DAMMU Swing Trading PRODammu Scalping Pro – Short Notes
1️⃣ Purpose:
Scalping and swing trading tool for 15-min and 1-min charts.
Designed for trend continuation, pullbacks, and reversals.
Works well with Heikin Ashi candles (optional).
2️⃣ Core Components:
EMAs:
Fast: EMA5-12
Medium: EMA12-36 Ribbon
Long: EMA75/89 (1-min), EMA180/200 (15-min), EMA540/633
Price Action Channel (PAC): EMA-based High, Low, Close channel.
Fractals: Regular & filtered (BW) fractals for swing recognition.
Higher Highs / Lower Highs / Higher Lows / Lower Lows (HH, LH, HL, LL).
Pivot Points: Optional display with labels.
3️⃣ Bar Coloring:
Blue: Close above PAC
Red: Close below PAC
Gray: Close inside PAC
4️⃣ Alerts:
Swing Buy/Sell arrows based on PAC breakout and EMA200 filter.
Optional “Big Arrows” mode for visibility.
Alert messages: "SWING_UP" and "SWING_DN"
5️⃣ Workflow / Usage Tips:
Set chart to 15-min (for trend) + 1-min (for entry).
Optionally enable Heikin Ashi candles.
Trade long only above EMA200, short only below EMA200.
Watch for pullbacks into EMA channels or ribbons.
Confirm trend resumption via PAC breakout & bar color change.
Use fractals and pivot points to draw trendlines and locate support/resistance.
6️⃣ Optional Filters:
Filter PAC signals with 200 EMA.
Filter fractals for “Pristine/Ideal” patterns (BW filter).
7️⃣ Visuals:
EMA ribbons, PAC fill, HH/LL squares, fractal triangles.
Pivot labels & candle numbering for patterns.
8️⃣ Notes:
No extra indicators needed except optionally SweetSpot Gold2 for major S/R levels.
Suitable for scalping pullbacks with trend confirmation.
If you want, I can make an even shorter “one-screen cheat sheet” with colors, alerts, and EMAs, perfect for real-time charT
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Forecast PriceTime Oracle [CHE] Forecast PriceTime Oracle — Prioritizes quality over quantity by using Power Pivots via RSI %B metric to forecast future pivot highs/lows in price and time
Summary
This indicator identifies potential pivot highs and lows based on out-of-bounds conditions in a modified RSI %B metric, then projects future occurrences by estimating time intervals and price changes from historical medians. It provides visual forecasts via diagonal and horizontal lines, tracks achievement with color changes and symbols, and displays a dashboard for statistical overview including hit rates. Signals are robust due to median-based aggregation, which reduces outlier influence, and optional tolerance settings for near-misses, making it suitable for anticipating reversals in ranging or trending markets.
Motivation: Why this design?
Standard pivot detection often lags or generates false signals in volatile conditions, missing the timing of true extrema. This design leverages out-of-bounds excursions in RSI %B to capture "Power Pivots" early—focusing on quality over quantity by prioritizing significant extrema rather than every minor swing—then uses historical deltas in time and price to forecast the next ones, addressing the need for proactive rather than reactive analysis. It assumes that pivot spacing follows statistical patterns, allowing users to prepare entries or exits ahead of confirmation.
What’s different vs. standard approaches?
- Reference baseline: Diverges from traditional ta.pivothigh/low, which require fixed left/right lengths and confirm only after bars close, often too late for dynamic markets.
- Architecture differences:
- Detects extrema during OOB runs rather than post-bar symmetry.
- Aggregates deltas via medians (or alternatives) over a user-defined history, capping arrays to manage resources.
- Applies tolerance thresholds for hit detection, with options for percentage, absolute, or volatility-adjusted (ATR) flexibility.
- Freezes achieved forecasts with visual states to avoid clutter.
- Practical effect: Charts show proactive dashed projections instead of retrospective dots; the dashboard reveals evolving hit rates, helping users gauge reliability over time without manual calculation.
How it works (technical)
The indicator first computes a smoothed RSI over a specified length, then applies Bollinger Bands to derive %B, flagging out-of-bounds below zero or above one hundred as potential run starts. During these runs, it tracks the extreme high or low price and bar index. Upon exit from the OOB state, it confirms the Power Pivot at that extreme and records the time delta (bars since prior) and price change percentage to rolling arrays.
For forecasts, it calculates the median (or selected statistic) of recent deltas, subtracts the confirmation delay (bars from apex to exit), and projects ahead by that adjusted amount. Price targets use the median change applied to the origin pivot value. Lines are drawn from the apex to the target bar and price, with a short horizontal at the endpoint. Arrays store up to five active forecasts, pruning oldest on overflow.
Tolerance adjusts hit checks: for highs, if the high reaches or exceeds the target (adjusted by tolerance); for lows, if the low drops to or below. Once hit, the forecast freezes, changing colors and symbols, and extends the horizontal to the hit bar. Persistent variables maintain last pivot states across bars; arrays initialize empty and grow until capped at history length.
Parameter Guide
Source: Specifies the data input for the RSI computation, influencing how price action is captured. Default is close. For conservative signals in noisy environments, switch to high; using low boosts responsiveness but may increase false positives.
RSI Length: Sets the smoothing period for the RSI calculation, with longer values helping to filter out whipsaws. Default is 32. Opt for shorter lengths like 14 to 21 on faster timeframes for quicker reactions, or extend to 50 or more in strong trends to enhance stability at the cost of some lag.
BB Length: Defines the period for the Bollinger Bands applied to %B, directly affecting how often out-of-bounds conditions are triggered. Default is 20. Align it with the RSI length: shorter periods detect more potential runs but risk added noise, while longer ones provide better filtering yet might overlook emerging extrema.
BB StdDev: Controls the multiplier for the standard deviation in the bands, where wider settings reduce false out-of-bounds alerts. Default is 2.0. Narrow it to 1.5 for highly volatile assets to catch more signals, or broaden to 2.5 or higher to emphasize only major movements.
Show Price Forecast: Enables or disables the display of diagonal and target lines along with their updates. Default is true. Turn it off for simpler chart views, or keep it on to aid in trade planning.
History Length: Determines the number of recent pivot samples used for median-based statistics, where more history leads to smoother but potentially less current estimates. Default is 50. Start with a minimum of 5 to build data; limit to 100 to 200 to prevent outdated regimes from skewing results.
Max Lookahead: Limits the number of bars projected forward to avoid overly extended lines. Default is 500. Reduce to 100 to 200 for intraday focus, or increase for longer swing horizons.
Stat Method: Selects the aggregation technique for time and price deltas: Median for robustness against outliers, Trimmed Mean (20%) for a balanced trim of extremes, or 75th Percentile for a conservative upward tilt. Default is Median. Use Median for even distributions; switch to Percentile when emphasizing potential upside in trending conditions.
Tolerance Type: Chooses the approach for flexible hit detection: None for exact matches, Percentage for relative adjustments, Absolute for fixed point offsets, or ATR for scaling with volatility. Default is None. Begin with Percentage at 0.5 percent for currency pairs, or ATR for adapting to cryptocurrency swings.
Tolerance %: Provides the relative buffer when using Percentage mode, forgiving small deviations. Default is 0.5. Set between 0.2 and 1.0 percent; higher values accommodate gaps but can overstate hit counts.
Tolerance Points: Establishes a fixed offset in price units for Absolute mode. Default is 0.0010. Tailor to the asset, such as 0.0001 for forex pairs, and validate against past wick behavior.
ATR Length: Specifies the period for the Average True Range in dynamic tolerance calculations. Default is 14. This is the standard setting; shorten to 10 to reflect more recent volatility.
ATR Multiplier: Adjusts the ATR scale for tolerance width in ATR mode. Default is 0.5. Range from 0.3 for tighter precision to 0.8 for greater leniency.
Dashboard Location: Positions the summary table on the chart. Default is Bottom Right. Consider Top Left for better visibility on mobile devices.
Dashboard Size: Controls the text scaling for dashboard readability. Default is Normal. Choose Tiny for dense overlays or Large for detailed review sessions.
Text/Frame Color: Sets the color scheme for dashboard text and borders. Default is gray. Align with your chart theme, opting for lighter shades on dark backgrounds.
Reading & Interpretation
Forecast lines appear as dashed diagonals from confirmed pivots to projected targets, with solid horizontals at endpoints marking price levels. Open targets show a target symbol (🎯); achieved ones switch to a trophy symbol (🏆) in gray, with lines fading to gray. The dashboard summarizes median time/price deltas, sample counts, and hit rates—rising rates indicate improving forecast alignment. Colors differentiate highs (red) from lows (lime); frozen states signal validated projections.
Practical Workflows & Combinations
- Trend following: Enter long on low forecast hits during uptrends (higher highs/lower lows structure); filter with EMA crossovers to ignore counter-trend signals.
- Reversal setups: Short above high projections in overextended rallies; use volume spikes as confirmation to reduce false breaks.
- Exits/Stops: Trail stops to prior pivot lows; conservative on low hit rates (below 50%), aggressive above 70% with tight tolerance.
- Multi-TF: Apply on 1H for entries, 4H for time projections; combine with Ichimoku clouds for confluence on targets.
- Risk management: Position size inversely to delta uncertainty (wider history = smaller bets); avoid low-liquidity sessions.
Behavior, Constraints & Performance
Confirmation occurs on OOB exit, so live-bar pivots may adjust until close, but projections update only on events to minimize repaint. No security or HTF calls, so no external lookahead issues. Arrays cap at history length with shifts; forecasts limited to five active, pruning FIFO. Loops iterate over small fixed sizes (e.g., up to 50 for stats), efficient on most hardware. Max lines/labels at 500 prevent overflow.
Known limits: Sensitive to OOB parameter tuning—too tight misses runs; assumes stationary pivot stats, which may shift in regime changes like low vol. Gaps or holidays distort time deltas.
Sensible Defaults & Quick Tuning
Defaults suit forex/crypto on 1H–4H: RSI 32/BB 20 for balanced detection, Median stats over 50 samples, None tolerance for exactness.
- Too many false runs: Increase BB StdDev to 2.5 or RSI Length to 50 for filtering.
- Lagging forecasts: Shorten History Length to 20; switch to 75th Percentile for forward bias.
- Missed near-hits: Enable Percentage tolerance at 0.3% to capture wicks without overcounting.
- Cluttered charts: Reduce Max Lookahead to 200; disable dashboard on lower TFs.
What this indicator is—and isn’t
This is a forecasting visualization layer for pivot-based analysis, highlighting statistical projections from historical patterns. It is not a standalone system—pair with price action, volume, and risk rules. Not predictive of all turns; focuses on OOB-derived extrema, ignoring volume or news impacts.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Cumulative Volume Delta Z Score [BackQuant]Cumulative Volume Delta Z Score
The Cumulative Volume Delta Z Score indicator is a sophisticated tool that combines the cumulative volume delta (CVD) with Z-Score normalization to provide traders with a clearer view of market dynamics. By analyzing volume imbalances and standardizing them through a Z-Score, this tool helps identify significant price movements and market trends while filtering out noise.
Core Concept of Cumulative Volume Delta (CVD)
Cumulative Volume Delta (CVD) is a popular indicator that tracks the net difference between buying and selling volume over time. CVD helps traders understand whether buying or selling pressure is dominating the market. Positive CVD signals buying pressure, while negative CVD indicates selling pressure.
The addition of Z-Score normalization to CVD makes it easier to evaluate whether current volume imbalances are unusual compared to past behavior. Z-Score helps in detecting extreme conditions by showing how far the current CVD is from its historical mean in terms of standard deviations.
Key Features
Cumulative Volume Delta (CVD): Tracks the net buying vs. selling volume, allowing traders to gauge the overall market sentiment.
Z-Score Normalization: Converts CVD into a standardized value to highlight extreme movements in volume that are statistically significant.
Divergence Detection: The indicator can spot bullish and bearish divergences between price and CVD, which can signal potential trend reversals.
Pivot-Based Divergence: Identifies price and CVD pivots, highlighting divergence patterns that are crucial for predicting price changes.
Trend Analysis: Colors bars according to trend direction, providing a visual indication of bullish or bearish conditions based on Z-Score.
How It Works
Cumulative Volume Delta (CVD): The CVD is calculated by summing the difference between buying and selling volume for each bar. It represents the net buying or selling pressure, giving insights into market sentiment.
Z-Score Normalization: The Z-Score is applied to the CVD to normalize its values, making it easier to compare current conditions with historical averages. A Z-Score greater than 0 indicates a bullish market, while a Z-Score less than 0 signals a bearish market.
Divergence Detection: The indicator detects regular and hidden bullish and bearish divergences between price and CVD. These divergences often precede trend reversals, offering traders a potential entry point.
Pivot-Based Analysis: The indicator uses pivot highs and lows in both price and CVD to identify divergence patterns. A bullish divergence occurs when price makes a lower low, but CVD fails to follow, suggesting weakening selling pressure. Conversely, a bearish divergence happens when price makes a higher high, but CVD doesn't confirm the move, indicating potential selling pressure.
Trend Coloring: The bars are colored based on the trend direction. Green bars indicate an uptrend (CVD is positive), and red bars indicate a downtrend (CVD is negative). This provides an easy-to-read visualization of market conditions.
Standard Deviation Levels: The indicator plots ±1σ, ±2σ, and ±3σ levels to indicate the degree of deviation from the average CVD. These levels act as thresholds for identifying extreme buying or selling pressure.
Customization Options
Anchor Timeframe: The user can define an anchor timeframe to aggregate the CVD, which can be customized based on the trader’s needs (e.g., daily, weekly, custom lower timeframes).
Z-Score Period: The period for calculating the Z-Score can be adjusted, allowing traders to fine-tune the indicator's sensitivity.
Divergence Detection: The tool offers controls to enable or disable divergence detection, with the ability to adjust the lookback periods for pivot detection.
Trend Coloring and Visuals: Traders can choose whether to color bars based on trend direction, display standard deviation levels, or visualize the data as a histogram or line plot.
Display Options: The indicator also allows for various display options, including showing the Z-Score values and divergence signals, with customizable colors and line widths.
Alerts and Signals
The Cumulative Volume Delta Z Score comes with pre-configured alert conditions for:
Z-Score Crossovers: Alerts are triggered when the Z-Score crosses the 0 line, indicating a potential trend reversal.
Shifting Trend: Alerts for when the Z-Score shifts direction, signaling a change in market sentiment.
Divergence Detection: Alerts for both regular and hidden bullish and bearish divergences, offering potential reversal signals.
Extreme Imbalances: Alerts when the Z-Score reaches extreme positive or negative levels, indicating overbought or oversold market conditions.
Applications in Trading
Trend Identification: Use the Z-Score to confirm bullish or bearish trends based on cumulative volume data, filtering out noise and false signals.
Reversal Signals: Divergences between price and CVD can help identify potential trend reversals, making it a powerful tool for swing traders.
Volume-Based Confirmation: The Z-Score allows traders to confirm price movements with volume data, providing more reliable signals compared to price action alone.
Divergence Strategy: Use the divergence signals to identify potential points of entry, particularly when regular or hidden divergences appear.
Volatility and Market Sentiment: The Z-Score provides insights into market volatility by measuring the deviation of CVD from its historical mean, helping to predict price movement strength.
The Cumulative Volume Delta Z Score is a powerful tool that combines volume analysis with statistical normalization. By focusing on volume imbalances and applying Z-Score normalization, this indicator provides clear, reliable signals for trend identification and potential reversals. It is especially useful for filtering out market noise and ensuring that trades are based on significant price movements driven by substantial volume changes.
This indicator is perfect for traders looking to add volume-based analysis to their strategy, offering a more robust and accurate way to gauge market sentiment and trend strength.
Enhanced Holt-Winters RSI [BOSWaves]Enhanced Holt-Winters RSI – Next-Level Momentum Smoothing & Signal Precision
Overview
The Enhanced Holt-Winters RSI transforms the classic Relative Strength Index into a robust, lag-minimized momentum oscillator through Holt-Winters triple exponential smoothing. By modeling the level, trend, and cyclical behavior of the RSI series, this indicator delivers smoother, more responsive signals that highlight overbought/oversold conditions, momentum shifts, and high-conviction trading setups without cluttering the chart with noise.
Unlike traditional RSI, which reacts to historical data and produces frequent whipsaws, the Enhanced Holt-Winters RSI filters transient price fluctuations, enabling traders to detect emerging momentum and potential reversal zones earlier.
Theoretical Foundation
The traditional RSI measures relative strength by comparing average gains and losses, but suffers from:
Lag in trend recognition : Signals often arrive after momentum has shifted.
Noise sensitivity : High-frequency price movements generate unreliable crossovers.
Limited insight into structural market shifts : Standard RSI cannot contextualize cyclical or momentum patterns.
The Enhanced Holt-Winters RSI addresses these limitations by applying triple exponential smoothing directly to the RSI series. This decomposes the series into:
Level (Lₜ) : Represents the smoothed central tendency of RSI.
Trend (Tₜ) : Captures rate-of-change in smoothed momentum.
Seasonal Component (Sₜ) : Models short-term cyclical deviations in momentum.
By incorporating these elements, the oscillator produces smoothed RSI values that react faster to emerging trends while suppressing erratic noise. Its internal forecast is mathematical, influencing the smoothed RSI output and signals, rather than being directly plotted.
How It Works
The Enhanced Holt-Winters RSI builds its signal framework through several layers:
1. Base RSI Calculation
Computes standard RSI over the selected period as the primary momentum input.
2. Triple Exponential Smoothing (Holt-Winters)
The RSI is smoothed recursively to extract underlying momentum structure:
Level, trend, and seasonal components are combined to produce a smoothed RSI.
This internal smoothing reduces lag and enhances signal reliability.
3. Momentum Analysis
Short-term momentum shifts are tracked via a moving average of the smoothed RSI, highlighting acceleration or deceleration in directional strength.
4. Volume Confirmation (Optional)
Buy/sell signals can be filtered through a configurable volume threshold, ensuring only high-conviction moves trigger alerts.
5. Visual Output
Colored Candles : Represent overbought (red), oversold (green), or neutral (yellow) conditions.
Oscillator Panel : Plots the smoothed RSI with dynamic color coding for immediate trend context.
Signals : Triangular markers indicate bullish or bearish setups, with stronger signals flagged in extreme zones.
Interpretation
The Enhanced Holt-Winters RSI provides a multi-dimensional perspective on price action:
Trend Strength : Smoothed RSI slope and color coding reflect the direction and momentum intensity.
Momentum Shifts : Rapid changes in the smoothed RSI indicate emerging strength or weakness.
Overbought/Oversold Zones : Highlight areas where price is stretched relative to recent momentum.
High-Conviction Signals : Combined with volume filtering, markers indicate optimal entries/exits.
Cycle Awareness : Smoothing reveals structural patterns, helping traders avoid reacting to noise.
By combining these elements, traders gain early insight into market structure and momentum without relying on raw, lag-prone RSI data.
Strategy Integration
The Enhanced Holt-Winters RSI can be applied across trading styles:
Trend Following
Enter when RSI is aligned with price momentum and color-coded signals confirm trend direction.
Strong slope in the smoothed RSI signals trend continuation.
Reversal Trading
Look for RSI extremes with momentum shifts and strong signal markers.
Compression in oscillator values often precedes reversal setups.
Breakout Detection
Oscillator flattening in neutral zones followed by directional expansion indicates potential breakout conditions.
Multi-Timeframe Confluence
Higher timeframes provide directional bias; lower timeframes refine entry timing using smoothed RSI dynamics.
Technical Implementation Details
Input Source : Close, open, high, low, or price.
