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.
