How meridianalgo/learn-quant works
Learn-Quant is a learning-oriented collection of runnable, mostly self-contained Python and JavaScript lessons and quantitative-finance tools—not one integrated trading application. The principal learner workflow is to choose a topic, run its lesson or utility, and inspect calculations, examples, and (in interactive tutorials) quiz prompts. Its domain coverage spans programming foundations, quantitative methods, market and portfolio analysis, strategy simulation, and machine learning. The sampled backtester and feature-engineering module illustrate internal calculation responsibilities; the README documents additional capabilities whose wiring is not established by the sampled source.
Main entry points: Learning Platform Learning Platform · functions_tutorial.py Python Basics · async_fetching.py Python Engineering · graph_algorithms.py Algorithms
Components 18
Learning Experience
- Learning Platform
Learning Platform - Lesson Content
Learning Platform/content.py
Programming Foundations
- Python Basics
Python Basics - Functions/functions_tutorial.py - Python Engineering
Advanced Python - AsyncIO/async_fetching.py - Algorithms
Algorithms - Graph/graph_algorithms.py
Quant Finance
- Option Pricing
Black-Scholes Option Pricing/black_scholes.py - Risk Analysis
Risk Metrics/risk_summary.py - Portfolio Tools
Portfolio Management/portfolio_utils.py - Quant Methods
Quantitative Methods - Statistics/statistical_analysis.py
Trading Systems
- Market Data
Market Data/market_data_utils.py - Trading Strategies
Strategies - Momentum Trading/momentum_strategy.py - Backtesting
Strategies - Backtesting Engine/backtest_engine.py - Execution Simulation
Order Execution Simulator/order_execution_simulator.py
Data And ML
- Feature Engineering
Machine Learning - Feature Engineering/feature_engineering.py - Machine Learning
Machine Learning - Random Forest/rf_predictor.py - News Sentiment
Sentiment Analysis on News/sentiment_analysis.py
Other components
- Learner
- Examples And Results
Connections 30
- Learner → Learning Platform: explores lessons
- Learning Platform → Lesson Content: uses lesson material
- Learner → Python Basics: runs lesson
- Learner → Python Engineering: runs lesson
- Learner → Algorithms: runs lesson
- Learner → Option Pricing: runs module
- Learner → Risk Analysis: runs module
- Learner → Portfolio Tools: runs module
- Learner → Quant Methods: runs module
- Learner → Market Data: runs module
- Learner → Trading Strategies: runs module
- Learner → Backtesting: runs module
- Learner → Execution Simulation: runs module
- Learner → Feature Engineering: runs module
- Learner → Machine Learning: runs module
- Learner → News Sentiment: runs module
- Python Basics → Examples And Results: shows examples
- Python Engineering → Examples And Results: demonstrates concepts
- Algorithms → Examples And Results: returns results
- Option Pricing → Examples And Results: computes prices
- Risk Analysis → Examples And Results: reports metrics
- Portfolio Tools → Examples And Results: computes analytics
- Quant Methods → Examples And Results: computes statistics
- Market Data → Examples And Results: provides data
- Trading Strategies → Examples And Results: demonstrates signals
- Backtesting → Examples And Results: returns performance
- Execution Simulation → Examples And Results: simulates orders
- Feature Engineering → Examples And Results: builds features and labels
- Machine Learning → Examples And Results: produces predictions
- News Sentiment → Examples And Results: analyzes news