All Modules

Complete index of all Learn-Quant lessons and utilities.

Python Fundamentals

Python Basics - Comprehensions

Python Basics – Comprehensions

Covers Python's concise data-transformation tools — list, dict, and set comprehensions, generator expressions, map, filter, functools.reduce, and itertools.accumulate — all applied to quantitative finance workflows. These patterns appear constantly in professional quant code and replace verbose loops with readable, performant one-liners.

Python Basics - Control Flow

Python Basics – Control Flow Utility

This utility teaches Python control flow structures essential for building trading algorithms and financial applications. Master conditionals, loops, and comprehensions to write efficient, readable code.

Python Basics - Functions

Python Basics – Functions Utility

This utility teaches Python functions - the building blocks of modular, reusable code. Learn to write efficient trading algorithms and financial tools using proper function design.

Python Basics - NumPy

Python Basics – NumPy

Covers the NumPy primitives that appear in virtually every quant codebase — from vectorised return calculations to portfolio variance via the quadratic form. All examples use realistic financial data so the connection between the NumPy API and actual quant work is immediate.

Python Basics - Numbers

Python Basics – Numbers Utility

This utility helps beginners practice Python number handling. It pairs with the foundational lessons in Documentation/Programs/level1_fundamentals.py and extends the concepts with finance-flavored examples.

Python Basics - Pandas

Python Basics – Pandas

Covers the Pandas patterns that power real quant research pipelines — from building a synthetic OHLCV DataFrame through rolling indicators, resampling, groupby analysis, and a simple SMA-crossover backtest. Every example is grounded in price data so the link from Pandas API to practical quant work stays concrete.

Python Basics - Strings

Python Basics – Strings Utility

This beginner-friendly utility introduces Python string fundamentals through hands-on examples. It is perfect for newcomers following the learning path in Documentation/Programs/level1_fundamentals.py and looking for extra practice manipulating text data.

Data Structures

Data Structures - Arrays

Arrays - Complete Guide to NumPy for Beginners and Beyond

Welcome to the comprehensive guide to NumPy arrays! This utility is designed to help both beginners and experienced Python programmers master array operations for data analysis, scientific computing, and quantitative finance.

Data Structures - Dictionaries

Dictionaries - Key-Value Data Structures for Financial Analysis

This utility provides comprehensive Python dictionary operations essential for financial data organization, lookup tables, and key-value mappings. Dictionaries are the backbone of feature engineering and data lookup in quantitative finance.

Data Structures - Lists

Lists - Python List Operations for Financial Data

This utility provides comprehensive Python list operations essential for financial data processing, algorithmic trading, and data manipulation. Lists are flexible containers that can store heterogeneous data types and are fundamental to Python programming.

Data Structures - Tuples and Sets

Data Structures – Tuples and Sets

Tuples and Sets are fundamental Python data structures that complement Lists and Dictionaries. Understanding when to use them is key to writing efficient, Pythonic code for financial applications.

Algorithms

Algorithms - Backtracking

Algorithms – Backtracking

Backtracking is a general algorithmic technique for solving problems by building candidates incrementally and abandoning a candidate ("backtracking") as soon as it is determined to violate the problem constraints. It is a systematic form of exhaustive search that prunes the search space to avoid exploring clearly invalid paths.

Algorithms - Dynamic Programming

Algorithms – Dynamic Programming

Dynamic Programming (DP) is an algorithmic technique for solving problems by breaking them into overlapping subproblems, solving each subproblem once, and storing the result to avoid redundant computation. It converts exponential-time recursive solutions into polynomial-time ones.

Algorithms - Graph

Algorithms – Graph

Graph algorithms operate on structures composed of vertices (nodes) and edges (connections). Many financial problems are naturally modelled as graphs: currency markets form weighted directed graphs, asset correlation matrices define undirected weighted graphs, and order routing networks are flow graphs.

Algorithms - Machine Learning

Algorithms – Machine Learning

This module implements fundamental machine learning algorithms from scratch using only NumPy — no scikit-learn or frameworks. Building these algorithms by hand is the most effective way to understand what happens inside the black boxes used in production trading systems.

