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Releases: MeridianAlgo/Learn-Quant

v2.1.0 - Interactive Quiz-Based Tutorials

16 Apr 00:20
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v2.1.0
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v2.1.0 - Interactive Quiz-Based Tutorials Latest
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What is New in v2.1.0

Four new interactive, quiz-based Python tutorials. Run them from the terminal to learn with live worked examples and immediate quiz feedback.

New Tutorials

  • 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): Historical and parametric VaR, CVaR/Expected Shortfall, drawdown, Sharpe and Sortino ratios
  • portfolio_tutorial.py (UTILS - Portfolio Optimizer): Portfolio variance, efficient frontier, Sharpe maximisation, MPT limits

How to Use

Run any tutorial directly:
python statistics_tutorial.py
python options_tutorial.py
python risk_tutorial.py
python portfolio_tutorial.py

Press ENTER to advance sections and type A/B/C/D to answer quiz questions.

Tests

91 new tests added (222 total passing, 0 failures).

Assets 2
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v2.0.0 - Performance Analysis Module

07 Apr 01:33
@ishaanman7898 ishaanman7898
v2.0.0
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v2.0.0 - Performance Analysis Module

What's New in v2.0.0

New Module: Quantitative Methods - Performance Analysis

Added 5 new production-quality utilities for evaluating strategy performance:

  • Hurst Exponent (hurst_exponent.py): Measure time series persistence using R/S analysis. H < 0.5 = mean-reverting, H > 0.5 = trending.
  • Omega Ratio (omega_ratio.py): Probability-weighted ratio of gains vs. losses relative to a target return threshold.
  • Tail Ratio ( ail_ratio.py): Ratio of the 95th to 5th percentile returns, quantifying asymmetry in the return distribution.
  • Gain-to-Pain Ratio (gain_to_pain_ratio.py): Cumulative return divided by the absolute sum of all losses (Schwager metric).
  • Active Performance (�ctive_performance.py): Tracking Error and Information Ratio for measuring active management skill vs. a benchmark.

Testing

  • Added ests/test_performance_analysis.py with 5 corresponding unit tests.
  • All 131 tests pass across the full suite.

Documentation

  • Updated main README.md to reference the new module under Level 4: Quantitative Methods.
  • Created UTILS - Quantitative Methods - Performance Analysis/README.md with full module documentation (no emojis).

CI/CD

  • All ruff lint and format checks pass cleanly.
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v1.9.0 - Releases

26 Mar 00:55
@MeridianAlgo-Developer MeridianAlgo-Developer
v1.9.0
25d2ced

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v1.9.0 - Releases

Learn-Quant: Master Quantitative Finance & Python (v1.9.0)

Lint

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

Overview

Learn-Quant is a massive, curated collection of over 55+ 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.
  • 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.

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.
  • 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.
  • UTILS - Risk Metrics: Value at Risk (VaR), CVaR, Drawdown, & Sortino Ratio.
  • 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 Context Managers

cd "UTILS - Advanced Python - Context Managers"
python context_managers_tutorial.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 v1.9.0
Quantitative Finance | Algorithmic Trading | Python Mastery
Maintained by MeridianAlgo

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v1.8.0 - New lessons and Lint Formatting

23 Feb 02:48
@MeridianAlgo-Developer MeridianAlgo-Developer
v1.8.0
960d8eb

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v1.8.0 - New lessons and Lint Formatting

Learn-Quant: Master Quantitative Finance & Python (v1.8.0)

Lint

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

Overview

Learn-Quant is a massive, curated collection of over 50+ 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, and more.
  • Engineer Robust Systems: Learn AsyncIO, Context Managers, Decorators, and advanced OOP.
  • Deep Dive into Math: Kalman Filters, Stochastic Processes, 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.

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.
  • UTILS - Quantitative Methods - Regression: Factor models & Alpha generation.
  • UTILS - Quantitative Methods - Linear Algebra: Portfolio optimization & risk modelling.

Level 5: Strategies & Finance

Applied quantitative finance.

