Hash Momentum Strategy# Hash Momentum Strategy
## 📊 Overview
The **Hash Momentum Strategy** is a professional-grade momentum trading system designed to capture strong directional price movements with precision timing and intelligent risk management. Unlike traditional EMA crossover strategies, this system uses momentum acceleration as its primary signal, resulting in earlier entries and better risk-to-reward ratios.
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## ⚡ What Makes This Strategy Unique
### 1. Momentum-Based Entry System
Most strategies rely on lagging indicators like moving average crossovers. This strategy captures momentum *acceleration* - entering when price movement is gaining strength, not after the move has already happened.
### 2. Programmable Risk-to-Reward
Set your exact R:R ratio (1:2, 1:2.5, 1:3, etc.) and the strategy automatically calculates stop loss and take profit levels. No more guessing or manual calculations.
### 3. Smart Partial Profit Taking
Lock in profits at multiple stages:
- **First TP**: Take 50% off at 2R
- **Second TP**: Take 40% off at 2.5R
- **Final TP**: Let 10% ride to maximum target
This approach locks in gains while letting winners run.
### 4. Dynamic Momentum Threshold
Uses ATR (Average True Range) multiplied by your threshold setting to adapt to market volatility. Volatile markets = higher threshold. Quiet markets = lower threshold.
### 5. Trade Cooldown System
Prevents overtrading and revenge trading by enforcing a cooldown period between trades. Configurable from 1-24 bars.
### 6. Optional Session & Weekend Filters
Filter trades by Tokyo, London, and New York sessions. Optional weekend-off toggle to avoid low-liquidity periods.
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## 🎯 How It Works
### Signal Generation
**STEP 1: Calculate Momentum**
- Momentum = Current Price - Price
- Check if Momentum > ATR × Threshold Multiplier
- Momentum must be accelerating (positive change in momentum)
**STEP 2: Confirm with EMA Trend Filter**
- Long: Price must be above EMA
- Short: Price must be below EMA
**STEP 3: Check Filters**
- Not in cooldown period
- Valid session (if enabled)
- Not weekend (if enabled)
**STEP 4: ENTRY SIGNAL TRIGGERED**
### Risk Management Example
**Example Long Trade:**
- Entry: $100
- Stop Loss: $97.80 (2.2% risk)
- Risk Amount: $2.20
**Take Profit Levels:**
- TP1: $104.40 (2R = $4.40) → Close 50%
- TP2: $105.50 (2.5R = $5.50) → Close 40%
- Final: $105.50 (2.5R) → Close remaining 10%
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## ⚙️ Settings Guide
### Core Strategy
**Momentum Length** (Default: 13)
Number of bars for momentum calculation. Higher = stronger but fewer signals.
**Momentum Threshold** (Default: 2.25)
ATR multiplier. Higher = only trade biggest moves.
**Use EMA Trend Filter** (Default: ON)
Only long above EMA, short below EMA.
**EMA Length** (Default: 28)
Period for trend-confirming EMA.
### Filters
**Use Trading Session Filter** (Default: OFF)
Restrict trading to specific sessions.
**Tokyo Session** (Default: OFF)
Trade during Asian hours (00:00-09:00 JST).
**London Session** (Default: OFF)
Trade during European hours (08:00-17:00 GMT).
**New York Session** (Default: OFF)
Trade during US hours (08:00-17:00 EST).
**Weekend Off** (Default: OFF)
Disable trading on Saturdays and Sundays.
### Risk Management
**Stop Loss %** (Default: 2.2)
Fixed percentage stop loss from entry.
**Risk:Reward Ratio** (Default: 2.5)
Your target reward as multiple of risk.
**Use Partial Profit Taking** (Default: ON)
Take profits in stages.
**First TP R:R** (Default: 2.0)
First target as multiple of risk.
**First TP Size %** (Default: 50)
Percentage of position to close at TP1.
**Second TP R:R** (Default: 2.5)
Second target as multiple of risk.
**Second TP Size %** (Default: 40)
Percentage of position to close at TP2.
### Trade Management
**Use Trade Cooldown** (Default: ON)
Prevent overtrading.
**Cooldown Bars** (Default: 6)
Bars to wait after closing a trade.
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## 🎨 Visual Elements
### Chart Indicators
🟢 **Green Dot** (below bar) = Long entry signal
🔴 **Red Dot** (above bar) = Short entry signal
🔵 **Blue X** (above bar) = Long position closed
🟠 **Orange X** (below bar) = Short position closed
**EMA Line** = Trend direction (green when bullish, red when bearish)
**White Line** = Entry price
**Red Line** = Stop loss level
**Green Lines** = Take profit levels (TP1, TP2, Final)
### Dashboard
When not in real-time mode, a dashboard displays:
- Current position (LONG/SHORT/FLAT)
- Entry price
- Stop loss price
- Take profit price
- R:R ratio
- Current momentum strength
- Total trades
- Win rate
- Net profit %
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## 📈 Recommended Settings by Timeframe
