quanttrader

99% of "StatArb" bots sold to retail traders use a static lookback window (OLS) to calculate the hedge ratio. When the market regime shifts, the spread blows past the Z-score threshold, and your account gets liquidated.
Real institutional arbitrage requires dynamic hedging. Here is the exact Kalman Filter update step I use in my Sentinel bot to dynamically track the relationship between two assets in real-time.
core/kalman_filter.py (Lines 42-56)import numpy as np
def kalman_update(price_x, price_y, state_mean, state_cov, observation_noise, transition_noise):
# Observation matrix
H = np.array([price_x, 1.0]).reshape(1, 2)
# Prediction
pred_cov = state_cov + transition_noise
# Update
innovation = price_y - np.dot(H, state_mean)
innovation_cov = np.dot(H, np.dot(pred_cov, H.T)) + observation_noise
kalman_gain = np.dot(pred_cov, H.T) / innovation_cov[0, 0]
new_state_mean = state_mean + kalman_gain * innovation[0]
new_state_cov = pred_cov - np.dot(kalman_gain, np.dot(H, pred_cov)) return new_state_mean, new_state_cov
If your bot does not recalculate the hedge ratio on every tick using state-space models, you are trading a lagging illusion.
I packaged my entire Python StatArb engine (Kalman + Z-Score) into a ready-to-deploy Docker container. It uses SQLite WAL for extreme read/write speeds, Redis for state management, and is fully controllable via Telegram polling. No manual intervention required.
Stop buying wrappers. Buy the math. Link in bio for the Docker image and SaaS license to run this for your clients. #StatArb #algoTrading #Python #BybitFutures #QuantTrader