pdmt5

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
• 💰 Advanced Trading Operations: Position management, margin calculations, and risk analysis tools
• 🧪 Dry Run Mode: Test trading strategies without executing real trades

Requirements

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

Installation

Using pip

pip install -U pdmt5 MetaTrader5

Using uv

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
)

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

Core Components

Mt5Client

The base client wrapper for all MetaTrader5 operations with context manager support:

• Connection Management:
  • initialize() - Establish connection with MT5 terminal (with optional path, login, password, server, timeout)
  • login() - Connect to trading account with credentials
  • shutdown() - Close MT5 terminal connection
  • Context manager support (with statement) for automatic initialization/cleanup
• Terminal Information:
  • version() - Get MT5 terminal version, build, and release date
  • last_error() - Get last error code and description
  • account_info() - Get current trading account information
  • terminal_info() - Get terminal status and settings
• Symbol Operations:
  • symbols_total() - Get total number of financial instruments
  • symbols_get() - Get all symbols or filter by group
  • symbol_info() - Get detailed data on specific symbol
  • symbol_info_tick() - Get last tick for symbol
  • symbol_select() - Show/hide symbol in MarketWatch
• Market Depth:
  • market_book_add() - Subscribe to Market Depth events
  • market_book_get() - Get current Market Depth data
  • market_book_release() - Unsubscribe from Market Depth
• Market Data:
  • copy_rates_from() - Get bars from specified date
  • copy_rates_from_pos() - Get bars from specified position
  • copy_rates_range() - Get bars for date range
  • copy_ticks_from() - Get ticks from specified date
  • copy_ticks_range() - Get ticks for date range
• Order Operations:
  • orders_total() - Get number of active orders
  • orders_get() - Get active orders with optional filters
  • order_calc_margin() - Calculate required margin
  • order_calc_profit() - Calculate potential profit
  • order_check() - Check if order can be placed
  • order_send() - Send order to trade server
• Position Operations:
  • positions_total() - Get number of open positions
  • positions_get() - Get open positions with optional filters
• Trading History:
  • history_orders_total() - Get number of historical orders
  • history_orders_get() - Get historical orders with filters
  • history_deals_total() - Get number of historical deals
  • history_deals_get() - Get historical deals with filters

Mt5DataClient

Extends Mt5Client with pandas DataFrame and dictionary conversions:

• Enhanced Connection:
  • initialize_and_login_mt5() - Combined initialization and login with retry logic
  • Configurable retry attempts via retry_count parameter
• DataFrame/Dictionary Conversions: All methods have both _as_df and _as_dict variants:
  • version_as_dict/df() - MT5 version information
  • last_error_as_dict/df() - Last error details
  • account_info_as_dict/df() - Account information
  • terminal_info_as_dict/df() - Terminal information
  • symbols_get_as_dicts/df() - Symbol list with optional group filter
  • symbol_info_as_dict/df() - Single symbol information
  • symbol_info_tick_as_dict/df() - Last tick data
  • market_book_get_as_dicts/df() - Market depth data
• OHLCV Data Methods:
  • copy_rates_from_as_dicts/df() - Historical bars from date
  • copy_rates_from_pos_as_dicts/df() - Historical bars from position
  • copy_rates_range_as_dicts/df() - Historical bars for date range
• Tick Data Methods:
  • copy_ticks_from_as_dicts/df() - Historical ticks from date
  • copy_ticks_range_as_dicts/df() - Historical ticks for date range
• Trading Data Methods:
  • orders_get_as_dicts/df() - Active orders with filters
  • order_check_as_dict/df() - Order validation results
  • order_send_as_dict/df() - Order execution results
  • positions_get_as_dicts/df() - Open positions with filters
  • history_orders_get_as_dicts/df() - Historical orders with date/ticket/position filters
  • history_deals_get_as_dicts/df() - Historical deals with date/ticket/position filters
• Features:
  • Automatic time conversion to datetime objects
  • Optional DataFrame indexing with index_keys parameter
  • Input validation for dates, counts, and positions
  • Pydantic-based configuration via Mt5Config

Mt5TradingClient

Advanced trading operations client that extends Mt5DataClient:

