Market OracleMarket Oracle Indicator: User Guide
Market Oracle is a powerful, all-in-one trading indicator for TradingView that combines multiple technical signals into a clear, actionable dashboard. Built for traders seeking a reliable edge, it analyzes trend, momentum, volatility, volume, and more to generate high-confidence buy and sell signals. With features like dynamic position sizing, market regime performance tracking, and customizable settings, it’s perfect for stocks, forex, crypto, or any market on any timeframe.
What It Does
Market Oracle evaluates market conditions using a blend of proven indicators (RSI, Stochastic RSI, EMAs, SuperTrend, MACD, Ichimoku Cloud, Bollinger Bands, ATR, ADX, and candlestick patterns) to produce a Composite Score (0-100) that reflects bullish or bearish strength. It generates:
Buy/Sell Signals: Triggered when the score crosses user-defined thresholds, filtered by volume, ADX, and trading hours.
Dynamic Position Sizing: Adjusts trade size based on volatility (ATR), reducing risk in choppy markets.
Market Regime Insights: Tracks performance in trending vs. ranging markets to highlight strategy strengths.
Comprehensive Dashboard: Displays real-time metrics like signal confidence, stop/take-profit levels, win rates, and more.
Whether you’re a day trader, swing trader, or long-term investor, Market Oracle simplifies decision-making with clear insights and robust backtesting.
Key Features
Composite Score:
Combines trend (EMAs, SuperTrend, MACD), momentum (RSI, Stochastic RSI), volatility (Bollinger Bands, ATR), volume (OBV, MA), Ichimoku Cloud, divergences, multi-timeframe trends, and candlestick patterns.
Score > 50 indicates bullishness; < 50 indicates bearishness. Extreme values (>90 or <10) flag overbought/oversold conditions.
Smart Signals:
Buy signals trigger when the score exceeds a threshold (default 70), with filters for volume, ADX strength, and Ichimoku/pattern confirmation.
Sell signals trigger below 100-threshold (default 30), with similar filters.
Signals include confidence levels (0-100%) based on indicator alignment.
Dynamic Position Sizing:
Scales trade size using ATR to limit risk in volatile markets (e.g., smaller positions when ATR is high).
Caps volatility adjustments (default 3x ATR) to maintain reasonable sizing.
Bases risk on a user-defined percentage (default 1% per trade).
Market Regime Performance:
Identifies trending (high ADX or wide Bollinger Bands) vs. ranging markets.
Tracks win rates separately for each regime, helping you optimize strategies for specific conditions.
Backtesting Metrics:
Shows buy/sell win rates, recent win rate, trending/ranging win rates, profit factor, max drawdown, and equity trend.
Helps evaluate strategy performance over time.
Customizable Dashboard:
Displays key metrics: Composite Score, Trend, Momentum, Volatility, Ichimoku, ADX, Divergences, Signal Status, Confidence, Stop/Take-Profit, Position Size, and more.
Toggles to show/hide backtest stats, Ichimoku Cloud, or multi-timeframe trends.
Clean layout with no clutter (e.g., no unnecessary “!” markers).
Alerts:
Set alerts for Buy/Sell Signals, Signal Exits, High Scores, or Overbought/Oversold conditions.
Messages include score and confidence (e.g., “Oracle+: Buy signal (Score: 75.2, Confidence: 80%)”).
Flexible Settings:
Adjust thresholds, indicator lengths, weights, risk parameters, and more.
Enable/disable features like trailing stops, market regime filters, or trading hour restrictions.
How to Use It
1. Add to TradingView
Copy the Market Oracle script into TradingView’s Pine Editor.
Click “Add to Chart” to apply it to your chosen market (e.g., BTC/USD, EUR/USD, SPY).
The dashboard appears in the bottom-right corner, showing real-time data.
2. Read the Dashboard
The dashboard is split into three sections:
Market State:
Composite Score: Current score and confidence (e.g., “75.2 (50.4%)”). “(OB)” or “(OS)” flags overbought/oversold.
Adjusted Threshold: Signal trigger level, adjusted for market regime.
Trend/MTF Trend: Bullish, Bearish, or Neutral (based on EMAs, SuperTrend, higher timeframe).
Momentum: Bullish, Bearish, or Neutral (RSI, Stochastic RSI).
Volatility: High or Low (Bollinger Bands, ATR).
Ichimoku Cloud: Bullish, Bearish, or Neutral (if enabled).
ADX Strength: Strong or Weak.
Divergence: Bullish, Bearish, or None (RSI, MACD).
Signals:
Signal Status: “Buy”, “Sell”, or “None”.
Signal Confidence: Percentage (e.g., “80%”) based on indicator agreement.
Stop Level/Take-Profit: Price levels for risk management.
Position Size: Units to trade, adjusted for volatility.
Avg Trade Duration: Average bars per trade.
Signal History: Recent signals/exits (e.g., “Buy | Buy Win”).
Backtest (optional):
Buy/Sell Win Rate: Percentage of winning trades.
Recent Win Rate: Win rate for recent trades.
Trending/Ranging Win Rate: Performance by market condition.
Profit Factor: Profit-to-loss ratio.
Max Drawdown: Largest equity drop.
Equity Trend: Up, Down, or Flat.
3. Customize Settings
Access the indicator’s settings by clicking the gear icon:
Signal Sensitivity:
Base Signal Threshold (default 70): Higher = stricter signals.
Min Confidence for Alerts (default 60%): Filters weaker alerts.
Risk Management:
Risk % per Trade (default 1%): Sets base risk.
Max ATR Multiplier (default 3.0): Caps position size reduction.
Take-Profit Ratio (default 2.0): Sets reward-to-risk ratio.
Use Trailing Stop (default false): Enable for dynamic stops.
Filters:
Use Volume Filter (default true): Requires above-average volume.
Use ADX Filter (default true): Requires strong trend (ADX > 25).
Use Market Regime Filter (default true): Adjusts thresholds for trending/ranging markets.
Avoid Specific Hours (default false): Skip signals during set hours (e.g., 0-6 UTC).
Indicators:
Adjust lengths for RSI (14), EMAs (20/50), MACD (12/26/9), etc.
Enable/disable Use Candlestick Patterns (default true) and choose type (Hammer, Engulfing, etc.).
Display:
Show Backtest Metrics (default true): Show win rates and stats.
Show Ichimoku Cloud (default true): Include cloud signals.
Show MTF Trend (default true): Include higher timeframe trend.
4. Set Alerts
In TradingView, click the alarm icon and select Market Oracle conditions:
Buy/Sell Signal: Triggers on new signals with sufficient confidence.
Signal Exit: Triggers when a trade closes (stop or take-profit hit).
High Score Alert: Triggers when score exceeds a threshold (default 80).
Overbought/Oversold: Warns of extreme conditions (if enabled).
Alerts include details for easy tracking (e.g., score, confidence).
5. Test and Trade
Test Markets: Try on forex (EUR/USD 1H), crypto (BTC/USD 4H), or stocks (AAPL daily).
Check Volatility: Monitor Position Size in volatile markets (e.g., XAU/USD); it shrinks to reduce risk.
Evaluate Regimes: Use “Trending Win Rate” and “Ranging Win Rate” to see where the strategy shines.
Backtest: Review Profit Factor and Max Drawdown to assess performance.
Start Small: Use a demo account to test signals before trading live.
Tips for Success
Match Your Style: Adjust scoreThreshold and timeframe to suit day trading (e.g., 15m, stricter threshold) or swing trading (e.g., 4H, looser threshold).
Monitor Regimes: If “Trending Win Rate” is higher, focus signals during strong trends (high ADX).
Manage Risk: Keep riskPerTrade low (e.g., 0.5-2%) and review Position Size for sanity.
Combine with Analysis: Use signals alongside support/resistance or news events for confirmation.
Tune Parameters: Experiment with atrVolatilityCap (e.g., 2.0 for tighter sizing) or adxThreshold (e.g., 30 for stronger trends).
Avoid Overtrading: Filter signals with minConfidenceForAlert (e.g., 70%) to focus on high-probability setups.
Example Scenarios
Crypto Breakout (BTC/USD 4H):
Composite Score jumps to 82, Signal Status shows “Buy” with 80% confidence.
Position Size is moderate due to high ATR during a rally.
“Trending Win Rate” is 65%, suggesting strength in directional moves.