  • UTILS - Strategies - Pairs Trading: Statistical arbitrage & mean reversion.
  • UTILS - Strategies - Momentum Trading: Trend following & signal generation.
  • UTILS - Black-Scholes Option Pricing: Greeks, implied volatility, & derivatives pricing.
  • UTILS - Finance - Volatility Calculator: Parkinson, Garman-Klass, & EWMA estimators.
  • UTILS - Portfolio Optimizer: Efficient Frontier, Sharpe Ratio, & Markowitz optimization.
  • UTILS - Risk Metrics: Value at Risk (VaR), CVaR, Drawdown, & Sortino Ratio.
  • 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 Context Managers

cd "UTILS - Advanced Python - Context Managers"
python context_managers_tutorial.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 v1.8.0
Quantitative Finance | Algorithmic Trading | Python Mastery
Maintained by MeridianAlgo

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v1.5.0 - Updated Lessons and Modules

10 Jan 21:30
@MeridianAlgo-Developer MeridianAlgo-Developer
v1.5.0
2280020

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v1.5.0 - Updated Lessons and Modules

Learn-Quant: Wanna learn quant through code? (v1.5.0)

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:

  • NEW IN v1.5.0: Master advanced strategies like Pairs Trading and Kalman Filters.
  • NEW IN v1.5.0: Learn high-frequency data engineering with AsyncIO.
  • 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.

NEW: Interactive Learning Platform

Experience algorithms like never before with our browser-based learning platform!

Features

  • Interactive Algorithm Browser: Filter by category and difficulty.
  • In-Browser Python Execution: Run code directly using PyScript.
  • Live Code Editor: Edit and test algorithms in real-time.
  • Visual Output: See results instantly in your browser.

Quick Start

# Option 1: Interactive launcher (Recommended)
python run_learning_platform.py interactive

# Option 2: Full platform demo
python run_learning_platform.py demo

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) - NEW!

  • UTILS - Advanced Python - AsyncIO/ — (New) 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) - NEW!

  • UTILS - Quantitative Methods - Kalman Filter/ — (New) 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/ — (New) Statistical arbitrage simulation.
  • 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.
  4. Educators: Use the interactive platform for teaching.

Getting Started

1. Installation

Clone the repo and install dependencies:

git clone https://github.com/MeridianAlgo/Learn-Quant
pip install numpy pandas scipy matplotlib requests

2. 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

3. Run the Web Platform

python run_learning_platform.py interactive

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 v1.5.0
Quantitative Finance, Algorithms, and Python Mastery.
Made by MeridianAlgo

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v1.2.0 - Standard Release

29 Dec 17:54
@MeridianAlgo-Developer MeridianAlgo-Developer
v1.2.0
54194f2

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v1.2.0 - Standard Release

Utils-main: Quantitative Finance & Python Utilities (v1.2.0)

Welcome to Utils-main, a comprehensive, beginner-friendly toolkit for learning and practicing quantitative finance, data analysis, coding, and financial engineering in Python!

🚀 What is This Repo?

A curated, growing collection of Python modules and reference material to help you:

  • Understand core quant finance concepts (risk, return, valuation, options, portfolios, data)
  • Practice with realistic scenarios or DIY finance scripts
  • Learn Python basics and data structures from scratch
  • Tinker with news and sentiment analysis, logging, AI, and more
  • Learn by doing—every tool is deeply commented and fully readable by learners new to finance and coding

📦 What's Inside?

All folders are independent, so you can learn or build projects one topic at a time!

