DafeRLMLLibDafeRLMLLib: The Reinforcement Learning & Machine Learning Engine
This is not an indicator. This is an artificial intelligence. A state-based, self-learning engine designed to bring the power of professional quantitative finance to the Pine Script ecosystem. Welcome to the next frontier of trading analysis.
█ CHAPTER 1: THE PHILOSOPHY - FROM STATIC RULES TO DYNAMIC LEARNING
Technical analysis has, for a century, been a discipline of static, human-defined rules. "If RSI is below 30, then buy." "If the 50 EMA crosses the 200 EMA, then sell." These are fixed heuristics. They are brittle. They fail to adapt to the market's ever-changing personality—its shifts between trend and range, high and low volatility, risk-on and risk-off sentiment. An indicator built on static rules is an automaton, destined to fail when the environment it was designed for inevitably changes.
The DafeRLMLLib was created to shatter this paradigm. It is not a tool with fixed rules; it is a framework for discovering optimal rules. It is a true Reinforcement Learning (RL) and Machine Learning (ML) engine, built from the ground up in Pine Script. Its purpose is not to follow a pre-programmed strategy, but to learn a strategy through trial, error, and feedback.
This library provides a complete, professional-grade toolkit for developers to build indicators that think, adapt, and evolve. It observes the market state, selects an action, receives a reward signal based on the outcome, and updates its internal "brain" to improve its future decisions. This is not just a step forward; it is a quantum leap into the future of on-chart intelligence.
█ CHAPTER 2: THE CORE INNOVATIONS - WHAT MAKES THIS A TRUE ML ENGINE?
This library is not a collection of simple moving averages labeled as "AI." It is a suite of genuine, academically recognized machine learning algorithms, adapted for the unique constraints and opportunities of the Pine Script environment.
Multi-Algorithm Architecture: You are not locked into one learning model. The library provides a choice of powerful RL algorithms:
Q-Learning with TD(λ) Eligibility Traces: A classic, robust algorithm for learning state-action values. We've enhanced it with eligibility traces (Lambda), allowing the agent to more efficiently assign credit or blame to a sequence of past actions, dramatically speeding up the learning process.
REINFORCE Policy Gradient with Baseline: A more advanced method that directly learns a "policy"—a probability distribution over actions—instead of just values. The baseline helps to stabilize learning by reducing variance.
Actor-Critic Architecture: The state-of-the-art. This hybrid model combines the best of both worlds. The "Actor" (the policy) decides what to do, and the "Critic" (the value function) evaluates how good that action was. The Critic's feedback is then used to directly improve the Actor's decisions.
Prioritized Experience Replay: Like a human, the AI learns more from surprising or significant events. Instead of learning from experiences in a simple chronological order, the library stores them in a ReplayBuffer. It then replays these memories to the learning algorithms, prioritizing experiences that resulted in a large prediction error. This makes learning incredibly efficient.
Meta-Learning & Self-Tuning: An AI that cannot learn how to learn is still a dumb machine. The MetaState module is a meta-learning layer that monitors the agent's own performance over time. If it detects that performance is degrading, it will automatically increase the learning rate ("Synaptic Plasticity"). If performance is improving, it will decrease the learning rate to stabilize the learned strategy. It tunes its own hyperparameters.
Catastrophic Forgetting Prevention: A common failure mode for simple neural networks is "catastrophic forgetting," where learning a new task completely erases knowledge of a previous one. This library includes mechanisms like soft_reset and L2 regularization to prevent the agent's learned weights from exploding or being wiped out by a single bad run of trades, ensuring more stable, long-term learning.
The Universal Socket Interface: How does the AI "see" the market? Through DataSockets. This brilliant, extensible interface allows a developer to connect any data series—an RSI, a volume metric, a volatility reading, a custom calculation—to the AI's "brain." Each socket normalizes its input, tracks its own statistics, and feeds into the state-building process. This makes the library universally adaptable to any trading idea.
█ CHAPTER 3: A DUAL-PURPOSE FRAMEWORK - MODES OF OPERATION
This library is a foundational component of the DAFE AI ecosystem, designed for ultimate flexibility. It can be used in two primary modes: as a powerful standalone intelligence, or as the core cognitive engine within a larger, bridged super-system. Understanding these modes is key to unlocking its full potential.
MODE 1: STANDALONE ENGINE OPERATION (Independent Power
The DafeRLMLLib can be used entirely on its own to create a complete, self-learning trading indicator. This approach is perfect for building focused, single-purpose tools that are designed to master a specific task. In this mode, the developer is responsible for creating the full feedback loop within their own indicator script.
The Workflow:
Your indicator initializes the ML agent.
On each bar, it feeds the agent market data via the socket interface.
It asks the agent for an action (e.g., Buy, Sell, Hold).
Your script then executes its own internal trade logic based on the agent's decision.
Your script is responsible for tracking the Profit & Loss (PnL) of the resulting simulated trade.
When the trade is closed, your script feeds the final PnL directly back into the agent's learn() function as the "reward" signal.
The Result: A pure, state-based learning system. The agent directly learns the consequences of its own actions. This is excellent for discovering novel, micro-level trading patterns and for building indicators that are designed to operate with complete autonomy.
MODE 2: BRIDGED SUPER-SYSTEM OPERATION (Synergistic Intelligence)
This is the pinnacle of the DAFE ecosystem. In this advanced mode, the DafeRLMLLib acts as the core "cognitive engine" or the "tactical brain" within a larger, multi-library system. It can be fused with a strategic portfolio management engine (like the DafeSPALib) via a master communication protocol (the DafeMLSPABridge).
The Workflow:
The ML engine (this library) generates a set of creative, state-based proposals or predictions.
The Bridge Library translates these proposals into a portfolio of micro-strategies.
The SPA (Strategy Portfolio Allocation) engine, acting as a high-level manager, analyzes the real-time performance of these micro-strategies and selects the one it trusts the most. This becomes the final decision. The PnL from the SPA's final, performance-vetted decision is then routed back through the Bridge as a highly-qualified reward signal for the ML engine.
The Result: A hybrid intelligence that is more robust and adaptive than either system alone. The ML engine provides tactical creativity, while the SPA engine provides ruthless, strategic, performance-based oversight. The ML proposes, the SPA disposes, and the ML learns from the SPA's wisdom. This creates a system of checks, balances, and continuous, synergistic learning, perfect for building an ultimate, all-in-one "drawing indicator" or trading system.
As a developer, the choice is yours. Use this library independently to build powerful, specialized learning tools, or use it as the foundational brain for a truly comprehensive trading AI.
█ CHAPTER 4: A GUIDE FOR DEVELOPERS - INTEGRATING THE BRAIN
We have made it incredibly simple to bring your indicators to life with the DAFE AI. This is the true purpose of the library—to empower you. This section provides the full, unabridged input template and usage guide.
PART I: THE INPUTS TEMPLATE
To give your users full control over the AI, copy this entire block of inputs into your indicator script. It is professionally organized with groups and detailed tooltips.
// ╔═════════════════════════════════════════════════════╗
// ║ INPUTS TEMPLATE (COPY INTO YOUR SCRIPT) ║
// ╚═════════════════════════════════════════════════════╝
// INPUT GROUPS
string G_RL_AGENT = "═══════════ 🧠 AGENT CONFIGURATION ════════════"
string G_RL_LEARN = "═══════════ 📚 LEARNING PARAMETERS ═══════════"
string G_RL_REWARD = "═══════════ 💰 REWARD SYSTEM ═══════════════"
string G_RL_REPLAY = "═══════════ 📼 EXPERIENCE REPLAY ════════════"
string G_RL_META = "═══════════ 🔮 META-LEARNING ═══════════════"
string G_RL_DASH = "═══════════ 📋 DIAGNOSTICS DASHBOARD ═════════"
// AGENT CONFIGURATION
string i_rl_algorithm = input.string("Actor-Critic", "🤖 Algorithm",
options= , group=G_RL_AGENT,
tooltip="Selects the core learning algorithm.\n\n" +
"• Q-Learning: Classic, robust, and fast for discrete states. Learns the 'value' of actions.\n" +
"• Policy Gradient: Learns a direct probability distribution over actions.\n" +
"• Actor-Critic: The state-of-the-art. The 'Actor' decides, the 'Critic' evaluates.\n" +
"• Ensemble: Runs both Q-Learning and Policy Gradient and chooses the action with the highest confidence.\n\n" +
"RECOMMENDATION: Start with 'Q-Learning' for stability or 'Actor-Critic' for performance.")
int i_rl_num_features = input.int(8, "Number of Features (Sockets)", minval=2, maxval=12, group=G_RL_AGENT,
tooltip="Defines the size of the AI's 'vision'. This MUST match the number of sockets you connect.")
int i_rl_num_actions = input.int(3, "Number of Actions", minval=2, maxval=5, group=G_RL_AGENT,
tooltip="Defines what the AI can do. 3 is standard (0=Neutral, 1=Buy, 2=Sell).")
// LEARNING PARAMETERS
float i_rl_learning_rate = input.float(0.05, "🎓 Learning Rate (Alpha)", minval=0.001, maxval=0.2, step=0.005, group=G_RL_LEARN,
tooltip="How strongly the AI updates its knowledge. Low (0.01-0.03) is stable. High (0.1+) is aggressive.")
float i_rl_discount = input.float(0.95, "🔮 Discount Factor (Gamma)", minval=0.8, maxval=0.99, step=0.01, group=G_RL_LEARN,
tooltip="Determines the agent's 'foresight'. High (0.95+) for trend following. Low (0.85) for scalping.")
float i_rl_epsilon = input.float(0.15, "🧭 Exploration Rate (Epsilon)", minval=0.01, maxval=0.5, step=0.01, group=G_RL_LEARN,
tooltip="For Q-Learning. The probability of taking a random action to explore. Decays automatically over time.")
float i_rl_lambda = input.float(0.7, "⚡ Eligibility Trace (Lambda)", minval=0.0, maxval=0.95, step=0.05, group=G_RL_LEARN,
tooltip="For Q-Learning. A powerful accelerator that allows a reward to be 'traced' back through a sequence of actions.")
// REWARD SYSTEM
string i_rl_reward_mode = input.string("Normalized", "💰 Reward Shaping Mode",
options= , group=G_RL_REWARD,
tooltip="Modifies the raw PnL reward signal to guide learning.\n\n" +
"• Normalized: Creates a stable reward signal (Recommended).\n" +
"• Asymmetric: Punishes losses more than it rewards gains. Teaches risk aversion.\n" +
"• Risk-Adjusted: Divides PnL by risk (e.g., ATR). Teaches better risk/reward.")
// EXPERIENCE REPLAY
bool i_rl_use_replay = input.bool(true, "📼 Enable Experience Replay", group=G_RL_REPLAY,
tooltip="Allows the agent to store and re-learn from past experiences. Dramatically improves learning stability. HIGHLY RECOMMENDED.")
int i_rl_replay_capacity = input.int(500, "Replay Buffer Size", minval=100, maxval=2000, group=G_RL_REPLAY)
int i_rl_replay_batch = input.int(4, "Replay Batch Size", minval=1, maxval=10, group=G_RL_REPLAY)
// META-LEARNING
bool i_rl_use_meta = input.bool(true, "🔮 Enable Meta-Learning", group=G_RL_META,
tooltip="Allows the agent to self-tune its own learning rate based on performance trends.")
// DIAGNOSTICS DASHBOARD
bool i_rl_show_dash = input.bool(true, "📋 Show Diagnostics Dashboard", group=G_RL_DASH)
PART II: THE IMPLEMENTATION LOGIC
This is the boilerplate code you will adapt to your indicator. It shows the complete Observe-Act-Learn loop.
// ╔═══════════════════════════════════════════════════════╗
// ║ USAGE EXAMPLE (ADAPT TO YOUR SCRIPT) ║
// ╚═══════════════════════════════════════════════════════╝
// 1. INITIALIZE THE AGENT (happens only on the first bar)
int algo_id = i_rl_algorithm == "Q-Learning" ? 0 : i_rl_algorithm == "Policy Gradient" ? 1 : i_rl_algorithm == "Actor-Critic" ? 2 : 3
int reward_id = i_rl_reward_mode == "Raw PnL" ? 0 : i_rl_reward_mode == "Normalized" ? 1 : i_rl_reward_mode == "Asymmetric" ? 2 : 3
var rl.RLAgent agent = rl.init(algo_id, i_rl_num_features, i_rl_num_actions, i_rl_learning_rate, 54, i_rl_replay_capacity, i_rl_epsilon, i_rl_discount, i_rl_lambda, reward_id)
// 2. CONNECT THE "SENSES" (happens only on the first bar)
if barstate.isfirst
// Connect your indicator's data series to the AI's sockets. The number MUST match 'i_rl_num_features'.
agent := rl.connect_socket(agent, "rsi", ta.rsi(close, 14), "oscillator", 1.0)
agent := rl.connect_socket(agent, "atr_norm", ta.atr(14)/close*100, "custom", 0.8)
// ... connect all other features ...
// 3. THE MAIN LOOP (Observe -> Act -> Learn) - runs on every bar
var bool in_trade = false
var int trade_direction = 0
var float entry_price = 0.0
var int last_state_hash = 0
var int last_action_taken = 0
// --- OBSERVE: Build the current market state ---
rl.RLState current_state = rl.build_state(agent)
// --- ACT: Ask the AI for a decision ---
= rl.select_action(agent, current_state)
agent := updated_agent // CRITICAL: Always update the agent state
// --- EXECUTE: Your custom trade logic goes here ---
if not in_trade and ai_action.action != 0 // Assuming 0 is "Hold"
in_trade := true
trade_direction := ai_action.action == 1 ? 1 : -1 // Assuming 1=Buy, 2=Sell
entry_price := close
last_state_hash := current_state.hash // Store the state at the moment of entry
last_action_taken := ai_action.action
// --- LEARN: Check for trade closure and provide feedback ---
bool trade_is_closed = false
float reward = 0.0
if in_trade
// Your custom exit condition here (e.g., stop loss, take profit, opposite signal)
bool exit_condition = bar_index > ta.valuewhen(in_trade, bar_index, 0) + 20
if exit_condition
trade_is_closed := true
pnl = trade_direction == 1 ? (close - entry_price) / entry_price : (entry_price - close) / entry_price
reward := pnl * 100
in_trade := false
// If a trade was closed on THIS bar, feed the experience to the AI
if trade_is_closed
agent := rl.learn(agent, last_state_hash, last_action_taken, reward, current_state, true)
// 4. DISPLAY DIAGNOSTICS
if i_rl_show_dash and barstate.islast
string diag_text = rl.diagnostics(agent)
label.new(bar_index, high, diag_text, style=label.style_label_down, color=color.new(#0A0A14, 10), textcolor=#00FF41, size=size.small, textalign=text.align_left)
█ DEVELOPMENT PHILOSOPHY
The DafeRLMLLib was born from a desire to push the boundaries of Pine Script and to empower the entire TradingView developer community. We believe that the future of technical analysis is not just in creating more complex algorithms, but in building systems that can learn, adapt, and optimize themselves. This library is an open-source framework designed to be a launchpad for a new generation of truly intelligent indicators on TradingView.
This library is designed to help you and your users discover what "the best trades" are, not by following a fixed set of rules, but by learning from the market's own feedback, one trade at a time.
█ DISCLAIMER & IMPORTANT NOTES
THIS IS A LIBRARY FOR ADVANCED DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be integrated into other indicators.
REINFORCEMENT LEARNING IS COMPLEX: RL is not a magic bullet. It requires careful feature engineering (choosing the right sockets), a well-defined reward signal, and a sufficient amount of training data (trades) to converge on a profitable strategy.
ALL TRADING INVOLVES RISK: The AI's decisions are based on statistical probabilities learned from past data. It does not predict the future with certainty.
"The goal of a successful trader is to make the best trades. Money is secondary."
— Alexander Elder
Taking you to school. - Dskyz, Create with RL.
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MA-trix Laboratory [DAFE]MA-trix Laboratory : The Ultimate Moving Average & Trend Following Engine
55+ Algorithms. Dual/Triple MA Systems. Advanced Signal Filtering. Quantum Smoothing. This is not just a moving average; it is the definitive toolkit for forging your perfect trend.
█ PHILOSOPHY: WELCOME TO THE LABORATORY
The moving average is the cornerstone of technical analysis. It is also, in its standard form, an obsolete, one-dimensional tool. A simple EMA or SMA is a blunt instrument in a market that demands surgical precision. It lags, it whipsaws, and it fails to adapt to the market's ever-changing character.
The MA-trix Laboratory was not created to be another moving average. It was engineered to be the final word on moving averages—a comprehensive, institutional-grade research and execution environment. This is not an indicator; it is a powerful, interactive sandbox where you, the trader, can move beyond the static "one-size-fits-all" approach. Here, you can experiment, test, and forge a moving average system that is perfectly synchronized with your specific market, timeframe, and analytical style.
We have deconstructed the very concept of "average" and rebuilt it from the ground up, creating a library of over 55 distinct mathematical algorithms —from timeless classics to proprietary quantum models—all housed within a single, unified, and infinitely configurable engine.
█ WHAT MAKES THIS A "LABORATORY"? THE CORE INNOVATIONS
This tool stands in a class of its own, offering a suite of features that collectively create an unparalleled analytical experience.
The 55+ Algorithm MA Core: This is the heart of the Laboratory. You are not limited to one or two MA types. You have a vast library of over 55 unique mathematical engines at your command, from classical SMAs to advanced adaptive algorithms like KAMA and FRAMA, to proprietary DAFE models like the "DAFE Flux Reactor" and "DAFE Quantum Step."
Multi-MA Architecture: Seamlessly switch between Single, Dual, and Triple MA operational modes. Build classic two-line crossover systems, three-line trend alignment confirmations, or beautiful, flowing ribbons with just a single click.
Advanced Post-Smoothing Engine: In a revolutionary step, you can apply a second layer of signal processing to your chosen MA. Select from a suite of over 20 professional-grade noise filters —including Ehlers' SuperSmoother, Kalman Filters, and the proprietary "DAFE Phase-Zero"—to surgically remove noise from your MA line after it has been calculated, achieving unprecedented smoothness without significant lag.
The Institutional Signal Filtering Suite: A signal is only as good as its filter. The Laboratory includes a powerful, multi-domain filter engine that acts as an intelligent gatekeeper for your signals. You can require signals to be confirmed by any combination of:
📦 Volume: Require a surge in volume to validate a crossover.
🌊 Volatility: Only take signals during low-volatility "squeeze" conditions or high-volatility expansions.
💪 Trend: Use the ADX to ensure you are only taking signals in the direction of a strong, established trend.
🚀 Momentum: Use RSI, MACD, or ROC to confirm that momentum is on your side.
Integrated Performance Engine: How do you know which of the 55+ algorithms is best? You test it. The built-in Performance Dashboard is a comprehensive backtesting engine that tracks every trade generated by your configuration, providing real-time data on Win Rate, Profit Factor, Net P&L, and Max Drawdown.
█ THE ARSENAL: A DEEP DIVE INTO THE ALGORITHMIC CORE
This is your library of mathematical DNA. The 55+ MA types are grouped into distinct families, each with a unique philosophy.
THE ALGORITHM FAMILIES
The Classics (SMA, EMA, WMA, etc.): The foundational building blocks. Simple, reliable, and universally understood. EMA for responsiveness, SMA for smoothness.
The Low-Lag Warriors (DEMA, TEMA, Hull MA, ZLEMA): A family of MAs engineered specifically to combat the inherent lag of classical averages. The Hull MA is a standout, offering a remarkable balance of extreme smoothness and near-zero lag.
The Adaptive Geniuses (KAMA, VIDYA, FRAMA, Volatility Adjusted MA): These are "smart" MAs. They contain internal logic that allows them to automatically change their speed based on market conditions. They will tighten up in fast-moving trends and loosen in sideways chop, intelligently filtering out noise.
The DSP & Quantitative Masters (Gaussian, Ehlers, Butterworth, Laguerre): These algorithms are born from the world of digital signal processing and advanced mathematics. They use sophisticated techniques like bell-curve weighting, non-linear feedback loops, and frequency filtering to separate the true trend "signal" from market "noise" with unparalleled precision.
The DAFE Proprietary Engines (The "Black Ops" MAs): The crown jewels of the Laboratory. These are custom-built, proprietary algorithms you will not find anywhere else:
DAFE Flux Reactor: A volatility-thermodynamic MA that adapts its alpha using a sigmoid function on Bollinger Band width, creating explosive responsiveness during volatility breakouts.
DAFE Tensor Flow: A multi-vector MA that uses a weighted average of the OHLC data (a "tensor") before applying Hull smoothing, creating an incredibly robust center of gravity.
DAFE Quantum Step: A non-linear, stepped MA that only moves if price exceeds a volatility-based quantum threshold, effectively ignoring all insignificant noise.
DAFE Gravity Well: An institutionally-focused MA that weights its calculation by both time (recency) and volume, pulling the average towards zones of heavy market participation.
THE POST-SMOOTHING FILTERS
This is a second layer of refinement. After your primary MA is calculated, you can pass it through one of over 20 advanced filters to achieve an even higher degree of clarity.
The Ehlers Filters (SuperSmoother, 2-Pole, 3-Pole): A suite of brilliant DSP filters for surgical noise removal.
The Kalman Filter: A predictive filter from robotics and aerospace engineering that provides an "optimal estimate" of the MA's true position.
DAFE Proprietary Smoothers:
DAFE Phase-Zero: Uses a de-trending feedback loop to achieve near-zero lag smoothing.
DAFE Spectral Smooth: A frequency-domain filter that removes jitter while preserving the primary trend.
█ OPERATIONAL MODES & SIGNAL GENERATION
The Laboratory is designed for ultimate flexibility.
Modes: Instantly switch between Single, Dual, and Triple MA modes. Each mode can be a standard line display or a beautiful, flowing Ribbon .
Signal Logic: You have complete control over what constitutes a "signal." Choose from nine different logic modes, including classic Price Cross , Dual MA Cross , Triple MA Alignment , or even advanced logic like Slope Change and Sequential Cross .
The Filter Gauntlet: Before a signal is plotted, it can be passed through the four-stage filtering suite. You can demand that a simple EMA crossover is also confirmed by high volume, ADX trend strength, and bullish RSI—all at the same time. This transforms a basic signal into a high-conviction, multi-factor setup.
█ THE MASTER DASHBOARD: YOUR MISSION CONTROL
The comprehensive dashboard is your unified command center for analysis and performance tracking.
Engine Status: See the currently selected Operation Mode and a detailed breakdown of the type and length of each active MA.
Market Dynamics: Get an at-a-glance view of the current Trend Status, Momentum intensity (based on MA slope), and the percentage deviation of price from your primary MA.
Filter Readout: If filters are enabled, the dashboard provides a live status for each active filter (Volume, Volatility, Trend, Momentum), showing you a "PASS" or "BLOCK" status in real-time.
Performance Readout: When enabled, this section provides a full breakdown of your backtesting results, including Trade Count, Win Rate, Profit Factor, Net P&L, and Max Drawdown.
█ DEVELOPMENT PHILOSOPHY
The MA-trix Laboratory was born from a deep respect for the moving average and a relentless desire to push its boundaries into the 21st century. We believe that in modern markets, static tools are obsolete. The future of trading lies in adaptation and customization. This indicator is for the serious trader, the tinkerer, the scientist—the individual who is not content with a black box, but who seeks to understand, test, and refine their edge with surgical precision. It is a tool for forging your own alpha, not just following someone else's.
