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.
18 documents
The document explains an exponentially weighted moving average crossover (EWMAC) forecast. It subtracts a slower exponential moving average of price from a faster one, then divides that difference by daily price volatility. A positive or negative result…
This guide lays out a futures data workflow for a trading system. It starts with instrument settings, spread costs, and roll parameters, then gathers individual contract histories, builds roll calendars, creates multiple-price series, derives back-adjusted…
This example assembles a futures trend-following system on hourly data and shows how to choose among vanilla accounting, simulated market orders, and simulated limit orders. The system combines raw data, trading rules, forecast scaling and combination,…
This document is a partial directory linking futures symbols to exchange product pages. It covers contracts across energy, metals, equity indexes, currencies, interest rates, and volatility. The stated use is practical: consult exchange data to investigate…
This configuration module sets parameters for a fast mean-reversion futures strategy and derives operating bounds for its estimated price range, R. It estimates that range from hourly high-low data: zero ranges are discarded, a rolling average is taken, and…
This short Python example shows how to assemble a daily futures trading system with an order simulator. It creates a data source, loads configuration, and constructs a system from account, portfolio, position-sizing, forecast-combination, forecast-scaling,…
This Python module provides diagnostics and configuration helpers for a systematic trading system. It compares each rule’s capped forecasts and each instrument’s combined forecasts with a target average forecast magnitude, ranking the largest discrepancies…
This Python entry point runs a futures mean reversion system through a broker controller. Before trading, it checks broker position consistency, obtains a price and an initial range estimate, and prompts the operator to accept or modify strategy parameters.…
This configuration defines a futures system that combines exponentially weighted moving-average crossover forecasts at several speeds with a carry forecast smoothed over 90 days. It assigns forecast scalars to the rules, caps combined forecasts, and…
This introduction shows how to build a futures trading rule and assemble it into a larger systematic trading process. Its example EWMAC forecast subtracts a slow exponential moving average from a fast one, then normalizes the difference by a robust estimate…
This guide describes how pysystemtrade connects to Interactive Brokers through the Gateway or Trader Workstation and a Python API library. It outlines gateway setup, trusted IP and API settings, connection creation, configuration, and client ID requirements.…
This configuration describes a futures trading system that estimates forecasts from several exponentially weighted moving average crossover rules and a carry rule. The EWMAC rules pair faster and slower lookback periods, while the carry forecast uses…
This user guide describes pysystemtrade as a framework for constructing futures backtests and modifying their components. It covers common tasks such as selecting instruments and date ranges, changing configurations, writing trading rules, inspecting…
This code defines an objective function for a dynamic portfolio optimizer that chooses integer contract positions. It compares candidate portfolio weights with an unconstrained optimal target using covariance-weighted tracking error, adds trading costs based…
This system component converts raw trading rule forecasts into scaled forecasts and then clips them between configured upper and lower bounds. It supports fixed forecast multipliers, which may be set per rule or through shared configuration, and estimated…
This document explains how a futures trading system uses several instrument sets: the full catalog, instruments sampled for price data, instruments with adjusted prices, and the smaller sets used in simulation or production backtests. It describes…
This documentation explains the production workflow for pysystemtrade, from obtaining market prices and generating desired positions to sending orders and reconciling accounting information. It covers the production system’s components and data flow, broker…
This document describes a raw-data stage in a futures trading system that prepares reusable price and carry calculations for later forecasting. It retrieves daily, natural-frequency, and hourly prices; computes absolute daily and hourly price changes; and…