### 1-Hour Timeframe (Default)
- Momentum Length: 13
- Momentum Threshold: 2.25
- EMA Length: 28
- Stop Loss: 2.2%
- R:R Ratio: 2.5
- Cooldown: 6 bars
### 4-Hour Timeframe
- Momentum Length: 24-36
- Momentum Threshold: 2.5
- EMA Length: 50
- Stop Loss: 3-4%
- R:R Ratio: 2.0-2.5
- Cooldown: 6-8 bars
### 15-Minute Timeframe
- Momentum Length: 8-10
- Momentum Threshold: 2.0
- EMA Length: 20
- Stop Loss: 1.5-2%
- R:R Ratio: 2.0
- Cooldown: 4-6 bars
---
## 🔧 Optimization Tips
### Want More Trades?
- Decrease Momentum Threshold (2.0 instead of 2.25)
- Decrease Momentum Length (10 instead of 13)
- Decrease Cooldown Bars (4 instead of 6)
### Want Higher Quality Trades?
- Increase Momentum Threshold (2.5-3.0)
- Increase Momentum Length (18-24)
- Increase Cooldown Bars (8-10)
### Want Lower Drawdown?
- Increase Cooldown Bars
- Use tighter stop loss
- Enable session filters (trade only high-liquidity sessions)
- Enable Weekend Off
### Want Higher Win Rate?
- Increase R:R Ratio (may reduce total profit)
- Increase Momentum Threshold (fewer but stronger signals)
- Use longer EMA for trend confirmation
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## 📊 Performance Expectations
Based on typical backtesting results:
- **Win Rate**: 35-45%
- **Profit Factor**: 1.5-2.0
- **Risk:Reward**: 1:2.5 (configurable)
- **Max Drawdown**: 10-20%
- **Trades/Month**: 8-15 (1H timeframe)
**Note:** Win rate may appear low, but with 2.5:1 R:R, you only need ~29% win rate to break even. The strategy aims for quality over quantity.
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## 🎓 Strategy Logic Explained
### Why Momentum > EMA Crossover?
**EMA Crossover Problems:**
- Signals lag behind price
- Late entries = poor R:R
- Many false signals in ranging markets
**Momentum Advantages:**
- Catches moves as they start accelerating
- Earlier entries = better R:R
- Adapts to volatility via ATR
### Why Partial Profit Taking?
**Without Partial TPs:**
- All-or-nothing approach
- Winners often turn to losers
- High stress watching open positions
**With Partial TPs:**
- Lock in 50% at first target
- Reduce risk to breakeven
- Let remainder ride for bigger gains
- Lower psychological pressure
### Why Trade Cooldown?
**Without Cooldown:**
- Revenge trading after losses
- Overtrading in choppy markets
- Emotional decision-making
**With Cooldown:**
- Forces discipline
- Waits for new setup to develop
- Reduces transaction costs
- Better signal quality
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## ⚠️ Important Notes
1. **This is a momentum strategy, not an EMA strategy**
The EMA only confirms trend direction. Momentum generates the actual signals.
2. **Backtest thoroughly before live trading**
Past performance ≠ future results. Test on your specific asset and timeframe.
3. **Use proper position sizing**
Risk 1-2% of account per trade maximum. The strategy uses 100% equity by default (adjust in Properties).
4. **Dashboard auto-hides in real-time**
Clean chart for live trading. Visible during backtesting.
5. **Customize for your trading style**
All settings are fully adjustable. No single "best" configuration.
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## 🚀 Quick Start Guide
1. **Add to Chart**: Apply to your preferred asset and timeframe
2. **Keep Defaults**: Start with default settings
3. **Backtest**: Review historical performance
4. **Paper Trade**: Test with simulated money first
5. **Go Live**: Start small and scale up
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## 💡 Pro Tips
**Tip 1: Combine Timeframes**
Use higher timeframe (4H) for trend direction, lower timeframe (1H) for entries.
**Tip 2: Avoid News Events**
Major news can cause whipsaws. Consider manual intervention during high-impact events.
**Tip 3: Monitor Momentum Strength**
Dashboard shows momentum in sigma (σ). Values >1.0σ indicate very strong momentum.
**Tip 4: Adjust for Volatility**
In high-volatility markets, increase threshold and stop loss. In quiet markets, decrease them.
**Tip 5: Review Losing Trades**
Check if losses are hitting stop loss or reversing. Adjust stop accordingly.
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## 📝 Changelog
**v1.0** - Initial Release
- Momentum-based signal generation
- EMA trend filter
- Programmable R:R ratio
- Partial profit taking (3 stages)
- Trade cooldown system
- Session filters (Tokyo/London/New York)
- Weekend off toggle
- Smart dashboard (auto-hides in real-time)
- Clean visual design
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## 🙏 Credits
Developed by **Hash Capital Research**
If you find this strategy useful, please give it a like and share with others!
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## ⚖️ Disclaimer
This strategy is for educational purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Always do your own research and consult with a qualified financial advisor before trading.
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## 📬 Feedback
Have suggestions or found a bug? Leave a comment below! I'm continuously improving this strategy based on community feedback.
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**Happy Trading! 🚀📈**
Analisis Tren
ILM & IFVG StrategyPlease feel free to adjust in any way possible. Let me know if you can create something better from this initial coding.
//═══════════════════════════════════════════════════════════════════════
// Inverted Liquidity Model (ILM) – Strategy
//═══════════════════════════════════════════════════════════════════════
//
// The **Inverted Liquidity Model (ILM)** is a liquidity-based algorithm
// built to capture high-probability reversals after:
//
// • A liquidity sweep (SSL/BSL taken)
// • Rejection back inside the range
// • A Fair Value Gap (FVG) forms
// • That FVG becomes invalidated → becomes an IFVG entry zone
//
// ILM combines:
// • LTF BOS / CHOCH structure confirmation
// • HTF structure (expansion) filtering
// • Premium / Discount filter (17:00 CST session midline)
// • Optional ATR volatility filter
// • Optional trading session restrictions
// • Optional partial profit-taking + runners
//
// When all conditions align, the strategy enters:
// ✔ Long after sweep of SSL + valid long IFVG + trend confirmation
// ✔ Short after sweep of BSL + valid short IFVG + trend confirmation
//
// Stops are placed at the sweep wick.
// Full target is set at the next structural high/low.
// Optional partial TP sends a runner to full target.
//
// Visual tools (labels, sweep lines, IFVG boxes, midline) assist
// with review and forward testing.
