Symbol Selection Statistical

Symbol Selection Statistical Analysis - Research Notes

Experiment Overview

Context

Simple Sharpe/momentum/volatility screening is insufficient for selecting symbols compatible with advanced trading systems that use:

• Predator-prey Markov regime detection (needs clear regime separation)
• GARCH-based position sizing (needs good volatility model fit)
• Mean-reversion strategies (needs appropriate half-life for trading timeframe)

Literature review identified key statistical metrics that predict trading compatibility better than traditional metrics.

Verified Workflow

1. Hurst Exponent Analysis

Determines mean-reverting (H<0.5) vs trending (H>0.5) behavior.

def calculate_hurst_dfa(prices, max_lag=100):
    """Detrended Fluctuation Analysis for Hurst exponent."""
    returns = prices.pct_change().dropna()
    y = (returns - returns.mean()).cumsum()

    scales = np.unique(np.logspace(1, np.log10(max_lag), 20).astype(int))
    fluctuations = []

    for scale in scales:
        n_segments = len(y) // scale
        f_list = []
        for i in range(n_segments):
            segment = y.iloc[i * scale:(i + 1) * scale].values
            # Detrend with linear fit
            x = np.arange(len(segment))
            coeffs = np.polyfit(x, segment, 1)
            trend = np.polyval(coeffs, x)
            detrended = segment - trend
            f = np.sqrt(np.mean(detrended ** 2))
            f_list.append(f)
        if f_list:
            fluctuations.append((scale, np.mean(f_list)))

    # Linear regression: log(F) = H * log(n) + c
    log_scales = np.log([x[0] for x in fluctuations])
    log_fluct = np.log([x[1] for x in fluctuations])
    hurst = np.polyfit(log_scales, log_fluct, 1)[0]
    return np.clip(hurst, 0, 1)

Key insight: Use BOTH short-term (window=50) and long-term (window=200) Hurst. Ideal symbols show:

• Short-term H < 0.45 (mean-reverting for VWAP reversion)
• Long-term H > 0.55 (trending for momentum continuation)

2. Half-Life of Mean Reversion (Ornstein-Uhlenbeck)

def calculate_half_life(prices):
    """Half-life using OU process: dP = theta(mu - P)dt"""
    price_lag = prices.shift(1).dropna()
    price_diff = prices.diff().dropna()

    # Align and regress: dP = alpha + beta*P(t-1)
    X = np.column_stack([np.ones(len(price_lag)), price_lag.values])
    y = price_diff.values[:len(price_lag)]
    coeffs = np.linalg.lstsq(X, y, rcond=None)[0]
    beta = coeffs[1]

    if beta >= 0:  # Not mean-reverting
        return float('inf')

    # half_life = -ln(2) / ln(1 + beta)
    half_life = -np.log(2) / np.log(1 + beta)
    return half_life

Target half-life for hourly trading: 4-24 hours

• < 1 hour: Too fast, transaction costs dominate

• 72 hours: Capital tied up too long

3. GARCH Fit Quality Scoring

from arch import arch_model

def score_garch_fit(returns):
    """Score GARCH(1,1) fit quality."""
    model = arch_model(returns * 100, vol='Garch', p=1, q=1, mean='Zero')
    result = model.fit(disp='off')

    alpha = result.params.get('alpha[1]', 0)
    beta = result.params.get('beta[1]', 0)
    persistence = alpha + beta

    # Check stationarity
    is_stationary = persistence < 0.9999

    # Score components
    score = 0.0
    if is_stationary:
        score += 0.20
    if 0.01 < alpha < 0.30 and 0.50 < beta < 0.95:
        score += 0.20
    # Add Ljung-Box and ARCH-LM test scores...

    return score, persistence

Good GARCH fit requires:

• Persistence (alpha + beta) between 0.90-0.97
• No remaining autocorrelation in residuals (Ljung-Box p > 0.05)
• No remaining ARCH effects (ARCH-LM p > 0.05)

4. Regime Persistence (HMM)

from hmmlearn import hmm

def calculate_regime_persistence(returns, n_regimes=2):
    """Measure regime duration using Hidden Markov Model."""
    model = hmm.GaussianHMM(n_components=n_regimes, covariance_type="full")
    model.fit(returns.values.reshape(-1, 1))

    states = model.predict(returns.values.reshape(-1, 1))
    transmat = model.transmat_

    # Calculate average regime duration
    durations = []
    current_duration = 1
    for i in range(1, len(states)):
        if states[i] == states[i-1]:
            current_duration += 1
        else:
            durations.append(current_duration)
            current_duration = 1

    return np.mean(durations), transmat

Target regime duration: 5-20 bars (hours)

• Enough time to identify and trade the regime
• Self-transition probability 0.85-0.95 (sticky but not too persistent)

5. Weighted Composite Scoring

weights = {
    'hurst_short': 0.20,   # Mean reversion at short timeframes
    'hurst_long': 0.15,    # Trending at long timeframes
    'half_life': 0.15,     # Appropriate reversion speed
    'garch': 0.15,         # Volatility modelability
    'regime': 0.10,        # Regime persistence
    'autocorr': 0.10,      # Return predictability
    'sharpe': 0.10,        # Risk-adjusted returns
    'drawdown': 0.05,      # Drawdown risk
}  # Sum = 1.0

Failed Attempts (Critical)

Final Parameters

# Selection Configuration
hurst_short_target: [0.30, 0.50]  # Mean-reverting
hurst_long_target: [0.50, 0.70]   # Trending
half_life_target_hours: [4.0, 24.0]
min_garch_score: 0.5
regime_duration_target: [5, 20]  # bars
max_correlation: 0.60
min_data_points: 500
min_price: 5.0
min_avg_volume: 500000

# Scoring Weights (must sum to 1.0)
hurst_short_weight: 0.20
hurst_long_weight: 0.15
half_life_weight: 0.15
garch_weight: 0.15
regime_weight: 0.10
autocorr_weight: 0.10
sharpe_weight: 0.10
drawdown_weight: 0.05

Key Insights

• Dual-behavior is key: The best trading symbols show mean-reversion at short timeframes AND momentum at longer timeframes. This supports both VWAP reversion and trend-following.

• Half-life must match trading frequency: For hourly trading, 4-24 hour half-life is ideal. Too fast = noise, too slow = capital inefficiency.

• GARCH fit predicts position sizing accuracy: Poor GARCH fit means unreliable volatility forecasts, leading to incorrect position sizes.

• Regime persistence matters for Markov models: Regimes should last 5-20 bars - long enough to identify but short enough to see transitions.

• DFA > R/S for Hurst: Detrended Fluctuation Analysis is more robust to non-stationarity than Rescaled Range method.

• Fallback implementations are essential: Not all environments have hmmlearn/arch packages. Implement EWMA-based fallbacks.

• Correlation filtering prevents over-concentration: Even high-scoring symbols should be filtered to maintain diversification.

References

• Hurst, H.E. (1951). "Long-term storage capacity of reservoirs"
• Peng, C.K. et al. (1994). "Mosaic organization of DNA nucleotides" - DFA method
• Bollerslev, T. (1986). "Generalized Autoregressive Conditional Heteroskedasticity" - GARCH
• Rabiner, L.R. (1989). "A tutorial on hidden Markov models" - HMM regime detection
• Avellaneda & Lee (2010). "Statistical Arbitrage in the US Equities Market" - Half-life trading