Set an alert and enter with a 2:1 take-profit (e.g., stop at 60,000, target at 62,000).
Forex Range (EUR/USD 1H):
Score hovers near 45, no signal (Neutral).
“Ranging Win Rate” is 55%, indicating decent performance in consolidation.
Wait for a score above 70 or use a higher timeframe (e.g., Daily MTF Trend).
Stock Swing (AAPL daily):
Score hits 75, “Buy” signal with a small Position Size (low ATR).
Dashboard shows Bullish Ichimoku and MTF Trend.
Trade with a trailing stop to capture a multi-day move.
Why Choose Market Oracle?
All-in-One: Combines multiple indicators into one easy-to-read dashboard, saving you time.
Adaptive: Adjusts signals and sizing for market conditions (trending, ranging, volatile).
Transparent: Shows confidence, win rates, and risk levels to build trust in trades.
Customizable: Tailor to any market, timeframe, or trading style with dozens of settings.
No Clutter: Clean interface with no distracting visuals, just actionable data.
Getting Started
Add Market Oracle to your chart and explore the dashboard.
Test on a market you trade (e.g., ETH/USD 1H) with default settings.
Adjust scoreThreshold or riskPerTrade to match your risk tolerance.
Set alerts for Buy/Sell Signals and monitor performance via “Trending Win Rate” and “Profit Factor”.
Join TradingView communities to share setups and tips!
For questions or tweaks (e.g., adding VWAP, changing alert conditions), consult your indicator’s developer or TradingView’s Pine Script resources. Happy trading!
Statistics
Average Body RangeThe Average Body Range (ABR) indicator calculates the average size of a candle's real body over a specified period. Unlike the traditional Average Daily Range (ADR), which measures the full range from high to low, the ABR focuses solely on the absolute difference between the open and close of each bar. This provides insight into market momentum and trading activity by reflecting how much price is actually moving from open to close , not just in total.
This indicator is especially useful for identifying:
Periods of strong directional movement (larger body sizes)
Low-volatility or indecisive markets (smaller body sizes)
Changes in trend conviction or momentum
Customization:
Length: Number of bars used to compute the average (default: 14)
Use ABR to enhance your understanding of price behavior and better time entries or exits based on market strength.
RTH and ETH RangesKey Functions :
Visualizes Regular Trading Hours (RTH) and Extended Trading Hours (ETH) price ranges
Tracks session highs, lows, and 50% levels where significant market reactions occur
Detects breakouts beyond previous session extremes
Trading Applications :
Exposes potential liquidity raids at session boundaries where smart money targets stop orders
Identifies critical price thresholds where institutional activity concentrates
Highlights divergences between RTH and ETH behavior that precede directional moves
Provides measurement of session volatility differences
Maps key price levels for objective entry and exit parameters
Reveals market dynamics at session transitions where institutional positioning changes
Dirty Market IndexThis indicator is designed to out an index displaying the level of dirtiness in market.
This level is defined by:
Sum of shadow lengths of last n candles (n is input and user can change it, it's 100 by default)
divided by
Sum of full candle bodies of last n candles (high - low)
This factor indicates how many percents of the market movement has been placed in shadows of candles, the higher this number, the dirtier market would be.
POF🔶 Smoothed POF Profile – Multi-Session Market Structure Tool 🔶
The Smoothed POF Profile is a precision-engineered market structure indicator that identifies the Point of Focus (POF) — the price level where market participation was most active — across Daily, Weekly, and Monthly sessions and plots them with smoothed over form to avoid whipsaws.
🔍 Powered by a custom-built algorithm for session profiling, this tool highlights:
🔶 POF: The most frequently traded or accepted price during a session
🟩 VAH / VAL: Dynamic Value Area High and Low markers (no cluttered lines — clean label-only display)
📐 The core logic utilizes a proprietary data refinement method that adapts to session volatility and filters out insignificant noise to avoid false shifts in structure. This results in smoothed POF readings that remain stable and meaningful — even during high-volatility periods.
🧠 Designed for traders who want to track evolving value, this tool provides a high-level view of where the market is finding agreement — and where price is likely to revert or expand from.
✅ Key Features:
Fully automated: Tracks Daily, Weekly, and Monthly sessions in real-time
Session-aware calculation of key structure levels
Elegant, non-obtrusive chart visuals (no histogram or volume bars)
Fully configurable Value Area % and display toggles
Multi-session color-coding (🟧 Daily, 🔵 Weekly, 🟣 Monthly)
🧭 Trading Applications:
POF Bias: Use POF as an evolving balance point. Price above = bullish lean, price below = bearish tilt
VAH/VAL Zones: Anticipate rejection or consolidation when price re-enters the value area. Use breakouts for continuation bias
Session Stack Confluence: When Daily, Weekly, and Monthly POFs cluster, it often signals strong interest zones and potential turning points
🧩 Use alongside your preferred price action, volume, or trend confirmation tools. This is not a signal-based system — it’s a contextual framework to help you align with market intent and structure.
⚠️ Disclaimer: This tool is intended for educational and informational purposes only. It is not financial advice. Use with proper risk management and your own due diligence.
Prop Firm Guard: Risk & Sizing Tracker by TFTProp Firm Guard: Risk & Sizing Tracker by TFT
Overview:
This script is designed to help prop firm traders stay within risk rules and avoid emotional overtrading. It tracks your max loss limits, daily loss rules, and gives real-time position sizing suggestions based on your account status.
This tool is especially helpful for newer traders navigating prop firm challenges and rules like trailing drawdowns and daily stopouts.
Key Features:
✅ Real-time tracking of max loss and daily loss limits
✅ Supports both Intraday and End-of-Day (EOD) drawdown styles
✅ Calculates remaining “distance” to max/daily loss levels
✅ Automatically locks max loss once it trails up to starting balance
✅ Provides smart, tier-based position sizing suggestions (5%–50%)
✅ Shows profit target progress and live daily P&L
Use Case Example:
Let’s say you’re trading a $50,000 prop account with a $2,000 max drawdown limit.
If you're using Intraday Drawdown:
• You start the day at $50,000.
• During the day, your balance grows to $51,000 (including unrealized profits).
• The drawdown logic will trail this intraday high — so your new max loss limit becomes $49,000 (51K - 2K).
• If your balance drops to $49,400, this tool will show you’re $400 away from breaching the limit.
• Sizing suggestions will adjust accordingly to keep you in a safe range.
If you're using End-of-Day (EOD) Drawdown:
• The same scenario (account grows to $51,000 intraday) won’t affect your max loss limit immediately.
• EOD drawdown is only updated based on your end-of-day closing balance.
• So even if you hit $51K intraday, your max loss limit still remains at $48,000 (50K - 2K) until the trading day closes and updates your best equity.
• This mode offers more flexibility during the day — and the tool reflects this in how it calculates distances and sizing.
📌 It will then suggest a conservative sizing range — maybe 5–10% of your allowed contract size — until you're safer again.
📌 Make sure you update your current balance after each trade and follow your risk settings.
Inputs Explained (with Tips):
• Overall Account Starting Balance: Your full prop account size (e.g., 50000 or 100000, 150000, 300000, so on)
• Day Start Balance: What your balance was when the trading day started
• Daily Max Loss: How much you’re allowed to lose in one day (used only for EOD drawdown)
• Daily Profit Target: Your goal for the day (e.g., 500 or 1000 or so on)
• Allowed Overall Drawdown: Usually 4% for prop firms — like 2000 on 50K, or 6000 on 300K
• Drawdown Mode:
→ Intraday: Includes floating/unrealized profits in drawdown logic
→ EOD: Uses only end-of-day equity for drawdown logic
• Best Day High: Your highest balance to date. If not above your starting balance, this is ignored
• Intraday High (Manual): Optional override if your peak balance isn’t same as equity (used only for intraday drawdown mode)
• Current Equity: Update this during the session to reflect your live balance — everything else updates automatically
What You’ll See on the Chart:
🟩 Equity Section: Start balance, current balance, intraday high, best day high
🟥 Risk Section:
• Max loss limit (based on trailing logic)
• Distance from current balance to that limit
• Daily loss limit and distance (EOD mode only)
🟦 Performance Metrics:
• Daily P&L in $ and %
• Progress to profit target (shows ✅ Accomplished when goal is hit)
📦 Sizing Suggestion:
Based on how close you are to a drawdown breach, and your total drawdown tier.
