Open Source Python Financial Software
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Python Financial Software
View 8033 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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Yahoo! Finance market data downloader
Yahoo! Finance market data downloader
Ever since Yahoo! finance decommissioned their historical data API, many programs that relied on it to stop working. yfinance aims to solve this problem by offering a reliable, threaded, and Pythonic way to download historical market data from Yahoo! finance. yfinance aimed to offer a temporary fix to the problem by scraping the data from Yahoo! Finance and returning a the data in the same format as pandas_datareader's get_data_yahoo(), thus keeping the code changes in existing software to a minimum. The latest version of yfinance is a complete re-write of the libray, offering a reliable method of downloading historical market data from Yahoo! Finance, up to 1 minute granularity, with a more Pythonic way. The Ticker() module allows you get market and metadata for security, using a Pythonic way. -
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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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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. -
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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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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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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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NeuralProphet
A simple forecasting package
NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in doubt. We are working on an improved documentation. We appreciate any help to improve and update the docs. Lagged regressors (measured features, e.g temperature sensor). Future regressors (in advance known features, e.g. temperature forecast). Country holidays & recurring special events. Sparsity of coefficients through regularization. Plotting for forecast components, model coefficients as well as final predictions. Automatic selection of training-related hyperparameters. -
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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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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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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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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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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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Polar.sh
Polar is the best funding & monetization platform for developers
Focus on building your passion, while we focus on the infrastructure to get you paid. Your Polar page can be displayed as an official funding option across your GitHub repositories. Get one-time donations of support from your community with ease. Turn issues into a crowdfunded backlog and share the funding with your contributors. -
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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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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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FinGPT
Open-Source Financial Large Language Models!
FinGPT is an open-source large language model tailored specifically for financial tasks. Developed by AI4Finance Foundation, it is designed to assist with various financial applications, such as forecasting, financial sentiment analysis, and portfolio management. FinGPT has been trained on a diverse range of financial datasets, making it a powerful tool for finance professionals looking to leverage AI for data-driven decision-making. The model is freely available on platforms like Hugging Face, allowing for easy access and customization. FinGPT's capabilities are extended by its ability to integrate with existing financial systems and enhance predictive analytics in finance. -
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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. -
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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. -
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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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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. -
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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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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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A quantitative finance C++ library for modeling, pricing, trading, and risk management in real-life. A cross-platform free/open-source tool for derivatives and financial engineering.
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OpenOffice.org Utility Library
Library modules for creating ODF documents.
OpenOffice.org Utility Library modules for creating Open Document Format (ODF) documents which can be read by Office Suites including OpenOffice.org, LibreOffice.org, and Microsoft Office. Currently, ooolib-python can create Calc spreadsheet ODS documents. These documents include many features including: - Create multiple table spreadsheets - Cells with text, numbers, dates, formulas - Ability to use built-in styles - Ability to create automatic styles (ie. bold, italics, underline, font size, font color, background color, etc.) - Set column and row attributes including width and height I am currently hosting the new development code at the following URL: https://github.com/josephcolton/ooolib-python I will be posting releases here on SourceForge as well as GitHub, so you can come to either place for the releases. The newest code will only be available using Git on GitHub.
