Open Source Python Financial Software
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Python Financial Software
View 8278 business solutionsBrowse free open source Python Financial Software and projects below. Use the toggles on the left to filter open source Python Financial Software by OS, license, language, programming language, and project status.
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Qbot
AI-powered Quantitative Investment Research Platform
Qbot is an open source quantitative research and trading platform that provides a full pipeline from data ingestion and strategy development to backtesting, simulation, and (optionally) live trading. It bundles a lightweight GUI client (built with wxPython) and a modular backend so researchers can iterate on strategies, run batch backtests, and validate ideas in a near-real simulated environment that models latency and slippage. The project places special emphasis on AI-driven strategies — including supervised learning, reinforcement learning and multi-factor models — and offers a “model zoo” and example strategies to help users get started. For evaluation and analysis, Qbot integrates reporting and visualization (tearsheets, metrics) so you can compare performance across runs and inspect trade-level behavior. It supports multiple strategy runtimes and backtesting engines, is organized for extensibility (strategies live in a dedicated folder). -
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NautilusTrader
A high-performance algorithmic trading platform
NautilusTrader is an open-source, high-performance, production-grade algorithmic trading platform, provides quantitative traders with the ability to backtest portfolios of automated trading strategies on historical data with an event-driven engine, and also deploy those same strategies live, with no code changes. The platform is 'AI-first', designed to develop and deploy algorithmic trading strategies within a highly performant and robust Python native environment. This helps to address the parity challenge of keeping the Python research/backtest environment, consistent with the production live trading environment. NautilusTraders design, architecture and implementation philosophy holds software correctness and safety at the highest level, with the aim of supporting Python native, mission-critical, trading system backtesting and live deployment workloads. -
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OpenBB Terminal
Investment research for everyone, anywhere
Fully written in python which is one of the most used programming languages due to its simplified syntax and shallow learning curve. It is the first time in history that users, regardless of their background, can so easily add features to an investment research platform. The MIT Open Source license allows any user to fork the project to either add features to the broader community or create their own customized terminal version. The terminal allows for users to import their own proprietary datasets to use on our econometric menu. In addition, users are allowed to export any type of data to any type of format whether that is raw data in Excel or an image in PNG. This is ideal for finance content creation. Create notebook templates (through papermill) which can be run on different tickers. This level of automation allows to speed up the development of your investment thesis and reduce human error. -
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Prophet
Tool for producing high quality forecasts for time series data
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well. Prophet is used in many applications across Facebook for producing reliable forecasts for planning and goal setting. We’ve found it to perform better than any other approach in the majority of cases. We fit models in Stan so that you get forecasts in just a few seconds. Get a reasonable forecast on messy data with no manual effort. Prophet is robust to outliers, missing data, and dramatic changes in your time series. -
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Google Spreadsheets Python
Google Sheets Python API
gspread is a Python API for Google Sheets. A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs [sic]. Since it’s a separate account, by default it does not have access to any spreadsheet until you share it with this account. Just like any other Google account. To access spreadsheets via Google Sheets API you need to authenticate and authorize your application. Older versions of gspread have used oauth2client. Google has deprecated it in favor of google-auth. If you’re still using oauth2client credentials, the library will convert these to google-auth for you, but you can change your code to use the new credentials to make sure nothing breaks in the future. If you familiar with the Jupyter Notebook, Google Colaboratory is probably the easiest way to get started using gspread. -
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Qlib
Qlib is an AI-oriented quantitative investment platform
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. With Qlib, users can easily try their ideas to create better Quant investment strategies. At the module level, Qlib is a platform that consists of above components. The components are designed as loose-coupled modules and each component could be used stand-alone. -
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VisiData
A terminal spreadsheet multitool for discovering and arranging data
