15m Continuation — prev → new (v6, styled)This indicator gives you backtested statistics on how often reversals vs continuations occur on 15 minute candles on any pair you want to trade. This is great for 15m binary markets like on Polymarket.
Cari skrip untuk "binary"
RSI+VOL——Binary（One bar）Overview
This indicator integrates Stochastic RSI, MACD trend alignment, ADX trend strength, and multi-dimensional volume analysis to provide intelligent signal guidance and market activity monitoring. It is suitable for short-term, swing, and event-driven trading, offering clear visualization of trend direction, market strength, and volume anomalies.
Core Features
1️⃣ Stochastic RSI Signals
Automatically identifies overbought and oversold conditions to generate buy and sell reference signals.
Signals are filtered with candle closing direction to reduce counter-trend entries.
2️⃣ MACD Trend Alignment
Signals trigger only when MACD trend direction aligns with Stochastic RSI, improving accuracy.
Real-time trend alignment reduces noise from ranging markets.
3️⃣ ADX Trend Strength Filter
Signals trigger only when ADX indicates a significant trend, filtering out low-strength movements.
Helps capture primary market directions.
4️⃣ Multi-Dimensional Volume Analysis
Differentiates bullish and bearish volume to identify breakout signals.
Relative volume (RVOL) ensures signals occur during periods of active trading.
Background highlights abnormal spikes and extreme volume, clearly reflecting market activity.
5️⃣ Signal Visualization and Alerts
Buy and sell labels with corresponding RSI values are displayed on the chart.
Built-in alert conditions support TradingView notifications and strategy integration.
Indicator Value
Multi-dimensional alignment: combines trend, momentum, and market activity for comprehensive assessment.
High-precision signal reference: filters noise and provides clear entry indications.
Market activity monitoring: highlights extreme volume to reflect market participation.
Broad applicability: suitable for short-term, swing, and event-driven trading across various markets.
[DEM] Parabolic SAR Bars (PSAR Bars) Parabolic SAR Bars is a visual enhancement of the traditional Parabolic SAR indicator that uses dynamic color coding to represent the relative position and momentum of price versus the SAR levels. The indicator calculates the percentage difference between the closing price and the Parabolic SAR value, then applies either a gradient color scheme that transitions from red to blue based on the relative strength within a 20-period range, or a momentum-based coloring system using purple, blue, and red to indicate directional changes. Both the SAR plot points and the price bars themselves are colored according to this system, creating an intuitive visual representation where traders can quickly assess not just whether price is above or below the SAR, but also the strength and momentum of that relationship. This approach transforms the binary nature of traditional Parabolic SAR signals into a more nuanced visual tool that helps identify the intensity of trending conditions and potential momentum shifts before actual SAR reversals occur.
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt
A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading.
"Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly."
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🤖 THE MACHINE LEARNING
Gaussian Mixture Models (GMM):
Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering :
Models data as coming from multiple Gaussian distributions
Each market regime is a different Gaussian component
Provides probability of belonging to each regime
More sophisticated than simple clustering
Expectation-Maximization Algorithm:
The indicator continuously learns and adapts using the E-M algorithm:
E-step: Calculate probability of current market belonging to each regime
M-step: Update regime parameters based on new data
Continuous learning without repainting
Adapts to changing market conditions
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🎯 THREE MARKET REGIMES
The GMM identifies three distinct market states:
Regime 1 - Low Volatility:
Quiet, ranging markets
Uses RSI-based momentum calculation
Reduces false signals in choppy conditions
Background: Pink tint
Regime 2 - Normal Market:
Standard trending conditions
Uses Rate of Change momentum
Balanced sensitivity
Background: Gray tint
Regime 3 - High Volatility:
Strong trends or volatility events
Uses Z-score based momentum
Captures extreme moves
Background: Cyan tint
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💡 KEY INNOVATIONS
1. Probabilistic Regime Detection:
Instead of binary regime assignment, provides probabilities:
30% Regime 1, 60% Regime 2, 10% Regime 3
Smooth transitions between regimes
No sudden indicator jumps
2. Weighted Momentum Calculation:
Combines three different momentum formulas
Weights based on regime probabilities
Automatically adapts to market conditions
3. Confidence Indicator:
Shows how certain the model is (white line)
High confidence = strong regime identification
Low confidence = transitional market state
Line transparency changes with confidence
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⚙️ PARAMETER OPTIMIZATION
Training Period (50-500):
50-100: Quick adaptation to recent conditions
100: Balanced (default)
200-500: Stable regime identification
Number of Components (2-5):
2: Simple bull/bear regimes
3: Low/Normal/High volatility (default)
4-5: More granular regime detection
Learning Rate (0.1-1.0):
0.1-0.3: Slow, stable learning
0.3: Balanced (default)
0.5-1.0: Fast adaptation
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📊 TRADING STRATEGIES
Visual Signals:
Cyan gradient: Bullish momentum
Magenta gradient: Bearish momentum
Background color: Current regime
Confidence line: Model certainty
1. Regime-Based Trading:
Regime 1 (pink): Expect mean reversion
Regime 2 (gray): Standard trend following
Regime 3 (cyan): Strong momentum trades
2. Confidence-Filtered Signals:
Only trade when confidence > 70%
High confidence = clearer market state
Avoid transitions (low confidence)
3. Adaptive Position Sizing:
Regime 1: Smaller positions (choppy)
Regime 2: Normal positions
Regime 3: Larger positions (trending)
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🚀 ADVANTAGES OVER OTHER ML INDICATORS
vs K-Means Clustering:
Soft clustering (probabilities) vs hard boundaries
Captures uncertainty and transitions
More mathematically robust
vs KNN (K-Nearest Neighbors):
Unsupervised learning (no historical labels needed)
Continuous adaptation
Lower computational complexity
vs Neural Networks:
Interpretable (know what each regime means)
No overfitting issues
Works with limited data
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📈 PERFORMANCE CHARACTERISTICS
Best Market Conditions:
Markets with clear regime shifts
Volatile to trending transitions
Multi-timeframe analysis
Cryptocurrency markets (high regime variation)
Key Strengths:
Automatically adapts to market changes
No manual parameter adjustment needed
Smooth transitions between regimes
Probabilistic confidence measure
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🔬 TECHNICAL BACKGROUND
Gaussian Mixture Models are used extensively in:
Speech recognition (Google Assistant)
Computer vision (facial recognition)
Astronomy (galaxy classification)
Genomics (gene expression analysis)
Finance (risk modeling at investment banks)
The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning.
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💡 PRO TIPS
Watch regime transitions: Best opportunities often occur when regimes change
Combine with volume: High volume + regime change = strong signal
Use confidence filter: Avoid low confidence periods
Multi-timeframe: Compare regimes across timeframes
Adjust position size: Scale based on identified regime
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⚠️ IMPORTANT NOTES
Machine learning adapts but doesn't predict the future
Best used with other confirmation indicators
Allow time for model to learn (100+ bars)
Not financial advice - educational purposes
Backtest thoroughly on your instruments
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🏆 CONCLUSION
The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides:
Automatic adaptation to market conditions
Clear regime identification with confidence levels
Smooth, professional signal generation
True unsupervised machine learning
This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in:
Google's speech recognition
Tesla's computer vision
NASA's data analysis
Wall Street risk models
"Let the machine learn the market regimes. Trade with statistical confidence."
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Developed by AlphaNatt | Machine Learning Trading Systems
Version: 1.0
Algorithm: Gaussian Mixture Model with E-M
Classification: Unsupervised Learning Oscillator
Not financial advice. Always DYOR.
Session Sniper Bands — Pro Overlay (Bollinger, Sessions, Engulf)The Session Sniper Bands — Pro Overlay combines three powerful tools into one clean, professional script designed to help traders spot high-probability setups across any market.
