Fractional Candlestick Long Only Experimental V10Fractional Candlestick Long-Only Strategy – Technical Description
This document provides a professional English description of the "Fractional Candlestick Long Only Experimental V6" strategy using pure CF/AB fractional kernels and wavelet-based filtering.
1. Fractional Candlesticks (CF / AB)
The strategy computes two fractional representations of price using Caputo–Fabrizio (CF) and Atangana–Baleanu (AB) kernels. These provide long-memory filtering without EMA approximations. Both CF and AB versions are applied to O/H/L/C, producing fractional candlesticks and fractional Heikin-Ashi variants.
2. Trend Stack Logic
Trend confirmation is based on a 4-component stack:
- CF close > AB close
- HA_CF close > HA_AB close
- HA_CF bullish
- HA_AB bullish
The user selects how many components must align (4, 3, or any 2).
3. Wavelet Filtering
A wavelet transform (Haar, Daubechies-4, Mexican Hat) is applied to a chosen source (e.g., HA_CF close). The wavelet response is used as:
- entry filter (4 modes)
- exit filter (4 modes)
Wavelet modes: off, confirm, wavelet-only, block adverse signals.
4. Trailing System
Trailing stop uses fractional AB low × buffer, providing long-memory dynamic trailing behavior. A fractional trend channel (CF/AB lows vs HA highs) is also plotted.
5. Exit Framework
Exit options include: stack flip, CF
Análise de Tendência
Supertrend +QQE + DEMASupertrend + QQE + DEMA — Strategy
Inspired by UNITED and my best friend ChatGPT
This strategy combines dual Supertrends, a QQE trend filter, and a 200-period DEMA directional filter to generate structured, trend-aligned entries. It is designed for Heikin Ashi charts , where trend noise is reduced and swing structure becomes clearer.
How It Works
The system fires a trade only when all conditions agree:
1. Both Supertrends flip in the same direction
This identifies strong directional shifts and removes weak reversals.
2. QQE Trend Confirmation
QQE acts as a momentum filter, requiring either a green (bullish) or red (bearish) state with optional consecutive-bar confirmation.
3. 200 DEMA Filter
Only longs above the DEMA and only shorts below the DEMA.
This keeps trades aligned with the higher-timeframe trend.
Because each component filters the other, signals are high-quality, controlled, and structured rather than frequent or reactive.
Expected Performance
Based on the design and typical market testing, this combination yields a 50–70% win rate, depending on:
The market (best on indices like NQ/MNQ, ES/MES, DAX, etc.)
Volatility conditions
Whether used on Heikin Ashi , which increases trend-cleanliness and reduces chop
Timeframe (1m–5m often optimal for intraday)
The system avoids rapid flip-flopping by using “arm → confirm → fire once” logic, which further improves win consistency and reduces whipsaw losses.
How to Properly Use It (IMPORTANT)
This strategy is meant to be run on a Heikin Ashi chart.
Why?
Heikin Ashi smooths candles, giving clearer:
Trend transitions
Pullbacks
Momentum continuation
Supertrend reliability
Running this on normal candles will still work, but the win rate and smoothness drop significantly because Supertrend + QQE respond more cleanly to HA structure.
Trade Behavior
Longs trigger when both Supertrends flip up, QQE is bullish, and price is above DEMA.
Shorts trigger when both Supertrends flip down, QQE is bearish, and price is below DEMA.
Strategy closes when the opposite Supertrend flip occurs.
Alerts fire automatically for buy/sell confirmations.
Best Use Cases
Intraday trend trading
Momentum continuation after a confirmed reversal
Avoiding chop with multi-layer confirmation
Backtesting rule-based execution
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning
RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System
RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention .
Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve.
Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume.
The Problem This System Solves
Traditional Technical Analysis Limitations
Most trading systems suffer from three fundamental challenges :
Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes.
Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions.
Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution.
How RPD Machine Learning Addresses These Challenges
Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation.
Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly.
Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success.
Machine Learning Architecture: What Makes This "Real" ML
Component 1: Contextual Multi-Armed Bandits (LinUCB)
What Is a Multi-Armed Bandit Problem?
Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies.
Academic Foundation:
RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems.
How It Works:
Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains:
Reward History: Tracks wins/losses for each policy
Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume)
Uncertainty Estimates: Confidence in each policy's performance
UCB Formula: predicted_reward + α × uncertainty
The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies.
Why This Matters:
Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics.
Component 2: Random Fourier Features (RFF)
The Non-Linearity Challenge:
Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions.
Academic Foundation:
RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading.
How It Works:
The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations:
Input: 8 base features
Projection: Through random Gaussian weights
Transformation: cos(W×features + b)
Output: 16 RFF dimensions
This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space.
Why This Matters:
Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection.
Component 3: Reinforcement Learning Stack
Beyond bandits, RPD implements a complete RL framework :
Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades.
TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades.
Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction.
Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases.
Why This Matters:
Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm.
Component 4: Policy Momentum Tracking (v2 Feature)
The Recency Challenge:
Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance.
RPD's Solution:
Each policy maintains a ring buffer of the last 10 outcomes. The system calculates:
Momentum: recent_win_rate - global_win_rate (range: -1 to +1)
Confidence: consistency of recent results (1 - variance)
Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty.
Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits.
Signal Generation: The Core Algorithm
Multi-Timeframe Fractal Detection
RPD identifies reversal points using three complementary methods :
1. Quantum State Analysis:
Divides price range into discrete states (default: 6 levels)
Peak signals require price in top states (≥ state 5)
Valley signals require price in bottom states (≤ state 1)
Prevents mid-range signals that may struggle in strong trends
2. Fractal Geometry:
Identifies swing highs/lows using configurable fractal strength
Confirms local extremum with neighboring bars
Validates reversal only if price crosses prior extreme
3. Multi-Timeframe Confirmation:
Analyzes higher timeframe (4× default) for alignment
MTF confirmation adds probability bonus
Designed to reduce false signals while preserving valid setups
Probability Scoring System
Each signal receives a dynamic probability score (40-99%) based on:
Base Components:
Trend Strength: EMA(velocity) / ATR × 30 points
Entropy Quality: (1 - entropy) × 10 points
Starting baseline: 40 points
Enhancement Bonuses:
Divergence Detection: +20 points (price/momentum divergence)
RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys)
Volume Confirmation: +5 points (volume >1.2× average)
Adaptive Momentum: +10 points (strong directional velocity)
MTF Alignment: +12 points (higher timeframe confirms)
Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment)
Regime Bonus: +8 points (trending ADX >25 with directional agreement)
Penalties:
High Entropy: -5 points (entropy >0.85, chaotic price action)
Consolidation Regime: -10 points (ADX <20, no directional conviction)
Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%)
Entropy-Based Quality Filter
What Is Entropy?
Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions.
Calculation:
Count up/down price changes over adaptive period
Calculate probability: p = ups / total_changes
Shannon entropy: -p×log(p) - (1-p)×log(1-p)
Normalized to 0-1 range
Application:
Entropy <0.5: Highly ordered (ELITE signals possible)
Entropy 0.5-0.75: Mixed (GOOD signals)
Entropy >0.85: Chaotic (signals blocked or heavily penalized)
Why This Matters:
Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable.
Regime Detection & Market Microstructure - ADX-Based Regime Classification
RPD uses Wilder's Average Directional Index to classify markets:
Bull Trend: ADX >25, +DI > -DI (directional conviction bullish)
Bear Trend: ADX >25, +DI < -DI (directional conviction bearish)
Consolidation: ADX <20 (no directional conviction)
Transitional: ADX 20-25 (forming direction, ambiguous)
Filter Logic:
Blocks all signals during Transitional regime (avoids trading during uncertain conditions)
Blocks Consolidation signals unless ADX ≥ Min Trend Strength
Adds probability bonus during strong trends (ADX >30)
Effect: Designed to reduce signal frequency while focusing on higher-quality setups.
Divergence Detection
Bearish Divergence:
Price makes higher high
Velocity (price momentum) makes lower high
Indicates weakening upward pressure → SHORT signal quality boost
Bullish Divergence:
Price makes lower low
Velocity makes higher low
Indicates weakening downward pressure → LONG signal quality boost
Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing.
