Scalable, event-driven, deep-learning-friendly backtesting library
- reinforcement-learning
- deep-reinforcement-learning
- gym-environment
- openai-gym
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Scalable, event-driven, deep-learning-friendly backtesting library
Python live trade execution library with zipline interface.
Oracle3: open-source trading engine and MCP server for prediction markets. Finds fee-adjusted no-arbitrage violations across related event contracts and trades them live on Kalshi, Polymarket and Solana, or on paper, under pre-trade risk limits. 13 MCP tools, JSON CLI, agent skills, 600+ tests.
A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine
Open Source Algorithmic Trading Engine
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
Building a population of models that trade crypto and mutate iteratively
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.
My algorithmic trading strategies with the Gekko cryptocurrency trading bot.
Wisp CLI — Interactive trading platform for backtesting and live trading across spot, perpetual futures, options, and prediction markets
Simple, modern ETH backtesting project using Pandas/Numpy: data prep, MA signals, portfolio simulation, and result visualization.