pair-trading

Mean-reversion trading signals are generated by computing the price ratio, rolling mean, standard deviation, and Z-score of two correlated assets to determine entry and exit points. You provide exactly two instruments, a lookback window, and entry/exit Z-score thresholds to output long/short signals.

Yes, you can use pandas to calculate Z-score entry and exit points for cryptocurrency pairs like BTC and ETH. The signal engine computes the price ratio and rolling statistics to output long/short signals for these correlated digital assets.

Calculating pair-trading signals requires exactly two instruments, a lookback window, and entry/exit Z-score thresholds. The engine applies these inputs to the ETF price data to compute the rolling mean and standard deviation for signal generation.

Equal-weight allocation in a mean-reversion pair-trading strategy automatically assigns 50% capital to each leg of the pair. This ensures the long and short positions are balanced when Z-score thresholds trigger entry and exit signals.

Automating daily signal generation involves computing the rolling price ratio and Z-score deviations for two correlated assets. The resulting long/short signals can be directly fed into an execution system to manage the mean-reversion trades.

No, this mean-reversion approach does not work with more than two assets at once. The signal generation requires exactly two instruments to compute the price ratio and Z-score deviations necessary for identifying entry and exit points.