OPEN-SOURCE SCRIPT
QFL StDev Mean Reversal σ-Based Levels v.1.0
🔹 Theory Behind the QFL σ-Based Mean Reversal Strategy
1. QFL Core Concept (Base + Bounce)
The QFL (Quickfingers Luc) method is a mean-reversion trading strategy built around the idea of “bases”:
A base is a strong support level, typically formed after a sharp move down, where buyers defended price.
When price drops below the base, it is considered an “overreaction” or “fake breakdown.”
The logic: after such a drop, price often snaps back upward (mean reversion).
In short:
Identify strong bases with volume confirmation.
Wait for a breakdown below the base (oversold condition).
Enter a long trade betting on a bounce back toward the mean.
2. σ-Based Levels (Standard Deviation Bands)
This version enhances QFL using statistics.
A moving average (SMA) of price defines the mean.
Standard Deviation (σ) measures volatility.
Multiple σ-levels define dynamic support/resistance:
Upper Band (Mean + 3σ) → Overbought zone.
Entry Band (Mean – 2σ) → Oversold trigger for entries.
TP Level (Mean + 3σ) → Take-profit target.
SL Level (Mean – 3σ) → Stop-loss safeguard.
This makes the strategy adaptive to volatility instead of relying on static levels.
3. Volume Confirmation
Not every dip below a base is worth trading. To filter noise:
The script requires pivot low detection (local support formation).
That pivot must coincide with volume spike confirmation:
Volume > SMA(Volume) × Factor.
This ensures breakdowns are meaningful, not just random dips.
4. Mean Reversion Logic
Entry triggers when:
A valid base has been established.
Price drops below the Entry Band (–2σ).
No active position is open.
Exit logic:
Take Profit → when price reaches the upper σ-based TP level.
Stop Loss → when price breaches the lower σ-based SL level.
This balances risk/reward using statistically significant levels.
🔹 Usage in TradingView
1. Adding to Chart
Copy and paste the script into TradingView Pine Editor.
Click Add to Chart → It overlays σ-bands, base levels, entry signals, and exit zones.
2. Inputs & Tuning Parameters
Volume Factor (default: 2.0)
Controls how strong a volume spike must be to confirm a base.
Higher = stricter filtering (fewer but stronger signals).
StDev Length (default: 20)
Window size for SMA + σ.
Shorter = more reactive (good for scalping).
Longer = smoother, more stable (good for swing trading).
Base Bounce Sigma (default: 3.0)
Defines how much price must bounce above pivot low to validate it as a base.
Drop Below Sigma (default: 2.0)
Defines how far below the mean price must drop to trigger entry (oversold).
Take Profit Sigma (default: 3.0)
Exit level above mean.
Higher = greedier (larger TP, fewer hits).
Lower = safer (quicker exits).
Stop Loss Sigma (default: 3.0)
Safety net if price continues falling instead of reverting.
Adjust based on asset volatility.
3. Chart Visuals
Blue line = Detected base.
Purple band = Entry zone (–2σ).
Green line = Take-profit target (+3σ).
Maroon line = Stop-loss boundary (–3σ).
Background purple highlight = Mean reversion signal zone.
Gray fill = Risk/reward channel from entry to TP.
4. Alerts
Entry Alert → When entry condition triggers.
Exit Alert → When trade closes (TP/SL).
Useful for automation with brokers via webhooks.
5. Best Markets & Timeframes
Works well on crypto, forex, and volatile equities.
Effective on 5m–1h charts for intraday trading.
On higher timeframes (4h–1D), it identifies swing trade reversals.
🔹 Strengths & Weaknesses
✅ Strengths
Combines QFL base logic with statistical volatility filtering.
Dynamic (σ-based) → adapts to changing volatility.
Filters weak setups with volume confirmation.
Provides automated TP & SL for risk management.
⚠️ Weaknesses
Mean reversion assumes price will bounce → vulnerable in strong trends.
Works better in ranging / sideways markets than trending ones.
Parameters must be optimized for each asset & timeframe.
Volume confirmation may be less reliable in markets with fake volume (e.g., some altcoins).
✅ In summary:
The QFL σ Mean Reversal Strategy is a volatility-adaptive, volume-filtered mean reversion system. It detects bases with pivot + volume logic, waits for an oversold drop below σ-bands, and enters trades betting on a bounce back toward the mean. TP and SL are defined statistically, making it more robust than traditional fixed-level QFL implementations.
1. QFL Core Concept (Base + Bounce)
The QFL (Quickfingers Luc) method is a mean-reversion trading strategy built around the idea of “bases”:
A base is a strong support level, typically formed after a sharp move down, where buyers defended price.
When price drops below the base, it is considered an “overreaction” or “fake breakdown.”
The logic: after such a drop, price often snaps back upward (mean reversion).
In short:
Identify strong bases with volume confirmation.
Wait for a breakdown below the base (oversold condition).
Enter a long trade betting on a bounce back toward the mean.
2. σ-Based Levels (Standard Deviation Bands)
This version enhances QFL using statistics.