🐍 Python Basics (Level 1)

  • UTILS - Python Basics - Numbers/ — Integers, floats, decimals, and financial math
  • UTILS - Python Basics - Strings/ — Text processing and formatting
  • UTILS - Python Basics - Control Flow/ — If/else, loops, and comprehensions
  • UTILS - Python Basics - Functions/ — Modular code, args/kwargs, and lambda functions

🏗️ Data Structures (Level 2)

  • UTILS - Data Structures - Lists/ — Sequences and array-like operations
  • UTILS - Data Structures - Dictionaries/ — Key-value mappings and lookups
  • UTILS - Data Structures - Tuples and Sets/ — Immutable records and unique collections
  • UTILS - Data Structures - Arrays/ — Introduction to NumPy arrays

🚀 Advanced Python (Level 3)

  • UTILS - Advanced Python - OOP/ — Classes, objects, inheritance, and trading systems
  • UTILS - Advanced Python - Error Handling/ — Try/except, logging, and robust code
  • UTILS - Advanced Python - Decorators and Generators/ — Efficient pipelines and function wrappers

📊 Quantitative Methods (Math & Stats)

  • UTILS - Quantitative Methods - Statistics/ — Descriptive stats, distributions, and hypothesis testing
  • UTILS - Quantitative Methods - Linear Algebra/ — Matrices, eigenvalues, and portfolio math
  • UTILS - Quantitative Methods - Regression Analysis/ — Beta calculation, factor models, and prediction
  • UTILS - Quantitative Methods - Optimization/ — Portfolio optimization, curve fitting, and root finding
  • UTILS - Quantitative Methods - Stochastic Processes/ — Brownian motion, GBM, and mean reversion
  • UTILS - Quantitative Methods - TVM/ — Time Value of Money (NPV, IRR, Annuities)
  • UTILS - Quantitative Methods - Time Series/ — ARIMA, GARCH, and trend analysis

💰 Finance Utilities & Simulators

  • UTILS - Sharpe and Sortino Ratio/ — Measure risk-adjusted returns
  • UTILS - CAPM/ — Asset pricing with Capital Asset Pricing Model
  • UTILS - Value at Risk (VaR)/ — Gauge risk of loss on any investment
  • UTILS - Black-Scholes Option Pricing/ — Price call & put options
  • UTILS - Monte Carlo Portfolio Simulator/ — Simulate many investment futures
  • UTILS - Bond Price and Yield/ — Price bonds & estimate YTM
  • UTILS - Discounted Cash Flow (DCF)/ — Value projects/stocks with DCF
  • UTILS - Portfolio Optimizer/ — Find the best asset mix using MPT
  • UTILS - Risk Metrics/ — Volatility, drawdown, skew, kurtosis, and more
  • UTILS - Technical Indicators/ — Compute trading/analysis indicators
  • UTILS - Options Chain Simulator/ — Simulate option chains
  • UTILS - Order Execution Simulator/ — Model execution and slippage
  • UTILS - Portfolio Tracker/ — Track investment values over time
  • UTILS - Dividend Tracker/ — Track, analyze, and project dividend income
  • UTILS - Economic Calendar/ — Access & analyze economic events

🌐 Data & Connectivity

  • UTILS - Historical Data/ — Fetch and parse price data from APIs
  • UTILS - News Fetching/ — Collect financial, trending or relevant news
  • UTILS - Sentiment Analysis on News/ — Analyze news sentiment with Python
  • UTILS - Currency Converter/ — Convert currencies and analyze rates
  • UTILS - Websocket Connection/ — Real-time data, eg. from exchanges
  • UTILS - Logging/ — Professional and educational logging (Python & JS)
  • UTILS - AI Development/ — Templates for basic AI/chatbots in Python/JS

🔧 Utility Libraries (NEW in v1.2.0)

Comprehensive utility collections organized by category:

  • 🔧 UTILS - Data Processing — Data validation, string manipulation, and cleaning

    • data_validation_utils.py — Email, phone, stock symbol validation, sanitization
    • string_utils.py — Case conversion, truncation, currency formatting, slug generation

  • ⚙️ UTILS - System Utilities — File operations and configuration management

    • file_io_utils.py — JSON/CSV operations, backup, file management
    • config_utils.py — Configuration loading, dot notation access, environment variables

  • 🧮 UTILS - Core Utilities — Mathematical and date/time foundations

    • datetime_utils.py — Trading days, market hours, timestamp operations
    • math_utils.py — Financial calculations, CAGR, moving averages, regression