Ranges from ⚠️ 5–10% to ✅ 40–50% of your max allowed contract size.
Who It's Best For:
• Built and optimized for 50K prop firm accounts
• Works well with 100K, 150K, or even 300K — but the sizing logic is most precise at 50K
• Best suited for futures or forex prop firm traders using account challenge-style rules
Manual Input Required:
Due to TradingView limitations, we cannot read your actual trades or live balance.
You'll need to update the Current Equity field yourself — but the rest is auto-calculated from there.
Most inputs (like overall balance and drawdown) are set once and rarely changed.
Beta Notice:
This tool is currently in beta and under testing. It's free for now and designed to help the trading community — but accuracy may vary.
Please send feedback if you'd like to suggest improvements or report bugs.
Disclaimer:
This tool is for educational purposes only and does not provide trading advice or signal any trades.
Always trade according to your firm’s rules. The author is not responsible for losses resulting from use of this script.
3 Candles Gap3 Candle Gap Indicator is made to detect these types of patterns:
1. 3 consecutive bullish or bearish candles
2. the middle candle true body (body excluding shadows) has a part that is not covered by previous and next candle shadows (gap)
This pattern helps traders to detect candles where price has moved in a direction and gap has formed (price is not covered by previous or next candles shadows), this is a signal showing price momentum where one side (bulls/bears) is so powerful at moving the price that the other side (bears/bulls) can't get price back to cover the gap.
This indicator has "repainting" by 1 candle which means, it uses the data from future to work, however this future data does not go further than 1 candle.
3 Way ValuationThe 3 Way Valuation (3WV) is a composite oscillator that evaluates market conditions by combining a diverse range of metrics into a single smoothed value between 0 and 1. It draws from multiple domains, including momentum, trend strength, volatility, risk-adjusted return ratios, and volume-based analytics to offer a well-rounded view of an asset’s relative positioning.
At its core, 3WV integrates three statistical approaches: Percentile Ranking to assess how current values compare to historical distributions, Z-Score Normalization to measure deviation from the average, and Empirical CDF (ECDF) to estimate the probability of observing a value based on past behavior. These methods are applied across all selected metrics, then normalized and averaged for a comprehensive market sentiment score.
Users can tailor the smoothing level and choose whether to include volume-based components for enhanced filtering. The indicator highlights potential overbought and oversold conditions with customizable thresholds, making it a versatile tool for timing entries, exits, or confirming broader strategies.
Leavitt Convolution ProbabilityTechnical Analysis of Markets with Leavitt Market Projections and Associated Convolution Probability
The aim of this study is to present an innovative approach to market analysis based on the research "Leavitt Market Projections." This technical tool combines one indicator and a probability function to enhance the accuracy and speed of market forecasts.
Key Features
Advanced Indicators : the script includes the Convolution line and a probability oscillator, designed to anticipate market changes. These indicators provide timely signals and offer a clear view of price dynamics.
Convolution Probability Function : The Convolution Probability (CP) is a key element of the script. A significant increase in this probability often precedes a market decline, while a decrease in probability can signal a bullish move. The Convolution Probability Function:
At each bar, i, the linear regression routine finds the two parameters for the straight line: y=mix+bi.
Standard deviations can be calculated from the sequence of slopes, {mi}, and intercepts, {bi}.
Each standard deviation has a corresponding probability.
Their adjusted product is the Convolution Probability, CP. The construction of the Convolution Probability is straightforward. The adjusted product is the probability of one times 1− the probability of the other.
Customizable Settings : Users can define oversold and overbought levels, as well as set an offset for the linear regression calculation. These options allow for tailoring the script to individual trading strategies and market conditions.
Statistical Analysis : Each analyzed bar generates regression parameters that allow for the calculation of standard deviations and associated probabilities, providing an in-depth view of market dynamics.
The results from applying this technical tool show increased accuracy and speed in market forecasts. The combination of Convolution indicator and the probability function enables the identification of turning points and the anticipation of market changes.
Additional information:
Leavitt, in his study, considers the SPY chart.
When the Convolution Probability (CP) is high, it indicates that the probability P1 (related to the slope) is high, and conversely, when CP is low, P1 is low and P2 is high.
For the calculation of probability, an approximate formula of the Cumulative Distribution Function (CDF) has been used, which is given by: CDF(x)=21(1+erf(σ2x−μ)) where μ is the mean and σ is the standard deviation.
For the calculation of probability, the formula used in this script is: 0.5 * (1 + (math.sign(zSlope) * math.sqrt(1 - math.exp(-0.5 * zSlope * zSlope))))
Conclusions
This study presents the approach to market analysis based on the research "Leavitt Market Projections." The script combines Convolution indicator and a Probability function to provide more precise trading signals. The results demonstrate greater accuracy and speed in market forecasts, making this technical tool a valuable asset for market participants.
Drawdown Visualizer v1.0Drawdown Visualizer
The Drawdown Visualizer tracks the percentage decline from all-time highs, providing valuable insights into market corrections and potential buying opportunities.
Key Features:
1) Real-Time Drawdown Tracking: The indicator continuously calculates and displays the current percentage drawdown from the all-time high price, color-coded from green (minimal drawdown) to red (severe drawdown) for instant visual feedback.
2) Maximum Drawdown Detection: Permanently tracks and displays the maximum historical drawdown encountered during the analyzed period, helping traders understand worst-case scenarios.
3) Statistical Analysis: Calculates and displays three important statistical measures:
* Average Drawdown: The mean value of all drawdowns recorded
* Median Drawdown: The middle value in the sorted list of all drawdowns, providing insight
into typical decline patterns
* Normal Drawdown Range: Visualizes the expected range of typical drawdowns based on
statistical standard deviation
Practical Applications:
1) Risk Management: Understand typical and extreme drawdowns to set appropriate stop-loss levels
2) Market Context: Gain perspective on whether current corrections are normal or exceptional
3) Entry Point Analysis: Identify potential buying opportunities when drawdowns reach statistical extremes
Daily LevelsOverview:
The Daily Levels indicator plots key price levels from the previous trading day, including the high, low, median (pivot), and projected extensions. These levels help traders identify potential support/resistance zones and anticipate breakout or reversal opportunities.
Key Features:
✅ Previous Day High & Low – Visualizes the prior day’s high and low as dynamic support/resistance levels.
✅ Median (Pivot) Line – Calculates the midpoint between the previous day’s high and low, acting as a key intraday reference.
✅ Projected Levels – Extends the high/low range symmetrically above and below the median, highlighting potential breakout zones.
✅ Customizable Display – Toggle visibility, adjust colors, and modify line styles (solid, dotted, dashed).
✅ Price Labels – Clear on-chart labels showing exact price values for quick reference.
✅ Built-in Alerts – Get notified when price crosses any of the key levels.
How to Use:
Trend Identification: If price holds above the median, the bias is bullish; below suggests bearish momentum.
Breakout Trading: Watch for moves beyond the projected levels for potential continuation.
Mean Reversion: Fade moves toward the previous day’s high/low if the median holds as support/resistance.
Ideal For:
Day Traders – Intraday support/resistance levels.
Swing Traders – Context for multi-day trends.
Breakout/Reversal Strategies – Clear levels for trade triggers.
Settings Recommendations:
High/Low Lines: Use semi-transparent colors (e.g., green/red) for clarity.
Projections: Helpful for anticipating extended moves (e.g., teal for upper, orange for lower).
Alerts: Enable notifications for key crosses (e.g., median or high/low breaks).
TS- Multitimeframe📊 The Trend Synchronizer – Multitimeframe Scalper 🔁
Indicator added at the of the chart. - Just in case anyone is confused, and one on chart as overlay is our own Delta zones indicator - as usual available to use for everyone.
🚀 Precision Aligned, Momentum Enhanced
Welcome to the Trend Synchronizer (TS) – a custom-built, multitimeframe momentum indicator developed for active traders looking to scalp lower timeframes (1–5 min) while staying in sync with broader market direction.
🔍 What Is It?
The Trend Synchronizer is an advanced momentum oscillator designed to identify entry opportunities only when multiple timeframes align.
It overlays real-time momentum signals from higher aggregations to ensure your trade is moving with the market, not against it.
✅ When short-term momentum aligns with higher timeframe direction, opportunities are clearer, stronger, and more reliable.
🧠 How to Use It (No Settings Needed)
This tool is ready to go out of the box.
