shashankvemuri/Finance
Finance
This project is a Python quantitative finance library designed for gathering, manipulating, and analyzing stock market data. It provides a suite of tools for quantitative stock analysis, including an equity screening framework for filtering stocks based on technical and fundamental criteria.
The library features a machine learning price predictor for classifying stock movements and forecasting future price directions. It also includes a financial technical analysis tool to calculate indicators such as Bollinger Bands, RSI, and MACD, alongside an algorithmic trading simulator for testing portfolio strategies against historical data.
The software covers a broad range of financial capabilities, including financial data acquisition via web scraping, individualized stock assessment, and the use of vectorized numerical computation to process large arrays of price data.
Features
- Quantitative Finance & Trading - A complete Python library for gathering, manipulating, and performing quantitative analysis on stock market data.
- Financial Price Forecasting - Implements machine learning models to classify stock movements and forecast future price directions.
- Algorithmic Trading Simulators - Evaluates the performance of automated investment strategies by simulating them against historical data.
- Criteria-Based Screeners - Provides a system for filtering stocks based on specific numerical thresholds and investment criteria.
- Technical Stock Screeners - Filters securities based on technical indicators and fundamental analysis to identify investment opportunities.
- Equity Assessments - Evaluates specific equities using detailed tools to determine current performance and intrinsic value.
- Screening Frameworks - Provides a comprehensive framework for filtering stocks based on technical and fundamental analysis criteria.
- Screening Workflows - Implements a workflow to filter stocks based on technical and fundamental criteria for investment identification.
- Stock Analysis Tools - Provides tools to analyze individual equities and determine intrinsic value using quantitative methods.
- Technical Indicators - Implements mathematical calculations for trend indicators such as Bollinger Bands and Relative Strength Index.
- Trading Strategy Backtesting - Applies algorithmic trading logic to historical financial data to simulate and evaluate performance.
- Trading Strategy Sandboxes - Provides an isolated environment to test and validate trading strategies against historical market data.
- Predictive Financial Models - Applies machine learning implementations tailored for financial forecasting and classifying stock movements.
- Financial Data Collection Pipelines - Aggregates financial market data and company information through automated collection pipelines.
- Web Data Collection - Automates the gathering of price action and corporate information from diverse web sources.
- Web Data Scraping - Extracts structured market data and company information from financial websites using automated scripts.
- Numerical Computing - Utilizes high-performance matrix operations and statistics to process large arrays of price data.
- Educational Resources - Collection of programs for gathering and analyzing market data.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
