Stock Trading Bot Using LSTM

[Karm-Dave]

Issue Title: Implement Stock Trading Bot using LSTM with yfinance and Backtesting

Description:
We need to implement a stock trading bot that utilizes Long Short-Term Memory (LSTM) networks for predicting stock prices based on historical data retrieved via the yfinance library. The bot will execute simulated trades based on these predictions and be backtested on historical stock data to evaluate its performance.

Key Requirements:

• LSTM Model: Develop an LSTM model to predict future stock prices using time series data.
• Data Retrieval: Use yfinance to pull historical stock data for training and testing the model.
• Trading Logic: Implement a simple trading strategy (e.g., buy if the predicted price is expected to rise, sell if it's expected to fall).
• Backtesting: Build a backtesting framework to evaluate the performance of the trading bot on historical data, focusing on key performance indicators such as profit/loss and total returns.

Additional Features:

• Visual comparison of predicted vs. actual stock prices.
• Performance report after backtesting, detailing overall profitability and key metrics.
• Option to test on multiple stocks and different timeframes.

Tech Stack:

• Python
• TensorFlow/Keras (for LSTM)
• yfinance (for stock data retrieval)
• pandas and numpy (for data processing)
• matplotlib or similar (for visualizations)

Please assign this issue to me for implementation under GSSOC 2024 Extd.

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