Smoothing : Holt-Winters triple exponential smoothing applied to RSI.
Parameters :
Level (α) : Controls smoothing of RSI.
Trend (β) : Adjusts responsiveness to momentum changes.
Seasonal Length : Defines cycles for short-term adjustments.
Delta Smoothing : Reduces choppiness in smoothed RSI difference.
Outputs :
Smoothed RSI
Colored candles and oscillator panel
Buy/Sell signal markers (with optional strength filtering)
Volume Filtering : Optional threshold to confirm signals.
Optimal Application Parameters
Asset-Specific Guidance:
Forex : Use moderate smoothing (α, β) to capture medium-term momentum swings while filtering minor price noise. Works best when combined with volume or volatility filters.
Equities : Balance responsiveness and smoothness to identify sustained sector momentum or rotational shifts; ideal for capturing clean directional transitions.
Cryptocurrency : Increase smoothing parameters slightly to stabilize RSI during extreme volatility; optional volume confirmation can help filter false signals.
Futures/Indices : Lower smoothing sensitivity emphasizes macro momentum and structural trend durability over short-term fluctuations.
Timeframe Optimization:
Scalping (1-5m) : Use higher sensitivity (lower smoothing factors) to react quickly to micro-momentum reversals.
Intraday (15m-1h) : Balance smoothing and responsiveness for detecting short-term acceleration and exhaustion zones.
Swing (4h-Daily) : Apply moderate smoothing to reveal underlying directional persistence and cyclical reversals.
Position (Daily-Weekly) : Use stronger smoothing to isolate dominant momentum trends and filter temporary pullbacks.
Integration Guidelines
Combine with trend filters (EMAs, SuperSmoother MA, ATR-based tools) for confirmation.
Use volume and signal strength markers to filter low-conviction trades.
Slope, color, and signal alignment can guide entry, stop placement, and scaling.
Disclaimer
The Enhanced Holt-Winters RSI is a technical analysis tool, not a guaranteed profit system. Effectiveness depends on proper settings, market structure, and disciplined risk management. Always backtest before live trading.
Oscillator Matrix [Alpha Extract]A comprehensive multi-oscillator system that combines volume-weighted money flow analysis with enhanced momentum detection, providing traders with a unified framework for identifying high-probability market opportunities across all timeframes. By integrating two powerful oscillators with advanced confluence analysis, this indicator delivers precise entry and exit signals while filtering out market noise through sophisticated threshold-based regime detection.
🔶 Volume-Weighted Money Flow Analysis
Utilizes an advanced money flow calculation that tracks volume-weighted price movements to identify institutional activity and smart money flow. This approach provides superior signal quality by emphasizing high-volume price movements while filtering out low-volume market noise.
// Volume-weighted flows
up_volume = price_up ? volume : 0
down_volume = price_down ? volume : 0
// Money Flow calculation
up_vol_sum = ta.sma(up_volume, mf_length)
down_vol_sum = ta.sma(down_volume, mf_length)
total_volume = up_vol_sum + down_vol_sum
money_flow_ratio = total_volume > 0 ? (up_vol_sum - down_vol_sum) / total_volume : 0
🔶 Enhanced Hyper Wave Oscillator
Features a sophisticated MACD-based momentum oscillator with advanced normalization techniques that adapt to different price ranges and market volatility. The system uses percentage-based calculations to ensure consistent performance across various instruments and timeframes.
// Enhanced MACD-based oscillator
fast_ma = ta.ema(src, hw_fast)
slow_ma = ta.ema(src, hw_slow)
macd_line = fast_ma - slow_ma
signal_line = ta.ema(macd_line, hw_signal)
// Proper normalization using percentage of price
price_base = ta.sma(close, 50)
macd_normalized = macd_line / price_base
hyper_wave = macd_range > 0 ? macd_normalized / macd_range : 0
🔶 Multi-Factor Confluence System
Implements an intelligent confluence scoring mechanism that combines signals from both oscillators to identify high-probability trading opportunities. The system assigns strength scores based on multiple confirmation factors, significantly reducing false signals.
🔶 Fixed Threshold Levels
Uses predefined threshold levels optimized for standard oscillator ranges to distinguish between normal market fluctuations and significant momentum shifts. The dual-threshold system provides clear visual cues for overbought/oversold conditions while maintaining consistent signal criteria across different market conditions.
🔶 Overflow Detection Technology
Advanced overflow indicators identify extreme market conditions that often precede major reversals or continuation patterns. These signals highlight moments when market momentum reaches critical levels, providing early warning for potential turning points.
🔶 Dual Oscillator Integration
The indicator simultaneously tracks volume-weighted money flow and momentum-based price action through two independent oscillators. This dual approach ensures comprehensive market analysis by capturing both institutional activity and technical momentum patterns.
// Multi-factor confluence scoring
confluence_bull = (mf_bullish ? 1 : 0) + (hw_bullish ? 1 : 0) +
(mf_overflow_bull ? 1 : 0) + (hw_overflow_bull ? 1 : 0)
confluence_bear = (mf_bearish ? 1 : 0) + (hw_bearish ? 1 : 0) +
(mf_overflow_bear ? 1 : 0) + (hw_overflow_bear ? 1 : 0)
confluence_strength = confluence_bull > confluence_bear ? confluence_bull / 4 : -confluence_bear / 4
🔶 Intelligent Signal Generation
The system generates two tiers of reversal signals: strong signals that require multiple confirmations across both oscillators, and weak signals that identify early momentum shifts. This hierarchical approach allows traders to adjust position sizing based on signal strength.
🔶 Visual Confluence Zones
Background coloring dynamically adjusts based on confluence strength, creating visual zones that immediately communicate market sentiment. The intensity of background shading corresponds to the strength of the confluent signals, making pattern recognition effortless.
🔶 Threshold Visualization
Color-coded threshold zones provide instant visual feedback about oscillator positions relative to key levels. The fill areas between thresholds create clear overbought and oversold regions with graduated color intensity.
🔶 Candle Color Integration
Optional candle coloring applies confluence-based color logic directly to price bars, creating a unified visual framework that helps traders correlate indicator signals with actual price movements for enhanced decision-making.
🔶 Overflow Alert System
Specialized circular markers highlight extreme overflow conditions on both oscillators, drawing attention to potential climax moves that often precede significant reversals or accelerated trend continuation.
🔶 Customizable Display Options
Comprehensive display controls allow traders to toggle individual components on or off, enabling focused analysis on specific aspects of the indicator. This modularity ensures the indicator adapts to different trading styles and analytical preferences.
1 Week
1 Day
15 Min
This indicator provides a complete analytical framework by combining volume analysis with momentum detection in a single, coherent system. By offering multiple confirmation layers and clear visual hierarchies, it empowers traders to identify high-probability opportunities while maintaining precise risk management across all market conditions and timeframes. The sophisticated confluence system ensures that signals are both timely and reliable, making it an essential tool for serious technical analysts.
Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired)
Version : PineScript™v6
📌Description
The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms.
The indicator employs a proprietary 9-factor logistic regression model that calculates real-time reaction probabilities for each detected zone. By incorporating CNN-inspired filtering techniques and dynamic zone management, it provides traders with unprecedented accuracy in identifying where institutional money is likely to react to price action.
