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