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.