pdmt5 0.0.7

Pandas-based data handler for MetaTrader 5

pdmt5

Pandas-based data handler for MetaTrader 5

Overview

pdmt5 is a Python package that provides a pandas-based interface for MetaTrader 5 (MT5), making it easier to work with financial market data in Python. It automatically converts MT5's native data structures into pandas DataFrames, enabling seamless integration with data science workflows.

Key Features

• 📊 Pandas Integration: All data returned as pandas DataFrames for easy analysis
• 🔧 Type Safety: Full type hints with strict pyright checking and pydantic validation
• 🏦 Comprehensive MT5 Coverage: Account info, market data, tick data, orders, positions, and more
• 🚀 Context Manager Support: Clean initialization and cleanup with with statements
• 📈 Time Series Ready: OHLCV data with proper datetime indexing
• 🛡️ Robust Error Handling: Custom exceptions with detailed MT5 error information

Requirements

• Operating System: Windows (required by MetaTrader5 API)
• Python: 3.11 or higher
• MetaTrader 5: Terminal must be installed

Installation

From GitHub

git clone https://github.com/dceoy/pdmt5.git
pip install -U --no-cache-dir ./pdmt5

Using uv (recommended for development)

git clone https://github.com/dceoy/pdmt5.git
cd pdmt5
uv sync

Quick Start

import MetaTrader5 as mt5
from datetime import datetime
from pdmt5 import Mt5DataClient, Mt5Config

# Configure connection
config = Mt5Config(
    login=12345678,
    password="your_password",
    server="YourBroker-Server",
    timeout=60000,
    portable=False
)

# Use as context manager
with Mt5DataClient(config=config) as client:
    # Get account information as DataFrame
    account_info = client.get_account_info_as_df()
    print(account_info)

    # Get OHLCV data as DataFrame
    rates = client.copy_rates_from_as_df(
        symbol="EURUSD",
        timeframe=mt5.TIMEFRAME_H1,
        date_from=datetime(2024, 1, 1),
        count=100
    )
    print(rates.head())

    # Get current positions as DataFrame
    positions = client.get_positions_as_df()
    print(positions)

Core Components

Mt5Client

The base client for MT5 operations with context manager support:

• Connection Management: initialize(), login(), shutdown()
• Account & Terminal Info: Access account details and terminal information
• Symbol Operations: Get symbol information and market data
• Trading Operations: Execute orders, manage positions and deals
• History Access: Retrieve historical orders and deals

Mt5DataClient

Extends Mt5Client with pandas DataFrame conversions:

• DataFrame Methods: All data methods have _as_df variants returning DataFrames
• Dictionary Methods: All data methods have _as_dict variants returning dictionaries
• Account Operations: get_account_info(), get_terminal_info()
• Market Data: copy_rates_*() methods for OHLCV data
• Tick Data: copy_ticks_*() methods for tick-level data
• Trading Info: get_orders(), get_positions(), get_deals()
• Symbol Info: get_symbols(), get_symbol_info()

Mt5TradingClient

Advanced trading operations interface that extends Mt5DataClient:

• Position Management: close_open_positions() - Close positions by symbol
• Order Filling Modes: IOC (Immediate or Cancel), FOK (Fill or Kill), or RETURN
• Dry Run Mode: Test trading logic without executing real trades
• Full Trading Operations: Includes all Mt5DataClient capabilities plus trading features

Configuration

from pdmt5 import Mt5Config

config = Mt5Config(
    login=12345678,          # MT5 account number
    password="password",     # MT5 password
    server="Broker-Server",  # MT5 server name
    timeout=60000,          # Connection timeout in ms
    portable=False          # Use portable mode
)

Examples

Getting Historical Data

import MetaTrader5 as mt5
from datetime import datetime

with Mt5DataClient(config=config) as client:
    # Get last 1000 H1 bars for EURUSD as DataFrame
    df = client.copy_rates_from_as_df(
        symbol="EURUSD",
        timeframe=mt5.TIMEFRAME_H1,
        date_from=datetime.now(),
        count=1000
    )

    # Data includes: time, open, high, low, close, tick_volume, spread, real_volume
    print(df.columns)
    print(df.describe())

Working with Tick Data

from datetime import datetime, timedelta

with Mt5DataClient(config=config) as client:
    # Get ticks for the last hour as DataFrame
    ticks = client.copy_ticks_from_as_df(
        symbol="EURUSD",
        date_from=datetime.now() - timedelta(hours=1),
        count=10000,
        flags=mt5.COPY_TICKS_ALL
    )

    # Tick data includes: time, bid, ask, last, volume, flags
    print(ticks.head())

Analyzing Positions

with Mt5DataClient(config=config) as client:
    # Get all open positions as DataFrame
    positions = client.get_positions_as_df()

    if not positions.empty:
        # Calculate summary statistics
        summary = positions.groupby('symbol').agg({
            'volume': 'sum',
            'profit': 'sum',
            'price_open': 'mean'
        })
        print(summary)

Trading Operations

from pdmt5 import Mt5TradingClient

# Create trading client with specific order filling mode
with Mt5TradingClient(config=config, order_filling_mode="IOC") as trader:
    # Close all EURUSD positions
    results = trader.close_open_positions(symbols="EURUSD")

    if results:
        for result in results:
            print(f"Closed position {result['position']} with result: {result['retcode']}")

    # Using dry run mode for testing
    trader_dry = Mt5TradingClient(config=config, dry_run=True)
    with trader_dry:
        # Test closing positions without actual execution
        test_results = trader_dry.close_open_positions(symbols=["EURUSD", "GBPUSD"])

Development

Setup Development Environment

# Clone repository
git clone https://github.com/dceoy/pdmt5.git
cd pdmt5

# Install with uv
uv sync

# Run tests
uv run pytest test/ -v

# Run type checking
uv run pyright .

# Run linting
uv run ruff check --fix .
uv run ruff format .

Code Quality

This project maintains high code quality standards:

• Type Checking: Strict mode with pyright
• Linting: Comprehensive ruff configuration with 40+ rule categories
• Testing: pytest with coverage tracking (minimum 90%)
• Documentation: Google-style docstrings

Error Handling

The package provides detailed error information:

from pdmt5 import Mt5RuntimeError

try:
    with Mt5DataClient(config=config) as client:
        data = client.copy_rates_from("INVALID", mt5.TIMEFRAME_H1, datetime.now(), 100)
except Mt5RuntimeError as e:
    print(f"MT5 Error: {e}")
    print(f"Error code: {e.error_code}")
    print(f"Description: {e.description}")

Limitations

• Windows Only: Due to MetaTrader5 API requirements
• MT5 Terminal Required: The MetaTrader 5 terminal must be installed
• Single Thread: MT5 API is not thread-safe

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Ensure tests pass and coverage is maintained
4. Submit a pull request

See CLAUDE.md for development guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Daichi Narushima, Ph.D.

Acknowledgments

• MetaTrader 5 for providing the Python API
• The pandas community for the excellent data manipulation tools