Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
6 documents
This document describes a parameter sweep for a grid trading backtest. It combines every configured symbol with candidate relative half-spread and grid-count values, then runs the resulting backtests in parallel over a selected date range. The grid interval…
This module describes a trading rule built around a pre-estimated multivariate cointegration vector. It calculates the weighted sum of log prices, differences that series across recent observations, and uses the sign of the summed changes to set trade…
This Python script launches a Rust grid-trading backtest for each symbol in a ticker configuration. It assembles daily market-data and latency-file paths for a specified date range, passes instrument and strategy settings to the backtest executable, and runs…
This reference describes metrics for evaluating trading strategies from records of equity, fees, trades, trading volume and value, positions, prices, and timestamps. It covers cumulative and annualized returns, Sharpe and Sortino ratios, return relative to…
This implementation describes a pairs strategy that estimates conditional probabilities from a fitted copula applied to each asset’s return ranks. It converts prices to returns, maps returns through marginal cumulative distribution functions, and uses the…
The code describes a grid market-making approach that repeatedly places buy and sell limit orders around a forecast mid-price. The forecast is simply the current best bid and ask midpoint, with no alpha adjustment in this implementation. A relative…