📌 What’s included:
Dual Bollinger Bands → track volatility squeezes, expansions, and mean reversion zones.
Customizable Trading Sessions (Tokyo / London / New York) → shaded regions with editable names, open/close lines, range, and average price markers.
Engulfing Candlestick Signals → automatic bullish and bearish engulfing arrows for precision entry timing.
✨ Features:
Session names and times are fully customizable (rename “Tokyo” to “Asia Open,” etc.).
Clear OB/OS volatility cues via Bollinger stack.
Lightweight visuals that won’t clutter your chart.
Works across Forex, Crypto, Indices, and Binary Options.
⚡ Why use it?
This overlay is built for traders who want to snipe entries with session context. Spot when volatility contracts, align with session flows, and confirm with engulfing momentum candles — all in one view.
⚠️ Disclaimer: This script is for educational purposes only and is not financial advice. Always test on demo before trading live.
MA20 & MA50 RisingMA20 & MA50 Rising Scanner
Detects when both the 20-period and 50-period simple moving averages turn upward on the most recent bar. Designed as a lightweight screener column for TradingView’s watchlists.
Overview
This indicator plots a binary flag (0 or 1) per symbol, where
- 1 means SMA(20) > SMA(20) and SMA(50) > SMA(50)
- 0 means one or both moving averages did not rise
Add it as a custom column in your watchlist to instantly surface stocks with both short- and medium-term trend acceleration.
How It Works
- Calculates ma20 = simple moving average of the last 20 closes
- Calculates ma50 = simple moving average of the last 50 closes
- Compares each with its prior value (ma20 and ma50 )
- Sets flag to 1 only when both are higher than yesterday’s values
When you filter your watchlist for flag == 1, you see only symbols whose 20- and 50-period SMAs both rose on the latest bar.
60 신고가 롱“60-Day New High Long” is a momentum breakout strategy that buys when price makes a fresh 60-day high, expecting continuation after resistance gives way.
Enter on the breakout close (or next open) with confirmation such as expanding volume, relative strength vs. a benchmark, and price above the 50/200-day MAs.
Manage risk with a stop below the recent swing low or 20-day low; take profits via ATR-based targets or a trailing stop, and be cautious around binary catalysts (earnings/news).
60 신고가 롱“60-Day New High Long” is a momentum breakout strategy that buys when price makes a fresh 60-day high, expecting continuation after resistance gives way.
Enter on the breakout close (or next open) with confirmation such as expanding volume, relative strength vs. a benchmark, and price above the 50/200-day MAs.
Manage risk with a stop below the recent swing low or 20-day low; take profits via ATR-based targets or a trailing stop, and be cautious around binary catalysts (earnings/news).
FVG Ultra Assertive - Individual Filters (mtbr)FVG Ultra Assertive - Individual Filters (mtbr)
What this script offers:
This strategy detects and highlights FVGs (Fair Value Gaps) on the chart, providing traders with a visual and systematic approach to identify potential price inefficiencies. The script plots bullish and bearish FVG zones using customizable boxes and labels, allowing users to easily spot high-probability trading areas. In addition, it opens and closes simulated trades based on the detected FVGs, enabling full backtesting and strategy performance evaluation. It integrates multiple independent filters to validate the strength of each FVG signal before entering a trade.
How it works:
The script identifies:
Bullish FVGs when the current low is higher than the high of two bars ago.
Bearish FVGs when the current high is lower than the low of two bars ago.
Once an FVG is detected, it applies three optional independent filters:
GAP/ATR Filter:
Measures the FVG size relative to the Average True Range (ATR). Only gaps exceeding a user-defined multiple of ATR are considered valid.
Support/Resistance (S/R) Filter:
Uses pivot points to check if the FVG overlaps with recent high/low pivot levels within a tolerance percentage. This ensures the gap aligns with meaningful market levels.
Stochastic Filter:
Applies a stochastic oscillator to confirm momentum. Bullish FVGs are validated when stochastic values are oversold, and bearish FVGs when overbought.
After passing the selected filters, the strategy opens trades:
LONG FVG for bullish signals (buy)
SHORT FVG for bearish signals (sell)
The strategy automatically closes positions when an opposite signal appears, generating a backtest report with trades, profits, and statistics. The final bullish or bearish FVG signals are plotted as colored boxes on the chart with labels “BULL FVG” or “BEAR FVG” for immediate visual reference.
How to configure it for use:
Use GAP/ATR Filter: Enable or disable the ATR-based filter and adjust the ATR period (ATR Length) and minimum gap multiplier (Minimum Gap x ATR).
Use S/R Filter: Enable or disable the pivot-based S/R filter. Configure the pivot lookback periods (Pivot Left and Pivot Right) and the tolerance percentage (Gap Tolerance %).
Use Stochastic Filter: Enable or disable stochastic confirmation. Adjust the K and D lengths (Stoch K Length and Stoch D Length) and the overbought/oversold thresholds (Stoch Overbought and Stoch Oversold).
Colors: Customize the colors for bullish and bearish FVGs (FVG Bull and FVG Bear) to match your chart preferences.
Usage Tips:
Apply this strategy to any timeframe; shorter timeframes generate more frequent FVGs, while higher timeframes highlight stronger gaps.
Combine FVG signals with other technical analysis tools for better trade confirmation.
Use the box and label visualization to quickly scan charts for trade opportunities without cluttering the chart.
The strategy’s trades (LONG and SHORT) provide backtesting results and performance statistics for each signal.
DM Impulse Enhanced [BackQuant]DM Impulse Enhanced
What this is (and what it isn’t)
DM Impulse Enhanced is a signal-driven overlay that classifies market action into two practical regimes: Long (risk-on) and Cash (risk-off). It’s built around a proprietary impulse model from the directional-movement family, wrapped in a persistence test and a state machine. Because this script is private, the core mechanics are intentionally abstracted here; what follows explains how to read and use it without revealing the protected calculation.
Why traders use it
Many tools oscillate or describe “how stretched” price is; fewer make a firm, operational call that you can automate. DM Impulse Enhanced aims to do exactly that declare when upside pressure is broad and durable enough to justify a long bias, and when deterioration is strong enough to stand aside (cash/short discretion). The emphasis is on impulse persistence rather than one-off spikes.
What you see on the chart
• Long / Cash markers – Green up-triangles (Long) and red down-triangles (Cash) plot at the bar where the regime changes.
• Regime-tinted bars (optional) – Candles can be softly shaded green during Long and red during Cash for at-a-glance context.
• Trend ribbon (context only) – A narrow ribbon (fast/slow moving averages) is tinted by the current regime to show trend alignment; it does not generate signals on its own.
• No separate sub-pane – Signals are intended to sit directly on price for immediate decision-making.
How the logic behaves (high-level)
Impulse core – A directional-movement–based engine estimates the strength of buying vs. selling pressure over a user-defined horizon.
Persistence gate – Instead of reacting to a single reading, the model evaluates how consistently that impulse dominates across a configurable lookback range.
State machine – When persistence clears (or fails) a pair of thresholds, the model flips and stays in that regime until evidence justifies a change. This “stickiness” is intentional; it reduces whipsaws in choppy tape.
Inputs & controls
Calculation Settings
• DM Length – The base horizon for the impulse engine. Longer = smoother/steadier; shorter = quicker/more reactive.
• Start / End – Defines the span of the persistence check. Expanding the span asks the market to prove itself against more history before changing regime.
Signal Settings
• Long Threshold – The persistence level required to promote the model into Long.
• Short Threshold – The level that, once crossed to the downside, demotes the model into Cash. Using a cross-under event for risk-off helps avoid premature exits on noise.
Visual Settings
• Long / Short colours – Customize marker and shading hues.
• Color Bars? – Toggle candle tinting by regime (off if you prefer a clean chart).
