30 open-source projects similar to alexandermerkel/binance-fix-connector-python, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Binance Fix Connector Python alternative.
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
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A TD Ameritrade API client for Python. Includes historical data for equities and ETFs, options chains, streaming order book data, complex order construction, and more.
Python live trade execution library with zipline interface.
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.
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
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.
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.
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.
This library provides a unified interface for interacting with hundreds of global cryptocurrency exchanges. It serves as a standardized framework for building automated trading systems, allowing developers to fetch real-time market data, manage account balances, and execute orders across multiple financial platforms through a single, predictable set of methods. The project distinguishes itself by abstracting the complexities of diverse exchange-specific application programming interfaces into a consistent internal schema. It includes a modular authentication layer that automatically handles c
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
Source code for Algorithmic Trading with Python (2020) by Chris Conlan
A high-performance execution engine utilizing LLVM-based JIT compilation to optimize mission-critical data processing. Engineered to handle high-throughput financial transactions and real-time infrastructure analysis.
Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.
Python-based framework for backtesting trading strategies & analyzing financial markets GUI :neckbeard:
finmarketpy is a quantitative trading framework and financial market analysis tool. It provides a Python-based library for simulating trading strategies against historical market data, computing the value of options contracts, and extracting trends from financial datasets. The system includes specialized engines for financial options pricing using numerical calculations and a backtesting library to assess risk and performance before live deployment. It further enables the detection of market seasonality and the execution of event studies to measure asset price behavior around specific time wi
🤖 AI Quant Fund — Multi-Agent Live Trading Analysis
OctoBot is an open-source automated trading platform that connects to over 15 cryptocurrency exchanges, enabling users to deploy grid, dollar-cost averaging, market-making, and AI-driven trading strategies. It functions as a unified multi-exchange trading platform, a TradingView alert executor, and a crypto trading bot, all within a single system. The platform is built on an event-driven trading loop with a plugin-based strategy engine, an exchange-agnostic connector layer, and a cloud-synced profile store for multi-device consistency. What distinguishes OctoBot is its integration of large la
Quant trading framework by OctoBot. Write, backtest & automate Python trading strategies like TradingView Pine Script. Work in progress.
A fast, extensible, transparent python library for backtesting quantitative strategies.