Learn-Quant: Master Quantitative Finance & Python (v2.5.0)

Welcome to Learn-Quant — your all-in-one, open-source toolkit for mastering algorithmic trading, quantitative finance theory, and professional Python engineering. Every folder is a fully self-contained lesson: run it, read it, extend it.

What is New in v2.5.0

Complete documentation coverage: all 5 previously undocumented v2.4.0 modules now have full READMEs — Python Basics - NumPy, Python Basics - Pandas, Python Basics - Comprehensions, Options Pricing - JavaScript, Monte Carlo Simulation - JavaScript
Auto-format workflow: new GitHub Actions workflow (autoformat.yml) automatically runs ruff format on every push and commits the result back — no more CI failures from formatting
Docs builder overhaul: docs_builder.py now correctly categorizes all 100+ modules into 13 dedicated sections instead of dumping half into "Other"

Previous Releases

Version	Highlights
v2.4.0	NumPy, Pandas, Comprehensions tutorials; Black-Scholes JS, Monte Carlo JS
v2.3.0	GARCH, Cointegration, Performance Attribution, Stress Testing, Trend Following
v2.2.0	13 quant finance modules: Kelly, FX, Exotic Options, Black-Litterman, Regime Detection
v2.1.0	Four interactive quiz-based tutorials (statistics, options, risk, portfolio)
v2.0.0	Performance Analysis utils: Hurst, Omega, Tail, Gain-Pain

Overview

Learn-Quant is a curated collection of 100+ 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 gives you the building blocks to go from Python fundamentals all the way to HFT execution systems and ML-driven strategies.

Key Learning Outcomes

Master Quant Strategies: Pairs Trading, Momentum, Mean Reversion, Market Making, Statistical Arbitrage
Engineer Robust Systems: AsyncIO, Context Managers, Decorators, Multiprocessing, Error Handling
Deep Dive into Math: Kalman Filters, Stochastic Processes, GARCH, Cointegration, Copulas, Factor Models
Build Core Tools: Option Pricers, Risk Engines, Portfolio Optimizers, Backtesting Simulators
CS Algorithms: Sorting, Graph Theory, Dynamic Programming, Backtracking applied to market data
Modern ML: Random Forests, Time-Series ML, Reinforcement Learning, Sentiment Analysis

Complete Module Directory

Level 1 — Python Fundamentals

Essential coding skills for financial analysis. Start here if you are new to Python.

Module	What you will learn
`Python Basics - Numbers`	Floating point precision, decimal module for currency math, compound interest
`Python Basics - Strings`	Ticker manipulation, string formatting, news headline parsing
`Python Basics - Control Flow`	Trading logic, conditional rules, loop patterns for time-series
`Python Basics - Functions`	Building reusable quant libraries, closures, type hints
`Python Basics - NumPy`	Arrays, vectorised returns, broadcasting, covariance, portfolio variance
`Python Basics - Pandas`	DataFrames, resampling, rolling windows, groupby, SMA crossover backtest
`Python Basics - Comprehensions`	List/dict/set comprehensions, generators, map/filter/reduce, accumulate

Level 2 — Data Structures & Algorithms

Optimising performance and implementing classical CS techniques on market data.

Data Structures

Module	What you will learn
`Data Structures - Arrays`	NumPy array ops, time-series slicing, OHLCV matrix manipulation
`Data Structures - Lists`	Dynamic portfolio lists, deque for order books, tick-by-tick storage
`Data Structures - Dictionaries`	Hash maps for symbol lookups, order routing tables, config stores
`Data Structures - Tuples and Sets`	Immutable records, set operations for universe filtering

Algorithms

Module	What you will learn
`Algorithms - Sorting`	Quicksort, Mergesort, Timsort benchmarks on market data
`Algorithms - Searching`	Binary search on sorted time-series, interpolation search
`Algorithms - Tree`	BST, heap-based priority queues for order books
`Algorithms - Graph`	Arbitrage detection via shortest paths (Bellman-Ford)
`Algorithms - Dynamic Programming`	Optimal execution paths, knapsack-style position sizing
`Algorithms - Backtracking`	Constraint-based portfolio construction, combinatorial search
`Algorithms - String`	Pattern matching on tick data, FIX message parsing helpers
`Algorithms - Machine Learning`	Classical ML algorithms from scratch: k-NN, decision trees, gradient descent

Level 3 — Advanced Python Engineering

Writing professional, production-ready financial systems.

Module	What you will learn
`Advanced Python - OOP`	Scalable Trading Engines and Portfolio Managers using classes and inheritance
`Advanced Python - AsyncIO`	High-throughput async data pipelines, concurrent order submission
`Advanced Python - Context Managers`	Database locks, atomic transactions, resource cleanup in trading systems
`Advanced Python - Decorators and Generators`	Custom logging wrappers, timing decorators, lazy price-stream generators
`Advanced Python - Error Handling`	Robust systems that never crash mid-trade: retry logic, circuit breakers
`Advanced Python - Multiprocessing`	Parallel Monte Carlo, parallel backtests, parameter sweeps across all CPU cores

Level 4 — Quantitative Methods

The mathematics underpinning modern finance.

