diff --git a/projects/forex_algo_trading.md b/projects/forex_algo_trading.md
index 313f07e..5e9ac3a 100644
--- a/projects/forex_algo_trading.md
+++ b/projects/forex_algo_trading.md
@@ -51,6 +51,71 @@ swing_trading_project/
 - 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 csv
+import os
+from oandapyV20 import API    # The Oanda API wrapper
+import oandapyV20.endpoints.instruments as instruments
+from datetime import datetime
+import pandas as pd
+
+# Configuration
+ACCOUNT_ID = 'your_account_id_here'
+ACCESS_TOKEN = 'your_access_token_here'
+INSTRUMENT = 'EUR_USD'
+GRANULARITY = 'H4'  # 4-hour candles
+OUTPUT_FILENAME = 'eur_usd_data.csv'
+
+# Directory for saving the data
+DATA_DIR = 'data'
+if not os.path.exists(DATA_DIR):
+    os.makedirs(DATA_DIR)
+
+def fetch_data(account_id, access_token, instrument, granularity):
+    """Fetch historical forex data for a specified instrument and granularity."""
+    client = API(access_token=access_token)
+    params = {
+        "granularity": granularity,
+        "count": 5000  # Maximum data points to fetch in one request
+    }
+    
+    # Create a data request
+    data_request = instruments.InstrumentsCandles(instrument=instrument, params=params)
+    data = client.request(data_request)
+    
+    return data['candles']
+
+def save_to_csv(data, filename):
+    """Save fetched forex data to a CSV file."""
+    filepath = os.path.join(DATA_DIR, filename)
+    with open(filepath, mode='w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow(['Time', 'Open', 'High', 'Low', 'Close', 'Volume'])
+        
+        for candle in data:
+            writer.writerow([
+                candle['time'],
+                candle['mid']['o'],
+                candle['mid']['h'],
+                candle['mid']['l'],
+                candle['mid']['c'],
+                candle['volume']
+            ])
+
+def main():
+    """Main function to fetch and save EUR/USD data."""
+    print("Fetching data...")
+    data = fetch_data(ACCOUNT_ID, ACCESS_TOKEN, INSTRUMENT, GRANULARITY)
+    print(f"Fetched {len(data)} data points.")
+    
+    print("Saving to CSV...")
+    save_to_csv(data, OUTPUT_FILENAME)
+    print(f"Data saved to {os.path.join(DATA_DIR, OUTPUT_FILENAME)}")
+
+if __name__ == '__main__':
+    main()
+```
+
 ## Step 4: Exploratory Data Analysis
 
 - Create a new Jupyter notebook in `notebooks/`.