# 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:
```bash
python -m venv venv
source venv/bin/activate  # Unix/macOS
venv\Scripts\activate     # Windows
deactivate
```

### 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.

```python
import os
import pandas as pd
from oandapyV20 import API
import oandapyV20.endpoints.instruments as instruments

# Configuration
ACCOUNT_ID = 'your_account_id_here'
ACCESS_TOKEN = 'your_access_token_here'
INSTRUMENTS = ['EUR_USD', 'USD_JPY', 'GBP_USD', 'AUD_USD', 'USD_CAD']  # Extendable to more pairs
GRANULARITY = 'H4'  # Can be parameterized as needed
DATA_DIR = 'data'

def fetch_and_save_data(account_id, access_token, instruments, granularity, data_dir):
    """Fetch historical forex data for specified instruments and save to CSV."""
    client = API(access_token=access_token)
    
    if not os.path.exists(data_dir):
        os.makedirs(data_dir)
    
    for instrument in instruments:
        params = {
            "granularity": granularity,
            "count": 5000  # Adjust based on needs
        }
        
        data_request = instruments.InstrumentsCandles(instrument=instrument, params=params)
        data = client.request(data_request)
        candles = data.get('candles', [])
        
        if candles:
            df = pd.DataFrame([{
                'Time': candle['time'],
                'Open': float(candle['mid']['o']),
                'High': float(candle['mid']['h']),
                'Low': float(candle['mid']['l']),
                'Close': float(candle['mid']['c']),
                'Volume': candle['volume']
            } for candle in candles])
            
            # Save to CSV
            output_filename = f"{instrument.lower()}_data.csv"
            df.to_csv(os.path.join(data_dir, output_filename), index=False)
            print(f"Data saved for {instrument} to {output_filename}")

def main():
    """Main function to orchestrate data fetching and saving."""
    print("Fetching data for instruments...")
    fetch_and_save_data(ACCOUNT_ID, ACCESS_TOKEN, INSTRUMENTS, GRANULARITY, DATA_DIR)
    print("Data fetching and saving complete.")

if __name__ == '__main__':
    main()
```

## 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.