"I don't think traders can follow rules for very long unless they reflect their own trading style. Eventually, a breaking point is reached and the trader has to quit or change, or find a new set of rules he can follow. This seems to be part of the process of evolution and growth of a trader."
█ DISCLAIMER AND BEST PRACTICES
THIS IS A TOOL, NOT A STRATEGY: This indicator provides a sophisticated trend and signal generation framework. It must be integrated into a complete trading plan that includes risk management, position sizing, and your own contextual analysis.
TEST, DON'T GUESS: The power of this tool is its adaptability. Use the Performance Dashboard to rigorously test different algorithms, settings, and filters on your chosen asset and timeframe. Find what works, and build your strategy around that data.
START SIMPLE: The possibilities can be overwhelming. Begin with a classic Dual MA mode (e.g., EMA 20/50) with no filters. Once you are comfortable, begin experimenting with more advanced MA types and layering on filters one by one.
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The backtesting results are hypothetical and do not account for slippage or psychological factors.
Never risk more capital than you are prepared to lose.
— Ed Seykota, Market Wizard
The MA-trix Laboratory is designed to be the ultimate tool for that evolution, allowing you to discover and codify the rules that truly fit you.
Taking you to school. - Dskyz, Don't be average. Trade with MA-trix. Trade with DAFE
Machine Learning Momentum Index (MLMI) [Zeiierman]█ Overview
The Machine Learning Momentum Index (MLMI) represents the next step in oscillator trading. By blending traditional momentum analysis with machine learning, MLMI delivers a potent and dynamic tool that aligns with the complexities of modern financial landscapes. Offering traders an adaptive way to understand and act on market momentum and trends, this oscillator provides real-time insights into market momentum and prevailing trends.
█ How It Works:
Momentum Analysis: MLMI employs a dual-layer analysis, utilizing quick and slow weighted moving averages (WMA) of the Relative Strength Index (RSI) to gauge the market's momentum and direction.
Machine Learning Integration: Through the k-Nearest Neighbors (k-NN) algorithm, MLMI intelligently examines historical data to make more accurate momentum predictions, adapting to the intricate patterns of the market.
MLMI's precise calculation involves:
Weighted Moving Averages: Calculations of quick (5-period) and slow (20-period) WMAs of the RSI to track short-term and long-term momentum.
k-Nearest Neighbors Algorithm: Distances between current parameters and previous data are measured, and the nearest neighbors are used for predictive modeling.
Trend Analysis: Recognition of prevailing trends through the relationship between quick and slow-moving averages.
█ How to use
The Machine Learning Momentum Index (MLMI) can be utilized in much the same way as traditional trend and momentum oscillators, providing key insights into market direction and strength. What sets MLMI apart is its integration of artificial intelligence, allowing it to adapt dynamically to market changes and offer a more nuanced and responsive analysis.
Identifying Trend Direction and Strength: The MLMI serves as a tool to recognize market trends, signaling whether the momentum is upward or downward. It also provides insights into the intensity of the momentum, helping traders understand both the direction and strength of prevailing market trends.
Identifying Consolidation Areas: When the MLMI Prediction line and the WMA of the MLMI Prediction line become flat/oscillate around the mid-level, it's a strong sign that the market is in a consolidation phase. This insight from the MLMI allows traders to recognize periods of market indecision.
Recognizing Overbought or Oversold Conditions: By identifying levels where the market may be overbought or oversold, MLMI offers insights into potential price corrections or reversals.
█ Settings
Prediction Data (k)
This parameter controls the number of neighbors to consider while making a prediction using the k-Nearest Neighbors (k-NN) algorithm. By modifying the value of k, you can change how sensitive the prediction is to local fluctuations in the data.
A smaller value of k will make the prediction more sensitive to local variations and can lead to a more erratic prediction line.
A larger value of k will consider more neighbors, thus making the prediction more stable but potentially less responsive to sudden changes.
Trend length
This parameter controls the length of the trend used in computing the momentum. This length refers to the number of periods over which the momentum is calculated, affecting how quickly the indicator reacts to changes in the underlying price movements.
A shorter trend length (smaller momentumWindow) will make the indicator more responsive to short-term price changes, potentially generating more signals but at the risk of more false alarms.
A longer trend length (larger momentumWindow) will make the indicator smoother and less responsive to short-term noise, but it may lag in reacting to significant price changes.
Please note that the Machine Learning Momentum Index (MLMI) might not be effective on higher timeframes, such as daily or above. This limitation arises because there may not be enough data at these timeframes to provide accurate momentum and trend analysis. To overcome this challenge and make the most of what MLMI has to offer, it's recommended to use the indicator on lower timeframes.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
loxxfftLibrary "loxxfft"
This code is a library for performing Fast Fourier Transform (FFT) operations. FFT is an algorithm that can quickly compute the discrete Fourier transform (DFT) of a sequence. The library includes functions for performing FFTs on both real and complex data. It also includes functions for fast correlation and convolution, which are operations that can be performed efficiently using FFTs. Additionally, the library includes functions for fast sine and cosine transforms.
Reference:
www.alglib.net
fastfouriertransform(a, nn, inversefft)
Returns Fast Fourier Transform
Parameters:
a (float ) : float , An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices.
nn (int) : int, The number of function values. It must be a power of two, but the algorithm does not validate this.
inversefft (bool) : bool, A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT.
Returns: float , Modifies the input array a in-place, which means that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. The transformed data will have real and imaginary parts interleaved, with the real parts at even indices and the imaginary parts at odd indices.
realfastfouriertransform(a, tnn, inversefft)
Returns Real Fast Fourier Transform
Parameters:
a (float ) : float , A float array containing the real-valued function samples.
tnn (int) : int, The number of function values (must be a power of 2, but the algorithm does not validate this condition).
inversefft (bool) : bool, A boolean flag that indicates the direction of the transformation (True for inverse, False for direct).
Returns: float , Modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution.
fastsinetransform(a, tnn, inversefst)
Returns Fast Discrete Sine Conversion
Parameters:
a (float ) : float , An array of real numbers representing the function values.
tnn (int) : int, Number of function values (must be a power of two, but the code doesn't validate this).
inversefst (bool) : bool, A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST.
Returns: float , The output is the transformed array 'a', which will contain the result of the transformation.
fastcosinetransform(a, tnn, inversefct)
Returns Fast Discrete Cosine Transform
Parameters:
a (float ) : float , This is a floating-point array representing the sequence of values (time-domain) that you want to transform. The function will perform the Fast Cosine Transform (FCT) or the inverse FCT on this input array, depending on the value of the inversefct parameter. The transformed result will also be stored in this same array, which means the function modifies the input array in-place.
tnn (int) : int, This is an integer value representing the number of data points in the input array a. It is used to determine the size of the input array and control the loops in the algorithm. Note that the size of the input array should be a power of 2 for the Fast Cosine Transform algorithm to work correctly.
inversefct (bool) : bool, This is a boolean value that controls whether the function performs the regular Fast Cosine Transform or the inverse FCT. If inversefct is set to true, the function will perform the inverse FCT, and if set to false, the regular FCT will be performed. The inverse FCT can be used to transform data back into its original form (time-domain) after the regular FCT has been applied.
Returns: float , The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array.
fastconvolution(signal, signallen, response, negativelen, positivelen)
Convolution using FFT
Parameters:
signal (float ) : float , This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1.
signallen (int) : int, This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array.
response (float ) : float , This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively.
negativelen (int) : int, This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero.
positivelen (int) : int, This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero.
Returns: float , The resulting convolved values are stored back in the input signal array.
fastcorrelation(signal, signallen, pattern, patternlen)
Returns Correlation using FFT
Parameters:
signal (float ) : float ,This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1.
signallen (int) : int, This is an integer representing the length of the input signal array.
pattern (float ) : float , This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1.
patternlen (int) : int, This is an integer representing the length of the pattern array.
Returns: float , The signal array containing the correlation values at points from 0 to SignalLen-1.
tworealffts(a1, a2, a, b, tn)
Returns Fast Fourier Transform of Two Real Functions
Parameters:
a1 (float ) : float , An array of real numbers, representing the values of the first function.
a2 (float ) : float , An array of real numbers, representing the values of the second function.
a (float ) : float , An output array to store the Fourier transform of the first function.
b (float ) : float , An output array to store the Fourier transform of the second function.
tn (int) : float , An integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition.
Returns: float , The a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b.
█ Detailed explaination of each function
Fast Fourier Transform
The fastfouriertransform() function takes three input parameters:
1. a: An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices.
2. nn: The number of function values. It must be a power of two, but the algorithm does not validate this.
3. inversefft: A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT.
The function performs the FFT using the Cooley-Tukey algorithm, which is an efficient algorithm for computing the discrete Fourier transform (DFT) and its inverse. The Cooley-Tukey algorithm recursively breaks down the DFT of a sequence into smaller DFTs of subsequences, leading to a significant reduction in computational complexity. The algorithm's time complexity is O(n log n), where n is the number of samples.
The fastfouriertransform() function first initializes variables and determines the direction of the transformation based on the inversefft parameter. If inversefft is True, the isign variable is set to -1; otherwise, it is set to 1.
Next, the function performs the bit-reversal operation. This is a necessary step before calculating the FFT, as it rearranges the input data in a specific order required by the Cooley-Tukey algorithm. The bit-reversal is performed using a loop that iterates through the nn samples, swapping the data elements according to their bit-reversed index.
After the bit-reversal operation, the function iteratively computes the FFT using the Cooley-Tukey algorithm. It performs calculations in a loop that goes through different stages, doubling the size of the sub-FFT at each stage. Within each stage, the Cooley-Tukey algorithm calculates the butterfly operations, which are mathematical operations that combine the results of smaller DFTs into the final DFT. The butterfly operations involve complex number multiplication and addition, updating the input array a with the computed values.
The loop also calculates the twiddle factors, which are complex exponential factors used in the butterfly operations. The twiddle factors are calculated using trigonometric functions, such as sine and cosine, based on the angle theta. The variables wpr, wpi, wr, and wi are used to store intermediate values of the twiddle factors, which are updated in each iteration of the loop.
Finally, if the inversefft parameter is True, the function divides the result by the number of samples nn to obtain the correct inverse FFT result. This normalization step is performed using a loop that iterates through the array a and divides each element by nn.
In summary, the fastfouriertransform() function is an implementation of the Cooley-Tukey FFT algorithm, which is an efficient algorithm for computing the DFT and its inverse. This FFT library can be used for a variety of applications, such as signal processing, image processing, audio processing, and more.
Feal Fast Fourier Transform
The realfastfouriertransform() function performs a fast Fourier transform (FFT) specifically for real-valued functions. The FFT is an efficient algorithm used to compute the discrete Fourier transform (DFT) and its inverse, which are fundamental tools in signal processing, image processing, and other related fields.
This function takes three input parameters:
1. a - A float array containing the real-valued function samples.
2. tnn - The number of function values (must be a power of 2, but the algorithm does not validate this condition).
3. inversefft - A boolean flag that indicates the direction of the transformation (True for inverse, False for direct).
The function modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution.
The algorithm uses a combination of complex-to-complex FFT and additional transformations specific to real-valued data to optimize the computation. It takes into account the symmetry properties of the real-valued input data to reduce the computational complexity.
Here's a detailed walkthrough of the algorithm:
1. Depending on the inversefft flag, the initial values for ttheta, c1, and c2 are determined. These values are used for the initial data preprocessing and post-processing steps specific to the real-valued FFT.
2. The preprocessing step computes the initial real and imaginary parts of the data using a combination of sine and cosine terms with the input data. This step effectively converts the real-valued input data into complex-valued data suitable for the complex-to-complex FFT.
3. The complex-to-complex FFT is then performed on the preprocessed complex data. This involves bit-reversal reordering, followed by the Cooley-Tukey radix-2 decimation-in-time algorithm. This part of the code is similar to the fastfouriertransform() function you provided earlier.
4. After the complex-to-complex FFT, a post-processing step is performed to obtain the final real-valued output data. This involves updating the real and imaginary parts of the transformed data using sine and cosine terms, as well as the values c1 and c2.
5. Finally, if the inversefft flag is True, the output data is divided by the number of samples (nn) to obtain the inverse DFT.
The function does not return a value explicitly. Instead, the transformed data is stored in the input array a. After the function execution, you can access the transformed data in the a array, which will have the real part at even indices and the imaginary part at odd indices.
Fast Sine Transform
This code defines a function called fastsinetransform that performs a Fast Discrete Sine Transform (FST) on an array of real numbers. The function takes three input parameters:
1. a (float array): An array of real numbers representing the function values.
2. tnn (int): Number of function values (must be a power of two, but the code doesn't validate this).
3. inversefst (bool): A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST.
The output is the transformed array 'a', which will contain the result of the transformation.
The code starts by initializing several variables, including trigonometric constants for the sine transform. It then sets the first value of the array 'a' to 0 and calculates the initial values of 'y1' and 'y2', which are used to update the input array 'a' in the following loop.
The first loop (with index 'jx') iterates from 2 to (tm + 1), where 'tm' is half of the number of input samples 'tnn'. This loop is responsible for calculating the initial sine transform of the input data.
The second loop (with index 'ii') is a bit-reversal loop. It reorders the elements in the array 'a' based on the bit-reversed indices of the original order.
The third loop (with index 'ii') iterates while 'n' is greater than 'mmax', which starts at 2 and doubles each iteration. This loop performs the actual Fast Discrete Sine Transform. It calculates the sine transform using the Danielson-Lanczos lemma, which is a divide-and-conquer strategy for calculating Discrete Fourier Transforms (DFTs) efficiently.
The fourth loop (with index 'ix') is responsible for the final phase adjustments needed for the sine transform, updating the array 'a' accordingly.
The fifth loop (with index 'jj') updates the array 'a' one more time by dividing each element by 2 and calculating the sum of the even-indexed elements.
Finally, if the 'inversefst' flag is True, the code scales the transformed data by a factor of 2/tnn to get the inverse Fast Sine Transform.
In summary, the code performs a Fast Discrete Sine Transform on an input array of real numbers, either in the direct or inverse direction, and returns the transformed array. The algorithm is based on the Danielson-Lanczos lemma and uses a divide-and-conquer strategy for efficient computation.
Fast Cosine Transform
This code defines a function called fastcosinetransform that takes three parameters: a floating-point array a, an integer tnn, and a boolean inversefct. The function calculates the Fast Cosine Transform (FCT) or the inverse FCT of the input array, depending on the value of the inversefct parameter.
The Fast Cosine Transform is an algorithm that converts a sequence of values (time-domain) into a frequency domain representation. It is closely related to the Fast Fourier Transform (FFT) and can be used in various applications, such as signal processing and image compression.
Here's a detailed explanation of the code:
1. The function starts by initializing a number of variables, including counters, intermediate values, and constants.
2. The initial steps of the algorithm are performed. This includes calculating some trigonometric values and updating the input array a with the help of intermediate variables.
3. The code then enters a loop (from jx = 2 to tnn / 2). Within this loop, the algorithm computes and updates the elements of the input array a.
4. After the loop, the function prepares some variables for the next stage of the algorithm.
5. The next part of the algorithm is a series of nested loops that perform the bit-reversal permutation and apply the FCT to the input array a.
6. The code then calculates some additional trigonometric values, which are used in the next loop.
7. The following loop (from ix = 2 to tnn / 4 + 1) computes and updates the elements of the input array a using the previously calculated trigonometric values.
8. The input array a is further updated with the final calculations.
9. In the last loop (from j = 4 to tnn), the algorithm computes and updates the sum of elements in the input array a.
10. Finally, if the inversefct parameter is set to true, the function scales the input array a to obtain the inverse FCT.
The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array.
Fast Convolution
This code defines a function called fastconvolution that performs the convolution of a given signal with a response function using the Fast Fourier Transform (FFT) technique. Convolution is a mathematical operation used in signal processing to combine two signals, producing a third signal representing how the shape of one signal is modified by the other.
The fastconvolution function takes the following input parameters:
1. float signal: This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1.
2. int signallen: This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array.
3. float response: This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively.
4. int negativelen: This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero.
5. int positivelen: This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero.
The function works by:
1. Calculating the length nl of the arrays used for FFT, ensuring it's a power of 2 and large enough to hold the signal and response.
2. Creating two new arrays, a1 and a2, of length nl and initializing them with the input signal and response function, respectively.
3. Applying the forward FFT (realfastfouriertransform) to both arrays, a1 and a2.
4. Performing element-wise multiplication of the FFT results in the frequency domain.
5. Applying the inverse FFT (realfastfouriertransform) to the multiplied results in a1.
6. Updating the original signal array with the convolution result, which is stored in the a1 array.
The result of the convolution is stored in the input signal array at the function exit.
Fast Correlation
This code defines a function called fastcorrelation that computes the correlation between a signal and a pattern using the Fast Fourier Transform (FFT) method. The function takes four input arguments and modifies the input signal array to store the correlation values.
Input arguments:
1. float signal: This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1.
2. int signallen: This is an integer representing the length of the input signal array.
3. float pattern: This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1.
4. int patternlen: This is an integer representing the length of the pattern array.
The function performs the following steps:
1. Calculate the required size nl for the FFT by finding the smallest power of 2 that is greater than or equal to the sum of the lengths of the signal and the pattern.
2. Create two new arrays a1 and a2 with the length nl and initialize them to 0.
3. Copy the signal array into a1 and pad it with zeros up to the length nl.
4. Copy the pattern array into a2 and pad it with zeros up to the length nl.
5. Compute the FFT of both a1 and a2.
6. Perform element-wise multiplication of the frequency-domain representation of a1 and the complex conjugate of the frequency-domain representation of a2.
7. Compute the inverse FFT of the result obtained in step 6.
8. Store the resulting correlation values in the original signal array.
At the end of the function, the signal array contains the correlation values at points from 0 to SignalLen-1.
Fast Fourier Transform of Two Real Functions
This code defines a function called tworealffts that computes the Fast Fourier Transform (FFT) of two real-valued functions (a1 and a2) using a Cooley-Tukey-based radix-2 Decimation in Time (DIT) algorithm. The FFT is a widely used algorithm for computing the discrete Fourier transform (DFT) and its inverse.
Input parameters:
1. float a1: an array of real numbers, representing the values of the first function.
2. float a2: an array of real numbers, representing the values of the second function.
3. float a: an output array to store the Fourier transform of the first function.
4. float b: an output array to store the Fourier transform of the second function.
5. int tn: an integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition.
The function performs the following steps:
1. Combine the two input arrays, a1 and a2, into a single array a by interleaving their elements.
2. Perform a 1D FFT on the combined array a using the radix-2 DIT algorithm.
3. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b.
Here is a detailed breakdown of the radix-2 DIT algorithm used in this code:
1. Bit-reverse the order of the elements in the combined array a.
2. Initialize the loop variables mmax, istep, and theta.
3. Enter the main loop that iterates through different stages of the FFT.
a. Compute the sine and cosine values for the current stage using the theta variable.
b. Initialize the loop variables wr and wi for the current stage.
c. Enter the inner loop that iterates through the butterfly operations within each stage.
i. Perform the butterfly operation on the elements of array a.
ii. Update the loop variables wr and wi for the next butterfly operation.
d. Update the loop variables mmax, istep, and theta for the next stage.
4. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b.
At the end of the function, the a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b.
█ Example scripts using functions contained in loxxfft
Real-Fast Fourier Transform of Price w/ Linear Regression
Real-Fast Fourier Transform of Price Oscillator
Normalized, Variety, Fast Fourier Transform Explorer
Variety RSI of Fast Discrete Cosine Transform
STD-Stepped Fast Cosine Transform Moving Average
AI Academy: Volume k-NN [PhenLabs]📊 AI Academy: Volume k-NN
Version: PineScript™ v6
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📌 Description
AI Academy: Volume k-NN (Theory Edition) is an educational indicator designed to demystify how artificial intelligence pattern recognition works directly on your TradingView charts. Rather than being a black-box signal generator, this tool visualizes the entire k-Nearest Neighbors algorithm process in real-time, showing you exactly how AI identifies similar historical patterns and generates predictions.
The indicator scans up to 2,000 historical bars to find patterns that match your current price action, then uses an ensemble of the closest matches to project potential future movement. What sets this apart is the integrated “AI Grimoire”—an interactive educational book overlay that teaches core machine learning concepts through four illuminating chapters.
Whether you’re a trader curious about AI methodology or a developer learning algorithmic concepts, this indicator transforms abstract machine learning theory into tangible, visual understanding.
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🚀 Points of Innovation
• First TradingView indicator to visualize k-NN algorithm execution in real-time with full transparency
• Interactive “AI Grimoire” educational overlay teaches machine learning concepts while you trade
• Dual-mode pattern matching combines price action with optional volume confirmation
• Confidence-based opacity system visually communicates prediction reliability
• Historical match visualization shows exactly which past patterns informed the prediction
• Ghost bar projections display averaged ensemble predictions with adjustable forecast horizons
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🔧 Core Components
• Pattern Capture Engine: Converts recent price action into logarithmic returns for normalized comparison across different price levels
• k-NN Search Algorithm: Calculates Euclidean distance between current pattern and historical patterns to find closest matches
• Volume Weighting System: Optional feature that incorporates volume patterns into distance calculations with adjustable influence
• Ensemble Predictor: Averages future returns from k-nearest historical matches to generate consensus forecast
• Confidence Calculator: Measures average distance of top matches to determine prediction reliability on 0-100% scale
• AI Grimoire Display: Table-based educational overlay rendering book-style content with chapter navigation
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🔥 Key Features
• Adjustable Pattern Length: Define how many bars constitute the current pattern for matching (5-100 bars)
• Configurable Search Depth: Control how far back the algorithm searches for historical matches (500-4,900 bars)
• Flexible k-Neighbors: Select how many closest matches inform the prediction (1-20 neighbors)
• Volume Toggle: Enable or disable volume pattern matching for different market conditions
• Volume Influence Slider: Fine-tune the weight given to volume vs. price patterns (0-100%)
• Ghost Bar Count: Adjust how many future bars the indicator projects (3-15 bars)
• Minimum Confidence Filter: Set threshold to hide low-confidence predictions
• Historical Match Display: Toggle visibility of colored boxes marking source patterns
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🎨 Visualization
• Blue Scanner Box: Highlights current pattern being analyzed labeled “AI INPUT (The Prompt)”
• Green Historical Boxes: Mark past patterns where price subsequently moved bullish
• Red Historical Boxes: Mark past patterns where price subsequently moved bearish
• Ghost Bars: Semi-transparent candles projecting into the future showing predicted price path
• Confidence Label: Displays prediction confidence percentage and number of matches used
• AI Grimoire Book: Leather-bound book overlay in top-right corner with navigable chapters
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📖 Usage Guidelines
Algorithm Settings
• Pattern Length — Default: 20 | Range: 5-100 | Controls how many recent bars define the pattern. Shorter values find more matches but less specific. Longer values find fewer but more precise matches.
• Search Depth — Default: 2000 | Range: 500-4900 | Determines how many historical bars to scan. Higher values find more potential matches but increase computation time.
• k-Neighbors — Default: 5 | Range: 1-20 | Number of closest matches to use for prediction. Higher values smooth predictions but may dilute strong signals.
• Ghost Bar Count — Default: 5 | Range: 3-15 | How many future bars to project. Shorter horizons are typically more reliable.
• Use Volume Matching — Default: Off | When enabled, patterns must match on both price AND volume characteristics.