Algorithms - Searching

Algorithms – Searching

Searching algorithms find a target value within a data structure. The choice of algorithm determines whether a search takes O(n) time (checking every element) or O(log n) time (dividing the search space in half each step). In latency-sensitive financial systems, this difference is meaningful at scale.

Algorithms - Sorting

Sorting Algorithms

A comprehensive implementation of fundamental sorting algorithms with detailed explanations, complexity analysis, and performance comparisons.

Algorithms - String

Algorithms – String

String algorithms handle efficient manipulation, searching, and analysis of text data. In quantitative finance, string processing is essential for parsing market data feeds, extracting information from news and filings, matching ticker symbols, and cleaning raw data from APIs.

Algorithms - Tree

Algorithms – Tree

Tree data structures organise data hierarchically to enable efficient search, insertion, and deletion. Binary Search Trees (BSTs) and their balanced variants (AVL trees, Red-Black trees) are the foundation of many performance-critical systems in finance, including order book matching engines, index structures for time-series databases, and priority queues for event-driven simulations.

Advanced Python

Advanced Python - AsyncIO

AsyncIO for High-Frequency Data

In quantitative finance, speed is edge. Python's asyncio library allows for concurrency, letting your program handle multiple tasks (like fetching data from 10 different exchanges) at once, rather than waiting for one to finish before starting the next.

Advanced Python - Context Managers

Advanced Python – Context Managers

Context Managers are a powerful Python feature for resource management. They allow you to allocate and release resources precisely when you want to. The most common usage is the with statement.

Advanced Python - Decorators and Generators

Advanced Python – Decorators and Generators

Decorators and Generators are powerful Python features that separate professional code from beginner scripts. Decorators allow you to modify function behavior cleanly, while Generators enable memory-efficient processing of large financial datasets.

Advanced Python - Error Handling

Advanced Python – Error Handling

Robust error handling is what separates a script that crashes overnight from a professional trading system that runs for years. This module teaches you how to anticipate, catch, and manage errors gracefully.

Advanced Python - Multiprocessing

Advanced Python Multiprocessing

Python Global Interpreter Lock prevents multiple threads from executing Python bytecode at the same time. This makes threads useless for intense algorithmic work. The multiprocessing module bypasses the lock entirely by spawning separate operating system processes. Each process has its own Python interpreter and memory space, enabling true parallelism across all processing cores.

Advanced Python - OOP

Advanced Python – Object-Oriented Programming

Object-Oriented Programming (OOP) is essential for building scalable, maintainable trading systems and financial applications. Learn to organize code using classes, objects, and OOP principles.

Quantitative Methods

Quantitative Methods - Cointegration

Cointegration & Pairs Trading Foundations

Cointegration: two non-stationary series whose linear combination is stationary. Backbone of statistical arbitrage and pairs trading.

Quantitative Methods - Copulas

Quantitative Methods - Copulas

This module demonstrates the concept of Copulas, specifically the Gaussian Copula, used in quantitative finance to model the dependency structure between multivariate random variables.

Quantitative Methods - Factor Models

Quantitative Methods – Factor Models

Factor models explain asset returns as a linear combination of systematic factors plus a stock-specific residual. The Fama-French 3-Factor Model (1992) extended CAPM by adding two well-documented risk premia: the Size premium (SMB) and the Value premium (HML), dramatically improving the explanation of cross-sectional stock returns.

Quantitative Methods - GARCH

GARCH Volatility Models

GARCH (Generalized Autoregressive Conditional Heteroskedasticity) captures volatility clustering — high-volatility days tend to follow high-volatility days. Used for risk forecasting, option pricing, and VaR.

Quantitative Methods - Interest Rate Models

Short Rate Interest Rate Models

Continuous-time models for the evolution of the short (instantaneous) interest rate. Used for bond pricing, interest rate derivatives, and yield curve modeling.

