Swing Trading Project with EUR/USD Using Oanda and scikit-learn

Step 1: Environment Setup

Install Python

Ensure Python 3.8+ is installed.

Create a Virtual Environment

Navigate to your project directory and run:

python -m venv venv
source venv/bin/activate  # Unix/macOS
venv\Scripts\activate     # Windows

Install Essential Libraries

Create requirements.txt with the following content:

pandas
numpy
matplotlib
seaborn
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

Step 3: Fetch Historical Data

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.

Step 4: Exploratory Data Analysis

Create a new Jupyter notebook in notebooks/.
Load the CSV with pandas and perform initial exploration. Plot closing prices and moving averages.

Step 5: Basic Feature Engineering

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.

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.

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