## 1. Understanding the Tools

### 1.1 Scikit-learn

* **Overview:** A versatile Python library offering a suite of machine learning algorithms for tasks like classification, regression, clustering, and dimensionality reduction.
* **Benefits:**
    * User-friendly API and extensive documentation.
    * Wide range of algorithms for diverse needs.
    * Supports feature engineering, model selection, and evaluation.
* **Limitations:**
    * Not specifically designed for finance.
    * Requires careful data preparation and interpretation.

### 1.2 Backtrader

* **Overview:** An open-source Python library built for backtesting trading strategies on historical data.
* **Benefits:**
    * Simulates trading based on user-defined strategies.
    * Analyzes performance metrics like profit, loss, Sharpe ratio, and drawdown.
    * Provides tools for order execution, position management, and visualization.
* **Limitations:**
    * Focuses on backtesting, not live trading.
    * Past performance not indicative of future results.

## 2. Synergistic Workflow

* **Step 1: Data Preparation and Feature Engineering (Scikit-learn)**
    * Gather historical financial data (e.g., prices, volumes, indicators).
    * Clean and preprocess data (e.g., handle missing values, outliers).
    * Extract meaningful features using techniques like:
        * **Technical indicators:** Moving averages, RSI, MACD.
        * **Lagged features:** Past price movements for momentum analysis.
        * **Volatility features:** ATR, Bollinger Bands.
        * **Market sentiment:** News analysis, social media data.
    * Utilize feature selection methods like PCA or LASSO.

## Step 2: Model Building and Training (Scikit-learn)

**Example 1: Predicting Future Closing Price**

* **Target variable:** Continuous future closing price of a specific asset.
* **Candidate models:**
    * **Linear Regression:** Simple baseline for linear relationships, but may struggle with non-linearities.
    * **Random Forest Regression:** Handles complex relationships well, but prone to overfitting.
    * **Support Vector Regression (SVR):** Identifies support and resistance levels, but sensitive to outliers.
    * **Long Short-Term Memory (LSTM):** Deep learning model capturing temporal dependencies, but requires more data and computational resources.
* **Features:**
    * **Technical indicators:** Moving averages, RSI, MACD, Bollinger Bands (consider normalization).
    * **Lagged features:** Past closing prices, volume, volatility (e.g., ATR).
    * **Market data:** Sector performance, interest rates, economic indicators (if relevant).
* **Feature engineering:**
    * Create new features like momentum indicators, price ratios, or technical indicator derivatives.
    * Consider dimensionality reduction techniques (e.g., PCA) to avoid overfitting.
* **Hyperparameter tuning:**
    * Tune regularization parameters for SVR, number of trees and max depth for Random Forest, and LSTM hyperparameters carefully.
* **Evaluation metrics:**
    * **Mean Squared Error (MSE):** Sensitive to outliers, use for interpretability.
    * **Mean Absolute Error (MAE):** Less sensitive to outliers, good for general performance.
    * **R-squared:** Proportion of variance explained, but can be misleading for non-linear models.
    * **Consider additional metrics:** Sharpe ratio (risk-adjusted return), MAPE (percentage error).

**Example 2: Trend Classification (Upward/Downward)**

* **Target variable:** Binary classification of price movement (e.g., next day).
* **Candidate models:**
    * **Logistic Regression:** Simple and interpretable, but may not capture complex trends.
    * **Decision Trees:** Handles non-linearities well, but prone to overfitting.
    * **Support Vector Machines (SVM):** Identifies clear trend boundaries, but sensitive to noise.
    * **Random Forest:** More robust than single Decision Trees, but requires careful tuning.
* **Features:** Similar to price prediction, but consider momentum indicators, volume changes, and market sentiment analysis (e.g., news sentiment).
* **Feature engineering:** Explore features specifically related to trend identification (e.g., rate of change, moving average convergence/divergence).
* **Hyperparameter tuning:** Regularization for Logistic Regression, tree depth/number of trees for Random Forest, kernel type for SVM.
* **Evaluation metrics:**
    * **Accuracy:** Overall percentage of correct predictions.
    * **Precision:** Ratio of true positives to predicted positives.
    * **Recall:** Ratio of true positives to all actual positives.
    * **F1-score:** Balanced metric considering both precision and recall.

**Remember:**

* Choose models and features aligned with your goals and asset class.
* Start simple and gradually add complexity based on data and performance.
* Evaluate thoroughly using appropriate metrics and avoid overfitting.
* Consider data quality, cleaning, and potential biases.