Quantitative Methods - Kalman Filter

Multi-Purpose Kalman Filter

This module provides a pure Python implementation of a 1-Dimensional Kalman Filter. Kalman filters are recursive algorithms used to estimate the state of a linear dynamic system from a series of noisy measurements.

Quantitative Methods - Linear Algebra

Quantitative Methods – Linear Algebra

Linear algebra is the mathematical foundation for portfolio optimization, risk modeling, factor analysis, and quantitative finance. This utility teaches essential concepts through practical financial applications.

Quantitative Methods - Optimization

Quantitative Methods – Optimization

Optimization is the mathematical engine behind modern finance. From finding the best portfolio weights to calibrating complex models, optimization techniques are essential for quantitative analysts.

Quantitative Methods - Performance Analysis

Performance Analysis Utilities

This module provides quantitative performance metrics to evaluate risk-adjusted returns and the quality of investment strategies. Beyond simple metrics like the Sharpe Ratio, these tools help quants analyze tail risk, active management skill, and the statistical properties of return series.

Quantitative Methods - Regime Detection

Market Regime Detection

Identifies distinct market states (bull/bear, low/high volatility) using statistical methods. Regime-aware strategies adapt parameters to the current market environment.

Quantitative Methods - Regression Analysis

Quantitative Methods – Regression Analysis

Regression analysis is the statistical "Swiss Army Knife" of quantitative finance. It allows you to quantify relationships between variables, such as how a stock moves relative to the market (Beta) or how factors drive returns.

Quantitative Methods - Statistics

Statistics - Essential Statistical Analysis for Quantitative Finance

This utility provides comprehensive statistical analysis tools essential for quantitative finance, risk management, and investment analysis. Statistics forms the foundation for understanding financial data patterns, risk assessment, and predictive modeling.

Quantitative Methods - Stochastic Processes

Quantitative Methods – Stochastic Processes

Stochastic processes are mathematical models for random systems evolving over time. In finance, they are used to model asset prices, interest rates, and volatility for pricing derivatives and managing risk.

Quantitative Methods - TVM

Time Value of Money (TVM) - Core Financial Calculations

This utility provides comprehensive Time Value of Money (TVM) calculations essential for financial analysis, investment evaluation, and capital budgeting. TVM is the foundation of quantitative finance and corporate finance.

Quantitative Methods - Time Series

Quantitative Methods – Time Series Utility

This utility introduces core time-series techniques used in quantitative finance. It serves as a bridge between the intermediate and advanced curriculum (Documentation/Programs/level3_financial.py and level4_advanced.py) and gives you reusable helpers for analyzing historical price data.

Options, Derivatives & Finance

Advanced Options Pricing

Advanced Options Pricing

This module covers advanced mathematical techniques for pricing financial derivatives. The focus is on models beyond the standard assumptions. Rather than assuming constant volatility, we explore dynamic and local volatility models. These models are crucial for correctly valuing exotic options and managing the risks of complex derivatives portfolios.

Black-Scholes Option Pricing

Black-Scholes Option Pricing Utility

This module lets you price basic stock options (calls and puts) using the Black-Scholes formula, a foundation of modern financial analysis.

Bond Price and Yield

Bond Price and Yield Calculator

This utility lets you calculate the fair price of a bond or estimate its yield to maturity (YTM), two of the most basic (and important!) ideas in investing.

CAPM

CAPM (Capital Asset Pricing Model) Utility

This module lets you calculate the expected return of any stock or portfolio according to CAPM, a core idea in modern finance for pricing risky assets.

Discounted Cash Flow (DCF)

Discounted Cash Flow (DCF) Calculator

This tool calculates the present value of a series of future cash flows—the basic principle behind valuing businesses, real estate, projects, and stocks!

Dividend Tracker

Dividend Tracker Utility (NO API)

This utility does NOT use any external APIs. All data is managed locally for learning and experimentation.

Finance - Beta Calculator

Beta Calculator

Calculate beta and related systematic risk metrics for portfolio analysis.

Finance - Correlation Analysis

Correlation Analysis

Analyze correlations between financial instruments for portfolio construction and risk management.

