A stock backtesting engine written in Java. And a pairs trading (cointegration) strategy implementation using a bayesian kalman filter model

An open-source Python backtesting engine for designing and evaluating daily stock trading algorithms against real historical market data.

Quant is a python-based system for stock trading strategy backtesting

National Stock Exchange (NSE), India based Stock screener program. Supports Live Data, Swing / Momentum Trading, Intraday Trading, Connect to online brokers as Zerodha Kite, Risk Management, Emotion Control, Screening, Strategies, Backtesting, Automatic Stock Downloading after closing, live free day trading data and much more

Platform for backtesting and live-trading intraday Stock/ETF/ELW using recurrent neural networks

Select a supervised algorithm that can predict stock prices of historical data based on the predictors (statistical indicators). Accordingly formulate a trading strategy based on predicted values to generate orders on same historical training set to backtest how much portfolio would have increased. Select the combination of Machine learning algorithm and Trading strategy to maximize gain for future orders placed automatically via the program.

stock-pairs-trading is a python library for backtest with stock pairs trading using kalman filter on Python 3.8 and above.

This project explores stock trading modelling with the use recurrent neural network (RNN) with long-short term memory (LSTM) architecture. This is for single stock prediction and backtesting, another RNN LSTM network and backtester for multiple-stock portfolio will be added soon.

软工三大作业，一个有点专业的股票量化交易系统。SECIII course project, a Python stock backtest & trading system.

A project that uses machine learning techniques to build a backtesting engine for trading and to analyze stock market data and make predictions.

Daily and Intraday OHLCV bars capture, fast message-driven parallel backtesting engine with trading strategy DSL, trading simulator, daily stock watcher

Stock trading strategy using tushare as datasource and pyalgotrade as backtesting platform

A professional framework for stock prediction using Transformer, LSTM, and tree-based models with comprehensive backtesting. Built on FinTSB and StockFormer research for financial practitioners seeking reliable, quantitative trading strategies.

This project uses Python to create an optimally weighted stock portfolio by combining 7 common technical indicators, generating trading signals, backtesting the strategy, and aiming to outperform the standard buy-and-hold strategy of the SPY ETF.

Broker Bot is an autonomous trading algorithm designed to continuously analyze New York Stock Exchange (NYSE) market conditions and execute profitable trades by utilizing advanced trading strategies. Built upon the Alpaca API, Broker Bot will be tuned through the extensive backtesting and paper trading capabilities provided.

Backtest and run stock trading CFD strategies tick by tick

Tools for automatic trading, stock market strategy backtesting and live trading

Using Python to Backtest and Evaluate Trading Strategies in the Dhaka Stock Exchange

Backtesting Yield Estimator for Index&Stock Options. A tool for testing long-term option-based trading/investment strategies.

💪📈 Powerfolio! is a stock screener and portfolio analysis. Backtest buy-and-hold vs. trading on RSI. Build a portfolio using efficient frontier and map hierarchical clustering results.

An Algorithm Manager that allows you to develop, test, and run stock trading algorithms on Alpaca. The Python library includes buy/sell functions, historical data, technical indicators, and more useful features to aid in the development quantitative algorithms. The manager includes a GUI (which can be opened from code or the python interactive interpreter) that allows you to track the progress of algorithms in backtests or in real time. Trades can be made in code by the algorithms (which can be scheduled to run at any times during the day), or manually from the python interactive interpreter. Multiple algorithms can run at a time. Algorithms can be added, removed, or paused, allocations can be changed, and assets can be rebalanced, all from within code or from the python interactive interpreter.

This article describes how to test your algorithmic trading strategy on a portfolio of stocks. A portfolio reduces risk as opposed to just applying a strategy to a single stock. Optimizing the portfolio can result in higher returns and reduce overall risk (Increases Sharpe Ratio).

Gym environment for stock trading featuring indicators, time series normalization and backtesting

This project implements Market Regime Detection using Hidden Markov Models (HMMs) to classify different market states and optimize trading strategies. It applies **backtesting techniques** on major stock indices to analyze performance.

In the dynamic world of stock trading, leveraging data-driven insights is key to staying ahead. Here’s a breakdown of a recent analysis I conducted, combining predictive modeling, technical indicators, and strategy backtesting to navigate market trends:

Stock dashboard for quick overview of a stock and backtesting of trading strategy

A unified framework for financial forecasting, integrating high-quality datasets and standardized evaluation tools. Supports easy comparison of algorithms on real-world financial tasks including stock prediction, trading strategy backtesting, and multimodal analysis.

QuantContext is an MCP server that gives AI agents real quant computation for better trading: stock screening, strategy backtesting, and factor analysis.

An efficient Python-based backtesting engine to validate quantitative trading strategies on historical stock market data.

Combines LLMs with fundamental analysis for algorithmic trading. Features: LLM evaluation of S&P 500 income statements Automated stock scoring and selection Strategy backtesting Performance visualization Uses Python, pandas, numpy, matplotlib, Groq API, and yfinance.