Hierarchical Policy System - The Six Trading Policies
1. AggressiveTrend (Policy 0):
Probability Threshold: 60% (trades more frequently)
Entropy Threshold: 0.70 (tolerates moderate chaos)
Stop Multiplier: 2.5× ATR (wider stops for trends)
Target Multiplier: 5.0R (larger targets)
Entry Mode: Pyramid (scales into winners)
Best For: Strong trending markets, breakouts, momentum continuation
2. ConservativeRange (Policy 1):
Probability Threshold: 75% (more selective)
Entropy Threshold: 0.60 (requires order)
Stop Multiplier: 1.8× ATR (tighter stops)
Target Multiplier: 3.0R (modest targets)
Entry Mode: Single (one-shot entries)
Best For: Range-bound markets, low volatility, mean reversion
3. VolatilityBreakout (Policy 2):
Probability Threshold: 65% (moderate)
Entropy Threshold: 0.80 (accepts high entropy)
Stop Multiplier: 3.0× ATR (wider stops)
Target Multiplier: 6.0R (larger targets)
Entry Mode: Tiered (splits entry)
Best For: Compression breakouts, post-consolidation moves, gap opens
4. EntropyScalp (Policy 3):
Probability Threshold: 80% (very selective)
Entropy Threshold: 0.40 (requires extreme order)
Stop Multiplier: 1.5× ATR (tightest stops)
Target Multiplier: 2.5R (quick targets)
Entry Mode: Single
Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns
5. DivergenceHunter (Policy 4):
Probability Threshold: 70% (quality-focused)
Entropy Threshold: 0.65 (balanced)
Stop Multiplier: 2.2× ATR (moderate stops)
Target Multiplier: 4.5R (balanced targets)
Entry Mode: Tiered
Best For: Divergence-confirmed reversals, exhaustion moves, trend climax
6. AdaptiveBlend (Policy 5):
Probability Threshold: 68% (balanced)
Entropy Threshold: 0.75 (balanced)
Stop Multiplier: 2.0× ATR (standard)
Target Multiplier: 4.0R (standard)
Entry Mode: Single
Best For: Mixed conditions, general trading, fallback when no clear regime
Policy Clustering (Advanced/Extreme Modes)
Policies are grouped into three clusters based on regime affinity:
Cluster 1 (Trending): AggressiveTrend, DivergenceHunter
High regime affinity (0.8): Performs well when ADX >25
Moderate vol affinity (0.6): Works in various volatility
Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend
Low regime affinity (0.3): Better suited for ADX <20
Low vol affinity (0.4): Optimized for calm markets
Cluster 3 (Breakout): VolatilityBreakout
Moderate regime affinity (0.6): Works in multiple regimes
High vol affinity (0.9): Requires high volatility for optimal characteristics
Hierarchical Selection Process:
Calculate cluster scores based on current regime and volatility
Select best-matching cluster
Run UCB selection within chosen cluster
Apply momentum boost/penalty
This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster.
Risk Management & Position Sizing
Dynamic Kelly Criterion Sizing (Optional)
Traditional Fixed Sizing Challenge:
Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions.
Kelly Formula:
f = (p × b - q) / b
Where:
p = win probability (from signal score)
q = loss probability (1 - p)
b = win/loss ratio (average_win / average_loss)
f = fraction of capital to risk
RPD Implementation:
Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits.
Enhancements:
Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier
Divergence Bonus: Additional sizing on divergence signals
Regime Bonus: Additional sizing during strong trends (ADX >30)
Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction
Safety Rails:
Minimum: 1 contract (floor)
Maximum: User-defined cap (default 10 contracts)
Portfolio Heat: Max total risk across all positions (default 4% equity)
Multi-Mode Stop Loss System
ATR Mode (Default):
Stop = entry ± (ATR × base_mult × policy_mult)
Consistent risk sizing
Ignores market structure
Best for: Futures, forex, algorithmic trading
Structural Mode:
Finds swing low (long) or high (short) over last 20 bars
Identifies fractal pivots within lookback
Places stop below/above structure + buffer (0.1× ATR)
Best for: Stocks, instruments that respect structure
Hybrid Mode (Intelligent):
Attempts structural stop first
Falls back to ATR if:
Structural level is invalid (beyond entry)
Structural stop >2× ATR away (too wide)
Best for: Mixed instruments, adaptability
Dynamic Adjustments:
Breakeven: Move stop to entry + 1 tick after 1.0R profit
Trailing: Trail stop 0.8R behind price after 1.5R profit
Timeout: Force close after 30 bars (optional)
Tiered Entry System
Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality.
Solution:
Tier 1 (40% of size): Enters immediately on all signals
Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%)
Example:
Calculated optimal size: 10 contracts
Signal probability: 72%
Tier 2 trigger: 75%
Result: Enter 4 contracts only (Tier 1)
Same signal at 80% probability
Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2)
Effect: Automatically scales size to signal quality, optimizing capital allocation.
Performance Optimization & Learning Curve
Warmup Phase (First 50 Trades)
Purpose: Ensure all policies get tested before system focuses on preferred strategies.
Modifications During Warmup:
Probability thresholds reduced 20% (65% becomes 52%)
Entropy thresholds increased 20% (more permissive)
Exploration rate stays high (30%)
Confidence width (α) doubled (more exploration)
Why This Matters:
Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies.
Curriculum Learning
Phase 1 (Trades 1-50): Exploration
Warmup active
All policies tested
High exploration (30%)
Learning fundamental patterns
Phase 2 (Trades 50-100): Refinement
Warmup ended, thresholds normalize
Exploration decaying (30% → 15%)
Policy preferences emerging
Meta-learning optimizing
Phase 3 (Trades 100-200): Specialization
Exploration low (15% → 8%)
Clear policy preferences established
Momentum tracking fully active
System focusing on learned patterns
Phase 4 (Trades 200+): Maturity
Exploration minimal (8% → 5%)
Regime-policy relationships learned
Auto-adaptation to market shifts
Stable performance expected
Convergence Indicators
System is learning well when:
Policy switch rate decreasing over time (initially ~50%, should drop to <20%)
Exploration rate decaying smoothly (30% → 5%)
One or two policies emerge with >50% selection frequency
Performance metrics stabilizing over time
Consistent behavior in similar market conditions
System may need adjustment when:
Policy switch rate >40% after 100 trades (excessive exploration)
Exploration rate not decaying (parameter issue)
All policies showing similar selection (not differentiating)
Performance declining despite relaxed thresholds (underlying signal issue)
Highly erratic behavior after learning phase
Advanced Features
Attention Mechanism (Extreme Mode)
Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally.
Solution:
Each RFF dimension has an importance weight . After each trade:
Calculate correlation: sign(feature - 0.5) × sign(reward)
Update importance: importance += correlation × 0.01
Clamp to range
Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes.
Temporal Context (Extreme Mode)
Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information.
Solution:
Includes 3-period historical context with exponential decay (0.85):
Current features (weight 1.0)
1 bar ago (weight 0.85)
2 bars ago (weight 0.72)
Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves.
Transfer Learning via Episodic Memory
Short-Term Memory (STM):
Last 20 trades
Fast adaptation to immediate regime
High learning rate
Long-Term Memory (LTM):
Condensed historical patterns
Preserved knowledge from past regimes
Low learning rate
Transfer Mechanism:
When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch.
Practical Implementation Guide - Recommended Settings by Instrument
Futures (ES, NQ, CL):
Adaptive Period: 20-25
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.5%
Stop Mode: ATR or Hybrid
Timeframe: 5-15 min
Forex Majors (EURUSD, GBPUSD):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.0-1.5%
Stop Mode: ATR
Timeframe: 5-30 min
Cryptocurrency (BTC, ETH):
Adaptive Period: 20-25
ML Mode: Extreme (handles non-stationarity)
RFF Dimensions: 32 (captures complexity)
Policies: 6
Base Risk: 1.0% (volatility consideration)
Stop Mode: Hybrid
Timeframe: 15 min - 4 hr
Stocks (Large Cap):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 5-6
Base Risk: 1.5-2.0%
Stop Mode: Structural or Hybrid
Timeframe: 15 min - Daily
Scaling Strategy
Phase 1 (Testing - First 50 Trades):
Max Contracts: 1-2
Goal: Validate system on your instrument
Monitor: Performance stabilization, learning progress
Phase 2 (Validation - Trades 50-100):
Max Contracts: 2-3
Goal: Confirm learning convergence
Monitor: Policy stability, exploration decay
Phase 3 (Scaling - Trades 100-200):
Max Contracts: 3-5
Enable: Kelly sizing (1/4 Kelly)
Goal: Optimize capital efficiency
Monitor: Risk-adjusted returns
Phase 4 (Full Deployment - Trades 200+):
Max Contracts: 5-10
Enable: Full momentum tracking
Goal: Sustained consistent performance
Monitor: Ongoing adaptation quality
Limitations & Disclaimers
Statistical Limitations
Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior.
Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment).
Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing.
Technical Limitations
Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded.
Pine Script Constraints:
No true matrix inversion (uses diagonal approximation for LinUCB)
No cryptographic RNG (uses market data as entropy)
No proper random number generation for RFF (uses deterministic pseudo-random)
These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications.
Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality.
Forward-Looking Bias Disclaimer
Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108.
Why This is Disclosed:
Entry signals do NOT look ahead - decisions use only data up to entry bar
Forward data used for learning only, not signal generation
In live trading, system learns identically as bars unfold in real-time
Simulates natural learning process (outcomes are only known after trades complete)
Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if:
- Positions held longer than 8 bars
- Slippage/commissions differ from backtest settings
- Market microstructure changes (wider spreads, different execution quality)
Risk Warnings
No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events.
Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations.
Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses.
Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance.
System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards.