It uses three internal timeframes (default: 1m, 5m, 30m) and processes their behavior to create momentum signals. Here's how to trade it:
📈 Entries
Buy Bias: When histogram bars turn bullish colors across layers and align positively.
Sell Bias: When histogram bars shift to bearish tones, confirming momentum is to the downside.
Avoid Signals when higher timeframe momentum and lower timeframe are diverging – that's when chop often occurs.
⏳ Timeframes
Default is tuned for scalping (1–5m charts), but can be adjusted.
You can change TF1, TF2, and TF3 to experiment with your preferred layers (e.g., 5m/15m/1H for intraday swing entries).
🟢 Color Cues
The color scheme helps you spot bullish and bearish dominance quickly.
Histograms are visually synced: above 0 = strength, below 0 = weakness.
⚙️ Settings
You don’t need to tweak anything unless you want to. The inputs are exposed only for fine-tuners.
TS1, TS2, TS3: Toggle momentum layers on/off.
Custom colors available for personalization.
Clean histogram-style display for clear, fast decision-making.
📌 Best Practices
Combine with price action and volume for higher conviction.
Always look for trend confirmation on your chart before executing.
It’s ideal for:
Momentum scalpers
Order flow traders
High-frequency setups
Trend pullbacks & breakouts
⚠️ Disclaimer
This indicator is for educational purposes only. It is not financial advice and does not guarantee profitability. Always do your own research and use proper risk management. You are solely responsible for your trading decisions.
✨ Final Word
The Trend Synchronizer is a tool designed to help you align with the flow of the market – not fight it. It simplifies the complexity of multiple timeframes into a visual format any trader can interpret.
If you find it useful, don’t forget to ⭐ it and drop a comment with your feedback!
Happy trading and stay in sync!
Day’s Open ForecastOverview
This Pine Script indicator combines two primary components:
1. Day’s Open Forecast:
o Tracks historical daily moves (up and down) from the day’s open.
o Calculates average up and down moves over a user-defined lookback period.
o Optionally includes standard deviation adjustments to forecast potential intraday levels.
o Plots lines on the chart for the forecasted up and down moves from the current day's open.
2. Session VWAP:
o Allows you to specify a custom trading session (by time range and UTC offset).
o Calculates and plots a Volume-Weighted Average Price (VWAP) during that session.
By combining these two features, you can gauge potential intraday moves relative to historical behavior from the open, while also tracking a session-specific VWAP that can act as a dynamic support/resistance reference.
How the Code Works
1. Collect Daily Moves
o The script detects when a new day starts using time("D").
o Once a new day is detected, it stores the previous day’s up-move (dayHigh - dayOpen) and down-move (dayOpen - dayLow) into arrays.
o These arrays keep track of the last N days (default: 126) of up/down move data.
2. Compute Statistics
o The script computes the average (f_average()) of up-moves and down-moves over the stored period.
o It also computes the standard deviation (f_stddev()) of up/down moves for optional “forecast bands.”
3. Forecast Lines
o Plots the current day’s open.
o Plots the average forecast lines above and below the open (Avg Up Move Level and Avg Down Move Level).
o If standard deviation is enabled, plots additional lines (Avg+StdDev Up and Avg+StdDev Down).
4. Session VWAP
o The script detects the start of a user-defined session (via input.session) and resets accumulation of volume and the numerator for VWAP.
o As each bar in the session updates, it accumulates volume (vwapCumulativeVolume) and a price-volume product (vwapCumulativeNumerator).
o The session VWAP is then calculated as (vwapCumulativeNumerator / vwapCumulativeVolume) and plotted.
5. Visualization Options
o Users can toggle standard deviation usage, historical up/down moves plotting, and whether to show the forecast “bands.”
o The vwapSession and vwapUtc inputs let you adjust which session (and time zone offset) the VWAP is calculated for.
________________________________________
How to Use This Indicator on TradingView
1. Create a New Script
o Open TradingView, then navigate to Pine Editor (usually found at the bottom of the chart).
o Copy and paste the entire code into the editor.
2. Save and Add to Chart
o Click Save (give it a relevant title if you wish), then click Add to chart.
o The indicator will appear on your chart with the forecast lines and VWAP.
o By default, it is overlayed on the price chart (because of overlay=true).
3. Customize Inputs
o In the indicator’s settings, you can:
Change lookback days (default: 126).
Enable or disable standard deviation (Include Standard Deviation in Forecast?).
Adjust the standard deviation multiplier.
Choose whether to plot bands (Plot Bands with Averages/StdDev?).
Plot historical moves if desired (Plot Historical Up/Down Moves for Reference?).
Set your custom session and UTC offset for the VWAP calculation.
4. Interpretation
o “Current Day Open” is simply today’s open price on your chart.
o Up/Down Move Lines: Indicate a potential forecast based on historical averages.
If standard deviation is enabled, the second set of lines acts as an extended range.
o VWAP: Helpful for determining intraday price equilibrium over the specified session.
Important Notes / Best Practices
• The script only updates the historical up/down move data once per day (when a new day starts).
• The VWAP portion resets at the start of the specified session each day.
• Standard deviation multiplies the average up/down range, giving you a sense of “volatility range” around the day’s open.
• Adjust the lookback length (dayCount) to balance how many days of data you want to average. More days = smoother but possibly slower to adapt; fewer days = more reactive but potentially less reliable historically.
Educational & Liability Disclaimers
1. Educational Disclaimer
o The information provided by this indicator is for educational and informational purposes only. It is a technical analysis tool intended to demonstrate how to use historical data and basic statistics in Pine Script.
2. No Financial Advice
o This script does not constitute financial or investment advice. All examples and explanations are solely illustrative. You should always do your own analysis before making any investment decisions.
3. No Liability
o The author of this script is not liable for any losses or damages—monetary or otherwise—that may occur from the application of this script.
o Past performance does not guarantee future results, and you should never invest money you cannot afford to lose.
By adding this indicator to your TradingView chart, you acknowledge and accept that you alone are responsible for your own trading decisions.
Enjoy using the “Day’s Open Forecast” and Session VWAP for better market insights!
Trend Targets [AlgoAlpha]OVERVIEW
This script combines a smoothed trend-following model with dynamic price rejection logic and ATR-based target projection to give traders a complete visual framework for trading trend continuations. It overlays on price and automatically detects potential trend shifts, confirms rejections near dynamic support/resistance, and displays calculated stop-loss and take-profit levels to support structured risk-reward management. Unlike traditional indicators that only show trend direction or signal entries, this tool brings together a unique mix of signal validation, volatility-aware positioning, and layered profit-taking to guide decision-making with more context.
CONCEPTS
The core trend logic is built on a custom Supertrend that uses an ATR-based band structure with long smoothing chains—first through a WMA, then an EMA—allowing the trend line to respond to major shifts while ignoring noise. A key addition is the use of rejection logic: the script looks for consolidation candles that "hug" the smoothed trend line and counts how many consecutive bars reject from it. This behavior often precedes significant moves. A user-defined threshold filters out weak tests and highlights only meaningful rejections.
FEATURES
Trend Detection : Automatically identifies trend direction using a smoothed Supertrend (WMA + EMA), with shape markers on trend shifts and color-coded bars for clarity.
Rejection Signals : Detects price rejections at the trend line after a user-defined number of consolidation bars; plots ▲/▼ icons to highlight strong continuation setups.
Target Projection : On trend confirmation, plots entry, stop-loss (ATR-based), and three dynamic take-profit levels based on customizable multiples.
Dynamic Updates : All levels (entry, SL, TP1–TP3) auto-adjust based on volatility and are labeled in real time on the chart.
Customization : Users can tweak trend parameters, rejection confirmation count, SL/TP ratios, smoothing lengths, and appearance settings.
Alerts : Built-in alerts for trend changes, rejection events, and when TP1, TP2, or TP3 are reached.
Chart Overlay : Plots directly on price chart with minimal clutter and clearly labeled levels for easy trading.
USAGE
Start by tuning the Supertrend factor and ATR period to fit your asset and timeframe—higher values will catch bigger swings, lower values catch faster moves. The confirmation count should match how tightly you want to filter rejection behavior—higher values make signals rarer but stronger. When the trend shifts, the indicator colors the bars and line accordingly, and if enabled, plots the full entry-TP-SL structure. Rejection markers appear only after enough qualifying bars confirm price pressure at the trend line. This is especially useful for continuation plays where price retests the trend but fails to break it. All calculations are based on volatility (ATR), so targets naturally adjust with market conditions. Add alerts to get notified of important signals even when away from the chart.