🚀Points of Innovation
● CNN-Inspired Pivot Analysis - Advanced binning system using convolutional neural network principles for superior pattern recognition
● Real-Time Probability Engine - Live reaction probability calculations using 9-factor logistic regression model
● Dynamic Zone Intelligence - Automatic zone merging using Intersection over Union (IoU) algorithms
● Volume-Weighted Scoring - Time-of-day volume Z-score analysis for enhanced zone strength assessment
● Adaptive Decay System - Intelligent zone lifecycle management based on touch frequency and recency
● Multi-Filter Architecture - Optional gradient, smoothing, and Difference of Gaussians (DoG) convolution filters
🔧Core Components
● Pivot Detection Engine - Advanced pivot identification with configurable left/right bars and ATR-normalized strength calculations
● Neural Network Binning - Price level clustering using CNN-inspired algorithms with ATR-based bin sizing
● Logistic Regression Model - 9-factor probability calculation including distance, width, volume, VWAP deviation, and trend analysis
● Zone Management System - Intelligent creation, merging, and decay algorithms for optimal zone lifecycle control
● Visualization Layer - Dynamic line drawing with opacity-based scoring and optional zone fills
🔥Key Features
● High-Probability Zone Detection - Automatically identifies institutional levels with reaction probabilities above configurable thresholds
● Real-Time Probability Scoring - Live calculation of zone reaction likelihood using advanced statistical modeling
● Session-Aware Analysis - Optional filtering to specific trading sessions for enhanced accuracy during active market hours
● Customizable Parameters - Full control over lookback periods, zone sensitivity, merge thresholds, and probability models
● Performance Optimized - Efficient processing with controlled update frequencies and pivot processing limits
● Non-Repainting Mode - Strict mode available for backtesting accuracy and live trading reliability
🎨Visualization
● Dynamic Zone Lines - Color-coded support and resistance levels with opacity reflecting zone strength and confidence scores
● Probability Labels - Real-time display of reaction probabilities, touch counts, and historical hit rates for active zones
● Zone Fills - Optional semi-transparent zone highlighting for enhanced visual clarity and immediate pattern recognition
● Adaptive Styling - Automatic color and opacity adjustments based on zone scoring and statistical significance
📖Usage Guidelines
● Lookback Bars - Default 500, Range 100-1000, Controls the historical data window for pivot analysis and zone calculation
● Pivot Left/Right - Default 3, Range 1-10, Defines the pivot detection sensitivity and confirmation requirements
● Bin Size ATR units - Default 0.25, Range 0.1-2.0, Controls price level clustering granularity for zone creation
● Base Zone Half-Width ATR units - Default 0.25, Range 0.1-1.0, Sets the minimum zone width in ATR units for institutional level boundaries
● Zone Merge IoU Threshold - Default 0.5, Range 0.1-0.9, Intersection over Union threshold for automatic zone merging algorithms
● Max Active Zones - Default 5, Range 3-20, Maximum number of zones displayed simultaneously to prevent chart clutter
● Probability Threshold for Labels - Default 0.6, Range 0.3-0.9, Minimum reaction probability required for zone label display and alerts
● Distance Weight w1 - Controls influence of price distance from zone center on reaction probability
● Width Weight w2 - Adjusts impact of zone width on probability calculations
● Volume Weight w3 - Modifies volume Z-score influence on zone strength assessment
● VWAP Weight w4 - Controls VWAP deviation impact on institutional level significance
● Touch Count Weight w5 - Adjusts influence of historical zone interactions on probability scoring
● Hit Rate Weight w6 - Controls prior success rate impact on future reaction likelihood predictions
● Wick Penetration Weight w7 - Modifies wick penetration analysis influence on probability calculations
● Trend Weight w8 - Adjusts trend context impact using ADX analysis for directional bias assessment
✅Best Use Cases
● Swing Trading Entries - Enter positions at high-probability institutional zones with 60%+ reaction scores
● Scalping Opportunities - Quick entries and exits around frequently tested institutional levels
● Risk Management - Use zones as dynamic stop-loss and take-profit levels based on institutional behavior
● Market Structure Analysis - Identify key institutional levels that define current market structure and sentiment
● Confluence Trading - Combine with other technical indicators for high-probability trade setups
● Session-Based Strategies - Focus analysis during high-volume sessions for maximum effectiveness
⚠️Limitations
● Historical Pattern Dependency - Algorithm effectiveness relies on historical patterns that may not repeat in changing market conditions
● Computational Intensity - Complex calculations may impact chart performance on lower-end devices or with multiple indicators
● Probability Estimates - Reaction probabilities are statistical estimates and do not guarantee actual market outcomes
● Session Sensitivity - Performance may vary significantly between different market sessions and volatility regimes
● Parameter Sensitivity - Results can be highly dependent on input parameters requiring optimization for different instruments
💡What Makes This Unique
● CNN Architecture - First indicator to apply convolutional neural network principles to institutional-level detection
● Real-Time ML Scoring - Live machine learning probability calculations for each zone interaction
● Advanced Zone Management - Sophisticated algorithms for zone lifecycle management and automatic optimization
● Statistical Rigor - Comprehensive 9-factor logistic regression model with extensive backtesting validation
● Performance Optimization - Efficient processing algorithms designed for real-time trading applications
🔬How It Works
● Multi-timeframe pivot identification - Uses configurable sensitivity parameters for advanced pivot detection
● ATR-normalized strength calculations - Standardizes pivot significance across different volatility regimes
● Volume Z-score integration - Enhanced pivot weighting based on time-of-day volume patterns
● Price level clustering - Neural network binning algorithms with ATR-based sizing for zone creation
● Recency decay applications - Weights recent pivots more heavily than historical data for relevance
● Statistical filtering - Eliminates low-significance price levels and reduces market noise
● Dynamic zone generation - Creates zones from statistically significant pivot clusters with minimum support thresholds
● IoU-based merging algorithms - Combines overlapping zones while maintaining accuracy using Intersection over Union
● Adaptive decay systems - Automatic removal of outdated or low-performing zones for optimal performance
● 9-factor logistic regression - Incorporates distance, width, volume, VWAP, touch history, and trend analysis
● Real-time scoring updates - Zone interaction calculations with configurable threshold filtering
● Optional CNN filters - Gradient detection, smoothing, and Difference of Gaussians processing for enhanced accuracy
💡Note
This indicator represents advanced quantitative analysis and should be used by traders familiar with statistical modeling concepts. The probability scores are mathematical estimates based on historical patterns and should be combined with proper risk management and additional technical analysis for optimal trading decisions.
FSVZO [Alpha Extract]A sophisticated volume-weighted momentum oscillator that combines Fourier smoothing with Volume Zone Oscillator methodology to deliver institutional-grade flow analysis and divergence detection. Utilizing advanced statistical filtering including ADF trend analysis and multi-dimensional volume dynamics, this indicator provides comprehensive market sentiment assessment through volume-price relationships with extreme zone detection and intelligent divergence recognition for high-probability reversal and continuation signals.
🔶 Advanced VZO Calculation Engine
Implements enhanced Volume Zone Oscillator methodology using relative volume analysis combined with smoothed price changes to create momentum-weighted oscillator values. The system applies exponential smoothing to both volume and price components before calculating positive and negative momentum ratios with trend factor integration for market regime awareness.
🔶 Fourier-Based Smoothing Architecture
Features advanced Fourier approximation smoothing using cosine-weighted calculations to reduce noise while preserving signal integrity. The system applies configurable Fourier length parameters with weighted sum normalization for optimal signal clarity across varying market conditions with enhanced responsiveness to genuine trend changes.
// Fourier Smoothing Algorithm
fourier_smooth(src, length) =>
sum = 0
weightSum = 0
for i = 0 to length - 1
weight = cos(2 * π * i / length)
sum += src * weight
weightSum += weight
sum / weightSum
🔶 Intelligent Divergence Detection System
Implements comprehensive divergence analysis using pivot point methodology with configurable lookback periods for both standard and hidden divergence patterns. The system validates divergence conditions through range analysis and provides visual confirmation through plot lines, labels, and color-coded identification for precise timing analysis.
15MIN
4H
12H
🔶 Flow Momentum Analysis Framework
Calculates flow momentum by measuring oscillator deviation from its exponential moving average, providing secondary confirmation of volume flow dynamics. The system creates momentum-based fills and visual indicators that complement the primary oscillator analysis for comprehensive market flow assessment.
🔶 Extreme Zone Detection Engine
Features sophisticated extreme zone identification at ±98 levels with specialized marker system including white X markers for signals occurring in extreme territory and directional triangles for potential reversal points. The system provides clear visual feedback for overbought/oversold conditions with institutional-level threshold accuracy.
🔶 Dynamic Visual Architecture
Provides advanced visualization engine with bullish/bearish color transitions, dynamic fill regions between oscillator and signal lines, and flow momentum overlay with configurable transparency levels. The system includes flip markers aligned to color junction points for precise signal timing with optional bar close confirmation to prevent repainting.
🔶 ADF Trend Filtering Integration
Incorporates Augmented Dickey-Fuller inspired trend filtering using normalized price statistics to enhance signal quality during trending versus ranging market conditions. The system calculates trend factors based on mean deviation and standard deviation analysis for improved oscillator accuracy across market regimes.
🔶 Comprehensive Alert System
Features intelligent multi-tier alert framework covering bullish/bearish flow detection, extreme zone reversals, and divergence confirmations with customizable message templates. The system provides real-time notifications for critical volume flow changes and structural market shifts with exchange and ticker integration.
🔶 Performance Optimization Framework
Utilizes efficient calculation methods with optimized variable management and configurable smoothing parameters to balance signal quality with computational efficiency. The system includes automatic pivot validation and range checking for consistent performance across extended analysis periods with minimal resource usage.
This indicator delivers sophisticated volume-weighted momentum analysis through advanced Fourier smoothing and comprehensive divergence detection capabilities. Unlike traditional volume oscillators that focus solely on volume patterns, the FSVZO integrates volume dynamics with price momentum and statistical trend filtering to provide institutional-grade flow analysis. The system's combination of extreme zone detection, intelligent divergence recognition, and multi-dimensional visual feedback makes it essential for traders seeking systematic approaches to volume-based market analysis across cryptocurrency, forex, and equity markets with clearly defined reversal and continuation signals.
Measured Move Volume XIndicator Description
The "Measured Move Volume X" indicator, developed for TradingView using Pine Script version 6, projects potential price targets based on the measured move concept, where the magnitude of a prior price leg (Leg A) is used to forecast a subsequent move. It overlays translucent boxes on the chart to visualize bullish (green) or bearish (red) price projections, extending them to the right for a user-specified number of bars. The indicator integrates volume analysis (relative to a simple moving average), RSI for momentum, and VWAP for price-volume weighting, combining these into a confidence score to filter entry signals, displayed as triangles on breakouts. Horizontal key level lines (large, medium, small) are drawn at significant price points derived from the measured moves, with customizable thresholds, colors, and styles. Exhaustion hints, shown as orange labels near box extremes, indicate potential reversal points. Anomalous candles, marked with diamond shapes, are identified based on volume spikes and body-to-range ratios. Optional higher timeframe candle coloring enhances context. The indicator is fully customizable through input groups for lookback periods, transparency, and signal weights, making it adaptable to various assets and timeframes.
Adjustment Tips for Optimization
To optimize the "Measured Move Volume X" indicator for specific assets or timeframes, adjust the following input parameters:
Leg A Lookback (default: 14 bars): Increase to 20-30 for volatile markets (e.g., cryptocurrencies) to capture larger price swings; decrease to 5-10 for intraday charts (e.g., stocks) for faster signals.
Extend Box to the Right (default: 30 bars): Extend to 50+ for daily or weekly charts to project further targets; shorten to 10-20 for lower timeframes to reduce clutter.
Volume SMA Length (default: 20) and Relative Volume Threshold (default: 1.5): Lower the threshold to 1.2-1.3 for low-volume assets (e.g., commodities) to detect subtler spikes; raise to 2.0+ for high-volume equities to filter noise. Match SMA length to RSI length for consistency.
RSI Parameters (default: length 14, overbought 70, oversold 30): Set overbought to 80 and oversold to 20 in trending markets to reduce premature exit signals; shorten length to 7-10 for scalping.
Key Level Thresholds (default: large 10%, medium 5%, small 5%): Increase thresholds (e.g., large to 15%) for volatile assets to focus on significant moves; disable medium or small lines to declutter charts.
Confidence Score Weights (default: volume 0.5, VWAP 0.3, RSI 0.2): Increase volume weight (e.g., 0.7) for volume-driven markets like futures; emphasize RSI (e.g., 0.4) for momentum-focused strategies.
