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.
7 documents
This document is a historical intraday dataset for the Dalian Commodity Exchange iron ore futures contract. Its rows report timestamped five-minute open, high, low, and close prices, along with volume, turnover, and open interest. The visible entries cover…
This script configures WtCtaOptimizer to run parameter searches for a Dual Thrust strategy on a China Financial Futures Exchange index futures contract. It fixes inputs such as bar count, bar interval, lookback days, and contract, then varies the strategy…
The example sketches a Python environment that connects a CTA backtesting engine to an agent-like training loop. It initializes a backtest, subscribes a strategy to five-minute bars, starts asynchronous execution, and advances the engine one step at a time.…
This overview catalogs Python examples for the WonderTrader framework, including CTA strategies, futures and stock backtests, futures arbitrage, optimization, reinforcement learning, and high-frequency trading. It names Dual Thrust as a sample strategy used…
This trading system uses the JFatl_Digit_System indicator to generate signals when a bar closes with a colored state that differs from the previous bar, including a transition from an uncolored state. An Expert Advisor acts on those indicator transitions.…
The document demonstrates a workflow for backtesting a Dual Thrust strategy and reviewing its performance with Pyfolio. It configures a CTA backtest engine, sets the date range and storage location, and creates a strategy instance with a futures contract,…
This example configures a genetic algorithm optimizer to search parameters for a Dual Thrust futures strategy. It shows how to define an objective from average winning and losing trade results, assign fixed and variable parameters, set a backtest environment…