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.
The main features of braverock/blotter are: Trading and Backtesting.
Open-source alternatives to braverock/blotter include: charliedream1/ai_quant_trade — ai_quant_trade is an AI-driven quantitative trading platform that enables the development, backtesting, and deployment… ai4finance-llc/finrl-library — FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and… alex-jb/orallexa-ai-trading-agent — Self-tuning multi-agent AI trading system. 8-source signal fusion (Polymarket + Kalshi + 10 ML models incl. Kronos… alexandermerkel/binance-fix-connector-python — Async Python connector for Binance SPOT FIX testing, latency research, and feed/session comparison. alexanderwanyoike/the0 — Open Source Algorithmic Trading Engine. achillesrasquinha/bulbea — :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling.
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
FinRL-Library is a reinforcement learning trading framework and algorithmic trading library used to develop and backtest automated financial trading strategies. It functions as a quantitative trading pipeline and financial market simulator, allowing users to build decision policies that optimize asset trading across various financial markets. The framework features a modular integration system for swapping reinforcement learning algorithms through a consistent API. It utilizes a standardized environment wrapper to encapsulate market dynamics into a state-action-reward interface, facilitating
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.
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling