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The user is open-sourcing their profitable quantitative trading code and introducing the QTrader platform, which offers strategy management, backtesting, and simulated trading features.
AI-Trader is an open-source Python framework that simplifies backtesting and optimizing algorithmic trading strategies across multiple markets using config-driven YAML files and AI assistant integration.
AQuA is a research system with two independent language-model-driven agents that recursively self-improve in quantitative trading research, achieving strong information coefficients on crypto and US equities while using sealed sandboxes to prevent data leakage.
Presents Backtrader-Bench, a benchmark for evaluating LLM coding agents in algorithmic trading using self-generated multiple-choice questions and a generator-solver filtering pipeline, showing tool-augmented agents achieve 90% accuracy.
zipline-reloaded is a continuation of the Zipline backtesting framework maintained by Stefan Jansen after Quantopian shut down, supporting Python 3.11+ and pandas 2.0+, retaining the event-driven architecture and Pipeline API, suitable for factor-based stock selection and machine learning strategy backtesting.
Promotional post about Horizon, an AI-driven trading strategy platform that lets users backtest and deploy strategies in plain English, citing a professor managing $200M with AI agents and returning 56% last year.
A backtest of the RSI<30 oversold strategy on BTC shows it was profitable (+12.44%) despite a 51.85% win rate, but with marginal risk-adjusted metrics, highlighting that payoff structure matters more than indicator accuracy. Promotes Horizon's backtesting platform.
This paper shows that the standard pre/post training-cutoff check for temporal leakage in LLM backtesting is uninformative, as recency effects mimic leakage. It proposes new estimators using known cutoffs and matched clean controls to measure leakage and compute adjusted scores, validated on frontier models.
Scalar Field (YC X25) launches its agentic trading desk execution engine, closing the loop from research and backtesting to live trading with support for Robinhood, Public, Alpaca, Hyperliquid, and Polymarket.
Introduces a full-stack hands-on Python quantitative trading video tutorial, covering data scraping, machine learning prediction, portfolio optimization, and strategy backtesting, and mentions the open-source quantitative trading execution system Kungfu.
Magents is an open-source Python framework for simulating and backtesting multiple trading strategies in one system, with isolated strategy pods, an event-driven engine, and central risk controls.
Introduces the open-source Python tool InStock (myhhub/stock) for A-share quantitative analysis, supporting data scraping, technical indicators, stock selection, backtesting, automated trading, etc., and can be deployed via Docker.
A curated list of 10 free GitHub repositories for algorithmic trading and quantitative finance, covering tools for backtesting, data analysis, portfolio optimization, and AI-driven trading strategies.
A user curated a free GitHub repository aggregating numerous open-source quantitative finance tools, including pricing engines, backtesting frameworks, order book simulators, and risk models, making institutional-grade research tools accessible to individuals at minimal cost.
A GitHub repo aggregates free, open-source quantitative finance tools—pricing engines, backtesters, order books, and HFT simulators—typically guarded by firms, including Microsoft's AI quant platform.
The post claims that quant firms' complex mathematical edge is now accessible via a plain-English chat interface that can describe, backtest, and execute trading strategies, democratizing quant finance.
This article details how to build a personal quantitative trading system using free AI open-source tools (such as OpenBB, Qlib, TradingAgents, etc.), covering five major modules: data, research, backtesting, risk control, and execution, and points out common pitfalls and discipline.
Open-source A-share quantitative workstation, based on TickFlow data, implements three functions: stock selection, real-time monitoring, and backtesting. Built-in 20 Polars vectorized strategies, supports AI strategy writing and Feishu push.
A developer demonstrates running Gemma 4 26B MoE model locally on an 8GB RTX 4060 with Hermes agent to fully automate backtesting of trading strategies, highlighting the growing capability of local LLMs as autonomous agents.
A thread introducing Loop Engineering as a solution to the common problem of quant strategies that backtest perfectly but fail in live trading, emphasizing the need for iterative optimization.