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.
13 documents
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 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 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 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 document lays out an ordered process for adding a strategy to a live trading system or replacing an existing one. It covers preparing instrument data, confirming a working backtest, configuring strategy and control files, implementing custom backtest,…
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 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 code models order and trade state for a scalping system. When flat with no open orders, it places buy and sell limit orders around the current price, with their distance based on a volatility-like measure R and a configurable multiplier. After one order…
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 code implements a portfolio stage that recalculates instrument positions across dates. For each date, it builds an optimization objective from target contract positions, a covariance estimate, contract values, transaction costs, previous positions,…
The code builds a portfolio of instruments through a greedy selection process. It first scores each eligible instrument individually, then repeatedly adds the candidate that gives the highest estimated portfolio Sharpe ratio. Correlations enter through a…