GCM Bull Bear RiderGCM Bull Bear Rider (GCM BBR)
Your Ultimate Trend-Riding Companion
GCM Bull Bear Rider is a comprehensive, all-in-one trend analysis tool designed to eliminate guesswork and provide a crystal-clear view of market direction. By leveraging a highly responsive Jurik Moving Average (JMA), this indicator not only identifies bullish and bearish trends with precision but also tracks their performance in real-time, helping you ride the waves of momentum from start to finish.
Whether you are a scalper, day trader, or swing trader, the GCM BBR adapts to your style, offering a clean, intuitive, and powerful visual guide to the market's pulse.
Key Features
JMA-Powered Trend Lines (UTPL & DTPL): The core of the indicator. A green "Up Trend Period Line" (UTPL) appears when the JMA's slope turns positive (buyers are in control), and a red "Down Trend Period Line" (DTPL) appears when the slope turns negative (sellers are in control). The JMA is used for its low lag and superior smoothing, giving you timely and reliable trend signals.
Live Profit Tracking Labels: This is the standout feature. As soon as a trend period begins, a label appears showing the real-time profit (P:) from the trend's starting price. This label moves with the trend, giving you instant feedback on its performance and helping you make informed trade management decisions.
Historical Performance Analysis: The profit labels remain on the chart for completed trends, allowing you to instantly review past performance. See at a glance which trends were profitable and which were not, aiding in strategy refinement and backtesting.
Automatic Chart Decluttering: To keep your chart clean and focused on significant moves, the indicator automatically removes the historical profit label for any trend that fails to achieve a minimum profit threshold (default is 0.5 points).
Dual-Ribbon Momentum System:
JMA / Short EMA Ribbon: Visualizes short-term momentum. A green fill indicates immediate bullish strength, while a red fill shows bearish pressure.
Short EMA / Long EMA Ribbon: Acts as a long-term trend filter, providing broader market context for your decisions.
"GCM Hunt" Entry Signals: The indicator includes optional pullback entry signals (green and red triangles). These appear when the price pulls back to a key moving average and then recovers in the direction of the primary trend, offering high-probability entry opportunities.
How to Use
Identify the Trend: Look for the appearance of a solid green line (UTPL) for a bullish bias or a solid red line (DTPL) for a bearish bias. Use the wider EMA ribbon for macro trend confirmation.
Time Your Entry: For aggressive entries, you can enter as soon as a new trend line appears. For more conservative entries, wait for a "GCM Hunt" triangle signal, which confirms a successful pullback.
Ride the Trend & Manage Your Trade: The moving profit label (P:) is your guide. As long as the trend line continues and the profit is increasing, you can confidently stay in the trade. A flattening JMA or a decreasing profit value can signal that the trend is losing steam.
Focus Your Strategy: Use the Display Mode setting to switch between "Buyers Only," "Sellers Only," or both. This allows you to completely hide opposing signals and focus solely on long or short opportunities.
Core Settings
Display Mode: The master switch. Choose to see visuals for "Buyers & Sellers," "Buyers Only," or "Sellers Only."
JMA Settings (Length, Phase): Fine-tune the responsiveness of the core JMA engine.
EMA Settings (Long, Short): Adjust the lengths of the moving averages that define the ribbons and "Hunt" signals.
Label Offset (ATR Multiplier): Customize the gap between the trend lines and the profit labels to avoid overlap with candles.
Filters (EMA, RSI, ATR, Strong Candle): Enable or disable various confirmation filters to strengthen the "Hunt" entry signals according to your risk tolerance.
Add the GCM Bull Bear Rider to your chart today and transform the way you see and trade the trend!
ENJOY
Cari dalam skrip untuk "profitable"
RFM Strategy - High QualityI trade high-probability resistance fades using a systematic 4-pillar approach that has delivered a proven 60%+ win rate with 2.5+ profit factor."
📊 Core Strategy Elements:
1. VRF Resistance Identification:
Multiple resistance level confluence (minimum 2 levels)
Dynamic resistance zones using 20-period high/low ranges
Only trade when price approaches clustered resistance
2. Volume Weakness Confirmation:
Volume ROC must be ≤ -30% (weak buying pressure)
Identifies exhaustion rallies with poor participation
Confirms institutional selling vs retail buying
3. Momentum Divergence:
SMI ≥ 60 (extreme overbought) OR 25-point momentum collapse
Multi-timeframe confirmation for higher reliability
Catches momentum exhaustion at key levels
4. Price Rejection Patterns:
Long upper wicks (2x body size) at resistance
Doji formations showing indecision
Failed breakout patterns with immediate rejection
⚡ Execution:
Entry: Only when ALL 4 conditions align simultaneously
Risk Management: 6-point stops, 12-point targets (2:1 R/R minimum)
Timeframe: 5-minute charts for precise entries
Selectivity: Quality over quantity - average 5 trades per period
🏆 Performance:
60% win rate (matches manual trading performance)
2.59 Profit Factor (highly profitable)
Systematic approach eliminates emotional decisions
"This strategy automates the discretionary resistance fade setups that institutional traders use, with strict filters ensuring only the highest-probability opportunities."
BG CloseCandleThis simple yet effective strategy script allows you to schedule automated entries (Buy or Sell) at three customizable times throughout the trading day. Each session can be individually enabled, with its own execution time and trade direction.
You can define:
• Entry time (Hour & Minute) for each session
• Whether each session should execute a Buy or a Sell order
• Your preferred Take Profit and Stop Loss levels (in ticks)
• Lot size per order
The strategy is designed specifically for the 1-minute timeframe, offering the most precise execution of time-based entries. It resets automatically each day and limits the number of trades to a maximum of three per session.
