Welcome to Learn-Quant! Your all-in-one, comprehensive toolkit for mastering algorithmic trading, quantitative finance theory, and professional Python software engineering.
Four new interactive, quiz-based tutorials have been added. Run them directly from the CLI to learn with worked examples and immediate feedback:
| Tutorial | Directory | Topics Covered |
|---|---|---|
| statistics_tutorial.py | UTILS - Quantitative Methods - Statistics | Normal distribution, Z-scores, correlation, hypothesis testing, skewness/kurtosis |
| options_tutorial.py | UTILS - Black-Scholes Option Pricing | Black-Scholes formula, all five Greeks, put-call parity, implied volatility |
| risk_tutorial.py | UTILS - Risk Metrics | VaR (historical and parametric), CVaR/Expected Shortfall, drawdown, Sharpe/Sortino |
| portfolio_tutorial.py | UTILS - Portfolio Optimizer | Portfolio variance, efficient frontier, Sharpe maximisation, diversification limits |
Each tutorial includes step-by-step explanations, live calculations using real financial inputs, and multiple-choice quiz questions with explanations after every section.
Learn-Quant is a massive, curated collection of over 60+ self-contained modules designed to bridge the gap between academic theory and production-grade code. Whether you are a student, a software engineer moving into finance, or a trader learning to code, this repository provides the building blocks you need.
- Master Quant Strategies: Implement Pairs Trading, Momentum, Mean Reversion, Position Sizing, and more.
- Engineer Robust Systems: Learn AsyncIO, Context Managers, Decorators, and advanced OOP.
- Deep Dive into Math: Kalman Filters, Stochastic Processes, Factor Models, Linear Algebra for Portfolio Theory.
- Build Core Tools: Create your own Option Pricers, Risk Engines (VaR), and Backtesting Simulators.
- CS Algorithms: Understand how Sorting, Graph Theory, and Dynamic Programming apply to market data.
Every folder is a fully functional lesson. Pick a topic and run the code.
Essential coding skills for financial analysis.
UTILS - Python Basics - Numbers: Floating point precision & financial math.UTILS - Python Basics - Strings: Ticker manipulation & news parsing.UTILS - Python Basics - Control Flow: Implementing trading logic & rules.UTILS - Python Basics - Functions: Building reusable quant libraries.
Optimizing performance for high-frequency environments.
UTILS - Data Structures: Efficient use of Lists, Sets, Tuples, and Dictionaries.UTILS - Algorithms - Sorting: Algorithmic efficiency (Quicksort, Mergesort).UTILS - Algorithms - Searching: Binary search on time-series data.UTILS - Algorithms - Graph: Arbitrage detection using shortest paths.UTILS - Algorithms - Dynamic Programming: Optimizing execution paths.
Writing professional, production-ready code.
UTILS - Advanced Python - AsyncIO: Building high-throughput data pipelines.UTILS - Advanced Python - OOP: Designing scalable Trading Engines & Portfolio Managers.UTILS - Advanced Python - Context Managers: Handling database locks and atomic transactions.UTILS - Advanced Python - Decorators: Custom logging, timing, and error handling wrappers.UTILS - Advanced Python - Error Handling: Robust systems that never crash mid-trade.UTILS - Advanced Python - Multiprocessing: Parallel Monte Carlo, backtests, and parameter sweeps across all CPU cores.
The mathematics of the markets.
UTILS - Quantitative Methods - Kalman Filter: Dynamic hedge ratios & noise filtering.UTILS - Quantitative Methods - Stochastic Processes: Geometric Brownian Motion & Monte Carlo.UTILS - Quantitative Methods - Statistics: Hypothesis testing, stationarity, and cointegration. Includes interactive tutorial (statistics_tutorial.py) with quizzes covering Z-scores, correlation, and fat tails.UTILS - Quantitative Methods - Regression: Factor models & Alpha generation.UTILS - Quantitative Methods - Linear Algebra: Portfolio optimization & risk modelling.UTILS - Quantitative Methods - Factor Models: Fama-French 3-Factor model, factor regression, alpha decomposition, and performance attribution.UTILS - Quantitative Methods - Performance Analysis: Hurst Exponent, Omega Ratio, Tail Ratio, and Active Metrics.
Applied quantitative finance.
UTILS - Strategies - Pairs Trading: Statistical arbitrage & mean reversion.UTILS - Strategies - Momentum Trading: Trend following & signal generation.UTILS - Strategies - Mean Reversion: Bollinger Band + RSI signals, Ornstein-Uhlenbeck process, and reversion-to-mean backtesting.UTILS - Black-Scholes Option Pricing: Greeks, implied volatility, & derivatives pricing. Includes interactive tutorial (options_tutorial.py) covering Black-Scholes, all five Greeks, and put-call parity.UTILS - Finance - Volatility Calculator: Parkinson, Garman-Klass, & EWMA estimators.UTILS - Finance - Yield Curve: Nelson-Siegel model fitting, forward rate extraction, and curve shape classification.UTILS - Finance - Position Sizing: Kelly Criterion, Fixed Fractional, Volatility Targeting, and Risk of Ruin.UTILS - Portfolio Optimizer: Efficient Frontier, Sharpe Ratio, & Markowitz optimization. Includes interactive tutorial (portfolio_tutorial.py) walking through MPT and portfolio construction.UTILS - Risk Metrics: Value at Risk (VaR), CVaR, Drawdown, & Sortino Ratio. Includes interactive tutorial (risk_tutorial.py) with worked examples and quizzes.UTILS - Technical Indicators: Custom implementations of RSI, MACD, Bollinger Bands.
Modern approaches to trading.
UTILS - AI Development: Basic market prediction models.UTILS - Sentiment Analysis on News: NLP for fundamental analysis.UTILS - Websocket Connection: Real-time market data streaming.
Understanding order book dynamics and market impact.
UTILS - Market Microstructure: Order book implementation, spread analysis, and market impact models.UTILS - High Frequency Trading: Latency optimization, execution algorithms, and HFT strategies.
Clone the repository and install the required dependencies.
git clone https://github.com/MeridianAlgo/Learn-Quant
pip install -r requirements.txtNavigate to any directory and run the tutorial script.
Example: Running the Momentum Strategy
cd "UTILS - Strategies - Momentum Trading"
python momentum_strategy.pyExample: Learning Performance Metrics
cd "UTILS - Quantitative Methods - Performance Analysis"
python hurst_exponent.pyWe believe in open-source knowledge. Contributions are welcome!
- Found a bug? Open an Issue.
- Have a new strategy? Fork the repo and submit a Pull Request.
- Documentation improvements? We love those too.
This project is open-sourced under the MIT License.
Learn-Quant v2.1.0 Quantitative Finance | Algorithmic Trading | Python Mastery Maintained by MeridianAlgo