• Volume Influence — Default: 30% | Range: 0-100% | Weight given to volume pattern when volume matching is enabled.
Visualization Settings
• Bullish/Bearish Match Colors — Customize colors for historical match boxes based on outcome direction.
• Min Confidence % — Default: 60 | Predictions below this threshold will not display.
• Show Historical Matches — Default: On | Toggle visibility of source pattern boxes on chart.
Education Settings
• Select Chapter — Navigate through AI Grimoire chapters or keep book closed for clean chart view.
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✅ Best Use Cases
• Learning how k-Nearest Neighbors algorithm functions in a trading context
• Understanding the relationship between historical patterns and forward predictions
• Identifying when current market conditions resemble past scenarios
• Supplementing discretionary analysis with pattern-based confluence
• Teaching others machine learning concepts through visual demonstration
• Validating whether volume confirms price pattern formations
• Building intuition for what AI “sees” when analyzing charts
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⚠️ Limitations
• Past pattern similarity does not guarantee future outcome similarity
• Requires sufficient historical data (minimum 500+ bars) to function properly
• Computation-intensive on lower timeframes with maximum search depth
• Cannot predict truly novel “black swan” events not represented in historical data
• Volume matching less effective on assets with inconsistent volume reporting
• Predictions become less reliable as forecast horizon extends further out
• Educational overlay may obstruct chart view on smaller screens
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💡 What Makes This Unique
• Full Transparency: Unlike black-box AI tools, every step of the algorithm is visualized on your chart
• Integrated Education: The AI Grimoire teaches machine learning concepts without leaving TradingView
• Theory Meets Practice: See exactly which historical patterns inform each prediction
• Honest Uncertainty: Confidence scoring and opacity fading acknowledge when the AI “doesn’t know”
• Dual-Mode Analysis: Optional volume weighting adds institutional-quality analysis dimension
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🔬 How It Works
1. Pattern Capture: On each bar, the indicator captures the most recent price changes as logarithmic returns, creating a normalized “fingerprint” of current market behavior. If volume matching is enabled, volume changes are captured similarly.
2. Historical Search: The algorithm iterates through up to 2,000 historical bars, calculating the Euclidean distance between the current pattern fingerprint and each historical pattern. Distance combines price similarity and optional volume similarity based on weight settings.
3. Neighbor Selection: All historical patterns are ranked by similarity (lowest distance = most similar). The k-closest matches are selected as the “ensemble council” that will inform the prediction.
4. Confidence Calculation: Average distance of top-k matches determines confidence. Tighter clustering of similar patterns yields higher confidence scores, while scattered or distant matches produce lower confidence.
5. Prediction Generation: Future returns from each historical match (what happened AFTER those patterns) are averaged together. This ensemble average is applied to current price to generate ghost bar projections.
6. Visualization: Historical match locations are marked with colored boxes (green for bullish outcomes, red for bearish). Ghost bars render with opacity tied to confidence level—higher confidence means more solid bars.
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💡 Note:
This indicator is designed primarily for educational purposes —to help traders understand how AI pattern recognition algorithms function. While the predictions can supplement your analysis, they should never be used as the sole basis for trading decisions. The AI Grimoire chapters explain key concepts including why AI “hallucinates” during unprecedented market events. Always combine with proper risk management and additional confirmation.
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Adaptive Market Wave TheoryAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
📋 AMWT vs AMWT-PRO:
This version includes all core AMWT functionality:
✓ Full Hidden Markov Model state estimation
✓ 3-Bandit Thompson Sampling consensus system
✓ Complete 5-factor chop detection engine
✓ All four signal modes
✓ Full trade management with TP/SL tracking
✓ Main dashboard with complete statistics
✓ All visual features (channels, zones, pools)
✓ Identical signal generation to PRO
✓ Six professional themes
✓ Full alert system
The PRO version adds the AMWT Advisor panel—a secondary dashboard providing:
• Real-time Market Pulse situation assessment
• Agent Matrix visualization (individual agent votes)
• Structure analysis breakdown
• "Watch For" upcoming setups
• Action Command coaching
Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals.
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
Optimized Grid with KNN_2.0Strategy Overview
This strategy, named "Optimized Grid with KNN_2.0," is designed to optimize trading decisions using a combination of grid trading, K-Nearest Neighbors (KNN) algorithm, and a greedy algorithm. The strategy aims to maximize profits by dynamically adjusting entry and exit thresholds based on market conditions and historical data.
Key Components
Grid Trading:
The strategy uses a grid-based approach to place buy and sell orders at predefined price levels. This helps in capturing profits from market fluctuations.
K-Nearest Neighbors (KNN) Algorithm:
The KNN algorithm is used to optimize entry and exit points based on historical price data. It identifies the nearest neighbors (similar price movements) and adjusts the thresholds accordingly.
Greedy Algorithm:
The greedy algorithm is employed to dynamically adjust the stop-loss and take-profit levels. It ensures that the strategy captures maximum profits by adjusting thresholds based on recent price changes.
Detailed Explanation
Grid Trading:
The strategy defines a grid of price levels where buy and sell orders are placed. The openTh and closeTh parameters determine the thresholds for opening and closing positions.
The t3_fast and t3_slow indicators are used to generate trading signals based on the crossover and crossunder of these indicators.
KNN Algorithm:
The KNN algorithm is used to find the nearest neighbors (similar price movements) in the historical data. It calculates the distance between the current price and historical prices to identify the most similar price movements.
The algorithm then adjusts the entry and exit thresholds based on the average change in price of the nearest neighbors.
Greedy Algorithm:
The greedy algorithm dynamically adjusts the stop-loss and take-profit levels based on recent price changes. It ensures that the strategy captures maximum profits by adjusting thresholds in real-time.
The algorithm uses the average_change variable to calculate the average price change of the nearest neighbors and adjusts the thresholds accordingly.
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
Adaptive MA constructor [lastguru]Adaptive Moving Averages are nothing new, however most of them use EMA as their MA of choice once the preferred smoothing length is determined. I have decided to make an experiment and separate length generation from smoothing, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource, so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Moving Averages.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers, but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers. I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power (exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Length Adaptaton section offer you a choice of Moving Average algorithms. Most of the Adaptations are originally used with EMA, so this is a good starting point for exploration.
SMA - Simple Moving Average
RMA - Running Moving Average
EMA - Exponential Moving Average
HMA - Hull Moving Average
VWMA - Volume Weighted Moving Average
2-pole Super Smoother - 2-pole Super Smoother by John F. Ehlers
3-pole Super Smoother - 3-pole Super Smoother by John F. Ehlers
Filt11 -a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers
Triangle Window - Triangle Window Filter by John F. Ehlers
Hamming Window - Hamming Window Filter by John F. Ehlers
Hann Window - Hann Window Filter by John F. Ehlers
Lowpass - removes cyclic components shorter than length (Price - Highpass)
DSSS - Derivation Scaled Super Smoother by John F. Ehlers
There are two Moving Averages that are drown on the chart, so length for both needs to be selected. If no Adaptation is selected ( None option), you can set Fast Length and Slow Length directly. If an Adaptation is selected, then Cycle multiplier can be selected for Fast and Slow MA.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Exotic SMA Explorations Treasure TroveThis is my "Exotic SMA Explorations Treasure Trove" intended for educational purposes, yet these functions will also have utility in special applications with other algorithms. Firstly, the Pine built-in sma() is exceedingly more efficient computationally on TV servers than these functions will be. I just wanted to make that very crystal clear. My notes elaborate on this in the code blatantly.
Anyhow, the simple moving average(SMA) is one of the most common averaging filters used in a wide variety of algorithms. "Simply put," it's name says a lot about it. The purpose of this script, is to demonstrate variations of it's calculation in a multitude of exotic forms. In certain scenarios our algorithms may require a specific mathemagical touch that is pertinent to our intended goals. Like screwdrivers, we often need different types depending on the objective we are trying to attain. The SMA also serves as the most basic of finite impulse response(FIR) algorithms. For example, things like weighted moving averages can be constructed by using the foundational code of SMA.
One other intended demonstration of this script, is running multiple functions for comparison. I have had to use this from time to time for my own comparisons of performance. Also, imbedded into this code is a method to generically and recklessly in this case, adapt an algorithm. I will warn you, RSI was NEVER intended to adapt an algorithm. It only serves as a crude method to display the versatility of these different algorithms, whether it be a benefit or hinderance concerning dynamic adaptability.
Lastly, this script shows the versatility of TV's NEW additions input(group=) and input(inline=) upgrades in action. The "Immense Power of Pine" is always evolving and will continue to do so, I assure you of that. We can now categorize our input()s without using the input(type=input.bool) hackTrick. Although, that still will have it's enduring versatility, at least for myself.
NOTICE: You have absolute freedom to use this source code any way you see fit within your new Pine projects. You don't have to ask for my permission to reuse these functions in your published scripts, simply because I have better things to do than answer requests for the reuse of these functions. Sufficient accreditation regarding this script and compliance with "TV's House Rules" regarding code reuse, is as easy as copying the functions in their entirety as is. Fair enough? Good!
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
MLMatrixLibOverview
MLMatrixLib is a comprehensive Pine Script v6 library implementing machine learning algorithms using native matrix operations. This library provides traders and developers with a toolkit of statistical and ML methods for building quantitative trading systems, performing data analysis, and creating adaptive indicators.
How It Works
The library leverages Pine Script's native matrix type to perform efficient linear algebra operations. Each algorithm is implemented from first principles, using matrix decomposition, iterative optimization, and statistical estimation techniques. All functions are designed for numerical stability with careful handling of edge cases.
---
Library Contents (34 Sections)
Section 1: Utility Functions & Matrix Operations
Core building blocks including:
• identity(n) - Creates n×n identity matrix
• diagonal(values) - Creates diagonal matrix from array
• ones(rows, cols) / zeros(rows, cols) - Matrix constructors
• frobeniusNorm(m) / l1Norm(m) - Matrix norm calculations
• hadamard(m1, m2) - Element-wise multiplication
• columnMeans(m) / rowMeans(m) - Statistical aggregations
• standardize(m) - Z-score normalization (zero mean, unit variance)
• minMaxNormalize(m) - Scale values to range
• fitStandardScaler(m) / fitMinMaxScaler(m) - Reusable scaler parameters
• addBiasColumn(m) - Prepend column of ones for regression
• arrayMedian(arr) / arrayPercentile(arr, p) - Array statistics
Section 2: Activation Functions
Numerically stable implementations:
• sigmoid(x) / sigmoidMatrix(m) - Logistic function with overflow protection
• tanhActivation(x) / tanhMatrix(m) - Hyperbolic tangent
• relu(x) / reluMatrix(m) - Rectified Linear Unit
• leakyRelu(x, alpha) - Leaky ReLU with configurable slope
• elu(x, alpha) - Exponential Linear Unit
• Derivatives for backpropagation: sigmoidDerivative, tanhDerivative, reluDerivative
Section 3: Linear Regression (OLS)
Ordinary Least Squares implementation using the normal equation (X'X)⁻¹X'y:
• fitLinearRegression(X, y) - Fits model, returns coefficients, R², standard error
• fitSimpleLinearRegression(x, y) - Single-variable regression
• predictLinear(model, X) - Generate predictions
• predictionInterval(model, X, confidence) - Confidence intervals using t-distribution
• Model type stores: coefficients, R-squared, residuals, standard error
Section 4: Weighted Linear Regression
Generalized least squares with observation weights:
• fitWeightedLinearRegression(X, y, weights) - Solves (X'WX)⁻¹X'Wy
• Useful for downweighting outliers or emphasizing recent data
Section 5: Polynomial Regression
Fits polynomials of arbitrary degree:
• fitPolynomialRegression(x, y, degree) - Constructs Vandermonde matrix
• predictPolynomial(model, x) - Evaluate polynomial at points
Section 6: Ridge Regression (L2 Regularization)
Adds penalty term λ||β||² to prevent overfitting:
• fitRidgeRegression(X, y, lambda) - Solves (X'X + λI)⁻¹X'y
• Lambda parameter controls regularization strength
Section 7: LASSO Regression (L1 Regularization)
Coordinate descent algorithm for sparse solutions:
• fitLassoRegression(X, y, lambda, maxIter, tolerance) - Iterative soft-thresholding
• Produces sparse coefficients by driving some to exactly zero
• softThreshold(x, lambda) - Core shrinkage operator
Section 8: Elastic Net (L1 + L2 Regularization)
Combines LASSO and Ridge penalties:
• fitElasticNet(X, y, lambda, alpha, maxIter, tolerance)
• Alpha balances L1 vs L2: alpha=1 is LASSO, alpha=0 is Ridge
Section 9: Huber Robust Regression
Iteratively Reweighted Least Squares (IRLS) for outlier resistance:
• fitHuberRegression(X, y, delta, maxIter, tolerance)
• Delta parameter defines transition between L1 and L2 loss
• Downweights observations with large residuals
Section 10: Quantile Regression
Estimates conditional quantiles using linear programming approximation:
• fitQuantileRegression(X, y, tau, maxIter, tolerance)
• Tau specifies quantile (0.5 = median, 0.25 = lower quartile, etc.)
Section 11: Logistic Regression (Binary Classification)
Gradient descent optimization of cross-entropy loss:
• fitLogisticRegression(X, y, learningRate, maxIter, tolerance)
• predictProbability(model, X) - Returns probabilities
• predictClass(model, X, threshold) - Returns binary predictions
Section 12: Linear SVM (Support Vector Machine)
Sub-gradient descent with hinge loss:
• fitLinearSVM(X, y, C, learningRate, maxIter)
• C parameter controls regularization (higher = harder margin)
• predictSVM(model, X) - Returns class predictions
Section 13: Recursive Least Squares (RLS)
Online learning with exponential forgetting:
• createRLSState(nFeatures, lambda, delta) - Initialize state
• updateRLS(state, x, y) - Update with new observation
• Lambda is forgetting factor (0.95-0.99 typical)
• Useful for adaptive indicators that update incrementally
Section 14: Covariance and Correlation
Matrix statistics:
• covarianceMatrix(m) - Sample covariance
• correlationMatrix(m) - Pearson correlations
• pearsonCorrelation(x, y) - Single correlation coefficient
• spearmanCorrelation(x, y) - Rank-based correlation
Section 15: Principal Component Analysis (PCA)
Dimensionality reduction via eigendecomposition:
• fitPCA(X, nComponents) - Power iteration method
• transformPCA(X, model) - Project data onto principal components
• Returns components, explained variance, and mean
Section 16: K-Means Clustering
Lloyd's algorithm with k-means++ initialization:
• fitKMeans(X, k, maxIter, tolerance) - Cluster data points
• predictCluster(model, X) - Assign new points to clusters
• withinClusterVariance(model) - Measure cluster compactness
Section 17: Gaussian Mixture Model (GMM)
Expectation-Maximization algorithm:
• fitGMM(X, k, maxIter, tolerance) - Soft clustering with probabilities
• predictProbaGMM(model, X) - Returns membership probabilities
• Models data as mixture of Gaussian distributions
Section 18: Kalman Filter
Linear state estimation:
• createKalman1D(processNoise, measurementNoise, ...) - 1D filter
• createKalman2D(processNoise, measurementNoise) - Position + velocity tracking
• kalmanStep(state, measurement) - Predict-update cycle
• Optimal filtering for noisy measurements
Section 19: K-Nearest Neighbors (KNN)
Instance-based learning:
• fitKNN(X, y) - Store training data
• predictKNN(model, X, k) - Classify by majority vote
• predictKNNRegression(model, X, k) - Average of k neighbors
• predictKNNWeighted(model, X, k) - Distance-weighted voting
Section 20: Neural Network (Feedforward)
Multi-layer perceptron:
• createNeuralNetwork(architecture) - Define layer sizes
• trainNeuralNetwork(nn, X, y, learningRate, epochs) - Backpropagation
• predictNN(nn, X) - Forward pass
• Supports configurable hidden layers
Section 21: Naive Bayes Classifier
Gaussian Naive Bayes:
• fitNaiveBayes(X, y) - Estimate class-conditional distributions
• predictNaiveBayes(model, X) - Maximum a posteriori classification
• Assumes feature independence given class
Section 22: Anomaly Detection
Statistical outlier detection:
• fitAnomalyDetector(X, contamination) - Mahalanobis distance-based
• detectAnomalies(model, X) - Returns anomaly scores
• isAnomaly(model, X, threshold) - Binary classification
Section 23: Dynamic Time Warping (DTW)
Time series similarity:
• dtw(series1, series2) - Compute DTW distance
• Handles sequences of different lengths
• Useful for pattern matching
Section 24: Markov Chain / Regime Detection
Discrete state transitions:
• fitMarkovChain(states, nStates) - Estimate transition matrix
• predictNextState(transitionMatrix, currentState) - Most likely next state
• stationaryDistribution(transitionMatrix) - Long-run probabilities
Section 25: Hidden Markov Model (Simple)
Baum-Welch algorithm:
• fitHMM(observations, nStates, maxIter) - EM training
• viterbi(model, observations) - Most likely state sequence
• Useful for regime detection
Section 26: Exponential Smoothing & Holt-Winters
Time series smoothing:
• exponentialSmooth(data, alpha) - Simple exponential smoothing
• holtWinters(data, alpha, beta, gamma, seasonLength) - Triple smoothing
• Captures trend and seasonality
Section 27: Entropy and Information Theory
Information measures:
• entropy(probabilities) - Shannon entropy in bits
• conditionalEntropy(jointProbs, marginalProbs) - H(X|Y)
• mutualInformation(probsX, probsY, jointProbs) - I(X;Y)
• kldivergence(p, q) - Kullback-Leibler divergence
Section 28: Hurst Exponent
Long-range dependence measure:
• hurstExponent(data) - R/S analysis
• H < 0.5: mean-reverting, H = 0.5: random walk, H > 0.5: trending
Section 29: Change Detection (CUSUM)
Cumulative sum control chart:
• cusumChangeDetection(data, threshold, drift) - Detect regime changes
• cusumOnline(value, prevCusumPos, prevCusumNeg, target, drift) - Streaming version
Section 30: Autocorrelation
Serial dependence analysis:
• autocorrelation(data, maxLag) - ACF for all lags
• partialAutocorrelation(data, maxLag) - PACF via Durbin-Levinson
• Useful for time series model identification
Section 31: Ensemble Methods
Model combination:
• baggingPredict(models, X) - Average predictions
• votingClassify(models, X) - Majority vote
• Improves robustness through aggregation
Section 32: Model Evaluation Metrics
Performance assessment:
• mse(actual, predicted) / rmse / mae / mape - Regression metrics
• accuracy(actual, predicted) - Classification accuracy
• precision / recall / f1Score - Binary classification metrics
• confusionMatrix(actual, predicted, nClasses) - Multi-class evaluation
• rSquared(actual, predicted) / adjustedRSquared - Goodness of fit
Section 33: Cross-Validation
Model validation:
• trainTestSplit(X, y, trainRatio) - Random split
• Foundation for walk-forward validation
Section 34: Trading Convenience Functions
Trading-specific utilities:
• priceMatrix(length) - OHLC data as matrix
• logReturns(length) - Log return series
• rollingSlope(src, length) - Linear trend strength
• kalmanFilter(src, processNoise, measurementNoise) - Filtered price
• kalmanFilter2D(src, ...) - Price with velocity estimate
• adaptiveMA(src, sensitivity) - Kalman-based adaptive moving average
• volAdjMomentum(src, length) - Volatility-normalized momentum
• detectSRLevels(length, nLevels) - K-means based S/R detection
• buildFeatures(src, lengths) - Multi-timeframe feature construction
• technicalFeatures(length) - Standard indicator feature set (RSI, MACD, BB, ATR, etc.)
• lagFeatures(src, lags) - Time-lagged features
• sharpeRatio(returns) - Risk-adjusted return measure
• sortinoRatio(returns) - Downside risk-adjusted return
• maxDrawdown(equity) - Maximum peak-to-trough decline
• calmarRatio(returns, equity) - Return/drawdown ratio
• kellyCriterion(winRate, avgWin, avgLoss) - Optimal position sizing
• fractionalKelly(...) - Conservative Kelly sizing
• rollingBeta(assetReturns, benchmarkReturns) - Market exposure
• fractalDimension(data) - Market complexity measure
---
Usage Example
```
import YourUsername/MLMatrixLib/1 as ml
// Create feature matrix
matrix X = ml.priceMatrix(50)
X := ml.standardize(X)
// Fit linear regression
ml.LinearRegressionModel model = ml.fitLinearRegression(X, y)
float prediction = ml.predictLinear(model, X_new)
// Kalman filter for smoothing
float smoothedPrice = ml.kalmanFilter(close, 0.01, 1.0)
// Detect support/resistance levels
array levels = ml.detectSRLevels(100, 3)
// K-means clustering for regime detection
ml.KMeansModel km = ml.fitKMeans(features, 3)
int cluster = ml.predictCluster(km, newFeature)
```
---
Technical Notes
• All matrix operations use Pine Script's native matrix type
• Numerical stability ensured through:
- Clamping exponential arguments to prevent overflow
- Division by zero protection with epsilon thresholds
- Iterative algorithms with convergence tolerance
• Designed for bar-by-bar execution in Pine Script's event-driven model
• Compatible with Pine Script v6
---
Disclaimer
This library provides mathematical tools for quantitative analysis. It does not constitute financial advice. Past performance of any algorithm does not guarantee future results. Users are responsible for validating models on their specific use cases and understanding the limitations of each method.
Adaptive Genesis Engine [AGE]ADAPTIVE GENESIS ENGINE (AGE)
Pure Signal Evolution Through Genetic Algorithms
Where Darwin Meets Technical Analysis
🧬 WHAT YOU'RE GETTING - THE PURE INDICATOR
This is a technical analysis indicator - it generates signals, visualizes probability, and shows you the evolutionary process in real-time. This is NOT a strategy with automatic execution - it's a sophisticated signal generation system that you control .
What This Indicator Does:
Generates Long/Short entry signals with probability scores (35-88% range)
Evolves a population of up to 12 competing strategies using genetic algorithms
Validates strategies through walk-forward optimization (train/test cycles)
Visualizes signal quality through premium gradient clouds and confidence halos
Displays comprehensive metrics via enhanced dashboard
Provides alerts for entries and exits
Works on any timeframe, any instrument, any broker
What This Indicator Does NOT Do:
Execute trades automatically
Manage positions or calculate position sizes
Place orders on your behalf
Make trading decisions for you
This is pure signal intelligence. AGE tells you when and how confident it is. You decide whether and how much to trade.
🔬 THE SCIENCE: GENETIC ALGORITHMS MEET TECHNICAL ANALYSIS
What Makes This Different - The Evolutionary Foundation
Most indicators are static - they use the same parameters forever, regardless of market conditions. AGE is alive . It maintains a population of competing strategies that evolve, adapt, and improve through natural selection principles:
Birth: New strategies spawn through crossover breeding (combining DNA from fit parents) plus random mutation for exploration
Life: Each strategy trades virtually via shadow portfolios, accumulating wins/losses, tracking drawdown, and building performance history
Selection: Strategies are ranked by comprehensive fitness scoring (win rate, expectancy, drawdown control, signal efficiency)
Death: Weak strategies are culled periodically, with elite performers (top 2 by default) protected from removal
Evolution: The gene pool continuously improves as successful traits propagate and unsuccessful ones die out
This is not curve-fitting. Each new strategy must prove itself on out-of-sample data through walk-forward validation before being trusted for live signals.