Anomaly Detection (default: volume multiplier 1.5, small body ratio 0.2, large body ratio 0.75): Adjust the volume multiplier higher for stricter anomaly detection in noisy markets; fine-tune body-to-range ratios based on asset-specific candle patterns.
Use TradingView’s replay feature to test adjustments on historical data, ensuring settings suit the chosen market and timeframe.
Tips for Using the Indicator
Interpreting Signals: Green upward triangles indicate bullish breakout entries when price exceeds the prior high with a confidence score ≥40; red downward triangles signal bearish breakouts. Use these to identify potential entry points aligned with the projected box targets.
Box Projections: Bullish boxes project upward targets (top of box) equal to the prior leg’s height added to the breakout price; bearish boxes project downward. Monitor price action near box edges for target completion or reversal.
Exhaustion Hints: Orange labels near box tops (bullish) or bottoms (bearish) suggest potential exhaustion when price deviates within the set percentage (default: 5%) and RSI or volume conditions are met. Use these as cues to watch for reversals.
Key Level Lines: Large, medium, and small lines mark significant price levels from box tops/bottoms. Use these as potential support/resistance zones, especially when drawn with high volume (colored differently).
Anomaly Candles: Orange diamonds highlight candles with unusual volume/body characteristics, indicating potential reversals or pauses. Combine with box levels for context.
Higher Timeframe Coloring: Enable to color bars based on higher timeframe candle closures (e.g., 1, 2, 5, or 15 minutes) for added trend context.
Customization: Toggle "Only Show Bullish Moves" to focus on bullish setups. Adjust transparency and line styles for visual clarity. Test settings to balance signal frequency and chart readability.
Inputs: Organized into groups (e.g., "Measured Move Settings") using input.int, input.float, input.color, and input.bool for user customization, with tooltips for clarity.
Calculations: Computes relative volume (ta.sma(volume, volLookback)), VWAP (ta.vwap(hlc3)), RSI (ta.rsi(close, rsiLength)), and prior leg extremes (ta.highest/lowest) using prior bar data ( ) to prevent repainting.
Boxes and Lines: Creates boxes (box.new) for bullish/bearish projections and lines (line.new) for key levels. The f_addLine function manages line arrays (array.new_line), capping at maxLinesCount to avoid clutter.
Confidence Score: Combines volume, VWAP distance, and RSI into a weighted score (confScore), filtering entries (≥40). Rounded for display.
Exhaustion Hints: Functions like f_plotBullExitHint assess price deviation, RSI, and volume decrease, using label.new for dynamic orange labels.
Entry Signals and Plots: plotshape displays triangles for breakouts; plot and hline show VWAP and RSI levels; request.security handles higher timeframe coloring.
Anomaly Detection: Identifies candles with small-body high-volume or large-body average-volume patterns via ratios, plotted as diamonds.
Guppy MMA [Alpha Extract]A sophisticated trend-following and momentum assessment system that constructs dynamic trader and investor sentiment channels using multiple moving average groups with advanced scoring mechanisms and smoothed CCI-style visualizations for optimal market trend analysis. Utilizing enhanced dual-group methodology with threshold-based trend detection, this indicator delivers institutional-grade GMMA analysis that adapts to varying market conditions while providing high-probability entry and exit signals through crossover and extreme value detection with comprehensive visual mapping and alert integration.
🔶 Advanced Channel Construction
Implements dual-group architecture using short-term and long-term moving averages as foundation points, applying customizable MA types to reduce noise and score-based averaging for sentiment-responsive trend channels. The system creates trader channels from shorter periods and investor channels from longer periods with configurable periods for optimal market reaction zones.
// Core Channel Calculation Framework
maType = input.string("EMA", title="Moving Average Type", options= )
// Short-Term Group Construction
stMA1 = ma(close, st1, maType)
stMA2 = ma(close, st2, maType)
// Long-Term Group Construction
ltMA1 = ma(close, lt1, maType)
ltMA2 = ma(close, lt2, maType)
// Smoothing Application
smoothedavg = ma(overallAvg, 10, maType)
🔶 Volatility-Adaptive Zone Framework
Features dynamic score-based averaging that expands sentiment signals during strong trend periods and contracts during consolidation phases, preventing false signals while maintaining sensitivity to genuine momentum shifts. The dual-group averaging system optimizes zone boundaries for realistic market behavior patterns.
// Dynamic Sentiment Adjustment
shortTermAvg = (stScore1 + stScore2 + ... + stScore11) / 11
longTermAvg = (ltScore1 + ltScore2 + ... + ltScore11) / 11
// Dual-Group Zone Optimization
overallAvg = (shortTermAvg + longTermAvg) / 2
allMAAvg = (shortTermAvg * 11 + longTermAvg * 11) / 22
🔶 Step-Like Boundary Evolution
Creates threshold-based trend boundaries that update on smoothed average changes, providing visual history of evolving bullish and bearish levels with performance-optimized threshold management limited to key zones for clean chart presentation and efficient processing.
🔶 Comprehensive Signal Detection
Generates buy and sell signals through sophisticated crossover analysis, monitoring smoothed average interaction with zero-line and thresholds for high-probability entry and exit identification. The system distinguishes between trend continuation and reversal patterns with precision timing.
🔶 Enhanced Visual Architecture
Provides translucent zone fills with gradient intensity scaling, threshold-based historical boundaries, and dynamic background highlighting that activates upon trend changes. The visual system uses institutional color coding with green bullish zones and red bearish zones for intuitive market structure interpretation.
🔶 Intelligent Zone Management
Implements automatic trend relevance filtering, displaying signals only when smoothed average proximity warrants analysis attention. The system maintains optimal performance through smart averaging management and historical level tracking with configurable MA periods for various market conditions.
🔶 Multi-Dimensional Analysis Framework
Combines trend continuation analysis through threshold crossovers with momentum detection via extreme markers, providing comprehensive market structure assessment suitable for both trending and ranging market conditions with score-normalized accuracy.
🔶 Advanced Alert Integration
Features comprehensive notification system covering buy signals, sell signals, strong bull conditions, and strong bear conditions with customizable alert conditions. The system enables precise position management through real-time notifications of critical sentiment interaction events and zone boundary violations.
🔶 Performance Optimization
Utilizes efficient MA smoothing algorithms with configurable types for noise reduction while maintaining responsiveness to genuine market structure changes. The system includes automatic visual level cleanup and performance-optimized visual rendering for smooth operation across all timeframes.
This indicator delivers sophisticated GMMA-based market analysis through score-adaptive averaging calculations and intelligent group construction methodology. By combining dynamic trader and investor sentiment detection with advanced signal generation and comprehensive visual mapping, it provides institutional-grade trend analysis suitable for cryptocurrency, forex, and equity markets. The system's ability to adapt to varying market conditions while maintaining signal accuracy makes it essential for traders seeking systematic approaches to trend trading, momentum reversals, and sentiment continuation analysis with clearly defined risk parameters and comprehensive alert integration.
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level:
Tzotchev Trend Measure: A Statistical Approach to Trend Following
The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies.
Mathematical Foundation and Statistical Theory
The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility.
This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework.
The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions.
Comparative Analysis with Traditional Trend Measurement Methods
The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically.
Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels.
The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment.
Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies.
The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification.
Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research.
Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach.
The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems.
Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting.
Advanced Noise Filtering and Market Regime Detection
A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies.
The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action.
The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions.
Professional Implementation and Trading Applications
The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements.
Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts.
The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes.
Performance Optimization and Computational Efficiency
The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached.
Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data.
Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold.
Visual Design and User Interface
The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications.
Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms.
The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously.
Theoretical Implications and Research Context
The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions.
The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions.
The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential.
Practical Applications and Trading Strategy Integration
The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions.
The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems.
Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions.
Implementation Guide and Parameter Configuration
The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization.
Core Calculation Parameters
The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification.
The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly.
The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument.
Trading Profile Configuration
The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels.
Advanced Noise Filtering System
The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications.
The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations.
The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions.
Visual Customization and Display Options
The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments.
The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations.
The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals.
Performance Optimization Settings
The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources.
The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications.
Alert System Configuration
The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations.
The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification.
Table Display and Information Management
The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements.
The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection.
Risk Management Integration
Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies.
The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly.
Timeframe Considerations and Multi-Timeframe Analysis
The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates.
For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment.
Conclusion
The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms.
The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management.
References
Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards.
Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182.
Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58.
Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720.
Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons.
Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250.
Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
POC Migration Velocity (POC-MV) [PhenLabs]📊POC Migration Velocity (POC-MV)
Version: PineScript™v6
📌Description
The POC Migration Velocity indicator revolutionizes market structure analysis by tracking the movement, speed, and acceleration of Point of Control (POC) levels in real-time. This tool combines sophisticated volume distribution estimation with velocity calculations to reveal hidden market dynamics that conventional indicators miss.
POC-MV provides traders with unprecedented insight into volume-based price movement patterns, enabling the early identification of continuation and exhaustion signals before they become apparent to the broader market. By measuring how quickly and consistently the POC migrates across price levels, traders gain early warning signals for significant market shifts and can position themselves advantageously.
The indicator employs advanced algorithms to estimate intra-bar volume distribution without requiring lower timeframe data, making it accessible across all chart timeframes while maintaining sophisticated analytical capabilities.
