## Example

```mermaid
graph TD
    style A fill:#333,stroke:#FFCC00,stroke-width:2px
    style B fill:#333,stroke:#FFCC00,stroke-width:2px
    style C fill:#555,stroke:#FFCC00,stroke-width:2px
    style D fill:#555,stroke:#FFCC00,stroke-width:2px
    style E fill:#555,stroke:#FFCC00,stroke-width:2px
    style F fill:#555,stroke:#FFCC00,stroke-width:2px
    style G fill:#777,stroke:#FFCC00,stroke-width:2px
    style H fill:#777,stroke:#FFCC00,stroke-width:2px

    A[Data Ingestion Service] -->|Fetches Data| B[Kubernetes Cluster]
    B -->|Stores| C[Data Storage: S3/GlusterFS]
    B -->|Analysis| D[Analysis Microservice]
    D -->|Generates Signals| E[Signal Generation Microservice]
    E -->|Executes Orders| F[Order Execution Service]
    F -->|Places Orders| G[Brokerage API: Oanda]
    B -->|Monitors System| H[Monitoring & Alerting: Prometheus/Grafana]

    linkStyle default interpolate basis
```

## Folder Structure

```mermaid
graph TD
    A("/mean-reversion-trading") --> B("/data")
    A --> C("/models")
    A --> D("/strategies")
    A --> E("/backtesting")
    A --> F("/trading")
    A --> G("/utils")
    A --> H("Dockerfile")
    A --> I("requirements.txt")
    A --> J("README.md")

    B --> B1("/raw")
    B --> B2("/processed")

    C --> C1("/trained")
    C --> C2("model_training.py")

    D --> D1("mean_reversion_strategy.py")

    E --> E1("backtest.py")

    F --> F1("live_trade.py")

    G --> G1("data_fetcher.py")
    G --> G2("feature_engineering.py")
    G --> G3("indicators.py")
```

---

```plaintext
/mean-reversion-trading
|-- /data
|   |-- /raw                      # Store raw historical data fetched from Oanda
|   `-- /processed                # Processed and feature-engineered datasets
|
|-- /models
|   |-- /trained                  # Saved models after training
|   `-- model_training.py         # Script for ML model training and evaluation
|
|-- /strategies
|   |-- mean_reversion_strategy.py  # Trading strategy implementation for Backtrader
|
|-- /backtesting
|   |-- backtest.py               # Script for backtesting strategies using Backtrader
|
|-- /trading
|   |-- live_trade.py             # Script for live trading on Oanda
|
|-- /utils
|   |-- data_fetcher.py           # Script for fetching data from Oanda
|   |-- feature_engineering.py    # Utilities for data cleaning and feature engineering
|   `-- indicators.py             # Custom indicators for strategy (e.g., Bollinger Bands, RSI)
|
|-- Dockerfile                    # Dockerfile for containerization
|-- requirements.txt              # Python dependencies
`-- README.md                     # Project documentation
```

---

# Mean Reversion Trading Strategy for EUR/USD with Machine Learning

## Overview
This guide is dedicated to developing a mean reversion trading strategy for the EUR/USD currency pair. It harnesses the power of machine learning (ML) via scikit-learn for strategy development, Backtrader for backtesting, and ultimately, deploying the optimized strategy for live trading on Oanda.

## Step 1: Data Preparation

### Fetch Historical EUR/USD Data
- **Objective**: Use `oandapyV20` to download 5 years of EUR/USD daily data from Oanda.
- **Rationale**: A 5-year period provides a balanced dataset to capture various market phases, essential for training robust mean reversion models.

### Clean and Preprocess Data
- **Tasks**: Eliminate duplicates and handle missing data. Standardize prices to ensure consistency across the dataset.
- **Normalization**: Apply Min-Max scaling to align features on a similar scale, enhancing model training efficiency.

## Step 2: Exploratory Data Analysis (EDA) and Feature Engineering

### Perform EDA
- **Visualization**: Plot price movements with `matplotlib` to identify mean reversion patterns. Analyze price volatility and its correlation with mean reversion points.

### Develop Technical Indicators
- **Indicators for Mean Reversion**: Calculate Bollinger Bands and RSI. These indicators help identify overbought or oversold conditions signaling potential mean reversions.

