A curated list of awesome financial software, libraries, resources, and tools.

• Trading & Investment Platforms
• Market Data
• Libraries & SDKs
• Trading Frameworks
• AI & Machine Learning
• Portfolio Management
• Cryptocurrency
• Personal Finance
• Risk Management
• Educational Resources
• Numerical Libraries
• Time Series Analysis
• Backtesting & Trading
• Reinforcement Learning Tools
• Trading Utilities
• Financial Datasets
• Time Series Forecasting Models

• Alpaca - Commission-free stock trading API for algorithmic trading
• Interactive Brokers - Professional trading platform with comprehensive API
• Robinhood - Commission-free investing platform
• TD Ameritrade - Trading platform with powerful APIs

• Alpha Vantage - Free APIs for realtime and historical financial data
• Yahoo Finance - Financial news and data platform
• IEX Cloud - Financial data infrastructure
• Finnhub - Real-time RESTful APIs for stocks, forex, and crypto
• Marketstack - Real-Time, Intraday & Historical Market Data API
• Financial Modeling Prep - Realtime and historical stock data
• SEC EDGAR Data - API to access annual reports of public US companies
• StockData - Real-Time, Intraday & Historical Market Data, News and Sentiment API
• Coinglass - Cryptocurrency market data API featuring:
  • Historical data with various time intervals (1min to 12h)
  • Real-time whale monitoring
  • Liquidation data and funding rates
  • Multiple request rate tiers
  • Support for major crypto exchanges
• MacroMicro - Financial data visualization platform featuring:
  • Global macro data analysis
  • Market sentiment indicators
  • Cross-asset correlations
  • Interactive charts and dashboards
  • Economic indicators and market trends

• QuantLib - Library for quantitative finance
• pandas-ta - Technical Analysis Indicators in Pandas
• FinanceAnalysis - Database for financial data
• yfinance - Yahoo Finance market data downloader
• metrics.py - Performance metrics for evaluating trading strategies
• indicators.py - Common technical indicators implemented in Pandas
• signals.py - Converting technical indicators into trading signals
• portfolio.py - Object-oriented portfolio simulation tools

• ccxt - Cryptocurrency trading API
• finance.js - Financial calculations in JavaScript
• accounting.js - Number, money and currency formatting

• Ito.jl - Quantitative finance in Julia
• QuantLib.jl - QuantLib implementation in pure Julia
• Miletus.jl - Financial contract modeling and valuation framework

• Strata - Modern open-source analytics and market risk library
• JQuantLib - Free, open-source quantitative finance framework
• finmath - Mathematical finance algorithms library

• QuantMath - Financial mathematics library
• Barter - Framework for building trading systems
• RustQuant - Quantitative finance library in Rust

• Portfolio Visualizer - Portfolio analysis and optimization tools
• OpenBB Terminal - Open-source investment research platform
• Quantopian - Platform for developing trading strategies

• CoinGecko - Cryptocurrency data aggregator
• Binance - Cryptocurrency exchange with extensive API
• MetaMask - Ethereum wallet and gateway
• CryptoCompare - Cryptocurrency market data

• Mint - Personal finance and budgeting tool
• YNAB - Zero-based budgeting software
• Personal Capital - Investment and wealth management platform
• GnuCash - Open-source accounting software

• OpenRisk - Open-source risk management tools
• Risk-API - Risk management API
• PyRisk - Python library for risk analytics

• Investopedia - Financial education website
• Khan Academy - Finance - Free finance courses
• Wall Street Oasis - Financial careers community

• "The Intelligent Investor" by Benjamin Graham
• "A Random Walk Down Wall Street" by Burton Malkiel
• "Options, Futures, and Other Derivatives" by John C. Hull

• AI Hedge Fund - Educational proof-of-concept for an AI-powered hedge fund using multiple agents (valuation, sentiment, fundamentals, technical analysis, risk management)
• FinRL - Deep reinforcement learning framework for quantitative finance
• OpenBB - Open source investment research platform with AI capabilities
• Qlib - AI-oriented quantitative investment platform by Microsoft
• FinGPT - Open source LLMs for financial applications
• CryptoMamba - State Space Model-based architecture for cryptocurrency price prediction featuring:
  • Novel Mamba-based architecture for time-series forecasting
  • Two trading algorithms (Vanilla and Smart) for real-world performance
  • Outperforms LSTM, Bi-LSTM, GRU, and S-Mamba baselines
  • Support for multiple features and time resolutions
  • Complete training and evaluation pipeline
• FinCon - A synthesized LLM multi-agent framework for financial decision making, featuring:
  • Manager-analyst communication hierarchy for collaborative decision making
  • Risk-control component with self-critiquing mechanism
  • Conceptual verbal reinforcement for knowledge updates
  • Applications in stock trading and portfolio management
  • Strong generalization capabilities across various financial tasks
• Deep Learning for Crypto - Comprehensive review and evaluation of deep learning models for cryptocurrency price prediction:
  • Evaluation of LSTM variants, CNN variants, and Transformer models
  • Comparison of univariate vs multivariate approaches
  • Multi-step ahead prediction capabilities
  • COVID-19 impact analysis on model performance
  • Best performance achieved by convolutional LSTM with multivariate approach
  • Implementation insights for different market conditions

• Fixer.io - Foreign exchange rates and currency conversion API
• Currency-api - Free Currency Exchange Rates API with 150+ Currencies
• ExchangeRate-API - Free currency conversion
• Frankfurter - Exchange rates, currency conversion and time series
• VATComply.com - Exchange rates, geolocation and VAT number validation

• Plaid - Connect with user's bank accounts and access transaction data
• Razorpay IFSC - Indian Financial Systems Code (Bank Branch Codes)
• Mono - Connect with users' bank accounts and access transaction data in Africa
• Nordigen - Connect to bank accounts using official bank APIs

• NumPy - Fundamental package for scientific computing with Python
• SciPy - Ecosystem of open-source software for mathematics, science, and engineering
• Pandas - High-performance data structures and data analysis tools
• Polars - Lightning-fast DataFrame library for data manipulation
• SymPy - Library for symbolic mathematics
• PyMC - Probabilistic programming with Bayesian modeling
• ArcticDB - High performance datastore for time series and tick data

• ARCH - Autoregressive Conditional Heteroskedasticity models in Python
• statsmodels - Statistical modeling and econometrics in Python
• PyFlux - Time series modeling and inference
• tsfresh - Automatic extraction of relevant features from time series
• Prophet - Tool for producing high quality forecasts for time series data
• pmdarima - ARIMA estimators for Python
• gluon-ts - Probabilistic time series modeling

• Zipline - Pythonic algorithmic trading library
• Backtrader - Python backtesting library for trading strategies
• QSTrader - Backtesting simulation engine
• bt - Flexible backtesting for Python
• PyAlgoTrade - Algorithmic trading library
• VectorBT - Fast backtesting and research platform
• FinRL - Deep reinforcement learning for trading
• Blankly - Unified interface for trading and backtesting

• TradeMaster - Open-source platform for quantitative trading empowered by reinforcement learning, featuring:
  • Multi-modality market data processing
  • High-fidelity market simulators
  • 13+ RL-based trading algorithms
  • Systematic evaluation toolkits
  • Support for multiple trading tasks (portfolio management, high-frequency trading, etc.)
  • Comprehensive visualization tools
• FinRL - Deep reinforcement learning framework for quantitative finance
• Blankly - Unified interface for trading and backtesting

• RLlib - Industry-grade library for reinforcement learning with support for:
  • 30+ RL algorithms (PPO, A2C, DDPG, TD3, SAC, IMPALA, etc.)
  • Multi-agent RL
  • Distributed training
  • TensorFlow, PyTorch, and JAX
• Stable Baselines3 - Set of reliable implementations of RL algorithms in PyTorch
• SpinningUp - Educational resource with clean implementations of key algorithms (PPO, TRPO, DDPG, TD3, SAC)
• Acme - DeepMind's framework for RL research, supporting:
  • TensorFlow and JAX
  • Distributed training
  • State-of-the-art algorithms (DDPG, D4PG, MPO, R2D2)
• Dopamine - Research framework for fast prototyping of RL algorithms by Google
• Coax - Lightweight RL framework with clean implementations using JAX
• Garage - Toolkit for reproducible reinforcement learning research with:
  • Comprehensive testing suite
  • Support for PyTorch and TensorFlow
  • Multi-task and meta-RL algorithms
• crypto-rl - Deep Reinforcement Learning toolkit for cryptocurrencies featuring:
  • Record and replay cryptocurrency limit order book data
  • Train DDQN agents for trading
  • Support for Coinbase Pro and Bitfinex exchanges
  • Integration with Arctic Tick Store for data persistence
  • Technical indicators with O(1) time complexity
  • Extended OpenAI Gym environment for trading

• algorithmic-trading-with-python - Comprehensive collection of trading utilities including:
  • Performance metrics evaluation tools
  • Technical indicators in pure Pandas
  • Ternary signal conversion
  • Grid search optimization
  • Portfolio simulation building blocks
  • Multi-core K-fold cross-validation
  • EOD stock data and alternative data streams

• Financial Datasets - A comprehensive platform offering a wide range of financial datasets, including:
  • Stock market data
  • Economic indicators
  • Alternative data sources
  • Historical financial data
  • APIs for easy access to datasets

• TimesFM - A pretrained time-series foundation model developed by Google Research for time-series forecasting, featuring:
  • Univariate time series forecasting for context lengths up to 2048 time points
  • Point forecasts with optional quantile heads
  • Support for various frequency indicators
  • Installation and usage instructions available on the Hugging Face page
  • Pretraining data includes a variety of datasets for robust performance

Please read the contribution guidelines first. Feel free to contribute by submitting a pull request.

To the extent possible under law, the authors have waived all copyright and related rights to this work.