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.
219 documents
This introduction explains how cointegration can help create a mean-reverting portfolio from price series that are not themselves mean-reverting. By combining multiple assets with suitable weights, a trader may construct a spread or portfolio whose value…
This implementation describes convergence trading for two cointegrated assets as a portfolio optimization problem. It estimates error-correction speeds and other model parameters from price data, then computes portfolio weights under both unconstrained and…
This example builds a BTCUSDT futures market-making strategy whose fair price is estimated from a spot reference price plus a smoothed spot–futures basis. It resamples spot and futures book-ticker mid-prices, carries observations forward, and calculates a…
This document describes a trading rule that measures a spread’s latest value against its recent average and standard deviation. It uses separate lookback windows for the mean and standard deviation, then calculates a z-score to identify unusually high or low…
This document introduces HftBacktest, a Rust framework for developing and running high-frequency trading and market-making strategies. Its backtesting approach replays tick-level market data and aims to model important execution effects, including feed…
The distance approach forms pairs by rescaling each asset’s training-period prices to a common range, calculating the sum of squared differences between each pair’s normalized series, and selecting the closest matches. In the cited original study, the…
This documentation landing page introduces ArbitrageLab, a Python library covering end-to-end pairs-trading strategies and tools for developing strategies. It organizes its subject matter around multiple approaches, including distance methods, cointegration,…
This overview describes research on forecasting and trading commodity spreads, including gasoline crack, soybean-oil crush, and corn-ethanol crush spreads. It explains why spreads can be less exposed to market-wide information shocks and speculative bubbles…
This document describes helper calculations commonly used to construct quantitative signals from price or other tabular time series. Its functions cover rolling sums, averages, standard deviations, correlations, covariances, ranks, products, extrema,…
The tutorial describes a faster backtesting approach that precomputes fill conditions across intervals, reducing the need to replay every depth update or estimate queue position. It retains feed and order-entry latency but omits order-response latency.…
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…
The document contains reusable strategy calculations for price returns, volatility scaling, trend following, and MACD signals. Its intermediate trend strategy combines the signs of one-month and one-year returns, weighted by a parameter, and applies that…
This code sample implements parts of the Alpha101 factor set using historical equity fields such as open, high, low, close, volume, returns, and volume-weighted average price. The formulas combine rolling ranks, moving averages, correlations, price changes,…
This Chinese equity screening proposal combines three conditions: daily price amplitude above 1%, a dividend ratio above 25% for 2019, and a 15-minute MACD histogram that is shortening while below zero. The rationale is to find volatile shares with a history…
This code excerpt implements neural-network components for a momentum forecasting model based on a temporal fusion transformer design. It includes feed-forward layers, gated linear units, gated residual networks with skip connections and normalization, and…
This module outlines an out-of-sample forecasting workflow built around Auto-ARIMA. It first applies an Augmented Dickey-Fuller test at a five percent significance level, repeatedly differencing the training series until the test indicates stationarity or a…
This module implements the bivariate Nelsen 13 copula, a tool for modeling dependence between two uniform variables. It provides the copula cumulative distribution and density, a conditional distribution, random pair generation, and a parameter estimator…
The document implements a threshold autoregressive model for testing whether a spread adjusts differently after positive and negative deviations. It first differences the input series to form changes, lags the spread by one period, and assigns each lagged…
The document introduces copulas as a way to model how two or more random variables depend on each other separately from their individual distributions. It explains transforming observations through their marginal cumulative distribution functions into…
This tutorial compares a high-frequency grid market-making strategy across cryptocurrency exchanges, emphasizing that different order flows can change results even for the same trading pair and parameters. The strategy places layered limit bids and offers…
This tutorial describes a high-frequency grid strategy that places passive limit orders at regular intervals around the mid-price. It maintains a fixed number of buy and sell levels, refreshes orders as the market moves, and limits new orders based on the…
This tutorial applies the Guéant–Lehalle–Fernandez-Tapia market-making model to grid quoting. It derives bid and ask quote depths from a fair price, volatility, trading intensity, and inventory. The resulting quotes combine a half-spread with an…
This module fits a bivariate mixture of Clayton, Student-t, and Gumbel copulas, motivated by a mixed-copula pairs trading approach. It first maps each input series to empirical cumulative probabilities, then estimates component parameters and mixture weights…
This code describes a stateful method for comparing consecutive limit-order-book snapshots. It stores bid and ask levels from the current and previous snapshots, then flags each current level as unchanged, changed, or inserted based on price and quantity.…