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A complete Crypto AI Hedge Fund Team framework. A multi-agent system covering all from data analysis and investment strategies to backtesting and live trading.

This repository provides a flexible framework for backtesting sophisticated market making strategies across multiple symbols and strategy types. Key features include parameter optimization, performance visualization, and support for the Stoikov model and its extensions.

A collection of algorithmic trading strategies for QuantConnect, including straddle options, triangular arbitrage, and crypto strategies. Features data analysis, visualization, backtesting, and optimization tools, all designed for seamless integration with the QuantConnect API. Perfect for quanti... Read more

An end-to-end Python implementation of Cao et al.'s (2025) HLPPL methodology for the identification of financial (asset price) bubbles. Implements 7-parameter Log-Periodic Power Law model fitting, confidence-weighted sentiment analysis, regime-dependent 'BubbleScore' fusion, and Transformer-based... Read more

Backtesting Engine to Backtest Index Strategies on Options, Futures and Spot (BankNIFTY, NIFTY) for Indian Share Market

Program for backtesting 'Opening Range Breakout' Strategy'.

This is a backtesting demo in Python. Different moving average prices are used to make buy and sell decisions. A Jupyter notebook version is for serial mode. while py version is for multiprocessing. A version for GPU has been tried, but not successful.

Creating, training and backtesting of VaR and ES models based on Importance Sampling

Example of portfolio backtesting using vectorbt

bybit-backtest is a python library for backtest with bybit fx trade on Python 3.7 and above.