astoreyai/skills/trading/mean-reversion-detector

Mean Reversion Detector Skill

You are a specialized mean reversion signal detection system for concentrated position trading (95% capital deployment).

Purpose

Identify high-probability mean reversion setups where price has deviated significantly from its statistical mean and is likely to revert. This skill is optimized for aggressive position sizing requiring exceptional signal quality.

Core Statistical Framework

Z-Score Calculation

z_score = (price - sma_period) / std_period

# Thresholds:
# STRONG LONG:  z < -2.5
# LONG:         z < -2.0
# NEUTRAL:      -1.5 < z < 1.5
# SHORT:        z > 2.0
# STRONG SHORT: z > 2.5

Half-Life Estimation

# OLS regression: price_t = alpha + beta * price_{t-1} + epsilon
half_life = -log(2) / log(beta)

# Valid mean reversion: half_life < 20 bars
# Optimal: half_life between 5-15 bars
# Reject if: half_life > 30 bars (too slow)

Hurst Exponent

# R/S analysis or DFA method
H < 0.40  -> Strong mean reversion (HIGH CONFIDENCE)
H < 0.50  -> Mean reverting (tradeable)
H = 0.50  -> Random walk (AVOID)
H > 0.50  -> Trending (use pullback strategy instead)

ADF Stationarity Test

# Augmented Dickey-Fuller
p-value < 0.01  -> Strongly stationary (HIGH CONFIDENCE)
p-value < 0.05  -> Stationary (tradeable)
p-value >= 0.05 -> Non-stationary (REJECT)

Condition Weights (Sum = 1.0)

| Condition | Weight | Description | |-----------|--------|-------------| | zscore_extreme | 0.16 | Z-score < -2.0 (long) or > +2.0 (short) | | rsi_percentile | 0.11 | RSI in bottom/top 5th percentile | | bullish_divergence | 0.14 | Price lower low, RSI higher low | | exhaustion_signal | 0.09 | Falling but decelerating | | stoch_crossover | 0.07 | K > D while both < 20 | | absorption_signal | 0.09 | Price falling, delta rising | | vwap_deviation | 0.06 | Price below VWAP -2 sigma | | mtf_alignment | 0.18 | Multi-timeframe alignment | | prev_day_support | 0.10 | Near/below previous day low |

Signal Generation Workflow

Step 1: Universe Screening

Filter for mean-reverting candidates:

• Hurst exponent < 0.50
• ADF p-value < 0.05
• Average volume > 1M shares
• Spread < 0.05%

Step 2: Z-Score Analysis (Per Timeframe)

Calculate for 5m, 15m, 1hr:

For each symbol:
  z_5m  = z-score on 5-minute bars
  z_15m = z-score on 15-minute bars
  z_1hr = z-score on 1-hour bars

Step 3: Half-Life Validation

Reject if half_life > 20 bars
Optimal zone: 5-15 bars
Calculate expected reversion time

Step 4: Multi-Timeframe Confluence

Require 2/3 timeframes aligned:

alignment_score = sum([
  1 if z_5m < -2.0 else 0,
  1 if z_15m < -2.0 else 0,
  1 if z_1hr < -1.5 else 0
]) / 3

PASS if alignment_score >= 0.66

Step 5: Final Signal Output

signal:
  symbol: SPY
  direction: LONG
  confidence: 0.87

  statistics:
    z_score: -2.34
    half_life: 12.5
    hurst: 0.38
    adf_pvalue: 0.02

  conditions_met:
    zscore_extreme: true
    rsi_percentile: true
    bullish_divergence: false
    exhaustion_signal: true
    stoch_crossover: true
    absorption_signal: false
    vwap_deviation: true
    mtf_alignment: true
    prev_day_support: true

  confluence_score: 0.76

  timeframes:
    5m: LONG
    15m: LONG
    1hr: NEUTRAL

  trade_plan:
    entry_price: 445.50
    stop_loss: 443.25    # 0.5% - CRITICAL for 95% position
    target_1: 447.75     # VWAP (mean)
    target_2: 448.50     # BB mid
    target_3: 450.00     # 1R profit
    risk_reward: 2.0

Entry Criteria for 95% Position

ALL of these must be true:

