Skip to content

Latest commit

 

History

History
132 lines (88 loc) · 3.77 KB

README.md

File metadata and controls

132 lines (88 loc) · 3.77 KB

Stocker - A Stock Analyzer

Overview

This Django application serves as a comprehensive financial data analysis platform. It offers functionalities for fetching and analyzing stock data, backtesting trading strategies, and leveraging machine learning for price predictions.

Features

  • Historical stock data retrieval from Alpha Vantage API
  • PostgreSQL database for data storage
  • Backtesting of simple buy/sell strategies
  • Integration of a pre-trained machine learning model for stock price prediction
  • Comprehensive report generation
  • Deployed on Google Cloud Run
  • Dockerfile included for easy deployment

Prerequisites

  • Python 3.x
  • pip
  • git
  • Docker

Installation

  1. Clone the repository:

    git clone https://github.com/1611Dhruv/stocker

  2. Set up the environment variables: Create a .env file in the project root with the following contents:

    ALPHA_VANTAGE_API_KEY=[your_api_key]
DB_URL=[your_psql_database_url]
DB_USER=[your_psql_username]
DB_PASSWORD=[your_psql_password]

  3. Install dependencies:

    python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Database Schema

The database schema consists of two tables:

  1. Symbol Table: Stores stock symbols.
  2. Financial Data Table: Stores financial data with symbol as a foreign key. There is an index built on the financial data, ensuring that data is always ordered by date. Retrievals are optimized with no sorting costs due to fixed ordering of symbol followed by date.

Machine Learning

The ML predictions are stored similarly to financial data. If the user specifies format=pdf, the predicted prices are returned as a Matplotlib-generated PDF and stored in the database.

Running the Application

Start the development server:

python manage.py runserver

API Endpoints

Financial Data

  • POST /financial_data/add

    • Add a new stock symbol
    • Expects symbol in the request body

  • POST /financial_data/refresh

    • Refresh data for existing symbols

  • GET /financial_data/data

    • List available symbols

  • GET /financial_data/data?symbol=<symbol>

    • Get data for a specific symbol

Backtesting

  • GET /backtesting?format=<pdf|json>&symbol=<symbol>&winsell=<sell_threshold>&winbuy=<buy_threshold>&amt=<initial_amt>
    • Perform backtesting with specified parameters

AI Prediction

  • GET /ai_prediction?symbol=<symbol>&format=<pdf|json>
    • Get predicted prices for a symbol. If format=pdf, the predictions are returned as a PDF and stored in the database.

Reporting

  • Access the reporting interface at /reporting/. This provides a user-friendly UI to interact with the various endpoints. Here are some views available: 1 2 3

Deployment

Docker

A Dockerfile is attached for easy deployment. Before running the Docker container, ensure the following environment variables are set:

  • ALPHA_VANTAGE_API_KEY: Key to interact with the API
  • DB_HOST: PostgreSQL database host
  • DB_PORT: PostgreSQL database port
  • DB_NAME: PostgreSQL database name
  • DB_USER: PostgreSQL username
  • DB_PASSWORD: PostgreSQL password
  • ALLOWED_HOST: Django specifications for allowed hosts (for CSRF tokens)
  • DEBUG: Django debug mode (set to True or False)

To build and run the Docker container:

docker build -t financial-data-analysis-app .
docker run -p 8000:8000 --env-file .env financial-data-analysis-app

Deployment

The platform is currently deployed on Google Cloud Run. You can access the site at this link.

The open-source repository is available at https://github.com/1611Dhruv/stock_analyzer.