//
//───────────────────────────────────────────────────────────────────────
// USER CONFIGURABLE FEATURES
//───────────────────────────────────────────────────────────────────────
//
// • **Liquidity & Structure**
// - pivotLen → swing length for pivots / liquidity
// - htfOn → toggle higher-timeframe pivots
// - htfTF → timeframe for HTF structure/liquidity
// - useStructureFilter → enforce LTF BOS/CHOCH trend
// - useHtfExpansionFilter → enforce HTF trend
// - showStructureLabels → show BOS/CHOCH labels
// - showHtfStructureLabels → show HTF BOS/CHOCH labels
//
// • **Premium / Discount Midline**
// - usePremiumDiscountFilter → only long in discount / short in premium
// - pdSession → session used for midline (default 17:00 CST)
// - showPdMidLine → show 50% midline
//
// • **FVG / IFVG Detection**
// - useBodyGapFVG → FVG uses candle bodies instead of wicks
// - useDisplacementFVG → require displacement bar
// - dispAtrMult → minimum ATR threshold for displacement
// - showIFVG → draw IFVG boxes
//
// • **ATR / Volatility / Sessions**
// - useRangeFilter → require minimum ATR%
// - atrLen → ATR period
// - minAtrPerc → minimum ATR% of price
// - useSessionFilter → restrict trading hours
// - sessionTimes → allowed trading session
//
// • **Sweep Visualization**
// - showSweepLines → draw sweep lines at SSL/BSL sweeps
// - sweepLineWidth → thickness of sweep lines
//
// • **Exits: Partial Targets & Runners**
// - usePartialTargets → enable partial TP logic
// - tp1QtyPercent → percent closed at TP1
// - tp1FractionOfPath → TP1 relative to path to full target
//
// • **Formatting / Visibility**
// - labelFontSizeInput → tiny / small / normal / large / huge
// - showEntries → entry markers
// - showTargets → target lines
//
//═══════════════════════════════════════════════════════════════════════
// END OF STRATEGY DESCRIPTION
//═══════════════════════════════════════════════════════════════════════
Rasta Long/Short — StrategyThe Rasta Long/Short Strategy is a visual and educational framework designed to help traders study momentum shifts that appear when a fast EMA interacts with a slower smoothed baseline.
It is not a signal service. Instead, it is a research tool that helps you observe transitions, structure, and behavior across different market conditions and smoothing contexts.
The script plots:
A primary EMA line (fast reaction wave).
A Smoothed line (your chosen smoothing method).
Color-coded fog regions showing directional bias.
Optional DNA rung connections between the two lines for structural comparison.
Together, these allow a deeper study of how momentum pushes, volatility compression, expansions, and drift emerge around fast/slow EMA interactions.
✦ Core Idea
The Rasta Long/Short mechanism studies how price behaves when the fast EMA crosses above or below a smoothed anchor.
Rather than predicting price, it reveals where transitions occur across different structures, timeframes, and smoothing techniques.
The Long/Short logic simply highlights flips in directional structure.
It is not intended for real-time signals or automated execution; it is intended for understanding market movement.
✦ Smoothing Types (Explained)
The strategy allows experimenting with several smoothing families to observe how they transform the fast EMA:
SMA (Simple Moving Average)
Averaged, slower response. Good for stability comparisons.
EMA (Exponential)
Faster reaction, more responsive, smoother behavior during momentum.
RMA (Wilder’s)
Used in RSI calculations; steady, well-balanced response.
WMA (Weighted)
More weight to recent bars; bridges SMA and EMA dynamics.
None
Raw EMA vs EMA interaction with no secondary smoothing.
Each smoothing type provides unique structural information and can lead to different interpretations.
✦ Modes of Study
Designed for multi-timeframe research:
1H / 4H — Momentum flow mapping and structural identification.
Daily / Weekly — Higher-timeframe rotations, macro structure transitions.
1–15m — Microstructure studies, noise vs trend emergence.
Use the built-in Strategy Tester to explore entry/exit context, but treat results as research, not predictive performance.
✦ Components (Visual Study Tools)
EMA Line (Fast)
Primary reactive wave. Shows fast directional shifts.
Smoothed Line (Slow)
Trend baseline / reference structure.
Fog Region
Highlights fast-vs-smoothed directional alignment.
DNA Rungs (Optional)
Structural “bridges” showing the exact relationship between waves on each bar.
Useful for studying separation, compression, and expansions.
✦ Educational Insights
This strategy helps illuminate:
How fast and slow EMAs interact dynamically.
How structure changes precede trend emergence.
Where volatility compresses before expansion.
How noise, drift, and clean reversals differ.
How different smoothers alter the interpretation of the same price data.
The goal is clarity — not prediction.
✦ How to Use
Apply to any timeframe or instrument.
Enable or disable fog depending on preferred visibility.
Use DNA rungs for close structural comparison.
Observe long/short flips as educational reference points — not signals.
Study transitions visually, then backtest using the Strategy Tester for pattern research.
✦ Disclaimer
This script is provided for educational and research purposes only.
It does not provide trading signals, financial advice, or recommendations.
Past behavior does not indicate future performance.
Always practice risk-aware study and consult qualified financial professionals when needed.
✦ Author
Michael Culpepper (mikeyc747)
Creator of the Rasta framework and related market structure studies.
Faraz Perfect Structure XL / XS (Trend-Filtered)Faraz’s Perfect Structure XL/XS identifies premium trend continuation and reversal setups using a three-filter system:
structural breakouts using dynamic swing-based support/resistance,
trend confirmation via 200-EMA slope,
momentum validation through RSI and MACD.
Signals only trigger when all factors align, eliminating noise, chop, and false signals.
Designed for traders who want clean, high-probability long (XL) and short (XS) entries.