🧪 THE DNA: WHAT EVOLVES
Every strategy carries a 10-gene chromosome controlling how it interprets market data:
Signal Sensitivity Genes
Entropy Sensitivity (0.5-2.0): Weight given to market order/disorder calculations. Low values = conservative, require strong directional clarity. High values = aggressive, act on weaker order signals.
Momentum Sensitivity (0.5-2.0): Weight given to RSI/ROC/MACD composite. Controls responsiveness to momentum shifts vs. mean-reversion setups.
Structure Sensitivity (0.5-2.0): Weight given to support/resistance positioning. Determines how much price location within swing range matters.
Probability Adjustment Genes
Probability Boost (-0.10 to +0.10): Inherent bias toward aggressive (+) or conservative (-) entries. Acts as personality trait - some strategies naturally optimistic, others pessimistic.
Trend Strength Requirement (0.3-0.8): Minimum trend conviction needed before signaling. Higher values = only trades strong trends, lower values = acts in weak/sideways markets.
Volume Filter (0.5-1.5): Strictness of volume confirmation. Higher values = requires strong volume, lower values = volume less important.
Risk Management Genes
ATR Multiplier (1.5-4.0): Base volatility scaling for all price levels. Controls whether strategy uses tight or wide stops/targets relative to ATR.
Stop Multiplier (1.0-2.5): Stop loss tightness. Lower values = aggressive profit protection, higher values = more breathing room.
Target Multiplier (1.5-4.0): Profit target ambition. Lower values = quick scalping exits, higher values = swing trading holds.
Adaptation Gene
Regime Adaptation (0.0-1.0): How much strategy adjusts behavior based on detected market regime (trending/volatile/choppy). Higher values = more reactive to regime changes.
The Magic: AGE doesn't just try random combinations. Through tournament selection and fitness-weighted crossover, successful gene combinations spread through the population while unsuccessful ones fade away. Over 50-100 bars, you'll see the population converge toward genes that work for YOUR instrument and timeframe.
📊 THE SIGNAL ENGINE: THREE-LAYER SYNTHESIS
Before any strategy generates a signal, AGE calculates probability through multi-indicator confluence:
Layer 1 - Market Entropy (Information Theory)
Measures whether price movements exhibit directional order or random walk characteristics:
The Math:
Shannon Entropy = -Σ(p × log(p))
Market Order = 1 - (Entropy / 0.693)
What It Means:
High entropy = choppy, random market → low confidence signals
Low entropy = directional market → high confidence signals
Direction determined by up-move vs down-move dominance over lookback period (default: 20 bars)
Signal Output: -1.0 to +1.0 (bearish order to bullish order)
Layer 2 - Momentum Synthesis
Combines three momentum indicators into single composite score:
Components:
RSI (40% weight): Normalized to -1/+1 scale using (RSI-50)/50
Rate of Change (30% weight): Percentage change over lookback (default: 14 bars), clamped to ±1
MACD Histogram (30% weight): Fast(12) - Slow(26), normalized by ATR
Why This Matters: RSI catches mean-reversion opportunities, ROC catches raw momentum, MACD catches momentum divergence. Weighting favors RSI for reliability while keeping other perspectives.
Signal Output: -1.0 to +1.0 (strong bearish to strong bullish)
Layer 3 - Structure Analysis
Evaluates price position within swing range (default: 50-bar lookback):
Position Classification:
Bottom 20% of range = Support Zone → bullish bounce potential
Top 20% of range = Resistance Zone → bearish rejection potential
Middle 60% = Neutral Zone → breakout/breakdown monitoring
Signal Logic:
At support + bullish candle = +0.7 (strong buy setup)
At resistance + bearish candle = -0.7 (strong sell setup)
Breaking above range highs = +0.5 (breakout confirmation)
Breaking below range lows = -0.5 (breakdown confirmation)
Consolidation within range = ±0.3 (weak directional bias)
Signal Output: -1.0 to +1.0 (bearish structure to bullish structure)
Confluence Voting System
Each layer casts a vote (Long/Short/Neutral). The system requires minimum 2-of-3 agreement (configurable 1-3) before generating a signal:
Examples:
Entropy: Bullish, Momentum: Bullish, Structure: Neutral → Signal generated (2 long votes)
Entropy: Bearish, Momentum: Neutral, Structure: Neutral → No signal (only 1 short vote)
All three bullish → Signal generated with +5% probability bonus
This is the key to quality. Single indicators give too many false signals. Triple confirmation dramatically improves accuracy.
📈 PROBABILITY CALCULATION: HOW CONFIDENCE IS MEASURED
Base Probability:
Raw_Prob = 50% + (Average_Signal_Strength × 25%)
Then AGE applies strategic adjustments:
Trend Alignment:
Signal with trend: +4%
Signal against strong trend: -8%
Weak/no trend: no adjustment
Regime Adaptation:
Trending market (efficiency >50%, moderate vol): +3%
Volatile market (vol ratio >1.5x): -5%
Choppy market (low efficiency): -2%
Volume Confirmation:
Volume > 70% of 20-bar SMA: no change
Volume below threshold: -3%
Volatility State (DVS Ratio):
High vol (>1.8x baseline): -4% (reduce confidence in chaos)
Low vol (<0.7x baseline): -2% (markets can whipsaw in compression)
Moderate elevated vol (1.0-1.3x): +2% (trending conditions emerging)
Confluence Bonus:
All 3 indicators agree: +5%
2 of 3 agree: +2%
Strategy Gene Adjustment:
Probability Boost gene: -10% to +10%
Regime Adaptation gene: scales regime adjustments by 0-100%
Final Probability: Clamped between 35% (minimum) and 88% (maximum)
Why These Ranges?
Below 35% = too uncertain, better not to signal
Above 88% = unrealistic, creates overconfidence
Sweet spot: 65-80% for quality entries
🔄 THE SHADOW PORTFOLIO SYSTEM: HOW STRATEGIES COMPETE
Each active strategy maintains a virtual trading account that executes in parallel with real-time data:
Shadow Trading Mechanics
Entry Logic:
Calculate signal direction, probability, and confluence using strategy's unique DNA
Check if signal meets quality gate:
Probability ≥ configured minimum threshold (default: 65%)
Confluence ≥ configured minimum (default: 2 of 3)
Direction is not zero (must be long or short, not neutral)
Verify signal persistence:
Base requirement: 2 bars (configurable 1-5)
Adapts based on probability: high-prob signals (75%+) enter 1 bar faster, low-prob signals need 1 bar more
Adjusts for regime: trending markets reduce persistence by 1, volatile markets add 1
Apply additional filters:
Trend strength must exceed strategy's requirement gene
Regime filter: if volatile market detected, probability must be 72%+ to override
Volume confirmation required (volume > 70% of average)
If all conditions met for required persistence bars, enter shadow position at current close price
Position Management:
Entry Price: Recorded at close of entry bar
Stop Loss: ATR-based distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit: ATR-based distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Position: +1 (long) or -1 (short), only one at a time per strategy
Exit Logic:
Check if price hit stop (on low) or target (on high) on current bar
Record trade outcome in R-multiples (profit/loss normalized by ATR)
Update performance metrics:
Total trades counter incremented
Wins counter (if profit > 0)
Cumulative P&L updated
Peak equity tracked (for drawdown calculation)
Maximum drawdown from peak recorded
Enter cooldown period (default: 8 bars, configurable 3-20) before next entry allowed
Reset signal age counter to zero
Walk-Forward Tracking:
During position lifecycle, trades are categorized:
Training Phase (first 250 bars): Trade counted toward training metrics
Testing Phase (next 75 bars): Trade counted toward testing metrics (out-of-sample)
Live Phase (after WFO period): Trade counted toward overall metrics
Why Shadow Portfolios?
No lookahead bias (uses only data available at the bar)
Realistic execution simulation (entry on close, stop/target checks on high/low)
Independent performance tracking for true fitness comparison
Allows safe experimentation without risking capital
Each strategy learns from its own experience
🏆 FITNESS SCORING: HOW STRATEGIES ARE RANKED
Fitness is not just win rate. AGE uses a comprehensive multi-factor scoring system:
Core Metrics (Minimum 3 trades required)
Win Rate (30% of fitness):
WinRate = Wins / TotalTrades
Normalized directly (0.0-1.0 scale)
Total P&L (30% of fitness):
Normalized_PnL = (PnL + 300) / 600
Clamped 0.0-1.0. Assumes P&L range of -300R to +300R for normalization scale.
Expectancy (25% of fitness):
Expectancy = Total_PnL / Total_Trades
Normalized_Expectancy = (Expectancy + 30) / 60
Clamped 0.0-1.0. Rewards consistency of profit per trade.
Drawdown Control (15% of fitness):
Normalized_DD = 1 - (Max_Drawdown / 15)
Clamped 0.0-1.0. Penalizes strategies that suffer large equity retracements from peak.
Sample Size Adjustment
Quality Factor:
<50 trades: 1.0 (full weight, small sample)
50-100 trades: 0.95 (slight penalty for medium sample)
100 trades: 0.85 (larger penalty for large sample)
Why penalize more trades? Prevents strategies from gaming the system by taking hundreds of tiny trades to inflate statistics. Favors quality over quantity.
Bonus Adjustments
Walk-Forward Validation Bonus:
if (WFO_Validated):
Fitness += (WFO_Efficiency - 0.5) × 0.1
Strategies proven on out-of-sample data receive up to +10% fitness boost based on test/train efficiency ratio.
Signal Efficiency Bonus (if diagnostics enabled):
if (Signals_Evaluated > 10):
Pass_Rate = Signals_Passed / Signals_Evaluated
Fitness += (Pass_Rate - 0.1) × 0.05
Rewards strategies that generate high-quality signals passing the quality gate, not just profitable trades.
Final Fitness: Clamped at 0.0 minimum (prevents negative fitness values)
Result: Elite strategies typically achieve 0.50-0.75 fitness. Anything above 0.60 is excellent. Below 0.30 is prime candidate for culling.
🔬 WALK-FORWARD OPTIMIZATION: ANTI-OVERFITTING PROTECTION
This is what separates AGE from curve-fitted garbage indicators.
The Three-Phase Process
Every new strategy undergoes a rigorous validation lifecycle:
Phase 1 - Training Window (First 250 bars, configurable 100-500):
Strategy trades normally via shadow portfolio
All trades count toward training performance metrics
System learns which gene combinations produce profitable patterns
Tracks independently: Training_Trades, Training_Wins, Training_PnL
Phase 2 - Testing Window (Next 75 bars, configurable 30-200):
Strategy continues trading without any parameter changes
Trades now count toward testing performance metrics (separate tracking)
This is out-of-sample data - strategy has never seen these bars during "optimization"
Tracks independently: Testing_Trades, Testing_Wins, Testing_PnL
Phase 3 - Validation Check:
Minimum_Trades = 5 (configurable 3-15)
IF (Train_Trades >= Minimum AND Test_Trades >= Minimum):
WR_Efficiency = Test_WinRate / Train_WinRate
Expectancy_Efficiency = Test_Expectancy / Train_Expectancy
WFO_Efficiency = (WR_Efficiency + Expectancy_Efficiency) / 2
IF (WFO_Efficiency >= 0.55): // configurable 0.3-0.9
Strategy.Validated = TRUE
Strategy receives fitness bonus
ELSE:
Strategy receives 30% fitness penalty
ELSE:
Validation deferred (insufficient trades in one or both periods)
What Validation Means
Validated Strategy (Green "✓ VAL" in dashboard):
Performed at least 55% as well on unseen data compared to training data
Gets fitness bonus: +(efficiency - 0.5) × 0.1
Receives priority during tournament selection for breeding
More likely to be chosen as active trading strategy
Unvalidated Strategy (Orange "○ TRAIN" in dashboard):
Failed to maintain performance on test data (likely curve-fitted to training period)
Receives 30% fitness penalty (0.7x multiplier)
Makes strategy prime candidate for culling
Can still trade but with lower selection probability
Insufficient Data (continues collecting):
Hasn't completed both training and testing periods yet
OR hasn't achieved minimum trade count in both periods
Validation check deferred until requirements met
Why 55% Efficiency Threshold?
If a strategy earned 10R during training but only 5.5R during testing, it still proved an edge exists beyond random luck. Requiring 100% efficiency would be unrealistic - market conditions change between periods. But requiring >50% ensures the strategy didn't completely degrade on fresh data.
The Protection: Strategies that work great on historical data but fail on new data are automatically identified and penalized. This prevents the population from being polluted by overfitted strategies that would fail in live trading.
🌊 DYNAMIC VOLATILITY SCALING (DVS): ADAPTIVE STOP/TARGET PLACEMENT
AGE doesn't use fixed stop distances. It adapts to current volatility conditions in real-time.
Four Volatility Measurement Methods
1. ATR Ratio (Simple Method):
Current_Vol = ATR(14) / Close
Baseline_Vol = SMA(Current_Vol, 100)
Ratio = Current_Vol / Baseline_Vol
Basic comparison of current ATR to 100-bar moving average baseline.
2. Parkinson (High-Low Range Based):
For each bar: HL = log(High / Low)
Parkinson_Vol = sqrt(Σ(HL²) / (4 × Period × log(2)))
More stable than close-to-close volatility. Captures intraday range expansion without overnight gap noise.
3. Garman-Klass (OHLC Based):
HL_Term = 0.5 × ²
CO_Term = (2×log(2) - 1) × ²
GK_Vol = sqrt(Σ(HL_Term - CO_Term) / Period)
Most sophisticated estimator. Incorporates all four price points (open, high, low, close) plus gap information.
4. Ensemble Method (Default - Median of All Three):
Ratio_1 = ATR_Current / ATR_Baseline
Ratio_2 = Parkinson_Current / Parkinson_Baseline
Ratio_3 = GK_Current / GK_Baseline
DVS_Ratio = Median(Ratio_1, Ratio_2, Ratio_3)
Why Ensemble?
Takes median to avoid outliers and false spikes
If ATR jumps but range-based methods stay calm, median prevents overreaction
If one method fails, other two compensate
Most robust approach across different market conditions
Sensitivity Scaling
Scaled_Ratio = (Raw_Ratio) ^ Sensitivity
Sensitivity 0.3: Cube root - heavily dampens volatility impact
Sensitivity 0.5: Square root - moderate dampening
Sensitivity 0.7 (Default): Balanced response to volatility changes
Sensitivity 1.0: Linear - full 1:1 volatility impact
Sensitivity 1.5: Exponential - amplified response to volatility spikes
Safety Clamps: Final DVS Ratio always clamped between 0.5x and 2.5x baseline to prevent extreme position sizing or stop placement errors.
How DVS Affects Shadow Trading
Every strategy's stop and target distances are multiplied by the current DVS ratio:
Stop Loss Distance:
Stop_Distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit Distance:
Target_Distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Example Scenario:
ATR = 10 points
Strategy's ATR_Mult gene = 2.5
Strategy's Stop_Mult gene = 1.5
Strategy's Target_Mult gene = 2.5
DVS_Ratio = 1.4 (40% above baseline volatility - market heating up)
Stop = 10 × 2.5 × 1.5 × 1.4 = 52.5 points (vs. 37.5 in normal vol)
Target = 10 × 2.5 × 2.5 × 1.4 = 87.5 points (vs. 62.5 in normal vol)
Result:
During volatility spikes: Stops automatically widen to avoid noise-based exits, targets extend for bigger moves
During calm periods: Stops tighten for better risk/reward, targets compress for realistic profit-taking
Strategies adapt risk management to match current market behavior
🧬 THE EVOLUTIONARY CYCLE: SPAWN, COMPETE, CULL
Initialization (Bar 1)
AGE begins with 4 seed strategies (if evolution enabled):
Seed Strategy #0 (Balanced):
All sensitivities at 1.0 (neutral)
Zero probability boost
Moderate trend requirement (0.4)
Standard ATR/stop/target multiples (2.5/1.5/2.5)
Mid-level regime adaptation (0.5)
Seed Strategy #1 (Momentum-Focused):
Lower entropy sensitivity (0.7), higher momentum (1.5)
Slight probability boost (+0.03)
Higher trend requirement (0.5)
Tighter stops (1.3), wider targets (3.0)
Seed Strategy #2 (Entropy-Driven):
Higher entropy sensitivity (1.5), lower momentum (0.8)
Slight probability penalty (-0.02)
More trend tolerant (0.6)
Wider stops (1.8), standard targets (2.5)
Seed Strategy #3 (Structure-Based):
Balanced entropy/momentum (0.8/0.9), high structure (1.4)
Slight probability boost (+0.02)
Lower trend requirement (0.35)
Moderate risk parameters (1.6/2.8)
All seeds start with WFO validation bypassed if WFO is disabled, or must validate if enabled.
Spawning New Strategies
Timing (Adaptive):
Historical phase: Every 30 bars (configurable 10-100)
Live phase: Every 200 bars (configurable 100-500)
Automatically switches to live timing when barstate.isrealtime triggers
Conditions:
Current population < max population limit (default: 8, configurable 4-12)
At least 2 active strategies exist (need parents)
Available slot in population array
Selection Process:
Run tournament selection 3 times with different seeds
Each tournament: randomly sample active strategies, pick highest fitness
Best from 3 tournaments becomes Parent 1
Repeat independently for Parent 2
Ensures fit parents but maintains diversity
Crossover Breeding:
For each of 10 genes:
Parent1_Fitness = fitness
Parent2_Fitness = fitness
Weight1 = Parent1_Fitness / (Parent1_Fitness + Parent2_Fitness)
Gene1 = parent1's value
Gene2 = parent2's value
Child_Gene = Weight1 × Gene1 + (1 - Weight1) × Gene2
Fitness-weighted crossover ensures fitter parent contributes more genetic material.
Mutation:
For each gene in child:
IF (random < mutation_rate):
Gene_Range = GENE_MAX - GENE_MIN
Noise = (random - 0.5) × 2 × mutation_strength × Gene_Range
Mutated_Gene = Clamp(Child_Gene + Noise, GENE_MIN, GENE_MAX)
Historical mutation rate: 20% (aggressive exploration)
Live mutation rate: 8% (conservative stability)
Mutation strength: 12% of gene range (configurable 5-25%)
Initialization of New Strategy:
Unique ID assigned (total_spawned counter)
Parent ID recorded
Generation = max(parent generations) + 1
Birth bar recorded (for age tracking)
All performance metrics zeroed
Shadow portfolio reset
WFO validation flag set to false (must prove itself)
Result: New strategy with hybrid DNA enters population, begins trading in next bar.
Competition (Every Bar)
All active strategies:
Calculate their signal based on unique DNA
Check quality gate with their thresholds
Manage shadow positions (entries/exits)
Update performance metrics
Recalculate fitness score
Track WFO validation progress
Strategies compete indirectly through fitness ranking - no direct interaction.
Culling Weak Strategies
Timing (Adaptive):
Historical phase: Every 60 bars (configurable 20-200, should be 2x spawn interval)
Live phase: Every 400 bars (configurable 200-1000, should be 2x spawn interval)
Minimum Adaptation Score (MAS):
Initial MAS = 0.10
MAS decays: MAS × 0.995 every cull cycle
Minimum MAS = 0.03 (floor)
MAS represents the "survival threshold" - strategies below this fitness level are vulnerable.
Culling Conditions (ALL must be true):
Population > minimum population (default: 3, configurable 2-4)
At least one strategy has fitness < MAS
Strategy's age > culling interval (prevents premature culling of new strategies)
Strategy is not in top N elite (default: 2, configurable 1-3)
Culling Process:
Find worst strategy:
For each active strategy:
IF (age > cull_interval):
Fitness = base_fitness
IF (not WFO_validated AND WFO_enabled):
Fitness × 0.7 // 30% penalty for unvalidated
IF (Fitness < MAS AND Fitness < worst_fitness_found):
worst_strategy = this_strategy
worst_fitness = Fitness
IF (worst_strategy found):
Count elite strategies with fitness > worst_fitness
IF (elite_count >= elite_preservation_count):
Deactivate worst_strategy (set active flag = false)
Increment total_culled counter
Elite Protection:
Even if a strategy's fitness falls below MAS, it survives if fewer than N strategies are better. This prevents culling when population is generally weak.
Result: Weak strategies removed from population, freeing slots for new spawns. Gene pool improves over time.
Selection for Display (Every Bar)
AGE chooses one strategy to display signals:
Best fitness = -1
Selected = none
For each active strategy:
Fitness = base_fitness
IF (WFO_validated):
Fitness × 1.3 // 30% bonus for validated strategies
IF (Fitness > best_fitness):
best_fitness = Fitness
selected_strategy = this_strategy
Display selected strategy's signals on chart
Result: Only the highest-fitness (optionally validated-boosted) strategy's signals appear as chart markers. Other strategies trade invisibly in shadow portfolios.
🎨 PREMIUM VISUALIZATION SYSTEM
AGE includes sophisticated visual feedback that standard indicators lack:
1. Gradient Probability Cloud (Optional, Default: ON)
Multi-layer gradient showing signal buildup 2-3 bars before entry:
Activation Conditions:
Signal persistence > 0 (same directional signal held for multiple bars)
Signal probability ≥ minimum threshold (65% by default)
Signal hasn't yet executed (still in "forming" state)
Visual Construction:
7 gradient layers by default (configurable 3-15)
Each layer is a line-fill pair (top line, bottom line, filled between)
Layer spacing: 0.3 to 1.0 × ATR above/below price
Outer layers = faint, inner layers = bright
Color transitions from base to intense based on layer position
Transparency scales with probability (high prob = more opaque)
Color Selection:
Long signals: Gradient from theme.gradient_bull_mid to theme.gradient_bull_strong
Short signals: Gradient from theme.gradient_bear_mid to theme.gradient_bear_strong
Base transparency: 92%, reduces by up to 8% for high-probability setups
Dynamic Behavior:
Cloud grows/shrinks as signal persistence increases/decreases
Redraws every bar while signal is forming
Disappears when signal executes or invalidates
Performance Note: Computationally expensive due to linefill objects. Disable or reduce layers if chart performance degrades.
2. Population Fitness Ribbon (Optional, Default: ON)
Histogram showing fitness distribution across active strategies:
Activation: Only draws on last bar (barstate.islast) to avoid historical clutter
Visual Construction:
10 histogram layers by default (configurable 5-20)
Plots 50 bars back from current bar
Positioned below price at: lowest_low(100) - 1.5×ATR (doesn't interfere with price action)
Each layer represents a fitness threshold (evenly spaced min to max fitness)
Layer Logic:
For layer_num from 0 to ribbon_layers:
Fitness_threshold = min_fitness + (max_fitness - min_fitness) × (layer / layers)
Count strategies with fitness ≥ threshold
Height = ATR × 0.15 × (count / total_active)
Y_position = base_level + ATR × 0.2 × layer
Color = Gradient from weak to strong based on layer position
Line_width = Scaled by height (taller = thicker)
Visual Feedback:
Tall, bright ribbon = healthy population, many fit strategies at high fitness levels
Short, dim ribbon = weak population, few strategies achieving good fitness
Ribbon compression (layers close together) = population converging to similar fitness
Ribbon spread = diverse fitness range, active selection pressure
Use Case: Quick visual health check without opening dashboard. Ribbon growing upward over time = population improving.