🚀Points of Innovation
Micro-POC calculation using advanced OHLC-based volume distribution estimation
Real-time velocity and acceleration tracking normalized by ATR for cross-market consistency
Persistence scoring system that quantifies directional consistency over multiple periods
Multi-signal detection combining continuation patterns, exhaustion signals, and gap alerts
Dynamic color-coded visualization system with intensity-based feedback
Comprehensive customization options for resolution, periods, and thresholds
🔧Core Components
POC Calculation Engine: Estimates volume distribution within each bar using configurable price bands and sophisticated weighting algorithms
Velocity Measurement System: Tracks the rate of POC movement over customizable lookback periods with ATR normalization
Acceleration Calculator: Measures the rate of change of velocity to identify momentum shifts in POC migration
Persistence Analyzer: Quantifies how consistently POC moves in the same direction using exponential weighting
Signal Detection Framework: Combines trend analysis, velocity thresholds, and persistence requirements for signal generation
Visual Rendering System: Provides dynamic color-coded lines and heat ribbons based on velocity and price-POC relationships
🔥Key Features
Real-time POC calculation with 10-100 configurable price bands for optimal precision
Velocity tracking with customizable lookback periods from 5 to 50 bars
Acceleration measurement for detecting momentum changes in POC movement
Persistence scoring to validate signal strength and filter false signals
Dynamic visual feedback with blue/orange color scheme indicating bullish/bearish conditions
Comprehensive alert system for continuation patterns, exhaustion signals, and POC gaps
Adjustable information table displaying real-time metrics and current signals
Heat ribbon visualization showing price-POC relationship intensity
Multiple threshold settings for customizing signal sensitivity
Export capability for use with separate panel indicators
🎨Visualization
POC Connecting Lines: Color-coded lines showing POC levels with intensity based on velocity magnitude
Heat Ribbon: Dynamic colored ribbon around price showing POC-price basis intensity
Signal Markers: Clear exhaustion top/bottom signals with labeled shapes
Information Table: Real-time display of POC value, velocity, acceleration, basis, persistence, and current signal status
Color Gradients: Blue gradients for bullish conditions, orange gradients for bearish conditions
📖Usage Guidelines
POC Calculation Settings
POC Resolution (Price Bands): Default 20, Range 10-100. Controls the number of price bands used to estimate volume distribution within each bar
Volume Weight Factor: Default 0.7, Range 0.1-1.0. Adjusts the influence of volume in POC calculation
POC Smoothing: Default 3, Range 1-10. EMA smoothing period applied to the calculated POC to reduce noise
Velocity Settings
Velocity Lookback Period: Default 14, Range 5-50. Number of bars used to calculate POC velocity
Acceleration Period: Default 7, Range 3-20. Period for calculating POC acceleration
Velocity Significance Threshold: Default 0.5, Range 0.1-2.0. Minimum normalized velocity for continuation signals
Persistence Settings
Persistence Lookback: Default 5, Range 3-20. Number of bars examined for persistence score calculation
Persistence Threshold: Default 0.7, Range 0.5-1.0. Minimum persistence score required for continuation signals
Visual Settings
Show POC Connecting Lines: Toggle display of colored lines connecting POC levels
Show Heat Ribbon: Toggle display of colored ribbon showing POC-price relationship
Ribbon Transparency: Default 70, Range 0-100. Controls transparency level of heat ribbon
Alert Settings
Enable Continuation Alerts: Toggle alerts for continuation pattern detection
Enable Exhaustion Alerts: Toggle alerts for exhaustion pattern detection
Enable POC Gap Alerts: Toggle alerts for significant POC gaps
Gap Threshold: Default 2.0 ATR, Range 0.5-5.0. Minimum gap size to trigger alerts
✅Best Use Cases
Identifying trend continuation opportunities when POC velocity aligns with price direction
Spotting potential reversal points through exhaustion pattern detection
Confirming breakout validity by monitoring POC gap behavior
Adding volume-based context to traditional technical analysis
Managing position sizing based on POC-price basis strength
⚠️Limitations
POC calculations are estimations based on OHLC data, not true tick-by-tick volume distribution
Effectiveness may vary in low-volume or highly volatile market conditions
Requires complementary analysis tools for complete trading decisions
Signal frequency may be lower in ranging markets compared to trending conditions
Performance optimization needed for very short timeframes below 1-minute
💡What Makes This Unique
Advanced Estimation Algorithm: Sophisticated method for calculating POC without requiring lower timeframe data
Velocity-Based Analysis: Focus on POC movement dynamics rather than static levels
Comprehensive Signal Framework: Integration of continuation, exhaustion, and gap detection in one indicator
Dynamic Visual Feedback: Intensity-based color coding that adapts to market conditions
Persistence Validation: Unique scoring system to filter signals based on directional consistency
🔬How It Works
Volume Distribution Estimation:
Divides each bar into configurable price bands for volume analysis
Applies sophisticated weighting based on OHLC relationships and proximity to close
Identifies the price level with maximum estimated volume as the POC
Velocity and Acceleration Calculation:
Measures POC rate of change over specified lookback periods
Normalizes values using ATR for consistent cross-market performance
Calculates acceleration as the rate of change of velocity
Signal Generation Process:
Combines trend direction analysis using EMA crossovers
Applies velocity and persistence thresholds to filter signals
Generates continuation, exhaustion, and gap alerts based on specific criteria
💡Note:
This indicator provides estimated POC calculations based on available OHLC data and should be used in conjunction with other analysis methods. The velocity-based approach offers unique insights into market structure dynamics but requires proper risk management and complementary analysis for optimal trading decisions.
BTC/USD 3-Min Binary Prediction [v7.2 EN]BTC/USD 3-Minute Binary Prediction Indicator v7.2 - Complete Guide
Overview
This is an advanced technical analysis indicator designed for Bitcoin/USD binary options trading with 3-minute expiration times. The system aims for an 83% win rate by combining multiple analysis layers and pattern recognition.
How It Works
Core Prediction Logic
- Timeframe: Predicts whether BTC price will be ±$25 higher (HIGH) or lower (LOW) after 3 minutes
- Entry Signals: Generates HIGH/LOW signals when confidence exceeds threshold (default 75%)
- Verification: Automatically tracks and displays win/loss statistics in real-time
5-Layer Filter System
The indicator uses a sophisticated scoring system (0-100 points):
1. Trend Filter (25 points) - Analyzes EMA alignments and price momentum
2. Leading Indicators (25 points) - RSI and MACD divergence analysis
3. Volume Confirmation (20 points) - Detects unusual volume patterns
4. Support/Resistance (15 points) - Identifies key price levels
5. Momentum Alignment (15 points) - Measures acceleration and deceleration
Pattern Recognition
Automatically detects and visualizes:
- Double Tops/Bottoms - Reversal patterns
- Triangles - Ascending, descending, symmetrical
- Channels - Trending price channels
- Candlestick Patterns - Engulfing, hammer, hanging man
Multi-Timeframe Analysis
- Uses 1-minute and 5-minute data for confirmation
- Aligns multiple timeframes for higher probability trades
- Monitors trend consistency across timeframes
Key Features
Display Panels
1. Statistics Panel (Top Right)
- Overall win rate percentage
- Hourly performance (wins/losses)
- Daily performance
- Current system status
2. Analysis Panel (Left Side)
- Market trend analysis
- RSI status (overbought/oversold)
- Volume conditions
- Filter scores for each component
- Final HIGH/LOW/WAIT decision
Visual Signals
- Green Triangle (↑) = HIGH prediction
- Red Triangle (↓) = LOW prediction
- Yellow Background = Entry opportunity
- Blue Background = Waiting for result
Configuration Options
Basic Settings
- Range Width: Target price movement (default $50 = ±$25)
- Min Confidence: Minimum confidence to enter (default 75%)
- Max Daily Trades: Risk management limit (default 5)
Filters (Can be toggled on/off)
- Trend Filter
- Volume Confirmation
- Support/Resistance Filter
- Momentum Alignment
Display Options
- Show/hide signals, statistics, analysis
- Minimal Mode for cleaner charts
- EMA line visibility
Important Risk Warnings
Binary Options Trading Risks:
1. High Risk Product - Binary options are extremely risky and banned in many countries
2. Not Investment Advice - This tool is for educational/analytical purposes only
3. No Guaranteed Returns - Past performance doesn't predict future results
4. Capital at Risk - You can lose your entire investment in seconds
Technical Limitations:
- Requires stable internet connection
- Performance varies with market conditions
- High volatility can reduce accuracy
- Not suitable for news events or low liquidity periods
Best Practices
1. Paper Trade First - Test thoroughly on demo accounts
2. Risk Management - Never risk more than 1-2% per trade
3. Market Conditions - Works best in normal volatility conditions
4. Avoid Major Events - Don't trade during major news releases
5. Monitor Performance - Track your actual results vs displayed statistics
Setup Instructions
1. Add to TradingView chart (BTC/USD preferred)
2. Use 30-second or 1-minute chart timeframe
3. Adjust settings based on your risk tolerance
4. Monitor F-Score (should be >65 for entries)
5. Wait for clear HIGH/LOW signals with high confidence
Alert Configuration
The indicator provides three alert types:
- HIGH Signal alerts
- LOW Signal alerts
- General entry opportunity alerts
Legal Disclaimer
Binary options trading may not be legal in your jurisdiction. Many countries including the USA, Canada, and EU nations have restrictions or outright bans on binary options. Always check local regulations and consult with financial advisors before trading.
Remember: This is a technical analysis tool, not a money-printing machine. Successful trading requires discipline, risk management, and continuous learning. The displayed statistics are historical and don't guarantee future performance.
Simple Symmetrical Triangle Strategy (6 points)Overview
This strategy identifies triangle patterns formed by a series of key high and low price points. A trade is triggered when the price breaks out from the pattern's final confirmation points: a buy signal occurs on a close above the last high point, and a sell signal on a close below the last low point. To ensure relevance, any pattern that doesn't break out within 10 bars is automatically discarded.
This helps filter out patterns that lose momentum and focuses only on the most imminent breakouts.
How It Works
1. Pattern Detection: The script continuously scans for a sequence of three declining highs (points H1, H2, H3) and three rising lows (points L1, L2, L3) to form a triangle.
2. Entry Logic: The logic is straightforward and based on breaking the last confirmed pivot:
* Long Entry: A buy order is executed if the price closes above the level of the last high (H3).
* Short Entry: A sell order is executed if the price closes below the level of the last low (L3).