Appropriate Use
This System Is:
✅ A machine learning framework for adaptive strategy selection
✅ A signal generation system with probabilistic scoring
✅ A risk management system with dynamic sizing
✅ A learning system designed to adapt over time
This System Is NOT:
❌ A price prediction system (does not forecast exact prices)
❌ A guarantee of profits (can and will experience losses)
❌ A replacement for due diligence (requires monitoring and understanding)
❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals)
Recommended Use:
Paper trade for 100 signals before risking capital
Start with minimal position sizing (1-2 contracts) regardless of calculated size
Monitor learning progress via dashboard
Scale gradually over several months only after consistent results
Combine with fundamental analysis and broader market context
Set account-level risk limits (e.g., maximum drawdown threshold)
Never risk more than you can afford to lose
What Makes This System Different
RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization:
✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.)
✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht)
✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook
✅ Meta-Learning - Learning rate adaptation based on feature correlation
✅ Online Learning - Real-time updates from streaming data
✅ Hierarchical Policies - Two-stage selection with clustering
✅ Momentum Tracking - Recent performance analysis for faster adaptation
✅ Attention Mechanism - Feature importance weighting
✅ Transfer Learning - Episodic memory consolidation
Key Differentiators:
Actually learns from trade outcomes (not just parameter optimization)
Updates model parameters in real-time (true online learning)
Adapts to changing market regimes (not static rules)
Improves over time through reinforcement learning
Implements published ML algorithms with proper citations
Conclusion
RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market.
The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches.
After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static.
Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Qullamagi EMA Breakout Autotrade (Crypto Futures L+S)Title: Qullamagi EMA Breakout – Crypto Autotrade
Overview
A crypto-focused, Qullamagi-style EMA breakout strategy built for autotrading on futures and perpetual swaps.
It combines a 5-MA trend stack (EMA 10/20, SMA 50/100/200), volatility contraction boxes, volume spikes and an optional higher-timeframe 200-MA filter. The script supports both long and short trades, partial take profit, trailing MA exits and percent-of-equity position sizing for automated crypto futures trading.
Key Features (Crypto)
Qullamagi MA Breakout Engine – trades only when price is aligned with a strong EMA/SMA trend and breaks out of a tight consolidation range. Longs use: Close > EMA10 > EMA20 > SMA50 > SMA100 > SMA200. Shorts are the mirror condition with all MAs sloping in the trend direction.
Strict vs Loose Modes – Strict (Daily) is designed for cleaner swing trades on 1H–4H (full MA stack, box+ATR and volume filters, optional HTF filter). Loose (Intraday) focuses on 10/20/50 alignment with relaxed filters for more frequent 15m–30m signals.
Volatility & Volume Filters for Crypto – ATR-based box height limit to detect volatility contraction, wide-candle filter to avoid chasing exhausted breakouts, and a volume spike condition requiring current volume to exceed an SMA of volume.
Higher-Timeframe Trend Filter (Optional) – uses a 200-period SMA on a higher timeframe (default: 1D). Longs only when HTF close is above the HTF 200-SMA, shorts only when it is below, helping avoid trading against dominant crypto trends.
Autotrade-Oriented Trade Management – position size as % of equity, initial stop anchored to a chosen MA (EMA10 / EMA20 / SMA50) with optional buffer, partial take profit at a configurable R-multiple, trailing MA exit for the remainder, and an optional cooldown after a full exit.
Markets & Timeframes
Best suited for BTC, ETH and major altcoin futures/perpetuals (Binance, Bybit, OKX, etc.).
Strict preset: 1H–4H charts for classic Qullamagi-style trend structure and fewer fake breakouts.
Loose preset: 15m–30m charts for higher trade frequency and more active intraday trading.
Always retune ATR length, box length, volume multiplier and position size for each symbol and exchange.
Strategy Logic (Quick Summary)
Long (Strict): MA stack in bullish alignment with all MAs sloping up → tight volatility box (ATR-based) → volume spike above SMA(volume) × multiplier → breakout above box high (close or intrabar) → optional HTF close above 200-SMA.
Short: Mirror logic: bearish MA stack, tight box, volume spike and breakdown below box low with optional HTF downtrend.
Best Practices for Crypto
Backtest on each symbol and timeframe you plan to autotrade, including commissions and slippage.
Start on higher timeframes (1H/4H) to learn the behavior, then move to 15m–30m if you want more signals.
Use the higher-timeframe filter when markets are strongly trending to reduce counter-trend trades.
Keep position-size percentage conservative until you fully understand the drawdowns.
Forward-test / paper trade before connecting to live futures accounts.
Webhook / Autotrade Integration
Designed to work with TradingView webhooks and external crypto trading bots.
Alert messages include structured fields such as: EVENT=ENTRY / SCALE_OUT / EXIT, SIDE=LONG / SHORT, STRATEGY=Qullamagi_MA.
Map each EVENT + SIDE combination to your bot logic (open long/short, partial close, full close, etc.) on your preferred exchange.
Important Notes & Disclaimer
Crypto markets are highly volatile and can change regime quickly. Backtest and forward-test thoroughly before using real capital. Higher timeframes generally produce cleaner MA structures and fewer fake breakouts.
This strategy is for educational and informational purposes only and does not constitute financial advice. Trading leveraged crypto products involves substantial risk of loss. Always do your own research, manage risk carefully, and never trade with money you cannot afford to lose.
Трендовые линии с продвинутыми стопамиtrend analysis strategy can work in every trend on the market.
Fractional Candlestick Long Only Experimental V4 Another example of use an idea of Fractional Candlestick , based on mathematical rules of Fractional Calculus , typical kernel Caputo-Fabrizio ( CF ) and Atangana-Baleanu is used, alfa factor ( esential for calculation ) is in range 0,1-0.9.
Let's fun with this script .
KELTNER + ADX 전략It's a trend strategy based on the Keltner channel and ADX. It's optimized for the Bitcoin Futures 15 Distribution Chart.
GMH : Tech Bubble Good Morning Holding
Simulating How to Ride the Bubble — and Jump Out Before the Crash
Be careful! Most simulation results show that this strategy sometimes underperforms a simple buy-and-hold, because it gives away positions during deep retracements and buys back at higher thresholds.
Humans often struggle with cutting losses. When the pain becomes too much, they lose the confidence needed to execute even a reasonable strategy.
But in terms of mentality, this approach reduces long-term portfolio volatility. It helps investors feel more at peace, especially during real market crashes like the tech bubble in 2021.
How to use : Select TimeFrame 4HR on trading view
Quasimodo Pattern Strategy Back Test [TradingFinder] QM Trading🔵 Introduction
The QM pattern, also known as the Quasimodo pattern, is one of the popular patterns in price action, and it is often used by technical analysts. The QM pattern is used to identify trend reversals and provides a very good risk-to-reward ratio. One of the advantages of the QM pattern is its high frequency and visibility in charts.
Additionally, due to its strength, it is highly profitable, and as mentioned, its risk-to-reward ratio is very good. The QM pattern is highly popular among traders in supply and demand, and traders also use this pattern.
The Price Action QM pattern, like other Price Action patterns, has two types: Bullish QM and Bearish QM patterns. To identify this pattern, you need to be familiar with its types to recognize it.
🔵 Identifying the QM Pattern
🟣 Bullish QM
In the bullish QM pattern, as you can see in the image below, an LL and HH are formed. As you can see, the neckline is marked as a dashed line. When the price reaches this range, it will start its upward movement.
🟣 Bearish QM
The Price Action QM pattern also has a bearish pattern. As you can see in the image below, initially, an HH and LL are formed. The neckline in this image is the dashed line, and when the LL is formed, the price reaches this neckline. However, it cannot pass it, and the downward trend resumes.
🔵 How to Use
The Quasimodo pattern is one of the clearest structures used to identify market reversals. It is built around the concept of a structural break followed by a pullback into an area of trapped liquidity. Instead of relying on lagging indicators, this pattern focuses purely on price action and how the market reacts after exhausting one side of liquidity. When understood correctly, it provides traders with precise entry points at the transition between trend phases.
🟣 Bullish Quasimodo
A bullish Quasimodo forms after a clear downtrend when sellers start losing control. The market continues to make lower lows until a sudden higher high appears, signaling that buyers are entering with strength. Price then pulls back to retest the previous low, creating what is known as the Quasimodo low.
This area often becomes the final trap for sellers before the market shifts upward. A visible rejection or displacement from this zone confirms bullish momentum. Traders usually place entries near this level, stops below the low, and targets at previous highs or the next resistance zone. Combining the setup with demand zones or Fair Value Gaps increases its accuracy.
🟣 Bearish Quasimodo
A bearish Quasimodo forms near the top of an uptrend when buyers begin to lose strength. The market continues to make higher highs until a sudden lower low breaks the bullish structure, showing that selling pressure is entering the market. Price then retraces upward to retest the previous high, forming the Quasimodo high, where breakout buyers are often trapped.
Once rejection appears at this level, it indicates a likely reversal. Traders can enter short near this area, with stop-losses placed above the high and targets near the next support or previous lows. The setup gains more reliability when aligned with supply zones, SMT divergence, or bearish Fair Value Gaps.
🔵 Setting
Pivot Period : You can use this parameter to use your desired period to identify the QM pattern. By default, this parameter is set to the number 5.