EMA 50/200 Pullback + RSI (BTC/USDT 15m - 2 Bar Logic)I recognize that combining indicators requires clear justification on how the components interact Therefore the new scripts description will explicitly detail the strategys operational logic
Objective The strategy is a Trend Following Pullback System designed for high frequency time frames 15m
Synergy The EMA50 EMA200 defines the primary Trend Direction Trend Filter It then utilizes a 2 Bar Pullback Logic to find an entry point where the price has momentarily reversed against the trendline and the RSI 14 serves as a Momentum Filter RSI greater than 50 for Long RSI less than 50 for Short to minimize false signals
EMA Cross Strategy v5 (30 lots) (15 min candle only)- safe flip🚀 EMA Cross Strategy v5 (30 Lots) (15 min candle only)— Safe Flip Edition
Fully Automated | Fast | Reliable | Battle-tested
Welcome to a clean, powerful, and automation-friendly EMA crossover system.
This strategy is built for traders who want consistent trend-based entries without the risk of unwanted pyramiding or doubled positions.
🔥 How It Works
This strategy uses a fast EMA (10) crossing a slow EMA (20) to detect trend shifts:
Bullish Crossover → LONG (30 lots)
Bearish Crossover → SHORT (30 lots)
Every opposite signal safely flips the position by first closing the current trade, then opening a fresh position of exactly 30 lots.
No doubling.
No runaway position size.
No surprises.
Just clean, mechanical trend-following.
📈 Why This Strategy Stands Out
Unlike basic EMA crossbots, this version:
✔ Prevents unintended pyramiding
✔ Never over-allocates capital
✔ Works perfectly with webhook-based automation
✔ Produces stable, systematic entries
✔ Executes directional flips with precision
🔍 Backtest Highlights (1-Year)
(Backtests will vary by instrument/timeframe)
1,500+ trades executed
Profit factor above 1.27
Strong trend performance
Balanced long/short behavior
No margin calls
Consistent trade execution
This strategy thrives in trending markets and maintains strict discipline even in choppy conditions.
⚙️ Automation Ready
Designed for automated execution via webhook and API setups on supported platforms.
Just connect, run, and let the bot follow the rules without hesitation.
No emotions.
No overtrading.
No fear or greed.
Pure logic.
Golden Cross 50/200 EMATrend-following systems are characterized by having a low win rate, yet in the right circumstances (trending markets and higher timeframes) they can deliver returns that even surpass those of systems with a high win rate.
Below, I show you a simple bullish trend-following system with clear execution rules:
System Rules
-Long entries when the 50-period EMA crosses above the 200-period EMA.
-Stop Loss (SL) placed at the lowest low of the 15 candles prior to the entry candle.
-Take Profit (TP) triggered when the 50-period EMA crosses below the 200-period EMA.
Risk Management
-Initial capital: $10,000
-Position size: 10% of capital per trade
-Commissions: 0.1% per trade
Important Note:
In the code, the stop loss is defined using the swing low (15 candles), but the position size is not adjusted based on the distance to the stop loss. In other words, 10% of the equity is risked on each trade, but the actual loss on the trade is not controlled by a maximum fixed percentage of the account — it depends entirely on the stop loss level. This means the loss on a single trade could be significantly higher or lower than 10% of the account equity, depending on volatility.
Implementing leverage or reducing position size based on volatility is something I haven’t been able to include in the code, but it would dramatically improve the system’s performance. It would fix a consistent percentage loss per trade, preventing losses from fluctuating wildly with changes in volatility.
For example, we can maintain a fixed loss percentage when volatility is low by using the following formula:
Leverage = % of SL you’re willing to risk / % volatility from entry point to stop loss
And when volatility is high and would exceed the fixed percentage we want to expose per trade (if the SL is hit), we could reduce the position size accordingly.
Practical example:
Imagine we only want to risk 15% of the position value if the stop loss is triggered on Tesla (which has high volatility), but the distance to the SL represents a potential 23.57% drop. In this case, we subtract the desired risk (15%) from the actual volatility-based loss (23.57%):
23.57% − 15% = 8.57%
Now suppose we normally use $200 per trade.
To calculate 8.57% of $200:
200 × (8.57 / 100) = $17.14
Then subtract that amount from the original position size:
$200 − $17.14 = $182.86
In summary:
If we reduce the position size to $182.86 (instead of the usual $200), even if Tesla moves 23.57% against us and hits the stop loss, we would still only lose approximately 15% of the original $200 position — exactly the risk level we defined. This way, we strictly respect our risk management rules regardless of volatility swings.
I hope this clearly explains the importance of capping losses at a fixed percentage per trade. This keeps risk under control while maintaining a consistent percentage of capital invested per trade — preventing both statistical distortion of the system and the potential destruction of the account.
About the code:
Strategy declaration:
The strategy is named 'Golden Cross 50/200 EMA'.
overlay=true means it will be drawn directly on the price chart.
initial_capital=10000 sets the initial capital to $10,000.
default_qty_type=strategy.percent_of_equity and default_qty_value=10 means each trade uses 10% of available equity.
margin_long=0 indicates no margin is used for long positions (this is likely for simulation purposes only; in real trading, margin would be required).
commission_type=strategy.commission.percent and commission_value=0.1 sets a 0.1% commission per trade.
Indicators:
Calculates two EMAs: a 50-period EMA (ema50) and a 200-period EMA (ema200).
Crossover detection:
bullCross is triggered when the 50-period EMA crosses above the 200-period EMA (Golden Cross).
bearCross is triggered when the 50-period EMA crosses below the 200-period EMA (Death Cross).
Recent swing:
swingLow calculates the lowest low of the previous 15 periods.
Stop Loss:
entryStopLoss is a variable initialized as na (not available) and is updated to the current swingLow value whenever a bullCross occurs.
Entry and exit conditions:
Entry: When a bullCross occurs, the initial stop loss is set to the current swingLow and a long position is opened.
Exit on opposite signal: When a bearCross occurs, the long position is closed.
Exit on stop loss: If the price falls below entryStopLoss while a position is open, the position is closed.
Visualization:
Both EMAs are plotted (50-period in blue, 200-period in red).