3. Pattern Expiration: A triangle only remains "active" for 10 bars after its formation. If a breakout doesn't occur within this window, the pattern is removed from analysis, avoiding trades on prolonged, unresolved consolidations.
Key Features
* Automatic Detection: Identifies and draws triangles for you.
* Simple Breakout Logic: Easy to understand, trades by following the price action.
* Time Filter: Its main advantage is discarding patterns that do not resolve quickly.
* Customizable: You can adjust the sensitivity of the pivot detection in the settings.
Important Disclaimer
This strategy is designed as an entry system and DOES NOT INCLUDE A STOP LOSS OR TAKE PROFIT.
Automation Ready
Want to automate this or ANY strategy on your broker or MetaTrader (MT4/MT5) without keeping your computer on or needing a VPS? You can use WebhookTrade.
Symmetrical Triangle Strategy (Real and Trap confirmation)Overview
This is an advanced strategy that not only detects symmetrical triangle patterns but also attempts to differentiate between a genuine breakout and a false breakout (a trap) to trade accordingly.
Instead of blindly following every breakout, it analyzes the "quality" of the move using Volume and RSI filters. If the breakout appears weak, it prepares to trade in the opposite direction, capitalizing on the pattern's failure.
How It Works
The strategy employs a dual logic that activates after the price breaks the last pivot (H3 or L3):
1. Scenario A: The Real Breakout
* If the price breaks the triangle AND the breakout is confirmed by a surge in volume and/or a favorable RSI, the strategy considers the move genuine and enters in the direction of the breakout.
2. Scenario B: The False Breakout (Trap)
* If the price breaks the triangle BUT the indicators fail to confirm it (e.g., low volume), the strategy interprets it as a potential trap.
* It waits for the price to return inside the pattern.
* Once the price has re-entered, it opens a trade AGAINST the initial breakout, betting that the first move was a fake-out.
Key Features
* Hybrid Logic: It's not just a simple breakout strategy; it adapts to market conditions.
* Confirmation Filters: Uses Volume and RSI to validate the strength of a breakout (fully configurable).
* Capitalizes on Traps: Its greatest strength is the ability to identify and trade false breakouts, a common market scenario.
* Optional Confirmation: For trap trades, an extra confirmation via an EMA crossover can be enabled for added safety.
* Opportunity Timeout: Potential traps have a time limit to be confirmed, preventing the strategy from getting stuck in an undecided scenario.
Important Disclaimer
This strategy is designed as an entry system and DOES NOT INCLUDE A STOP LOSS OR TAKE PROFIT.
Automation Ready
Want to automate this or ANY strategy on your broker or MetaTrader (MT4/MT5) without keeping your computer on or needing a VPS? You can use WebhookTrade.
Average hourly move by @zeusbottradingThis Pine Script called "Average hourly move by @zeusbottrading" calculates and displays the average percentage price movement for each hour of the day using the full available historical data.
How the script works:
It tracks the high and low price within each full hour (e.g., 10:00–10:59).
It calculates the percentage move as the range between high and low relative to the average price during that hour.
For each hour of the day, it stores the total of all recorded moves and the count of occurrences across the full history.
At the end, the script computes the average move for each hour (0 to 23) and determines the minimum and maximum averages.
Using these values, it creates a color gradient, where the hours with the lowest average volatility are red and the highest are green.
It then displays a table in the top-right corner of the chart showing each hour and its average percentage move, color‑coded according to volatility.
What it can be used for:
Identifying when the market is historically most volatile or calm during the day.
Helping plan trade entries and exits based on expected volatility.
Comparing hourly volatility patterns across different markets or instruments.
Adjusting position size and risk management according to the anticipated volatility in a particular hour.
Using long-term historical data to understand recurring daily volatility patterns.
In short, this script is a useful tool for traders who want to fine‑tune their trading strategies and risk management by analyzing time‑based volatility profiles.
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
Quant Signals: Entropy w/ ForecastThis is the first of many quantitative signals I plan to create for TV users.
Most technical analysis (TA) tools—like moving averages, oscillators, or chart patterns—are heuristic: they’re based on visually identifiable shapes, threshold crossovers, or empirically chosen rules. These methods rarely quantify the information content or structural complexity of market data. By quantifying market predictability before making a forecast, this method filters out noise and focuses your trading only during statistically favorable conditions—something traditional TA cannot objectively measure.
This MEPP-based approach is quantitative and model-free:
It comes from information theory and measures Shannon entropy rate to assess how predictable the market is at any moment.
Instead of interpreting price formations, it uses a data-compression algorithm (Lempel–Ziv) to capture hidden structure in the sequence of returns.
Forecasts are generated using a principle from statistical physics (Maximum Entropy Production), not historical chart patterns.
In short, this method measures the market's predictability BEFORE deciding a directional forecast is worth trusting. This tool is to inform TA traders on the market's current regime, whether it is smooth and predictable or it is volatile and turbulent.
Technical Introduction:
In information theory, Shannon entropy measures the uncertainty (or information content) in a sequence of data. For markets, the entropy rate captures how much new information price returns generate over time:
Low entropy rate → price changes are more structured and predictable.
High entropy rate → price changes are more random and unpredictable.
By discretizing recent returns into quartile-based states, this indicator:
Calculates the normalized entropy rate as a regime filter.
Uses MEPP to forecast the next state that maximizes entropy production.
Displays both the regime status (predictable vs chaotic) and the forecast bias (bullish/bearish) in a dashboard.
Measurements & How to Use Them
TLDR: HIGH ENTROPY -> information generation/market shift -> Don't trust forecast/strategy
1. H (bits/sym)
Shannon entropy rate of the last μ discrete returns, in bits per symbol (0–2).
Lower → more predictable; higher → more random.
Use as a raw measure of market structure.
2. H_max (log₂Ω)
Theoretical maximum entropy for Ω states. Here Ω = 4 → H_max = 2.0 bits.
Reference value for normalization.
3. Entropy (norm)
H / H_max, scaled between 0 and 1.
< 0.5–0.6 → predictable regime; > 0.6 → chaotic regime.
Main regime filter — forecasts are more reliable when below your threshold.
4. Regime
Label based on Entropy (norm) vs your entThresh.
LOW (predictable) = higher odds forecast will be correct.
HIGH (chaotic) = forecasts less reliable.
5. Next State (MEPP Forecast)
Discrete return state (1–4) predicted to occur next, chosen to maximize entropy production:
Large Down (strong bearish)
Small Down (mild bearish)
Small Up (mild bullish)
Large Up (strong bullish)
Use as your bias direction.
6. Bias
Simplified label from the Next State:
States 1–2 = Bearish bias (red)
States 3–4 = Bullish bias (green)
Align strategy direction with bias only in LOW regime.
Squeeze Pro Momentum BAR color - KLTDescription:
The Squeeze Pro Momentum indicator is a powerful tool designed to detect volatility compression ("squeeze" zones) and visualize momentum shifts using a refined color-based system. This script blends the well-known concepts of Bollinger Bands and Keltner Channels with an optimized momentum engine that uses dynamic color gradients to reflect trend strength, direction, and volatility.
It’s built for traders who want early warning of potential breakouts and clearer insight into underlying market momentum.
🔍 How It Works:
📉 Squeeze Detection:
This indicator identifies "squeeze" conditions by comparing Bollinger Bands and Keltner Channels:
When Bollinger Bands are inside Keltner Channels → Squeeze is ON
When Bollinger Bands expand outside Keltner Channels → Squeeze is OFF
You’ll see squeeze zones classified as:
Wide
Normal
Narrow
Each represents varying levels of compression and breakout potential.
⚡ Momentum Engine:
Momentum is calculated using linear regression of the price's deviation from a dynamic average of highs, lows, and closes. This gives a more accurate representation of directional pressure in the market.
🧠 Smart Candle Coloring (Optimized):
The momentum color logic is inspired by machine learning principles (no hardcoded thresholds):
EMA smoothing and rate of change (ROC) are used to detect momentum acceleration.
ATR-based filters help remove noise and false signals.
Colors are dynamically assigned based on both direction and trend strength.
🧪 How to Use It:
Look for Squeeze Conditions — especially narrow squeezes, which tend to precede high-momentum breakouts.
Confirm with Momentum Color — strong colors often indicate trend continuation; fading colors may signal exhaustion.
Combine with Price Action — use this tool with support/resistance or patterns for higher probability setups.
Recommended For:
Trend Traders
Breakout Traders
Volatility Strategy Users
Anyone who wants visual clarity on trend strength
📌 Tip: This indicator works great when layered with volume and price action patterns. It is fully non-repainting and supports overlay on price charts.
Reversal IndicatorWhat does this indicator do?
This indicator is designed to help traders spot potential reversal points in the market by combining multiple conditions:
✅ Multi-Timeframe RSI – Checks RSI on a lower timeframe (like 5m) to see if the market is oversold or overbought.
✅ Higher Timeframe SMA Filter – Uses a higher timeframe SMA (like 1h) as a trend filter, so signals only trigger in the direction of the bigger trend.
✅ Candle Pattern Confirmation – Looks for bullish or bearish engulfing candles to confirm price exhaustion before signaling a reversal.
When all these conditions align, the indicator plots a triangle under/above the candle to highlight a possible reversal.
Why is this useful?
Many traders struggle with false RSI signals or candle patterns that fail because they don’t respect the larger trend.
This indicator filters out weak setups by requiring alignment between:
A lower timeframe RSI oversold/overbought condition,
A higher timeframe trend filter (SMA),
And a strong candle reversal pattern.
This multi-layer approach helps avoid chasing every RSI dip and focuses only on high-probability reversal zones.
How does it work?
Bullish reversal signal → appears when RSI on the lower TF is oversold, price is still above the higher TF SMA (trend still intact), AND a bullish engulfing candle forms.
Bearish reversal signal → appears when RSI on the lower TF is overbought, price is below the higher TF SMA, AND a bearish engulfing candle forms.
