A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities

Free, open-source crypto trading bot, automated bitcoin / cryptocurrency trading software, algorithmic trading bots. Visually design your crypto trading bot, leveraging an integrated charting system, data-mining, backtesting, paper trading, and multi-server crypto bot deployments.

Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/

Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.

Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market support, CLI tools, and an integrated MCP server for professional traders.

Coin Trader is a Java-based backend for algorithmically trading cryptocurrencies. It provides data collection and export, complex event processing and triggering, and backtesting - paper trading - live trading.

algorithmic trading backtest and optimization examples using order book imbalances. (bitcoin, cryptocurrency, bitmex, binance futures, market making)

An open-source Python backtesting engine for designing and evaluating daily stock trading algorithms against real historical market data.

An open source simulated options brokerage and UI for paper trading, algorithmic interfaces and backtesting.

A stream-based approach to algorithmic trading and backtesting in Node.js

Codera Quant is a Java framework for algorithmic trading strategies development, execution and backtesting via Interactive Brokers TWS API or other brokers API

C++ Trading Algorithm Backtest Environment

Quantitative Clawdbot trading system for Polymarket that applies statistical models and algorithmic strategies to identify high-probability market entries. Backtests strategies against historical odds data and executes live trades when model conditions are satisfied.

Grid trading model for algorithmic trading. Script strategy for Trading View. Copy and paste the code on your Pine console. Backtest the strategy for a spot market.

An algorithmic trading platform written in rust. Focused on backtesting, charting, live trading. With an emphasis for semi-automated strategies. fund-forge is built to allow simple abstractions for common strategy functionality: trade multiple symbols, with multiple indicators and data streams.

Algorithmic trading and backtesting framework in pure julia

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

Securities master database for algorithmic trading, backtesting, and data mining.

[HackIllinois 17 Winner] Bitcoin Trading Algorithm Backtesting and Analysis Toolkit

Composable Python toolkit for algorithmic trading on Polymarket crypto prediction markets. Plugin-based strategies, indicators, backtesting, and multi-source data feeds.

The toolbox for developing systematic trading strategies. It includes datasets and strategy ideas to assist in developing and backtesting trading algorithms.

:crystal_ball::moneybag: Backtesting and execution of algorithmic trading strategies in Node.js

Backtesting an algorithmic trading strategy using Machine Learning and Sentiment Analysis.

The backtest of turtle trading algorithm on cryptocurrency data in python

Algorithmic backtesting and live trading system; no database required, simple setup, config and out-of-the-box exchange support

Select a supervised algorithm that can predict stock prices of historical data based on the predictors (statistical indicators). Accordingly formulate a trading strategy based on predicted values to generate orders on same historical training set to backtest how much portfolio would have increased. Select the combination of Machine learning algorithm and Trading strategy to maximize gain for future orders placed automatically via the program.

Algorithmic and manual trading strategies, backtests, and analysis for IMC’s 15‑day Prosperity 3 challenge that resulted in rank 25.

A next-generation AI-powered algorithmic trading platform supporting strategy backtesting, live/paper trading, parameter optimization, and intelligent analysis.

An algorithmic trading platform including backtesting, indicators

Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.