📌 I personally use this strategy on the Nasdaq 100 E-mini Futures (NQ) for intraday setups and session-based candle closes.
Perfect for testing market behavior at defined moments — ideal for overnight, premarket, or close-of-candle strategies.
Thank you for your interest, and wishing you profitable trading
CLMM Vault策略回测 (专业版) v5Explanation of the CLMM (Concentrated Liquidity - Market Maker) Strategy Backtesting Model Developed for the Sui Chain Vaults Protocol
Why Are We Doing This?
Conducting strategy backtesting is a crucial step for us to make data-driven decisions, validate the feasibility of strategies, and manage potential risks before committing real funds and significant development resources. A strategy that appears to have a high APY may perform entirely differently once real-world frictional costs (such as rebalancing fees and slippage) are deducted. The goal of this backtesting model is to quickly and cost-effectively identify which strategy parameter combinations have the potential to be profitable and which ones pose risks before formal development, thereby avoiding significant losses and providing data support for the project's direction.
Core Features of the Backtesting Model
We have built a "pro version" (v5) strategy simulator using TradingView's Pine Script. It can quickly simulate the core performance of our auto-compounding and rebalancing Vaults on historical price data, with the following main features:
Auto-Compounding: Continuously adds the generated fee income to the principal based on the set profit range (e.g., 0.01%).
Auto-Rebalancing: Simulates automatic rebalancing actions when the price exceeds the preset profit range and deducts the corresponding costs.
Smart Filtering Mechanism: To make the simulation closer to our ideal "smart" decision-making, it integrates three freely combinable filtering mechanisms:
Buffer Zone: Tolerates minor and temporary breaches of the profit range to avoid unnecessary rebalancing.
Breakout Confirmation: Requires the price to be in the trigger zone for N consecutive candles to confirm a breakout, filtering out market noise from "false breakouts."
Time Cooldown: Enforces a minimum time interval between two rebalances to prevent value-destroying high-frequency trading in extreme market conditions.
Important: Simplifications and Assumptions of the Model
To quickly prototype and iterate on the TradingView platform, we have made some key simplifications to the model.
A fully accurate backtest would require a deep simulation of on-chain liquidity pools (Pool Pair), calculating the price impact (Slippage) and impermanent loss (IL) caused by each rebalance on the pool. Since TradingView cannot access real-time on-chain liquidity data, we have made the following simplifications:
Simplified Rebalancing Costs: Instead of simulating real transaction slippage, we use a unified input parameter of single rebalance cost (%) to "bundle" and approximate the total of Gas fees, slippage, and realized impermanent loss.
Simplified Fee Income: Instead of calculating fees based on real-time trading volume, we directly input an average fee annualized return (%) as the core income assumption for our strategy.
How to Use and Test
Team members can load this script and test different strategies by adjusting the input parameters on the panel. The most critical parameters include: position profit range, average fee annualized return, single rebalance cost, and the switches and corresponding values of the above three smart filters.
cd_secret_candlestick_patterns_CxHi traders,
With this indicator, we aim to uncover secret candlestick formations that even advanced traders may miss—especially those that can't be detected by classic pattern indicators, unless you're a true master of candlestick patterns or candle math.
________________________________________
General Idea:
We'll try to identify candlestick patterns by regrouping candles into custom-sized segments that you define.
You might ask: “Why do I need this? I can just look at different timeframes and spot the structure anyway.” But it’s not the same.
For example, if you're using a 1-minute chart and add a higher-timeframe candle overlay (like 5-minute), the candles you see start at fixed timestamps like 0, 5, 10, etc.
However, in this indicator, we redraw new candles by grouping them from the current candle backward in batches of five.
These candles won't match the standard view—only when aligned with exact time multiples (e.g., 0 and 5 minutes) will they look the same.
In classic charts:
• You see 5-minute candles that begin every 0 and 5 minutes.
In this tool:
• You see a continuously updating set of 5 merged 1-minute candles redrawn every minute.
What about the structures forming in between those fixed timeframes?
That’s exactly what we’ll be able to detect—while also making the lower timeframe chart more readable.
________________________________________
Candle Merging:
Let’s continue with an example.
Assume we choose to merge 5 candles. Then the new candle will be formed using:
open = open
close = close
high = math.max(high , high , high , high , high)
low = math.min(low , low , low , low , low)
This logic continues backward on the chart, creating merged candles in groups of 5.
Since the selected patterns are made up of 3, 4, or 5 candles, we redraw 5 such merged candles to analyze.
________________________________________
Which Patterns Are Included?
A total of 18 bullish and bearish patterns are included.
You’ll find both widely known formations and a few personal ones I use, marked as (MeReT).
You can find the pattern list and visual reference here:
________________________________________
Entry and Filtering Suggestions:
Let me say this clearly:
Entering a trade every time a pattern forms will not make you profitable in the long run.
You need a clear trade plan and should only act when you can answer questions like:
• Where did the pattern appear?
• When and under what conditions?
It’s more effective to trade in the direction of the trend and look for setups around support/resistance, supply/demand zones, key levels, or areas confirmed by other indicators.
Whether you enter immediately after the pattern or wait for a retest is a personal choice—but risk management is non-negotiable.
One of the optional filters I’ve included is a Higher Timeframe (HTF) condition, which is my personal preference:
When enabled, the highest or lowest price among the pattern candles must match the high or low of the current HTF candle.
You can see in the image below the decrease in the number of detected patterns on the 1-minute chart when using no filter (blue labels) compared to when the 1-hour timeframe filter is applied (red labels).
Additionally, I’ve added a “protected” condition for engulfing patterns to help filter out weak classic engulf patterns.
________________________________________
Settings:
From the menu, you can configure:
• Number of candles for regrouping
• Distance between the last candle and newly drawn candles
• Show/hide options
• HTF filter toggle and timeframe selection
• Color, label placement, and text customization
• Pattern list (select which to display or trigger alerts for)
My preferred setup:
While trading on the 1-minute chart, I typically set the higher timeframe to 15m or 1H, and switch the candle count between 2 and 3 depending on the situation.
⚠️ Important note:
The “Show” and “Alert” options are controlled by a single command.
Alerts are automatically created for any pattern you choose to display.
________________________________________
What’s Next?
In future updates, I plan to add:
• Pattern success rate statistics
• Multi-broker confirmation for pattern validation
Lastly, keep in mind:
The more candles a pattern is based on, the more reliable it may be.
I'd love to hear your feedback and suggestions.
Cheerful trading! 🕊️📈
[Myth Busting] [ORB] Casper SMC - 16 JunJust showcase of YouTube strategy claimed to be profitable and fool proof. Not on every asset and not long-term though
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator
The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Timeshifter Triple Timeframe Strategy w/ SessionsOverview
The "Enhanced Timeshifter Triple Timeframe Strategy with Session Filtering" is a sophisticated trading strategy designed for the TradingView platform. It integrates multiple technical indicators across three different timeframes and allows traders to customize their trading Sessions. This strategy is ideal for traders who wish to leverage multi-timeframe analysis and session-based trading to enhance their trading decisions.
Features
Multi-Timeframe Analysis and direction:
Higher Timeframe: Set to a daily timeframe by default, providing a broader view of market trends.
Trading Timeframe: Automatically set to the current chart timeframe, ensuring alignment with the trader's primary analysis period.
Lower Timeframe: Set to a 15-minute timeframe by default, offering a granular view for precise entry and exit points.
Indicator Selection:
RMI (Relative Momentum Index): Combines RSI and MFI to gauge market momentum.
TWAP (Time Weighted Average Price): Provides an average price over a specified period, useful for identifying trends.
TEMA (Triple Exponential Moving Average): Reduces lag and smooths price data for trend identification.
DEMA (Double Exponential Moving Average): Similar to TEMA, it reduces lag and provides a smoother trend line.
MA (Moving Average): A simple moving average for basic trend analysis.
MFI (Money Flow Index): Measures the flow of money into and out of a security, useful for identifying overbought or oversold conditions.
VWMA (Volume Weighted Moving Average): Incorporates volume data into the moving average calculation.
PSAR (Parabolic SAR): Identifies potential reversals in price movement.
Session Filtering:
London Session: Trade during the London market hours (0800-1700 GMT+1).
New York Session: Trade during the New York market hours (0800-1700 GMT-5).
Tokyo Session: Trade during the Tokyo market hours (0900-1800 GMT+9).
Users can select one or multiple sessions to align trading with specific market hours.
Trade Direction:
Long: Only long trades are permitted.
Short: Only short trades are permitted.
Both: Both long and short trades are permitted, providing flexibility based on market conditions.
ADX Confirmation:
ADX (Average Directional Index): An optional filter to confirm the strength of a trend before entering a trade.
How to Use the Script
Setup:
Add the script to your TradingView chart.
Customize the input parameters according to your trading preferences and strategy requirements.
Indicator Selection:
Choose the primary indicator you wish to use for generating trading signals from the dropdown menu.
Enable or disable the ADX confirmation based on your preference for trend strength analysis.
Session Filtering:
Select the trading sessions you wish to trade in. You can choose one or multiple Sessions based on your trading strategy and market focus.
Trade Direction:
Set your preferred trade direction (Long, Short, or Both) to align with your market outlook and risk tolerance. You can use this feature to gauge the market and understand the possible directions.
Tips for Profitable and Safe Trading:
Recommended Timeframes Combination:
LT: 1m , CT: 5m, HT: 1H
LT: 1-5m , CT: 15m, HT: 4H
LT: 5-15m , CT: 4H, HT: 1W
Backtesting:
Always backtest the strategy on historical data to understand its performance under various market conditions.
Adjust the parameters based on backtesting results to optimize the strategy for your specific trading style.
Risk Management:
Use appropriate risk management techniques, such as setting stop-loss and take-profit levels, to protect your capital.
Avoid over-leveraging and ensure that you are trading within your risk tolerance.
Market Analysis:
Combine the script with other forms of market analysis, such as fundamental analysis or market sentiment, to make well-rounded trading decisions.
Stay informed about major economic events and news that could impact market volatility and trading sessions.
Continuous Monitoring:
Regularly monitor the strategy's performance and make adjustments as necessary.
Keep an eye on the results and settings for real-time statistics and ensure that the strategy aligns with current market conditions.
Education and Practice:
Continuously educate yourself on trading strategies and market dynamics.
Practice using the strategy in a demo account before applying it to live trading to gain confidence and understanding.
X-Day Capital Efficiency ScoreThis indicator helps identify the Most Profitable Movers for Your fixed Capital (ie, which assets offer the best average intraday profit potential for a fixed capital).
Unlike traditional volatility indicators (like ATR or % change), this script calculates how much real dollar profit you could have made each day over a custom lookback period — assuming you deployed your full capital into that ticker daily.
How it works:
Calculates the daily intraday range (high − low)
Filters for clean candles (where body > 60% of the candle range)
Assumes you invested the full amount of capital ($100K set as default) on each valid day
Computes an average daily profit score based on price action over the selected period (default set to 20 days)
Plots the score in dollars — higher = more efficient use of capital
Why It’s Useful:
Compare tickers based on real dollar return potential — not just % volatility
Spot low-priced, high-volatility stocks that are better suited for intraday or momentum trading
Inputs:
Capital ($): Amount you're hypothetically deploying (e.g., 100,000)
Look Back Period: Number of past days to average over (e.g., 20)
Haven Average Daily RangeOverview
This indicator is an enhanced version of the traditional ADR tool that adapts to intraday price movements. Unlike static ADR levels, this indicator dynamically adjusts its range boundaries based on real-time price action while maintaining the original ADR calculation framework.
Key Features
ADR calculation based on multiple periods (5, 10, and 20 days)
ADR levels displayed with automatic style changes upon range reach
Customizable display settings (color, line style)
Price labels for better visualization
The indicator helps traders assess the instrument's volatility, identify potential reversal zones, and plan daily trading targets.
Suitable for all timeframes up to D1 and any trading instrument.
How It Works
Session Start (UTC+0): Calculates ADR based on historical data and sets initial High/Low levels
Dynamic Phase: Monitors price action and adjusts the opposite boundary (ADR Low or High) when new extremes are reached.
When price creates new Day high price above the opening price, the ADR Low level moves upward proportionally.
When price creates new Day low price below the opening price, the ADR High level moves downward proportionally.
Completion Phase: Stops adjustments and highlights breach when price reaches either boundary
Trading Application
Entry and Exit Signals
The ADR boundaries serve as key decision points for trade execution. When price approaches the upper ADR boundary, it often signals a potential selling zone, particularly when confluence exists with other overbought indicators such as RSI divergence or resistance levels. Conversely, price reaching the lower ADR boundary frequently indicates potential buying opportunities, especially when supported by oversold conditions or support confluences.
Trend Continuation Assessment
One of the most valuable applications is gauging the probability of continued directional movement. When the current session's price action has not yet reached either ADR boundary, statistical probability favors trend continuation in the established direction. This information helps traders stay with profitable positions longer rather than exiting prematurely.
Reversal and Consolidation Zones
The visual color change to orange when ADR boundaries are reached provides immediate feedback that the normal daily range has been exhausted. At this point, the probability of trend reversal or sideways consolidation increases significantly. This signal helps traders prepare for potential position adjustments or new counter-trend opportunities.
Volatility Quality [Alpha Extract]The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
vqiRaw = ta.ema(weightedVol, vqiLen)
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
vqiStdev = ta.stdev(vqiSmoothed, vqiLen)
upperBand1 = vqiSmoothed + (vqiStdev * stdevMultiplier1)
upperBand2 = vqiSmoothed + (vqiStdev * stdevMultiplier2)
upperBand3 = vqiSmoothed + (vqiStdev * stdevMultiplier3)
lowerBand1 = vqiSmoothed - (vqiStdev * stdevMultiplier1)
lowerBand2 = vqiSmoothed - (vqiStdev * stdevMultiplier2)
lowerBand3 = vqiSmoothed - (vqiStdev * stdevMultiplier3)
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
TitanGrid L/S SuperEngineTitanGrid L/S SuperEngine
Experimental Trend-Aligned Grid Signal Engine for Long & Short Execution
🔹 Overview
TitanGrid is an advanced, real-time signal engine built around a tactical grid structure.
It manages Long and Short trades using trend-aligned entries, layered scaling, and partial exits.
Unlike traditional strategy() -based scripts, TitanGrid runs as an indicator() , but includes its own full internal simulation engine.
This allows it to track capital, equity, PnL, risk exposure, and trade performance bar-by-bar — effectively simulating a custom backtest, while remaining compatible with real-time alert-based execution systems.
The concept was born from the fusion of two prior systems:
Assassin’s Grid (grid-based execution and structure) + Super 8 (trend-filtering, smart capital logic), both developed under the AssassinsGrid framework.
🔹 Disclaimer
This is an experimental tool intended for research, testing, and educational use.
It does not provide guaranteed outcomes and should not be interpreted as financial advice.
Use with demo or simulated accounts before considering live deployment.
🔹 Execution Logic
Trend direction is filtered through a custom SuperTrend engine. Once confirmed:
• Long entries trigger on pullbacks, exiting progressively as price moves up
• Short entries trigger on rallies, exiting as price declines
Grid levels are spaced by configurable percentage width, and entries scale dynamically.
🔹 Stop Loss Mechanism
TitanGrid uses a dual-layer stop system:
• A static stop per entry, placed at a fixed percentage distance matching the grid width
• A trend reversal exit that closes the entire position if price crosses the SuperTrend in the opposite direction
Stops are triggered once per cycle, ensuring predictable and capital-aware behavior.
🔹 Key Features
• Dual-side grid logic (Long-only, Short-only, or Both)
• SuperTrend filtering to enforce directional bias
• Adjustable grid spacing, scaling, and sizing
• Static and dynamic stop-loss logic
• Partial exits and reset conditions
• Webhook-ready alerts (browser-based automation compatible)
• Internal simulation of equity, PnL, fees, and liquidation levels
• Real-time dashboard for full transparency
🔹 Best Use Cases
TitanGrid performs best in structured or mean-reverting environments.
It is especially well-suited to assets with the behavioral profile of ETH — reactive, trend-intraday, and prone to clean pullback formations.
While adaptable to multiple timeframes, it shows strongest performance on the 15-minute chart , offering a balance of signal frequency and directional clarity.
🔹 License
Published under the Mozilla Public License 2.0 .
You are free to study, adapt, and extend this script.