  • 💼 UTILS - Portfolio Management — Portfolio analysis and risk metrics

    • portfolio_utils.py — Portfolio valuation, allocation, rebalancing, diversification
    • risk_utils.py — VaR, drawdown, Sharpe/Sortino ratios, stress testing, correlations

  • 📊 UTILS - Market Data — Data processing and API integration

    • market_data_utils.py — Returns, outliers, sentiment analysis, data validation
    • api_utils.py — HTTP requests, retry logic, API key management, error handling

📚 Learning Platform

  • UTILS - Learning Platform/ — Extendable Python learning hub
  • Documentation/ — Learning paths, tutorials, reference, examples, quizzes, and extra resources
  • tests/ — Example and reference tests for practicing and validating code

🎯 Who Is This For?

  • Absolute beginners to Python or finance (comments explain all math & code)
  • Tinkerers wanting to simulate, value, or analyze investments in plain code
  • Anyone wanting a practical, realistic finance or coding codebase for reference, study, or projects

🛠️ Getting Started

  1. Clone this repo: git clone https://github.com/MeridianAlgo/Learn-Quant
  2. Install Python 3.8+ and required packages: 
    pip install numpy pandas scipy matplotlib requests
  3. Explore any folder, read the README, and run the Python script for live examples
  4. Visit the Documentation/ folder for guided paths and exercises

🆕 What's New in v1.2.0

  • 🔧 5 New Utility Libraries: Comprehensive utility collections organized by category
  • 📊 Enhanced Portfolio Tools: Advanced portfolio management and risk analysis utilities
  • 🌐 Market Data Processing: Professional-grade data validation and API integration
  • ⚙️ System Integration: Robust file I/O and configuration management
  • 🧮 Core Mathematical Functions: Financial calculations and date/time utilities
  • 📚 Improved Documentation: Each utility includes comprehensive README and examples

🌱 Contributing

  • See something missing? Want to add modules, improve docs, or fix code? PRs welcome—this project is designed to grow for all learners!

Learn-Quant v1.2.0
25 files added, 5,720+ lines of new utility code
Made with ❤️ by MeridianAlgo

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Standard Release - v1.1.0

29 Nov 02:03
@MeridianAlgo-Developer MeridianAlgo-Developer
v1.1.0
e651d0f

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Standard Release - v1.1.0

Utils-main: Quantitative Finance & Python Utilities (v1.1.0)

Welcome to Utils-main, a comprehensive, beginner-friendly toolkit for learning and practicing quantitative finance, data analysis, coding, and financial engineering in Python!

🚀 What is This Repo?

A curated, growing collection of Python modules and reference material to help you:

  • Understand core quant finance concepts (risk, return, valuation, options, portfolios, data)
  • Practice with realistic scenarios or DIY finance scripts
  • Learn Python basics and data structures from scratch
  • Tinker with news and sentiment analysis, logging, AI, and more
  • Learn by doing—every tool is deeply commented and fully readable by learners new to finance and coding

📦 What's Inside?

All folders are independent, so you can learn or build projects one topic at a time!

🐍 Python Basics (Level 1)

  • UTILS - Python Basics - Numbers/ — Integers, floats, decimals, and financial math
  • UTILS - Python Basics - Strings/ — Text processing and formatting
  • UTILS - Python Basics - Control Flow/ — If/else, loops, and comprehensions
  • UTILS - Python Basics - Functions/ — Modular code, args/kwargs, and lambda functions

🏗️ Data Structures (Level 2)

  • UTILS - Data Structures - Lists/ — Sequences and array-like operations
  • UTILS - Data Structures - Dictionaries/ — Key-value mappings and lookups
  • UTILS - Data Structures - Tuples and Sets/ — Immutable records and unique collections
  • UTILS - Data Structures - Arrays/ — Introduction to NumPy arrays

🚀 Advanced Python (Level 3)

  • UTILS - Advanced Python - OOP/ — Classes, objects, inheritance, and trading systems
  • UTILS - Advanced Python - Error Handling/ — Try/except, logging, and robust code
  • UTILS - Advanced Python - Decorators and Generators/ — Efficient pipelines and function wrappers