3. Confidence Halo (Optional, Default: ON)
Circular polyline around entry signals showing probability strength:
Activation: Draws when new position opens (shadow_position changes from 0 to ±1)
Visual Construction:
20-segment polyline forming approximate circle
Center: Low - 0.5×ATR (long) or High + 0.5×ATR (short)
Radius: 0.3×ATR (low confidence) to 1.0×ATR (elite confidence)
Scales with: (probability - min_probability) / (1.0 - min_probability)
Color Coding:
Elite (85%+): Cyan (theme.conf_elite), large radius, minimal transparency (40%)
Strong (75-85%): Strong green (theme.conf_strong), medium radius, moderate transparency (50%)
Good (65-75%): Good green (theme.conf_good), smaller radius, more transparent (60%)
Moderate (<65%): Moderate green (theme.conf_moderate), tiny radius, very transparent (70%)
Technical Detail:
Uses chart.point array with index-based positioning
5-bar horizontal spread for circular appearance (±5 bars from entry)
Curved=false (Pine Script polyline limitation)
Fill color matches line color but more transparent (88% vs line's transparency)
Purpose: Instant visual probability assessment. No need to check dashboard - halo size/brightness tells the story.
4. Evolution Event Markers (Optional, Default: ON)
Visual indicators of genetic algorithm activity:
Spawn Markers (Diamond, Cyan):
Plots when total_spawned increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.spawn_marker (cyan/bright blue)
Size: tiny
Indicates new strategy just entered population
Cull Markers (X-Cross, Red):
Plots when total_culled increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.cull_marker (red/pink)
Size: tiny
Indicates weak strategy just removed from population
What It Tells You:
Frequent spawning early = population building, active exploration
Frequent culling early = high selection pressure, weak strategies dying fast
Balanced spawn/cull = healthy evolutionary churn
No markers for long periods = stable population (evolution plateaued or optimal genes found)
5. Entry/Exit Markers
Clear visual signals for selected strategy's trades:
Long Entry (Triangle Up, Green):
Plots when selected strategy opens long position (position changes 0 → +1)
Location: below bar (location.belowbar)
Color: theme.long_primary (green/cyan depending on theme)
Transparency: Scales with probability:
Elite (85%+): 0% (fully opaque)
Strong (75-85%): 10%
Good (65-75%): 20%
Acceptable (55-65%): 35%
Size: small
Short Entry (Triangle Down, Red):
Plots when selected strategy opens short position (position changes 0 → -1)
Location: above bar (location.abovebar)
Color: theme.short_primary (red/pink depending on theme)
Transparency: Same scaling as long entries
Size: small
Exit (X-Cross, Orange):
Plots when selected strategy closes position (position changes ±1 → 0)
Location: absolute (at actual exit price if stop/target lines enabled)
Color: theme.exit_color (orange/yellow depending on theme)
Transparency: 0% (fully opaque)
Size: tiny
Result: Clean, probability-scaled markers that don't clutter chart but convey essential information.
6. Stop Loss & Take Profit Lines (Optional, Default: ON)
Visual representation of shadow portfolio risk levels:
Stop Loss Line:
Plots when selected strategy has active position
Level: shadow_stop value from selected strategy
Color: theme.short_primary with 60% transparency (red/pink, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Take Profit Line:
Plots when selected strategy has active position
Level: shadow_target value from selected strategy
Color: theme.long_primary with 60% transparency (green, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Purpose:
Shows where shadow portfolio would exit for stop/target
Helps visualize strategy's risk/reward ratio
Useful for manual traders to set similar levels
Disable for cleaner chart (recommended for presentations)
7. Dynamic Trend EMA
Gradient-colored trend line that visualizes trend strength:
Calculation:
EMA(close, trend_length) - default 50 period (configurable 20-100)
Slope calculated over 10 bars: (current_ema - ema ) / ema × 100
Color Logic:
Trend_direction:
Slope > 0.1% = Bullish (1)
Slope < -0.1% = Bearish (-1)
Otherwise = Neutral (0)
Trend_strength = abs(slope)
Color = Gradient between:
- Neutral color (gray/purple)
- Strong bullish (bright green) if direction = 1
- Strong bearish (bright red) if direction = -1
Gradient factor = trend_strength (0 to 1+ scale)
Visual Behavior:
Faint gray/purple = weak/no trend (choppy conditions)
Light green/red = emerging trend (low strength)
Bright green/red = strong trend (high conviction)
Color intensity = trend strength magnitude
Transparency: 50% (subtle, doesn't overpower price action)
Purpose: Subconscious awareness of trend state without checking dashboard or indicators.
8. Regime Background Tinting (Subtle)
Ultra-low opacity background color indicating detected market regime:
Regime Detection:
Efficiency = directional_movement / total_range (over trend_length bars)
Vol_ratio = current_volatility / average_volatility
IF (efficiency > 0.5 AND vol_ratio < 1.3):
Regime = Trending (1)
ELSE IF (vol_ratio > 1.5):
Regime = Volatile (2)
ELSE:
Regime = Choppy (0)
Background Colors:
Trending: theme.regime_trending (dark green, 92-93% transparency)
Volatile: theme.regime_volatile (dark red, 93% transparency)
Choppy: No tint (normal background)
Purpose:
Subliminal regime awareness
Helps explain why signals are/aren't generating
Trending = ideal conditions for AGE
Volatile = fewer signals, higher thresholds applied
Choppy = mixed signals, lower confidence
Important: Extremely subtle by design. Not meant to be obvious, just subconscious context.
📊 ENHANCED DASHBOARD
Comprehensive real-time metrics in single organized panel (top-right position):
Dashboard Structure (5 columns × 14 rows)
Header Row:
Column 0: "🧬 AGE PRO" + phase indicator (🔴 LIVE or ⏪ HIST)
Column 1: "POPULATION"
Column 2: "PERFORMANCE"
Column 3: "CURRENT SIGNAL"
Column 4: "ACTIVE STRATEGY"
Column 0: Market State
Regime (📈 TREND / 🌊 CHAOS / ➖ CHOP)
DVS Ratio (current volatility scaling factor, format: #.##)
Trend Direction (▲ BULL / ▼ BEAR / ➖ FLAT with color coding)
Trend Strength (0-100 scale, format: #.##)
Column 1: Population Metrics
Active strategies (count / max_population)
Validated strategies (WFO passed / active total)
Current generation number
Total spawned (all-time strategy births)
Total culled (all-time strategy deaths)
Column 2: Aggregate Performance
Total trades across all active strategies
Aggregate win rate (%) - color-coded:
Green (>55%)
Orange (45-55%)
Red (<45%)
Total P&L in R-multiples - color-coded by positive/negative
Best fitness score in population (format: #.###)
MAS - Minimum Adaptation Score (cull threshold, format: #.###)
Column 3: Current Signal Status
Status indicator:
"▲ LONG" (green) if selected strategy in long position
"▼ SHORT" (red) if selected strategy in short position
"⏳ FORMING" (orange) if signal persisting but not yet executed
"○ WAITING" (gray) if no active signal
Confidence percentage (0-100%, format: #.#%)
Quality assessment:
"🔥 ELITE" (cyan) for 85%+ probability
"✓ STRONG" (bright green) for 75-85%
"○ GOOD" (green) for 65-75%
"- LOW" (dim) for <65%
Confluence score (X/3 format)
Signal age:
"X bars" if signal forming
"IN TRADE" if position active
"---" if no signal
Column 4: Selected Strategy Details
Strategy ID number (#X format)
Validation status:
"✓ VAL" (green) if WFO validated
"○ TRAIN" (orange) if still in training/testing phase
Generation number (GX format)
Personal fitness score (format: #.### with color coding)
Trade count
P&L and win rate (format: #.#R (##%) with color coding)
Color Scheme:
Panel background: theme.panel_bg (dark, low opacity)
Panel headers: theme.panel_header (slightly lighter)
Primary text: theme.text_primary (bright, high contrast)
Secondary text: theme.text_secondary (dim, lower contrast)
Positive metrics: theme.metric_positive (green)
Warning metrics: theme.metric_warning (orange)
Negative metrics: theme.metric_negative (red)
Special markers: theme.validated_marker, theme.spawn_marker
Update Frequency: Only on barstate.islast (current bar) to minimize CPU usage
Purpose:
Quick overview of entire system state
No need to check multiple indicators
Trading decisions informed by population health, regime state, and signal quality
Transparency into what AGE is thinking
🔍 DIAGNOSTICS PANEL (Optional, Default: OFF)
Detailed signal quality tracking for optimization and debugging:
Panel Structure (3 columns × 8 rows)
Position: Bottom-right corner (doesn't interfere with main dashboard)
Header Row:
Column 0: "🔍 DIAGNOSTICS"
Column 1: "COUNT"
Column 2: "%"
Metrics Tracked (for selected strategy only):
Total Evaluated:
Every signal that passed initial calculation (direction ≠ 0)
Represents total opportunities considered
✓ Passed:
Signals that passed quality gate and executed
Green color coding
Percentage of evaluated signals
Rejection Breakdown:
⨯ Probability:
Rejected because probability < minimum threshold
Most common rejection reason typically
⨯ Confluence:
Rejected because confluence < minimum required (e.g., only 1 of 3 indicators agreed)
⨯ Trend:
Rejected because signal opposed strong trend
Indicates counter-trend protection working
⨯ Regime:
Rejected because volatile regime detected and probability wasn't high enough to override
Shows regime filter in action
⨯ Volume:
Rejected because volume < 70% of 20-bar average
Indicates volume confirmation requirement
Color Coding:
Passed count: Green (success metric)
Rejection counts: Red (failure metrics)
Percentages: Gray (neutral, informational)
Performance Cost: Slight CPU overhead for tracking counters. Disable when not actively optimizing settings.
How to Use Diagnostics
Scenario 1: Too Few Signals
Evaluated: 200
Passed: 10 (5%)
⨯ Probability: 120 (60%)
⨯ Confluence: 40 (20%)
⨯ Others: 30 (15%)
Diagnosis: Probability threshold too high for this strategy's DNA.
Solution: Lower min probability from 65% to 60%, or allow strategy more time to evolve better DNA.
Scenario 2: Too Many False Signals
Evaluated: 200
Passed: 80 (40%)
Strategy win rate: 45%
Diagnosis: Quality gate too loose, letting low-quality signals through.
Solution: Raise min probability to 70%, or increase min confluence to 3 (all indicators must agree).
Scenario 3: Regime-Specific Issues
⨯ Regime: 90 (45% of rejections)
Diagnosis: Frequent volatile regime detection blocking otherwise good signals.
Solution: Either accept fewer trades during chaos (recommended), or disable regime filter if you want signals regardless of market state.
Optimization Workflow:
Enable diagnostics
Run 200+ bars
Analyze rejection patterns
Adjust settings based on data
Re-run and compare pass rate
Disable diagnostics when satisfied
⚙️ CONFIGURATION GUIDE
🧬 Evolution Engine Settings
Enable AGE Evolution (Default: ON):
ON: Full genetic algorithm (recommended for best results)
OFF: Uses only 4 seed strategies, no spawning/culling (static population for comparison testing)
Max Population (4-12, Default: 8):
Higher = more diversity, more exploration, slower performance
Lower = faster computation, less exploration, risk of premature convergence
Sweet spot: 6-8 for most use cases
4 = minimum for meaningful evolution
12 = maximum before diminishing returns
Min Population (2-4, Default: 3):
Safety floor - system never culls below this count
Prevents population extinction during harsh selection
Should be at least half of max population
Elite Preservation (1-3, Default: 2):
Top N performers completely immune to culling
Ensures best genes always survive
1 = minimal protection, aggressive selection
2 = balanced (recommended)
3 = conservative, slower gene pool turnover
Historical: Spawn Interval (10-100, Default: 30):
Bars between spawning new strategies during historical data
Lower = faster evolution, more exploration
Higher = slower evolution, more evaluation time per strategy
30 bars = ~1-2 hours on 15min chart
Historical: Cull Interval (20-200, Default: 60):
Bars between culling weak strategies during historical data
Should be 2x spawn interval for balanced churn
Lower = aggressive selection pressure
Higher = patient evaluation
Live: Spawn Interval (100-500, Default: 200):
Bars between spawning during live trading
Much slower than historical for stability
Prevents population chaos during live trading
200 bars = ~1.5 trading days on 15min chart
Live: Cull Interval (200-1000, Default: 400):
Bars between culling during live trading
Should be 2x live spawn interval
Conservative removal during live trading
Historical: Mutation Rate (0.05-0.40, Default: 0.20):
Probability each gene mutates during breeding (20% = 2 out of 10 genes on average)
Higher = more exploration, slower convergence
Lower = more exploitation, faster convergence but risk of local optima
20% balances exploration vs exploitation
Live: Mutation Rate (0.02-0.20, Default: 0.08):
Mutation rate during live trading
Much lower for stability (don't want population to suddenly degrade)
8% = mostly inherits parent genes with small tweaks
Mutation Strength (0.05-0.25, Default: 0.12):
How much genes change when mutated (% of gene's total range)
0.05 = tiny nudges (fine-tuning)
0.12 = moderate jumps (recommended)
0.25 = large leaps (aggressive exploration)
Example: If gene range is 0.5-2.0, 12% strength = ±0.18 possible change
📈 Signal Quality Settings
Min Signal Probability (0.55-0.80, Default: 0.65):
Quality gate threshold - signals below this never generate
0.55-0.60 = More signals, accept lower confidence (higher risk)
0.65 = Institutional-grade balance (recommended)
0.70-0.75 = Fewer but higher-quality signals (conservative)
0.80+ = Very selective, very few signals (ultra-conservative)
Min Confluence Score (1-3, Default: 2):
Required indicator agreement before signal generates
1 = Any single indicator can trigger (not recommended - too many false signals)
2 = Requires 2 of 3 indicators agree (RECOMMENDED for balance)
3 = All 3 must agree (very selective, few signals, high quality)
Base Persistence Bars (1-5, Default: 2):
Base bars signal must persist before entry
System adapts automatically:
High probability signals (75%+) enter 1 bar faster
Low probability signals (<68%) need 1 bar more
Trending regime: -1 bar (faster entries)
Volatile regime: +1 bar (more confirmation)
1 = Immediate entry after quality gate (responsive but prone to whipsaw)
2 = Balanced confirmation (recommended)
3-5 = Patient confirmation (slower but more reliable)
Cooldown After Trade (3-20, Default: 8):
Bars to wait after exit before next entry allowed
Prevents overtrading and revenge trading
3 = Minimal cooldown (active trading)
8 = Balanced (recommended)
15-20 = Conservative (position trading)
Entropy Length (10-50, Default: 20):
Lookback period for market order/disorder calculation
Lower = more responsive to regime changes (noisy)
Higher = more stable regime detection (laggy)
20 = works across most timeframes
Momentum Length (5-30, Default: 14):
Period for RSI/ROC calculations
14 = standard (RSI default)
Lower = more signals, less reliable
Higher = fewer signals, more reliable
Structure Length (20-100, Default: 50):
Lookback for support/resistance swing range
20 = short-term swings (day trading)
50 = medium-term structure (recommended)
100 = major structure (position trading)
Trend EMA Length (20-100, Default: 50):
EMA period for trend detection and direction bias
20 = short-term trend (responsive)
50 = medium-term trend (recommended)
100 = long-term trend (position trading)
ATR Period (5-30, Default: 14):
Period for volatility measurement
14 = standard ATR
Lower = more responsive to vol changes
Higher = smoother vol calculation
📊 Volatility Scaling (DVS) Settings
Enable DVS (Default: ON):
Dynamic volatility scaling for adaptive stop/target placement
Highly recommended to leave ON
OFF only for testing fixed-distance stops
DVS Method (Default: Ensemble):
ATR Ratio: Simple, fast, single-method (good for beginners)
Parkinson: High-low range based (good for intraday)
Garman-Klass: OHLC based (sophisticated, considers gaps)
Ensemble: Median of all three (RECOMMENDED - most robust)
DVS Memory (20-200, Default: 100):
Lookback for baseline volatility comparison
20 = very responsive to vol changes (can overreact)
100 = balanced adaptation (recommended)
200 = slow, stable baseline (minimizes false vol signals)
DVS Sensitivity (0.3-1.5, Default: 0.7):
How much volatility affects scaling (power-law exponent)
0.3 = Conservative, heavily dampens vol impact (cube root)
0.5 = Moderate dampening (square root)
0.7 = Balanced response (recommended)
1.0 = Linear, full 1:1 vol response
1.5 = Aggressive, amplified response (exponential)
🔬 Walk-Forward Optimization Settings
Enable WFO (Default: ON):
Out-of-sample validation to prevent overfitting
Highly recommended to leave ON
OFF only for testing or if you want unvalidated strategies
Training Window (100-500, Default: 250):
Bars for in-sample optimization
100 = fast validation, less data (risky)
250 = balanced (recommended) - about 1-2 months on daily, 1-2 weeks on 15min
500 = patient validation, more data (conservative)
Testing Window (30-200, Default: 75):
Bars for out-of-sample validation
Should be ~30% of training window
30 = minimal test (fast validation)
75 = balanced (recommended)
200 = extensive test (very conservative)
Min Trades for Validation (3-15, Default: 5):
Required trades in BOTH training AND testing periods
3 = minimal sample (risky, fast validation)
5 = balanced (recommended)
10+ = conservative (slow validation, high confidence)
WFO Efficiency Threshold (0.3-0.9, Default: 0.55):
Minimum test/train performance ratio required
0.30 = Very loose (test must be 30% as good as training)
0.55 = Balanced (recommended) - test must be 55% as good
0.70+ = Strict (test must closely match training)
Higher = fewer validated strategies, lower risk of overfitting
🎨 Premium Visuals Settings
Visual Theme:
Neon Genesis: Cyberpunk aesthetic (cyan/magenta/purple)
Carbon Fiber: Industrial look (blue/red/gray)
Quantum Blue: Quantum computing (blue/purple/pink)
Aurora: Northern lights (teal/orange/purple)
⚡ Gradient Probability Cloud (Default: ON):
Multi-layer gradient showing signal buildup
Turn OFF if chart lags or for cleaner look
Cloud Gradient Layers (3-15, Default: 7):
More layers = smoother gradient, more CPU intensive
Fewer layers = faster, blockier appearance
🎗️ Population Fitness Ribbon (Default: ON):
Histogram showing fitness distribution
Turn OFF for cleaner chart
Ribbon Layers (5-20, Default: 10):
More layers = finer fitness detail
Fewer layers = simpler histogram
⭕ Signal Confidence Halo (Default: ON):
Circular indicator around entry signals
Size/brightness scales with probability
Minimal performance cost
🔬 Evolution Event Markers (Default: ON):
Diamond (spawn) and X (cull) markers
Shows genetic algorithm activity
Minimal performance cost
🎯 Stop/Target Lines (Default: ON):
Shows shadow portfolio stop/target levels
Turn OFF for cleaner chart (recommended for screenshots/presentations)
📊 Enhanced Dashboard (Default: ON):
Comprehensive metrics panel
Should stay ON unless you want zero overlays
🔍 Diagnostics Panel (Default: OFF):
Detailed signal rejection tracking
Turn ON when optimizing settings
Turn OFF during normal use (slight performance cost)
📈 USAGE WORKFLOW - HOW TO USE THIS INDICATOR
Phase 1: Initial Setup & Learning
Add AGE to your chart
Recommended timeframes: 15min, 30min, 1H (best signal-to-noise ratio)
Works on: 5min (day trading), 4H (swing trading), Daily (position trading)
Load 1000+ bars for sufficient evolution history
Let the population evolve (100+ bars minimum)
First 50 bars: Random exploration, poor results expected
Bars 50-150: Population converging, fitness improving
Bars 150+: Stable performance, validated strategies emerging
Watch the dashboard metrics
Population should grow toward max capacity
Generation number should advance regularly
Validated strategies counter should increase
Best fitness should trend upward toward 0.50-0.70 range
Observe evolution markers
Diamond markers (cyan) = new strategies spawning
X markers (red) = weak strategies being culled
Frequent early activity = healthy evolution
Activity slowing = population stabilizing
Be patient. Evolution takes time. Don't judge performance before 150+ bars.
Phase 2: Signal Observation
Watch signals form
Gradient cloud builds up 2-3 bars before entry
Cloud brightness = probability strength
Cloud thickness = signal persistence
Check signal quality
Look at confidence halo size when entry marker appears
Large bright halo = elite setup (85%+)
Medium halo = strong setup (75-85%)
Small halo = good setup (65-75%)
Verify market conditions
Check trend EMA color (green = uptrend, red = downtrend, gray = choppy)
Check background tint (green = trending, red = volatile, clear = choppy)
Trending background + aligned signal = ideal conditions
Review dashboard signal status
Current Signal column shows:
Status (Long/Short/Forming/Waiting)
Confidence % (actual probability value)
Quality assessment (Elite/Strong/Good)
Confluence score (2/3 or 3/3 preferred)
Only signals meeting ALL quality gates appear on chart. If you're not seeing signals, population is either still learning or market conditions aren't suitable.
Phase 3: Manual Trading Execution
When Long Signal Fires:
Verify confidence level (dashboard or halo size)
Confirm trend alignment (EMA sloping up, green color)
Check regime (preferably trending or choppy, avoid volatile)
Enter long manually on your broker platform
Set stop loss at displayed stop line level (if lines enabled), or use your own risk management
Set take profit at displayed target line level, or trail manually
Monitor position - exit if X marker appears (signal reversal)
When Short Signal Fires:
Same verification process
Confirm downtrend (EMA sloping down, red color)
Enter short manually
Use displayed stop/target levels or your own
AGE tells you WHEN and HOW CONFIDENT. You decide WHETHER and HOW MUCH.
Phase 4: Set Up Alerts (Never Miss a Signal)
Right-click on indicator name in legend
Select "Add Alert"
Choose condition:
"AGE Long" = Long entry signal fired
"AGE Short" = Short entry signal fired
"AGE Exit" = Position reversal/exit signal
Set notification method:
Sound alert (popup on chart)
Email notification
Webhook to phone/trading platform
Mobile app push notification
Name the alert (e.g., "AGE BTCUSD 15min Long")
Save alert
Recommended: Set alerts for both long and short, enable mobile push notifications. You'll get alerted in real-time even if not watching charts.
Phase 5: Monitor Population Health
Weekly Review:
Check dashboard Population column:
Active count should be near max (6-8 of 8)
Validated count should be >50% of active
Generation should be advancing (1-2 per week typical)
Check dashboard Performance column:
Aggregate win rate should be >50% (target: 55-65%)
Total P&L should be positive (may fluctuate)
Best fitness should be >0.50 (target: 0.55-0.70)
MAS should be declining slowly (normal adaptation)
Check Active Strategy column:
Selected strategy should be validated (✓ VAL)
Personal fitness should match best fitness
Trade count should be accumulating
Win rate should be >50%
Warning Signs:
Zero validated strategies after 300+ bars = settings too strict or market unsuitable
Best fitness stuck <0.30 = population struggling, consider parameter adjustment
No spawning/culling for 200+ bars = evolution stalled (may be optimal or need reset)
Aggregate win rate <45% sustained = system not working on this instrument/timeframe
Health Check Pass:
50%+ strategies validated
Best fitness >0.50
Aggregate win rate >52%
Regular spawn/cull activity
Selected strategy validated
Phase 6: Optimization (If Needed)
Enable Diagnostics Panel (bottom-right) for data-driven tuning:
Problem: Too Few Signals
Evaluated: 200
Passed: 8 (4%)
⨯ Probability: 140 (70%)
Solutions:
Lower min probability: 65% → 60% or 55%
Reduce min confluence: 2 → 1
Lower base persistence: 2 → 1
Increase mutation rate temporarily to explore new genes
Check if regime filter is blocking signals (⨯ Regime high?)