Green triangles are plotted below the bar on a bullCross, and red triangles above the bar on a bearCross.
A horizontal orange line is drawn that shows the stop loss level whenever a position is open.
Alerts:
Alerts are created for:Long entry
Exit on bearish crossover (Death Cross)
Exit triggered by stop loss
Favorable Conditions:
Tesla (45-minute timeframe)
June 29, 2010 – November 17, 2025
Total net profit: $12,458.73 or +124.59%
Maximum drawdown: $1,210.40 or 8.29%
Total trades: 107
Winning trades: 27.10% (29/107)
Profit factor: 3.141
Tesla (1-hour timeframe)
June 29, 2010 – November 17, 2025
Total net profit: $7,681.83 or +76.82%
Maximum drawdown: $993.36 or 7.30%
Total trades: 75
Winning trades: 29.33% (22/75)
Profit factor: 3.157
Netflix (45-minute timeframe)
May 23, 2002 – November 17, 2025
Total net profit: $11,380.73 or +113.81%
Maximum drawdown: $699.45 or 5.98%
Total trades: 134
Winning trades: 36.57% (49/134)
Profit factor: 2.885
Netflix (1-hour timeframe)
May 23, 2002 – November 17, 2025
Total net profit: $11,689.05 or +116.89%
Maximum drawdown: $844.55 or 7.24%
Total trades: 107
Winning trades: 37.38% (40/107)
Profit factor: 2.915
Netflix (2-hour timeframe)
May 23, 2002 – November 17, 2025
Total net profit: $12,807.71 or +128.10%
Maximum drawdown: $866.52 or 6.03%
Total trades: 56
Winning trades: 41.07% (23/56)
Profit factor: 3.891
Meta (45-minute timeframe)
May 18, 2012 – November 17, 2025
Total net profit: $2,370.02 or +23.70%
Maximum drawdown: $365.27 or 3.50%
Total trades: 83
Winning trades: 31.33% (26/83)
Profit factor: 2.419
Apple (45-minute timeframe)
January 3, 2000 – November 17, 2025
Total net profit: $8,232.55 or +80.59%
Maximum drawdown: $581.11 or 3.16%
Total trades: 140
Winning trades: 34.29% (48/140)
Profit factor: 3.009
Apple (1-hour timeframe)
January 3, 2000 – November 17, 2025
Total net profit: $9,685.89 or +94.93%
Maximum drawdown: $374.69 or 2.26%
Total trades: 118
Winning trades: 35.59% (42/118)
Profit factor: 3.463
Apple (2-hour timeframe)
January 3, 2000 – November 17, 2025
Total net profit: $8,001.28 or +77.99%
Maximum drawdown: $755.84 or 7.56%
Total trades: 67
Winning trades: 41.79% (28/67)
Profit factor: 3.825
NVDA (15-minute timeframe)
January 3, 2000 – November 17, 2025
Total net profit: $11,828.56 or +118.29%
Maximum drawdown: $1,275.43 or 8.06%
Total trades: 466
Winning trades: 28.11% (131/466)
Profit factor: 2.033
NVDA (30-minute timeframe)
January 3, 2000 – November 17, 2025
Total net profit: $12,203.21 or +122.03%
Maximum drawdown: $1,661.86 or 10.35%
Total trades: 245
Winning trades: 28.98% (71/245)
Profit factor: 2.291
NVDA (45-minute timeframe)
January 3, 2000 – November 17, 2025
Total net profit: $16,793.48 or +167.93%
Maximum drawdown: $1,458.81 or 8.40%
Total trades: 172
Winning trades: 33.14% (57/172)
Profit factor: 2.927
ATR Trend + RSI Pullback Strategy [Profit-Focused]This strategy is designed to catch high-probability pullbacks during strong trends using a combination of ATR-based volatility filters, RSI exhaustion levels, and a trend-following entry model.
Strategy Logic
Rather than relying on lagging crossovers, this model waits for RSI to dip into oversold zones (below 40) while price remains above a long-term EMA (default: 200). This setup captures pullbacks in strong uptrends, allowing traders to enter early in a move while controlling risk dynamically.
To avoid entries during low-volatility conditions or sideways price action, it applies a minimum ATR filter. The ATR also defines both the stop-loss and take-profit levels, allowing the model to adapt to changing market conditions.
Exit logic includes:
A take-profit at 3× the ATR distance
A stop-loss at 1.5× the ATR distance
An optional early exit if RSI crosses above 70, signaling overbought conditions
Technical Details
Trend Filter: 200 EMA – must be rising and price must be above it
Entry Signal: RSI dips below 40 during an uptrend
Volatility Filter: ATR must be above a user-defined minimum threshold
Stop-Loss: 1.5× ATR below entry price
Take-Profit: 3.0× ATR above entry price
Exit on Overbought: RSI > 70 (optional early exit)
Backtest Settings
Initial Capital: $10,000
Position Sizing: 5% of equity per trade
Slippage: 1 tick
Commission: 0.075% per trade
Trade Direction: Long only
Timeframes Tested: 15m, 1H, and 30m on trending assets like BTCUSD, NAS100, ETHUSD
This model is tuned for positive P&L across trending environments and volatile markets.
Educational Use Only
This strategy is for educational purposes only and should not be considered financial advice. Past performance does not guarantee future results. Always validate performance on multiple markets and timeframes before using it in live trading.
BTC EMA 5-9 Flip Strategy AutobotThis strategy is designed for fast and accurate trend-following trades on Bitcoin.
It uses a crossover between EMA 5 and EMA 9 to detect instant trend reversals and automatically flips between Long and Short positions.