Reading the signals
• Long prints only when the model observes sustained upside pressure across the configured span. Treat this as permission to engage with pullbacks, breakouts, or your preferred setups in the direction of the trend.
• Cash prints when downside deterioration is strong enough to invalidate the prior regime. It’s a risk-off directive—flatten, hedge, or switch to short strategies according to your plan.
• Regime persistence is a feature: once Long, the model won’t flip on minor dips; once Cash, it won’t re-arm on minor bounces. If you want more flips, shorten the spans and relax thresholds; if you want fewer, do the opposite.
Practical tuning guide
Match DM Length to your timeframe
– Intraday: smaller length for timely response.
– Swing/Position: larger length to filter desk-noise and track higher-timeframe flows.
Size the persistence span to your goal
– Narrow span: faster regime changes, more trades, more noise.
– Wide span: fewer, higher-conviction calls, longer holds.
Set realistic thresholds
– The Long threshold should be reachable with your chosen span; the Short threshold should be low enough to catch genuine deterioration but not so tight that it flips on every dip.
Decide on cosmetics
– Turn on bar tinting for discretionary reading, or keep it off when exporting screenshots or running other overlays.
Suggested workflows
• Trend-following with discipline – Trade only in the Long regime; use structure (higher lows, anchored VWAP, or pullbacks to your MA stack) for entries and the Cash flip as a portfolio-level exit.
• Risk overlay – Keep your normal strategy, but: reduce size when Cash appears; re-enable full risk only after Long reasserts.
• Multi-timeframe gating – Require Long on a higher timeframe (e.g., 4H or 1D), then take entries on a lower one. If the high-TF posts Cash, stand down.
How the ribbon fits in
The ribbon visualizes short- vs. intermediate-term trend in the same colour as the regime. It’s deliberately “dumb”: it does not change the signal, it just helps you see when price action and regime are in harmony (e.g., pullbacks during Long that hold above the ribbon).
Alerts included
• DM Impulse LONG – Triggers as the persistence measure clears the Long threshold.
• DM Impulse CASH – Triggers when deterioration crosses the Short threshold from above.
Configure alerts to fire on bar close if you want final (non-intrabar) decisions.
Strengths
• Actionable binary output – Long/Cash is unambiguous and easy to automate.
• Persistence-aware – Focuses on runs that endure, not one-bar excitement.
• Asset/timeframe agnostic – Works anywhere you trust directional-movement concepts (equities, futures, crypto, FX).
Limitations & cautions
• Not a reversal caller – It’s a regime classifier. If you need early bottoms/tops, pair it with your own exhaustion or liquidity tools.
• Parameter feasibility matters – If your thresholds are set beyond what your span can reasonably achieve, signals may rarely (or never) trigger.
• Chop happens – In mean-reverting or news-driven tape, expect more frequent flips unless you widen spans and thresholds.
• Intrabar movement – Like any responsive model, provisional intrabar states can appear before the bar closes. Use “bar close” alerts for finality.
Getting started (safe defaults you can adapt)
• Intraday bias – Shorter DM Length, modest span, moderately tight thresholds.
• Swing filter – Longer DM Length, wider span, stricter Long and sufficiently low Short.
• Conservative overlay – Keep thresholds firm and spans wide; use signals to scale risk rather than flip directions frequently.
Summary
DM Impulse Enhanced is a persistence-focused regime classifier built on directional-movement concepts. It answers a narrow question clearly “Risk-on or risk-off?” and stays with that answer until the evidence meaningfully changes. Use it as a bias switch, a portfolio risk overlay, or a gate for your existing entry logic, and size its spans/thresholds to the cadence of the market you trade.
RCI Buy/Sell Signals RCI Buy/Sell Signals — Dual-RCI State Machine with EMA Bias (Protected)
A purpose-built signal tool that combines two RCI horizons with a deterministic state machine and an EMA-based background bias. It is not a simple mashup: the components are designed to work together so that trend context, timing, and exits form a coherent workflow.
The code remains closed-source to protect a proprietary implementation; this description explains what it does and how the pieces interact so traders can evaluate it.
What it does
On-chart signals: Marks potential BUY/SELL entries and EXITs directly on the chart (markers are drawn with a −1 bar offset for readability).
Trend bias at a glance: Background shading reflects EMA context:
Green when price is above both EMA1 and EMA2
Red when price is below both EMA1 and EMA2
No shading otherwise
(EMA lengths and timeframes are user-configurable.)
Deterministic state machine: Ensures only one active side at a time (flat → long/short → exit), so entries/exits do not overlap or contradict each other.
How it works (conceptual)
Two-horizon RCI framework:
A MID-RCI monitors regime transitions using mid-range thresholds.
A LONG-RCI acts as a slower directional filter/validator.
Two timing modes (you can enable either or both):
Steadily — MID-RCI transitions across mid-band thresholds (e.g., around −50/50) govern entries; LONG-RCI direction is used to confirm/align.
Above — LONG-RCI crosses its pivotal level (around zero) with directional agreement.
These modes are integrated—not stacked randomly—so that one provides timing, the other directional context.
Exits & risk guard: Exits trigger on MID-RCI reaching extreme bands (e.g., ±85) or when price violates a simple N-bar extremum stop (default: 20-bar low/high), whichever comes first. This makes exits explicit rather than relying on a trailing overlay.
Why this is not “just a merge”
The EMAs are not a separate indicator pasted on top; they only provide a binary bias that gates background shading and helps filter entries visually.
The RCI pair is functionally split (timing vs. confirmation) and then synchronized through a state machine that prevents conflicting signals and enforces clean transitions.
The script ships with signal placement discipline (−1 offset markers for clarity) and built-in exit logic based on RCI extremes plus a simple context stop—an integrated design choice, not an ad-hoc mix.
Inputs (overview)
Display: Show Entry Signals / Show Exit Signals / Show Background
Context: EMA1/EMA2 lengths & timeframes (background bias only)
RCI: Long/Mid lengths and source
How to use
Apply the script on a clean chart (no other indicators unless you explain why).
Use the background color as high-level bias, then use the on-chart signals for timing.
Optionally set alerts with “Any alert() function call” to receive signal notifications.
Confirm with your own risk management, liquidity checks, and higher-timeframe confluence.
Notes on publication (for moderators & traders)
Closed-source rationale: The specific RCI ranking/threshold scheme and the state-machine selection logic are part of ongoing proprietary research; the code is protected.
This description details the concepts and interactions sufficiently to understand what the script does and how components work together, while preserving implementation specifics.
Disclaimer
For educational/informational purposes only; not financial advice. Test thoroughly before live use. Trading involves risk.
VWAP For Loop [BackQuant]VWAP For Loop
What this tool does—in one sentence
A volume-weighted trend gauge that anchors VWAP to a calendar period (day/week/month/quarter/year) and then scores the persistence of that VWAP trend with a simple for-loop “breadth” count; the result is a clean, threshold-driven oscillator plus an optional VWAP overlay and alerts.
Plain-English overview
Instead of judging raw price alone, this indicator focuses on anchored VWAP —the market’s average price paid during your chosen institutional period. It then asks a simple question across a configurable set of lookback steps: “Is the current anchored VWAP higher than it was i bars ago—or lower?” Each “yes” adds +1, each “no” adds −1. Summing those answers creates a score that reflects how consistently the volume-weighted trend has been rising or falling. Extreme positive scores imply persistent, broad strength; deeply negative scores imply persistent weakness. Crossing predefined thresholds produces objective long/short events and color-coded context.
Under the hood
• Anchoring — VWAP using hlc3 × volume resets exactly when the selected period rolls:
Day → session change, Week → new week, Month → new month, Quarter/Year → calendar quarter/year.
• For-loop scoring — For lag steps i = , compare today’s VWAP to VWAP .
– If VWAP > VWAP , add +1.