Multi-Timeframe Anchored VWAP Valuation# Multi-Timeframe Anchored VWAP Valuation
## Overview
This indicator provides a unique perspective on potential price valuation by comparing the current price to the Volume Weighted Average Price (VWAP) anchored to the start of multiple timeframes: Weekly, Monthly, Quarterly, and Yearly. It synthesizes these comparisons into a single oscillator value, helping traders gauge if the current price is potentially extended relative to significant volume-weighted levels.
## Core Concept & Calculation
1. **Anchored VWAP:** The script calculates the VWAP separately for the current Week, Month, Quarter (3 Months), and Year (12 Months), starting the calculation from the first bar of each period.
2. **Price Deviation:** It measures how far the current `close` price is from each of these anchored VWAPs. This distance is measured in terms of standard deviations calculated *within* that specific anchor period (e.g., how many weekly standard deviations the price is away from the weekly VWAP).
3. **Deviation Score (Multiplier):** Based on this standard deviation distance, a score is assigned. The further the price is from the VWAP (in terms of standard deviations), the higher the absolute score. The indicator uses linear interpolation to determine scores between the standard deviation levels (defaulted at 1, 2, and 3 standard deviations corresponding to scores of +/-2, +/-3, +/-4, with a score of 1 at the VWAP).
4. **Timeframe Weighting:** Longer timeframes are considered more significant. The deviation scores are multiplied by fixed scalars: Weekly (x1), Monthly (x2), Quarterly (x3), Yearly (x4).
5. **Final Valuation Metric:** The weighted scores from all four timeframes are summed up to produce the final oscillator value plotted in the indicator pane.
## How to Interpret and Use
* **Histogram (Indicator Pane):**
* The main output is the histogram representing the `Final Valuation Metric`.
* **Positive Values:** Suggest the price is generally trading above its volume-weighted averages across the timeframes, potentially indicating strength or relative "overvaluation."
* **Negative Values:** Suggest the price is generally trading below its volume-weighted averages, potentially indicating weakness or relative "undervaluation."
* **Values Near Zero:** Indicate the price is relatively close to its volume-weighted averages.
* **Histogram Color:**
* The color of the histogram bars provides context based on the metric's *own recent history*.
* **Green (Positive Color):** The metric is currently *above* its recent average plus a standard deviation band (dynamic upper threshold). This highlights potentially significant "overvalued" readings relative to its normal range.
* **Red (Negative Color):** The metric is currently *below* its recent average minus a standard deviation band (dynamic lower threshold). This highlights potentially significant "undervalued" readings relative to its normal range.
* **Gray (Neutral Color):** The metric is within its typical recent range (between the dynamic upper and lower thresholds).
* **Orange Line:** Plots the moving average of the `Final Valuation Metric` itself (based on the "Threshold Lookback Period"), serving as the centerline for the dynamic thresholds.
* **On-Chart Table:**
* Provides a detailed breakdown for transparency.
* Shows the calculated VWAP, the raw deviation multiplier score, and the final weighted (adjusted) metric for each individual timeframe (W, M, Q, Y).
* Displays the current price, the final combined metric value, and a textual interpretation ("Overvalued", "Undervalued", "Neutral") based on the dynamic thresholds.
## Potential Use Cases
* Identifying potential exhaustion points when the indicator reaches statistically high (green) or low (red) levels relative to its recent history.
* Assessing whether price trends are supported by underlying volume-weighted average prices across multiple timeframes.
* Can be used alongside other technical analysis tools for confirmation.
## Settings
* **Calculation Settings:**
* `STDEV Level 1`: Adjusts the 1st standard deviation level (default 1.0).
* `STDEV Level 2`: Adjusts the 2nd standard deviation level (default 2.0).
* `STDEV Level 3`: Adjusts the 3rd standard deviation level (default 3.0).
* **Interpretation Settings:**
* `Threshold Lookback Period`: Defines the number of bars used to calculate the average and standard deviation of the final metric for dynamic thresholds (default 200).
* `Threshold StDev Multiplier`: Controls how many standard deviations above/below the metric's average are used to set the "Overvalued"/"Undervalued" thresholds (default 1.0).
* **Table Settings:** Customize the position and colors of the data table displayed on the chart.
## Important Considerations
* This indicator measures price deviation relative to *anchored* VWAPs and its *own historical range*. It is not a standalone trading system.
* The interpretation of "Overvalued" and "Undervalued" is relative to the indicator's logic and calculations; it does not guarantee future price movement.
* Like all indicators, past performance is not indicative of future results. Use this tool as part of a comprehensive analysis and risk management strategy.
* The anchored VWAP and Standard Deviation values reset at the beginning of each respective period (Week, Month, Quarter, Year).
Chau RSI+MA for DHChau RSI+MA for DH – Indicator Description & Usage
Overview:
The Chau RSI+MA for DH indicator is a custom RSI-based analysis tool designed to provide a deeper and more dynamic view of market momentum. It plots three configurable RSI lines along with three moving averages (MA) of the main RSI, helping traders identify overbought/oversold zones, trend strength, and potential reversal points.
🔧 Inputs & Configuration:
Three RSI Lines:
RSI 1 (default: 7)
RSI 2 (default: 14) → used as the base for MA calculations
RSI 3 (default: 21)
These allow multi-timeframe or multi-speed momentum analysis in a single panel.
Three MAs of RSI 2:
MA 1, MA 2, MA 3 – customizable lengths, defaulting to 5, 10, and 20
These help smooth out RSI 2 to detect trend direction or divergence.
Overbought/Oversold Levels:
Customizable dual thresholds (Level 1 & Level 2), offering flexible signal filtering.
🎯 Core Features & Strengths:
Multi-RSI Display:
Combines short, mid, and long RSI to give a layered view of market strength and potential turning points.
RSI MA Tracking:
Smoothing RSI 2 with three MAs helps visualize momentum trends and potential trend-following signals.
Dynamic Signal Zones:
Overbought and Oversold regions are highlighted with background colors.
Dual level alert system (e.g., 70/80 and 30/20) increases precision and adaptability for different strategies.
Highly Customizable Visualization:
Fully adjustable color schemes for all RSI and MA lines.
Easily identify confluences or divergences at a glance.
✅ Best Use Cases:
Trend Confirmation:
Use RSI 2 crossing above/below its MAs as a momentum confirmation signal.
Reversal Detection:
Identify overbought or oversold conditions combined with RSI-MA divergence.
Filtering Entries/Exits:
Combine with price action or other indicators to filter out low-probability trades.
Scalping & Swing Trading:
Adaptable to multiple timeframes and styles due to customizable RSI/MA settings.
Delta Zones🔶 Delta Zones — A Precision Tool for Time-Price Mapping 🔶
The Delta Zones indicator is a refined structure-mapping tool that dynamically tracks zones of dominant trading activity across recent sessions.
These zones are projected forward in time, offering traders a reliable visual guide to where significant interactions between buyers and sellers are likely to take place.
This tool was designed for intraday use, but its adaptability makes it powerful even on higher timeframes, giving traders insights into market behavior without the noise. You need to change session setting from indicator to higher TF that the chart. For intra, its by default on daily.
🔧 What This Indicator Does
Detects and displays the key activity zone for the current session (today).
Recalls the most active zone from the previous session, allowing you to track momentum or reversal bias.
Color codes each zone based on where price currently trades relative to it:
Neutral gradient (orange/white) for today’s zone, showing where price is consolidating or reacting.
Bullish green fade if price is trading above yesterday’s zone.
Bearish red fade if price is trading below yesterday’s zone.
Extends each zone forward (default 200 bars) so you can observe price behavior as it revisits these areas over time.
📈 How to Use Delta Zones
Trend Continuation:
If price pushes beyond today's zone and maintains momentum, it may suggest strength in that direction. Watch how price reacts on retests of this zone.
Fade or Mean Reversion:
When price strays far from a Delta Zone and struggles to gain ground, it often rotates back into that region. These situations can offer attractive risk-reward setups.
Zone Polarity from Prior Sessions:
Yesterday’s zone serves as a directional cue — if price opens and stays above it (green-filled), sentiment favors strength. If it stays below (red-filled), weakness may persist.