Take Profit Mode : You can choose your desired Take Profit in three ways. Based on the logic of the QM strategy, you can select two Take Profit levels, TP1 and TP2. You can also choose your take profit based on the Reward to Risk ratio. You must enter your desired R/R in the Reward to Risk Ratio parameter.
Stop Loss Refine : The loss limit of the QM strategy is based on its logic on the Head pattern. You can refine it using the ATR Refine option to prevent Stop Hunt. You can enter your desired coefficient in the Stop Loss ATR Adjustment Coefficient parameter.
Reward to Risk Ratio : If you set Take Profit Mode to R/R, you must enter your desired R/R here. For example, if your loss limit is 10 pips and you set R/R to 2, your take profit will be reached when the price is 20 pips away from your entry point.
Stop Loss ATR Adjustment Coefficient : If you set Stop Loss Refine to ATR Refine, you must adjust your loss limit coefficient here. For example, if your buy position's loss limit is at the price of 1000, and your ATR is 10, if you set Stop Loss ATR Adjustment Coefficient to 2, your loss limit will be at the price of 980.
Entry Level Validity : Determines how long the Entry level remains valid. The higher the level, the longer the entry level will remain valid. By default it is 2 and it can be set between 2 and 15.
🔵 Results
The following examples show the backtest results of the Quasimodo (QM) strategy in action. Each image is based on specific settings for the symbol, timeframe, and input parameters, illustrating how the QM logic can generate signals under different market conditions. The detailed configuration for each backtest is also displayed on the image.
⚠ Important Note : Even with identical settings and the same symbol, results may vary slightly across different brokers due to data feed variations and pricing differences.
Default Properties of Backtests :
OANDA:XAUUSD | TimeFrame: 5min | Duration: 1 Year :
BINANCE:BTCUSD | TimeFrame: 5min | Duration: 1 Year :
CAPITALCOM:US30 | TimeFrame: 5min | Duration: 1 Year :
NASDAQ:QQQ | TimeFrame: 5min | Duration: 5 Year :
OANDA:EURUSD | TimeFrame: 5min | Duration: 5 Year :
PEPPERSTONE:US500 | TimeFrame: 5min | Duration: 5 Year :
XAU/USD Weekly Volatility Strategy by WeTradeAIWeTradeAI - XAU/USD Weekly Volatility Strategy
This strategy is designed for Gold (XAU/USD) trading, leveraging a weekly market structure and volatility projection model. It dynamically identifies high-probability zones based on the prior week’s price action and adapts to intraday movement.
🔍 Core Logic:
Weekly High, Low & Midpoint: Defines structural balance for directional bias.
Projected Volatility Zones:
Green Zone: Upward projection from last week’s low.
Red Zone: Downward projection from last week’s high.
Half-Volatility Lines: Act as breakout or reversal triggers.
Monday Open: Serves as a temporary directional reference until midweek.
Daily High, Low, and Mid: Used for intraday stop-loss placement and validation.
🎯 Trade Entries:
Breakout Entries: Triggered when price breaks and holds above/below the Half-Vol levels.
Reversal Entries: Triggered by strong rejections near outer zones, reverting back toward equilibrium.
🛡️ Risk Management:
Dynamic Stop-Loss: Based on the previous day’s midpoint.
⏱️ Multi-Timeframe Usage:
4H – Weekly structure & context
1H – Trend alignment
15M – Precision entries
RastaRasta — Educational Strategy (Pine v5)
Momentum · Smoothing · Trend Study
Overview
The Rasta Strategy is a visual and educational framework designed to help traders study momentum transitions using the interaction between a fast-reacting EMA line and a slower smoothed reference line.
It is not a signal generator or profit system; it’s a learning tool for understanding how smoothing, crossovers, and filters interact under different market conditions.
The script displays:
A primary EMA line (the fast reactive wave).
A Smoothed line (using your chosen smoothing method).
Optional fog zones between them for quick visual context.
Optional DNA rungs connecting both lines to illustrate volatility compression and expansion.
Optional EMA 8 / EMA 21 trend filter to observe higher-time-frame alignment.
Core Idea
The Rasta model focuses on wave interaction. When the fast EMA crosses above the smoothed line, it reflects a shift in short-term momentum relative to background trend pressure. Cross-unders suggest weakening or reversal.
Rather than treating this as a trading “signal,” use it to observe structure, study trend alignment, and test how smoothing type affects reaction speed.
Smoothing Types Explained
The script lets you experiment with multiple smoothing techniques:
Type Description Use Case
SMA (Simple Moving Average) Arithmetic mean of the last n values. Smooth and steady, but slower. Trend-following studies; filters noise on higher time frames.
EMA (Exponential Moving Average) Weights recent data more. Responds faster to new price action. Momentum or reactive strategies; quick shifts and reversals.
RMA (Relative Moving Average) Used internally by RSI; smooths exponentially but slower than EMA. Momentum confirmation; balanced response.
WMA (Weighted Moving Average) Linear weights emphasizing the most recent data strongly. Intraday scalping; crisp but potentially noisy.
None Disables smoothing; uses the EMA line alone. Raw comparison baseline.
Each smoothing method changes how early or late the strategy reacts:
Faster smoothing (EMA/WMA) = more responsive, good for scalping.
Slower smoothing (SMA/RMA) = more stable, good for trend following.
Modes of Study
🔹 Scalper Mode
Use short EMA lengths (e.g., 3–5) and fast smoothing (EMA or WMA).
Focus on 1 min – 15 min charts.
Watch how quick crossovers appear near local tops/bottoms.
Fog and rung compression reveal volatility contraction before bursts.
Goal: study short-term rhythm and liquidity pulses.
🔹 Momentum Mode
Use moderate EMA (5–9) and RMA smoothing.
Ideal for 1 H–4 H charts.
Observe how the fog color aligns with trend shifts.
EMA 8 / 21 filter can act as macro bias; “Enter” labels will appear only in its direction when enabled.
Goal: study sustained motion between pullbacks and acceleration waves.
🔹 Trend-Follower Mode
Use longer EMA (13–21) with SMA smoothing.
Great for daily/weekly charts.
Focus on periods where fog stays unbroken for long stretches — these illustrate clear trend dominance.
Watch rung spacing: tight clusters often precede consolidations; wide rungs signal expanding volatility.
Goal: visualize slow-motion trend transitions and filter whipsaw conditions.
Components
EMA Line (Red): Fast-reacting short-term direction.
Smoothed Line (Yellow): Reference trend baseline.
Fog Zone: Green when EMA > Smoothed (up-momentum), red when below.
DNA Rungs: Thin connectors showing volatility structure.
EMA 8 / 21 Filter (optional):
When enabled, the strategy will only allow Enter events if EMA 8 > EMA 21.
Use this to study higher-trend gating effects.
Educational Applications
Momentum Visualization: Observe how the fast EMA “breathes” around the smoothed baseline.
Trend Transitions: Compare different smoothing types to see how early or late reversals are detected.
Noise Filtering: Experiment with fog opacity and smoothing lengths to understand trade-off between responsiveness and stability.
Risk Concept Simulation: Includes a simple fixed stop-loss parameter (default 13%) for educational demonstrations of position management in the Strategy Tester.
How to Use
Add to Chart → “Strategy.”
Works on any timeframe and instrument.
Adjust Parameters:
Length: base EMA speed.
Smoothing Type: choose SMA, EMA, RMA, or WMA.
Smoothing Length: controls delay and smoothness.
EMA 8 / 21 Filter: toggles trend gating.
Fog & Rungs: visual study options only.
Study Behavior:
Use Strategy Tester → List of Trades for entry/exit context.
Observe how different smoothing types affect early vs. late “Enter” points.
Compare trend periods vs. ranging periods to evaluate efficiency.
Combine with External Tools:
Overlay RSI, MACD, or Volume for deeper correlation analysis.
Use replay mode to visualize crossovers in live sequence.
Interpreting the Labels
Enter: Marks where fast EMA crosses above the smoothed line (or when filter flips positive).
Exit: Marks where fast EMA crosses back below.
These are purely analytical markers — they do not represent trade advice.
Educational Value
The Rasta framework helps learners explore:
Reaction time differences between moving-average algorithms.
Impact of smoothing on signal clarity.
Interaction of local and global trends.
Visualization of volatility contraction (tight DNA rungs) and expansion (wide fog zones).
It’s a sandbox for studying price structure, not a promise of profit.
Disclaimer
This script is provided for educational and research purposes only.
It does not constitute financial advice, trading signals, or performance guarantees. Past market behavior does not predict future outcomes.
Users are encouraged to experiment responsibly, record observations, and develop their own understanding of price behavior.
Author: Michael Culpepper (mikeyc747)
License: Educational / Open for study and modification with credit.