– Else, add −1.
The final score ∈ , where N = (end − start + 1). With defaults (1→45), N = 45.
• Signal logic (stateful)
– Long when score > upper (e.g., > 40 with N = 45 → VWAP higher than ~89% of checked lags).
– Short on crossunder of lower (e.g., dropping below −10).
– A compact state variable ( out ) holds the current regime: +1 (long), −1 (short), otherwise unchanged. This “stickiness” avoids constant flipping between bars without sufficient evidence.
Why VWAP + a breadth score?
• VWAP aggregates both price and volume—where participants actually traded.
• The breadth-style count rewards consistency of the anchored trend, not one-off spikes.
• Thresholds give you binary structure when you need it (alerts, automation), without complex math.
What you’ll see on the chart
• Sub-pane oscillator — The for-loop score line, colored by regime (long/short/neutral).
• Main-pane VWAP (optional) — Even though the indicator runs off-chart, the anchored VWAP can be overlaid on price (toggle visibility and whether it inherits trend colors).
• Threshold guides — Horizontal lines for the long/short bands (toggle).
• Cosmetics — Optional candle painting and background shading by regime; adjustable line width and colors.
Input map (quick reference)
• VWAP Anchor Period — Day, Week, Month, Quarter, Year.
• Calculation Start/End — The for-loop lag window . With 1→45, you evaluate 45 comparisons.
• Long/Short Thresholds — Default upper=40, lower=−10 (asymmetric by design; see below).
• UI/Style — Show thresholds, paint candles, background color, line width, VWAP visibility and coloring, custom long/short colors.
Interpreting the score
• Near +N — Current anchored VWAP is above most historical VWAP checkpoints in the window → entrenched strength.
• Near −N — Current anchored VWAP is below most checkpoints → entrenched weakness.
• Between — Mixed, choppy, or transitioning regimes; use thresholds to avoid reacting to noise.
Why the asymmetric default thresholds?
• Long = score > upper (40) — Demands unusually broad upside persistence before declaring “long regime.”
• Short = crossunder lower (−10) — Triggers only on downward momentum events (a fresh breach), not merely being below −10. This combination tends to:
– Capture sustained uptrends only when they’re very strong.
– Flag downside turns as they occur, rather than waiting for an extreme negative breadth.
Tuning guide
Choose an anchor that matches your horizon
– Intraday scalps : Day anchor on intraday charts.
– Swing/position : Month or Quarter anchor on 1h/4h/D charts to capture institutional cycles.
Pick the for-loop window
– Larger N (bigger end) = stronger evidence requirement, smoother oscillator.
– Smaller N = faster, more reactive score.
Set achievable thresholds
– Ensure upper ≤ N and lower ≥ −N ; if N=30, an upper of 40 can never trigger.
– Symmetric setups (e.g., +20/−20) are fine if you want balanced behavior.
Match visuals to intent
– Enabling VWAP coloring lets you see regime directly on price.
– Background shading is useful for discretionary reading; turn it off for cleaner automation displays.
Playbook examples
• Trend confirmation with disciplined entries — On Month anchor, N=45, upper=38–42: when the long regime engages, use pullbacks toward anchored VWAP on the main pane for entries, with stops just beyond VWAP or a recent swing.
• Downside transition detection — Keep lower around −8…−12 and watch for crossunders; combine with price losing anchored VWAP to validate risk-off.
• Intraday bias filter — Day anchor on a 5–15m chart, N=20–30, upper ~ 16–20, lower ~ −6…−10. Only take longs while score is positive and above a midline you define (e.g., 0), and shorts only after a genuine crossunder.
Behavior around resets (important)
Anchored VWAP is hard-reset each period. Immediately after a reset, the series can be young and comparisons to pre-reset values may span two periods. If you prefer within-period evaluation only, choose end small enough not to bridge typical period length on your timeframe, or accept that the breadth test intentionally spans regimes.
Alerts included
• VWAP FL Long — Fires when the long condition is true (score > upper and not in short).
• VWAP FL Short — Fires on crossunder of the lower threshold (event-driven).
Messages include {{ticker}} and {{interval}} placeholders for routing.
Strengths
• Simple, transparent math — Easy to reason about and validate.
• Volume-aware by construction — Decisions reference VWAP, not just price.
• Robust to single-bar noise — Needs many lags to agree before flipping state (by design, via thresholds and the stateful output).
Limitations & cautions
• Threshold feasibility — If N < upper or |lower| > N, signals will never trigger; always cross-check N.
• Path dependence — The state variable persists until a new event; if you want frequent re-evaluation, lower thresholds or reduce N.
• Regime changes — Calendar resets can produce early ambiguity; expect a few bars for the breadth to mature.
• VWAP sensitivity to volume spikes — Large prints can tilt VWAP abruptly; that behavior is intentional in VWAP-based logic.
Suggested starting profiles
• Intraday trend bias : Anchor=Day, N=25 (1→25), upper=18–20, lower=−8, paint candles ON.
• Swing bias : Anchor=Month, N=45 (1→45), upper=38–42, lower=−10, VWAP coloring ON, background OFF.
• Balanced reactivity : Anchor=Week, N=30 (1→30), upper=20–22, lower=−10…−12, symmetric if desired.
Implementation notes
• The indicator runs in a separate pane (oscillator), but VWAP itself is drawn on price using forced overlay so you can see interactions (touches, reclaim/loss).
• HLC3 is used for VWAP price; that’s a common choice to dampen wick noise while still reflecting intrabar range.
• For-loop cap is kept modest (≤50) for performance and clarity.
How to use this responsibly
Treat the oscillator as a bias and persistence meter . Combine it with your entry framework (structure breaks, liquidity zones, higher-timeframe context) and risk controls. The design emphasizes clarity over complexity—its edge is in how strictly it demands agreement before declaring a regime, not in predicting specific turns.
Summary
VWAP For Loop distills the question “How broadly is the anchored, volume-weighted trend advancing or retreating?” into a single, thresholded score you can read at a glance, alert on, and color through your chart. With careful anchoring and thresholds sized to your window length, it becomes a pragmatic bias filter for both systematic and discretionary workflows.
Six Meridian Divine Swords [theUltimator5]The Six Meridian Divine Sword is a legendary martial arts technique in the classic wuxia novel “Demi-Gods and Semi-Devils” (天龙八部) by Jin Yong (金庸). The technique uses powerful internal energy (qi) to shoot invisible sword-like energy beams from the six meridians of the hand. Each of the six fingers/meridians corresponds to a “sword,” giving six different sword energies.
The Six Meridian Divine Swords indicator is a compact “signal dashboard” that fuses six classic indicators (fingers)—MACD, KDJ, RSI, LWR (Williams %R), BBI, and MTM—into one pane. Each row is a traffic-light dot (green/bullish, red/bearish, gray/neutral). When all six align, the script draws a confirmation line (“All Bullish” or “All Bearish”). It’s designed for quick consensus reads across trend, momentum, and overbought/oversold conditions.
How to Read the Dashboard
The pane has 6 horizontal rows (explained in depth later):
MACD
KDJ
RSI
LWR (Larry Williams %R)
BBI (Bull & Bear Index)
MTM (Momentum)
Each tick in the row is a dot, with sentiment identified by a color.
Green = bullish condition met
Red = bearish condition met
Gray = inside a neutral band (filtering chop), shown when Use Neutral (Gray) Colors is ON
There are two lines that track the dots on the top or bottom of the pane.
All Bullish Signal Line: appears only if all 6 are strongly bullish (default color = white)
All Bearish Signal Line: appears only if all 6 are strongly bearish (default color = fuchsia)
The Six Meridians (Indicators) — What They Mean:
1) MACD — Trend & Momentum
What it is: A trend-following momentum indicator based on the relationship between two moving averages (typically 12-EMA and 26-EMA)
Logic used: Classic MACD line (EMA12−EMA26) vs its 9-EMA signal.