Support/Resistance Anchors:
Use zones as dynamic S/R levels — watch for wick tests, engulfing candles, or volume surges at zone edges for potential trade entries or exits.
🎛️ Inputs You Can Control
Session Length (Default: Daily): Defines how often a new zone is calculated.
💡 Pro Tip
These zones act like magnetic fields around price — not only can they contain price, but they also attract it. The key is to recognize when price is respecting, rejecting, or absorbing at the edges of the zone.
Pair Delta Zones with your favorite price action, momentum, or volume tools for sharper decision-making. For example, "Accumulation/Distribution Money Flow" script which I published few days ago.
⚠️ Note
This is a conceptually adaptive framework designed to simplify the visual structure of the market. While no model guarantees predictive accuracy, Delta Zones are especially useful for contextualizing price behavior and anticipating where meaningful reactions may occur.
This is an educational idea, use it at your own risk.
Past performance does not guarantee future success.
Intrinsic Event (Multi DC OS)Overview
This indicator implements an event-based approach to analyze price movements in the foreign exchange market, inspired by the intrinsic time framework introduced in Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller et al. (1995). It identifies significant price events using an intrinsic time perspective and supports multi-agent analysis to reflect the heterogeneous nature of financial markets. The script plots these events as lines and labels on the chart, offering a visual tool for traders to understand market dynamics at different scales.
Key Features
Intrinsic Events : The indicator detects directional change (DC) and overshoot (OS) events based on user-defined thresholds (delta), aligning with the paper’s concept of intrinsic time (Section 6). Intrinsic time redefines time based on market activity, expanding during volatile periods and contracting during inactive ones, rather than relying on a physical clock.
Multi-Agent Analysis : Supports up to five agents, each with its own threshold and color settings, reflecting the heterogeneous market hypothesis (Section 5). This allows the indicator to capture the perspectives of market participants with different time horizons, such as short-term FX dealers and long-term central banks.
How It Works
Intrinsic Events Detection : The script identifies two types of events using intrinsic time principles:
Directional Change (DC) : Triggered when the price reverses by the threshold (delta) against the current trend (e.g., a drop by delta in an uptrend signals a "Down DC").
Overshoot (OS) : Occurs when the price continues in the trend direction by the threshold (e.g., a rise by delta in an uptrend signals an "Up OS").
DC events are plotted as solid lines, and OS events as dashed lines, with labels like "Up DC" or "OS Down" for clarity. The label style adjusts based on the trend to ensure visibility.
Multi-Agent Setup : Each agent operates independently with its own threshold, mimicking market participants with varying time horizons (Section 5). Smaller thresholds detect frequent, short-term events, while larger thresholds capture broader, long-term movements.
Settings
Each agent can be configured with:
Enable Agent : Toggle the agent on or off.
Threshold (%) : The percentage threshold (delta) for detecting DC and OS events (default values: 0.1%, 0.2%, 0.5%, 1%, 2% for agents 1–5).
Up Mode Color : Color for lines and labels in up mode (DC events).
Down Mode Color : Color for lines and labels in down mode (OS events).
Usage Notes
This indicator is designed for the foreign exchange market, leveraging its high liquidity, as noted in the paper (Section 1). Adjust the threshold values based on the instrument’s volatility—higher volatility leads to more intrinsic events (Section 4). It can be adapted to other markets where event-based analysis applies.
Reference
The methodology is based on:
Fractals and Intrinsic Time - A Challenge to Econometricians by U. A. Müller, M. M. Dacorogna, R. D. Davé, O. V. Pictet, R. B. Olsen, and J. R. Ward (June 28, 1995). Olsen & Associates Preprint.
Quantify [Trading Model] | FractalystNote: In this description, "TM" refers to Trading Model (not trademark) and "EM" refers to Entry Model
What’s the indicator’s purpose and functionality?
You know how to identify market bias but always struggle with figuring out the best exit method, or even hesitating to take your trades?
I've been there. That's why I built this solution—once and for all—to help traders who know the market bias but need a systematic and quantitative approach for their entries and trade management.
A model that shows you real-time market probabilities and insights, so you can focus on execution with confidence—not doubt or FOMO.
How does this Quantify differentiate from Quantify ?
Have you managed to code or even found an indicator that identifies the market bias for you, so you don’t have to manually spend time analyzing the market and trend?
Then that’s exactly why you might need the Quantify Trading Model.
With the Trading Model (TM) version, the script automatically uses your given bias identification method to determine the trend (bull vs bear and neutral), detect the bias, and provide instant insight into the trades you could’ve taken.
To avoid complications from consecutive signals, it uses a kNN machine learning algorithm that processes market structure and probabilities to predict the best future patterns.
(You don’t have to deal with any complexity—it’s all taken care of for you.)
Quantify TM uses the k-Nearest Neighbors (kNN) machine learning algorithm to learn from historical market patterns and adapt to changing market structures. This means it can recognize similar market conditions from the past and apply those lessons to current trading decisions.
On the other hand, Quantify EM requires you to manually select your directional bias. It then focuses solely on generating entry signals based on that pre-determined bias.
While the entry model version (EM) uses your manual bias selection to determine the trend, it then provides insights into trades you could’ve taken and should be taking.
Trading Model (TM)
- Uses `input.source()` to incorporate your personal methodology for identifying market bias
- Automates everything—from bias detection to entry and exit decisions
- Adapts to market bias changes through kNN machine learning optimization
- Reduces human intervention in trading decisions, limiting emotional interference
Entry Model (EM)
- Focuses specifically on optimizing entry points within your pre-selected directional bias
- Requires manual input for determining market bias
- Provides entry signals without automating alerts or bias rules
Can the indicator be applied to any market approach/trading strategy?
Yes, if you have clear rules for identifying the market bias, then you can code your bias detection and then use the input.source() user input to retrieve the direction from your own indicator, then the Quantify uses machine-learning identify the best setups for you.
Here's an example:
//@version=6
indicator('Moving Averages Bias', overlay = true)
// Input lengths for moving averages
ma10_length = input.int(10, title = 'MA 10 Length')
ma20_length = input.int(20, title = 'MA 20 Length')
ma50_length = input.int(50, title = 'MA 50 Length')
// Calculate moving averages
ma10 = ta.sma(close, ma10_length)
ma20 = ta.sma(close, ma20_length)
ma50 = ta.sma(close, ma50_length)
// Identify bias
var bias = 0
if close > ma10 and close > ma20 and close > ma50 and ma10 > ma20 and ma20 > ma50
bias := 1 // Bullish
bias
else if close < ma10 and close < ma20 and close < ma50 and ma10 < ma20 and ma20 < ma50
bias := -1 // Bearish
bias
else
bias := 0 // Neutral
bias
// Plot the bias
plot(bias, title = 'Identified Bias', color = color.blue,display = display.none)
Once you've created your custom bias indicator, you can integrate it with Quantify :
- Add your bias indicator to your chart
- Open the Quantify settings
- Set the Bias option to "Auto"
- Select your custom indicator as the bias source
The machine learning algorithms will then analyze historical price action and identify optimal setups based on your defined bias parameters. Performance statistics are displayed in summary tables, allowing you to evaluate effectiveness across different timeframes.
Can the indicator be used for different timeframes or trading styles?
Yes, regardless of the timeframe you’d like to take your entries, the indicator adapts to your trading style.
Whether you’re a swing trader, scalper, or even a position trader, the algorithm dynamically evaluates market conditions across your chosen timeframe.
How Quantify Helps You Trade Profitably?
The Quantify Trading Model offers several powerful features that can significantly improve your trading profitability when used correctly:
Real-Time Edge Assessment
It displays real-time probability of price moving in your favor versus hitting your stoploss
This gives you immediate insight into risk/reward dynamics before entering trades
You can make more informed decisions by knowing the statistical likelihood of success
Historical Edge Validation
Instantly shows whether your trading approach has demonstrated an edge in historical data
Prevents you from trading setups that historically haven't performed well
Gives confidence when entering trades that have proven statistical advantages
Optimized Position Sizing
Analyzes each setup's success rate to determine the adjusted Kelly criterion formula
Customizes position sizing based on your selected maximum drawdown tolerance
Helps prevent account-destroying losses while maximizing growth potential
Advanced Exit Management
Utilizes market structure-based trailing stop-loss mechanisms
Maximizes the average risk-reward ratio profit per winning trade
Helps capture larger moves while protecting gains during market reversals
Emotional Discipline Enforcement
Eliminates emotional bias by adhering to your pre-defined rules for market direction
Prevents impulsive decisions by providing objective entry and exit signals
Creates psychological distance between your emotions and trading decisions
Overtrading Prevention
Highlights only setups that demonstrate positive expectancy
Reduces frequency of low-probability trades
Conserves capital for higher-quality opportunities
Systematic Approach Benefits
By combining machine learning algorithms with your personal bias identification methods, Quantify helps transform discretionary trading approaches into more systematic, probability-based strategies.
