This repository contains notebooks and datasets for a comprehensive project focused on forecasting Tesla (TSLA) stock prices. The predictive models employed include a single-step LSTM for immediate forecasting and a multi-stacked LSTM for extended predictions, spanning multiple days into the future (equivalent to a month in business days). The documentation not only presents the practical implementation but also provides an insightful exploration of the intuitive and mathematical underpinnings of LSTM networks.

• Context: This dataset explores Tesla's stock price from its initial public offering (IPO) to a specific date.
• Content:
  • Date
  • Open
  • High
  • Low
  • Close
  • Volume
  • Adjusted Close
• Acknowledgements: Acquired from Yahoo Finance through Python programming, with inspiration drawn from Sentdex.

• Context: Explores Tesla's recent stock performance, capturing a period of significant growth.
• Content: End-of-day (EOD) data for Tesla's stock from 2010 to 2020.
• Acknowledgements: Data sourced from Yahoo Finance.
• Inspiration: Investigating the recent surge using technical indicators alone.

• Context: Provides historical data of TESLA INC. stock (TSLA) at a daily level, denominated in USD.
• About Tesla: Tesla, Inc. is an American electric vehicle and clean energy company based in Palo Alto, California. Their products include electric cars, battery energy storage, solar panels, and related products and services.

For more details and access to the full code, visit the Kaggle notebook here.