📊 Quantitative Methods (Math & Stats)

  • UTILS - Quantitative Methods - Statistics/ — Descriptive stats, distributions, and hypothesis testing
  • UTILS - Quantitative Methods - Linear Algebra/ — Matrices, eigenvalues, and portfolio math
  • UTILS - Quantitative Methods - Regression Analysis/ — Beta calculation, factor models, and prediction
  • UTILS - Quantitative Methods - Optimization/ — Portfolio optimization, curve fitting, and root finding
  • UTILS - Quantitative Methods - Stochastic Processes/ — Brownian motion, GBM, and mean reversion
  • UTILS - Quantitative Methods - TVM/ — Time Value of Money (NPV, IRR, Annuities)
  • UTILS - Quantitative Methods - Time Series/ — ARIMA, GARCH, and trend analysis

💰 Finance Utilities & Simulators

  • UTILS - Sharpe and Sortino Ratio/ — Measure risk-adjusted returns
  • UTILS - CAPM/ — Asset pricing with Capital Asset Pricing Model
  • UTILS - Value at Risk (VaR)/ — Gauge risk of loss on any investment
  • UTILS - Black-Scholes Option Pricing/ — Price call & put options
  • UTILS - Monte Carlo Portfolio Simulator/ — Simulate many investment futures
  • UTILS - Bond Price and Yield/ — Price bonds & estimate YTM
  • UTILS - Discounted Cash Flow (DCF)/ — Value projects/stocks with DCF
  • UTILS - Portfolio Optimizer/ — Find the best asset mix using MPT
  • UTILS - Risk Metrics/ — Volatility, drawdown, skew, kurtosis, and more
  • UTILS - Technical Indicators/ — Compute trading/analysis indicators
  • UTILS - Options Chain Simulator/ — Simulate option chains
  • UTILS - Order Execution Simulator/ — Model execution and slippage
  • UTILS - Portfolio Tracker/ — Track investment values over time
  • UTILS - Dividend Tracker/ — Track, analyze, and project dividend income
  • UTILS - Economic Calendar/ — Access & analyze economic events

🌐 Data & Connectivity

  • UTILS - Historical Data/ — Fetch and parse price data from APIs
  • UTILS - News Fetching/ — Collect financial, trending or relevant news
  • UTILS - Sentiment Analysis on News/ — Analyze news sentiment with Python
  • UTILS - Currency Converter/ — Convert currencies and analyze rates
  • UTILS - Websocket Connection/ — Real-time data, eg. from exchanges
  • UTILS - Logging/ — Professional and educational logging (Python & JS)
  • UTILS - AI Development/ — Templates for basic AI/chatbots in Python/JS

📚 Learning Platform

  • UTILS - Learning Platform/ — Extendable Python learning hub
  • Documentation/ — Learning paths, tutorials, reference, examples, quizzes, and extra resources
  • tests/ — Example and reference tests for practicing and validating code

🎯 Who Is This For?

  • Absolute beginners to Python or finance (comments explain all math & code)
  • Tinkerers wanting to simulate, value, or analyze investments in plain code
  • Anyone wanting a practical, realistic finance or coding codebase for reference, study, or projects

🛠️ Getting Started

  1. Clone this repo: git clone https://github.com/MeridianAlgo/Learn-Quant
  2. Install Python 3.8+ and required packages: 
    pip install numpy pandas scipy matplotlib
  3. Explore any folder, read the README, and run the Python script for live examples
  4. Visit the Documentation/ folder for guided paths and exercises

🌱 Contributing

  • See something missing? Want to add modules, improve docs, or fix code? PRs welcome—this project is designed to grow for all learners!

Learn-Quant v1.1.0
Made with ❤️ by MeridianAlgo

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V1.0.0

16 Nov 02:25
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V1.0.0
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V1.0.0

Utils-main: Quantitative Finance & Python Utilities (v1.0.0)

Welcome to Utils-main, a comprehensive, beginner-friendly toolkit for learning and practicing quantitative finance, data analysis, coding, and financial engineering in Python!

🚀 What is This Repo?

A curated, growing collection of Python modules and reference material to help you:

  • Understand core quant finance concepts (risk, return, valuation, options, portfolios, data)
  • Practice with realistic scenarios or DIY finance scripts
  • Learn Python basics and data structures from scratch
  • Tinker with news and sentiment analysis, logging, AI, and more
  • Learn by doing—every tool is deeply commented and fully readable by learners new to finance and coding

📦 What's Inside?

All folders are independent, so you can learn or build projects one topic at a time!

Beginner Finance & Data Utilities

  • UTILS - Sharpe and Sortino Ratio/ — Measure risk-adjusted returns
  • UTILS - CAPM/ — Asset pricing with Capital Asset Pricing Model
  • UTILS - Value at Risk (VaR)/ — Gauge risk of loss on any investment
  • UTILS - Black-Scholes Option Pricing/ — Price call & put options
  • UTILS - Monte Carlo Portfolio Simulator/ — Simulate many investment futures
  • UTILS - Bond Price and Yield/ — Price bonds & estimate YTM
  • UTILS - Discounted Cash Flow (DCF)/ — Value projects/stocks with DCF
  • UTILS - Portfolio Optimizer/ — Find the best asset mix using MPT
  • UTILS - Risk Metrics/ — Volatility, drawdown, skew, kurtosis, and more
  • UTILS - Technical Indicators/ — Compute trading/analysis indicators
  • UTILS - Options Chain Simulator/ — Simulate option chains
  • UTILS - Order Execution Simulator/ — Model execution and slippage
  • UTILS - Portfolio Tracker/ — Track investment values over time
  • UTILS - Dividend Tracker/ — Track, analyze, and project dividend income
  • UTILS - Economic Calendar/ — Access & analyze economic events
  • UTILS - Historical Data/ — Fetch and parse price data from APIs
  • UTILS - News Fetching/ — Collect financial, trending or relevant news
  • UTILS - Sentiment Analysis on News/ — Analyze news sentiment with Python
  • UTILS - Currency Converter/ — Convert currencies and analyze rates
  • UTILS - Websocket Connection/ — Real-time data, eg. from exchanges
  • UTILS - Logging/ — Professional and educational logging (Python & JS)
  • UTILS - AI Development/ — Templates for basic AI/chatbots in Python/JS

Python & Data Structure Learning

  • UTILS - Python Basics - Numbers/ — Numbers and math in Python
  • UTILS - Python Basics - Strings/ — String skills and operations
  • UTILS - Data Structures - Arrays/ — Foundational data type for beginners
  • UTILS - Data Structures - Lists/ — Python’s powerful list type
  • UTILS - Data Structures - Dictionaries/ — Key-value maps, practical for all data work

Quantitative Methods

  • UTILS - Quantitative Methods - Statistics/ — Practical basics of statistics
  • UTILS - Quantitative Methods - Time Series/ — Analyze and model times series data
  • UTILS - Quantitative Methods - TVM/ — Time value of money tools & concepts

Learning & Reference Platform

  • UTILS - Learning Platform/ — Extendable Python learning hub
  • Documentation/ — Learning paths, tutorials, reference, examples, quizzes, and extra resources
  • tests/ — Example and reference tests for practicing and validating code

🎯 Who Is This For?

  • Absolute beginners to Python or finance (comments explain all math & code)
  • Tinkerers wanting to simulate, value, or analyze investments in plain code
  • Anyone wanting a practical, realistic finance or coding codebase for reference, study, or projects

🛠️ Getting Started

  1. Clone this repo: git clone https://github.com/MeridianAlgo/Learn-Quant
  2. Install Python 3.7+ and required packages (numpy, scipy, matplotlib, etc. as needed)
  3. Explore any folder, read the README, and run the Python script for live examples
  4. Visit the Documentation/ folder for guided paths and exercises

🌱 Contributing

  • See something missing? Want to add modules, improve docs, or fix code? PRs welcome—this project is designed to grow for all learners!

Learn-Quant v1.0.0
Made with ❤️ by MeridianAlgo

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