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Learn-Quant: Master Quantitative Finance & Python (v2.1.0)

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Welcome to Learn-Quant! Your all-in-one, comprehensive toolkit for mastering algorithmic trading, quantitative finance theory, and professional Python software engineering.

What is New in v2.1.0

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.

Overview

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.

Key Learning Outcomes

  • 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.

Repository Structure

Every folder is a fully functional lesson. Pick a topic and run the code.

Level 1: Python Fundamentals

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.

Level 2: Data Structures & Algorithms

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.

Level 3: Advanced Engineering

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.

Level 4: Quantitative Methods

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.

Level 5: Strategies & Finance

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.

Level 6: AI & Alternative Data

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.

Level 7: Market Microstructure

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.

Usage

1. Installation

Clone the repository and install the required dependencies.

git clone https://github.com/MeridianAlgo/Learn-Quant
pip install -r requirements.txt

2. Running a Module

Navigate to any directory and run the tutorial script.

Example: Running the Momentum Strategy

cd "UTILS - Strategies - Momentum Trading"
python momentum_strategy.py

Example: Learning Performance Metrics

cd "UTILS - Quantitative Methods - Performance Analysis"
python hurst_exponent.py

Contributing

We 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.

License

This project is open-sourced under the MIT License.

Learn-Quant v2.1.0 Quantitative Finance | Algorithmic Trading | Python Mastery Maintained by MeridianAlgo