## Step 3: Strategy Implementation and Backtesting (Backtrader)

**Example 1: Trend-Following Strategy (Price Prediction based)**

* **Entry rule:** Buy when predicted price exceeds actual price by a threshold (consider volatility).
* **Exit rule:** Sell when predicted price falls below actual price by a threshold or after a holding period (set stop-loss).
* **Position sizing:** Based on predicted price movement, confidence level, and risk tolerance.
* **Risk management:** Implement stop-loss orders, consider trailing stops and position size adjustments.
* **Backtesting:** Analyze performance metrics (profit, loss, Sharpe ratio, drawdown) for different models, thresholds, and holding periods.
* **Additional considerations:** Transaction costs, slippage, commissions, walk-forward testing for robustness.

**Example 2: Mean Reversion Strategy (Trend Classification based)**

* **Entry rule:** Buy when classified as downtrend and reaches a support level (defined by technical indicators or historical data).
* **Exit rule:** Sell when classified as uptrend or reaches a take-profit target (set based on risk tolerance and expected return).
* **Position sizing:** Fixed percentage or dynamic based on confidence in trend classification.
* **Risk management:** Stop-loss orders, consider trailing stops and position adjustments based on trend strength.
* **Backtesting:** Analyze performance across different trend classification models, support/resistance levels, and holding periods.
* **Additional considerations:** Transaction costs

* **Step 4: Continuous Improvement and Feedback Loop**
    * Analyze backtesting results and identify areas for improvement.
    * Refine feature engineering, model selection, hyperparameters.
    * Update models with new data and re-evaluate performance.
    * Adapt the strategy as market dynamics change.

## 3. Additional Considerations

* **Responsible Trading:** Backtesting is not a guarantee of success in real markets. Practice responsible risk management and seek professional advice before making trading decisions.
* **Data Quality:** The quality of your historical data significantly impacts model performance. Ensure proper cleaning and preprocessing.
* **Model Overfitting:** Avoid overfitting models to training data. Use techniques like cross-validation and regularization.
* **Market Complexity:** Financial markets are complex and dynamic. Models may not always capture all relevant factors.
* **Further Exploration:** This guide provides a starting point. Each step involves deeper exploration and best practices specific to your goals.

---

# 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 the Oanda API client
import oandapyV20.endpoints.instruments as instruments

# Set your Oanda API credentials and configuration for data fetching
ACCOUNT_ID = 'your_account_id_here'
ACCESS_TOKEN = 'your_access_token_here'
# List of currency pairs to fetch. Add or remove pairs as needed.
CURRENCY_PAIRS = ['EUR_USD', 'USD_JPY', 'GBP_USD', 'AUD_USD', 'USD_CAD']
TIME_FRAME = 'H4'  # 4-hour candles, change as per your analysis needs
DATA_DIRECTORY = 'data'  # Directory where fetched data will be saved

# Ensure the data directory exists, create it if it doesn't
if not os.path.exists(DATA_DIRECTORY):
    os.makedirs(DATA_DIRECTORY)

def fetch_and_save_forex_data(account_id, access_token, currency_pairs, time_frame, data_dir):
    """Fetch historical forex data for specified currency pairs and save it to CSV files."""
    # Initialize the Oanda API client with your access token
    api_client = API(access_token=access_token)
    
    for pair in currency_pairs:
        # Define the parameters for the data request: time frame and number of data points
        request_params = {"granularity": time_frame, "count": 5000}
        
        # Prepare the data request for fetching candle data for the current currency pair
        data_request = instruments.InstrumentsCandles(instrument=pair, params=request_params)
        # Fetch the data
        response = api_client.request(data_request)
        # Extract the candle data from the response
        candle_data = response.get('candles', [])
        
        # If data was fetched, proceed to save it
        if candle_data:
            # Convert the candle data into a pandas DataFrame
            forex_data_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 candle_data])
            
            # Construct the filename for the CSV file
            csv_filename = f"{pair.lower()}_data.csv"
            # Save the DataFrame to a CSV file in the specified data directory
            forex_data_df.to_csv(os.path.join(data_dir, csv_filename), index=False)
            print(f"Data for {pair} saved to {csv_filename}")

def main():
    """Orchestrates the data fetching and saving process."""
    print("Starting data fetching process...")
    # Call the function to fetch and save data for the configured currency pairs
    fetch_and_save_forex_data(ACCOUNT_ID, ACCESS_TOKEN, CURRENCY_PAIRS, TIME_FRAME, DATA_DIRECTORY)
    print("Data fetching process completed.")

if __name__ == '__main__':
    # Execute the script
    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.