Bullish: MACD > Signal and |MACD−Signal| > Neutral Threshold
Bearish: MACD < Signal and |diff| > threshold
Neutral: |diff| ≤ threshold
Why: Small crosses can whipsaw. The neutral band ignores tiny separations to reduce noise.
Inputs: Fast/Slow/Signal lengths, Neutral Threshold.
2) KDJ — Stochastic with J-line boost
What it is: A variation of the stochastic oscillator popular in Chinese trading systems
Logic used: K = SMA(Stochastic, smooth), D = SMA(K, smooth), J = 3K − 2D.
Bullish: K > D and |K−D| > 2
Bearish: K < D and |K−D| > 2
Neutral: |K−D| ≤ 2
Why: K–D separation filters tiny wiggles; J offers an “extreme” early-warning context in the value label.
Inputs: Length, Smoothing.
3) RSI — Momentum balance (0–100)
What it is: A momentum oscillator measuring speed and magnitude of price changes (0–100)
Logic used: RSI(N).
Bullish: RSI > 50 + Neutral Zone
Bearish: RSI < 50 − Neutral Zone
Neutral: Between those bands
Why: Centerline/adaptive bands (around 50) give a directional bias without relying on fixed 70/30.
Inputs: Length, Neutral Zone (± around 50).
4) LWR (Williams %R) — Overbought/Oversold
What it is: An oscillator similar to stochastic, measuring how close the close is to the high-low range over N periods
Logic used: %R over N bars (0 to −100).
Bullish: %R > −50 + Neutral Zone
Bearish: %R < −50 − Neutral Zone
Neutral: Between those bands
Why: Uses a centered band around −50 instead of only −20/−80, making it act like a directional filter.
Inputs: Length, Neutral Zone (± around −50).
5) BBI (Bull & Bear Index) — Smoothed trend bias
What it is: A composite moving average, essentially the average of several different moving averages (often 3, 6, 12, 24 periods)
Logic used: Average of 4 SMAs (3/6/12/24 by default):
BBI = (MA3 + MA6 + MA12 + MA24) / 4
Bullish: Close > BBI and |Close−BBI| > 0.2% of BBI
Bearish: Close < BBI and |diff| > threshold
Neutral: |diff| ≤ threshold
Why: Multiple MAs blended together reduce single-MA whipsaw. A dynamic 0.2% band ignores tiny drift.
Inputs: 4 lengths (default 3/6/12/24). Threshold is auto-scaled at 0.2% of BBI.
6) MTM (Momentum) — Rate of change in price
What it is: A simple measure of rate of change
Logic used: MTM = Close − Close
Bullish: MTM > 0.5% of Close
Bearish: MTM < −0.5% of Close
Neutral: |MTM| ≤ threshold
Why: A percent-based gate adapts across prices (e.g., $5 vs $500) and mutes insignificant moves.
Inputs: Length. Threshold auto-scaled to 0.5% of current Close.
Display & Inputs You Can Tweak
🎨 Use Neutral (Gray) Colors
ON (default): 3-color mode with clear “no-trade”/“weak” states.
OFF: classic binary (green/red) without neutral filtering.
Nasdaq Sentiment DashboardBuilds a composite sentiment state — RISK-ON / NEUTRAL / RISK-OFF — using three legs:
Volatility: CBOE VXN vs its moving average and absolute thresholds (risk-on when low & below MA; risk-off when high & above MA).
Breadth (quality of participation): QQEW/QQQ ratio vs its MA (equal-weight beating cap-weight = healthier breadth).
Advance/Decline (intraday breadth): advdec.nq vs its MA, with a magnitude filter (ignores tiny A/D days).
How it works
Pulls each series on your chosen signal timeframe (default Daily).
Creates binary signals per leg:
Vol: volOn if VXN < MA and < vxnLower; volOff if VXN > MA and > vxnUpper.
Breadth: brOn if QQEW/QQQ is above its MA by a deadband; brOff if below.
A/D: adOn if A/D > MA and above adMin; adOff if below MA and < -adMin.
Scores each leg (+1 on, −1 off, 0 neutral) → sums to −3…+3.
State rule (default): RISK-ON if score ≥ +2, RISK-OFF if ≤ −2, else NEUTRAL (i.e., need 2 of 3 to agree).
Detects flips (changes in state) and provides alert conditions that fire only on the flip bar.
What you see
Lines for VXN & MA, QQEW/QQQ & MA, A/D & MA.
Background color shows current composite state.
Triangle markers on the flip bar (up for ON, down for OFF).
A top-right table summarizing state, each leg vs its MA, and the composite score.
How to tune
Vol thresholds: vxnLower / vxnUpper.
Breadth whipsaw control: deadbandBps around the ratio’s MA.
A/D sensitivity: adMin and adMaLen.
Stricter regime: require all 3 to agree by changing the state line to score == 3 / -3.
Timeless Command | QuantEdgeB🔍 Overview
Timeless Command is a multi-asset, multi-timeframe “sentiment dashboard” built around a custom Universal Strategy. It fuses two independent proprietary oscillators into one normalized signal, then snapshots that signal across six user-chosen assets and six user-chosen timeframes—right on your price chart. You instantly see whether Bitcoin, Ethereum, Gold, the U.S. Dollar Index, the S&P 500 or the Nasdaq are “Bullish” or “Bearish” from the 2-day down to the 15-minute horizon, plus an overall bias and bar-color overlays.
✨ Key Features
• 🧠 Universal Strategy
o Combines two independent strategic modules into a single oscillator.
o Applies upper/lower thresholds to generate Long/Short/Neutral signals.
• 🌐 Multi-Asset, Multi-TF Grid
o Up to six symbols (e.g. BTC, ETH, SPX, NDX, GOLD, DXY).
o Six configurable timeframes (days, hours, minutes).
o Automatic conversion of “4H” → “240” minutes for seamless request.security calls.
• 📊 Live Sentiment Table
o Arrow icons per asset/timeframe (“⬆️” vs “⬇️”).
o Per-asset average bias (“Bullish” / “Bearish” / “Neutral”), color-coded.
o Clean, right-aligned table overlay with asset labels and timeframe headers.
• 🎨 Chart Overlays
o Bar coloring driven by the first asset’s average TPI bias.
o Two EMAs (default 12/21) filled to show trend direction.
o Optional mini info table to explain bar-color logic.
⚙️ How It Works
1. Signal Calculation
o Applies thresholds (±0.1) to yield discrete signals from a Universal Strategy: +1 (long), –1 (short), 0 (neutral).
2. Multi-TF Signal Gathering
o For each asset, the script uses request.security to pull the TPI on each selected timeframe, locking values at bar close for consistency.
o Converts each reading into a binary direction (up/down).
3. Averaging & Labeling
o Averages the six directional values per asset to gauge overall bias.
o Renders a “Bullish” or “Bearish” label (or “Neutral” if exactly zero).
4. Visual Overlay
o Bar Color: The chart’s candles recolor based on the first asset’s average bias—blue for bullish, orange for bearish, gray for neutral.
o EMAs: Two exponential moving averages sweep the chart, filled to highlight trending regimes.
5. Dashboard Table
o Rows = assets, columns = timeframes + “Average” column.
o Each cell shows an arrow icon with background shading.
o Last column spells out the per-asset average bias in styled text and color.
🎯 Who Should Use It
• Macro Traders who want a quick cross-market heatmap.
• Multi-Asset Strategists balancing exposure across crypto, equities, FX and commodities.
• Systematic & Discretionary players looking for unified, threshold-based signals.
• Risk Managers needing a real-time sentinel on regime shifts across key markets.
