Open-source alternatives to AlphaPy

aiquanttrade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment of trading strategies powered by machine learning and artificial intelligence. It provides a complete local environment for quantitative research, simulation, and automated live trading through brokerage APIs, supporting both historical backtesting and real-time paper trading without capital risk. The platform distinguishes itself through a modular, event-driven architecture that separates strategy logic from execution, allowing rule-based and machine learning models to be co

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

This is a Bayesian optimization library for Python designed to find the maximum value of expensive black box functions. It operates as a global optimizer that uses probabilistic models to identify the peak value of unknown functions through iterative sampling. The tool is specifically designed for hyperparameter tuning in machine learning, where it maximizes model performance while minimizing the number of required training runs. It treats the target function as a black box, selecting optimal input parameters based on statistical priors to reduce manual trial and error. The system utilizes G

Open Source Algorithmic Trading Engine

Async Python connector for Binance SPOT FIX testing, latency research, and feed/session comparison

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

EvalML is an AutoML library written in python.

Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering

AI-powered trading research platform. Test any idea on stocks, futures, and crypto with event studies, backtesting, and statistical validation. MCP server with 8 tools. pip install varrd.

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

Automatic architecture search and hyperparameter optimization for PyTorch

Python live trade execution library with zipline interface.

Self-tuning multi-agent AI trading system. 8-source signal fusion (Polymarket Kalshi 10 ML models incl. Kronos foundation model), Bull/Bear/Judge debate on Claude Opus 4.7, Portfolio Manager gate.

RoBO: a Robust Bayesian Optimization framework

Collection of hyperparameter optimization benchmark problems

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

AutoGluon is an automated machine learning framework designed to optimize model selection and hyperparameter tuning across tabular, text, image, and time series data. It functions as an ensemble learning library and a tabular data prediction engine, aiming to build high-accuracy predictive models without manual algorithm selection. The framework integrates multimodal machine learning pipelines that combine disparate data types into a single representation using specialized encoders. It also includes a probabilistic time series forecaster that fits multiple statistical and deep learning models

MLBox is a powerful Automated Machine Learning python library.

Backtrader is a Python backtesting framework and algorithmic trading platform. It provides a toolkit for developing automated trading rules and simulating investment strategies using historical financial time-series data. The system functions as a quantitative analysis tool, combining a simulation engine for testing trading rules with a financial data visualizer that generates price action charts. It allows for the calculation of technical indicators and the evaluation of portfolio performance through risk-adjusted returns. The platform covers live trading integration via brokerage APIs and

Open-source Rust framework for building event-driven live-trading & backtesting systems

Pipeline Extension for Live Trading

Local-first backtesting engine with built-in overfitting detection. Asset-class agnostic. MCP-native.

🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.

a distributed Hyperband implementation on Steroids

blotter provides transaction infrastructure for defining transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.

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This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur