Stock Prediction and Trading Strategy

Overview

This repository contains the code and documentation for a stock prediction and trading strategy project. The project aims to predict stock prices using an ARIMA model and implement a trading strategy based on the model's predictions.

Table of Contents

• Installation
• Usage
• Project Structure
• Dependencies
• Data
• Methodology
• Results
• How to Contribute

Installation

1. Clone the repository:

git clone https://github.com/your-username/stock-prediction-trading.git

2. Install the required dependencies:

pip install -r requirements.txt

Usage

• Open the Jupyter Notebook file Stock_Prediction_Trading.ipynb in a Jupyter environment.
• Run the cells to execute the code and generate predictions.
• Explore the simulation results and visualize trading performance.

Project Structure

• Jupyter Notebook containing the code for stock prediction and trading simulation.
• Requirements file

Dependencies

• Python
• Jupyter Notebook
• pandas
• yfinance
• matplotlib
• seaborn
• statsmodels
• tqdm

Install the dependencies using the provided requirements.txt file:

pip install -r requirements.txt

Data

The historical stock price data for Microsoft (MSFT) is downloaded using the yfinance library.

Methodology

1. Data Collection:
   • Historical stock price data for MSFT is downloaded using the yfinance library.
2. Data Preprocessing:
   • Data is cleaned and explored to handle null values.
3. Stationarity Check:
   • The Augmented Dickey-Fuller and KPSS tests are performed to check stationarity.
   • Differencing is applied to make the data stationary.
4. ARIMA Modeling:
   • A suitable ARIMA model is chosen based on ACF and PACF plots.
   • The model is trained on the differenced data.
5. Trading Strategy Simulation:
   • A simulation is run using the ARIMA model to make buy/sell decisions based on specified thresholds.
   • Simulation results, including returns, are recorded.

Results

Simulation Results:

1. Threshold = 0:
   • Initial Amount: $100
   • Positive Return: 0.38%
   • Total Return Amount: $100.38

2. Threshold = 0.001:
   • Initial Amount: $100
   • Positive Return: 3.4%
   • Total Return Amount: $103.4

3. Threshold = 0.005:
   • Initial Amount: $100
   • Positive Return: 7.45%
   • Total Return Amount: $107.45

How to Contribute

• If you'd like to contribute to this project, please follow these steps:
• Fork the repository.
• Create a new branch for your feature: git checkout -b feature-name
• Commit your changes: git commit -m 'Add new feature'
• Push to the branch: git push origin feature-name
• Submit a pull request.