⚙️ Default Settings
• Assets: BTCUSD, ETHUSD, SPX, NDX, GOLD, DXY
• Timeframes: 2D, 1D, 12H, 4H, 1H, 15m
• Thresholds: ±0.1 for long/short entries
📌 Conclusion
With Timeless Command, you gain an at-a-glance “command center” for cross-market sentiment. It turns complex, multi-TF oscillator data into a simple arrow-and-table view, coloring your price bars to reinforce the prevailing bias. Whether you’re hunting trend continuations, regime changes or mean-reversion setups, this overlay gives you the high-level context you need—without digging through six different charts.
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
BTC/USD sainBTC/USD 30s Trend-Following Alert v2
Description
This script is designed for short-term trading on BTC/USD, especially for 30-second entries.
It combines EMA (trend direction) and RSI (momentum filter) to generate trend-following buy/sell alerts.
• Core logic
1. EMA (Exponential Moving Average) identifies the main market direction.
2. RSI (Relative Strength Index) checks overbought/oversold conditions within the short-term trend.
3. A signal appears only when both conditions align with the trend, filtering out weak entries.
• Entry conditions
・High (BUY): Price closes above EMA and RSI is above the high threshold → bullish continuation.
・Low (SELL): Price closes below EMA and RSI is below the low threshold → bearish continuation.
• Features
・Simple but effective trend-following method for very short timeframes.
・Customizable parameters: EMA length, RSI length, RSI thresholds.
・Clear chart labels (“HIGH” / “LOW”) with real-time alerts for automated or manual trading decisions.
• Usage
Apply on lower timeframes (e.g., 30s–1m) to catch quick trend continuations.
Signals can be used for scalping or binary options style entries.
• Disclaimer
This script does not guarantee profits. Always manage risk and combine with price action or additional confirmation tools.
EMA Trend Regime Filter by JaeheeOverview
This indicator defines bullish/bearish regimes using a five-EMA stack and emits one signal per confirmed regime flip. Optional ATR gap gating and an ADX gate require structure and strength before a switch is confirmed. An optional, subtle center line improves readability. This is not a strategy and it does not execute trades.
Note: This tool is not the ATR-based Supertrend; it uses EMA stacking with ATR/ADX gating.
Why this combination (originality & value)
• EMA stacking provides a clear directional framework.
• ATR gap gating filters compressed/fragile stacks by requiring each adjacent EMA distance to exceed ATR × multiplier.
• A state machine limits signals to one per direction change, reducing alert fatigue.
• Confirm bars + ADX gate elevate the quality of regime recognition under directional pressure.
Together, these components interact to emphasize durable regime shifts while curbing noise typical of sideways phases.
How it works (concept)
EMA stack: Bullish when EMA1 > EMA2 > EMA3 > EMA4 > EMA5; bearish is the reverse.
ATR spacing (optional): When enabled, each EMA gap must exceed ATR × k to qualify for a flip.
Confirmation streak: Conditions must persist for confirmBars before a flip is validated.
Trend-strength gate: A flip is allowed only when ADX ≥ adxMin.
Flip & signal: On validation, a single marker/label is emitted; duplicates are suppressed.
Visual layer (optional): Subtle background/center line for context; visuals do not affect logic.
Why it’s useful
• Regime clarity: A binary bullish/bearish state reduces decision fatigue and aligns your playbook with market context.
• Counter-trend filter: In a bullish regime, counter-trend shorts are discouraged; in a bearish regime, counter-trend longs are discouraged—until the regime flips.
• Signal economy: One signal per confirmed flip helps avoid alert fatigue and over-trading.
• Volatility awareness: ATR gap gating filters compressed EMA stacks that often precede whipsaws.
• Strength confirmation: The ADX gate requires directional pressure before a switch is allowed.
Practical workflows (how it can be used)
• HTF compass (e.g., H4): Use a higher timeframe such as the 4-hour chart to set directional bias; execute on your lower timeframe with your own triggers and risk rules.
• Alignment rule: Trade in the direction of the active regime—prefer long setups during a bullish regime and short setups during a bearish regime—until a confirmed flip occurs.
• Pullback playbook: In a bullish regime, consider pullbacks to structure/MA confluence; in a bearish regime, consider rallies into resistance. Always size risk independently of the indicator.
• Parameter tuning: Adapt confirmBars, ATR × multiplier, and ADX minimum to the instrument’s volatility. Higher thresholds generally reduce noise but may delay flips.
• Alerts/automation: Set alerts on regime flips but confirm on bar close; intrabar values can update.
Context note (BTC, H4)
On higher timeframes such as the 4-hour chart, trends are often more stable. For BTC, the regime can help distinguish whether the broader market is trending up or down: when the H4 regime is bullish, favor long-side opportunities even if lower-timeframe candles retrace; when the regime turns bearish, favor short-side opportunities. This is context, not signals—entries/exits and risk management remain your responsibility.
Key inputs
• EMA lengths (1–5), Confirm Bars, Min Spacing by ATR
• ADX Length, ADX Minimum
• Visualization toggles (background opacity, center line, label/marker colors)
Alerts
• EMA REGIME LONG — fires once on a confirmed bullish regime
• EMA REGIME SHORT — fires once on a confirmed bearish regime
Notes & limitations
• Designed without future-bar references. Values can update intrabar, so confirm on close before acting on signals.
• This is an indicator for study purposes; it does not place trades.
• Parameters may require tuning across symbols/timeframes.
• Publish with a clean chart so the indicator’s output is clearly identifiable.
• Use on standard bar types (e.g., candles). Non-standard chart types can yield unrealistic behavior for signal logic.
Franco Varacalli binary options |ENGLISH|
What if you could know, with mathematical precision, when your trades have the highest probability of success?
Franco V. ~ Stats is not just an indicator: it’s a real-time performance tracking and analysis system that transforms price action into clear, actionable metrics.
🔍 What it does
It analyzes candle sequences and detects changes in price dynamics, filtering opportunities according to your settings (buy only, sell only, or both). From there, it records each entry, counts wins and losses, and calculates success probabilities for different scenarios.
🛠 How it works (core concepts)
-Evaluates proportional relationships between open, close, high, and low prices.
-Detects shifts in the balance of buying/selling pressure.
-Classifies trades by the number of prior consecutive losses.
-Calculates success probabilities based on accumulated historical data.
📈 What you get
-On-chart table showing entries, wins, losses, and win percentage.
-Dynamic colors to instantly spot the best-performing scenarios.
-Optional arrows marking moments when conditions are met.
-Filters and thresholds to adapt the analysis to your trading style.
💡 How to use it
-Set your preferred signal type and consecutive loss threshold.
-Monitor the table to see which sequences show higher probability.
-Use the signals as a reference and confirm with your own technical analysis.
⚠ Disclaimer: This tool is designed for market analysis and performance tracking. It should be used in combination with your own research, risk management, and decision-making process.
Franco Varacalli
FlowScape PredictorFlowScape Predictor is a non-repainting, regime-aware entry qualifier that turns complex market context into two readiness scores (Long & Short, each 0/25/50/75/100) and clean, confirmed-bar signals. It blends three orthogonal pillars so you act only when trend energy, momentum, and location agree:
Regime (energy): ATR-normalized linear-regression slope of a smooth HMA → EMA baseline, gated by ADX to confirm when pressure is meaningful.
Momentum (push): RSI slope alignment so price has directional follow-through, not just drift.
Structure (location): proximity to pivot-confirmed swings, scaled by ATR, so “ready” appears near constructive pullbacks—not mid-trend chases.
A soft ATR cloud wraps the baseline for context. A yellow Predictive Baseline extends beyond the last bar to visualize near-term trajectory. It is visual-only: scores/alerts never use it.
What you see
Baseline line that turns green/red when regime is strong in that direction; gray when weak.
ATR cloud around the baseline (context for stretch and pullbacks).