Problem: Too Many False Signals
Evaluated: 200
Passed: 90 (45%)
Win rate: 42%
Solutions:
Raise min probability: 65% → 70% or 75%
Increase min confluence: 2 → 3
Raise base persistence: 2 → 3
Enable WFO if disabled (validates strategies before use)
Check if volume filter is being ignored (⨯ Volume low?)
Problem: Counter-Trend Losses
⨯ Trend: 5 (only 5% rejected)
Losses often occur against trend
Solutions:
System should already filter trend opposition
May need stronger trend requirement
Consider only taking signals aligned with higher timeframe trend
Use longer trend EMA (50 → 100)
Problem: Volatile Market Whipsaws
⨯ Regime: 100 (50% rejected by volatile regime)
Still getting stopped out frequently
Solutions:
System is correctly blocking volatile signals
Losses happening because vol filter isn't strict enough
Consider not trading during volatile periods (respect the regime)
Or disable regime filter and accept higher risk
Optimization Workflow:
Enable diagnostics
Run 200+ bars with current settings
Analyze rejection patterns and win rate
Make ONE change at a time (scientific method)
Re-run 200+ bars and compare results
Keep change if improvement, revert if worse
Disable diagnostics when satisfied
Never change multiple parameters at once - you won't know what worked.
Phase 7: Multi-Instrument Deployment
AGE learns independently on each chart:
Recommended Strategy:
Deploy AGE on 3-5 different instruments
Different asset classes ideal (e.g., ES futures, EURUSD, BTCUSD, SPY, Gold)
Each learns optimal strategies for that instrument's personality
Take signals from all 5 charts
Natural diversification reduces overall risk
Why This Works:
When one market is choppy, others may be trending
Different instruments respond to different news/catalysts
Portfolio-level win rate more stable than single-instrument
Evolution explores different parameter spaces on each chart
Setup:
Same settings across all charts (or customize if preferred)
Set alerts for all
Take every validated signal across all instruments
Position size based on total account (don't overleverage any single signal)
⚠️ REALISTIC EXPECTATIONS - CRITICAL READING
What AGE Can Do
✅ Generate probability-weighted signals using genetic algorithms
✅ Evolve strategies in real-time through natural selection
✅ Validate strategies on out-of-sample data (walk-forward optimization)
✅ Adapt to changing market conditions automatically over time
✅ Provide comprehensive metrics on population health and signal quality
✅ Work on any instrument, any timeframe, any broker
✅ Improve over time as weak strategies are culled and fit strategies breed
What AGE Cannot Do
❌ Win every trade (typical win rate: 55-65% at best)
❌ Predict the future with certainty (markets are probabilistic, not deterministic)
❌ Work perfectly from bar 1 (needs 100-150 bars to learn and stabilize)
❌ Guarantee profits under all market conditions
❌ Replace your trading discipline and risk management
❌ Execute trades automatically (this is an indicator, not a strategy)
❌ Prevent all losses (drawdowns are normal and expected)
❌ Adapt instantly to regime changes (re-learning takes 50-100 bars)
Performance Realities
Typical Performance After Evolution Stabilizes (150+ bars):
Win Rate: 55-65% (excellent for trend-following systems)
Profit Factor: 1.5-2.5 (realistic for validated strategies)
Signal Frequency: 5-15 signals per 100 bars (quality over quantity)
Drawdown Periods: 20-40% of time in equity retracement (normal trading reality)
Max Consecutive Losses: 5-8 losses possible even with 60% win rate (probability says this is normal)
Evolution Timeline:
Bars 0-50: Random exploration, learning phase - poor results expected, don't judge yet
Bars 50-150: Population converging, fitness climbing - results improving
Bars 150-300: Stable performance, most strategies validated - consistent results
Bars 300+: Mature population, optimal genes dominant - best results
Market Condition Dependency:
Trending Markets: AGE excels - clear directional moves, high-probability setups
Choppy Markets: AGE struggles - fewer signals generated, lower win rate
Volatile Markets: AGE cautious - higher rejection rate, wider stops, fewer trades
Market Regime Changes:
When market shifts from trending to choppy overnight
Validated strategies can become temporarily invalidated
AGE will adapt through evolution, but not instantly
Expect 50-100 bar re-learning period after major regime shifts
Fitness may temporarily drop then recover
This is NOT a holy grail. It's a sophisticated signal generator that learns and adapts using genetic algorithms. Your success depends on:
Patience during learning periods (don't abandon after 3 losses)
Proper position sizing (risk 0.5-2% per trade, not 10%)
Following signals consistently (cherry-picking defeats statistical edge)
Not abandoning system prematurely (give it 200+ bars minimum)
Understanding probability (60% win rate means 40% of trades WILL lose)
Respecting market conditions (trending = trade more, choppy = trade less)
Managing emotions (AGE is emotionless, you need to be too)
Expected Drawdowns:
Single-strategy max DD: 10-20% of equity (normal)
Portfolio across multiple instruments: 5-15% (diversification helps)
Losing streaks: 3-5 consecutive losses expected periodically
No indicator eliminates risk. AGE manages risk through:
Quality gates (rejecting low-probability signals)
Confluence requirements (multi-indicator confirmation)
Persistence requirements (no knee-jerk reactions)
Regime awareness (reduced trading in chaos)
Walk-forward validation (preventing overfitting)
But it cannot prevent all losses. That's inherent to trading.
🔧 TECHNICAL SPECIFICATIONS
Platform: TradingView Pine Script v5
Indicator Type: Overlay indicator (plots on price chart)
Execution Type: Signals only - no automatic order placement
Computational Load:
Moderate to High (genetic algorithms + shadow portfolios)
8 strategies × shadow portfolio simulation = significant computation
Premium visuals add additional load (gradient cloud, fitness ribbon)
TradingView Resource Limits (Built-in Caps):
Max Bars Back: 500 (sufficient for WFO and evolution)
Max Labels: 100 (plenty for entry/exit markers)
Max Lines: 150 (adequate for stop/target lines)
Max Boxes: 50 (not heavily used)
Max Polylines: 100 (confidence halos)
Recommended Chart Settings:
Timeframe: 15min to 1H (optimal signal/noise balance)
5min: Works but noisier, more signals
4H/Daily: Works but fewer signals
Bars Loaded: 1000+ (ensures sufficient evolution history)
Replay Mode: Excellent for testing without risk
Performance Optimization Tips:
Disable gradient cloud if chart lags (most CPU intensive visual)
Disable fitness ribbon if still laggy
Reduce cloud layers from 7 to 3
Reduce ribbon layers from 10 to 5
Turn off diagnostics panel unless actively tuning
Close other heavy indicators to free resources
Browser/Platform Compatibility:
Works on all modern browsers (Chrome, Firefox, Safari, Edge)
Mobile app supported (full functionality on phone/tablet)
Desktop app supported (best performance)
Web version supported (may be slower on older computers)
Data Requirements:
Real-time or delayed data both work
No special data feeds required
Works with TradingView's standard data
Historical + live data seamlessly integrated
🎓 THEORETICAL FOUNDATIONS
AGE synthesizes advanced concepts from multiple disciplines:
Evolutionary Computation
Genetic Algorithms (Holland, 1975): Population-based optimization through natural selection metaphor
Tournament Selection: Fitness-based parent selection with diversity preservation
Crossover Operators: Fitness-weighted gene recombination from two parents
Mutation Operators: Random gene perturbation for exploration of new parameter space
Elitism: Preservation of top N performers to prevent loss of best solutions
Adaptive Parameters: Different mutation rates for historical vs. live phases
Technical Analysis
Support/Resistance: Price structure within swing ranges
Trend Following: EMA-based directional bias
Momentum Analysis: RSI, ROC, MACD composite indicators
Volatility Analysis: ATR-based risk scaling
Volume Confirmation: Trade activity validation
Information Theory
Shannon Entropy (1948): Quantification of market order vs. disorder
Signal-to-Noise Ratio: Directional information vs. random walk
Information Content: How much "information" a price move contains
Statistics & Probability
Walk-Forward Analysis: Rolling in-sample/out-of-sample optimization
Out-of-Sample Validation: Testing on unseen data to prevent overfitting
Monte Carlo Principles: Shadow portfolio simulation with realistic execution
Expectancy Theory: Win rate × avg win - loss rate × avg loss
Probability Distributions: Signal confidence quantification
Risk Management
ATR-Based Stops: Volatility-normalized risk per trade
Volatility Regime Detection: Market state classification (trending/choppy/volatile)
Drawdown Control: Peak-to-trough equity measurement
R-Multiple Normalization: Performance measurement in risk units
Machine Learning Concepts
Online Learning: Continuous adaptation as new data arrives
Fitness Functions: Multi-objective optimization (win rate + expectancy + drawdown)
Exploration vs. Exploitation: Balance between trying new strategies and using proven ones
Overfitting Prevention: Walk-forward validation as regularization
Novel Contribution:
AGE is the first TradingView indicator to apply genetic algorithms to real-time indicator parameter optimization while maintaining strict anti-overfitting controls through walk-forward validation.
Most "adaptive" indicators simply recalibrate lookback periods or thresholds. AGE evolves entirely new strategies through competitive selection - it's not parameter tuning, it's Darwinian evolution of trading logic itself.
The combination of:
Genetic algorithm population management
Shadow portfolio simulation for realistic fitness evaluation
Walk-forward validation to prevent overfitting
Multi-indicator confluence for signal quality
Dynamic volatility scaling for adaptive risk
...creates a system that genuinely learns and improves over time while avoiding the curse of curve-fitting that plagues most optimization approaches.
🏗️ DEVELOPMENT NOTES
This project represents months of intensive development, facing significant technical challenges:
Challenge 1: Making Genetics Actually Work
Early versions spawned garbage strategies that polluted the gene pool:
Random gene combinations produced nonsensical parameter sets
Weak strategies survived too long, dragging down population
No clear convergence toward optimal solutions
Solution:
Comprehensive fitness scoring (4 factors: win rate, P&L, expectancy, drawdown)
Elite preservation (top 2 always protected)
Walk-forward validation (unproven strategies penalized 30%)
Tournament selection (fitness-weighted breeding)
Adaptive culling (MAS decay creates increasing selection pressure)
Challenge 2: Balancing Evolution Speed vs. Stability
Too fast = population chaos, no convergence. Too slow = can't adapt to regime changes.
Solution:
Dual-phase timing: Fast evolution during historical (30/60 bar intervals), slow during live (200/400 bar intervals)
Adaptive mutation rates: 20% historical, 8% live
Spawn/cull ratio: Always 2:1 to prevent population collapse
Challenge 3: Shadow Portfolio Accuracy
Needed realistic trade simulation without lookahead bias:
Can't peek at future bars for exits
Must track multiple portfolios simultaneously
Stop/target checks must use bar's high/low correctly
Solution:
Entry on close (realistic)
Exit checks on current bar's high/low (realistic)
Independent position tracking per strategy
Cooldown periods to prevent unrealistic rapid re-entry
ATR-normalized P&L (R-multiples) for fair comparison across volatility regimes
Challenge 4: Pine Script Compilation Limits
Hit TradingView's execution limits multiple times:
Too many array operations
Too many variables
Too complex conditional logic
Solution:
Optimized data structures (single DNA array instead of 8 separate arrays)
Minimal visual overlays (only essential plots)
Efficient fitness calculations (vectorized where possible)
Strategic use of barstate.islast to minimize dashboard updates
Challenge 5: Walk-Forward Implementation
Standard WFO is difficult in Pine Script:
Can't easily "roll forward" through historical data
Can't re-optimize strategies mid-stream
Must work in real-time streaming environment
Solution:
Age-based phase detection (first 250 bars = training, next 75 = testing)
Separate metric tracking for train vs. test
Efficiency calculation at fixed interval (after test period completes)
Validation flag persists for strategy lifetime
Challenge 6: Signal Quality Control
Early versions generated too many signals with poor win rates:
Single indicators produced excessive noise
No trend alignment
No regime awareness
Instant entries on single-bar spikes
Solution:
Three-layer confluence system (entropy + momentum + structure)
Minimum 2-of-3 agreement requirement
Trend alignment checks (penalty for counter-trend)
Regime-based probability adjustments
Persistence requirements (signals must hold multiple bars)
Volume confirmation
Quality gate (probability + confluence thresholds)
The Result
A system that:
Truly evolves (not just parameter sweeps)
Truly validates (out-of-sample testing)
Truly adapts (ongoing competition and breeding)
Stays within TradingView's platform constraints
Provides institutional-quality signals
Maintains transparency (full metrics dashboard)
Development time: 3+ months of iterative refinement
Lines of code: ~1500 (highly optimized)
Test instruments: ES, NQ, EURUSD, BTCUSD, SPY, AAPL
Test timeframes: 5min, 15min, 1H, Daily
🎯 FINAL WORDS
The Adaptive Genesis Engine is not just another indicator - it's a living system that learns, adapts, and improves through the same principles that drive biological evolution. Every bar it observes adds to its experience. Every strategy it spawns explores new parameter combinations. Every strategy it culls removes weakness from the gene pool.
This is evolution in action on your charts.
You're not getting a static formula locked in time. You're getting a system that thinks , that competes , that survives through natural selection. The strongest strategies rise to the top. The weakest die. The gene pool improves generation after generation.
AGE doesn't claim to predict the future - it adapts to whatever the future brings. When markets shift from trending to choppy, from calm to volatile, from bullish to bearish - AGE evolves new strategies suited to the new regime.
Use it on any instrument. Any timeframe. Any market condition. AGE will adapt.
This indicator gives you the pure signal intelligence. How you choose to act on it - position sizing, risk management, execution discipline - that's your responsibility. AGE tells you when and how confident . You decide whether and how much .
Trust the process. Respect the evolution. Let Darwin work.
"In markets, as in nature, it is not the strongest strategies that survive, nor the most intelligent - but those most responsive to change."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
— Happy Holiday's
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt
A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.
"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."
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🤖 THE MACHINE LEARNING
Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering :
Models data as coming from multiple Gaussian distributions
Each market regime is a different Gaussian component
Provides probability of belonging to each regime
More sophisticated than simple clustering
Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
E-step: Calculate probability of current market belonging to each regime
M-step: Update regime parameters based on new data
Continuous learning without repainting
Adapts to changing market conditions
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🎯 THREE MARKET REGIMES
The GMM identifies three distinct market states:
Regime 1 - Low Volatility:
Quiet, ranging markets
Uses RSI-based momentum calculation
Reduces false signals in choppy conditions
Background: Pink tint
Regime 2 - Normal Market:
Standard trending conditions
Uses Rate of Change momentum
Balanced sensitivity
Background: Gray tint
Regime 3 - High Volatility:
Strong trends or volatility events
Uses Z-score based momentum
Captures extreme moves
Background: Cyan tint
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💡 KEY INNOVATIONS
1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
30% Regime 1, 60% Regime 2, 10% Regime 3
Smooth transitions between regimes
No sudden indicator jumps
2. Weighted Momentum Calculation:
Combines three different momentum formulas
Weights based on regime probabilities
Automatically adapts to market conditions
3. Confidence Indicator:
Shows how certain the model is (white line)
High confidence = strong regime identification
Low confidence = transitional market state
Line transparency changes with confidence
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⚙️ PARAMETER OPTIMIZATION
Training Period (50-500):
50-100: Quick adaptation to recent conditions
100: Balanced (default)
200-500: Stable regime identification
Number of Components (2-5):
2: Simple bull/bear regimes
3: Low/Normal/High volatility (default)
4-5: More granular regime detection
Learning Rate (0.1-1.0):
0.1-0.3: Slow, stable learning
0.3: Balanced (default)
0.5-1.0: Fast adaptation
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📊 TRADING STRATEGIES
Visual Signals:
Cyan gradient: Bullish momentum
Magenta gradient: Bearish momentum
Background color: Current regime
Confidence line: Model certainty
1. Regime-Based Trading:
Regime 1 (pink): Expect mean reversion
Regime 2 (gray): Standard trend following
Regime 3 (cyan): Strong momentum trades
2. Confidence-Filtered Signals:
Only trade when confidence > 70%
High confidence = clearer market state
Avoid transitions (low confidence)
3. Adaptive Position Sizing:
Regime 1: Smaller positions (choppy)
Regime 2: Normal positions
Regime 3: Larger positions (trending)
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🚀 ADVANTAGES OVER OTHER ML INDICATORS
vs K-Means Clustering:
Soft clustering (probabilities) vs hard boundaries
Captures uncertainty and transitions
More mathematically robust
vs KNN (K-Nearest Neighbors):
Unsupervised learning (no historical labels needed)
Continuous adaptation
Lower computational complexity
vs Neural Networks:
Interpretable (know what each regime means)
No overfitting issues
Works with limited data
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📈 PERFORMANCE CHARACTERISTICS
Best Market Conditions:
Markets with clear regime shifts
Volatile to trending transitions
Multi-timeframe analysis
Cryptocurrency markets (high regime variation)
Key Strengths:
Automatically adapts to market changes
No manual parameter adjustment needed
Smooth transitions between regimes
Probabilistic confidence measure
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🔬 TECHNICAL BACKGROUND
Gaussian Mixture Models are used extensively in:
Speech recognition (Google Assistant)
Computer vision (facial recognition)
Astronomy (galaxy classification)
Genomics (gene expression analysis)
Finance (risk modeling at investment banks)
The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.
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💡 PRO TIPS
Watch regime transitions: Best opportunities often occur when regimes change
Combine with volume: High volume + regime change = strong signal
Use confidence filter: Avoid low confidence periods
Multi-timeframe: Compare regimes across timeframes
Adjust position size: Scale based on identified regime
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⚠️ IMPORTANT NOTES
Machine learning adapts but doesn't predict the future
Best used with other confirmation indicators
Allow time for model to learn (100+ bars)
Not financial advice - educational purposes
Backtest thoroughly on your instruments
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🏆 CONCLUSION
The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:
Automatic adaptation to market conditions
Clear regime identification with confidence levels
Smooth, professional signal generation
True unsupervised machine learning
This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
Google's speech recognition
Tesla's computer vision
NASA's data analysis
Wall Street risk models
"Let the machine learn the market regimes. Trade with statistical confidence."
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Developed by AlphaNatt | Machine Learning Trading Systems
Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Goertzel Adaptive JMA T3Hello Fellas,
The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs.
It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive.
Used Adaptive Length Algorithms
Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length.
Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach.
Scoring System
The scoring system determines if bars are red or green and collects them.
Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA.
Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars.
Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0–100.
Signals
Is the price above MA? -> bullish market
Is the price below MA? -> bearish market
Usage
Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA.
It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score.
Now, follows a bunch of knowledge for people who don't know about the concepts used here.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Goertzel Algorithm
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency components¹.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence .
Usage of DFT/Goertzel In Adaptive Length Algorithms
Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy.
The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms.
I hope this can help you somehow.
Thanks for reading, and keep it up.
Best regards,
simwai
---
Credits to:
@ClassicScott
@yatrader2
@cheatcountry
@loxx
Normalized, Variety, Fast Fourier Transform Explorer [Loxx]Normalized, Variety, Fast Fourier Transform Explorer demonstrates Real, Cosine, and Sine Fast Fourier Transform algorithms. This indicator can be used as a rule of thumb but shouldn't be used in trading.
What is the Discrete Fourier Transform?
In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle.
What is the Complex Fast Fourier Transform?
The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T).
The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward.
What is Real-Fast Fourier Transform?
Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded.
What is the Real-Fast Fourier Transform?
In many applications, the input data for the DFT are purely real, in which case the outputs satisfy the symmetry
X(N-k)=X(k)
and efficient FFT algorithms have been designed for this situation (see e.g. Sorensen, 1987). One approach consists of taking an ordinary algorithm (e.g. Cooley–Tukey) and removing the redundant parts of the computation, saving roughly a factor of two in time and memory. Alternatively, it is possible to express an even-length real-input DFT as a complex DFT of half the length (whose real and imaginary parts are the even/odd elements of the original real data), followed by O(N) post-processing operations.
It was once believed that real-input DFTs could be more efficiently computed by means of the discrete Hartley transform (DHT), but it was subsequently argued that a specialized real-input DFT algorithm (FFT) can typically be found that requires fewer operations than the corresponding DHT algorithm (FHT) for the same number of inputs. Bruun's algorithm (above) is another method that was initially proposed to take advantage of real inputs, but it has not proved popular.
There are further FFT specializations for the cases of real data that have even/odd symmetry, in which case one can gain another factor of roughly two in time and memory and the DFT becomes the discrete cosine/sine transform(s) (DCT/DST). Instead of directly modifying an FFT algorithm for these cases, DCTs/DSTs can also be computed via FFTs of real data combined with O(N) pre- and post-processing.
What is the Discrete Cosine Transform?
A discrete cosine transform ( DCT ) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT , first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC ), digital television (such as SDTV, HDTV and VOD ), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.
The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the type-II DCT , which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT , is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform ( DST ), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs ( MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT . A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT . One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT ,: ix, xiii, 1, 141–304 used in several ISO /IEC and ITU-T international standards.
What is the Discrete Sine Transform?
In mathematics, the discrete sine transform (DST) is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using a purely real matrix. It is equivalent to the imaginary parts of a DFT of roughly twice the length, operating on real data with odd symmetry (since the Fourier transform of a real and odd function is imaginary and odd), where in some variants the input and/or output data are shifted by half a sample.
A family of transforms composed of sine and sine hyperbolic functions exists. These transforms are made based on the natural vibration of thin square plates with different boundary conditions.
The DST is related to the discrete cosine transform (DCT), which is equivalent to a DFT of real and even functions. See the DCT article for a general discussion of how the boundary conditions relate the various DCT and DST types. Generally, the DST is derived from the DCT by replacing the Neumann condition at x=0 with a Dirichlet condition. Both the DCT and the DST were described by Nasir Ahmed T. Natarajan and K.R. Rao in 1974. The type-I DST (DST-I) was later described by Anil K. Jain in 1976, and the type-II DST (DST-II) was then described by H.B. Kekra and J.K. Solanka in 1978.
Notable settings
windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm
SS = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods
Fmin1 = zeroes out frequencies not passing this test for min value
Fmax1 = zeroes out frequencies not passing this test for max value
barsback = moves the window backward
Inverse = whether or not you wish to invert the FFT after first pass calculation
Related indicators
Real-Fast Fourier Transform of Price Oscillator
STD-Stepped Fast Cosine Transform Moving Average
Real-Fast Fourier Transform of Price w/ Linear Regression
Variety RSI of Fast Discrete Cosine Transform
Additional reading
A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al.
Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al.
Cooley–Tukey FFT algorithm
Ahmed, Nasir (January 1991). "How I Came Up With the Discrete Cosine Transform". Digital Signal Processing. 1 (1): 4–5. doi:10.1016/1051-2004(91)90086-Z.
DCT-History - How I Came Up With The Discrete Cosine Transform
Comparative Analysis for Discrete Sine Transform as a suitable method for noise estimation
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following:
w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter
and
w += math.pow(array.get(x, i), 2), the sum of the squared source parameter
This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method
Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price?
In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Further reading
A high-resolution modified Burg algorithm for spectral estimation
Related Indicators
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
DafeSPALibDafeSPALib: The Shadow Portfolio Adaptation & Strategy Selection Engine
This is not a backtester. This is a live, adaptive portfolio manager. It is a reinforcement learning system that learns which of your strategies to trust in the ever-changing chaos of the market.