Philosophy:
“Learning the rhythm of the market is more valuable than chasing its profits.” — Rasta
Adaptive ATR Guardian PRO+ (Locked Lines)🎯 核心交易功能 / Core Trading Features
1. 智能参数配置系统 / Intelligent Parameter Configuration
多风格选择：稳健/激进/保守三种交易风格
Multi-style Selection: Conservative/Aggressive/Moderate trading styles
多时间周期：M5/M15/H1三种时间框架
Multi-timeframe: M5/M15/H1 timeframes
自适应参数：根据风格自动调整所有技术参数
Adaptive Parameters: Automatically adjusts all technical parameters based on style
2. 高级信号生成系统 / Advanced Signal Generation
双均线策略：快慢EMA交叉信号
Dual MA Strategy: Fast/Slow EMA crossover signals
趋势过滤：100周期EMA作为趋势方向过滤
Trend Filter: 100-period EMA for trend direction filtering
ADX强度确认：ADX > 最小值才确认趋势有效
ADX Strength Confirmation: ADX > minimum value for valid trend
交易时段控制：可设置交易开始和结束时间
Trading Session Control: Configurable start and end times
3. 智能风险管理 / Intelligent Risk Management
动态止损：基于ATR的智能止损计算
Dynamic Stop Loss: ATR-based intelligent stop loss calculation
分批止盈：TP1平仓50%，TP2平仓剩余50%
Partial Take Profit: TP1 closes 50%, TP2 closes remaining 50%
追踪止损：TP2部分启用追踪止损功能
Trailing Stop: TP2 portion uses trailing stop functionality
品种自适应：BTC和黄金品种特殊参数调整
Symbol Adaptation: Special parameter adjustments for BTC and Gold
4. 专业订单管理 / Professional Order Management
自动平仓：新信号自动平掉反向仓位
Auto Close: New signals automatically close opposite positions
仓位管理：基于账户权益的百分比仓位
Position Management: Percentage-based position sizing
佣金计算：包含交易佣金成本
Commission Calculation: Includes trading commission costs
📊 高级可视化功能 / Advanced Visualization Features
1. 实时交易线系统 / Real-time Trading Lines System
入场线：蓝色虚线，显示入场价格
Entry Line: Blue dashed line showing entry price
止损线：红色实线，显示止损价格
Stop Loss Line: Red solid line showing stop loss price
TP1线：青色实线，显示第一目标位
TP1 Line: Teal solid line showing first target
TP2线：青色实线，显示第二目标位
TP2 Line: Teal solid line showing second target
2. 智能标签管理 / Intelligent Label Management
动态字号：根据时间周期自动调整标签大小
Dynamic Font Size: Auto-adjusts label size based on timeframe
位置优化：标签固定在入场K线右侧3根位置
Position Optimization: Labels fixed 3 bars right of entry candle
实时更新：线条和标签随图表滚动延伸
Real-time Updates: Lines and labels extend with chart scrolling
3. 专业信息面板 / Professional Information Panel
策略状态：交易风格、时间周期、持仓方向
Strategy Status: Trading style, timeframe, position direction
指标数据：ADX强度、ATR波动率数值
Indicator Data: ADX strength, ATR volatility values
交易信息：入场价格、止损价格、止盈价格
Trade Information: Entry price, stop loss, take profit prices
实时更新：每根K线更新最新数据
Real-time Updates: Updates data on every candle
4. 模式状态标签 / Mode Status Label
顶部状态栏：显示周期、风格、ADX、ATR、持仓状态
Top Status Bar: Shows timeframe, style, ADX, ATR, position status
颜色编码：蓝色主题，专业视觉效果
Color Coding: Blue theme, professional visual appearance
⚙️ 技术特色功能 / Technical Special Features
1. 自适应波动率调整 / Adaptive Volatility Adjustment
ATR基准：基于14周期ATR计算
ATR Baseline: Based on 14-period ATR calculation
波动率调整：ATR相对于50周期均线的调整系数
Volatility Adjustment: ATR adjustment coefficient relative to 50-period MA
动态止盈：止盈距离根据波动率动态调整
Dynamic Take Profit: TP distances dynamically adjusted based on volatility
2. 多品种优化 / Multi-Symbol Optimization
BTC特殊处理：更大的止损倍数和TP2倍数
BTC Special Handling: Larger stop loss and TP2 multipliers
黄金特殊处理：适中的参数调整
Gold Special Handling: Moderate parameter adjustments
通用品种：标准参数适用于其他品种
General Symbols: Standard parameters for other symbols
3. 时间智能控制 / Intelligent Time Control
交易时段：可配置的交易时间窗口
Trading Sessions: Configurable trading time windows
时段逻辑：支持跨午夜的时间段设置
Session Logic: Supports cross-midnight time periods
时间过滤：只在交易时段内产生信号
Time Filtering: Only generates signals during trading hours
4. 内存管理优化 / Memory Management Optimization
自动清理：平仓时自动删除所有线条和标签
Auto Cleanup: Automatically deletes all lines and labels on position close
资源回收：避免图表元素堆积
Resource Recycling: Prevents chart element accumulation
性能优化：高效的实时更新机制
Performance Optimization: Efficient real-time update mechanism
🛡️ 风险控制功能 / Risk Control Features
1. 多层过滤系统 / Multi-layer Filtering System
趋势方向过滤 / Trend direction filtering
ADX强度过滤 / ADX strength filtering
交易时间过滤 / Trading time filtering
品种特性过滤 / Symbol characteristic filtering
2. 动态参数系统 / Dynamic Parameter System
快慢均线周期自适应 / Fast/slow MA period adaptation
止损倍数动态调整 / Stop loss multiplier dynamic adjustment
止盈倍数风格化配置 / Take profit multiplier style-based configuration
追踪止损灵敏度设置 / Trailing stop sensitivity settings
3. 资金管理 / Money Management
固定百分比仓位 / Fixed percentage position sizing
佣金成本计入 / Commission costs included
无金字塔加仓 / No pyramiding (no adding to positions)
自动反向平仓 / Automatic opposite position closing
📈 用户体验功能 / User Experience Features
1. 可视化定制 / Visualization Customization
交易线显示/隐藏开关 / Trading lines show/hide toggle
信息面板显示控制 / Information panel display control
线条延伸长度可调 / Line extension length adjustable
颜色方案统一管理 / Color scheme unified management
2. 实时监控 / Real-time Monitoring
持仓状态实时显示 / Real-time position status display
关键价格水平标记 / Key price level markings
指标数值动态更新 / Indicator values dynamic updates
交易统计信息 / Trading statistics information
3. 专业布局 / Professional Layout
右上角信息面板 / Top-right information panel
顶部状态标签 / Top status label
图表交易线条 / Chart trading lines
整洁的视觉层次 / Clean visual hierarchy
MaayaTrippleST_Bullish_PRODMaayaTrippleST_Bullish_PROD
MaayaTrippleST_Bullish_PROD
MaayaTrippleST_Bullish_PRODMaayaTrippleST_Bullish_PRODMaayaTrippleST_Bullish_PRODvvMaayaTrippleST_Bullish_PRODMaayaTrippleST_Bullish_PROD
KriptoBotik 5 min TFCryptobot is effective on a 5-minute timeframe on any coin (with appropriate settings), with a grid of 10 averages, where each average is equal to the sum of previous entries, with a stop-loss. It can be configured for both local volatility and strong channel coverage, with adjustable entry amounts for each average and a percentage grid between averages. It can be connected to any exchange via API keys.
KriptoBotik 15 min TFCryptobot is effective on a 15-minute timeframe on any coin (with appropriate settings), with a grid of 10 averages (5 averages are sufficient with global settings), where each average is equal to the sum of previous entries, with a stop-loss. It can be configured for both local volatility and strong channel coverage, with adjustable entry amounts for each average and a percentage grid between averages. It can be connected to any exchange using API keys.
Blackflag FTS Strategy – Based on Extremum (Fractional CF/AB)Example of usage concepts of Fractional calculus Math rules, script use two type of kernel (Caputo-Fabrizio and Atangana-Baleanu), basic parametr alpha ( independently for each kernel) could be change in range 0.1-0.9.
WMAX-D-TPS1 (USD setup)Here I show you a strategy that I have been developing for years based on breakouts of maximum and minimum price levels.This work good in 1d
Range Trading StrategyOVERVIEW
The Range Trading Strategy is a systematic trading approach that identifies price ranges
from higher timeframe candles or trading sessions, tracks pivot points, and generates
trading signals when range extremes are mitigated and confirmed by pivot levels.
CORE CONCEPT
The strategy is based on the principle that when a candle (or session) closes within the
range of the previous candle (or session), that previous candle becomes a "range" with
identifiable high and low extremes. When price breaks through these extremes, it creates
trading opportunities that are confirmed by pivot levels.
RANGE DETECTION MODES
1. HTF (Higher Timeframe) Mode:
Automatically selects a higher timeframe based on the current chart timeframe
Uses request.security() to fetch HTF candle data
Range is created when an HTF candle closes within the previous HTF candle's range
The previous HTF candle's high and low become the range extremes
2. Sessions Mode:
- Divides the trading day into 4 sessions (UTC):
* Session 1: 00:00 - 06:00 (6 hours)
* Session 2: 06:00 - 12:00 (6 hours)
* Session 3: 12:00 - 20:00 (8 hours)
* Session 4: 20:00 - 00:00 (4 hours, spans midnight)
- Tracks high, low, and close for each session
- Range is created when a session closes within the previous session's range
- The previous session's high and low become the range extremes
PIVOT DETECTION
Pivots are detected based on candle color changes (bullish/bearish transitions):
1. Pivot Low:
Created when a bullish candle appears after a bearish candle
Pivot low = minimum of the current candle's low and previous candle's low
The pivot bar is the actual bar where the low was formed (current or previous bar)
2. Pivot High:
Created when a bearish candle appears after a bullish candle
Pivot high = maximum of the current candle's high and previous candle's high
The pivot bar is the actual bar where the high was formed (current or previous bar)
IMPORTANT: There is always only ONE active pivot high and ONE active pivot low at any
given time. When a new pivot is created, it replaces the previous one.
