Project Goals:

Develop a Sequence-to-Sequence Model: Implement an LSTM-based sequence-to-sequence model, a type of neural network specifically designed to handle sequential data, capable of learning patterns and dependencies in the historical stock price time series.

‍Data Collection: Gather extensive historical stock data for Palantir from reliable financial sources, such as Bloomberg and Yahoo! Finance. This data will include relevant features such as open price, high price, low price, closing price, trading volume, and earnings changes (in percentage change) for PLTR and associated competitor stocks, and broader market indicators such as the volatility (VIX), percentage returns on the SPY (expressed as a YoY % change), and the relation between equity returns and macroeconomic factors such as the federal funds rate.

Data Preprocessing: Clean, preprocess, and transform the historical stock data to make it suitable for training the LSTM model. This step involves handling missing values, normalization, and splitting the dataset into training and testing sets.

Model Training: Train the LSTM sequence-to-sequence model using the processed historical data. Fine-tune hyperparameters and experiment with various network architectures to optimize the model's performance.

Sequence Prediction: Implement the LSTM model to predict future stock prices of Palantir based on historical data. The model should generate sequences of stock prices that indicate the possible price movements over a given time horizon.

Model Evaluation: Assess the accuracy and reliability of the LSTM model's predictions by comparing them with the actual stock price movements. Utilize metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to quantify the model's performance.

Visualization: Create visualizations that illustrate the model's predictions alongside the actual stock price data. These visualizations will provide valuable insights into the model's strengths and weaknesses and its ability to capture trends and patterns.

Challenges:

Financial markets are highly dynamic and subject to various external factors. The model may struggle to capture sudden shifts in stock prices caused by unexpected events.

LSTM models can be computationally intensive, requiring significant computational resources and time for training, especially when dealing with large datasets.

Financial data can be noisy and volatile, requiring careful preprocessing and feature engineering to improve the model's predictive performance.

To Enhance our Model:

Incorporating sentiment analysis of news and social media data to gauge market sentiment and its impact on stock prices.

Exploring other deep learning architectures, such as attention-based models, to improve the LSTM model's predictive capabilities.

Implementing an automated trading strategy that utilizes the LSTM predictions to make informed buy/sell decisions in a simulated trading environment.

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