VisiData is an interactive multitool for tabular data. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility that can handle millions of rows with ease. A terminal interface for exploring and arranging tabular data. VisiData supports tsv, CSV, SQLite, JSON, xlsx (Excel), hdf5, and many other formats. Requires Linux, OS/X, or Windows (with WSL). Hundreds of other commands and options are also available; see the documentation. Code in the stable branch of this repository, including the main vd application, loaders, and plugins, is available for use and redistribution under GPLv3. VisiData is a free, open-source tool that lets you quickly open, explore, summarize, and analyze datasets in your computer’s terminal. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources. -
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Odoo
Open-source business management software
Odoo 18 is a comprehensive open-source business management software that offers a suite of integrated applications to streamline various organizational processes. Designed for flexibility and scalability, it provides tools for managing functions like sales, inventory, accounting, human resources, and customer relationships. Odoo's modular structure allows businesses to adopt only the features they need while maintaining the option to expand functionality as they grow. The open-source version is community-driven, making it cost-effective and continuously improving through global developer contributions. Its user-friendly interface and robust customization options make it a popular choice for small to medium-sized businesses seeking an adaptable and efficient ERP solution. -
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AI Hedge Fund
An AI Hedge Fund Team
This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions. Its structure is educational: intended more as a proof-of-concept than a ready-to-use financial product, giving learners insight into the mechanics of quantitative finance automation. The project underlines AI’s potential in investment strategies but also carries disclaimers that it is for research and not financial advice. The implementation is designed so developers can study the pipeline end-to-end: from data ingestion through modeling to simulated portfolio management. -
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Finance Database
This is a database of 300.000+ symbols containing Equities, ETFs, etc.
As a private investor, the sheer amount of information that can be found on the internet is rather daunting. Trying to understand what type of companies or ETFs are available is incredibly challenging with there being millions of companies and derivatives available on the market. Sure, the most traded companies and ETFs can quickly be found simply because they are known to the public (for example, Microsoft, Tesla, S&P500 ETF or an All-World ETF). However, what else is out there is often unknown. This database tries to solve that. It features 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets. It, therefore, allows you to obtain a broad overview of sectors, industries, types of investments and much more. The aim of this database is explicitly not to provide up-to-date fundamentals or stock data as those can be obtained with ease (with the help of this database) by using yfinance, FundamentalAnalysis or ThePassiveInvestor. -
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alpha_vantage
A python wrapper for Alpha Vantage API for financial data.
Alpha Vantage delivers a free API for real time financial data and most used finance indicators in a simple json or pandas format. This module implements a python interface to the free API provided by Alpha Vantage. You can have a look at all the API calls available in their API documentation. For code-less access to the APIs, you may also consider the official Google Sheet Add-on or the Microsoft Excel Add-on by Alpha Vantage. To get data from the API, simply import the library and call the object with your API key. Next, get ready for some awesome, free, realtime finance data. Your API key may also be stored in the environment variable ALPHAVANTAGE_API_KEY. The library supports giving its results as json dictionaries (default), pandas dataframe (if installed) or csv, simply pass the parameter output_format='pandas' to change the format of the output for all the API calls in the given class. -
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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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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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QuickFIX is the worlds first Open Source C++ FIX (Financial Information eXchange) engine, helping financial institutions easily integrate with each other. The SVN repository is now locked. Latest code is hosted at github. https://github.com/quickfix/quickfix
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Awesome-Quant
A curated list of insanely awesome libraries, packages and resources
awesome-quant is a curated list (“awesome list”) of libraries, packages, articles, and resources for quantitative finance (“quants”). It includes tools, frameworks, research papers, blogs, datasets, etc. It aims to help people working in algorithmic trading, quant investing, financial engineering, etc., find useful open source or educational resources. Licensed under typical “awesome” list standards. -
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Optopsy
A nimble options backtesting library for Python
Optopsy is a Python-based, nimble backtesting and statistics library focused on evaluating options trading strategies like calls, puts, straddles, spreads, and more, using pandas-driven analysis. The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. Optopsy is a small simple library that offloads the heavy work of backtesting option strategies, the API is designed to be simple and easy to implement into your regular Panda's data analysis workflow. As such, we just need to call the long_calls() function to have Optopsy generate all combinations of a simple long call strategy for the specified time period and return a DataFrame. Here we also use Panda's round() function afterwards to return statistics within two decimal places. -