How the strategy works
EMA 5 crossing above EMA 9 → Long
EMA 5 crossing below EMA 9 → Short
Automatically closes the opposite trade during a flip
Executes trades only on candle close
Prevents double entries with internal position-state logic
Fully compatible with automated trading via webhooks (Delta Exchange)
Why this strategy works
EMA 5–9 is extremely responsive for BTC’s volatility
Captures trend reversals early
Works best on 15-minute timeframe
Clean, simple logic without over-filtering reduces missed opportunities
Performs well in both uptrends and downtrends
Automation Ready
This strategy includes alert conditions and webhook-ready JSON for automated execution.
This is a fast-reacting BTC bot designed for intraday and swing crypto trend trading.
SP500 Session Gap Fade StrategySummary in one paragraph
SPX Session Gap Fade is an intraday gap fade strategy for index futures, designed around regular cash sessions on five minute charts. It helps you participate only when there is a full overnight or pre session gap and a valid intraday session window, instead of trading every open. The original part is the gap distance engine which anchors both stop and optional target to the previous session reference close at a configurable flat time, so every trade’s risk scales with the actual gap size rather than a fixed tick stop.
Scope and intent
• Markets. Primarily index futures such as ES, NQ, YM, and liquid index CFDs that exhibit overnight gaps and regular cash hours.
• Timeframes. Intraday timeframes from one minute to fifteen minutes. Default usage is five minute bars.
• Default demo used in the publication. Symbol CME:ES1! on a five minute chart.
• Purpose. Provide a simple, transparent way to trade opening gaps with a session anchored risk model and forced flat exit so you are not holding into the last part of the session.
• Limits. This is a strategy. Orders are simulated on standard candles only.
Originality and usefulness
• Unique concept or fusion. The core novelty is the combination of a strict “full gap” entry condition with a session anchored reference close and a gap distance based TP and SL engine. The stop and optional target are symmetric multiples of the actual gap distance from the previous session’s flat close, rather than fixed ticks.
• Failure mode it addresses. Fixed sized stops do not scale when gaps are unusually small or unusually large, which can either under risk or over risk the account. The session flat logic also reduces the chance of holding residual positions into late session liquidity and news.
• Testability. All key pieces are explicit in the Inputs: session window, minutes before session end, whether to use gap exits, whether TP or SL are active, and whether to allow candle based closes and forced flat. You can toggle each component and see how it changes entries and exits.
• Portable yardstick. The main unit is the absolute price gap between the entry bar open and the previous session reference close. tp_mult and sl_mult are multiples of that gap, which makes the risk model portable across contracts and volatility regimes.
Method overview in plain language
The strategy first defines a trading session using exchange time, for example 08:30 to 15:30 for ES day hours. It also defines a “flat” time a fixed number of minutes before session end. At the flat bar, any open position is closed and the bar’s close price is stored as the reference close for the next session. Inside the session, the strategy looks for a full gap bar relative to the prior bar: a gap down where today’s high is below yesterday’s low, or a gap up where today’s low is above yesterday’s high. A full gap down generates a long entry; a full gap up generates a short entry. If the gap risk engine is enabled and a valid reference close exists, the strategy measures the distance between the entry bar open and that reference close. It then sets a stop and optional target as configurable multiples of that gap distance and manages them with strategy.exit. Additional exits can be triggered by a candle color flip or by the forced flat time.
Base measures
• Range basis. The main unit is the absolute difference between the current entry bar open and the stored reference close from the previous session flat bar. That value is used as a “gap unit” and scaled by tp_mult and sl_mult to build the target and stop.
Components
• Component one: Gap Direction. Detects full gap up or full gap down by comparing the current high and low to the previous bar’s high and low. Gap down signals a long fade, gap up signals a short fade. There is no smoothing; it is a strict structural condition.
• Component two: Session Window. Only allows entries when the current time is within the configured session window. It also defines a flat time before the session end where positions are forced flat and the reference close is updated.
• Component three: Gap Distance Risk Engine. Computes the absolute distance between the entry open and the stored reference close. The stop and optional target are placed as entry ± gap_distance × multiplier so that risk scales with gap size.
• Optional component: Candle Exit. If enabled, a bullish bar closes short positions and a bearish bar closes long positions, which can shorten holding time when price reverses quickly inside the session.
• Session windows. Session logic uses the exchange time of the chart symbol. When changing symbols or venues, verify that the session time string still matches the new instrument’s cash hours.
Fusion rule
All gates are hard conditions rather than weighted scores. A trade can only open if the session window is active and the full gap condition is true. The gap distance engine only activates if a valid reference close exists and use_gap_risk is on. TP and SL are controlled by separate booleans so you can use SL only, TP only, or both. Long and short are symmetric by construction: long trades fade full gap downs, short trades fade full gap ups with mirrored TP and SL logic.
Signal rule
• Long entry. Inside the active session, when the current bar shows a full gap down relative to the previous bar (current high below prior low), the strategy opens a long position. If the gap risk engine is active, it places a gap based stop below the entry and an optional target above it.
• Short entry. Inside the active session, when the current bar shows a full gap up relative to the previous bar (current low above prior high), the strategy opens a short position. If the gap risk engine is active, it places a gap based stop above the entry and an optional target below it.
• Forced flat. At the configured flat time before session end, any open position is closed and the close price of that bar becomes the new reference close for the following session.
• Candle based exit. If enabled, a bearish bar closes longs, and a bullish bar closes shorts, regardless of where TP or SL sit, as long as a position is open.
What you will see on the chart
• Markers on entry bars. Standard strategy entry markers labeled “long” and “short” on the gap bars where trades open.
• Exit markers. Standard exit markers on bars where either the gap stop or target are hit, or where a candle exit or forced flat close occurs. Exit IDs “long_gap” and “short_gap” label gap based exits.
• Reference levels. Horizontal lines for the current long TP, long SL, short TP, and short SL while a position is open and the gap engine is enabled. They update when a new trade opens and disappear when flat.
• Session background. This version does not add background shading for the session; session logic runs internally based on time.