What Entry Models are used in Quantify Trading Model version?
The Quantify Trading Model utilizes two primary entry models to identify high-probability trade setups:
Breakout Entry Model
- Identifies potential trade entries when price breaks through significant swing highs and swing lows
- Captures momentum as price moves beyond established trading ranges
- Particularly effective in trending markets when combined with the appropriate bias detection
- Optimized by machine learning to filter false breakouts based on historical performance
Fractals Entry Model
- Utilizes fractal patterns to identify potential reversal or continuation points
- Also uses swing levels to determine optimal entry locations
- Based on the concept that market structure repeats across different timeframes
- Identifies local highs and lows that form natural entry points
- Enhanced by machine learning to recognize the most profitable fractal formations
- These entry models work in conjunction with your custom bias indicator to ensure trades are taken in the direction of the overall market trend. The machine learning component analyzes historical performance of these entry types across different market conditions to optimize entry timing and signal quality.
How Does This Indicator Identify Market Structure?
1. Swing Detection
• The indicator identifies key swing points on the chart. These are local highs or lows where the price reverses direction, forming the foundation of market structure.
2. Structural Break Validation
• A structural break is flagged when a candle closes above a previous swing high (bullish) or below a previous swing low (bearish).
• Break Confirmation Process:
To confirm the break, the indicator applies the following rules:
• Valid Swing Preceding the Break: There must be at least one valid swing point before the break.
3. Numeric Labeling
• Each confirmed structural break is assigned a unique numeric ID starting from 1.
• This helps traders track breaks sequentially and analyze how the market structure evolves over time.
4. Liquidity and Invalidation Zones
• For every confirmed structural break, the indicator highlights two critical zones:
1. Liquidity Zone (LIQ): Represents the structural liquidity level.
2. Invalidation Zone (INV): Acts as Invalidation point if the structure fails to hold.
How does the trailing stop-loss work? what are the underlying calculations?
A trailing stoploss is a dynamic risk management tool that moves with the price as the market trend continues in the trader’s favor. Unlike a fixed take profit, which stays at a set level, the trailing stoploss automatically adjusts itself as the market moves, locking in profits as the price advances.
In Quantify, the trailing stoploss is enhanced by incorporating market structure liquidity levels (explain above). This ensures that the stoploss adjusts intelligently based on key price levels, allowing the trader to stay in the trade as long as the trend remains intact, while also protecting profits if the market reverses.
What is the Kelly Criterion, and how does it work in Quantify?
The Kelly Criterion is a mathematical formula used to determine the optimal position size for each trade, maximizing long-term growth while minimizing the risk of large drawdowns. It calculates the percentage of your portfolio to risk on a trade based on the probability of winning and the expected payoff.
Quantify integrates this with user-defined inputs to dynamically calculate the most effective position size in percentage, aligning with the trader’s risk tolerance and desired exposure.
How does Quantify use the Kelly Criterion in practice?
Quantify uses the Kelly Criterion to optimize position sizing based on the following factors:
1. Confidence Level: The model assesses the confidence level in the trade setup based on historical data and sample size. A higher confidence level increases the suggested position size because the trade has a higher probability of success.
2. Max Allowed Drawdown (User-Defined): Traders can set their preferred maximum allowed drawdown, which dictates how much loss is acceptable before reducing position size or stopping trading. Quantify uses this input to ensure that risk exposure aligns with the trader’s risk tolerance.
3. Probabilities: Quantify calculates the probabilities of success for each trade setup. The higher the probability of a successful trade (based on historical price action and liquidity levels), the larger the position size suggested by the Kelly Criterion.
How can I get started to use the indicator?
1. Set Your Market Bias
• Choose Auto.
• Select the source you want Quantify to use as for bias identification method (explained above)
2. Choose Your Entry Timeframes
• Specify the timeframes you want to focus on for trade entries.
• The indicator will dynamically analyze these timeframes to provide optimal setups.
3. Choose Your Entry Model and BE/TP Levels
• Choose a model that suits your personality
• Choose a level where you'd like the script to take profit or move stop-loss to BE
4. Set and activate the alerts
What tables are used in the Quantify?
• Quarterly
• Monthly
• Weekly
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
- By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Correlation Heatmap█ OVERVIEW
This indicator creates a correlation matrix for a user-specified list of symbols based on their time-aligned weekly or monthly price returns. It calculates the Pearson correlation coefficient for each possible symbol pair, and it displays the results in a symmetric table with heatmap-colored cells. This format provides an intuitive view of the linear relationships between various symbols' price movements over a specific time range.
█ CONCEPTS
Correlation
Correlation typically refers to an observable statistical relationship between two datasets. In a financial time series context, it usually represents the extent to which sampled values from a pair of datasets, such as two series of price returns, vary jointly over time. More specifically, in this context, correlation describes the strength and direction of the relationship between the samples from both series.
If two separate time series tend to rise and fall together proportionally, they might be highly correlated. Likewise, if the series often vary in opposite directions, they might have a strong anticorrelation . If the two series do not exhibit a clear relationship, they might be uncorrelated .
Traders frequently analyze asset correlations to help optimize portfolios, assess market behaviors, identify potential risks, and support trading decisions. For instance, correlation often plays a key role in diversification . When two instruments exhibit a strong correlation in their returns, it might indicate that buying or selling both carries elevated unsystematic risk . Therefore, traders often aim to create balanced portfolios of relatively uncorrelated or anticorrelated assets to help promote investment diversity and potentially offset some of the risks.
When using correlation analysis to support investment decisions, it is crucial to understand the following caveats:
• Correlation does not imply causation . Two assets might vary jointly over an analyzed range, resulting in high correlation or anticorrelation in their returns, but that does not indicate that either instrument directly influences the other. Joint variability between assets might occur because of shared sensitivities to external factors, such as interest rates or global sentiment, or it might be entirely coincidental. In other words, correlation does not provide sufficient information to identify cause-and-effect relationships.
• Correlation does not predict the future relationship between two assets. It only reflects the estimated strength and direction of the relationship between the current analyzed samples. Financial time series are ever-changing. A strong trend between two assets can weaken or reverse in the future.
Correlation coefficient
A correlation coefficient is a numeric measure of correlation. Several coefficients exist, each quantifying different types of relationships between two datasets. The most common and widely known measure is the Pearson product-moment correlation coefficient , also known as the Pearson correlation coefficient or Pearson's r . Usually, when the term "correlation coefficient" is used without context, it refers to this correlation measure.
The Pearson correlation coefficient quantifies the strength and direction of the linear relationship between two variables. In other words, it indicates how consistently variables' values move together or in opposite directions in a proportional, linear manner. Its formula is as follows:
𝑟(𝑥, 𝑦) = cov(𝑥, 𝑦) / (𝜎𝑥 * 𝜎𝑦)
Where:
• 𝑥 is the first variable, and 𝑦 is the second variable.
• cov(𝑥, 𝑦) is the covariance between 𝑥 and 𝑦.
• 𝜎𝑥 is the standard deviation of 𝑥.
• 𝜎𝑦 is the standard deviation of 𝑦.
In essence, the correlation coefficient measures the covariance between two variables, normalized by the product of their standard deviations. The coefficient's value ranges from -1 to 1, allowing a more straightforward interpretation of the relationship between two datasets than what covariance alone provides:
• A value of 1 indicates a perfect positive correlation over the analyzed sample. As one variable's value changes, the other variable's value changes proportionally in the same direction .
• A value of -1 indicates a perfect negative correlation (anticorrelation). As one variable's value increases, the other variable's value decreases proportionally.
• A value of 0 indicates no linear relationship between the variables over the analyzed sample.
Aligning returns across instruments
In a financial time series, each data point (i.e., bar) in a sample represents information collected in periodic intervals. For instance, on a "1D" chart, bars form at specific times as successive days elapse.