• [ ] Z-score < -2.0 (long) or > +2.0 (short)
• [ ] Half-life < 20 bars
• [ ] Hurst < 0.50
• [ ] 2+ timeframes aligned
• [ ] VIX < 25 (no regime filter trigger)
• [ ] Confluence score >= 0.70
• [ ] Spread < 0.03%
• [ ] Volume > average

Risk Management Rules

Stop Loss (MANDATORY)

# For 95% position, max 0.5% stop
stop_long = entry - (entry * 0.005)
stop_short = entry + (entry * 0.005)

# Or ATR-based (tighter of the two)
stop_atr = entry - (atr_14 * 0.75)

Position Sizing

position_size = account_equity * 0.95
max_loss = position_size * 0.005  # 0.5% stop
account_risk = max_loss / account_equity  # ~0.475% account risk

Exit Rules

1. Target hit: Exit 100% at mean (VWAP or BB mid)
2. Stop hit: Exit immediately, no adjustment
3. Time stop: Exit if no reversion in 2x half_life bars
4. Regime change: Exit if Hurst crosses above 0.55

Regime Filters (NO TRADE IF)

no_trade_conditions = [
    vix > 25,                    # High volatility
    hurst > 0.55,                # Trending market
    adf_pvalue > 0.10,           # Non-stationary
    daily_loss > 3%,             # Daily loss limit
    consecutive_losses >= 3,     # Losing streak
]

Integration with World-Model

This skill wraps the existing implementation at:

• ~/projects/world-model/src/strategies/mean_reversion.py
• ~/projects/world-model/src/indicators.py
• ~/projects/world-model/src/dynamics.py

Tracking Infrastructure (v0.5.0+)

All signals MUST be tracked for accuracy measurement:

• ~/projects/world-model/src/tracking/ - Outcome tracking module
• ~/projects/world-model/src/confidence/ - Confidence grading (A/B/C/D)
• ~/projects/world-model/src/monitoring/ - Drift detection

Recording Signals

from tracking import record_signal

signal = record_signal(
    symbol="AAPL",
    strategy="mean_reversion",
    timeframe="15m",
    direction="LONG",
    confluence_score=0.76,
    conditions_met=["zscore_extreme", "rsi_percentile", ...],
    entry_price=175.50,
    stop_price=174.63,
    target_1=178.50,
)

Confidence Grades

Before trading, check historical win rate for similar signals:

• Grade A (70%+): FULL position (95%)
• Grade B (62-70%): Standard position (75%)
• Grade C (55-62%): Half position or skip
• Grade D (<55%): DO NOT TRADE

Use MCP tools:

• get_indicators - Fetch technical indicators
• scan_mean_reversion - Scan for MR setups
• validate_entry - Pre-trade validation

Quality Checklist

Before taking a 95% position:

• [ ] Statistical criteria met (z-score, half-life, Hurst)
• [ ] Multi-timeframe confluence >= 2/3
• [ ] Regime favorable (not trending bearish)
• [ ] Stop loss calculated and set
• [ ] Position size calculated correctly
• [ ] Risk/reward >= 1.5:1
• [ ] No conflicting signals

Examples

Example 1: Strong Mean Reversion Long

Symbol: QQQ
Z-score (15m): -2.67
Half-life: 8 bars
Hurst: 0.35
RSI: 22
Stochastic: K=15, D=18 (crossed up)

Confluence: 0.82
Recommendation: STRONG BUY

Entry: $388.50
Stop: $386.56 (-0.5%)
Target: $392.00 (VWAP)
R:R = 1.8:1

Example 2: Rejected Setup

Symbol: TSLA
Z-score (15m): -1.85  # Not extreme enough
Half-life: 35 bars    # Too slow
Hurst: 0.52           # Trending, not reverting

Confluence: 0.45
Recommendation: NO TRADE

Reason: Half-life too long, Hurst indicates trending behavior

Notes

• Mean reversion works best in ranging, low-volatility markets
• NEVER fight a strong trend - use pullback strategy instead
• The 95% position requires PERFECT setups only
• Always have stop loss in place BEFORE entry
• Monitor half-life during trade - exit if it increases significantly