Module	What you will learn
`Quantitative Methods - Statistics`	Hypothesis testing, stationarity tests, cointegration, Z-scores, fat tails — includes interactive quiz
`Quantitative Methods - Regression Analysis`	OLS, GLS, factor regressions, residual diagnostics
`Quantitative Methods - Linear Algebra`	Matrix decompositions for portfolio optimisation, PCA, eigenvalues
`Quantitative Methods - Factor Models`	Fama-French 3-factor, factor regression, alpha decomposition, performance attribution
`Quantitative Methods - Time Series`	ARIMA, ACF/PACF, stationarity, forecasting
`Quantitative Methods - Stochastic Processes`	GBM, Ornstein-Uhlenbeck, Brownian Bridge, Heston model
`Quantitative Methods - GARCH`	EWMA, GARCH(1,1) MLE fitting, multi-step volatility forecasting
`Quantitative Methods - Cointegration`	ADF unit-root, Engle-Granger two-step, OU half-life, rolling z-score for pairs
`Quantitative Methods - Kalman Filter`	Dynamic hedge ratios, noise filtering, state-space models
`Quantitative Methods - Optimization`	Convex optimisation, quadratic programming, scipy.optimize for portfolio problems
`Quantitative Methods - Performance Analysis`	Hurst Exponent, Omega Ratio, Tail Ratio, Gain-Pain, active return metrics
`Quantitative Methods - Copulas`	Gaussian and t-copulas, tail dependence, joint return simulation
`Quantitative Methods - Interest Rate Models`	Vasicek, CIR, Hull-White short rate models
`Quantitative Methods - Regime Detection`	Hidden Markov Models, changepoint detection, bull/bear regime classification
`Quantitative Methods - TVM`	Time Value of Money: PV, FV, NPV, IRR, bond pricing foundations

Level 5 — Options, Derivatives & Finance

Pricing, Greeks, valuation, and core financial instruments.

Options & Derivatives

Module	What you will learn
`Black-Scholes Option Pricing`	European call/put pricing, all five Greeks, implied vol — includes interactive tutorial
`Advanced Options Pricing`	Binomial trees, finite-difference methods, American options
`Options Pricing - JavaScript`	Full Black-Scholes in pure JS: price, Greeks, IV bisection solver
`Finance - Greeks Calculator`	Delta, Gamma, Theta, Vega, Rho across a full options chain
`Finance - Exotic Options`	Barriers, Asian, Lookback, Digital — analytical and Monte Carlo pricing
`Finance - Options Strategies`	Spreads, straddles, strangles, condors — payoff diagrams and breakeven analysis
`Options Chain Simulator`	Synthetic options chain generation with vol surface interpolation
`Technical Indicators`	SMA, EMA, RSI (Wilder), MACD, Bollinger Bands, ATR — Python and JavaScript
`Monte Carlo Simulation - JavaScript`	GBM paths, correlated multi-asset portfolio MC, antithetic variates, VaR/CVaR

Fixed Income & Valuation

Module	What you will learn
`Bond Price and Yield`	Yield-to-maturity, duration, convexity, zero-coupon and coupon bonds
`Finance - Duration Convexity`	Modified/effective duration, convexity adjustment, immunisation
`Finance - Yield Curve`	Nelson-Siegel fitting, forward rate extraction, curve shape classification
`Discounted Cash Flow (DCF)`	DCF valuation, terminal value, sensitivity analysis
`CAPM`	Capital Asset Pricing Model, beta estimation, security market line
`Finance - Beta Calculator`	Rolling beta, market beta vs. sector beta
`Finance - Correlation Analysis`	Pearson and Spearman correlation, rolling correlation, correlation heatmaps
`Finance - Covariance Estimation`	Sample, shrinkage (Ledoit-Wolf), and robust covariance estimators
`Finance - Credit Risk`	PD/LGD/EAD framework, Merton model, credit spreads
`Finance - Volatility Calculator`	Parkinson, Garman-Klass, EWMA volatility estimators
`Finance - Kelly Criterion`	Full Kelly, fractional Kelly, continuous Kelly for multi-asset portfolios
`Finance - Position Sizing`	Fixed fractional, volatility targeting, risk of ruin analysis
`Finance - Transaction Cost Analysis`	Implementation shortfall, VWAP slippage, market impact models
`Finance - FX Tools`	Spot/forward rates, PPP, interest rate parity, triangular arbitrage
`Finance - Expected Shortfall`	ES (CVaR) calculation, parametric and historical methods
`Dividend Tracker`	Dividend yield, growth rate modelling, dividend discount model

Level 6 — Risk, Portfolio & Strategies

Applied quantitative finance: measure risk, build portfolios, run strategies.

