Learn-Quant: Wanna learn quant through code? (v1.6.0)

Welcome to Learn-Quant, your comprehensive, beginner-friendly toolkit for mastering quantitative finance, algorithmic trading, and Python programming.

What is This Repo?

A massive, curated collection of Python modules, strategies, and reference materials designed to help you:

• Master advanced strategies like Pairs Trading and Kalman Filters
• Learn high-frequency data engineering with AsyncIO
• Calculate beta, volatility, and correlation metrics
• Understand core quant concepts (Risk, Return, Derivatives, Portfolio Theory)
• Practice with realistic trading algorithms and backtesting simulators
• Learn Python from scratch: from basic loops to advanced OOP and Decorators
• Master computer science algorithms (Sorting, Searching, Graphs, DP) tailored for finance

What's Inside?

Every folder is a self-contained lesson. Pick a topic and dive in.

Python Basics (Level 1)

• UTILS - Python Basics - Numbers/ — Financial math and precision.
• UTILS - Python Basics - Strings/ — Text processing for tickers and news.
• UTILS - Python Basics - Control Flow/ — Logic for trading rules.
• UTILS - Python Basics - Functions/ — Building modular quant tools.

Data Structures & Algorithms (Level 2)

• UTILS - Data Structures/ — Lists, Dictionaries, Sets, and NumPy Arrays.
• UTILS - Algorithms - Sorting/ — bubblesort, quicksort, etc.
• UTILS - Algorithms - Searching/ — binary search, interpolation search.
• UTILS - Algorithms - Graph/ — Shortest paths for arbitrage.
• UTILS - Algorithms - Dynamic Programming/ — Optimization techniques.

Advanced Python & Engineering (Level 3)

• UTILS - Advanced Python - AsyncIO/ — Concurrent data fetching for high-frequency setups
• UTILS - Advanced Python - OOP/ — Building a scalable trading engine
• UTILS - Advanced Python - Decorators/ — Measuring execution time and logging

Quantitative Methods (Math & Stats)

• UTILS - Quantitative Methods - Kalman Filter/ — Dynamic hedging and noise filtering
• UTILS - Quantitative Methods - Statistics/ — Hypothesis testing and distributions
• UTILS - Quantitative Methods - Linear Algebra/ — Portfolio math
• UTILS - Quantitative Methods - Stochastic Processes/ — Geometric Brownian Motion
• UTILS - Quantitative Methods - Regression/ — Beta calculation and factor models

Finance Utilities & Simulators

• UTILS - Strategies - Pairs Trading/ — Statistical arbitrage simulation
• UTILS - Finance - Beta Calculator/ — Beta, levered/unlevered beta, upside/downside beta
• UTILS - Finance - Volatility Calculator/ — Historical, Parkinson, Garman-Klass, EWMA volatility
• UTILS - Finance - Correlation Analysis/ — Pearson, rolling, EWMA, tail correlation
• UTILS - Black-Scholes Option Pricing/ — Valuation of derivatives
• UTILS - Portfolio Optimizer/ — Efficient Frontier and Sharpe Ratio maximization
• UTILS - Monte Carlo Portfolio Simulator/ — Stress testing portfolios
• UTILS - Risk Metrics/ — VaR, Drawdown, Sortino Ratio
• UTILS - Technical Indicators/ — RSI, MACD, Bollinger Bands implementation

Data & AI

• UTILS - AI Development/ — Chatbots and simple market predictors
• UTILS - Sentiment Analysis on News/ — Natural Language Processing for trading
• UTILS - Websocket Connection/ — Real-time exchange streaming

Who Is This For?

1. Aspiring Quants: Bridge the gap between theory and code
2. Students & Developers: Learn algorithms with a financial context
3. Traders: Prototype strategies like Pairs Trading or Option Pricing

Getting Started

Installation

Clone the repo and install dependencies:

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

Run a Lesson

Navigate to any folder and run the Python script. For example, to try Pairs Trading:

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

Contributing

We welcome contributions:

• Found a bug? Open an issue
• Want to add a strategy? Submit a Pull Request
• Learn-Quant is designed to grow with the community

Learn-Quant v1.6.0 Quantitative Finance, Algorithms, and Python Mastery. Made by MeridianAlgo