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mt5gw: MetaTrader 5 Gateway

mt5gw is a comprehensive Python library that transforms MetaTrader 5 into a versatile and reliable data pipeline for machine learning (ML) applications. It simplifies the process of retrieving, enriching, and managing financial data while ensuring reproducibility across ML workflows.

Table of Contents

Installation

Prerequisites

  • Python: Ensure you have Python 3.7 or later installed. You can download it from the official website.
  • MetaTrader 5 Terminal: Install and configure MetaTrader 5 on your Windows machine. Download it from the MetaQuotes website.
  • Windows OS: mt5gw is designed to work on Windows operating systems.

Installation Steps

  1. Clone the Repository 
    git clone https://github.com/emerzon/mt5gw.git
cd mt5gw
  2. Install from Source
    • Run: 
      pip install -e .

Quick Start

Basic Data Retrieval

To fetch basic data for a specific instrument and timeframe:

from mt5gw import MetaTraderManager

# Initialize the manager
mt = MetaTraderManager()

# Fetch 100 bars of EURUSD at 1-hour intervals
df = mt.fetch(
    instrument="EURUSD",
    timeframe="1h",
    bars=100,
    add_meta_dates=True
)

print(df)

Data Retrieval with Technical Indicators

To fetch data along with technical indicators:

from mt5gw import MetaTraderManager

# Initialize the manager
mt = MetaTraderManager()

# Fetch 100 bars of EURUSD at 1-hour intervals with RSI and MACD indicators
df = mt.fetch(
    instrument="EURUSD",
    timeframe="1h",
    bars=100,
    talib_indicators=[
        {"method": "RSI", "args": ["c"], "kwargs": {"timeperiod": 14}},
        {"method": "MACD", "args": ["c"]}
    ],
    mas=[
        {"method": "SMA", "field": "close", "periods": [20, 50]}
    ]
)

print(df)

Data Retrieval with Custom Parameters

To fetch data with custom parameters such as date ranges and open/close behaviors:

from mt5gw import MetaTraderManager
import datetime

# Initialize the manager
mt = MetaTraderManager()

# Fetch EURUSD from 2023-01-01 to 2023-01-31 at 1-hour intervals
df = mt.fetch(
    instrument="EURUSD",
    timeframe="1h",
    start_date=datetime.datetime(2023, 1, 1),
    end_date=datetime.datetime(2023, 1, 31),
    add_meta_dates=True,
    open_close_behavior="open"
)

print(df)

Key Features

1. Structured Data Retrieval

  • Flexible Timeframes: Supports multiple timeframes from 1 minute to 1 month, enabling granular or broad data analysis.
  • Multi-Instrument Support: Fetch data for one or more instruments simultaneously, with consistent and unified outputs.
  • Customizable Parameters: Specify date ranges, bar limits, and open/close behaviors for tailored data extraction.

2. Technical Indicator Integration

  • Broad Indicator Support: Leverages popular libraries like ta, talib, pandas_ta, and tulipy to compute a wide range of technical indicators.
  • Candle Pattern Detection: Recognizes candlestick patterns using TA-Lib for advanced technical analysis.
  • Moving Averages and Lookbacks: Provides methods such as Simple Moving Average (SMA), Exponential Moving Average (EMA), and customizable lookback calculations.

3. Data Enrichment

  • Pivot Levels: Adds standard, Fibonacci, Camarilla, Woodie, and Demark pivot levels along with distances to these levels for deeper insights.
  • Meta Information: Enhances data with metadata including day, month, hour, minute, and price summaries.
  • Support and Resistance Levels: Calculates key support and resistance levels based on rolling windows and specific fields like close prices.

4. Advanced Filtering and Smoothing

  • Wavelet Denoising: Applies wavelet transforms to smooth data, reducing noise and improving signal clarity.
  • Gap Analysis: Identifies gaps between consecutive bars for anomaly detection or trading strategies.
  • Null Handling: Fills missing values using forward-fill or back-fill techniques to ensure consistent datasets.

5. Trading and Order Management

  • Order Placement: Simplifies placing market, limit, and stop orders with MetaTrader 5.
  • Position Management: Facilitates fetching and managing open positions and orders for active trading.
  • Trade Execution: Supports position closing and ensures robust order sending mechanisms.

6. Reproducibility and Workflow Integration

  • Replicable Settings: Allows defining retrieval settings to ensure consistency between training and inference datasets.
  • Multi-Instrument Aggregation: Combines data from multiple instruments into a unified dataset, ensuring consistency in multi-asset analysis.

Example Use Cases

  • ML Pipeline: Use mt5gw to fetch, preprocess, and enrich financial data for training predictive models.
  • Quantitative Analysis: Perform detailed technical analysis using enriched data and advanced indicators.
  • Automated Trading: Develop and backtest trading strategies with the included trading and order management features.

Documentation

Troubleshooting

Common Issues

  1. MetaTrader Connection Failed

    • Ensure MetaTrader 5 terminal is running
    • Check if the symbol is available in your MT5 terminal
    • Verify your MT5 account has necessary permissions

  2. Missing Indicators

    • Install required dependencies: pip install ta-lib pandas-ta tulipy
    • For TA-Lib on Windows, use: pip install TA-Lib-binary

  3. Data Quality Issues

    • Verify symbol availability during the requested timeframe
    • Check for sufficient historical data in MT5
    • Ensure proper market hours for the requested instrument

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

mt5gw is released under the MIT License. See the LICENSE file for more details.