• No on chart table. All decisions are visible through orders and exit levels. Use the Strategy Tester for performance metrics.
Inputs with guidance
Session Settings
• Trading session (sess). Session window in exchange time. Typical value uses the regular cash session for each contract, for example “0830-1530” for ES. Adjust if your broker or symbol uses different hours.
• Minutes before session end to force exit (flat_before_min). Minutes before the session end where positions are forced flat and the reference close is stored. Typical range is 15 to 120. Raising it closes trades earlier in the day; lowering it allows trades later in the session.
Gap Risk
• Enable gap based TP/SL (use_gap_risk). Master switch for the gap distance exit engine. Turning it off keeps entries and forced flat logic but removes automatic TP and SL placement.
• Use TP limit from gap (use_gap_tp). Enables gap based profit targets. Typical values are true for structured exits or false if you want to manage exits manually and only keep a stop.
• Use SL stop from gap (use_gap_sl). Enables gap based stop losses. This should normally remain true so that each trade has a defined initial risk in ticks.
• TP multiplier of gap distance (tp_mult). Multiplier applied to the gap distance for the target. Typical range is 0.5 to 2.0. Raising it places the target further away and reduces hit frequency.
• SL multiplier of gap distance (sl_mult). Multiplier applied to the gap distance for the stop. Typical range is 0.5 to 2.0. Raising it widens the stop and increases risk per trade; lowering it tightens the stop and may increase the number of small losses.
Exit Controls
• Exit with candle logic (use_candle_exit). If true, closes shorts on bullish candles and longs on bearish candles. Useful when you want to react to intraday reversal bars even if TP or SL have not been reached.
• Force flat before session end (use_forced_flat). If true, guarantees you are flat by the configured flat time and updates the reference close. Turn this off only if you understand the impact on overnight risk.
Filters
There is no separate trend or volatility filter in this version. All trades depend on the presence of a full gap bar inside the session. If you need extra filtering such as ATR, volume, or higher timeframe bias, they should be added explicitly and documented in your own fork.
Usage recipes
Intraday conservative gap fade
• Timeframe. Five minute chart on ES regular session.
• Gap risk. use_gap_risk = true, use_gap_tp = true, use_gap_sl = true.
• Multipliers. tp_mult around 0.7 to 1.0 and sl_mult around 1.0.
• Exits. use_candle_exit = false, use_forced_flat = true. Focus on the structured TP and SL around the gap.
Intraday aggressive gap fade
• Timeframe. Five minute chart.
• Gap risk. use_gap_risk = true, use_gap_tp = false, use_gap_sl = true.
• Multipliers. sl_mult around 0.7 to 1.0.
• Exits. use_candle_exit = true, use_forced_flat = true. Entries fade full gaps, stops are tight, and candle color flips flatten trades early.
Higher timeframe gap tests
• Timeframe. Fifteen minute or sixty minute charts on instruments with regular gaps.
• Gap risk. Keep use_gap_risk = true. Consider slightly higher sl_mult if gaps are structurally wider on the higher timeframe.
• Note. Expect fewer trades and be careful with sample size; multi year data is recommended.
Properties visible in this publication
• On average our risk for each position over the last 200 trades is 0.4% with a max intraday loss of 1.5% of the total equity in this case of 100k $ with 1 contract ES. For other assets, recalculations and customizations has to be applied.
• Initial capital. 100 000.
• Base currency. USD.
• Default order size method. Fixed with size 1 contract.
• Pyramiding. 0.
• Commission. Flat 2 USD per order in the Strategy Tester Properties. (2$ buying + 2$selling)
• Slippage. One tick in the Strategy Tester Properties.
• Process orders on close. ON.
Realism and responsible publication
• No performance claims are made. Past results do not guarantee future outcomes.
• Costs use a realistic flat commission and one tick of slippage per trade for ES class futures.
• Default sizing with one contract on a 100 000 reference account targets modest per trade risk. In practice, extreme slippage or gap through events can exceed this, so treat the one and a half percent risk target as a design goal, not a guarantee.
• All orders are simulated on standard candles. Shapes can move while a bar is forming and settle on bar close.
Honest limitations and failure modes
• Economic releases, thin liquidity, and limit conditions can break the assumptions behind the simple gap model and lead to slippage or skipped fills.
• Symbols with very frequent or very large gaps may require adjusted multipliers or alternative risk handling, especially in high volatility regimes.
• Very quiet periods without clean gaps will produce few or no trades. This is expected behavior, not a bug.
• Session windows follow the exchange time of the chart. Always confirm that the configured session matches the symbol.
• When both the stop and target lie inside the same bar’s range, the TradingView engine decides which is hit first based on its internal intrabar assumptions. Without bar magnifier, tie handling is approximate.
Legal
Education and research only. This strategy is not investment advice. You remain responsible for all trading decisions. Always test on historical data and in simulation with realistic costs before considering any live use.
Moving Average Band StrategyOverview
The Moving Average Band Strategy is a fully customizable breakout and trend-continuation system designed for traders who need both simplicity and control.
The strategy creates adaptive bands around a user-selected moving average and executes trades when price breaks out of these bands, with advanced risk-management settings including optional Risk:Reward targets.
This script is suitable for intraday, swing, and positional traders across all markets — equities, futures, crypto, and forex.
Key Features
✔ Six Moving Average Types
Choose the MA that best matches your trading style:
SMA
EMA
WMA
HMA
VWMA
RMA
✔ Dynamic Bands
Upper Band built from MA of highs
Lower Band built from MA of lows
Adjustable band offset (%)
Color-coded band fill indicating price position
✔ Configurable Strategy Preferences
Toggle Long and/or Short trades
Toggle Risk:Reward Take-Profit
Adjustable Risk:Reward Ratio
Default position sizing: % of equity (configurable via strategy settings)
Entry Conditions
Long Entry
A long trade triggers when:
Price crosses above the Upper Band
Long trades are enabled
No existing long position is active
Short Entry
A short trade triggers when:
Price crosses below the Lower Band
Short trades are enabled
No existing short position is active
Clear entry markers and price labels appear on the chart.
