backtesting

GPT-6 Astra optimized a Rust backtesting engine using a 2014 math paper, solving a problem other AI models couldn't, and the author argues this indicates imminent AGI.

Indicator Go is a Go library that provides 80+ technical analysis indicators, a backtesting framework, and integrates with AI tools via MCP for algorithmic trading.

Horizon is an AI-powered tool that allows traders to describe trading strategies in plain language, automatically generating backtests with detailed metrics for iterative testing and improvement.

This GitHub repository named finance-quant-skills provides 13 installable Agent Skills specifically designed for A-share quantitative trading, covering functions such as data acquisition, strategy backtesting, and document query.

The paper explains how removing market beta and factor exposure reveals true trading signals, emphasizing rigorous stress testing to avoid overfitting and false confidence in backtests.

The article argues that AI trading agents should include a skeptic agent to check for flaws like data leakage and impossible assumptions, rather than just generating more strategies.

NautilusTrader is a high-performance, open-source trading engine with a Rust core and Python strategy support, enabling seamless transition from backtesting to live trading across multiple assets and exchanges.

The author built PortfolioLab, an AI agent pipeline for trading that stages models through backtesting, paper trading, and read-only API execution to prevent premature exposure to real money. They are seeking feedback on trust patterns in AI agent architectures for high-stakes applications.

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.