█ CHAPTER 1: THE PHILOSOPHY - BEYOND A SINGLE STRATEGY
The search for a single "holy grail" trading strategy is a fool's errand. No single set of rules can perform optimally in all market conditions. A trend-following system that thrives in a bull run will be decimated by a choppy, range-bound market. A mean-reversion strategy that profits from ranges will be run over by a powerful breakout.
The DafeSPALib (Shadow Portfolio Adaptation Library) was created to solve this fundamental problem. It is built on a powerful principle from modern quantitative finance: instead of searching for one perfect strategy, a truly robust system should intelligently allocate to a portfolio of different strategies, dynamically favoring the one that is currently most effective.
This is not just a concept; it is a complete, production-grade engine built in Pine Script. It allows a developer to run multiple "shadow portfolios"—hypothetical trading accounts for each of your strategies—in parallel, in real time. The library tracks the actual equity curve, win rate, Sharpe ratio, and drawdown of each strategy. It then uses a sophisticated selection algorithm to determine which strategy is the "alpha" in the current market regime and tells you which one to follow. It is an AI portfolio manager that lives on your chart.
█ CHAPTER 2: THE CORE INNOVATIONS - WHAT MAKES THIS A REVOLUTIONARY ENGINE?
This library is not a simple strategy switcher. It is a suite of genuine, academically recognized machine learning and statistical concepts, adapted for the Pine Script environment.
Shadow Portfolio Tracking: This is the heart of the system. For each of your strategy "arms," the library maintains a complete, independent set of performance analytics. It doesn't just keep a simple "score." It tracks every hypothetical trade, calculates real P&L;, and updates a full suite of institutional metrics, including the Sharpe Ratio (risk-adjusted return), Sortino Ratio (downside-risk-adjusted return), Profit Factor , and Maximum Drawdown . This provides a rich, data-driven foundation for all decision-making.
Advanced Selection Algorithms: The library doesn't just pick the strategy with the highest recent win rate. It uses sophisticated, battle-tested algorithms from the "multi-armed bandit" problem in machine learning to solve the critical "explore vs. exploit" dilemma:
Thompson Sampling: The default and most powerful. Instead of just picking the "best" arm, it samples from each arm's learned probability distribution of success (its Beta distribution). This naturally balances "exploitation" (using the strategy that works) with "exploration" (giving less-proven strategies a chance to shine), making it incredibly robust against changing conditions.
Upper Confidence Bound (UCB): A deterministic algorithm that is "optimistic in the face of uncertainty." It favors strategies that have both a high win rate and a high degree of uncertainty (fewer trades), encouraging intelligent exploration.
Epsilon-Greedy: A classic RL algorithm that mostly exploits the best-known strategy but, with a small probability (epsilon), explores a random one to prevent getting stuck on a sub-optimal choice.
Trauma-Based Memory Compression: This is a groundbreaking, proprietary concept. When the market experiences a "regime shock" (a sudden explosion in volatility, a violent trend reversal), a simple learning system can be paralyzed or make catastrophic errors. The SPA engine's "trauma" cycle is an intelligent response. It does not erase all learned knowledge. Instead, it compresses the memory : it preserves the direction of what it has learned (e.g., "Strategy A is generally better than B") but it destroys the confidence. The AI "remembers" its experiences but becomes highly uncertain, forcing it to re-learn and adapt to the new market personality with incredible speed. Think of it like PTSD for an AI: the memory of the event remains, but the trust is shattered.
Multi-Layer Concept Drift Detection: This is the system's "earthquake detector." It is constantly scanning for signs that the market's fundamental character is changing ("concept drift"). It uses three layers of detection— Structural (trend slope changes), Volatility (ATR explosions), and Participation (volume anomalies)—to identify a regime shock and trigger the trauma compression cycle.
█ CHAPTER 3: A DUAL-PURPOSE FRAMEWORK - MODES OF OPERATION
This library, along with its companion DAFE libraries, is designed for ultimate flexibility. As a developer, you have complete freedom to use these tools independently or as a fully integrated system.
MODE 1: STANDALONE ENGINE OPERATION (Independent Power)
The DafeSPALib can be used entirely on its own to build a powerful portfolio-of-strategies indicator without any external ML. This approach is perfect for comparing, validating, and dynamically selecting from your own existing, rule-based trading ideas.
The Workflow:
Your indicator initializes the SPA engine with a set number of "arms" (e.g., 4).
On each bar, you calculate the signals for each of your independent strategies (e.g., an EMA Crossover, an RSI Mean Reversion, a Bollinger Breakout).
You feed this array of signals ( ) into the SPA's feed_signals() function.
The SPA engine updates the shadow portfolio for each of the four strategies based on these signals. You then call the select() function, and the SPA's chosen algorithm (e.g., Thompson Sampling) will return the index of the single strategy arm that it trusts the most right now.
Your indicator's final output signal is the signal from that selected arm.
The Result: A complete, self-contained meta-strategy. Your indicator is no longer just one strategy; it is an intelligent manager that dynamically switches between multiple strategies, adapting to the market by selecting the one with the best real-time, risk-adjusted performance.
MODE 2: BRIDGED SUPER-SYSTEM OPERATION (The Ultimate AI)
This is the pinnacle of the DAFE ecosystem. In this advanced mode, the DafeSPALib acts as the "strategic brain" or "portfolio manager" that is fused with a tactical machine learning engine (like the DafeRLMLLib) via a master communication protocol (the DafeMLSPABridge).
The Workflow:
The ML engine generates proposals.
The Bridge Library translates these proposals into a portfolio of micro-strategies.
The SPA engine (this library) receives this portfolio of signals, tracks their shadow performance, and uses its advanced selection algorithms to choose the single best micro-strategy to follow. This becomes the final trade decision.
The final P&L; from the SPA's selection is then routed back through the Bridge to the ML engine as a highly qualified reward signal for learning.
The Result: A hybrid intelligence that is more robust and adaptive than either system alone. The ML provides tactical creativity, while the SPA provides ruthless, performance-based strategic oversight.
█ CHAPTER 4: THE DEVELOPER'S MASTERCLASS - IMPLEMENTATION GUIDE
This library is a professional framework. This guide provides the complete, unabridged instructions and templates required to integrate the DAFE SPA engine into your own custom Pine Script indicators.
PART I: THE INPUTS TEMPLATE (THE CONTROL PANEL)
To give your users full control over the AI, copy this entire block of inputs into your indicator script. It is professionally organized with groups and detailed tooltips.
// ╔════════════════════════════════════════════════════════╗
// ║ INPUTS TEMPLATE (COPY INTO YOUR SCRIPT) ║
// ╚════════════════════════════════════════════════════════╝
// INPUT GROUPS
string G_SPA_ENGINE = "════════════ 🧠 SPA ENGINE ════════════"
string G_SPA_DRIFT = "════════════ 🌊 CONCEPT DRIFT ══════════"
string G_SPA_DASH = "════════════ 📋 DIAGNOSTICS ═══════════"
// SPA ENGINE
int i_spa_num_arms = input.int(4, "Number of Strategy Arms", minval=2, maxval=10, group=G_SPA_ENGINE,
tooltip="The number of parallel strategies the SPA will track.")
string i_spa_selection = input.string("Thompson Sampling", "🤖 Selection Algorithm",
options= , group=G_SPA_ENGINE,
tooltip="The machine learning algorithm used to select the best arm.\n\n" +
"• Thompson Sampling: Bayesian approach, samples from each arm's success probability. Balances explore/exploit perfectly (Recommended).\n" +
"• UCB: Optimistic approach that favors arms with high uncertainty. Excellent for exploration.\n" +
"• Epsilon-Greedy: Mostly exploits the best arm, but explores randomly with a small probability (epsilon).\n" +
"• Softmax: Selects arms based on a probability distribution weighted by their performance.")
float i_spa_epsilon = input.float(0.15, "🧭 Epsilon (for Epsilon-Greedy)", minval=0.01, maxval=0.5, step=0.01, group=G_SPA_ENGINE,
tooltip="The probability of taking a random action to explore. This value automatically decays over time.")
float i_spa_decay = input.float(0.995, "🧠 Memory Decay Rate", minval=0.98, maxval=0.9999, step=0.0005, group=G_SPA_ENGINE,
tooltip="Controls recency bias. A value of 0.995 means the AI gives slightly more weight to recent performance. Lower values create a very short-term memory.")
// CONCEPT DRIFT & TRAUMA
bool i_spa_use_drift = input.bool(true, "🌊 Enable Concept Drift & Trauma", group=G_SPA_DRIFT,
tooltip="Allows the engine to detect market regime shocks and trigger a 'Trauma Compression' cycle to accelerate re-learning.")
float i_spa_trauma_sens = input.float(2.0, "Trauma Sensitivity", minval=1.2, maxval=4.0, step=0.1, group=G_SPA_DRIFT,
tooltip="How sensitive the shock detector is. A lower value will trigger trauma cycles more frequently on smaller volatility/volume spikes.")
// DIAGNOSTICS
bool i_spa_show_dash = input.bool(true, "📋 Show Diagnostics Dashboard", group=G_SPA_DASH)
PART II: THE IMPLEMENTATION LOGIC (THE HEART OF YOUR SCRIPT)
This is the boilerplate code you will adapt to your indicator. It shows the complete loop of feeding signals, detecting drift, and selecting the best strategy.
// ╔═══════════════════════════════════════════════════════╗
// ║ USAGE EXAMPLE (ADAPT TO YOUR SCRIPT) ║
// ╚═══════════════════════════════════════════════════════╝
// 1. INITIALIZE THE ENGINE (happens only on the first bar)
int sel_method_id = i_spa_selection == "Thompson Sampling" ? 0 : i_spa_selection == "Upper Confidence Bound (UCB)" ? 1 : i_spa_selection == "Epsilon-Greedy" ? 2 : 3
var spa.SPAEngine engine = spa.init(
num_arms = i_spa_num_arms,
arm_names = array.from("TrendArm", "ReversionArm", "BreakoutArm", "MomentumArm"), // Give your arms names!
selection_method = sel_method_id,
decay_rate = i_spa_decay,
trauma_sensitivity = i_spa_trauma_sens,
epsilon = i_spa_epsilon
)
// 2. DEFINE YOUR STRATEGY SIGNALS (runs on every bar)
// These are your own custom, rule-based strategies. The signal should be +1 for Buy, -1 for Sell, 0 for Neutral.
int trend_signal = close > ta.ema(close, 200) and ta.crossover(ta.ema(close, 20), ta.ema(close, 50)) ? 1 :
close < ta.ema(close, 200) and ta.crossunder(ta.ema(close, 20), ta.ema(close, 50)) ? -1 : 0
int reversion_signal = ta.crossunder(ta.rsi(close, 14), 30) ? 1 : ta.crossover(ta.rsi(close, 14), 70) ? -1 : 0
int breakout_signal = ta.crossover(close, ta.highest(high, 20) ) ? 1 : ta.crossunder(close, ta.lowest(low, 20) ) ? -1 : 0
int momentum_signal = ta.crossover(ta.mom(close, 10), 0) ? 1 : ta.crossunder(ta.mom(close, 10), 0) ? -1 : 0
// Create an array of your signals. The order MUST be consistent.
array all_signals = array.from(trend_signal, reversion_signal, breakout_signal, momentum_signal)
// 3. THE MAIN LOOP (Feed -> Detect -> Select) - runs on every bar
// --- FEED: Update the shadow portfolios with the latest signals and price ---
engine := spa.feed_signals(engine, all_signals, close)
// --- DETECT: Run the concept drift engine ---
if i_spa_use_drift
float trend_slope = ta.linreg(close, 20, 0) - ta.linreg(close, 20, 1)
engine := spa.detect_drift(engine, close, volume, ta.atr(14), trend_slope)
engine := spa.apply_trauma_cycle(engine) // This will compress memory if a shock was detected
// --- SELECT: Ask the engine for its best choice ---
= spa.select(engine)
engine := updated_engine // CRITICAL: Always update the engine state
// --- ACT: Use the final, selected signal for your indicator's logic ---
int final_signal = array.get(all_signals, selected_arm)
string selected_name = spa.get_name(engine, selected_arm)
// Example: Color bars based on the final, SPA-vetted signal
barcolor(final_signal == 1 ? color.new(color.green, 70) : final_signal == -1 ? color.new(color.red, 70) : na)
// 4. DISPLAY DIAGNOSTICS
if i_spa_show_dash and barstate.islast
string diag_text = spa.diagnostics(engine)
label.new(bar_index, high, diag_text,
style=label.style_label_down,
color=color.new(#0A0A14, 10),
textcolor=#00E5FF,
size=size.small,
textalign=text.align_left)
█ DEVELOPMENT PHILOSOPHY
The DafeSPALib was born from the realization that market adaptation is the true holy grail of trading. While any single strategy is brittle, a portfolio of strategies, managed by an intelligent selection algorithm, is antifragile—it can learn, adapt, and potentially thrive in the face of chaos. This library is an open-source tool for the systems thinker, the quantitative analyst, and the professional developer. It is designed to provide the foundational architecture for building the most robust, adaptive, and intelligent trading systems on the TradingView platform.
This library is a tool for that wisdom. It is not about having the single smartest algorithm, but about having a disciplined, data-driven process for selecting the one that is working right now.
█ DISCLAIMER & IMPORTANT NOTES
THIS IS A LIBRARY FOR ADVANCED DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be integrated into other indicators and fed with valid strategy signals.
PERFORMANCE IS HYPOTHETICAL: The shadow portfolio tracking is a simulation. It does not account for slippage, fees (unless manually added to P&L;), or the psychological pressure of live trading.
LEARNING REQUIRES DATA: The selection algorithms require a sufficient number of trades (at least 20-30 per arm) to make statistically meaningful decisions. The engine will be less reliable during the initial "warm-up" period.
"You don't need to be a rocket scientist. Investing is not a game where the guy with the 160 IQ beats the guy with the 130 IQ."
— Warren Buffett
Taking you to school. - Dskyz, Create with RL.
Ichimoku Cloud Laboratory [DAFE]Ichimoku Cloud Laboratory : The Ultimate All-In-One Trend & Equilibrium Engine
50+ Cloud Engines. Multi-Cloud Architecture. Advanced Signal Filtering. This is Not Just Ichimoku. This is the Evolution of Market Equilibrium.
█ PHILOSOPHY: BEYOND THE CLOUD, INTO THE LABORATORY
The Ichimoku Kinko Hyo is more than an indicator; it is a complete trading philosophy, a masterpiece of market analysis that provides an "at-a-glance" view of trend, momentum, and equilibrium. However, its core calculation—the simple midpoint of the high and low—was conceived in a pre-computer era. While brilliant, it is blind to the modern market's most critical force: the nuanced character of volume, volatility, and microstructure.
The Ichimoku Cloud Laboratory was not created to be another Ichimoku clone. It was engineered to be the definitive evolution of Goichi Hosoda's original vision. This is not just an indicator; it is a powerful, interactive research environment. It is a laboratory where you, the trader, can move beyond the static "one-size-fits-all" approach and forge an Ichimoku system that is perfectly synchronized with the unique physics of your market, timeframe, and analytical style.
We have deconstructed the very DNA of the Cloud, replacing its rigid 1930s-era calculation with a library of over 50 distinct, mathematically diverse calculation engines . From classical moving averages and advanced DSP filters to proprietary DAFE quantum models, this suite provides an unparalleled arsenal for visualizing the true, underlying architecture of market equilibrium.
█ WHAT MAKES THIS A "LABORATORY"? THE CORE INNOVATIONS
This tool stands in a class of its own. It is a collection of what could be 50 separate indicators, all seamlessly integrated into one powerful, unified engine.
The 50+ Algorithm Engine: This is the heart of the Laboratory. You are no longer bound by the simple Donchian midpoint. You can now swap the core calculation engine of the Tenkan-sen, Kijun-sen, and Senkou Span B with any of over 50 algorithms. Want a zero-lag, Hull MA-based cloud? A volume-weighted cloud that gravitates towards liquidity? A cloud that adapts its speed based on market entropy? You now have the power to construct it.
Multi-Cloud Architecture: This revolutionary feature allows you to stack up to three layers of the Ichimoku cloud on your chart, each calculated with a progressively longer timeframe multiplier. This transforms the flat, two-dimensional cloud into a rich, three-dimensional "heatmap" of support and resistance. You can instantly see the alignment (or conflict) between the short-term, medium-term, and long-term trends.
Advanced Signal Logic & Filtering: Go beyond the simple TK Cross. The Laboratory includes eight distinct, built-in signal strategies, from the classic "Kumo Breakout" to the high-conviction "Perfect Order." Crucially, you can then fortify these signals with a professional-grade filter module, requiring confirmation from Volume, ATR (volatility), or ADX (trend strength) before a signal is even considered valid.
Proprietary DAFE Engines: The crown jewels of the Laboratory. These are custom-built, proprietary algorithms you will not find anywhere else, designed to infuse the cloud with modern quantitative analysis:
DAFE Flux Reactor: A cloud that breathes with volatility, automatically tightening in squeezes and expanding in trends.
DAFE Tensor Cloud: Uses a 4-dimensional average (OHLC) to create a cloud that tracks the "true" center of price action.
DAFE Quantum Step: A noise-canceling cloud that only moves when price exceeds a volatility-based threshold.
DAFE Gravity Well: A volume-weighted cloud that is magnetically pulled towards high-liquidity zones.
Integrated Performance Engine & Dashboard: How do you know which of the 50+ engines is best? You test it. The built-in Performance Dashboard tracks every trade generated by your chosen configuration, while the main dashboard provides a comprehensive, at-a-glance summary of the entire Ichimoku system's current state.
█ A GUIDED TOUR OF THE ALGORITHMIC CORE
This is your library of mathematical DNA. The 50+ engines are your tools to build the perfect cloud.
THE ENGINE FAMILIES
The Classics (Hull MA, ZLEMA, KAMA, VIDYA): Replace the choppy Donchian midpoint with smooth, low-lag, or adaptive moving averages to create a more responsive and readable cloud.
The DSP & Quantitative Masters (SuperSmoother, Kalman, Gaussian, Laguerre): Employ advanced digital signal processing and statistical filtering to construct a cloud that is surgically precise in its separation of trend "signal" from market "noise."
The Volume-Based (VWMA, VWAP, Money Flow Weighted): Build a cloud that is not just based on price, but is weighted by participation. This creates a cloud that automatically respects high-liquidity zones as stronger levels of support and resistance.
The Adaptive Geniuses (ATR-Scaled, Volatility-Modulated, Efficiency Ratio, Entropy): These are "smart" engines that analyze the market's character—its volatility, trendiness, or disorder—and adapt the cloud's calculation in real-time. The result is a cloud that is stable in chop and dynamic in trends.
The DAFE Proprietary Engines: The pinnacle of cloud engineering. These exclusive algorithms allow you to build clouds based on principles of physics, institutional analysis, and quantum mechanics, creating a truly next-generation analytical tool.
█ STRATEGIC APPLICATION: FROM SIGNALS TO STRUCTURE
The Laboratory transforms Ichimoku from a simple signal generator into a complete market structure framework.
The Signal Logic: You are not limited to one strategy.
TK Cross: For classic momentum signals.
Kumo Breakout: For pure price action breakout strategies.
Perfect Order: The ultimate filter. By requiring Price > Cloud > Tenkan > Kijun, you filter for only the strongest, most established trends, eliminating the majority of false signals.
Cloud Twist: A forward-looking, predictive signal. The twist of the future cloud often pinpoints the exact timing of a potential trend reversal.
The Multi-Cloud Strategy: This is the professional's view. By enabling 3 Cloud Layers, you can see the market's fractal nature.
Layer 1 (Standard): Your short-term operational trend.
Layer 2 (e.g., 2x Periods): Your medium-term structural trend.
Layer 3 (e.g., 3x Periods): Your long-term macro trend.
The Strategy: Wait for price to pull back into the space between the 2nd and 3rd cloud layers—the "macro support/resistance zone"—and then take a signal from the 1st layer in the direction of the overall trend. This is a high-probability institutional setup.
█ THE MASTER DASHBOARD: YOUR "AT-A-GLANCE" COMMAND CENTER
The dashboard provides a comprehensive, real-time summary of the entire Ichimoku system's state.
Engine & Periods: Instantly confirm which of the 50+ engines and period settings are active.
Status Readout: Get an immediate, color-coded verdict on the three core Ichimoku components: Price vs. Cloud, the TK Cross, and the Future Cloud bias.
Momentum & Strength Gauge: A proprietary score that quantifies the overall bullish or bearish momentum of the system, and a "Strength" bar that visualizes the conviction of the current alignment.
Performance Data: If enabled, the dashboard will display your strategy's key performance metrics, including Win Rate, Profit Factor, and Net P&L.
█ DEVELOPMENT PHILOSOPHY
The Ichimoku Cloud Laboratory was born from a deep respect for Goichi Hosoda's original work and a relentless desire to push it into the 21st century. We believe that in modern markets, static tools are obsolete. The future of trading lies in adaptation, customization, and multi-dimensional analysis. This tool is for the serious trader, the systems thinker, the architect—the individual who is not content with a black box, but who seeks to understand, test, and refine their edge with surgical precision.
The Ichimoku Laboratory is designed to be the ultimate tool for that reaction, providing a crystal-clear, multi-layered view of what the market is telling you—not just through price, but through the very fabric of its equilibrium.
█ DISCLAIMER AND BEST PRACTICES
THIS IS AN ADVANCED ANALYTICAL TOOL: This indicator provides a sophisticated market structure and signal framework. It must be integrated into a complete trading plan that includes your own analysis and risk management.
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. Never risk more capital than you are prepared to lose.
START WITH A ROBUST BASE: Begin with the "Traditional" preset and the "Standard Donchian" engine to master the classic feel. Then, experiment with a low-lag engine like the "Hull Moving Average" to see the immediate benefit of a smoother, more responsive cloud.
USE CONFLUENCE: The highest probability signals come from confluence. A "TK Cross" buy signal that occurs above a bullish "Multi-Cloud" structure, confirmed by a "Perfect Order" and high volume, is an A++ setup.
"The essence of success in the market is not forecasting, but reacting to what the market is telling you right now."
— J. Welles Wilder Jr.
Taking you to school. - Dskyz, Trade with Anticipation. Trade with Strength. Trade with RSI: Evolved
Flexible S/R Channels🟩 Flexible S/R Channels is a visualization tool that draws curved support and resistance boundaries through user-defined anchor points. Unlike traditional trendlines and channels that force linear interpretation onto price action, this indicator captures the curved structures that markets frequently form—rounded tops and bottoms, parabolic advances and declines, arcing rallies and pullbacks. Three anchor points per curve define the shape; the indicator fits a smooth mathematical curve through these points and projects it forward. The approach is simple: draw what you see. Curved market structure that resists precise definition with traditional tools can now be rendered with mathematical accuracy.
The indicator bridges the gap between static drawing tools and programmable indicators. TradingView's arc tool draws curves but produces only visual pixels with no analytical value. Flexible S/R Channels creates live data series that integrate with other analysis tools. Four curve-fitting methods—Quadratic, Quadratic-Linear, Weighted Linear, and Natural Cubic Spline—accommodate different market structures. The curved levels naturally lend themselves to breakout and reversion strategies—applications left to the trader's discretion. The open-source code invites experimentation and customization.