However, the times of the data points for a symbol's standard dataset depend on its active sessions , and sessions vary across instrument types. For example, the daily session for NYSE stocks is 09:30 - 16:00 UTC-4/-5 on weekdays, Forex instruments have 24-hour sessions that span from 17:00 UTC-4/-5 on one weekday to 17:00 on the next, and new daily sessions for cryptocurrencies start at 00:00 UTC every day because crypto markets are consistently open.
Therefore, comparing the standard datasets for different asset types to identify correlations presents a challenge. If two symbols' datasets have bars that form at unaligned times, their correlation coefficient does not accurately describe their relationship. When calculating correlations between the returns for two assets, both datasets must maintain consistent time alignment in their values and cover identical ranges for meaningful results.
To address the issue of time alignment across instruments, this indicator requests confirmed weekly or monthly data from spread tickers constructed from the chart's ticker and another specified ticker. The datasets for spreads are derived from lower-timeframe data to ensure the values from all symbols come from aligned points in time, allowing a fair comparison between different instrument types. Additionally, each spread ticker ID includes necessary modifiers, such as extended hours and adjustments.
In this indicator, we use the following process to retrieve time-aligned returns for correlation calculations:
1. Request the current and previous prices from a spread representing the sum of the chart symbol and another symbol ( "chartSymbol + anotherSymbol" ).
2. Request the prices from another spread representing the difference between the two symbols ( "chartSymbol - anotherSymbol" ).
3. Calculate half of the difference between the values from both spreads ( 0.5 * (requestedSum - requestedDifference) ). The results represent the symbol's prices at times aligned with the sample points on the current chart.
4. Calculate the arithmetic return of the retrieved prices: (currentPrice - previousPrice) / previousPrice
5. Repeat steps 1-4 for each symbol requiring analysis.
It's crucial to note that because this process retrieves prices for a symbol at times consistent with periodic points on the current chart, the values can represent prices from before or after the closing time of the symbol's usual session.
Additionally, note that the maximum number of weeks or months in the correlation calculations depends on the chart's range and the largest time range common to all the requested symbols. To maximize the amount of data available for the calculations, we recommend setting the chart to use a daily or higher timeframe and specifying a chart symbol that covers a sufficient time range for your needs.
█ FEATURES
This indicator analyzes the correlations between several pairs of user-specified symbols to provide a structured, intuitive view of the relationships in their returns. Below are the indicator's key features:
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab accepts a comma-separated list of symbols or ticker identifiers with optional spaces (e.g., "XOM, MSFT, BITSTAMP:BTCUSD"). The indicator dynamically requests returns for each symbol in the list, then calculates the correlation between each pair of return series for its heatmap display.
Each item in the list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script raises a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Note that the number of symbols allowed in the list depends on the user's plan. Users with non-professional plans can compare up to 20 symbols with this indicator, and users with professional plans can compare up to 32 symbols.
Timeframe and data length selection
The "Returns timeframe" input specifies whether the indicator uses weekly or monthly returns in its calculations. By default, its value is "1M", meaning the indicator analyzes monthly returns. Note that this script requires a chart timeframe lower than or equal to "1M". If the chart uses a higher timeframe, it causes a runtime error.
To customize the length of the data used in the correlation calculations, use the "Max periods" input. When enabled, the indicator limits the calculation window to the number of periods specified in the input field. Otherwise, it uses the chart's time range as the limit. The top-left corner of the table shows the number of confirmed weeks or months used in the calculations.
It's important to note that the number of confirmed periods in the correlation calculations is limited to the largest time range common to all the requested datasets, because a meaningful correlation matrix requires analyzing each symbol's returns under the same market conditions. Therefore, the correlation matrix can show different results for the same symbol pair if another listed symbol restricts the aligned data to a shorter time range.
Heatmap display
This indicator displays the correlations for each symbol pair in a heatmap-styled table representing a symmetric correlation matrix. Each row and column corresponds to a specific symbol, and the cells at their intersections correspond to symbol pairs . For example, the cell at the "AAPL" row and "MSFT" column shows the weekly or monthly correlation between those two symbols' returns. Likewise, the cell at the "MSFT" row and "AAPL" column shows the same value.
Note that the main diagonal cells in the display, where the row and column refer to the same symbol, all show a value of 1 because any series of non-na data is always perfectly correlated with itself.
The background of each correlation cell uses a gradient color based on the correlation value. By default, the gradient uses blue hues for positive correlation, orange hues for negative correlation, and white for no correlation. The intensity of each blue or orange hue corresponds to the strength of the measured correlation or anticorrelation. Users can customize the gradient's base colors using the inputs in the "Color gradient" section of the "Settings/Inputs" tab.
█ FOR Pine Script® CODERS
• This script uses the `getArrayFromString()` function from our ValueAtTime library to process the input list of symbols. The function splits the "string" value by its commas, then constructs an array of non-empty strings without leading or trailing whitespaces. Additionally, it uses the str.upper() function to convert each symbol's characters to uppercase.
• The script's `getAlignedReturns()` function requests time-aligned prices with two request.security() calls that use spread tickers based on the chart's symbol and another symbol. Then, it calculates the arithmetic return using the `changePercent()` function from the ta library. The `collectReturns()` function uses `getAlignedReturns()` within a loop and stores the data from each call within a matrix . The script calls the `arrayCorrelation()` function on pairs of rows from the returned matrix to calculate the correlation values.
• For consistency, the `getAlignedReturns()` function includes extended hours and dividend adjustment modifiers in its data requests. Additionally, it includes other settings inherited from the chart's context, such as "settlement-as-close" preferences.
• A Pine script can execute up to 40 or 64 unique `request.*()` function calls, depending on the user's plan. The maximum number of symbols this script compares is half the plan's limit, because `getAlignedReturns()` uses two request.security() calls.
• This script can use the request.security() function within a loop because all scripts in Pine v6 enable dynamic requests by default. Refer to the Dynamic requests section of the Other timeframes and data page to learn more about this feature, and see our v6 migration guide to learn what's new in Pine v6.
• The script's table uses two distinct color.from_gradient() calls in a switch structure to determine the cell colors for positive and negative correlation values. One call calculates the color for values from -1 to 0 based on the first and second input colors, and the other calculates the colors for values from 0 to 1 based on the second and third input colors.
Look first. Then leap.
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US Recessions with SPX reversals v3 [FornaxTV]In addition to highlighting periods of official US recessions (as defined by the NBER) this script also displays vertical lines for the SPX market top and bottom associated with each recession .
This facilitates more detailed analysis of potential leading and coincident indicators for market tops and bottoms. This is particularly relevant for market tops, which typically precede the start of a recession by several months.
In addition to recessions with SPX market tops and market bottoms:
- A horizontal line can optionally be displayed for the last market top . (NOTE: this line will only be displayed for SPX tickers.)
- Labels can optionally be displayed for market tops & bottoms, plus the start and end of recessions. If the statistics are enabled (see below) these labels will also indicate the number of weeks between key market events, e.g. a market top and the start of a recession.
- A statistics table can optionally be displayed, contained statistics such as the number of weeks wince the last recession & market bottom, as well as averages for all recessions included in the analysis set.
For the recession statistics:
- "Outlier" recessions such as 1945 (WWII, where the market top occurred well after the recession itself) and 2020 (COVID pandemic, which was arguably not a "true" economic recession) can optionally be excluded.
- You can choose to exclude recessions occurring before a specific year.
Timed Reversion Markers (Custom Session Alerts)This script plots vertical histogram markers at specific intraday time points defined by the user. It is designed for traders who follow time-based reversion or breakout setups tied to predictable market behavior at key clock times, such as institutional opening moves, midday reversals, or end-of-day volatility.
Unlike traditional price-action indicators, this tool focuses purely on time-based triggers, a technique often used in time cycle analysis, market internals, and volume-timing strategies.
The indicator includes eight fully customizable time inputs, allowing users to mark any intraday minute with precision using a decimal hour format (for example, 9.55 for 9:55 AM). Each input is automatically converted into hour and minute format, and a visual histogram marker is plotted once per day at that exact time.
Example use cases:
Mark institutional session opens (e.g., 9:30, 10:00, 15:30)
Time-based mean reversion or volatility windows
Backtest recurring time-based reactions
Highlight algorithmic spike zones
The vertical plots serve as non-intrusive, high-contrast visual markers for scalping setups, session analysis, and decision-making checkpoints. All markers are displayed at the top of the chart without interfering with price candles.
