Add check_symbols.py for ETH perpetuals filtering and enhance backtester with size handling
- Introduced `check_symbols.py` to load and filter ETH perpetual markets from the OKX exchange using CCXT. - Updated the backtester to normalize signals to a 5-tuple format, incorporating size management for trades. - Enhanced portfolio functions to support variable size and leverage adjustments based on initial capital. - Added a new method in `CryptoQuantClient` for chunked historical data fetching to avoid API limits. - Improved market symbol normalization in `market.py` to handle different formats. - Updated regime strategy parameters based on recent research findings for optimal performance.
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@@ -28,21 +28,39 @@ class RegimeReversionStrategy(BaseStrategy):
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- Eliminates look-ahead bias for realistic backtest results
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"""
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# Optimal parameters from walk-forward research (2025-10 to 2025-12)
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# Research: research/horizon_optimization_results.csv
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OPTIMAL_HORIZON = 102 # 4.25 days - best Net PnL (+232%)
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OPTIMAL_Z_WINDOW = 24 # 24h rolling window for spread Z-score
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OPTIMAL_TRAIN_RATIO = 0.7 # 70% train / 30% test split
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OPTIMAL_PROFIT_TARGET = 0.005 # 0.5% profit threshold for target definition
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OPTIMAL_Z_ENTRY = 1.0 # Enter when |Z| > 1.0
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def __init__(self,
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model_path: str = "data/regime_model.pkl",
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horizon: int = 96, # 4 Days based on research
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z_window: int = 24,
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stop_loss: float = 0.06, # 6% to survive 2% avg MAE
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take_profit: float = 0.05, # Swing target
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train_ratio: float = 0.7 # Walk-forward: train on first 70%
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horizon: int = OPTIMAL_HORIZON,
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z_window: int = OPTIMAL_Z_WINDOW,
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z_entry_threshold: float = OPTIMAL_Z_ENTRY,
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profit_target: float = OPTIMAL_PROFIT_TARGET,
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stop_loss: float = 0.06, # 6% - accommodates 1.95% avg MAE
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take_profit: float = 0.05, # 5% swing target
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train_ratio: float = OPTIMAL_TRAIN_RATIO,
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trend_window: int = 0, # Disable SMA filter
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use_funding_filter: bool = True, # Enable Funding Rate filter
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funding_threshold: float = 0.005 # Tightened to 0.005%
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):
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super().__init__()
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self.model_path = model_path
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self.horizon = horizon
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self.z_window = z_window
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self.z_entry_threshold = z_entry_threshold
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self.profit_target = profit_target
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self.stop_loss = stop_loss
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self.take_profit = take_profit
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self.train_ratio = train_ratio
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self.trend_window = trend_window
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self.use_funding_filter = use_funding_filter
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self.funding_threshold = funding_threshold
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# Default Strategy Config
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self.default_market_type = MarketType.PERPETUAL
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@@ -68,7 +86,8 @@ class RegimeReversionStrategy(BaseStrategy):
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try:
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# Load BTC data (Context) - Must match the timeframe of the backtest
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# Research was done on 1h candles, so strategy should be run on 1h
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df_btc = self.dm.load_data("okx", "BTC-USDT", "1h", MarketType.SPOT)
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# Use PERPETUAL data to match the trading instrument (ETH Perp)
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df_btc = self.dm.load_data("okx", "BTC-USDT", "1h", MarketType.PERPETUAL)
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# Align BTC to ETH (close)
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df_btc = df_btc.reindex(close.index, method='ffill')
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@@ -141,11 +160,76 @@ class RegimeReversionStrategy(BaseStrategy):
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probs = self.model.predict_proba(X_test)[:, 1]
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# 8. Generate Entry Signals (TEST period only)
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# If Z > 1 (Spread High, ETH Expensive) -> Short ETH
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# If Z < -1 (Spread Low, ETH Cheap) -> Long ETH
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# If Z > threshold (Spread High, ETH Expensive) -> Short ETH
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# If Z < -threshold (Spread Low, ETH Cheap) -> Long ETH
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z_thresh = self.z_entry_threshold
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short_signal_test = (probs > 0.5) & (test_features['z_score'].values > 1.0)
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long_signal_test = (probs > 0.5) & (test_features['z_score'].values < -1.0)
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short_signal_test = (probs > 0.5) & (test_features['z_score'].values > z_thresh)
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long_signal_test = (probs > 0.5) & (test_features['z_score'].values < -z_thresh)
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# 8b. Apply Trend Filter (Macro Regime)
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# Rule: Long only if BTC > SMA (Bull), Short only if BTC < SMA (Bear)
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if self.trend_window > 0:
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# Calculate SMA on full BTC history first
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btc_sma = btc_close.rolling(window=self.trend_window).mean()
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# Align with test period
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test_btc_close = btc_close.reindex(test_features.index)
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test_btc_sma = btc_sma.reindex(test_features.index)
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# Define Regimes
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is_bull = (test_btc_close > test_btc_sma).values
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is_bear = (test_btc_close < test_btc_sma).values
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# Apply Filter
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long_signal_test = long_signal_test & is_bull
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short_signal_test = short_signal_test & is_bear
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# 8c. Apply Funding Rate Filter
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# Rule: If Funding > Threshold (Greedy) -> No Longs.
