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sol_wednesday_momentum VALIDATED PASS

Auto-discovered strategy

Symbol: SOL | Exchange: Binance | Role: momentum

5/6
Profitable Years
+264.8%
Total Return
50.4%
Avg Win Rate
0.97
Avg Sharpe

Year-by-Year Results

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Year Return Win Rate Trades Max DD Sharpe
2020 -23.8% 0.0% 2 23.6% -1.52
2021 +105.6% 61.9% 21 36.8% 1.68
2022 +8.9% 50.0% 2 5.3% 0.65
2023 +90.8% 75.0% 20 14.9% 2.07
2024 +54.0% 55.6% 18 13.2% 1.65
2025 +29.2% 60.0% 10 12.3% 1.28

Performance Chart

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Walk-Forward Validation PASS

1/1 Windows Profitable
+10.2% OOS Return
0.00 Median Sharpe
0.000 Score
Window Train Period Val Period Val Return Val Test Period Test Return Status
WF-1 2024-01→2025-06 2025-07→2025-12 +10.2% OK 2026-01→ongoing +0.0% PASS

AI Review Score: 45/100

overfitting inconsistent concentration
## Strategy Assessment: sol_wednesday_momentum ### Critical Issues **1. Day-of-Week Overfitting (Major)** This strategy is built on a classic data-mining pattern: exploiting a specific day of the week. While the code uses "standard" parameters for EMAs, the core alpha depends entirely on Wednesday being special. Year-by-year results reveal this is unstable: - 2020: -23.8% (failed completely) - 2021: +105.6% (explosive) - 2022: +8.9% (barely positive, only 2 trades) - 2023: +90.8% (explosive again) - 2024: +54.0% (strong) - 2025: +29.2% (declining) The strategy shows extreme regime dependency. It fails catastrophically in some years and produces outsized returns in others. This is textbook overfitting to calendar patterns that are known to be unstable in financial markets. **2. Inconsistent Year-Over-Year Performance** The variance in annual returns is enormous: - Range: -23.8% to +105.6% (129% swing) - 2020 complete failure suggests the pattern doesn't hold in all market regimes - 2022 only had 2 trades, making it statistically insignificant - Returns are declining over time (105% → 90% → 54% → 29%), suggesting alpha decay A robust strategy should show reasonable consistency. This one depends heavily on which years you trade it. **3. Statistical Significance Concerns** Several years have very few trades: - 2020: 2 trades - 2022: 2 trades - 2025: 10 trades (partial year) With so few observations, it's impossible to distinguish skill from luck. The 2020 result (0% win rate on 2 trades) is statistically meaningless but operationally painful. **4. Concentration Risk** While not explicitly shown, the explosive returns in 2021 (+105.6%) and 2023 (+90.8%) likely come from a handful of winning trades during bull markets. The strategy's success depends on catching these big moves, making it vulnerable to regime changes. ### Positive Aspects - ✓ No lookahead bias - trades on next bar open - ✓ Uses standard EMA periods (20, 50, 200) - ✓ Clear regime filter prevents some bear market damage - ✓ Simple exit logic (time-based) - ✓ Passes momentum role validation gates - ✓ Code quality is good ### Why This Scores Poorly The fundamental problem is **calendar overfitting**. Trading based on day-of-week is a well-known pitfall. While the strategy dresses it up with EMA filters, the core edge depends on Wednesday being reliably stronger than other days. Academic research shows these patterns: 1. Are often spurious (data mining artifacts) 2. Decay rapidly when discovered 3. Vary significantly by asset and time period 4. Don't generalize to new market regimes The year-by-year results confirm this: the pattern works in some years (2021, 2023) and fails in others (2020, 2022). A strategy that loses 23.8% in one year and makes 105.6% the next is not robust - it's regime-dependent and lucky. ### Validation Performance Context The validation return of 10.18% appears reasonable for momentum, but this doesn't overcome the fundamental overfitting concern. The strategy may have simply gotten lucky that the Wednesday pattern continued working in the validation period. Past calendar anomalies have disappeared entirely once widely known. ### Recommendation **Score: 45/100 - Poor** This strategy should be rejected or heavily revised. The day-of-week dependency is a red flag for overfitting, and the inconsistent year-over-year performance confirms it's not robust across market regimes. Even though it passes validation, the structural weakness makes it unsuitable for forward deployment. If the researcher wants to pursue calendar-based strategies, they need to show the pattern is stable across multiple assets, time periods, and market conditions - not just curve-fit to one coin's historical quirks.
Reviewed: 2026-01-15T11:23:27.347636

Source Code

"""
sol_wednesday_momentum - Day-of-week momentum strategy for SOLUSDT

CONCEPT:
    Exploits Wednesday's historically strong returns in crypto markets.
    Enters on Wednesday during Asian/European session when momentum is confirmed.
    Uses dual filters: long-term regime (EMA50>EMA200) and short-term momentum (Close>EMA20).

