Search Results for "backtesting" - Page 2
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Showing 50 open source projects for "backtesting"
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QuantResearch
Quantitative analysis, strategies and backtests
...The repository also explores financial modeling topics such as vector autoregression, Gaussian mixture models, and option pricing techniques. Many notebooks demonstrate backtesting pipelines that allow users to evaluate trading strategies using historical market data. The project integrates machine learning methods with traditional quantitative finance models, illustrating how statistical techniques can be applied to asset management and trading. -
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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quantitative
Quantized transactions python3
...The repo is evidently tied to a popular video series (on Bilibili) that reportedly drew substantial attention, suggesting the material is meant to be both educational and hands-on. The README and associated lessons walk the user through implementing algorithms, likely covering data handling, backtesting, and maybe simple trading logic. As an open-source educational resource, it’s designed for Python users interested in automatic trading, algorithmic strategies, and financial data analysis. -
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MarketStore
DataFrame server for financial timeseries data
...You can think of it as an extensible DataFrame service that is accessible from anywhere in your system, at higher scalability. It is designed from the ground up to address scalability issues around handling large amounts of financial market data used in algorithmic trading backtesting, charting, and analyzing price history with data spanning many years, and granularity down to tick-level for the all US equities or the exploding cryptocurrencies space. If you are struggling with managing lots of HDF5 files, this is perfect solution to your problem. The batteries are included with the basic install, you can start pulling crypto price data from GDAX and writing it to the db with a simple plugin configuration. ... -
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SGX-Full-OrderBook-Tick-Data-Trading
Providing the solutions for high-frequency trading (HFT) strategies
SGX-Full-OrderBook-Tick-Data-Trading-Strategy is an open-source research project focused on modeling high-frequency financial market behavior using machine learning techniques. The repository analyzes tick-level order book data from the Singapore Exchange and attempts to capture the dynamics of limit order book movements. By extracting features such as order depth ratios and price movement indicators, the system trains machine learning models to predict short-term market changes. Several... -
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BitcoinExchangeFH
Cryptocurrency exchange market data feed handler
BitcoinExchangeFH is a slim application to record the price depth and trades in various exchanges. You can set it up quickly and record all the exchange data in a few minutes! If the exchange is not supported with the WebSocket API feed, it will automatically fall into using its REST API feed. The subscription section specifies the exchange and instruments to subscribe. After receiving the order book or trade update, each handler is updated. For example, for SQL database handler, it is... -
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Machine Learning Financial Laboratory
MlFinLab helps portfolio managers and traders
...The library also includes tools for constructing specialized financial data structures, generating predictive features, and evaluating trading strategies through backtesting. Its architecture emphasizes reproducibility, robust testing, and well-documented code so that researchers and practitioners can reliably experiment with financial machine learning models. -
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Pandas TA
Python 3 Pandas Extension with 130+ Indicators
Technical Analysis Indicators - Pandas TA is an easy-to-use Python 3 Pandas Extension with 130+ Indicators. Pandas Technical Analysis (Pandas TA) is an easy-to-use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), Simple Moving Average (sma) Moving Average Convergence Divergence (macd), Hull Exponential Moving Average... -
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MachineLearningStocks
Using python and scikit-learn to make stock predictions
...The model attempts to predict whether specific stocks will outperform a benchmark index such as the S&P 500. The repository includes scripts for parsing financial statistics, building training datasets, and performing backtesting to evaluate model performance over historical periods. Because it is structured as a template project, developers are encouraged to extend or modify the pipeline to test different algorithms, features, or investment strategies. -
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. -
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Quantitative-Notebooks
Educational notebooks on quantitative finance, algorithmic trading
...While each individual notebook is aimed at practical finance workflows, the overall repository helps practitioners and learners use Python, pandas, and numerical libraries to build, test, and evaluate financial strategies using historical market data. The notebooks typically showcase how to perform backtesting, factor analysis, risk assessment, and other quantitative workflows in a reproducible, exploratory format. Because quantitative analysis often requires visualization, statistics, and time series processing, these notebooks also serve as templates for real financial research and strategy prototyping. Users can adapt the examples to their own data sources, financial instruments, and modeling techniques. -
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Strategems
Quantitative systematic trading strategy development and backtesting
