This article reviews nine highly regarded open-source projects for quantitative trading, covering data acquisition, strategy backtesting, AI analysis, and live trading, and explains which types of users each tool is best suited for.
Moon Dev AI Agents is an open‑source Python framework that bundles backtesting, live trading, risk management, arbitrage and market monitoring into over 18 ready‑made AI agents, using a swarm of large language models to automate strategy development and execution for cryptocurrency traders.
This article compiles a categorized collection of Python packages for quantitative finance, covering scientific computation, pricing, technical indicators, backtesting, risk analysis, data sources, Excel integration, and visualization, with brief descriptions and reference links for each tool.
This tutorial shows how to replace cumbersome PDF reports with dynamic HTML reports generated by Python and Jinja2, covering template creation, inserting text, tables, images, and styling to fully automate fund backtesting result publishing.
This article introduces two low‑frequency Python trading strategies—a grid‑based price‑difference approach and an intraday T‑strategy—explains their implementation on the RiceQuant platform, provides sample code, and presents back‑testing results that demonstrate their performance and practical considerations.
This article explains why accurate trade data is essential for strategy backtesting, why Binance is chosen, and provides a detailed Python workflow—including argument parsing, using the Binance aggTrades endpoint, handling pagination with from_id, cleaning the resulting DataFrame, saving to CSV, and validating the data integrity.
This step‑by‑step guide shows how to implement, backtest, and run a single‑stock 5‑day versus 30‑day moving‑average trading strategy on the Ricequant platform, covering code setup, cash handling, order execution, and both daily and minute‑level simulations.