🔹 Panel Reference
The real-time dashboard displays performance metrics, capital state, and position behavior:
• Asset Type – Automatically detects the instrument class (e.g., Crypto, Stock, Forex) from symbol metadata
• Equity – Total simulated capital: realized PnL + floating PnL + remaining cash
• Available Cash – Capital not currently allocated to any position
• Used Margin – Capital locked in open trades, based on position size and leverage
• Net Profit – Realized gain/loss after commissions and fees
• Raw Net Profit – Gross result before trading costs
• Floating PnL – Unrealized profit or loss from active positions
• ROI – Return on initial capital, including realized and floating PnL. Leverage directly impacts this metric, amplifying both gains and losses relative to account size.
• Long/Short Size & Avg Price – Open position sizes and volume-weighted average entry prices
• Leverage & Liquidation – Simulated effective leverage and projected liquidation level
• Hold – Best-performing hold side (Long or Short) over the session
• Hold Efficiency – Performance efficiency during holding phases, relative to capital used
• Profit Factor – Ratio of gross profits to gross losses (realized)
• Payoff Ratio – Average profit per win / average loss per loss
• Win Rate – Percent of profitable closes (including partial exits)
• Expectancy – Net average result per closed trade
• Max Drawdown – Largest recorded drop in equity during the session
• Commission Paid – Simulated trading costs: maker, taker, funding
• Long / Short Trades – Count of entry signals per side
• Time Trading – Number of bars spent in active positions
• Volume / Month – Extrapolated 30-day trading volume estimate
• Min Capital – Lowest equity level recorded during the session
🔹 Reference Ranges by Strategy Type
Use the following metrics as reference depending on the trading style:
Grid / Mean Reversion
• Profit Factor: 1.2 – 2.0
• Payoff Ratio: 0.5 – 1.2
• Win Rate: 50% – 70% (based on partial exits)
• Expectancy: 0.05% – 0.25%
• Drawdown: Moderate to high
• Commission Impact: High
Trend-Following
• Profit Factor: 1.5 – 3.0
• Payoff Ratio: 1.5 – 3.5
• Win Rate: 30% – 50%
• Expectancy: 0.3% – 1.0%
• Drawdown: Low to moderate
Scalping / High-Frequency
• Profit Factor: 1.1 – 1.6
• Payoff Ratio: 0.3 – 0.8
• Win Rate: 80% – 95%
• Expectancy: 0.01% – 0.05%
• Volume / Month: Very high
Breakout Strategies
• Profit Factor: 1.4 – 2.2
• Payoff Ratio: 1.2 – 2.0
• Win Rate: 35% – 60%
• Expectancy: 0.2% – 0.6%
• Drawdown: Can be sharp after failed breakouts
🔹 Note on Performance Simulation
TitanGrid includes internal accounting of fees, slippage, and funding costs.
While its logic is designed for precision and capital efficiency, performance is naturally affected by exchange commissions.
In frictionless environments (e.g., zero-fee simulation), its high-frequency logic could — in theory — extract substantial micro-edges from the market.
However, real-world conditions introduce limits, and all results should be interpreted accordingly.
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.
Volume-Enhanced Candlestick Patterns 1
Overview
Scans for four major candlestick reversal patterns:
Harami
Engulfing
Morning/Evening Star
Piercing Line/Dark Cloud Cover
Underlying logic assumes that, at a turning point, the dominant side (bulls or bears) often delivers a “final” push—either a last surge of buying or selling—before the reversal truly takes hold.
Pattern Toggles
Each individual pattern can be turned on or off in the inputs.
Enable only the patterns you want to monitor to reduce chart clutter and speed up performance.
Volume Filter Toggle
On: Requires volume-based exhaustion or climax to confirm each pattern.
Off: Relies purely on price-action candlestick logic (no volume checks).
Grouped Labels & Confluence
When one or more patterns trigger on the same bar close, a single label is drawn:
Grouping multiple confirmed patterns on one bar increases confluence and signal strength.
Climax Volume × Multiplier
Adjusting this input affects signal frequency and conviction:
Higher multiplier → fewer signals but with stronger volume confirmation
Lower multiplier → more signals, each with a looser volume requirement
Alerts
Built-in alert condition for each individual pattern (bullish/bearish Harami, Engulfing, Star, Piercing, Dark Cloud Cover), so you can receive real-time notifications whenever a confirmation occurs.
Follow for Weekly Scripts
If you find this helpful, please hit Follow and 🚀button —I release a new scripts every week.
Disclaimer
Not Financial Advice. This script is for educational and research purposes only.
Use as Part of a Larger System. It should not be used in isolation; combine it with your own risk management rules, additional indicators, and broader market analysis.
No Guarantees. Candlestick patterns and volume filters can improve signal quality, but they do not guarantee profitable trades. Always perform your own due diligence before entering any position.
Pin Bar Reversal StrategyStrategy: Pin Bar Reversal with Trend Filter
One effective high-probability setup is a Pin Bar reversal in the direction of the larger trend. A pin bar is a candlestick with a tiny body and a long wick, signaling a sharp rejection of price
By itself, a pin bar often marks a potential reversal, but not all pin bars lead to profitable moves. To boost reliability, this strategy trades pin bars only when they align with the prevailing trend – for example, taking a bullish pin bar while the market is in an uptrend, or a bearish pin bar in a downtrend. The trend bias can be determined by a long-term moving average or higher timeframe analysis.
Why it works: In an uptrend, a bullish pin bar after a pullback often indicates that sellers tried to push price down but failed, and buyers are resuming control. Filtering for pin bars near key support or moving averages further improves odds of success. This aligns the entry with both a strong price pattern and the dominant market direction, yielding a higher win rate. The pin bar’s own structure provides natural levels for stop and target placement, keeping risk management straightforward.
Example Setup:
USDCHF - 4 Hour Chart
Trend SMA 12
Max Body - 34
Min Wick - 66
ATR -15
ATR Stop Loss Multiplier - 2.3
ATR Take Profit Multiplier - 2.9
Minimum ATR to Enter - 0.0025
Trend Scanner ProTrend Scanner Pro, Robust Trend Direction and Strength Estimator
Trend Scanner Pro is designed to evaluate the current market trend with maximum robustness, providing both direction and strength based on statistically reliable data.
This indicator builds upon the core logic of a previous script I developed, called Best SMA Finder. While the original script focused on identifying the most profitable SMA length based on backtested trade performance, Trend Scanner Pro takes that foundation further to serve a different purpose: analyzing and quantifying the actual trend state in real time.
It begins by testing hundreds of SMA lengths, from 10 to 1000 periods. Each one is scored using a custom robustness formula that combines profit factor, number of trades, and win rate. Only SMAs with a sufficient number of trades are retained, ensuring statistical validity and avoiding curve fitting.
The SMA with the highest robustness score is selected as the dynamic reference point. The script then calculates how far the price deviates from it using rolling standard deviation, assigning a trend strength score from -5 (strong bearish) to +5 (strong bullish), with 0 as neutral.
Two detection modes are available:
Slope mode, based on SMA slope reversals
Bias mode, based on directional shifts relative to deviation zones
Optional features:
Deviation bands for visual structure
Candle coloring to reflect trend strength
Compact table showing real-time trend status
This tool is intended for traders who want an adaptive, objective, and statistically grounded assessment of market trend conditions.
5EMA_BB_ScalpingWhat?
In this forum we have earlier published a public scanner called 5EMA BollingerBand Nifty Stock Scanner , which is getting appreciated by the community. That works on top-40 stocks of NSE as a scanner.
Whereas this time, we have come up with the similar concept as a stand-alone indicator which can be applied for any chart, for any timeframe to reap the benifit of reversal trading.
How it works?
This is essentially a reversal/divergence trading strategy, based on a widely used strategy of Power-of-Stocks 5EMA.
To know the divergence from 5-EMA we just check if the high of the candle (on closing) is below the 5-EMA. Then we check if the closing is inside the Bollinger Band (BB). That's a Buy signal. SL: low of the candle, T: middle and higher BB.
Just opposite for selling. 5-EMA low should be above 5-EMA and closing should be inside BB (lesser than BB higher level). That's a Sell signal. SL: high of the candle, T: middle and lower BB.
Along with we compare the current bar's volume with the last-20 bar VWMA (volume weighted moving average) to determine if the volume is high or low.
Present bar's volume is compared with the previous bar's volume to know if it's rising or falling.
VWAP is also determined using `ta.vwap` built-in support of TradingView.
The Bolling Band width is also notified, along with whether it is rising or falling (comparing with previous candle).
What's special?
We love this reversal trading, as it offers many benifits over trend following strategies:
Risk to Reward (RR) is superior.
It _Does Hit_ stop losses, but the stop losses are tiny.
Means, althrough the Profit Factor looks Nahh , however due to superior RR, end of day it ended up in green.
When the day is sideways, it's difficult to trade in trending strategies. This sort of volatility, reversal strategies works better.
It's always tempting to go agaist the wind. Whole world is in Put/PE and you went opposite and enter a Call/CE. And turns out profitable! That's an amazing feeling, as a trader :)
How to trade using this?
* Put any chart
* Apply this screener from Indicators (shortcut to launch indicators is just type / in your keyboard).
* It will show you the Green up arrow when buy alert comes or red down arrow when sell comes. * Also on the top right it will show the latest signal with entry, SL and target.
Disclaimer
* This piece of software does not come up with any warrantee or any rights of not changing it over the future course of time.
* We are not responsible for any trading/investment decision you are taking out of the outcome of this indicator.
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
MNQ EMA StrategyThis strategy is not perfected yet. ONE MINUTE TIMEFRAME
The goal is to take Longs above the 5 ema when price is above all the 200, 30, and 5 ema.
Short side is when candle closes below the 5 ema and price is below the 300, 30, and 5 ema.
I use candle range blocks for different time zones to avoid excess orders from being triggered. As well as blocks when stoploss is hit or after a profitable trade of certain ticks.
There is an RSI to avoid trades when there isn't too much movement.
My goal is to get an entry when price trades above the 5 ema and then next candle passes it by .25 instead of entering immediately. The stoploss as the low of candle before entry and TP as 3 times the stoploss. I've tried a million times to make it like this but I don't know how to use pine script or Code.
The sell side is basically the same, enter at candle close below 5 ema wait for low to get swept to enter and stoploss above previous high, with TP 3 times the stoploss.
Publishing in hopes anyone knows how to adjust this
CAUTION THIS STRATEGY WORKS WITH CURRENT PRICE ACTION DUE TO ME USING RECENT TICK COUNT RATHER THAN BASED ON CANDLES OR PERCENTAGES. THIS WILL ONLY WORK AS LONG AS MARKET MOVES AS IT HAS BEEN SINCE 2024. CME_MINI:MNQ1!
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis
A Journey from Theory to Trading Reality
What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals.
The Theoretical Foundation
Lorentzian Distance in Market Space
Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure:
d(x,y) = Σ ln(1 + |xi - yi|)
This logarithmic formulation naturally handles:
Scale invariance: Large price moves don't overwhelm small but significant patterns
Outlier robustness: Extreme values are dampened rather than dominating
Non-linear relationships: Captures market behavior better than linear metrics
K-Nearest Neighbors with Relativistic Weighting
The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance:
w = 1 / (1 + distance)
This creates a "gravitational" effect where closer patterns have stronger influence on predictions.
The Implementation Challenge
Creating meaningful market features required extensive experimentation:
Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average
Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio
Each feature undergoes min-max normalization to ensure equal weighting in distance calculations.