Strategems is a Julia package aimed at simplifying and streamlining the process of developing, testing, and optimizing algorithmic/systematic trading strategies. This package is inspired in large part by the quantstrat1,2 package in R, adopting a similar general structure to the building blocks that make up a strategy. Given the highly iterative nature of event-driven trading strategy development, Julia's high-performance design (particularly in the context of loops) and straightforward... -
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AIAlpha
Use unsupervised and supervised learning to predict stocks
...The project typically involves collecting market data, transforming financial indicators into machine learning features, and training models to identify patterns that may predict market trends. It also demonstrates how models can be evaluated through backtesting frameworks that simulate how a strategy would perform using historical market conditions. By combining financial analytics with machine learning algorithms, the repository illustrates the process of building data-driven investment strategies. -
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pyfolio
Portfolio and risk analytics in Python
pyfolio is a Python library for performance and risk analysis of financial portfolios developed by Quantopian Inc. It works well with the Zipline open source backtesting library. At the core of pyfolio is a so-called tear sheet that consists of various individual plots that provide a comprehensive image of the performance of a trading algorithm. Here's an example of a simple tear sheet analyzing a strategy. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. -
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Catalyst
An Algorithmic Trading Library for Crypto-Assets in Python
...Users can express strategies in Python, run backtests against historical price data, and analyze performance through built-in metrics and analytics to evaluate profitability, risk, and behavior under different market conditions. Beyond backtesting, Catalyst was designed to support live trading on multiple crypto exchanges such as Binance, Bitfinex, Bittrex, and Poloniex, bridging simulation and production within the same framework. The library includes a rich set of examples, Docker and conda configurations, and integration points for community resources like forums and Discord for sharing strategies and troubleshooting. -
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Gekko
A bitcoin trading bot written in node
Gekko is an open source platform for automating trading strategies over bitcoin markets. Define your own trading strategy and Gekko will take care of everything else. Gekko is free and 100% open source, you download and run the software on your own machine. Gekko only talks to the exchanges (if you want it too). We don't know anyhing about your strategies, usage, portfolio size or anything else (unless you tell us). Gekko supports 16 different exchanges (including Bitfinex, Bitstamp and... -
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fooltrader
Quant framework for stock
Build a standard data schema, and then implement various connectors to import systems you are familiar with for analysis. fooltrader is a quantitative analysis trading system designed using big data technology, including data capture, cleaning, structuring, calculation, display, backtesting and trading. Its goal is to provide a unified framework for the whole market (stock, futures, bonds, foreign exchange, digital currency, macroeconomics, etc.) for research, backtesting, forecasting, and trading. Its applicable objects include quantitative traders, teachers, and students majoring in finance, people interested in economic data, programmers, and people who like freedom and the spirit of exploration. ... -
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PyAlgoTrade
Python Algorithmic Trading Library
PyAlgoTrade is a Python library for backtesting stock trading strategies. -
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Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
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AIStockBot
Stock Analysis Program
AIStockBot aims to become the greatest Technical and Fundamental Stock Analysis program using different approaches including Artificial Intelligence. It strives to recommend stocks better than your average Financial Adviser. -
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QuantComponents
Free Java components for Quantitative Finance and Algorithmic Trading
... * Highly modular: usable as plain java API, OSGi components, or integrated into Eclipse * Standalone or client-server architecture, depending on performance and reliability needs * Integrated with Interactive Brokers through IB Java API * Generic broker API, it can easily be extended to work with other brokers * It works with historical and/or realtime market data * Backtesting facility * Extensible SWT charting library -
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CATSBF CCFEAAlgorithmic Trading Strategy Backtesting Framework enables historical backtesting. Integration to the backtesting exchange happens via FIX. Pre-integrated with Marketcetera 1.5.0, and includes a Marketcetera (1.5.0) market data adapter
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GeniusTrader
GeniusTrader is a stock market trading systems toolkit
...GeniusTrader implements trading systems defined in terms of: specific entry-exit points, current cash, current positions, trade type and most traditional technical analysis indicators. Market data time-periods range from ticks to years. Backtesting defined trading systems as well as tools to develop and test technical market indicators are provided. Visit geniustrader.org for full details and to download the most current version. Written in perl. -
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Data Model that will be applied to backtesting. The main goal of this project is to create a data model that is able to process large amounts of data using a simple script.
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