Trading SMA

This Jupyter-based application is designed to implement a 'simple moving averages' trading strategy on provided Excel data using PostgreSQL, Plotly, Pandas, and NumPy. The project seamlessly inserts ticker data into a PostgreSQL database, extracts and processes it into datasets for analysis, and includes robust unit testing and validation.

Features

• Simple Moving Averages Strategy: Implement a simple moving averages trading strategy on the provided Excel data.

• PostgreSQL Database: Utilize PostgreSQL as the database to store and manage ticker data efficiently.

• Data Extraction and Processing: Extract data from the database and process it into datasets suitable for analysis using Pandas and NumPy.

• Interactive Visualizations: Create interactive visualizations of the trading strategy results using Plotly.

• Unit Testing and Validation: Ensure the reliability and accuracy of the implemented strategy with robust unit testing and validation procedures.

Getting Started

1. Prerequisites: Make sure you have Python installed along with the necessary libraries: PostgreSQL, Pandas, NumPy, and Plotly.

2. Database Setup: Set up a PostgreSQL database and configure the connection details in the application.

3. Data Import: Import the provided Excel data into the PostgreSQL database using Python.

4. Jupyter Notebook: Open the Jupyter notebook provided in this repository to run the trading strategy and analyze the results.

Usage

1. Open the Jupyter notebook.

2. Run the notebook cells sequentially to execute the trading strategy and generate visualizations.

3. Analyze the results and make informed trading decisions based on the strategy's output.