Risk & Performance

Module	What you will learn
`Risk Metrics`	VaR, CVaR, Drawdown, Sortino Ratio — includes interactive tutorial with quizzes
`Risk Metrics - Drawdown Analysis`	Max drawdown, drawdown duration, Calmar ratio, underwater equity curves
`Risk Metrics - Stress Testing`	2008 GFC, 2020 COVID, 1987 crash, dotcom, 2022 scenarios; sensitivity analysis
`Value at Risk (VaR)`	Parametric, historical, and Monte Carlo VaR; backtesting and Kupiec test
`Sharpe and Sortino Ratio`	Risk-adjusted return metrics, annualisation, rolling Sharpe
`Finance - Performance Attribution`	Brinson-Hood-Beebower allocation/selection/interaction decomposition
`Finance - Expected Shortfall`	ES/CVaR: parametric, historical, and Monte Carlo approaches

Portfolio Management

Module	What you will learn
`Portfolio Optimizer`	Efficient Frontier, max-Sharpe, min-variance, Markowitz — includes interactive tutorial
`Portfolio Tracker`	Position tracking, P&L attribution, multi-asset portfolio dashboard
`Portfolio Management`	Portfolio construction, rebalancing rules, turnover constraints
`Portfolio Management - Black Litterman`	Black-Litterman model, investor views, posterior allocation
`Monte Carlo Portfolio Simulator`	Multi-path portfolio simulation, probability-of-ruin, wealth distribution

Strategies

Module	What you will learn
`Strategies - Pairs Trading`	Cointegration-based stat arb, spread z-score entry/exit, hedge ratio
`Strategies - Momentum Trading`	Cross-sectional and time-series momentum, signal generation, backtest
`Strategies - Mean Reversion`	Bollinger Band + RSI signals, Ornstein-Uhlenbeck process, reversion-to-mean
`Strategies - Trend Following`	Donchian channel, MA crossover, ATR-based volatility position sizing
`Strategies - Market Making`	Bid/ask spread optimisation, inventory management, Avellaneda-Stoikov
`Strategies - Statistical Arbitrage`	Factor-neutral stat arb, residual momentum, cross-sectional z-score
`Order Execution Simulator`	VWAP, TWAP, POV execution algorithms; market impact simulation

Level 7 — AI, ML & Market Microstructure

Modern data-driven approaches and low-latency market structure.

Machine Learning & AI

Module	What you will learn
`Machine Learning - Random Forest`	Random forests for return prediction, feature importance, walk-forward CV
`Machine Learning Time Series`	LSTM, gradient boosting on financial time-series, train/test discipline
`Reinforcement Learning Q Learning`	Q-learning trading agent, reward engineering, policy evaluation
`AI Development`	Integrating LLMs and ML models into trading pipelines
`Sentiment Analysis on News`	NLP for fundamental analysis, FinBERT, news-driven signals

Data & Connectivity

Module	What you will learn
`Market Data`	Historical and real-time data ingestion, cleaning, normalisation
`Historical Data`	JavaScript module for fetching and processing historical price data
`News Fetching`	JavaScript module for scraping and structuring financial news
`Websocket Connection`	Real-time market data streaming, reconnection logic
`Learning Platform`	Interactive CLI learning platform with progress tracking

Market Microstructure & HFT

Module	What you will learn
`Market Microstructure`	Order book implementation, spread analysis, market impact models
`High Frequency Trading`	Latency optimisation, co-location considerations, HFT execution strategies

Utilities & Tools

Module	What you will learn
`Core Utilities`	Shared helpers: date utils, config loaders, math primitives
`Data Processing`	Cleaning, normalising, resampling raw market data at scale
`Logging`	Structured logging for trading systems in Python and JavaScript
`System Utilities`	Process management, environment introspection, cross-platform helpers
`Currency Converter`	Real-time and historical FX conversion utilities
`Economic Calendar`	Macro event calendar parsing and impact classification
`Finance - Transaction Cost Analysis`	TCA pipeline: slippage measurement, benchmark comparison

Usage

Installation

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

Running a Module

Navigate to any directory and run the script directly. All modules are self-contained.

# Python Basics
cd "Python Basics - NumPy"
python numpy_tutorial.py

# Quantitative methods
cd "Quantitative Methods - GARCH"
python garch_model.py

# Options pricing
cd "Black-Scholes Option Pricing"
python black_scholes.py

# Strategies
cd "Strategies - Pairs Trading"
python pairs_trading.py

# JavaScript modules (requires Node.js)
cd "Options Pricing - JavaScript"
node blackScholes.js

cd "Monte Carlo Simulation - JavaScript"
node monteCarlo.js

Recommended Learning Path

Level 1 → Level 2 → Level 3 → Level 4 → Level 5 → Level 6 → Level 7

Each level builds on the last. Complete the interactive tutorials (files ending in _tutorial.py) inside each module for quizzes and worked examples.

Repository Stats

Category	Count
Total modules	100+
Python lesson files	175+
JavaScript modules	7
Modules with interactive tutorials	4
Test files	90+

Contributing

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 v2.5.0 Quantitative Finance | Algorithmic Trading | Python Mastery Maintained by MeridianAlgo