Finance - Covariance Estimation

Robust Covariance Estimation

Sample covariance is noisy and often poorly conditioned with many assets. Shrinkage estimators blend sample covariance with a structured target for more stable portfolio optimization.

Finance - Credit Risk

Merton Credit Risk Model

The Merton (1974) structural credit model treats a firm's equity as a call option on its assets. Default occurs when asset value falls below debt face value at maturity.

Finance - Duration Convexity

Bond Duration, Convexity, and DV01

Fixed income sensitivity measures that quantify how bond prices respond to changes in interest rates.

Finance - Exotic Options

Exotic Options Pricing

Monte Carlo pricing for path-dependent options that have no simple closed-form solution (or where the path matters, not just the terminal price).

Finance - Expected Shortfall

Expected Shortfall (CVaR)

Expected Shortfall (ES), also called Conditional Value at Risk (CVaR), measures the expected loss given that losses exceed the VaR threshold. It is a coherent risk measure — unlike VaR, it captures tail severity, not just frequency.

Finance - FX Tools

FX (Foreign Exchange) Tools

Core analytics for foreign exchange markets: no-arbitrage pricing, option valuation, and cross-rate calculations.

Finance - Greeks Calculator

Finance – Greeks Calculator

The Options Greeks measure the sensitivity of an option's price to changes in underlying market parameters. They are the primary tools used by options traders and risk managers to understand, hedge, and monitor options positions.

Finance - Kelly Criterion

Kelly Criterion Position Sizing

The Kelly Criterion determines the optimal fraction of capital to allocate to maximize the long-run geometric growth rate of wealth.

Finance - Options Strategies

Finance – Options Strategies

Options strategies combine multiple option legs (calls and puts at different strikes and expiries) to create specific risk/reward profiles. Rather than taking a directional bet with a single option, multi-leg strategies allow traders to express nuanced views on direction, volatility, time decay, and risk limits.

Finance - Position Sizing

Finance – Position Sizing

Position sizing is the most underrated skill in quantitative trading. A strategy with a mediocre edge and excellent position sizing will outperform a brilliant strategy with reckless sizing. This module covers four fundamental frameworks every trader and quant must understand before risking real capital.

Finance - Transaction Cost Analysis

Transaction Cost Analysis (TCA)

Tools for measuring execution quality and estimating market impact. TCA is essential for evaluating whether a strategy's theoretical alpha survives real-world trading costs.

Finance - Volatility Calculator

Volatility Calculator

Calculate various volatility metrics for financial instruments.

Finance - Yield Curve

Finance – Yield Curve

The yield curve is the most closely watched chart in global finance. It plots interest rates (yields) across different maturities for bonds of equal credit quality — most commonly US Treasury bonds. Its shape and movements drive pricing for virtually every financial asset, from mortgages to corporate bonds to equity discount rates.

Monte Carlo Simulation - JavaScript

Monte Carlo Simulation – JavaScript

A pure JavaScript Monte Carlo engine for portfolio simulation and European option pricing via geometric Brownian motion (GBM). Implements correlated multi-asset paths using Cholesky decomposition, antithetic variates for variance reduction, and VaR/CVaR estimation from the simulated return distribution. No external dependencies — runs directly in Node.js.

Options Chain Simulator

Options Chain Simulator Utility (NO API)

This utility does NOT use any external APIs. All calculations are done locally for learning and experimentation.

Options Pricing - JavaScript

Options Pricing – JavaScript

A pure JavaScript implementation of the Black-Scholes European options pricing model with all five Greeks and implied volatility via bisection. No external dependencies — runs directly in Node.js and can be imported as a module into any JS project.

Technical Indicators

Technical Indicators Calculator Utility (NO API)

This utility does NOT use any external APIs. All calculations are done locally for learning and experimentation.

Risk & Performance

Finance - Performance Attribution

Performance Attribution

Brinson decomposition splits portfolio active return into allocation and selection effects — answering "did we beat the benchmark by picking the right sectors or the right stocks?"