When all conditions match, the indicator plots a triangle under the candle for bullish signals and above the candle for bearish signals.
How to use it?
Choose your timeframes:
A timeframe for trend filtering (e.g. 1h).
A timeframe for RSI (e.g. 4h).
NOTICE: THE RSI TIMEFRAME SHOULD BE GREATER THEN THE TIMEFRAME FOR THE SMA
Otherwise it will not generate that much signals.
Watch for signals ONLY in the direction of the higher trend.
Use the signals as potential reversal points, not as guaranteed entries. Combine with your own confluence.
Optionally set alerts for bullish or bearish reversal conditions so you never miss a setup.
Customization
✅ Choose your RSI length & overbought/oversold levels.
✅ Select which timeframes you want for SMA & RSI.
✅ Toggle the higher TF SMA display on/off.
✅ Adjust signal appearance (triangles).
Important Notes
⚠️ This is not a standalone trading system. It’s a tool to help spot possible reversal areas. Always confirm with price action, support/resistance, or your own strategy
Normalized Fibonacci Retracement (MTF/LOG)A question: Instead of creating indicators that constantly plot Fibonacci Retracement levels in a visually overwhelming way, why don't we redefine them on a different scale? 🤨
Overview
The Normalized Fibonacci Retracement indicator converts price data to a 0-100 scale based on the selected timeframe's high-low range, displaying normalized candlesticks alongside standard Fibonacci levels (23.6%, 38.2%, 50%, 61.8%, 78.6%). This normalization reveals patterns that may be hidden in absolute price charts and allows consistent analysis across different instruments.
Originality
By normalizing prices to percentages, this indicator enables pattern recognition independent of absolute price levels. The same formation at $10-$20 and $1000-$2000 appears identical on the normalized scale, helping traders identify recurring structures across various assets and timeframes.
Concepts
The indicator uses a simple formula to transform price data into percentages. This creates a bounded scale where patterns become comparable regardless of the underlying asset's price range. The normalized view often reveals symmetries and relationships not visible in traditional price charts.
Mechanics
The system tracks highs and lows within the selected timeframe as anchor points. When a new period begins, fresh boundaries are established and prices recalculated. Trend direction is determined by timing of extremes. Linear scaling uses direct percentage calculation, while logarithmic scaling applies exponential interpolation for assets with large percentage moves.
Functions
Timeframe Selection: Higher timeframe analysis on any chart resolution
Normalized Display: OHLC data converted to 0-100 percentage scale
Fibonacci Levels: Standard retracement levels plotted automatically
Scaling Options: Linear or logarithmic calculation methods
Pattern Recognition: Reveals formations hidden in absolute price charts
Moving Average: Optional 20-period SMA overlay
Notes
Ensure chart data covers the full selected timeframe for accurate calculations. Use logarithmic scaling for volatile assets with large percentage moves. The normalized scale is effective at revealing patterns and structures that remain consistent across different price ranges, making it particularly useful for comparative analysis and pattern-based trading strategies.
I hope it helps everyone. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
ETF Leverage VerificationDo leveraged ETFs really return what they promise?
Do they return the exact 2x or 3x? Or a slightly different multiple?
How much do they deviate from the promised leverage multiples?
Do these deviations impact investors in a positive or negative manner?
These are the questions that I want to answer with this indicator.
The ETF Leverage Verification indicator challenges the conventional understanding of leveraged ETFs by measuring how they actually perform versus their theoretical targets.
Instead of assuming leveraged ETFs perfectly track their target multiple, this indicator quantifies the real-world behavior by comparing the expected returns versus the actual results on every trading day.
Key Features
Measures actual versus expected performance of leveraged ETFs
Tracks deviation patterns across thousands of trading days
Identifies asymmetric behavior in up versus down markets
Quantifies beneficial "cushioning effect" during market declines
Provides statistical summary of performance patterns
Works with any leverage factor (2x, 3x, -1x, etc.)
Compatible with all leveraged ETFs (equity, bond, commodity, volatility)
How to Use the Indicator
Enter the Expected Leverage Factor (default: 2.0)
Select the Base Asset (underlying index, e.g., SPX)
Select the Leveraged Asset (leveraged ETF, e.g., SSO)
Understanding the Results
Green markers: Days when the ETF outperformed its expected multiple
Red markers: Days when the ETF underperformed its expected multiple
Data Table:
Positive Deviations: Count of days with better-than-expected performance
Negative Deviations: Count of days with worse-than-expected performance
Avg Deviation: Average magnitude of deviation from expected returns
Frequency Skew: Difference between beneficial deviations in down vs. up markets
Impact: Overall assessment of pattern benefit to investors
Summary Label:
Percentage of positive deviations in up and down markets
Total sample size for statistical significance
Key Patterns to Look For
Positive Deviation in Negative Days:
This occurs when a leveraged ETF falls less than expected during market declines. For example, if SPX falls 1% and a 2x ETF falls only 1.8% (instead of the expected 2%), this creates a +0.2% deviation. This pattern is beneficial as it provides downside protection.
Negative Deviation in Positive Days:
This happens when a leveraged ETF rises less than expected during market advances. For example, if SPX rises 1% and a 2x ETF rises only 1.9% (instead of the expected 2%), this creates a -0.1% deviation. This pattern reduces upside performance.
Frequency Skew:
The most critical metric that measures how much more frequently beneficial deviations occur in down markets compared to up markets. A higher positive skew indicates a stronger asymmetric pattern that helps long-term performance.
Mathematical Background
The indicator computes the deviation between expected and actual performance:
Deviation = Actual Return - Expected Return
Where:
Expected Return = Base Asset Return × Leverage Factor
The deviation is then categorized into four possible outcomes:
Positive deviation on positive market days
Negative deviation on positive market days
Positive deviation on negative market days
Negative deviation on negative market days
In short, more positive deviations are good for investors.
Please feel free to criticize. I'm happy to improve the indicator.
Flexible Reversal DetectorFlexible Reversal Detector
An advanced, fully customizable analytical tool designed to identify local trend reversal zones based on candlestick formations. Users have full control over all logic parameters, making it adaptable to different trading styles and preferences.
Key Features
Adjustable maximum pattern length (number of candles)
Customizable body size ratios for initial, middle, and final candles
Configurable minimum price movement (in %) required before a pattern is considered valid
Colored horizontal lines showing the full length of the pattern – helpful in identifying structure, potential support/resistance zones
Optional volume filter – the volume of the final candle is compared to a volume SMA; multiplier can be adjusted (e.g. 1 = equal to average, 0.8 = 80% of average)
Logic Overview
U Pattern
Each bearish candle is treated as a potential start of a reversal pattern.
Subsequent candles, if small enough and within defined thresholds, form the middle part of the structure.
When a bullish candle with a sufficiently large body appears, it is marked as the final candle of the pattern.
The pattern is considered valid if it was preceded by a defined percentage price drop and – optionally – if the volume condition was met.
∩ Pattern
Each bullish candle may act as the initial candle of a potential reversal pattern in the opposite direction.
Following smaller candles form the middle part, as long as they meet the defined criteria.
The appearance of a strong bearish candle marks the end of the formation.
If this pattern is preceded by a certain price increase (and optionally meets the volume filter), it is highlighted on the chart.
Note: On markets with low volatility or on lower timeframes, it is recommended to reduce the percentage thresholds for signal detection. For more dynamic price action or higher timeframes, consider increasing them accordingly.
Visualization
The final candle of the pattern is marked visually on the chart (depending on direction)
Colored horizontal lines indicate the full span of the pattern – from initial to final candle
GStrategy 1000Pepe 15mTrend Following Candlestick Strategy with EMA Filter and Exit Delay
Strategy Concept
This strategy combines candlestick patterns with EMA trend filtering to identify high-probability trade entries, featuring:
Entry Signals: Hammer and Engulfing patterns confirmed by EMA trend
Trend Filter: Fast EMA (20) vs Slow EMA (50) crossover system
Risk Management: 5% stop-loss + 1% trailing stop
Smart Exit: 2-bar delay after exit signals to avoid whipsaws
Key Components
Trend Identification:
Uptrend: Fast EMA > Slow EMA AND rising
Downtrend: Fast EMA < Slow EMA AND falling
Entry Conditions:
pinescript
// Bullish Entry (Long)
longCondition = (Hammer OR Bullish Engulfing)
AND Uptrend
AND no existing position
// Bearish Entry (Short)
shortCondition = Bearish Engulfing
AND Downtrend
AND no existing position
Exit Mechanics:
Primary Exit: EMA crossover (Fast crosses Slow)
Delayed Execution: Waits 2 full candles after signal
Emergency Exits:
5% fixed stop-loss
1% trailing stop
Visual Dashboard:
Colored EMA lines (Blue=Fast, Red=Slow)
Annotated candlestick patterns
Background highlighting for signals
Distinct markers for entries/exits
Unique Features
Pattern Recognition:
Enhanced Hammer detection (strict body/wick ratios)
Multi-candle engulfing confirmation
Trend-Confirmation:
Requires price and EMA alignment
Filters counter-trend patterns
Exit Optimization:
pinescript
// Delay implementation
if exit_signal_triggered
counter := 2 // Start countdown
else if counter > 0
counter -= 1 // Decrement each bar
exit_trade = (counter == 1) // Execute on final bar
Risk Parameters
Parameter Value Description
Stop Loss 5% Fixed risk per trade
Trailing Stop 1% Locks in profits
Exit Delay 2 bars Reduces false exits
Position Size 100% No pyramiding
Visualization Examples
🟢 Green Triangle: Bullish entry
🔴 Red Triangle: Bearish entry
⬇️ Blue X: Long exit (after delay)
⬆️ Green X: Short exit (after delay)
🎯 Pattern Labels: Identifies hammer/engulfing
Recommended Use
Timeframes: 1H-4H (reduces noise)
Markets: Trend-prone assets (FX, indices)
Best Conditions: Strong trending markets
Avoid: Choppy/Ranging markets