### Feature Engineering
- **Feature Creation**: Derive features like the distance from moving averages, Bollinger Band width, and RSI levels to capture market states indicative of mean reversion.

## Step 3: Machine Learning Model Development with Scikit-learn

### Choose an ML Model
- **Model Selection**: Start with Logistic Regression to classify potential mean reversion opportunities. Consider Random Forest for a more nuanced understanding of feature relationships.

### Train and Validate the Model
- **Cross-Validation**: Implement cross-validation to assess model performance, minimizing the risk of overfitting.
- **Metrics**: Evaluate models based on accuracy, precision, recall, and the F1 score to ensure a balanced assessment of the model's predictive capabilities.

## Step 4: Backtesting Strategy with Backtrader

### Integrate ML Model into Backtrader Strategy
- **Strategy Implementation**: Embed your scikit-learn model within a custom Backtrader strategy. Use model predictions to drive trade entries and exits based on identified mean reversion signals.

### Execute Backtesting
- **Configuration**: Set up Backtrader with historical EUR/USD data, including transaction costs and initial capital.
- **Analysis**: Utilize Backtrader's analyzers to evaluate the strategy's performance, focusing on net profit, drawdown, and Sharpe ratio.

## Step 5: Live Trading Preparation

### Paper Trading with Oanda Demo
- **Objective**: Validate the strategy under current market conditions using Oanda's demo account.
- **Adjustments**: Fine-tune strategy parameters and risk management settings based on paper trading outcomes.

### Transition to Live Trading
- **Live Account Switch**: Transition the strategy to a live Oanda account for real trading.
- **Capital Management**: Start with conservative capital allocation, gradually scaling based on live performance and risk appetite.

### Continuous Monitoring and Optimization
- **Live Performance Tracking**: Closely monitor trading activity and performance metrics.
- **Strategy Iteration**: Regularly review and adjust the trading model and strategy parameters in response to evolving market conditions and performance insights.

## Conclusion

This guide provides a concise roadmap for creating a mean reversion trading strategy for the EUR/USD pair, leveraging machine learning for signal generation, Backtrader for rigorous backtesting, and Oanda for deployment. It emphasizes a systematic approach from data analysis to live trading, ensuring a well-founded strategy backed by empirical evidence and optimized through practical experience.

---

# 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.

---

# From Backtesting to Live Trading with Backtrader and Oanda

## Setup and Installation

- **Install Required Packages**
  ```bash
  pip install backtrader oandapyV20
  ```

- **Oanda API Credentials**
  - Obtain API credentials from your Oanda demo account.

## Backtesting

### 1. Data Preparation
- Fetch historical data using Oanda's API for your target currency pairs.

### 2. Strategy Development
- Code your trading strategy within a subclass of `bt.Strategy`.
- Define indicators, entry and exit logic.

### 3. Backtesting Execution
- Initialize a `bt.Cerebro()` engine, adding your strategy and data.
- Set initial capital and other parameters.
- Run backtest and analyze results using Backtrader's built-in analyzers.

## Transition to Paper Trading

### 1. Configure Live Data Feed
- Setup a live data feed from Oanda using the `oandapyV20` package.

### 2. Integrate Oanda Demo as Broker
- Configure Backtrader to use Oanda as the broker with your demo account credentials.
- This simulates order execution in the demo environment.

### 3. Run Paper Trading
- Execute your strategy with Backtrader against the live data feed in simulation mode.
- Monitor performance and make adjustments as necessary.

## Going Live

### 1. Strategy Review and Adjustment
- Fine-tune your strategy based on insights gained from paper trading.

### 2. Switch to Live Account
- Change the API credentials in your script to those of your live Oanda account.

### 3. Start Live Trading
- Begin with the smallest lot sizes.
- Closely monitor the strategy's live trading performance.

## Key Considerations

- **Monitoring**: Keep a close watch on your system's operation during live trading.
- **Incremental Deployment**: Gradually increase your trading size based on the strategy's live performance.
- **Continuous Improvement**: Regularly update your strategy based on live trading data and market conditions.

This markdown guide outlines a focused and actionable path from developing and backtesting trading strategies with Backtrader and Oanda, to paper trading and eventually live trading.
---