Forex Historical Data Ingestion Module ( 😉FREE )

Overview

This Python module facilitates the ingestion of historical forex data for the EUR/USD currency pair using the dukascopy_python library. It retrieves 15-minute OHLCV (Open, High, Low, Close, Volume) data for a specified date range, adjusts the timestamps, and saves the data to a CSV file. This module is designed for traders, data analysts, and developers who need reliable forex historical data for backtesting trading strategies or conducting market analysis.

Features

• Data Retrieval: Fetches historical OHLCV data for EUR/USD from Dukascopy.
• Customizable Date Range: Allows users to specify start and end dates for data collection.
• Timestamp Adjustment: Adjusts timestamps by a user-defined offset (e.g., +3 hours).
• CSV Export: Saves the retrieved data to a CSV file for further analysis.
• Simple Integration: Easy-to-use script that integrates with Python data analysis workflows.

Prerequisites

Before using this module, ensure you have the following installed:

• Python 3.6 or higher
• dukascopy_python library
• pandas library (included as a dependency with dukascopy_python)

Installation

1. Install Python from python.org if not already installed.

2. Install the required library using pip:

   pip install dukascopy-python

3. Verify the installation:

   python -c "import dukascopy_python; print(dukascopy_python.__version__)"

Usage

Code Example

The following script demonstrates how to use the module to fetch 15-minute EUR/USD data, adjust timestamps, and save the output to a CSV file:

from datetime import datetime, timedelta
import dukascopy_python
from dukascopy_python.instruments import INSTRUMENT_FX_MAJORS_EUR_USD

# Define date range
start = datetime(2020, 1, 1)
end = datetime(2025, 8, 1)

# Fetch 15-minute OHLCV data
df = dukascopy_python.fetch(
    instrument=INSTRUMENT_FX_MAJORS_EUR_USD,
    interval=dukascopy_python.INTERVAL_MIN_15,
    offer_side=dukascopy_python.OFFER_SIDE_BID,
    start=start,
    end=end,
)

# Adjust timestamp by +3 hours
df.index = df.index + timedelta(hours=3)

# Save to CSV
df.to_csv("EURUSD_15min.csv")
print(df.head(), df.shape)

Explanation of Parameters

• instrument: Specifies the forex pair (e.g., INSTRUMENT_FX_MAJORS_EUR_USD for EUR/USD).
• interval: Sets the time interval for data (e.g., INTERVAL_MIN_15 for 15-minute data).
• offer_side: Defines the bid or ask price (OFFER_SIDE_BID for bid prices).
• start and end: Datetime objects specifying the date range for data retrieval.
• timedelta(hours=3): Adjusts the timestamp of the data by adding 3 hours.

Output

• The script generates a CSV file named EURUSD_15min.csv containing the OHLCV data.
• The print statement displays the first five rows of the DataFrame and its shape (rows, columns).

Example output of print(df.head(), df.shape):

                           open    high     low   close  volume
2025-01-01 03:00:00  1.0800  1.0810  1.0795  1.0805    1000
2025-01-01 03:15:00  1.0805  1.0820  1.0800  1.0815    1200
2025-01-01 03:30:00  1.0815  1.0825  1.0810  1.0820    1100
2025-01-01 03:45:00  1.0820  1.0830  1.0815  1.0825    1300
2025-01-01 04:00:00  1.0825  1.0835  1.0820  1.0830    1150
(10000, 5)

Repository Structure

forex-data-ingestion/
├── README.md              # This file
├── fetch_forex_data.py    # Main script for data ingestion
├── EURUSD_15min.csv       # Output CSV file (generated after running the script)

Installation Instructions for Repository

1. Clone the repository:

   git clone https://github.com/frostyalce000/forex-data-ingestion-free-dukacopy.git
   cd forex-data-ingestion

2. Install dependencies:

   pip install dukascopy-python

3. Run the script:

   python fetch_forex_data.py

Customization

• Change Currency Pair: Modify the instrument parameter to fetch data for other pairs (e.g., INSTRUMENT_FX_MAJORS_USD_JPY for USD/JPY). Refer to dukascopy_python.instruments for available pairs.
• Adjust Time Interval: Use other intervals like INTERVAL_MIN_1, INTERVAL_HOUR_1, or INTERVAL_DAY_1 as needed.
• Timestamp Adjustment: Modify the timedelta value to adjust timestamps differently (e.g., timedelta(hours=-5) for a 5-hour subtraction).
• Output Format: Replace df.to_csv with df.to_parquet or other formats for alternative storage options.

Notes

• Data Availability: Ensure the date range specified is within the available data period provided by Dukascopy. Future dates (e.g., beyond the current date) will result in no data being returned.
• API Limits: Be aware of any rate limits or restrictions imposed by Dukascopy's data service. Check the Dukascopy documentation for details.
• Error Handling: Add try-except blocks for robust error handling in production environments.

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit (git commit -m "Add new feature").
4. Push to the branch (git push origin feature-branch).
5. Open a pull request with a detailed description of your changes.

Acknowledgments

• Dukascopy for providing historical forex data.
• dukascopy-python for the Python wrapper used in this module.