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AIQuant
AI-powered platform for quantitative trading
ai_quant_trade is an AI-powered, one-stop open-source platform for quantitative trading—ranging from learning and simulation to actual trading. It consolidates stock trading knowledge, strategy examples, factor discovery, traditional rules-based strategies, various machine learning and deep learning methods, reinforcement learning, graph neural networks, high-frequency trading, C++ deployment, and Jupyter Notebook examples for practical hands-on use. Stock trading strategies: large models, factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, high-frequency trading, etc. Resource summary: network-wide resource summary, practical cases, paper interpretation, and code implementation. -
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AlphaPy
Python AutoML for Trading Systems and Sports Betting
AlphaPy is a Python-based AutoML framework tailored for trading systems and sports betting applications. Built on popular libraries like scikit-learn and pandas, it enables data scientists and speculators to craft predictive models, ensemble strategies, and automated forecasting systems with minimal setup. Run machine learning models using scikit-learn, Keras, xgboost, LightGBM, and CatBoost. Generate blended or stacked ensembles. Create models for analyzing the markets with MarketFlow. Develop trading systems and analyze portfolios using MarketFlow and Quantopian's pyfolio. -
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AutoTrader
A Python-based development platform for automated trading systems
AutoTrader is a Python-based platform—now archived—designed to facilitate the full lifecycle of automated trading systems. It provides tools for backtesting, strategy optimization, visualization, and live trading integration. A feature-rich trading simulator, supporting backtesting and paper trading. The 'virtual broker' allows you to test your strategies in a risk-free, simulated environment before going live. Capable of simulating multiple order types, stop-losse,s and take-profits, cross-exchange arbitrage and portfolio strategies, AutoTrader has more than enough to build a profitable trading system. -
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PyTorch Forecasting
Time series forecasting with PyTorch
PyTorch Forecasting aims to ease state-of-the-art time series forecasting with neural networks for both real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. A time series dataset class that abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. A base model class that provides basic training of time series models along with logging in tensorboard and generic visualizations such actual vs predictions and dependency plots. Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box. -
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ThetaGang
ThetaGang is an IBKR bot for collecting money
ThetaGang is an IBKR trading bot for collecting premiums by selling options using "The Wheel" strategy. The Wheel is a strategy that surfaced on Reddit but has been used by many in the past. This bot implements a slightly modified version of The Wheel, with my own personal tweaks. The strategy, as implemented here, does a few things differently from the one described in the post above. For one, it's intended to be used to augment a typical index-fund-based portfolio with specific asset allocations. For example, you might want to use a 60/40 portfolio with SPY (S&P500 fund) and TLT (20-year treasury fund). This strategy reduces risk, but may also limit gains from big market swings. By reducing risk, one can increase leverage. ThetaGang will try to acquire your desired allocation of each stock or ETF according to the weights you specify in the config. To acquire the positions, the script will write puts when conditions are met. -
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TradeMaster
TradeMaster is an open-source platform for quantitative trading
TradeMaster is a first-of-its-kind, best-in-class open-source platform for quantitative trading (QT) empowered by reinforcement learning (RL), which covers the full pipeline for the design, implementation, evaluation and deployment of RL-based algorithms. TradeMaster is composed of 6 key modules: 1) multi-modality market data of different financial assets at multiple granularities; 2) whole data preprocessing pipeline; 3) a series of high-fidelity data-driven market simulators for mainstream QT tasks; 4) efficient implementations of over 13 novel RL-based trading algorithms; 5) systematic evaluation toolkits with 6 axes and 17 measures; 6) different interfaces for interdisciplinary users. -
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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. Installing Zipline is slightly more involved than the average Python package. For a development installation (used to develop Zipline itself), create and activate a virtualenv, then run the etc/dev-install script. Please note that Zipline is not a community-led project. Zipline is maintained by the Quantopian engineering team, and we are quite small and often busy. -
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OpenMoneyBox
Budget management
OpenMoneyBox is an application designed to manage small personal budgets in the easiest way. Check the homepage to download apps/packages for additional Operating Systems. -
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Trading system written in Python including Quotes Management, Historical and live data, Import/Export data, Charting, Candlestick, Technical analysis, automated alerts, portfolio management, risk management, currency exchange, and much much more ...
