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hub / github.com/shashankvemuri/Finance

github.com/shashankvemuri/Finance @main sqlite

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274 symbols 1,212 edges 183 files 52 documented · 19%
README

Finance

Introduction

Welcome! Finance is a collection of 150+ Python for Finance programs for gathering, manipulating, and analyzing stock market data.

Below you will find more information about how the repository is organized as well as usage and setup instructions!

Organization

Our repository is organized into several key sections:

find_stocks

Programs to screen stocks based on technical and fundamental analysis.

machine_learning

Introductory machine learning applications for stock classification and prediction.

portfolio_strategies

Simulations of trading strategies and portfolio analysis tools.

stock_analysis

Detailed analysis tools for individual stock assessment.

stock_data

Tools for collecting stock price action and company data via APIs and web scraping.

technical_indicators

Visual tools for popular technical indicators like Bollinger Bands, RSI, and MACD.

Installation

To get started, clone the repository and install the required dependencies:

git clone https://github.com/shashankvemuri/Finance.git
cd Finance
pip install -r requirements.txt

Usage

Detailed instructions on how to use each program can be found within their respective directories. Explore different modules to discover their functionalities.

Each script in this collection is stand-alone. Here's how you can run a sample program:

python example_program.py

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

Authors

License

This project is licensed under the MIT License.

Acknowledgements

Disclaimer

The material in this repository is for educational purposes only and should not be considered professional investment advice.

Core symbols most depended-on inside this repo

log
called by 13
machine_learning/sklearn_trading_bot.py
portfolio_performance
called by 8
portfolio_strategies/portfolio_optimization.py
sendMessage
called by 8
stock_data/tradingview_intraday_data.py
scrape_section
called by 7
stock_data/finviz_home_scraper.py
get_stock_backtest_data
called by 4
stock_analysis/backest_all_indicators.py
prepare_stock_ta_backtest_data
called by 4
stock_analysis/backest_all_indicators.py
run_stock_ta_backtest
called by 4
stock_analysis/backest_all_indicators.py
sharpe_ratio
called by 3
portfolio_strategies/optimal_portfolio.py

Shape

Function 247
Method 20
Class 6
Route 1

Languages

Python100%

Modules by API surface

ta_functions.py35 symbols
stock_analysis/twitter_sentiment_analysis.py13 symbols
stock_analysis/backest_all_indicators.py13 symbols
stock_analysis/seasonal_stock_analysis.py11 symbols
portfolio_strategies/portfolio_optimization.py10 symbols
stock_data/tradingview_intraday_data.py9 symbols
machine_learning/sklearn_trading_bot.py8 symbols
machine_learning/deep_learning_bot.py8 symbols
stock_data/get_dividend_calendar.py7 symbols
portfolio_strategies/backtrader_backtest.py7 symbols
portfolio_strategies/portfolio_analysis.py6 symbols
machine_learning/technical_indicators_clustering.py6 symbols

Dependencies from manifests, versioned

Flask2.2.2 · 1×
FundamentalAnalysis0.3.1 · 1×
Requests2.31.0 · 1×
autoscraper1.1.14 · 1×
backtrader1.9.78.123 · 1×
beautifulsoup44.12.2 · 1×
config0.5.1 · 1×
factor_analyzer0.5.0 · 1×
fastai2.7.13 · 1×
fbprophet0.7.1 · 1×
googletrans3.0.0 · 1×
ipython8.15.0 · 1×

For agents

$ claude mcp add Finance \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact