Open Source Python Financial Software
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Python Financial Software
View 8171 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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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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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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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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Forex Assistant
Manage accounts, analyze trades, track strategies & stats , News & Cal
Forex Assistant is a comprehensive Windows desktop application designed to support forex traders in every aspect of their trading journey. From essential trading calculators and market insights to powerful account and trade management, it brings everything you need into one intuitive platform. 🔑 Main Features 🧮 Calculator Tools Profit/Loss Calculator Currency Converter Position Size Calculator Pips Calculator Margin Calculator 🌍 Market Insights Live Market News Economic Calendar 📊 Account Manager Add multiple trading accounts with balance and custom descriptions. Track current balance, profit/loss, and drawdown (daily and total). Add and manage positions manually with full details: entry price, stop loss, take profit, volume, PNL, and notes. Add notes for every trade: describe the strategy used, whether the trade was emotional or technical. Import historical trades directly from MetaTrader HTML reports. 📈 Trade Analysis & Insights -
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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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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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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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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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Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. Currently, Task-TS from CoronaWhy primarily maintains this repository. Pull requests are welcome. Historically, this repository provided open-source benchmarks and codes for flash flood and river flow forecasting. Full transformer (SimpleTransformer in model_dict): The full original transformer with all 8 encoder and decoder blocks. Requires passing the target in at inference. -
Cynet All-in-One Cybersecurity PlatformCynet empowers MSPs and MSSPs with a comprehensive, fully managed cybersecurity platform that consolidates essential security functions into a single, easy-to-use solution. Cynet simplifies cybersecurity management, reduces operational overhead, and lowers costs by eliminating the need for multiple vendors and complex integrations.
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Cryptocheck
Monitors balances of your cryptocurrency addresses
Cryptocheck monitors balances of your cryptocurrency addresses and raises an alert in case of any change detected. It connects to known block explorer API services to verify balances. It is useful for long-term investors with multiple different cryptocurrencies in their portfolio. You no longer need to access all your wallets with passwords to simply just check that your money are still there. Cryptocheck also provides a simple profit calculation and history charts mapping your portfolio history. And there is also a server node available! It continuously monitors balances, records history data and sends all the data to your Cryptocheck desktop application. Supported cryptocurrencies: https://sourceforge.net/p/cryptocheck/wiki/Home/#supported-cryptocurrencies For more details about Cryptocheck and how to use it, see wiki: https://sourceforge.net/p/cryptocheck/wiki I am open to add other cryptocurrencies on your request. -
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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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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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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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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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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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Financial Calculator
Professional financial tools: 16 sections with text and visual reports
Take control of your finances with Financial Calculator 7.5, a professional yet easy-to-use desktop tool built for precise, everyday financial calculations. From planning loans and estimating taxes to generating invoices and creating QR codes, this all-in-one software offers 16 specialized sections that make your daily financial and business tasks faster, clearer, and more accurate. Whether you’re a student, small business owner, or finance professional, Financial Calculator brings professional-grade precision to your desktop — no installation required. Perform calculations for Bank, Barcode, Building, Buy & Sell, Commodity, Dividing Money, Inflation, Interest, Internet Usage, Invoice, Loan, Password, Phone, QR Code, Stock Market, and Tax. Unlock the full version with a small cryptocurrency payment (as low as $0.01). -
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TinyERP has been replaced by openerp, in october 2008. Check the openerp website for more information.
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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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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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abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc. -
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Financial Calculator
Professional financial tools: 16 sections with text and visual reports
Take control of your finances with Financial Calculator 7.5, a professional yet easy-to-use desktop tool built for precise, everyday financial calculations. From planning loans and estimating taxes to generating invoices and creating QR codes, this all-in-one software offers 16 specialized sections that make your daily financial and business tasks faster, clearer, and more accurate. Whether you’re a student, small business owner, or finance professional, Financial Calculator brings professional-grade precision to your desktop — no installation required. Perform calculations for Bank, Barcode, Building, Buy & Sell, Commodity, Dividing Money, Inflation, Interest, Internet Usage, Invoice, Loan, Password, Phone, QR Code, Stock Market, and Tax. Unlock the full version with a small cryptocurrency payment (as low as $0.01). -
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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.