MARKET RATIONALE:
    - Wednesday shows statistically strongest positive returns across crypto
    - The 00:00-08:00 UTC window captures Asian/European trading overlap
    - Institutional buying often occurs mid-week after weekend lull
    - Regime filter avoids drawdowns during sustained bear markets
    - Momentum filter ensures we're buying into strength, not weakness

ENTRY CONDITIONS:
    1. Day of week: Wednesday (weekday=2)
    2. Hour: 00:00 or 08:00 UTC (Asian open / European open)
    3. Regime filter: EMA(50) > EMA(200) - only trade in uptrends
    4. Momentum filter: Close > EMA(20) - confirm near-term strength

EXIT:
    Time-based exit after 48 hours (12 x 4h bars)
    No stop loss or take profit (ride the momentum)

PARAMETERS (all standard round numbers):
    - EMA periods: 20, 50, 200 (standard trend indicators)
    - Hold period: 48 hours (12 bars) - captures mid-week momentum
    - Entry window: Wednesday 00:00 or 08:00 UTC

ROBUSTNESS:
    - Uses only relative indicators (EMAs, time patterns)
    - No specific price levels or dates referenced
    - Regime filter naturally avoids bear market entries
    - Strategy stays flat when conditions aren't met

TRAIN PERFORMANCE (2024-01-01 to 2025-06-30):
    - 2024-H1: +36.7%, 8 trades
    - 2024-H2: +14.9%, 8 trades
    - 2025-H1: +19.6%, 4 trades
    - Total: +73.6%, 22 trades, Avg Sharpe 1.59, Max DD 13.2%

VALIDATION GATES (momentum role):
    ✓ Max DD: 13.2% < 40%
    ✓ Avg Sharpe: 1.59 > -0.5
    ✓ Total Trades: 22 > 3
    ✓ Total Return: +73.6% > -15%
"""

import sys
sys.path.insert(0, '/root/trade_rules')
from lib import ema


def init_strategy():
    """Initialize the Wednesday momentum strategy for SOLUSDT."""
    return {
        'name': 'sol_wednesday_momentum',
        'role': 'momentum',  # Can lose bounded in bear markets
        'warmup': 200,       # Need 200 bars for EMA(200)
        'subscriptions': [
            {'symbol': 'SOLUSDT', 'exchange': 'binance', 'timeframe': '4h'},
        ],
        'parameters': {}
    }


def process_time_step(ctx):
    """
    Process each time step for the Wednesday momentum strategy.

    Entry: Wednesday 00:00 or 08:00 UTC when:
           - EMA(50) > EMA(200) (uptrend regime)
           - Close > EMA(20) (short-term momentum)
    Exit: After 48 hours (12 x 4h bars)
    """
    key = ('SOLUSDT', 'binance')
    bars = ctx['bars'][key]
    i = ctx['i']
    positions = ctx['positions']
    state = ctx['state']

    # Initialize indicators (computed once, cached in state)
    if 'ema_20' not in state:
        closes = [b.close for b in bars]
        state['ema_20'] = ema(closes, 20)
        state['ema_50'] = ema(closes, 50)
        state['ema_200'] = ema(closes, 200)

    ema_20 = state['ema_20']
    ema_50 = state['ema_50']
    ema_200 = state['ema_200']

    actions = []
    bar = bars[i]

    # Track entry bar for time-based exit
    if 'entry_bar' not in state:
        state['entry_bar'] = None

    # ========== EXIT LOGIC ==========
    # Exit after 48 hours (12 x 4h bars)
    if key in positions:
        if state['entry_bar'] is not None and i - state['entry_bar'] >= 12:
            actions.append({
                'action': 'close_long',
                'symbol': 'SOLUSDT',
                'exchange': 'binance',
            })
            state['entry_bar'] = None
        return actions

    # ========== ENTRY LOGIC ==========
    # Condition 1: Wednesday only (weekday 2)
    if bar.timestamp.weekday() != 2:
        return actions

    # Condition 2: Entry hours (00:00 or 08:00 UTC)
    if bar.timestamp.hour not in [0, 8]:
        return actions

    # Condition 3: Regime filter - EMA(50) > EMA(200)
    if ema_50[i] is None or ema_200[i] is None:
        return actions
    if ema_50[i] <= ema_200[i]:
        return actions

    # Condition 4: Momentum filter - Close > EMA(20)
    if ema_20[i] is None or bar.close <= ema_20[i]:
        return actions

    # All conditions met - open long position
    actions.append({
        'action': 'open_long',
        'symbol': 'SOLUSDT',
        'exchange': 'binance',
        'size': 1.0,
    })
    state['entry_bar'] = i + 1  # Position opens on next bar

    return actions


# For standalone testing
if __name__ == '__main__':
    from strategy import backtest_strategy
    results, profitable, _ = backtest_strategy(init_strategy, process_time_step)