The Prediction Mechanism
For each current market state:
Feature Vector Construction: 12-dimensional representation of market conditions
Historical Search: Scan lookback period for similar patterns using Lorentzian distance
Neighbor Selection: Identify K nearest historical matches
Outcome Analysis: Examine what happened N bars after each match
Weighted Prediction: Combine outcomes using distance-based weights
Confidence Calculation: Measure agreement between neighbors
Technical Hurdles Overcome
Array Management: Complex indexing to prevent look-ahead bias
Distance Calculations: Optimizing nested loops for performance
Memory Constraints: Balancing lookback depth with computational limits
Signal Filtering: Preventing clustering of identical signals
Advanced Dashboard System
Main Control Panel
The primary dashboard provides real-time market intelligence:
Signal Status: Current prediction with confidence percentage
Neighbor Analysis: How many historical patterns match current conditions
Market Regime: Trend strength, volatility, and volume analysis
Temporal Context: Real-time updates with timestamp
Performance Analytics
Comprehensive tracking system monitors:
Win Rate: Percentage of successful predictions
Signal Count: Total predictions generated
Streak Analysis: Current winning/losing sequence
Drawdown Monitoring: Maximum equity decline
Sharpe Approximation: Risk-adjusted performance estimate
Risk Assessment Panel
Multi-dimensional risk analysis:
RSI Positioning: Overbought/oversold conditions
ATR Percentage: Current volatility relative to price
Bollinger Position: Price location within volatility bands
MACD Alignment: Momentum confirmation
Confidence Heatmap
Visual representation of prediction reliability:
Historical Confidence: Last 10 periods of prediction certainty
Strength Analysis: Magnitude of prediction values over time
Pattern Recognition: Color-coded confidence levels for quick assessment
Input Parameters Deep Dive
Core Algorithm Settings
K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets.
Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes.
Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe.
Feature Selection
Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation
Signal Generation
Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading.
Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities.
Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag.
Visual Design Philosophy
Color Themes
Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics
Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors
Information Hierarchy
The dashboard system prioritizes information by importance:
Primary Signals: Largest, most prominent display
Confidence Metrics: Secondary but clearly visible
Supporting Data: Detailed but unobtrusive
Historical Context: Available but not distracting
Trading Applications
Signal Interpretation
Long Signals: Prediction > threshold with high confidence
Look for volume confirmation
- Check trend alignment
- Verify support levels
Short Signals: Prediction < -threshold with high confidence
Confirm with resistance levels
- Check for distribution patterns
- Verify momentum divergence
- Market Regime Adaptation
Trending Markets: Higher confidence in directional signals
Ranging Markets: Focus on reversal signals at extremes
Volatile Markets: Require higher confidence thresholds
Low Volume: Reduce position sizes, increase caution
Risk Management Integration
Confidence-Based Sizing: Larger positions for higher confidence signals
Regime-Aware Stops: Wider stops in volatile regimes
Multi-Timeframe Confirmation: Align signals across timeframes
Volume Confirmation: Require volume support for major signals
Originality and Innovation
This indicator represents genuine innovation in several areas:
Mathematical Approach
First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities.
Feature Engineering
Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form.
Visualization System
Professional-grade dashboard system providing comprehensive market intelligence in intuitive format.
Performance Tracking
Real-time performance analytics typically found only in institutional trading systems.
Development Journey
Creating this indicator involved overcoming numerous technical challenges:
Mathematical Complexity: Translating theoretical concepts into practical code
Performance Optimization: Balancing accuracy with computational efficiency
User Interface Design: Making complex data accessible and actionable
Signal Quality: Filtering noise while maintaining responsiveness
The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation.
Best Practices
- Parameter Optimization
- Start with default settings and adjust based on:
Market Characteristics: Volatile vs. stable
Trading Timeframe: Scalping vs. swing trading
Risk Tolerance: Conservative vs. aggressive
Signal Confirmation
Never trade on Lorentzian signals alone:
Price Action: Confirm with support/resistance
Volume: Verify with volume analysis
Multiple Timeframes: Check higher timeframe alignment
Market Context: Consider overall market conditions
Risk Management
Position Sizing: Scale with confidence levels
Stop Losses: Adapt to market volatility
Profit Targets: Based on historical performance
Maximum Risk: Never exceed 2-3% per trade
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution.
Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization.
Thank you for sharing the idea. You're more than a follower, you're a leader!
@vasanthgautham1221
Trade with precision. Trade with insight.
— Dskyz , for DAFE Trading Systems
SMA Backtest Optimizer [Mr_Rakun]The SMA Backtest Optimizer is a powerful Pine Script tool designed to systematically analyze and compare various Simple Moving Average (SMA) periods to identify the most profitable configuration for trading strategies. This indicator tests multiple SMA periods (from 10 to 100) using a crossover strategy where buys occur when price crosses above the SMA and sells when price crosses below it.
Key Features:
Tests 10 different SMA periods to determine optimal settings
Calculates profit/loss based on a defined starting capital
Tracks total profit and number of trades for each period
Visually highlights the best performing SMA on your chart
Displays comprehensive results in an easy-to-read table
Labels the chart with key performance metrics
This code serves as a core framework that traders can customize for their specific needs. You can easily modify the strategy parameters, test different technical indicators, adjust capital settings, or implement more complex entry/exit rules. The optimization methodology can be applied to virtually any trading approach you wish to evaluate.
Feel free to adapt this framework to test your own trading ideas and discover which parameters work best in different market conditions.