RANGE CREATION
A range is created when:
(HTF Mode) An HTF candle closes within the previous HTF candle's range AND a new HTF
candle has just started
(Sessions Mode) A session closes within the previous session's range AND a new session
has just started
Or Range Can Be Created when the Extreme of Another Range Gets Mitigated and We Have a Pivot low Just Above the Range Low or Pivot High just Below the Range High
Range Properties:
rangeHigh: The high extreme of the range
rangeLow: The low extreme of the range
highStartTime: The timestamp when the range high was actually formed (found by looping
backwards through bars)
lowStartTime: The timestamp when the range low was actually formed (found by looping
backwards through bars)
highMitigated / lowMitigated: Flags tracking whether each extreme has been broken
isSpecial: Flag indicating if this is a "special range" (see Special Ranges section)
RANGE MITIGATION
A range extreme is considered "mitigated" when price interacts with it:
High is mitigated when: high >= rangeHigh (any interaction at or above the level)
Low is mitigated when: low <= rangeLow (any interaction at or below the level)
Mitigation can happen:
At the moment of range creation (if price is already beyond the extreme)
At any point after range creation when price touches the extreme
SIGNAL GENERATION
1. Pending Signals:
When a range extreme is mitigated, a pending signal is created:
a) BEARISH Pending Signal:
- Triggered when: rangeHigh is mitigated
- Confirmation Level: Current pivotLow
- Signal is confirmed when: close < pivotLow
- Stop Loss: Current pivotHigh (at time of confirmation)
- Entry: Short position
Signal Confirmation
b) BULLISH Pending Signal:
- Triggered when: rangeLow is mitigated
- Confirmation Level: Current pivotHigh
- Signal is confirmed when: close > pivotHigh
- Stop Loss: Current pivotLow (at time of confirmation)
- Entry: Long position
IMPORTANT: There is only ever ONE pending bearish signal and ONE pending bullish signal
at any given time. When a new pending signal is created, it replaces the previous one
of the same type.
2. Signal Confirmation:
- Bearish: Confirmed when price closes below the pivot low (confirmation level)
- Bullish: Confirmed when price closes above the pivot high (confirmation level)
- Upon confirmation, a trade is entered immediately
- The confirmation line is drawn from the pivot bar to the confirmation bar
TRADE EXECUTION
When a signal is confirmed:
1. Position Management:
- Any existing position in the opposite direction is closed first
- Then the new position is entered
2. Stop Loss:
- Bearish (Short): Stop at pivotHigh
- Bullish (Long): Stop at pivotLow
3. Take Profit:
- Calculated using Risk:Reward Ratio (default 2:1)
- Risk = Distance from entry to stop loss
- Target = Entry ± (Risk × R:R Ratio)
- Can be disabled with "Stop Loss Only" toggle
4. Trade Comments:
- "Range Bear" for short trades
- "Range Bull" for long trades
SPECIAL RANGES
Special ranges are created when:
- A range high is mitigated AND the current pivotHigh is below the range high
- A range low is mitigated AND the current pivotLow is above the range low
In these cases:
- The pivot value is stored in an array (storedPivotHighs or storedPivotLows)
- A "special range" is created with only ONE extreme:
* If pivotHigh < rangeHigh: Creates a range with rangeHigh = pivotLow, rangeLow = na
* If pivotLow > rangeLow: Creates a range with rangeLow = pivotHigh, rangeHigh = na
- Special ranges can generate signals just like normal ranges
- If a special range is mitigated on the creation bar or the next bar, it is removed
entirely without generating signals (prevents false signals)
Special Ranges
REVERSE ON STOP LOSS
When enabled, if a stop loss is hit, the strategy automatically opens a trade in the
opposite direction:
1. Long Stop Loss Hit:
- Detects when: position_size > 0 AND position_size <= 0 AND low <= longStopLoss
- Action: Opens a SHORT position
- Stop Loss: Current pivotHigh
- Trade Comment: "Reverse on Stop"
2. Short Stop Loss Hit:
- Detects when: position_size < 0 AND position_size >= 0 AND high >= shortStopLoss
- Action: Opens a LONG position
- Stop Loss: Current pivotLow
- Trade Comment: "Reverse on Stop"
The reverse trade uses the same R:R ratio and respects the "Stop Loss Only" setting.
VISUAL ELEMENTS
1. Range Lines:
- Drawn from the time when the extreme was formed to the mitigation point (or current
time if not mitigated)
- High lines: Blue (or mitigated color if mitigated)
- Low lines: Red (or mitigated color if mitigated)
- Style: SOLID
- Width: 1
2. Confirmation Lines:
- Drawn when a signal is confirmed
- Extends from the pivot bar to the confirmation bar
- Bearish: Red, solid line
- Bullish: Green, solid line
- Width: 1
- Can be toggled on/off
STRATEGY SETTINGS
1. Range Detection Mode:
- HTF: Uses higher timeframe candles
- Sessions: Uses trading session boundaries
2. Auto HTF:
- Automatically selects HTF based on current chart timeframe
- Can be disabled to use manual HTF selection
3. Risk:Reward Ratio:
- Default: 2.0 (2:1)
- Minimum: 0.5
- Step: 0.5
4. Stop Loss Only:
- When enabled: Trades only have stop loss (no take profit)
- Trades close on stop loss or when opposite signal confirms
5. Reverse on Stop Loss:
- When enabled: Hitting a stop loss opens opposite trade with stop at opposing pivot
6. Max Ranges to Display:
- Limits the number of ranges kept in memory
- Oldest ranges are purged when limit is exceeded
KEY FEATURES
1. Dynamic Pivot Tracking:
- Pivots update on every candle color change
- Always maintains one high and one low pivot
2. Range Lifecycle:
- Ranges are created when price closes within previous range
- Ranges are tracked until mitigated
- Mitigation creates pending signals
- Signals are confirmed by pivot levels
3. Signal Priority:
- Only one pending signal of each type at a time
- New signals replace old ones
- Confirmation happens on close of bar
4. Position Management:
- Closes opposite positions before entering new trades
- Tracks stop loss levels for reverse functionality
- Respects pyramiding = 1 (only one position per direction)
5. Time-Based Drawing:
- Uses time coordinates instead of bar indices for line drawing
- Prevents "too far from current bar" errors
- Lines can extend to any historical point
USAGE NOTES
- Best suited for trending and ranging markets
- Works on any timeframe, but HTF mode adapts automatically
- Sessions mode is ideal for intraday trading
- Pivot detection requires clear candle color changes
- Range detection requires price to close within previous range
- Signals are generated on bar close, not intra-bar
The strategy combines range identification, pivot tracking, and signal confirmation to
create a systematic approach to trading breakouts and reversals based on price structure, past performance does not in any way predict future performance
5-min Strat Strategy V2 (With Stop Loss)README: 5-min Strat Strategy V2 – $7,500 Stop Loss Version
✅ Description
This is a rules-based intraday trading strategy developed for use on futures contracts like MNQ (Micro Nasdaq) or MES (Micro S&P). It focuses on momentum-based breakout entries above pre-market highs, during regular trading hours, and uses EMAs to define trend alignment.
⚙️ Strategy Components
✅ Trade Type
Long-Only strategy
Entry and exit based on EMAs, price position, and time windows
✅ Time Frame
Built for 5-minute charts
✅ Symbols
Optimized for MNQ (Micro Nasdaq Futures)
Works on MES or other U.S. index futures with similar structure
📅 Time Windows
Pre-Market Hours (PMH/PML): 04:00 – 09:30 AM EST
Regular Trading Hours (RTH): 09:30 AM – 4:00 PM EST
Auto Exit Time: 4:59 PM EST (to comply with prop firm rules)
📌 Entry Conditions (Long)
48 EMA > 200 EMA (Bullish alignment)
Price > Locked Pre-Market High
Green Candle (close > open)
During RTH (9:30–16:00 EST)
Cooldown: Must wait 4 candles after last entry
Max Trades per Day: 3
💥 Exit Conditions
Primary Exit: Close below the 48 EMA
Max Loss Exit: Stop loss set to $7,500 per trade
EOD Exit: All positions are closed at 4:59 PM EST
💰 Risk Management
Contracts: 6 Micro contracts per trade
Stop Loss: Dynamic point-based SL calculated based on:
MNQ point value = $20/point per contract
30 contracts = $120/point
Max SL points = $7,500 / $120 = 62.5 points
📊 Key Variables for Logging
Parameter Value
Max Stop Loss $7,500
Position Size 30 Micro Contracts - ***Varies depending on account size***
Cooldown Bars 4 (20 min)
Max Daily Trades 3
Strategy Version V2 – $7.5K SL
ULTIMATE COAIUSDT AUTOMATED STRATEGY# ULTIMATE COAIUSDT AUTOMATED STRATEGY
## 🚀 OVERVIEW
High-performance automated trading strategy for COAIUSDT.P perpetual contract achieving 5,962% returns in one month with 68.58% win rate across 611 trades.
## 📊 PERFORMANCE METRICS
• Net Profit: +5,962.87%
• Total Trades: 611
• Win Rate: 68.58% (419 wins)
• Profit Factor: 6.41
• Max Drawdown: -6.11%
• Timeframe: 5 minutes
• Backtest Period: Oct 2 - Nov 7, 2025
## 🎯 CORE TRADING LOGIC
Primary Indicator: ALMA (Arnaud Legoux Moving Average)
• Dual ALMA crossovers for precise entry signals
• Optimized parameters: Offset 0.85, Sigma 5, Period 2
• Reduces lag while maintaining signal accuracy
Multi-Timeframe Confirmation:
• HTF Filter: 15-minute timeframe validation
• HTF Multiplier: 8x for trend confirmation
• Eliminates false signals by requiring 5m + 15m alignment
Entry Signals:
• LONG: Fast ALMA crosses above slow ALMA (both timeframes)
• SHORT: Fast ALMA crosses below slow ALMA (both timeframes)
• EXIT: Opposite crossover triggers position close
## 💰 RISK MANAGEMENT
Take Profit: 13.5%
• Dynamically calculated from entry price
• Automatically locked when target reached
Stop Loss: 2.8%
• Tight stop to protect capital
• Optimized through extensive backtesting
Position Sizing:
• Pyramiding: Up to 12 positions
• Trade size: 20% of equity per trade
• Commission: 0.04% included in calculations
## 🤖 AUTOMATION FEATURES
Built-in Alert System:
• Webhook-ready
• Real-time signal transmission
• Pipe-delimited format for easy parsing
Alert Types:
• BUY: BUY|COAIUSDT.P|price=X|tp=Y|sl=Z
• SELL: SELL|COAIUSDT.P|price=X|tp=Y|sl=Z
• CLOSE LONG: CLOSE|COAIUSDT.P|side=LONG
• CLOSE SHORT: CLOSE|COAIUSDT.P|side=SHORT
## 📈 STRATEGY STRENGTHS
1. High Win Rate: 68.58% consistent edge
2. Excellent Profit Factor: 6.41 (profits 6.4x larger than losses)
3. Low Drawdown: Only -6.11% despite massive returns
4. Active Trading: 611 opportunities in 1 month
5. Automation Ready: Built-in webhook alerts
6. Optimized Risk/Reward: 4.8:1 ratio (13.5% TP / 2.8% SL)
## 🎨 VISUAL SIGNALS
• Green triangle + "BUY" label for long entries
• Red triangle + "SELL" label for short entries
• Clear exit markers for position closes
• Minimal chart clutter
## ⚠️ IMPORTANT DISCLAIMERS
• Strategy optimized specifically for COAIUSDT.P on 5-minute timeframe
• Past performance (5,962% returns) does not guarantee future results
• Use proper position sizing - never risk more than you can afford to lose
• Monitor strategy regularly, especially during high-impact news events
• Backtested results may differ from live trading due to slippage and execution
## 🔧 BEST PRACTICES
• Start with paper trading to verify signals
• Use stop-loss protection at all times
• Monitor drawdown levels regularly
• Adjust position size based on account risk tolerance
• Consider market conditions and volatility
The ULTIMATE COAIUSDT AUTOMATED STRATEGY combines sophisticated technical analysis with robust risk management and full automation capabilities - designed to capture consistent profits from COAI's price movements while maintaining strict capital protection.
Super Frog Power - Cluster Flip %Super Frog Power - Cluster Flip %
🔄 Trade Smarter, Not Harder: Let the Cluster Decide
Welcome to the "Super Frog Power - Cluster Flip %" strategy, a sophisticated multi-system confluence engine designed to filter out market noise and pinpoint high-probability trade setups. This isn't just another indicator; it's a comprehensive trading system that aggregates signals from eight distinct technical methodologies, waiting for them to align into a powerful "cluster" before you enter a trade.
🎯 Core Philosophy: The Power of Confluence
A single indicator can give false signals. A cluster of indicators from uncorrelated systems agreeing on a direction is a much stronger signal. This strategy continuously monitors multiple independent systems and only executes a trade when a significant number of them flip to a consensus, dramatically increasing the likelihood of a successful move.
✨ The 8 Systems of Super Frog Power
This strategy synthesizes signals from the following powerful components:
Bollinger Bands®: Identifies overbought and oversold conditions relative to recent volatility.
CMI (Cluster Momentum Index) System: A unique multi-period momentum oscillator that identifies convergence and breakout moments with custom "Lion" (SELL) and "Car" (BUY) signals.
SMI (Stochastic Momentum Index) System: A refined momentum indicator that generates "Mouse" (BUY) signals and combines with CMI for "Green Angel" and "Red Devil" super signals.
Lucky Balls (NVI/PVI): Utilizes Negative and Positive Volume Index to gauge smart money flow and identify accumulation/distribution zones.
Momentum System: A triple-threat combo of RSI, CCI, and PPO, scaled and combined to generate robust momentum-based entries and exits.
Lucky Table (Oscillator Overload): Counts the number of key oscillators (SMI, RSI, CCI) in overbought or oversold territory, triggering a signal when a threshold is met.
Apples & Pairs System: A complex system analyzing price swings, accumulation, mass index, and doji patterns with fun, emoji-based signals like "Apple Cross Up" 🍎 and "Pig Cross Down" 🐖.
ZBT (Zonal Breakout Trend) System: A multi-timeframe trend-following system using dynamic EMA channels and an ATR-based trailing stop to identify the primary trend and robust breakout points.
⚙️ How It Works: The Cluster Flip Logic
The magic happens in the signal aggregation. The strategy counts every single BUY and SELL signal from all active systems.
A "Strong Buy" is triggered when 6 or more independent BUY signals occur simultaneously.
A "Strong Sell" is triggered when 5 or more independent SELL signals occur simultaneously.
This "cluster flip" mechanism ensures you are only trading when there is broad-based technical agreement, keeping you out of choppy and uncertain market conditions.
🛡️ Integrated Risk Management
We believe a strategy is nothing without proper risk management. This system comes with built-in, percentage-based order management:
User-Defined Profit Target (%): Lock in profits automatically at your specified percentage gain.
User-Defined Stop Loss (%): Protect your capital with a hard stop loss.
Position Sizing: Control your risk per trade with a customizable position size.
Trades are also managed logically: a new strong signal in the opposite direction will automatically close any existing position, ensuring you're always on the right side of the cluster's consensus.
🎨 Visual Features & Customization
Fully Customizable: Don't like one system? Turn it off! Every system can be toggled on/off from the inputs.
Clear Visuals: Each system is plotted in a distinct color, making the chart a rich source of information without being cluttered.
Signal Markers: Strong Buy and Strong Sell clusters are clearly marked with large circles below and above the bars.
Alert Ready: Built-in alerts for Strong Buy and Strong Sell signals so you never miss a cluster setup.
🚀 How to Use
Add the script to your chart (1H, 4H, or Daily timeframes are recommended for swing trading).
Adjust the inputs to your liking, especially the Profit Target %, Stop Loss %, and Position Size under the "Strategy Parameters" section.
Observe the clusters. Wait for the "Strong Buy" or "Strong Sell" circle to appear.
Enter the trade. The strategy will automatically plot the profit target and stop loss levels on the chart for your reference.
Manage your trade. Let the logic handle the exits, or use your own discretion.
💡 Ideal For
Swing Traders looking for high-confidence set-and-forget setups.
Technical Analysts who appreciate the depth of multi-system confluence.
Traders who want to avoid the paralysis of analyzing too many indicators separately.
Unleash the power of cluster trading. Add the "Super Frog Power - Cluster Flip %" to your chart today!
Sigma Trinity ModelAbstract
Sigma Trinity Model is an educational framework that studies how three layers of market behavior interact within the same trend: (1) structural momentum (Rasta), (2) internal strength (RSI), and (3) continuation/compounding structure (Pyramid). The model deliberately combines bar-close momentum logic with intrabar, wick-aware strength checks to help users see how reversals form, confirm, and extend. It is not a signal service or automation tool; it is a transparent learning instrument for chart study and backtesting.
Why this is not “just a mashup”
Many scripts merge indicators without explaining the purpose. Sigma Trinity is a coordinated, three-engine study designed for a specific learning goal:
Rasta (structure): defines when momentum actually flips using a dual-line EMA vs smoothed EMA. It gives the entry/exit framework on bar close for clean historical study.
RSI (energy): measures internal strength with wick-aware triggers. It uses RSI of LOW (for bottom touches/reclaims) and RSI of HIGH (for top touches/exhaustion) so users can see intrabar strength/weakness that the close can hide.
Pyramid (progression): demonstrates how continuation behaves once momentum and strength align. It shows the logic of adds (compounding) as a didactic layer, also on bar close to keep historical alignment consistent.
These three roles are complementary, not redundant: structure → strength → progression.
Architecture Overview
Execution model
Rasta & Pyramid: bar close only by default (historically stable, easy to audit).
RSI: per tick (realtime) with bar-close backup by default, using RSI of LOW for entries and RSI of HIGH for exits. This makes the module sensitive to intra-bar wicks while still giving a close-based safety net for backtests.
Stops (optional in strategy builds): wick-accurate: trail arms/ratchets on HIGH; stop hit checks with LOW (or Close if selected) with a small undershoot buffer to avoid micro-noise hits.
Visual model
Dual lines (EMA vs smoothed EMA) for Rasta + color fog to see direction and compression/expansion.
Rungs (small vertical lines) drawn between the two Rasta lines to visualize wave spacing and rhythm.
Clean labels for Entry/Exit/Pyramid Add/RSI events. Everything is state-locked to avoid spamming.
Module 1 — Rasta (Structural Momentum Layer)
Goal: Identify structural momentum reversals and maintain a consistent, replayable backbone for study.
Method:
Compute an EMA of a chosen price source (default Close), and a smoothed version (SMA/EMA/RMA/WMA/None selectable).
Flip points occur when the EMA line crosses the smoothed line.
Optional EMA 8/21 trend filter can gate entries (long-bias when EMA8 > EMA21). A small “adaptive on flip” option lets an entry fire when the filter itself flips to ON and the EMA is already above the smoothed line—useful for trend resumption.
Why bar close only?
Bar-close Rasta gives a stable, auditable timeline for the structure of the trend. It teaches users to separate “structure” (close-resolved) from “energy” (intrabar, via RSI).
Visuals:
Fog between the lines (green/red) to show regime.
Rungs between lines to show spread (compression vs expansion).
Optional plotting of EMA8/EMA21 so users can see the gating effect.
Module 2 — RSI (Internal Strength / Energy Layer)
Goal: Reveal the intrabar strength/weakness that often precedes or confirms structural flips.
Method:
Standard RSI with adjustable length and signal smoothing for the panel view.
Logic uses wick-aware sources:
Entry trigger: RSI of LOW (same RSI length) touching or below a lower band (default 15). Think of it as intraband reactivation from the bottom, using the candle’s deepest excursion.
Exit trigger: RSI of HIGH touching or above an upper band (default 85). Think of it as exhaustion at the top, using the candle’s highest excursion.
Realtime + Close Backup: fires intrabar on tick, but if the realtime event was missed, the close backup will note it at bar end.
Cooldown control: optional bars-between-signals to avoid rapid re-triggers on choppy sequences.
Why wick-aware RSI?
A close-only RSI can miss the true micro-extremes that cause reversals. Using LOW/HIGH for triggers captures the behavior that traders actually react to during the bar, while the bar-close backup preserves historical reproducibility.
Module 3 — Pyramid (Continuation / Compounding Layer)
Goal: Teach how continuation behaves once a trend is underway, and how adds can be structured.
Method:
Same dual-line logic as Rasta (EMA vs smoothed EMA), but only fires when already in a position (or after prior entry conditions).
Supports the same EMA 8/21 filter and optional adaptive-on-flip behavior.
Bar close only to maintain historical cohesion.
What it teaches:
Adds tend to cluster when momentum persists.
Students can experiment with add spacing and compare “one-shot entries” vs “laddered adds” during strong regimes.
How the Pieces Work Together
Rasta establishes the structural frame (when the wave flip is real enough to record at close).
RSI validates or challenges that structure by tracking intrabar energy at the extremes (low/high touches).
Pyramid shows what sustained continuation looks like once (1) and (2) align.
This produces a layered view: Structure → Energy → Progression. Users can see when all three line up (strongest phases) and when they diverge (riskier phases or transitions).
How to Use It (Step-by-Step)
Quick Start
Apply script to any symbol/timeframe.
In Strategy/Indicator Properties:
Enable On every tick (recommended).
If available, enable Using bar magnifier and choose a lower resolution (e.g., 1m) to simulate intrabar fills more realistically.
Keep On bar close unchecked if you want to observe realtime logic in live charts (strategies still place orders on close by platform design).
Default behavior: Rasta & Pyramid = bar close; RSI = per tick with close backup.
Reading the Chart
Watch for Rasta Entry/Exit labels: they define clean structural turns on close.
Watch RSI Entry (LOW touch at/below lower band) and RSI Exit (HIGH touch at/above upper band) to gauge internal energy extremes.
Pyramid Add labels reveal continuation phases once a move is already in progress.
Tuning
Rasta smoothing: choose SMA/EMA/RMA/WMA or None. Higher smoothing → later but cleaner flips; lower smoothing → earlier but choppier.
RSI bands: a common educational setting is 15/85 for strong extremes; 20/80 is a bit looser.
Cooldown: increase if you see too many RSI re-fires in chop.
EMA 8/21 filter: toggle ON to study “trend-gated” entries, OFF to study raw momentum flips.
Backtesting Notes (for Strategy Builds)
Stops (optional): trail is armed when price advances by a trigger (default D–F₀), ratchets only upward from HIGH, and hits from LOW (or Close if chosen) with a tiny undershoot buffer to avoid micro-wicks.
Order sequencing per bar (mirrors the script’s code comments):
Trail ratchet via HIGH
Intrabar stop hit via LOW/CLOSE → immediate close
If still in position at bar close: process exits (Rasta/RSI)
If still in position at bar close: process Pyramid Add
If flat at bar close: process entries (Rasta/RSI)
Platform reality: strategies place orders at bar close in historical testing; the intrabar logic improves realism for stops and event marking but final order timestamps are still close-resolved.
Inputs Reference (common)
Modules: enable/disable RSI and Pyramid learning layers.
Rasta: EMA length, smoothing type/length, EMA8/21 filter & adaptive flip, fog opacity, rungs on/off & limit.
RSI: RSI length, signal MA length (panel), Entry band (LOW), Exit band (HIGH), cooldown bars, labels.
Pyramid: EMA length, smoothing, EMA8/21 filter & adaptive adds.
Execution: toggle Bar Close Only for Rasta/Pyramid; toggle Realtime + Close Backup for RSI.
Stops (strategy): Fixed Stop % (first), Fixed Stop % (add), Trail Distance %, Trigger rule (auto D–F₀ or custom), undershoot buffer %, and hit source (LOW/CLOSE).
What to Study With It
Convergence: how often RSI-LOW entry touches precede the next Rasta flip.
Divergence: cases where RSI screams exhaustion (HIGH >= upper band) but Rasta hasn’t flipped yet—often transition zones.
Continuation: how Pyramid adds cluster in strong moves; how spacing changes with smoothing/filter choices.
Regime changes: use EMA8/21 filter toggles to see what happens at macro turns vs chop.
Limitations & Scope
This is a learning tool, not a trade copier. It does not provide financial advice or automated execution.
Intrabar results depend on data granularity; bar magnifier (when available) can help simulate lower-resolution ticks, but true tick-by-tick fills are a platform-level feature and not guaranteed across all symbols.
Suggested Publication Settings (Strategy)
Initial capital: 100
Order size: 100 USD (cash)
Pyramiding: 10
Commission: 0.25%
Slippage: 3 ticks
Recalculate: ✓ On every tick
Fill orders: ✓ Using bar magnifier (choose 1m or similar); leave On bar close unchecked for live viewing.
Educational License
Released under the Michael Culpepper Gratitude License (2025).
Use and modify freely for education and research with attribution. No resale. No promises of profitability. Purpose is understanding, not signals.
Turtles StrategyBorn from the 1980s "Turtle" experiment, this method of trading captures breakouts and places or closes trades with intrabar entries or exits and realized-equity risk controls.
How It Works
The strategy buys/sells on breakouts from recent highs/lows, using ATR for volatility-adjusted stops and sizing. It risks a fixed % (default 1%) of realized equity per trade—initial capital plus closed P&L, ignoring open positions for conservatism. Drawdown protection auto-reduces risk by 20% at 10% drops (up to three times), resetting only on full peak recovery. Single positions only, with 1-tick slippage simulated for realistic fills. Best for trending assets like forex,commodities, crypto, stocks. Backtest for optimal parameters.
Main Operations
The strategy works on any timeframe but it's meant to be used on daily charts.
Entry Signals:
Long: Buy-stop 1 tick above 20-bar high (default "Entry Period") when no position—enters intrabar on breakout.
Short: Sell-stop 1 tick below 20-bar low. OCA cancels opposites.
Size: (Realized equity × adjusted risk %) ÷ (2× ATR stop distance), scaled by point value.
Exit Signals:
Longs: Stop at tighter of (entry - 2× ATR) or (10-bar low - 1 tick trailing, default "Exit Period").
Shorts: Stop at tighter of (entry + 2× ATR) or (10-bar high + 1 tick trailing).
Locks profits in trends, exits fast on fades.
Risk Controls:
Tracks realized equity peak.
10% drawdown: Risk ×0.8; 20%/30%: Further ×0.8 (max 3x).
Full reset above peak—preserves capital in slumps.