• Position Management:
  • close_open_positions() - Close all positions for specified symbol(s)
  • place_market_order() - Place market orders with configurable side, volume, and execution modes
  • update_sltp_for_open_positions() - Modify stop loss and take profit levels for open positions
• Margin Calculations:
  • calculate_minimum_order_margin() - Calculate minimum required margin for a specific order side
  • calculate_volume_by_margin() - Calculate maximum volume for given margin amount
  • calculate_spread_ratio() - Calculate normalized bid-ask spread ratio
  • calculate_new_position_margin_ratio() - Calculate margin ratio for potential new positions
• Simplified Data Access:
  • fetch_latest_rates_as_df() - Get recent OHLC data with timeframe strings (e.g., "M1", "H1", "D1")
  • fetch_latest_ticks_as_df() - Get tick data for specified seconds around last tick
  • collect_entry_deals_as_df() - Filter and collect entry deals (BUY/SELL) from history
  • fetch_positions_with_metrics_as_df() - Get open positions with calculated metrics (elapsed time, margin, profit ratios)
• Features:
  • Smart order routing with configurable filling modes
  • Comprehensive error handling with Mt5TradingError
  • Support for batch operations on multiple symbols
  • Automatic position closing with proper order type reversal

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
)

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.positions_get_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 Mt5TradingClient(config=config) as trader:
    # Place a market buy order
    order_result = trader.place_market_order(
        symbol="EURUSD",
        volume=0.1,
        order_side="BUY",
        order_filling_mode="IOC",  # Immediate or Cancel
        order_time_mode="GTC"      # Good Till Cancelled
    )
    print(f"Order placed: {order_result['retcode']}")

    # Update stop loss and take profit for open positions
    update_results = trader.update_sltp_for_open_positions(
        symbol="EURUSD",
        stop_loss=1.0950,   # New stop loss
        take_profit=1.1050  # New take profit
    )
    for result in update_results:
        print(f"Position updated: {result['retcode']}")

    # Calculate margin ratio for a new position
    margin_ratio = trader.calculate_new_position_margin_ratio(
        symbol="EURUSD",
        new_position_side="SELL",
        new_position_volume=0.2
    )
    print(f"New position margin ratio: {margin_ratio:.2%}")

    # Close all EURUSD positions with specific order filling mode
    results = trader.close_open_positions(
        symbols="EURUSD",
        order_filling_mode="FOK"  # Fill or Kill
    )

    if results:
        for symbol, close_results in results.items():
            for result in close_results:
                print(f"Closed position {result.get('position')} with result: {result['retcode']}")

Market Analysis with Mt5TradingClient

with Mt5TradingClient(config=config) as trader:
    # Calculate spread ratio for EURUSD
    spread_ratio = trader.calculate_spread_ratio("EURUSD")
    print(f"EURUSD spread ratio: {spread_ratio:.5f}")

    # Get minimum order margin for BUY and SELL
    buy_margin = trader.calculate_minimum_order_margin("EURUSD", "BUY")
    sell_margin = trader.calculate_minimum_order_margin("EURUSD", "SELL")
    print(f"Minimum BUY margin: {buy_margin['margin']} (volume: {buy_margin['volume']})")
    print(f"Minimum SELL margin: {sell_margin['margin']} (volume: {sell_margin['volume']})")

    # Calculate volume by margin
    available_margin = 1000.0
    max_buy_volume = trader.calculate_volume_by_margin("EURUSD", available_margin, "BUY")
    max_sell_volume = trader.calculate_volume_by_margin("EURUSD", available_margin, "SELL")
    print(f"Max BUY volume for ${available_margin}: {max_buy_volume}")
    print(f"Max SELL volume for ${available_margin}: {max_sell_volume}")

    # Get recent OHLC data with custom timeframe
    rates_df = trader.fetch_latest_rates_as_df(
        symbol="EURUSD",
        granularity="M15",  # 15-minute bars
        count=100
    )
    print(rates_df.tail())

    # Get tick data for the last 60 seconds
    ticks_df = trader.fetch_latest_ticks_as_df(
        symbol="EURUSD",
        seconds=60
    )
    print(f"Received {len(ticks_df)} ticks")

    # Collect entry deals for the last hour
    deals_df = trader.collect_entry_deals_as_df(
        symbol="EURUSD",
        history_seconds=3600
    )
    if not deals_df.empty:
        print(f"Found {len(deals_df)} entry deals")
        print(deals_df[['time', 'type', 'volume', 'price']].head())

    # Get positions with calculated metrics
    positions_df = trader.fetch_positions_with_metrics_as_df("EURUSD")
    if not positions_df.empty:
        print(f"Open positions with metrics:")
        print(positions_df[['ticket', 'volume', 'profit', 'elapsed_seconds', 'underlier_profit_ratio']].head())

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 tests/ -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:

• Fork the repository
• Create a feature branch
• Ensure tests pass and coverage is maintained
• 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