A moving average (SMA) of price defines the mean.
Standard Deviation (σ) measures volatility.
Multiple σ-levels define dynamic support/resistance:
Upper Band (Mean + 3σ) → Overbought zone.
Entry Band (Mean – 2σ) → Oversold trigger for entries.
TP Level (Mean + 3σ) → Take-profit target.
SL Level (Mean – 3σ) → Stop-loss safeguard.
This makes the strategy adaptive to volatility instead of relying on static levels.
3. Volume Confirmation
Not every dip below a base is worth trading. To filter noise:
The script requires pivot low detection (local support formation).
That pivot must coincide with volume spike confirmation:
Volume > SMA(Volume) × Factor.
This ensures breakdowns are meaningful, not just random dips.
4. Mean Reversion Logic
Entry triggers when:
A valid base has been established.
Price drops below the Entry Band (–2σ).
No active position is open.
Exit logic:
Take Profit → when price reaches the upper σ-based TP level.
Stop Loss → when price breaches the lower σ-based SL level.
This balances risk/reward using statistically significant levels.
🔹 Usage in TradingView
1. Adding to Chart
Copy and paste the script into TradingView Pine Editor.
Click Add to Chart → It overlays σ-bands, base levels, entry signals, and exit zones.
2. Inputs & Tuning Parameters
Volume Factor (default: 2.0)
Controls how strong a volume spike must be to confirm a base.
Higher = stricter filtering (fewer but stronger signals).
StDev Length (default: 20)
Window size for SMA + σ.
Shorter = more reactive (good for scalping).
Longer = smoother, more stable (good for swing trading).
Base Bounce Sigma (default: 3.0)
Defines how much price must bounce above pivot low to validate it as a base.
Drop Below Sigma (default: 2.0)
Defines how far below the mean price must drop to trigger entry (oversold).
Take Profit Sigma (default: 3.0)
Exit level above mean.
Higher = greedier (larger TP, fewer hits).
Lower = safer (quicker exits).
Stop Loss Sigma (default: 3.0)
Safety net if price continues falling instead of reverting.
Adjust based on asset volatility.
3. Chart Visuals
Blue line = Detected base.
Purple band = Entry zone (–2σ).
Green line = Take-profit target (+3σ).
Maroon line = Stop-loss boundary (–3σ).
Background purple highlight = Mean reversion signal zone.
Gray fill = Risk/reward channel from entry to TP.
4. Alerts
Entry Alert → When entry condition triggers.
Exit Alert → When trade closes (TP/SL).
Useful for automation with brokers via webhooks.
5. Best Markets & Timeframes
Works well on crypto, forex, and volatile equities.
Effective on 5m–1h charts for intraday trading.
On higher timeframes (4h–1D), it identifies swing trade reversals.
🔹 Strengths & Weaknesses
✅ Strengths
Combines QFL base logic with statistical volatility filtering.
Dynamic (σ-based) → adapts to changing volatility.
Filters weak setups with volume confirmation.
Provides automated TP & SL for risk management.
⚠️ Weaknesses
Mean reversion assumes price will bounce → vulnerable in strong trends.
Works better in ranging / sideways markets than trending ones.
Parameters must be optimized for each asset & timeframe.
Volume confirmation may be less reliable in markets with fake volume (e.g., some altcoins).
✅ In summary:
The QFL σ Mean Reversal Strategy is a volatility-adaptive, volume-filtered mean reversion system. It detects bases with pivot + volume logic, waits for an oversold drop below σ-bands, and enters trades betting on a bounce back toward the mean. TP and SL are defined statistically, making it more robust than traditional fixed-level QFL implementations.
סקריפט קוד פתוח
ברוח TradingView אמיתית, היוצר של הסקריפט הזה הפך אותו לקוד פתוח, כך שסוחרים יכולים לבדוק ולאמת את הפונקציונליות שלו. כל הכבוד למחבר! למרות שאתה יכול להשתמש בו בחינם, זכור שפרסום מחדש של הקוד כפוף לכללי הבית שלנו.
כתב ויתור
המידע והפרסומים אינם אמורים להיות, ואינם מהווים, עצות פיננסיות, השקעות, מסחר או סוגים אחרים של עצות או המלצות שסופקו או מאושרים על ידי TradingView. קרא עוד בתנאים וההגבלות.
סקריפט קוד פתוח
ברוח TradingView אמיתית, היוצר של הסקריפט הזה הפך אותו לקוד פתוח, כך שסוחרים יכולים לבדוק ולאמת את הפונקציונליות שלו. כל הכבוד למחבר! למרות שאתה יכול להשתמש בו בחינם, זכור שפרסום מחדש של הקוד כפוף לכללי הבית שלנו.
כתב ויתור
המידע והפרסומים אינם אמורים להיות, ואינם מהווים, עצות פיננסיות, השקעות, מסחר או סוגים אחרים של עצות או המלצות שסופקו או מאושרים על ידי TradingView. קרא עוד בתנאים וההגבלות.