Scores (Long & Short, 0–100 in steps of 25) and optional “L/S” icons on bar close.
Yellow Predictive Baseline that extends to the right for a few bars (visual trajectory of the smoothed baseline).
The scoring system (simple and transparent)
Each side (Long/Short) sums four binary checks, 25 points each:
Regime aligned: trendStrong is true and LR slope sign favors that side.
Momentum aligned: RSI side (>50 for Long, <50 for Short) and RSI slope confirms direction.
Baseline side: price is above (Long) / below (Short) the baseline.
Location constructive: distance from the last confirmed pivot is healthy (ATR-scaled; not overstretched).
Valid totals are 0, 25, 50, 75, 100.
Best-quality signal: 100/0 (your side/opposite) on bar close.
Good, still valid: 75/0, especially when the missing block is only “location” right as price re-engages the cloud/baseline.
Avoid: 75/25 or any opposition > 0 in a weak (gray) regime.
The Predictive (Kalman) line — what it is and isn’t
The yellow line is a visual forward extension of the smoothed baseline to help you see the current trajectory and time pullback resumptions. It does not predict price and is excluded from scores and alerts.
How it’s built (plain English):
We maintain a one-dimensional Kalman state x as a smoothed estimate of the baseline. Each bar we observe the current baseline z.
The filter adjusts its trust using the Kalman gain K = P / (P + R) and updates:
x := x + K*(z − x), then P := (1 − K)*P + Q.
Q (process noise): Higher Q → expects faster change → tracks turns quicker (less smoothing).
R (measurement noise): Higher R → trusts raw baseline less → smoother, steadier projection.
What you control:
Lead (how many bars forward to draw).
Kalman Q/R (visual smoothness vs. responsiveness).
Toggle the line on/off if you prefer a minimal chart.
Important: The predictive line extends the baseline, not price. It’s a visual timing aid—don’t automate off it.
How to use (step-by-step)
Keep the chart clean and use a standard OHLC/candlestick chart.
Read the regime: Prefer trades with green/red baseline (trendStrong = true).
Check scores on bar close:
Take Long 100 / Short 0 or Long 75 / Short 0 when the chart shows a tidy pullback re-engaging the cloud/baseline.
Mirror the logic for shorts.
Confirm location: If price is > ~1.5 ATR from its reference pivot, let it come back—avoid chasing.
Set alerts: Add an alert on Long Ready or Short Ready; these fire on closed bars only.
Risk management: Use ATR-buffered stops beyond the recent pivot; target fixed-R multiples (e.g., 1.5–3.0R). Manage the trade with the baseline/cloud if you trail.
Best-practice playbook (quick rules)
Green light: 100/0 (best) or 75/0 (good) on bar close in a colored (non-gray) regime.
Location first: Prefer entries near the baseline/cloud right after a pullback, not far above/below it.
Avoid mixed signals: Skip 75/25 and anything with opposition while the baseline is gray.
Use the yellow line with discretion: It helps you see rhythm; it’s not a signal source.
Timeframes & tuning (practical defaults)
Intraday indices/FX (5m–15m): Demand 100/0 in chop; allow 75/0 when ADX is awake and pullback is clean.
Crypto intraday (15m–1h): Prefer 100/0; 75/0 on the first pullback after a regime turn.
Swing (1h–4h/D1): 75/0 is often sufficient; 100/0 is excellent (fewer but cleaner signals).
If choppy: raise ADX threshold, raise the readiness bar (insist on 100/0), or lengthen the RSI slope window.
What makes FlowScape different
Energy-first regime filter: ATR-normalized LR slope + ADX gate yields a consistent read of trend quality across symbols and timeframes.
Location-aware entries: ATR-scaled pivot proximity discourages mid-air chases, encouraging pullback timing.
Separation of concerns: The predictive line is visual-only, while scores/alerts are confirmed on close for non-repainting behavior.
One simple score per side: A single 0–100 readiness figure is easier to tune than juggling multiple indicators.
Transparency & limitations
Scores are coarse by design (25-point blocks). They’re a gatekeeper, not a promise of outcomes.
Pivots confirm after right-side bars, so structure signals appear after swings form (non-repainting by design).
Avoid using non-standard chart types (Heikin Ashi, Renko, Range, etc.) for signals; use a clean, standard chart.
No lookahead, no higher-timeframe requests; alerts fire on closed bars only.
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.
HyperChannelThought for 1m 33sHyperChannel Exhaustion Indicator
Overview
This open-source indicator creates a dynamic volatility channel to identify trend directions and potential support/resistance levels, while incorporating momentum analysis, trend strength assessment, and a composite exhaustion metric to highlight possible trend fatigue and reversal points. The core idea is to combine volatility adaptation with multi-layered exhaustion signals, providing a unified tool that goes beyond standalone channels or oscillators. By integrating these elements, it offers a comprehensive view: the channel tracks price within volatile bounds, momentum detects strength or weakening, and exhaustion quantifies reversal risks—allowing users to spot continuations in strong trends or prepare for reversals during fatigue. This synergy creates a unique, actionable framework not found in isolated indicators, helping users make informed decisions across various market conditions.
The indicator builds on public domain concepts like ATR-based channels and standard exhaustion ratios (with credits to Franklin Moormann for foundational exhaustion logic, significantly enhanced here through integration and scoring). Improvements include a custom composite score weighting multiple factors, adaptive coloring for visual clarity, and a dashboard for quick stats—resulting in a tool that's more than a simple merge, but a cohesive system for trend management.
Key Features
Volatility Channel: Plots adaptive upper and lower bands based on smoothed true range multiples around a price midpoint, with trend confirmation requiring consecutive closes beyond bands for reliability.
Momentum Layer: Uses averaged relative changes across varying periods to flag strong impulses or pullbacks, enhancing channel breakouts with contextual strength.
Trend Strength: Differentiates strong trends from ranges or transitions, altering band colors for intuitive reading (e.g., vibrant in trends, subdued otherwise).
Exhaustion Metrics:
A ratio-based signal comparing price advances to highs, smoothed to detect fading momentum.
A composite score (0-100%) aggregating normalized exhaustion, divergence flags, and volume surges—low scores suggest trend health, medium warn of fatigue, high indicate reversal potential.
Visuals:
Band plots (active/inactive) with fills for trend highlighting.
Circles on candles for pullback warnings.
Candle coloring: Dark shades for robust trends (e.g., deep green/up, maroon/down), lighter/warning tones (yellow/up, orange/down) for weakening phases.
Divergence labels on price vs. momentum for hidden/regular setups.
Dashboard: Compact table with trend, risk score (integrated exhaustion), composite value, regime, and higher-timeframe levels; background gradients from green (low risk) to red (high) for at-a-glance reversal probability.
Alerts: For channel events, momentum shifts, exhaustion thresholds, and signals.
How It Works
The indicator operates on core technical concepts without relying on external data:
Channel Construction: Starts with true range (high-low, gaps) smoothed over a period (default 120) to form ATR. Bands are midpoint ± ATR multiple (default 3.0), tightened/loosened based on closes and momentum to avoid whipsaws. Trends flip only after confirmed breaches (default 2 bars), reducing false signals.
Momentum Calculation: Aggregates percentage changes from short to long moving averages (defaults 10-200 periods), smoothed into dynamic thresholds. This detects "strong" (beyond multiples) vs. "exhausting" (pullbacks below fractions), feeding into channel logic and warnings.
Strength and Regime: ADX (default period 14) classifies markets: above high threshold (25) as trending, below low (20) as ranging, in-between as transitioning (with bias if rising and momentum aligns).
Exhaustion and Scoring:
Compares cumulative closes above priors vs. new highs, smoothed (default length 10) into a slope: positive/negative for bull/bear, intensifying for strength.