💡 THEORY AND CONCEPT 💡
Traders have long relied on horizontal levels and diagonal trendlines to define support and resistance. Linear tools assume constant slope—a property rarely exhibited by actual market movement. When momentum accelerates or decelerates, price trajectories curve rather than hold to fixed angles. The resulting structures—parabolic advances during expansion phases, arcing pullbacks during consolidation, rounded formations at reversal points—represent changes in the rate of change itself. Traditional drawing tools cannot accommodate this variable geometry without sacrificing mathematical precision..
Flexible S/R Channels extends familiar support and resistance concepts into curved space. The approach is simple: draw what you see. When the eye recognizes a curved boundary in price action, this indicator provides the means to define it precisely. Three anchor points per curve—an initial point, an intermediate point, and a recent point—are all that is required. The indicator fits a smooth mathematical curve through these points and extends it forward as a projection.
This indicator represents a blend of human pattern recognition and algorithmic precision. Fully automated indicators make decisions without user input—efficient but detached from trader discretion. Manual drawing tools rely entirely on freehand skill—expressive but imprecise. Flexible S/R Channels occupies the middle ground. The trader identifies the curved structure; the algorithm renders it mathematically. The result is human insight expressed with computational accuracy—for traders who recognize curved structure in price action but lack precise tools to define it.
This projection is not a prediction. It is a visual hypothesis—a structured way of asking "if this trajectory continues, where would price be?" The underlying assumption is simple: like Newton's first law of motion, a trajectory in motion tends to continue unless acted upon by an external force. Future price action validates or invalidates the projection, just as it does with any trendline or channel.
TradingView offers an arc drawing tool for freehand curved lines, but these are purely visual—static pixels on a screen with no programmable value. Flexible S/R Channels bridges this gap. The fitted curves exist as data series that can generate alerts, trigger signals, and interact with other analysis tools. The visual drawing becomes operational structure.
🔁 CURVE METHODS 🔁
The indicator offers four curve-calculation methods, each producing different shapes suited to different market structures:
Quadratic — Fits a parabolic arc through the three anchor points. Best for smooth, continuous curves such as rounded tops and bottoms. It captures the natural "swing" of the market, assuming the momentum will maintain its current rate of acceleration or deceleration.
Quadratic-Linear — Uses a parabolic curve through the anchor points, then transitions to a straight line after the final anchor. Useful when curved structure gives way to linear trend continuation. This is the "bridge" between a turning market and a steady, directed move, preventing the projection from curving back on itself when the price begins to run.
Weighted Linear — Connects anchor points with straight line segments rather than a smooth curve. Suited for angular market structures with distinct inflection points. It treats the market as a series of rigid shifts, providing a clear "corridor" when the price is bouncing between sharp, diagonal levels.
Natural Cubic Spline — Produces the smoothest curve by minimizing abrupt directional changes. Ideal for organic, flowing market movements. It acts as a flexible spine that adapts to complex transitions without the rigid constraints of a fixed geometric shape.
Quadratic Fitting : A smooth, parabolic arc defines a curved resistance boundary. By fitting a mathematical path through three anchor points, the curve captures rounded structures and arcing price action that traditional linear trendlines fail to represent.
Weighted Linear Fitting : This method produces an angular, segmented path by connecting anchor points with distinct linear slopes. Unlike the continuous smoothness of a quadratic arc, the weighted linear approach creates a more jointed geometry, allowing for a precise match to market structures that exhibit sharp, localized changes in trajectory.
Natural Cubic Spline Fitting : This method creates a highly fluid, elastic curve that can accommodate complex price oscillations. In this instance, the curves define a narrowing range as support and resistance converge, highlighting the volatility compression that often precedes a significant breakout or breakdown from established structures.
🖱️ HOW IT WORKS 🖱️
1️⃣ Initial Setup
Unlike traditional indicators that calculate values automatically from price data, Flexible S/R Channels requires user-defined anchor points. This is intentional. The trader's eye is the pattern recognition engine—no algorithm can see the curved structure that experience and intuition reveal. The indicator waits for this input, then applies mathematical precision to render what the trader has identified.
The Recognition of Natural Structure : Effective analysis begins when a curved rhythm becomes visible within price action that traditional trendlines cannot satisfy. Identifying the specific swing highs and swing lows that define these boundaries is the first step in organizing a chart. By isolating three key pivots for resistance and three for support, the underlying framework of the market's trajectory is established, providing the necessary coordinates to accurately map the path.
Interactive Setup Workflow : Upon loading, the indicator prompts for the sequential selection of six points—three swing highs and three swing lows—to serve as the raw data for the calculation. While the chart remains blank during this initial phase, the curves generate instantly once the final anchor is confirmed. These points are not permanent; they appear as interactive grips that can be dragged in real time to refine the boundaries as the market structure evolves.
The indicator prompts for six sequential selections—three for resistance, three for support. The first three selections define the resistance boundary; the final three define support. This sequential grouping is distinct from zigzag-style selection patterns. Within each group, clicking order is flexible—the algorithm automatically sorts points chronologically, allowing traders to select visually prominent pivots in whatever sequence feels natural.
Structural Anchor Identification : Identifying three key swing highs and three key swing lows provides the foundation for the dual-curve geometry. These specific structural peaks and troughs serve as the coordinates for the mathematical models, ensuring that the resulting boundaries accurately reflect the underlying skeleton of the market action.
2️⃣ Interactive Adjustment
After the initial setup, all six anchor points are fully adjustable:
Points are automatically sorted chronologically regardless of selection order
Grip handles appear at each anchor location
Any point can be repositioned by clicking and dragging its grip handle
The curves recalculate instantly as points are adjusted
The algorithm produces a mathematically perfect curve based on the anchor points provided. If the result does not match the trader's vision, adjustments are immediate. This iterative refinement—see, adjust, refine—continues until the rendered curve represents what the trader sees in the price action. The user remains in control; the algorithm remains in service.
Interactive Channel Boundaries : Six user-defined anchor points—three for resistance and three for support —establish a non-linear range that moves beyond the constraints of a flat, horizontal channel. This configuration captures the arcing trajectory of the market while showing price action respecting the curved boundaries in a classic reversion pattern. By manually positioning these anchors, a dynamic dimension is added to the chart that maintains structural integrity even as the price follows a rounded path.
🛠️ SETTINGS 🛠️
Customizable Visual Feedback : Beyond the core geometry, the visualization offers various user-defined settings to tailor the chart's information density. From identifying specific price targets to toggling structural labels, these options allow the trader to adjust the level of detail to suit their personal analysis style while maintaining a clear view of the non-linear boundaries.
Configuration Options
Curve Method — Select the curve-fitting algorithm: Quadratic, Quadratic-Linear, Weighted Linear, or Natural Cubic Spline.
Projection Length — Number of bars to project the curves beyond current price action. Projections appear as dashed lines.
Visual Settings
Grip Size — Size of the draggable handles displayed at each anchor point. Set to zero to hide grips entirely.
Line Width — Thickness of the support and resistance curves.
Support Color / Resistance Color — Color settings for each curve.
Show Info Table — Toggle display of the info table showing the current curve method in the chart corner.
Advanced: Time/Price Coordinates
The settings panel includes precise time and price values for each of the six anchor points, grouped under Resistance Time/Price and Support Time/Price. These values are populated automatically when points are selected on the chart.
Adjusting anchor points by dragging the grip handles directly on the chart is faster and more intuitive. The time/price fields are available for situations requiring exact coordinate entry—such as aligning an anchor to a specific candle timestamp or a precise price level. These fields can be safely ignored unless fine-tuning is necessary.
🖼️ CHART EXAMPLES 🖼️
The Flexible S/R Channels indicator adapts to diverse market structures across multiple timeframes and instruments. Curved boundaries can define subtle momentum shifts in near-linear trends, dramatic reversals in rounding formations, or volatility compression as channels converge toward breakout points. The four curve-fitting methods accommodate different geometries—smooth parabolic arcs for continuous momentum changes, segmented linear paths for angular structures, and elastic splines for complex oscillations. Each anchor point adjustment instantly recalculates the curves, allowing iterative refinement until the rendered boundaries align with the trader's interpretation of market structure. Forward projections extend these mathematical relationships into future territory, providing visual context for hypothetical support and resistance levels if current trajectories persist.
Subtle Curve Alignment : Even in structures that appear linear, subtle curvature allows the channel boundaries to breathe with the market’s internal momentum. By utilizing three anchor points rather than two, the channel adapts to the slight acceleration of a trend, providing a more precise fit than a rigid, straight corridor.
Decelerating Momentum and Convergence : This classic rounding structure illustrates a transition where the initial wide oscillations between highs and lows begin to contract. As the boundaries converge, the curve captures the diminishing volatility and the shift in market energy, providing a clear visual representation of a trend losing its expansive momentum as it approaches a potential turning point.
Organic Trend Modeling : In an accelerating uptrend, the Natural Cubic Spline provides a highly adaptable boundary that mirrors the organic flow of momentum. This non-traditional approach allows the channel to follow complex price pulses that a standard linear trendline would likely cut through, maintaining a precise fit even as the angle of the trend shifts over time.
Non-Linear Projections : Unlike standard trendlines that converge at a fixed rate, curved projections adapt to the historical momentum of the move. This allows the indicator to map a dynamic squeeze, capturing the subtle nuances of how price action tightens toward an apex. It provides a more sophisticated view of future convergence points that traditional linear channels often fail to anticipate.
The "Draw What You See" Philosophy : Market structures are rarely perfect, and this example highlights the indicator’s ability to map unconventional rhythms. Rather than forcing price into a predefined category, the tool remains flexible enough to define any structural path the trader identifies. If you can see a trend's trajectory, the indicator can provide the mathematical framework to support it.
Comparative Projection Modeling : Using identical anchor points as above, this example demonstrates how selecting a different calculation method can alter the projected path. While the historical fit remains precise, the variation in the forward-looking trajectory allows traders to explore multiple mathematical interpretations of the same market structure, choosing the model that best aligns with the current volatility and trend behavior.
Extended Timeframe Channel Definition : This multi-year perspective demonstrates the indicator's ability to define curved channel boundaries across extended timeframes spanning hundreds of bars and multiple market cycles. The resistance curve captures the rounded distribution of swing highs while the support curve follows the accelerating base formation, creating a non-linear channel that frames long-term structural trends more precisely than traditional parallel channels or static trendlines.
Rounding Bottom Reversal and Channel Convergence : This example captures a classic rounding bottom formation—a reversal pattern that linear tools cannot adequately define. The Quadratic method produces a smooth parabolic arc through the resistance anchors, tracing the deceleration of the downtrend, the capitulation low, and the subsequent re-acceleration upward as a single continuous curve. The support boundary mirrors this momentum shift from below, creating a curved channel that narrows toward current price. This convergence represents structural compression—the boundaries tightening as volatility contracts and directional resolution approaches. Price action oscillates within these non-linear boundaries, demonstrating that channel behavior persists even when the geometry is curved rather than parallel. The projection extends both curves forward, mapping the hypothetical trajectory if the current momentum structure continues, providing visual context for potential breakout or breakdown levels as the channel reaches its apex.
Built-in Precision vs. Algorithmic Power : While TradingView offers basic curve drawing tools (shown here as dashed lines), the Flexible S/R Channels indicator elevates this concept into a functional analytical framework. By converting manual observations into mathematical models, it moves beyond mere drawing to provide a data-driven structure that can be utilized for advanced technical analysis and future Pine Script trading logic.
⚙️ TECHNICAL DETAILS ⚙️
Curve Fitting vs. Overfitting: The term curve fitting often carries negative connotations in quantitative analysis due to its association with overfitting—the practice of adjusting a model until it perfectly matches historical data, producing an illusion of accuracy that fails when applied to new data. The application here is fundamentally different. Flexible S/R Channels does not optimize parameters to maximize historical fit; it constructs a mathematical curve through user-selected anchor points, then projects that curve into unknown territory. The curve is not fitted to price data—it is fitted to structural pivots identified by the trader. The projection represents a hypothesis about trajectory continuation, not a prediction derived from statistical optimization. Future price action validates or invalidates this hypothesis in real time, exactly as it does with any trendline or channel. The anchor points remain fixed unless manually adjusted, ensuring the curve does not adapt to new data retroactively.
Non-Repainting Behavior: The indicator does not repaint historical bars. The mathematical coefficients that define each curve are calculated once—when the final anchor point is set—and stored as fixed values. These coefficients remain constant unless an anchor point is manually repositioned. The backfit polyline is drawn once using these coefficients, spanning the known range from the first to last anchor point. The plot() function applies the same coefficients to each subsequent bar, updating in real-time as new bars form but never altering previously plotted values. The projection polyline extends forward from the current bar using the same fixed coefficients, projecting a user-defined number of future bars (maximum 500). This projection redraws on each tick to maintain its position relative to the moving current bar, but the mathematical trajectory remains constant—only the starting point advances. The current bar's curve value will update tick-by-tick as price develops, which is standard real-time behavior, not repainting. Once a bar closes, all curve values on that bar are permanent. The hybrid architecture (backfit polyline for known history, plot() for unlimited real-time range, projection polyline for controlled forward extension) prevents overflow errors while maintaining non-repainting integrity across all components.
🗒️ NOTES 🗒️
The indicator renders curves based on any anchor points provided without validation. Unusual anchor placement produces mathematically accurate but potentially non-useful results. Adjustment is iterative—if the curve doesn't match expectations, reposition the anchors.
Because anchor points are stored as specific time and price coordinates, a new instance of the indicator should be added when analyzing a different chart or timeframe.
Grip handles can be hidden by setting Grip Size to zero in the settings. This is useful for clean chart screenshots or presentations where interactive elements are not needed.
Projection length can be set to zero if forward-looking curves are not desired. The indicator will still render the backfit curves through the anchor points and continue plotting in real-time without the dotted projection extensions.
Anchor points remain fixed at their selected time-price coordinates as new bars form. The curves extend forward automatically from these historical anchors, allowing observation of how projected trajectories align with developing price action.
⚠️ DISCLAIMER ⚠️
The Flexible S/R Channels indicator is a visual analysis tool designed to illustrate geometric market inertia and serve as a framework for understanding dynamic support and resistance. While the indicator generates structural channels and projected paths, no guarantee is made regarding the accuracy or profitability of these projections. Like all technical indicators, the curves and boundaries generated by this tool may appear to align with favorable trading opportunities in hindsight. However, these visualizations are not intended as standalone recommendations for trading decisions. This indicator is intended for educational and analytical purposes, complementing other tools and methods of market analysis.
🧠 BEYOND THE CODE 🧠
Flexible S/R Channels is part of a broader collection of tools designed to provide structured market analysis. This includes the Grid Bot Simulator , the Grid Bot Auto , the Grid Bot Parabolic , and the Gridbot Ping Pong . While each tool serves a distinct purpose, they all utilize dynamic anchor mechanics and non-linear boundaries to adapt to evolving market conditions.
This indicator shares the same educational philosophy as the Fibonacci Time-Price Zones and the Fibonacci Geometry Series - providing frameworks for understanding market concepts through visualization and experimentation rather than black-box signals.
The Flexible S/R Channels indicator, like other xxattaxx indicators , is designed to encourage both education and community engagement. Feedback and insights are invaluable to refining and enhancing this tool. We look forward to the creative applications, observations, and discussions this indicator inspires within the trading community.
SMC Post-Analysis Lab [PhenLabs]📊 SMC Post-Analysis Lab
Version: PineScript™ v6
📌 Description
The SMC Post-Analysis Lab is a dedicated hindsight analysis tool built for traders who want to understand what really happened during any historical trading period. Unlike forward-looking indicators, this tool lets you scroll back through time and instantly receive algorithmic classification of market states using Smart Money Concepts methodology.
Whether you’re reviewing a losing trade, studying a successful session, or building your pattern recognition skills, this indicator provides immediate context. The expansion-aware algorithm processes price action within your selected window and outputs clear, actionable classifications ranging from Parabolic Expansion to Consolidation Inducements.
Stop relying on subjective post-trade analysis. Let the algorithm objectively tell you whether institutional players were accumulating, distributing, or running inducements during your trades.
🚀 Points of Innovation
First indicator specifically designed for SMC-based post-trade review rather than live signal generation
Dual-mode analysis system allowing both dynamic scrollback and precise date selection
Expansion-aware classification algorithm that weighs range position against net displacement
Real-time efficiency metrics calculating directional quality of price movement
Integrated visual FVG detection within the analysis window only
Interactive table with clickable date range adjustment via chart interface
🔧 Core Components
Pivot Detection Engine: Uses configurable pivot length to identify significant swing highs and lows for structure break detection
Window Calculator: Determines active analysis zone based on either bar offset or timestamp boundaries
Data Aggregator: Tracks window open, high, low, close and counts bullish/bearish structure break events
State Classification Algorithm: Applies hierarchical logic to determine market state from six possible classifications
Visual Renderer: Draws structure breaks, FVG boxes, and window highlighting within the active zone
🔥 Key Features
Sliding Window Mode: Use the Scroll Back slider to dynamically move your analysis zone backwards through history bar-by-bar
Date Range Mode: Select specific start and end timestamps for precise session or trade review
Six Market State Classifications: Parabolic Expansion (Bull/Bear), Bullish/Bearish Order Flow, Accumulation/Distribution Reversal, and Consolidation/Inducement
Range Position Percentile: See exactly where price closed relative to the window’s high-low range as a percentage
Bull/Bear Event Counter: Quantified count of structure breaks in each direction during the analysis period
Efficiency Calculation: Net move divided by total range reveals trending quality versus chop
🎨 Visualization
Blue Window Highlight: Active analysis zone is clearly marked with blue background shading on the chart
Structure Break Lines: Dashed lines appear at each bullish or bearish structure break within the window
FVG Boxes: Fair Value Gaps automatically render as semi-transparent boxes in bullish or bearish colors
Dashboard Table: Top-right positioned table displays State, Analysis description, and Metrics in real-time
Color-Coded States: Each classification uses distinct coloring for immediate visual recognition
Interactive Tip Row: Optional help text guides users on clicking the table to adjust date range
📖 Usage Guidelines
General Configuration
Analysis Mode: Default is Sliding Window. Choose Date Range for specific timestamp analysis.
Sliding Window Settings
Scroll Back (Bars): Default 0. Increase to move window backwards into history.
Window Width (Bars): Default 100. Range 20-50 for scalping, 100+ for swing analysis.
Date Range Settings
Start Date: Select the beginning timestamp for your analysis period.
End Date: Select the ending timestamp for your analysis period.
Visual Settings
Show Help Tip: Default true. Toggle to hide instructional row in dashboard.
Bullish Color: Default teal. Customize for bullish elements.
Bearish Color: Default red. Customize for bearish elements.
SMC Parameters
Pivot Length: Default 5. Lower values (3-5) catch minor breaks. Higher values (10+) focus on major swings.
✅ Best Use Cases
Post-trade review to understand why entries succeeded or failed
Session analysis to identify institutional activity patterns
Trade journaling with objective algorithmic classifications
Pattern recognition training through historical scrollback
Identifying whether stop hunts were inducements or legitimate breaks
Comparing your real-time read versus what the algorithm detected
⚠️ Limitations
Designed for historical analysis only, not live trade signals
Classification accuracy depends on appropriate pivot length for the timeframe
FVG detection uses simple gap logic without mitigation tracking
State classification is based on window data only, not broader context
Requires manual scrolling or date input to review different periods
💡 What Makes This Unique
Purpose-Built for Review: Unlike most indicators focused on live signals, this is designed specifically for post-trade analysis
Expansion-Aware Logic: Algorithm weighs both position in range AND directional efficiency for accurate state detection
Interactive Date Control: Click the dashboard table to reveal draggable anchors for window adjustment directly on chart
🔬 How It Works
1. Window Definition:
User selects either Sliding Window or Date Range mode
System calculates which bars fall within the active analysis zone
Active zone receives blue background highlighting
2. Data Collection:
Algorithm captures window open, running high, running low, and current close
Structure breaks are detected when price crosses above last pivot high or below last pivot low
Bullish and bearish events are counted separately
3. State Classification:
Range Position calculates where close sits as percentage of high-low range
Efficiency calculates net move divided by total range
Hierarchical logic applies priority rules from Parabolic states down to Consolidation
4. Output Rendering:
Dashboard table updates with State title, Analysis description, and Metrics
Visual elements render within window only to keep chart clean
Colors reflect bullish, bearish, or neutral classification
💡 Note:
This indicator is intended for educational and review purposes. Use it to develop your understanding of Smart Money Concepts by analyzing what institutional order flow looked like during historical periods. Combine insights with your own analysis methodology for best results.
Dresteghamat:Adaptive Multi-TF Decision Engine**Dresteghamat: Adaptive Multi-Timeframe Decision Engine**
This open-source indicator is an algorithmic decision-support system designed to filter market noise by quantifying three core market dimensions: **Regime**, **Direction**, and **Exhaustion**.
**⚠️ Technical Note on Originality:**
This script solves the "Timeframe Irrelevance" problem found in standard dashboards. Instead of using static HTF references, it implements a custom **"Adaptive Context Engine"** (see lines 245-270 in source code). It calculates the user's current `timeframe.multiplier` and dynamically maps the mathematically relevant Higher Timeframes.
* *Innovation:* A 5m chart automatically weights 15m/1H structure, whereas a 1H chart weights 4H/Daily structure. This dynamic logic is proprietary and ensures contextual accuracy.
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### 🛠️ Logic & Calculation Methodology
The script does not simply overlay indicators. It processes raw market data through a **Weighted Scoring Engine** (lines 275-285) to output a unified market state.
**1. Regime Identification (Volatility Normalized)**
We calculate a custom "Volatility Ratio" to distinguish Trend vs. Range regimes.
* **Logic:** `Range / Smoothed_ATR`.
* **Function:** If Ratio > 2.0, the market is in Expansion (Trend). If < 1.2, it is in Compression (Range). This normalizes volatility across assets (Crypto/Forex/Stocks).
**2. Directional Bias (Composite Metric)**
Direction is calculated via a voting system of three sub-components (lines 80-130):
* **Structural Pivots:** Detects Swing Highs/Lows using a 25-bar lookback to define market structure.
* **Cumulative Body Delta:** Tracks the net buying/selling pressure within candle bodies.
* **Micro-Flow:** A short-term (5-bar) momentum filter to detect immediate order flow shifts.
**3. Exhaustion Model (Risk Management)**
The script prevents late entries by calculating an "Exhaustion Score" (lines 150-200). It aggregates:
* **VRSD (Volatility Regime Shift):** Detects when volatility expands > 2 standard deviations (Mean Reversion risk).
* **Volume Decay (VEFF):** Identifies Divergence where price makes new highs on declining Volume MA.
* **RSI/Impulse Divergence:** Standard momentum divergence logic.
**4. The Decision Output (MODE)**
The dashboard renders a final signal based on a hierarchical algorithm:
* **BUY/SELL ONLY:** Triggered when Current Momentum aligns with the Dynamically Selected HTF Structure AND the Exhaustion Score is low.
* **PULLBACK:** Triggered when HTF Structure is bullish, but Current Momentum is bearish (indicating a corrective phase).
* **HTF EXHAUST:** Overrides signals when the Higher Timeframe metrics hit extreme levels.
* **WAIT:** Default state during Range Regimes or conflicting signals.
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### 📊 Usage Guide
1. Apply to chart (Auto-adapts to any timeframe).
2. **Status Column:** Shows the raw health of the trend (Strong/Weakening/Exhausted).
3. **MODE Column:** Displays the final actionable bias based on the scoring algorithm.
**Disclaimer:** This tool provides statistical analysis based on historical data. It does not guarantee future results.
