Update projects/forex_algo_trading.md

projects/forex_algo_trading.md

@@ -1,112 +1,75 @@
-To set up a virtual environment for your forex trading analysis project, you'll first need to decide on a folder structure that organizes your work efficiently. I'll guide you through creating this structure, setting up a virtual environment, and providing a basic `requirements.txt` file.
+# Swing Trading Project with EUR/USD Using Oanda and scikit-learn
 
-### Project Folder Structure
+## Step 1: Environment Setup
 
-Here's a suggested structure to keep your project organized:
-
-```plaintext
-forex_analysis_project/
-│
-├── data/                        # Store raw and processed forex data
-│   ├── raw/
-│   └── processed/
-│
-├── notebooks/                   # Jupyter notebooks for exploration and analysis
-│
-├── src/                         # Source code for the project
-│   ├── data_retrieval/          # Scripts for fetching and preprocessing data
-│   │   ├── __init__.py
-│   │   └── fetch_forex_data.py
-│   │
-│   ├── feature_engineering/     # Generate features from forex data
-│   │   ├── __init__.py
-│   │   └── features.py
-│   │
-│   ├── models/                  # Machine learning and deep learning models
-│   │   ├── __init__.py
-│   │   ├── lstm_model.py        # LSTM for time series prediction
-│   │   └── rag_model.py         # RAG model integration
-│   │
-│   ├── inference/               # Inference logic for making predictions
-│   │   ├── __init__.py
-│   │   └── predict.py
-│   │
-│   └── utils/                   # Utility functions and classes
-│       ├── __init__.py
-│       └── utils.py
-│
-├── tests/                       # Unit and integration tests
-│   ├── __init__.py
-│   └── test_fetch_forex_data.py
-│
-├── requirements.txt             # Project dependencies
-│
-└── Dockerfile                   # Containerize your project (optional)
-```
-
-### Setting Up a Virtual Environment
-
-**1. Creating a Virtual Environment:**
-
-First, navigate to your project directory in the terminal, then run:
+### Install Python
+Ensure Python 3.8+ is installed.
 
+### Create a Virtual Environment
+Navigate to your project directory and run:
 ```bash
-python3 -m venv venv
+python -m venv venv
+source venv/bin/activate  # Unix/macOS
+venv\Scripts\activate     # Windows
 ```
 
-This command creates a virtual environment named `venv` within your project directory.
-
-**2. Activating the Virtual Environment:**
-
-- **On Windows:**
-
-```bash
-venv\Scripts\activate
-```
-
-- **On macOS and Linux:**
-
-```bash
-source venv/bin/activate
-```
-
-Once activated, you'll see the name of the virtual environment (`venv`) in your terminal prompt, indicating that any Python or pip commands will use the environments' packages and settings.
-
-**3. Deactivating the Virtual Environment:**
-
-When you're done working in the virtual environment, you can deactivate it by running:
-
-```bash
-deactivate
-```
-
-### `requirements.txt` File
-
-Create a `requirements.txt` file in your project root directory (`forex_project/`) with the following content to specify the project dependencies:
-
+### Install Essential Libraries
+Create `requirements.txt` with the following content:
 ```
 pandas
 numpy
 matplotlib
 seaborn
-requests
-oandapyV20
+scikit-learn
 jupyterlab
+oandapyV20
+requests
+```
+Install with `pip install -r requirements.txt`.
+
+## Step 2: Project Structure
+
+Organize your directory as follows:
+```
+swing_trading_project/
+├── data/
+├── notebooks/
+├── src/
+│   ├── __init__.py
+│   ├── data_fetcher.py
+│   ├── feature_engineering.py
+│   ├── model.py
+│   └── backtester.py
+├── tests/
+├── requirements.txt
+└── README.md
 ```
 
-**Installing Dependencies:**
+## Step 3: Fetch Historical Data
 
-With your virtual environment activated, install the project dependencies by running:
+- Sign up for an Oanda practice account and get an API key.
+- Use `oandapyV20` in `data_fetcher.py` to request historical EUR/USD data. Consider H4 or D granularity.
+- Save the data to `data/` as CSV.
 
-```bash
-pip install -r requirements.txt
-```
+## Step 4: Exploratory Data Analysis
 
-This command reads the `requirements.txt` file and installs the specified versions of the packages into your virtual environment.
+- Create a new Jupyter notebook in `notebooks/`.
+- Load the CSV with `pandas` and perform initial exploration. Plot closing prices and moving averages.
 
-### Final Steps
+## Step 5: Basic Feature Engineering
 
-- **Developing Your Project:** Place your Python scripts in the `src/` directory, Jupyter notebooks in `notebooks/`, and any tests in `tests/`. Use the `data/` directory to store fetched data, such as CSV files.
-- **Using Jupyter Notebook:** If you want to use Jupyter Notebook for analysis, start it with `jupyter notebook` or `jupyter lab` if you're using JupyterLab, and it will open in your web browser. Ensure you're doing this with your virtual environment activated so that Jupyter can access your project's dependencies.
+- In the notebook, add technical indicators as features (e.g., SMA 50, SMA 200, RSI) using `pandas`.
+- Investigate the relationship between these features and price movements.
 
-This setup provides a solid foundation for developing your forex trading analysis project, offering a clear separation of concerns and making it easier to manage dependencies and share your work with others.
+## Step 6: Initial Model Training
+
+- In `model.py`, fit a simple `scikit-learn` model (e.g., LinearRegression, LogisticRegression) to predict price movements.
+- Split data into training and testing sets to evaluate the model's performance.
+
+## Step 7: Documentation
+
+- Document your project's setup, objectives, and findings in `README.md`.
+
+## Next Steps
+
+- Refine features, try different models, and develop a backtesting framework as you progress.