Composite score weights this normalization (40%), binary divergence checks on a standard oscillator (30%), and volume ratios (30%)—scaled to 0-100%. Thresholds (e.g., 80 for high) trigger color shifts.
Reversal risk (0-100%) blends exhaustion depth, divergences, unconfirmed bars, and the score—labeled Low (<30%), Medium (30-70%), High (>70%).
These interact: e.g., channel bands adjust with momentum, exhaustion colors candles/dashboard, creating a feedback loop for holistic analysis.
Usage Suggestions
Setup: Add to a clean chart (no other indicators unless combining for confluence, e.g., with volume—explain in notes). Use defaults for most assets; tweak ATR period/multiplier for volatility (shorter for crypto, longer for stocks). Set higher timeframe (default 60min) for context.
Interpreting Trends: Green-filled uptrends (active support band) signal buys on pullbacks; red downtrends for shorts. Vibrant colors indicate ADX strength—trade with trend.
Spotting Exhaustion/Reversals: Watch for yellow/orange candles (weakening signal) or circles (pullback warnings). Composite >80% (red dashboard cell) or high risk (yellow/orange table background) suggests exits/preparation. Divergences add confirmation: bullish (green label) near supports, bearish (red) at resistances.
Regimes: Trending: Follow channel breaks. Ranging: Fade extremes. Transitioning: Wait for emerging bias.
Alerts: Enable for real-time notifications—e.g., high exhaustion for potential tops/bottoms.
Customization: Adjust weights for risk sensitivity (e.g., boost exhaustion for conservative trading). Test on historical data to align with strategy; aim for balanced risk (e.g., <5% per trade).
This tool visualizes concepts like volatility clustering and momentum divergence, aiding in trend-following or mean-reversion setups. Always combine with personal analysis—it's not a signal generator but a decision aid.
Credits and Notes
Builds on public domain ATR/ADX ideas; exhaustion ratio inspired by Franklin Moormann (cheatcountry), with major enhancements like multi-momentum integration, composite scoring, and visual/dashboard features for originality.
Compliant with Pine v6; open-source for community use. No ads/guarantees—past performance isn't indicative. Manage risk; this is educational. For chart: Publish clean, with this script only, showing clear outputs.
Altcoin Breadth | QuantumResearch🔹 Altcoin Breadth | QuantumResearch
Purpose:
Altcoin Breadth measures the strength of the altcoin market by tracking how many assets trade above key moving averages (50-day and 200-day). It offers a normalized view of trend participation across 40 major crypto assets.
How It Works:
For each of the 40 altcoins:
The script checks whether the asset's current price is above its 50-day and/or 200-day simple moving average.
Each condition counts as a binary "1" (trend up) or "0" (trend down).
The total values are averaged, yielding two normalized values between 0 and 1:
Breadth 50: % of assets above their 50 SMA
Breadth 200: % of assets above their 200 SMA
Visual Display:
Plots Breadth 50 and Breadth 200 separately as two gradient-colored lines.
Dynamic labels at the latest bar indicate current breadth values.
Optional bar coloring to reflect underlying breadth momentum.
Key Features:
Evaluates short-term and long-term trend strength across the altcoin sector.
Dynamic visualization of market participation breadth.
Clear trend shifts and sector-wide bullish/bearish transitions.
Separate toggles to show either Breadth 50, Breadth 200, or both.
Trading Application:
Identify broad altcoin uptrends or breakdowns.
Use Breadth 200 for macro confirmation; Breadth 50 for tactical shifts.
Align altcoin exposure with healthy trend participation levels.
⚠️ Breadth tools offer market-wide context, not individual entry signals. Use in combination with trend or momentum indicators.
Disclaimer: Past performance does not guarantee future results. This tool is intended for informational and educational use only. Cryptocurrency markets are volatile and involve high risk.
Fear Volatility Gate [by Oberlunar]The Fear Volatility Gate by Oberlunar is a filter designed to enhance operational prudence by leveraging volatility-based risk indices. Its architecture is grounded in the empirical observation that sudden shifts in implied volatility often precede instability across financial markets. By dynamically interpreting signals from globally recognized "fear indices", such as the VIX, the indicator aims to identify periods of elevated systemic uncertainty and, accordingly, restrict or flag potential trade entries.
The rationale behind the Fear Volatility Gate is rooted in the understanding that implied volatility represents a forward-looking estimate of market risk. When volatility indices rise sharply, it reflects increased demand for options and a broader perception of uncertainty. In such contexts, price movements can become less predictable, more erratic, and often decoupled from technical structures. Rather than relying on price alone, this filter provides an external perspective—derived from derivative markets—on whether current conditions justify caution.
The indicator operates in two primary modes: single-source and composite . In the single-source configuration, a user-defined volatility index is monitored individually. In composite mode, the filter can synthesize input from multiple indices simultaneously, offering a more comprehensive macro-risk assessment. The filtering logic is adaptable, allowing signals to be combined using inclusive (ANY), strict (ALL), or majority consensus logic. This allows the trader to tailor sensitivity based on the operational context or asset class.
The indices available for selection cover a broad spectrum of market sectors. In the equity domain, the filter supports the CBOE Volatility Index ( CBOE:VIX VIX) for the S&P 500, the Nasdaq-100 Volatility Index ( CBOE:VXN VXN), the Russell 2000 Volatility Index ( CBOEFTSE:RVX RVX), and the Dow Jones Volatility Index ( CBOE:VXD VXD). For commodities, it integrates the Crude Oil Volatility Index ( CBOE:OVX ), the Gold Volatility Index ( CBOE:GVZ ), and the Silver Volatility Index ( CBOE:VXSLV ). From the fixed income perspective, it includes the ICE Bank of America MOVE Index ( OKX:MOVEUSD ), the Volatility Index for the TLT ETF ( CBOE:VXTLT VXTLT), and the 5-Year Treasury Yield Index ( CBOE:FVX.P FVX). Within the cryptocurrency space, it incorporates the Bitcoin Volmex Implied Volatility Index ( VOLMEX:BVIV BVIV), the Ethereum Volmex Implied Volatility Index ( VOLMEX:EVIV EVIV), the Deribit Bitcoin Volatility Index ( DERIBIT:DVOL DVOL), and the Deribit Ethereum Volatility Index ( DERIBIT:ETHDVOL ETHDVOL). Additionally, the user may define a custom instrument for specialized tracking.
To determine whether market conditions are considered high-risk, the indicator supports three modes of evaluation.
The moving average cross mode compares a fast Hull Moving Average to a slower one, triggering a signal when short-term volatility exceeds long-term expectations.
The Z-score mode standardizes current volatility relative to historical mean and standard deviation, identifying significant deviations that may indicate abnormal market stress.
The percentile mode ranks the current value against a historical distribution, providing a relative perspective particularly useful when dealing with non-normal or skewed distributions.
When at least one selected index meets the condition defined by the chosen mode, and if the filtering logic confirms it, the indicator can mark the trading environment as “blocked”. This status is visually highlighted through background color changes and symbolic markers on the chart. An optional tabular interface provides detailed diagnostics, including raw values, fast-slow MA comparison, Z-scores, percentile levels, and binary risk status for each active index.
The Fear Volatility Gate is not a predictive tool in itself but rather a dynamic constraint layer that reinforces discipline under conditions of macro instability. It is particularly valuable when trading systems are exposed to highly leveraged or short-duration strategies, where market noise and sentiment can temporarily override structural price behavior. By synchronizing trading signals with volatility regimes, the filter promotes a more cautious, informed approach to decision-making.
This approach does not assume that all volatility spikes are harmful or that market corrections are imminent. Rather, it acknowledges that periods of elevated implied volatility statistically coincide with increased execution risk, slippage, and spread widening, all of which may erode the profitability of even the most technically accurate setups.
Therefore, the Fear Volatility Gate acts as a protective mechanism.
Oberlunar 👁️⭐