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# If Funding < -Threshold (Fearful) -> No Shorts.
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if self.use_funding_filter and 'btc_funding' in test_features.columns:
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funding = test_features['btc_funding'].values
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thresh = self.funding_threshold
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# Greedy Market (High Positive Funding) -> Risk of Long Squeeze -> Block Longs
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# (Or implies trend is up? Actually for Mean Reversion, high funding often marks tops)
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# We block Longs because we don't want to buy into an overheated market?
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# Actually, "Greedy" means Longs are paying Shorts.
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# If we Long, we pay funding.
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# If we Short, we receive funding.
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# So High Funding = Good for Shorts (receive yield + reversion).
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# Bad for Longs (pay yield + likely top).
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is_overheated = funding > thresh
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is_oversold = funding < -thresh
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# Block Longs if Overheated
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long_signal_test = long_signal_test & (~is_overheated)
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# Block Shorts if Oversold (Negative Funding) -> Risk of Short Squeeze
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short_signal_test = short_signal_test & (~is_oversold)
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n_blocked_long = (is_overheated & (probs > 0.5) & (test_features['z_score'].values < -z_thresh)).sum()
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n_blocked_short = (is_oversold & (probs > 0.5) & (test_features['z_score'].values > z_thresh)).sum()
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if n_blocked_long > 0 or n_blocked_short > 0:
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logger.info(f"Funding Filter: Blocked {n_blocked_long} Longs, {n_blocked_short} Shorts")
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# 9. Calculate Position Sizing (Probability-Based)
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# Base size = 1.0 (100% of equity)
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# Scale: 1.0 + (Prob - 0.5) * 2
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# Example: Prob=0.6 -> Size=1.2, Prob=0.8 -> Size=1.6
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# Align probabilities to close index
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probs_series = pd.Series(0.0, index=test_features.index)
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probs_series[:] = probs
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probs_aligned = probs_series.reindex(close.index, fill_value=0.0)
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# Calculate dynamic size
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dynamic_size = 1.0 + (probs_aligned - 0.5) * 2.0
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# Cap leverage between 1x and 2x
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size = dynamic_size.clip(lower=1.0, upper=2.0)
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# Create full-length signal series (False for training period)
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long_entries = pd.Series(False, index=close.index)
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@@ -171,7 +255,7 @@ class RegimeReversionStrategy(BaseStrategy):
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n_short = short_entries.sum()
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logger.info(f"Generated {n_long} long signals, {n_short} short signals (test period only)")
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return long_entries, long_exits, short_entries, short_exits
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return long_entries, long_exits, short_entries, short_exits, size
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def prepare_features(self, df_btc, df_eth, cq_df=None):
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"""Replicate research feature engineering"""
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@@ -236,19 +320,20 @@ class RegimeReversionStrategy(BaseStrategy):
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Args:
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train_features: DataFrame containing features for training period only
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"""
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threshold = 0.005
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threshold = self.profit_target
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horizon = self.horizon
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z_thresh = self.z_entry_threshold
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# Define targets using ONLY training data
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# For Short Spread (Z > 1): Did spread drop below target within horizon?
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# For Short Spread (Z > threshold): Did spread drop below target within horizon?
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future_min = train_features['spread'].rolling(window=horizon).min().shift(-horizon)
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target_short = train_features['spread'] * (1 - threshold)
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success_short = (train_features['z_score'] > 1.0) & (future_min < target_short)
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success_short = (train_features['z_score'] > z_thresh) & (future_min < target_short)
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# For Long Spread (Z < -1): Did spread rise above target within horizon?
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# For Long Spread (Z < -threshold): Did spread rise above target within horizon?
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future_max = train_features['spread'].rolling(window=horizon).max().shift(-horizon)
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target_long = train_features['spread'] * (1 + threshold)
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success_long = (train_features['z_score'] < -1.0) & (future_max > target_long)
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success_long = (train_features['z_score'] < -z_thresh) & (future_max > target_long)
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targets = np.select([success_short, success_long], [1, 1], default=0)
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