Risk Metrics

Risk Metrics Summary Utility

This module gives you quick, professional stats about risk in any list or array of investment returns. It's used by investors, analysts, and students everywhere!

Risk Metrics - Drawdown Analysis

Drawdown Analysis

Comprehensive drawdown metrics for quantifying portfolio loss risk over time. Drawdown measures capture both the depth and duration of losses — dimensions VaR ignores.

Risk Metrics - Stress Testing

Portfolio Stress Testing

Stress tests answer: "What happens if 2008 repeats?" or "How big a shock kills the portfolio?" Required by Basel III, CCAR, and most institutional risk frameworks.

Sharpe and Sortino Ratio

Sharpe and Sortino Ratio Calculator

This utility offers easy-to-use Python functions to calculate Sharpe and Sortino ratios for financial returns. These ratios help you understand whether a series of investment returns is attractive on a risk-adjusted basis.

Value at Risk (VaR)

Value at Risk (VaR) Calculator

This utility lets you estimate the potential losses on a portfolio or investment using Value at Risk (VaR), one of the most important tools in financial risk management.

Portfolio Management

Monte Carlo Portfolio Simulator

Monte Carlo Portfolio Simulator

This utility helps you forecast possible futures for a portfolio using random simulations—a key idea in finance, risk management, and statistics!

Portfolio Management

Portfolio Management Utilities

This folder contains utilities for portfolio management, risk analysis, and investment optimization.

Portfolio Management - Black Litterman

Black-Litterman Portfolio Optimization

The Black-Litterman (1990) model addresses the instability of mean-variance optimization by blending market equilibrium returns with investor views using Bayesian updating.

Portfolio Optimizer

Portfolio Optimizer (Mean-Variance)

This utility helps you find the best mix of assets for a portfolio, balancing risk and return using the foundation of Modern Portfolio Theory (MPT).

Portfolio Tracker

Portfolio Tracker Utility (USES yfinance API)

This utility uses the yfinance API to fetch current prices automatically. All other calculations and data are managed locally for learning and experimentation.

Strategies

Order Execution Simulator

Order Execution Simulator Utility (NO API)

This utility does NOT use any external APIs. All trades and portfolio data are managed locally for learning and experimentation.

Strategies - Market Making

Avellaneda-Stoikov Market Making Model

Implementation of the Avellaneda-Stoikov (2008) continuous-time market making model. A dealer posts bid/ask quotes to maximize expected PnL while penalizing inventory accumulation.

Strategies - Mean Reversion

Strategies – Mean Reversion

Mean reversion is the statistical tendency for an asset's price to return to its historical average after deviating from it. While Momentum strategies bet on continuation, Mean Reversion strategies bet on reversal — buying when something is "too cheap" and selling when it is "too expensive" relative to recent history.

Strategies - Momentum Trading

Strategies – Momentum Trading

Momentum trading is a strategy that capitalizes on the continuance of existing trends in the market. The core philosophy is "buy high, sell higher." If an asset's price is rising strongly, momentum traders assume it will continue to rise.

Strategies - Pairs Trading

Pairs Trading Strategy

This module demonstrates a statistical arbitrage strategy known as Pairs Trading. It identifies two assets that move together and trades the convergence of their spread. When the correlation weakens temporarily, executing trades on both assets allows for capturing profits as they revert to their historical relationship. This quantitative technique relies strictly on mathematical relationships rather than fundamental valuation.

Strategies - Statistical Arbitrage

Strategies - Statistical Arbitrage

This module demonstrates a basic Statistical Arbitrage strategy, specifically pairs trading.

Strategies - Trend Following

Trend Following

Trend-following: ride momentum with discipline. Backbone of CTAs and managed-futures funds (AHL, Winton, Man, MLP). Profits from extended directional moves; pays for it during chop.

AI & Machine Learning

AI Development

Gemini API Chatbot

This project provides simple command-line chatbots for Google's Gemini API in both Python and Node.js.