Dynamic and ATR Supertrend**Dynamic and ATR Supertrend Indicator**
The Dynamic and ATR Supertrend indicator is a powerful tool for traders who want to identify trends and make informed decisions about their investments. This indicator combines the benefits of the Supertrend indicator with the dynamic multiplier adjustment and ATR (Average True Range) calculation to provide a more accurate and reliable trend identification system.
**Key Features:**
* **Dynamic Multiplier Adjustment:** The indicator uses a dynamic multiplier adjustment to adapt to changing market conditions. This ensures that the indicator remains sensitive to trend changes and provides accurate signals.
* **ATR Calculation:** The indicator uses the ATR (Average True Range) calculation to determine the volatility of the market. This helps to identify the optimal multiplier value for the Supertrend calculation.
* **Supertrend Calculation:** The indicator uses the Supertrend calculation to identify trends and provide buy and sell signals.
* **Higher Timeframe Analysis:** The indicator allows for higher timeframe analysis, which enables traders to identify trends and make decisions based on a broader market perspective.
* **Alert System:** The indicator includes an alert system that notifies traders of trend changes and price crosses, allowing them to make timely and informed decisions.
**How it Works:**
1. The indicator calculates the ATR (Average True Range) of the market to determine the volatility.
2. The indicator uses the dynamic multiplier adjustment to adapt to changing market conditions.
3. The indicator calculates the Supertrend value using the ATR and dynamic multiplier.
4. The indicator identifies trends and provides buy and sell signals based on the Supertrend value.
5. The indicator includes an alert system that notifies traders of trend changes and price crosses.
**Benefits:**
* **Improved Trend Identification:** The indicator provides a more accurate and reliable trend identification system, allowing traders to make informed decisions.
* **Adaptability:** The indicator adapts to changing market conditions, ensuring that it remains sensitive to trend changes and provides accurate signals.
* **Flexibility:** The indicator allows for higher timeframe analysis, enabling traders to identify trends and make decisions based on a broader market perspective.
* **Alert System:** The indicator includes an alert system that notifies traders of trend changes and price crosses, allowing them to make timely and informed decisions.
**Conclusion:**
The Dynamic and ATR Supertrend indicator is a powerful tool for traders who want to identify trends and make informed decisions about their investments. With its dynamic multiplier adjustment, ATR calculation, and Supertrend calculation, this indicator provides a more accurate and reliable trend identification system. The indicator's adaptability, flexibility, and alert system make it an essential tool for traders who want to stay ahead of the market and make profitable trades.
Smart Fib StrategySmart Fibonacci Strategy
This advanced trading strategy combines the power of adaptive SMA entries with Fibonacci-based exit levels to create a comprehensive trend-following system that self-optimizes based on historical market conditions. Credit goes to Julien_Eche who created the "Best SMA Finder" which received an Editors Pick award.
Strategy Overview
The Smart Fibonacci Strategy employs a two-pronged approach to trading:
1. Intelligent Entries: Uses a self-optimizing SMA (Simple Moving Average) to identify optimal entry points. The system automatically tests multiple SMA lengths against historical data to determine which period provides the most robust trading signals.
2. Fibonacci-Based Exits: Implements ATR-adjusted Fibonacci bands to establish precise exit targets, with risk-management options ranging from conservative to aggressive.
This dual methodology creates a balanced system that adapts to changing market conditions while providing clear visual reference points for trade management.
Key Features
- **Self-Optimizing Entries**: Automatically calculates the most profitable SMA length based on historical performance
- **Adjustable Risk Parameters**: Choose between low-risk and high-risk exit targets
- **Directional Flexibility**: Trade long-only, short-only, or both directions
- **Visualization Tools**: Customizable display of entry lines and exit bands
- **Performance Statistics**: Comprehensive stats table showing key metrics
- **Smoothing Option**: Reduces noise in the Fibonacci bands for cleaner signals
Trading Rules
Entry Signals
- **Long Entry**: When price crosses above the blue center line (optimal SMA)
- **Short Entry**: When price crosses below the blue center line (optimal SMA)
### Exit Levels
- **Low Risk Option**: Exit at the first Fibonacci band (1.618 * ATR)
- **High Risk Option**: Exit at the second Fibonacci band (2.618 * ATR)
Strategy Parameters
Display Settings
- Toggle visibility of the stats table and indicator components
Strategy Settings
- Select trading direction (long, short, or both)
- Choose exit method (low risk or high risk)
- Set minimum trades threshold for SMA optimization
SMA Settings
- Option to use auto-optimized or fixed-length SMA
- Customize SMA length when using fixed option
Fibonacci Settings
- Adjust ATR period and SMA basis for Fibonacci bands
- Enable/disable smoothing function
- Customize Fibonacci ratio multipliers
Appearance Settings
- Modify colors, line widths, and transparency
Optimization Methodology
The strategy employs a sophisticated optimization algorithm that:
1. Tests multiple SMA lengths against historical data
2. Evaluates performance based on trade count, profit factor, and win rate
3. Calculates a "robustness score" that balances profitability with statistical significance
4. Selects the SMA length with the highest robustness score
This ensures that the strategy's entry signals are continuously adapting to the most effective parameters for current market conditions.
Risk Management
Position sizing is fixed at $2,000 per trade, allowing for consistent exposure across all trading setups. The Fibonacci-based exit system provides two distinct risk management approaches:
- **Conservative Approach**: Using the first Fibonacci band for exits produces more frequent but smaller wins
- **Aggressive Approach**: Using the second Fibonacci band allows for larger potential gains at the cost of increased volatility
Ideal Usage
This strategy is best suited for:
- Trending markets with clear directional moves
- Timeframes from 4H to Daily for most balanced results
- Instruments with moderate volatility (stocks, forex, commodities)
Traders can further enhance performance by combining this strategy with broader market analysis to confirm the prevailing trend direction.
