Projects sharing features with PandoraTrader

RQAlpha is a Python-native quantitative trading backtesting framework and live trading execution system. It provides an event-driven engine for simulating trading strategies against historical market data, with realistic transaction costs, slippage models, and corporate action handling. The platform supports multi-asset class trading including stocks, futures, options, and REITs, with separate sub-accounts for different asset types and configurable margin requirements. The framework distinguishes itself through a plugin-based extensible architecture that allows users to swap out core componen

Backtestable AI trading agents and Python algorithmic trading strategies for stocks, options, crypto, futures, forex, SEC filings, FRED macro data, and real brokers.

hftbacktest is a high-frequency trading backtesting framework and level 3 market data engine. It serves as a simulation environment for cryptocurrency trading bots and market-making strategies, utilizing a limit order book simulator to model precise market microstructures and liquidity. The system differentiates itself through high-fidelity simulation components, including queue-position modeling to predict fill times and latency-aware execution to simulate network and exchange processing delays. It reconstructs order book states from level 2 and level 3 data and uses raw exchange trade and q

Lean is an algorithmic trading engine and quantitative finance platform designed for the development, backtesting, and live execution of automated trading strategies. It provides a comprehensive framework for processing time-series market data, managing multi-asset portfolios, and conducting quantitative research across diverse financial markets. The platform distinguishes itself through a modular, event-driven architecture that decouples strategy logic from data ingestion and brokerage connectivity. By utilizing standardized interfaces for data providers and brokerage abstractions, it enable

VeighNa is an event-driven, modular platform designed for the development, backtesting, and execution of automated financial trading strategies. It provides a comprehensive suite of tools that includes a centralized trading terminal for monitoring portfolios and market conditions, alongside a robust algorithmic trading engine that manages real-time data processing and order execution. The platform distinguishes itself through a highly decoupled architecture that isolates algorithmic logic from market connectivity, allowing for independent strategy development and testing. It utilizes a dynami

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

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

Systematic Trading in python

VectorBT is a vectorized trading strategy backtesting framework that simulates thousands of strategy configurations in a single pass over historical price data. It operates as a parameter optimization engine, a portfolio performance analyzer, a technical indicator calculator, and a financial data fetcher, all built around a DataFrame-centric data model that uses NumPy broadcasting for signal alignment and compiled code acceleration for performance. The framework distinguishes itself through its ability to run large-scale parameter sweeps by constructing every combination of strategy parameter

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

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

🤖 AI Quant Fund — Multi-Agent Live Trading Analysis

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.

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

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.

Open Source Algorithmic Trading Engine

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

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:

Asynchronous, event-driven algorithmic trading in Python and C++

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

AI-based stock analysis and trading system

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