Learning Platform

Interactive Python Learning Platform

An all-in-one learning hub that delivers progressive Python lessons through both a guided CLI and a hostable Flask web interface. Lessons combine narrative walkthroughs, executable code examples, mini quizzes, and follow-up practice ideas geared toward aspiring quantitative developers.

Machine Learning - Random Forest

Machine Learning - Random Forest

This module provides a basic implementation of a Random Forest Predictor for quantitative finance. It uses scikit-learn's RandomForestRegressor to predict time series data or returns based on a set of features.

Machine Learning Time Series

Machine Learning for Sequential Financial Data

Applying incredibly sophisticated statistical and advanced computational matrix calculating algorithms to historical sequential asset prices explicitly enables quantitative researchers to discover heavily latent non linear correlation patterns. Standard basic linear techniques lack the internal theoretical mapping memory required to fully process continuous progression data natively. Therefore, explicit sequential data pattern prediction necessitates deeply specialized memory architectures uniquely capable of successfully retaining vast contextual numerical memory safely across thousands of chronologically independent market observations simultaneously.

Reinforcement Learning Q Learning

Reinforcement Learning for Quantitative Finance

This module extensively covers the core mathematical algorithms necessary to construct entirely autonomous quantitative execution agents. Rather than relying on rigid statistical parameters or explicit condition based trading logic, reinforcement learning allows an agent to discover the most optimal sequences of action through continuous simulated trial and error. The intelligent agent dynamically interprets complex environmental states and receives explicit scalar rewards or punitive penalties based directly upon its transactional profitability and risk management threshold maintenance. Over thousands of episodes, the model organically maps the market mechanics to develop a mathematically optimal trading policy without human intervention.

Sentiment Analysis on News

Sentiment Analysis on News Utility (NO API)

This utility does NOT use any external APIs. All sentiment analysis is done locally using a simple rule-based approach for learning and experimentation.

Market Microstructure

High Frequency Trading

High Frequency Trading

High Frequency Trading (HFT) encompasses algorithmic strategies that execute a large number of orders at extremely high speeds — typically microseconds to milliseconds. HFT firms compete primarily on latency: the fastest participant to react to new information captures the profit.

Market Microstructure

Market Microstructure

Market microstructure studies how trading mechanisms — the rules, protocols, and participants in a market — affect price formation, liquidity, and transaction costs. Understanding microstructure is essential for designing realistic execution algorithms, building order books, estimating market impact, and analysing bid-ask spreads.

Utilities & Tools

Core Utilities

Core Utilities

This folder contains core mathematical and date/time utilities that form the foundation for quantitative finance calculations.

Currency Converter

Currency Converter Utility (NO API)

This utility does NOT use any external APIs. All exchange rates are entered manually for learning and experimentation.

Data Processing

Data Processing Utilities

This folder contains utilities for data processing, validation, and manipulation in financial applications.

Economic Calendar

Economic Calendar Simulator Utility (NO API)

This utility does NOT use any external APIs. All data is managed locally for learning and experimentation.

Historical Data

Alpaca Historical Data Fetcher

This Node.js script fetches historical bars (OHLCV data) for stocks or crypto from the Alpaca Market Data API. It prompts you for the symbol type, symbol, timeframe, and date range, then displays the results in JSON format.

Logging

Logging Utilities

This project provides simple logging utilities in both Python and JavaScript. You can add, read, edit, and delete log entries using either language. All logs are stored in a file named log.txt in the same directory.

Market Data

Market Data Utilities

This folder contains utilities for processing, analyzing, and fetching market data for financial applications.

News Fetching

Google News Fetcher

This utility provides a Google News headline scraper using the google-news-json package. It no longer requires any API keys, making it ideal for beginners who want to experiment with news-driven trading ideas or sentiment analysis without signing up for external services.

System Utilities

System Utilities

This folder contains utilities for system-level operations, file management, and configuration in financial applications.

Websocket Connection

WebSocket Connection Utilities

This project provides WebSocket clients for connecting to various financial data providers, including YFLive and Finnhub. These utilities are designed for real-time market data streaming and analysis.