My First Commit
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@@ -1,14 +1,47 @@
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# original script moved to vecm_rolling_fit_01.py
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# 09.09.25 Added GARCH model - predicting volatility
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# Rule of thumb:
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# alpha + beta ≈ 1 → strong volatility clustering, persistence.
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# If much lower → volatility mean reverts quickly.
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# If > 1 → model is unstable / non-stationary (bad).
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# the VECM disequilibrium (mean reversion signal) and
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# the GARCH volatility forecast (risk measure).
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# combine them → e.g., only enter trades when:
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# high_volatility = 1 → persistence > 0.95 or volatility > 2 (rule of thumb: unstable / risky regime).
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# high_volatility = 0 → stable regime.
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# VECM disequilibrium z-score > threshold and
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# GARCH-forecasted volatility is not too high (avoid noise-driven signals).
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# This creates a volatility-adjusted pairs trading strategy, more robust than plain VECM
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# now pair_predict_result_ DataFrame includes:
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# disequilibrium, scaled_disequilibrium, z-scores, garch_alpha, garch_beta, garch_persistence (α+β rule-of-thumb)
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# garch_vol_forecast (1-step volatility forecast)
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# Would you like me to also add a warning flag column
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# (e.g., "high_volatility" = 1 if persistence > 0.95 or vol_forecast > threshold)
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# so you can easily detect unstable regimes?
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# VECM/GARCH
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# vecm_rolling_fit.py:
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from typing import Any, Dict, Optional, cast
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import numpy as np
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import pandas as pd
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from typing import Any, Dict, Optional
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from pt_trading.results import BacktestResult
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from pt_trading.rolling_window_fit import RollingFit
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from pt_trading.trading_pair import TradingPair
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from statsmodels.tsa.vector_ar.vecm import VECM, VECMResults
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from arch import arch_model
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NanoPerMin = 1e9
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class VECMTradingPair(TradingPair):
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vecm_fit_: Optional[VECMResults]
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pair_predict_result_: Optional[pd.DataFrame]
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@@ -23,20 +56,23 @@ class VECMTradingPair(TradingPair):
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super().__init__(config, market_data, symbol_a, symbol_b)
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self.vecm_fit_ = None
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self.pair_predict_result_ = None
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self.garch_fit_ = None
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self.sigma_spread_forecast_ = None
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self.garch_alpha_ = None
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self.garch_beta_ = None
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self.garch_persistence_ = None
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self.high_volatility_flag_ = None
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def _train_pair(self) -> None:
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self._fit_VECM()
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assert self.vecm_fit_ is not None
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diseq_series = self.training_df_[self.colnames()] @ self.vecm_fit_.beta
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# print(diseq_series.shape)
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self.training_mu_ = float(diseq_series[0].mean())
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self.training_std_ = float(diseq_series[0].std())
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self.training_df_["dis-equilibrium"] = (
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self.training_df_[self.colnames()] @ self.vecm_fit_.beta
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)
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# Normalize the dis-equilibrium
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self.training_df_["scaled_dis-equilibrium"] = (
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self.training_df_["disequilibrium"] = diseq_series
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self.training_df_["scaled_disequilibrium"] = (
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diseq_series - self.training_mu_
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) / self.training_std_
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@@ -45,61 +81,96 @@ class VECMTradingPair(TradingPair):
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vecm_df = self.training_df_[self.colnames()].reset_index(drop=True)
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vecm_model = VECM(vecm_df, coint_rank=1)
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vecm_fit = vecm_model.fit()
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assert vecm_fit is not None
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# URGENT check beta and alpha
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# Check if the model converged properly
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if not hasattr(vecm_fit, "beta") or vecm_fit.beta is None:
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print(f"{self}: VECM model failed to converge properly")
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self.vecm_fit_ = vecm_fit
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pass
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# Error Correction Term (spread)
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ect_series = (vecm_df @ vecm_fit.beta).iloc[:, 0]
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# Difference the spread for stationarity
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dz = ect_series.diff().dropna()
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if len(dz) < 30:
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print("Not enough data for GARCH fitting.")
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return
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# Rescale if variance too small
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if dz.std() < 0.1:
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dz = dz * 1000
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# print("Scale check:", dz.std())
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try:
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garch = arch_model(dz, vol="GARCH", p=1, q=1, mean="Zero", dist="normal")
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garch_fit = garch.fit(disp="off")
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self.garch_fit_ = garch_fit
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# Extract parameters
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params = garch_fit.params
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self.garch_alpha_ = params.get("alpha[1]", np.nan)
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self.garch_beta_ = params.get("beta[1]", np.nan)
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self.garch_persistence_ = self.garch_alpha_ + self.garch_beta_
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# print (f"GARCH α: {self.garch_alpha_:.4f}, β: {self.garch_beta_:.4f}, "
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# f"α+β (persistence): {self.garch_persistence_:.4f}")
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# One-step-ahead volatility forecast
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forecast = garch_fit.forecast(horizon=1)
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sigma_next = np.sqrt(forecast.variance.iloc[-1, 0])
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self.sigma_spread_forecast_ = float(sigma_next)
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# print("GARCH sigma forecast:", self.sigma_spread_forecast_)
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# Rule of thumb: persistence close to 1 or large volatility forecast
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self.high_volatility_flag_ = int(
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(self.garch_persistence_ is not None and self.garch_persistence_ > 0.95)
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or (self.sigma_spread_forecast_ is not None and self.sigma_spread_forecast_ > 2)
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)
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except Exception as e:
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print(f"GARCH fit failed: {e}")
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self.garch_fit_ = None
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self.sigma_spread_forecast_ = None
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self.high_volatility_flag_ = None
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def predict(self) -> pd.DataFrame:
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self._train_pair()
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assert self.testing_df_ is not None
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assert self.vecm_fit_ is not None
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# VECM predictions
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predicted_prices = self.vecm_fit_.predict(steps=len(self.testing_df_))
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# Convert prediction to a DataFrame for readability
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predicted_df = pd.DataFrame(
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predicted_prices, columns=pd.Index(self.colnames()), dtype=float
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)
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predicted_df = pd.merge(
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self.testing_df_.reset_index(drop=True),
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pd.DataFrame(
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predicted_prices, columns=pd.Index(self.colnames()), dtype=float
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),
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pd.DataFrame(predicted_prices, columns=pd.Index(self.colnames()), dtype=float),
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left_index=True,
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right_index=True,
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suffixes=("", "_pred"),
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).dropna()
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# Disequilibrium and z-scores
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predicted_df["disequilibrium"] = (
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predicted_df[self.colnames()] @ self.vecm_fit_.beta
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)
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predicted_df["signed_scaled_disequilibrium"] = (
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predicted_df["disequilibrium"] - self.training_mu_
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) / self.training_std_
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predicted_df["scaled_disequilibrium"] = abs(
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predicted_df["signed_scaled_disequilibrium"]
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)
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predicted_df = predicted_df.reset_index(drop=True)
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# Add GARCH parameters + volatility forecast
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predicted_df["garch_alpha"] = self.garch_alpha_
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predicted_df["garch_beta"] = self.garch_beta_
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predicted_df["garch_persistence"] = self.garch_persistence_
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predicted_df["garch_vol_forecast"] = self.sigma_spread_forecast_
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predicted_df["high_volatility"] = self.high_volatility_flag_
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# Save results
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if self.pair_predict_result_ is None:
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self.pair_predict_result_ = predicted_df
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else:
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self.pair_predict_result_ = pd.concat(
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[self.pair_predict_result_, predicted_df], ignore_index=True
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)
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# Reset index to ensure proper indexing
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self.pair_predict_result_ = self.pair_predict_result_.reset_index(drop=True)
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return self.pair_predict_result_
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@@ -117,6 +188,6 @@ class VECMRollingFit(RollingFit):
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return VECMTradingPair(
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config=config,
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market_data=market_data,
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symbol_a=symbol_a,
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symbol_b=symbol_b,
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)
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symbol_a = symbol_a,
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symbol_b = symbol_b,
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)
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