Risk Management
This strategy includes a complete set of risk-controls:
Stop-Loss (Fixed at Entry)
Long SL: Lower Band
Short SL: Upper Band
These levels remain constant for the entire trade.
Optional Risk:Reward Take-Profit
Enabled/disabled using a toggle switch.
When enabled:
Long TP = Entry + (Risk × Risk:Reward Ratio)
Short TP = Entry – (Risk × Risk:Reward Ratio)
When disabled:
Exits are handled by reverse crossover signals.
Exit Conditions
Long Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Short Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Exit markers and price labels are plotted automatically.
Visual Tools
To improve clarity:
Upper & Lower Band (blue, adjustable width)
Middle Line
Dynamic band fill (green/red/yellow)
SL & TP line plotting when in position
Entry/Exit markers
Price labels for all executed trades
These are built to help users visually follow the strategy logic.
Alerts Included
Every trading event is covered:
Long Entry
Short Entry
Long SL / TP / Cross Exit
Short SL / TP / Cross Exit
Combined Alert for webhook/automation (JSON-formatted)
Perfect for algo trading, Discord bots, or automation platforms.
Best For
This strategy performs best in:
Trending markets
Breakout environments
High-momentum instruments
Clean intraday swings
Works seamlessly on:
Stocks
Index futures
Commodities
Crypto
Forex
⚠️ Important Disclaimer
This script is for educational purposes only.
Trading involves risk. Backtest results are not indicative of future performance.
Always validate settings and use proper position sizing.
MOMO – Imbalance Trend (SIMPLE BUY/SELL)MOMO – Imbalance Trend (SIMPLE BUY/SELL)
This strategy combines trend breaks, imbalance detection, and first-tap supply/demand entries to create a clean and disciplined trading model.
It automatically highlights imbalance candles, draws fresh zones, and waits for the first retest to deliver precise BUY and SELL signals.
Performance
On optimized settings, this strategy shows an estimated 57%–70% win-rate, depending on the asset and timeframe.
Actual performance may vary, but the model is built for consistency, discipline, and improved decision-making.
How it works
Detects trend structure shifts (BOS / Break of Trend)
Identifies displacement (imbalance) candles
Creates supply and demand zones from imbalance origin
Waits for first tap only (no second chances)
Confirms direction using trend logic
Generates clean BUY/SELL arrows
Automatic SL/TP based on user settings
Features
Clean BUY/SELL markers
Auto-drawn supply & demand zones
Trend break markers
Imbalance tags
Smart first-tap confirmation
Customizable stop loss & take profit
Works on crypto, gold, forex, indices
Best on M5–H1 for day trading
Note
This strategy is designed for day traders who want clarity, structure, and zero emotional trading.
Use it with discipline — and it will serve you well.
Good luck, soldier.
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
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.
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 .
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 :
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
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
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.
Vandan V2Vandan V2 is an automated trading strategy for NQ1! (E-mini Nasdaq-100) based on short-term mean reversion with dynamic risk control. It combines volatility filters and overbought/oversold signals to capture local market imbalances.
Backtested from 2015 to 2025, it achieved a +730% total return, Profit Factor of 1.40, max drawdown of only 1.61%, and over 106,000 trades. Designed for systematic scalping or intraday arbitrage with a limit of 3 simultaneous contracts.
HEK Dinamik Fiyat Kanalı Stratejisi v1HEK Dynamic Price Channel Strategy
Concept
The HEK Dynamic Price Channel provides a channel structure that expands and contracts according to price momentum and time-based equilibrium.
Unlike fixed-band systems, it evaluates the interaction between price and its balance line through an adaptive channel width that dynamically adjusts to changing market conditions.
How It Works
When the price reacts to the midline, the channel bands automatically reposition themselves.
Touching the upper band indicates a strengthening trend, while touching the lower band signals weakening momentum.
This adaptive mechanism helps filter out false signals during sudden directional changes, enhancing overall signal quality.
Advantages
✅ Maintains trend continuity while avoiding overtrading.
✅ Automatically adapts to changing volatility conditions.
✅ Detects early signals of short- and mid-term trend reversals.
Applications
Directional confirmation in spot and futures markets.
A supporting tool in channel breakout strategies.
Identifying price consolidation and equilibrium zones.
Note
This strategy is intended for educational and research purposes only.
It should not be considered financial advice. Always consult a professional financial advisor before making investment decisions.
© HEK — Adaptive Channel Approach on Dynamic Market Structures
6 gün önce
Sürüm Notları
HEK Dynamic Price Channel Strategy
Concept
The HEK Dynamic Price Channel provides a channel structure that expands and contracts according to price momentum and time-based equilibrium.
Unlike fixed-band systems, it evaluates the interaction between price and its balance line through an adaptive channel width that dynamically adjusts to changing market conditions.
How It Works
When the price reacts to the midline, the channel bands automatically reposition themselves.
Touching the upper band indicates a strengthening trend, while touching the lower band signals weakening momentum.
This adaptive mechanism helps filter out false signals during sudden directional changes, enhancing overall signal quality.
Advantages
✅ Maintains trend continuity while avoiding overtrading.
✅ Automatically adapts to changing volatility conditions.
✅ Detects early signals of short- and mid-term trend reversals.
Applications
Directional confirmation in spot and futures markets.
A supporting tool in channel breakout strategies.
Identifying price consolidation and equilibrium zones.
Note
This strategy is intended for educational and research purposes only.
It should not be considered financial advice. Always consult a professional financial advisor before making investment decisions.
© HEK — Adaptive Channel Approach on Dynamic Market Structures
