``` |--- data/ | |--- raw/ | |--- processed/ | |--- external/ | |--- models/ | |--- saved_models/ | |--- notebooks/ | |--- src/ | |--- data/ | |--- features/ | |--- models/ | |--- visualization/ | |--- tests/ | |--- data/ | |--- features/ | |--- models/ | |--- visualization/ | |--- .gitignore |--- README.md |--- requirements.txt |--- main.py ``` 1. `data/`: This directory holds all your data files. `raw/` might contain the data as it was originally obtained, while `processed/` contains data that has been cleaned or transformed for analysis or modeling. `external/` could hold any external data sources. 2. `models/`: This is where you would store trained model files for easy reuse later. `saved_models/` would contain the serialized versions of your trained models. 3. `notebooks/`: This is a good place to put Jupyter notebooks or other interactive documents used for exploration and visualization. 4. `src/`: This directory would contain the source code for your project. `data/` could contain code for loading and cleaning data, `features/` might hold code for feature extraction or selection, `models/` would have the code for your machine learning models, and `visualization/` might contain code for creating plots and other visualizations. 5. `tests/`: Contains unit tests for your project, organized in a similar way to `src/`. 6. `.gitignore`: This file tells git which files or directories to ignore. 7. `README.md`: This file provides an overview of your project and instructions on how to run the code. 8. `requirements.txt`: This file lists the Python dependencies required to run your project. 9. `main.py`: This is the main script that ties together all the parts of your project. For a trading bot, our goal could be to predict the future price of a specific asset, like a stock, given historical data. This is essentially a time-series forecasting problem. To keep things relatively simple, we could consider using a linear regression model to start with. This is a good starting point for regression tasks and can give us a baseline to compare with more complex models. Here's a high-level overview of the steps involved: Data Preparation: Collect historical price data using the Alpaca API or another data source. Preprocess this data to create useful features and split it into training and testing sets. Model Training: Train a linear regression model on the training data. This involves fitting the model to the input features and the target variable (the price). Model Evaluation: Evaluate the model's performance on the testing data. This involves using the model to predict the prices in the testing data and comparing these predictions to the actual prices.