pdmt5

Low-level MetaTrader 5 wrapper and pandas/dict conversion package

Overview

pdmt5 is a Python package that provides a low-level wrapper around the MetaTrader 5 (MT5) API with pandas DataFrame and dictionary conversion helpers. It exposes raw MT5 API calls, converts results into pandas DataFrames and dicts, and provides canonical constant parsing for timeframes, tick copy flags, and order types.

High-level trading orchestration (market-order construction, margin-budget sizing, position lifecycle management, strategy or batch workflows) is out of scope. Those concerns belong in downstream applications or tools such as mt5cli.

Key Features

• Pandas Integration: DataFrame and dictionary helpers 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
• Canonical MT5 Constants: Shared parsing for timeframes, COPY_TICKS flags, and ORDER_TYPE values with official names, short aliases, or valid integers
• Context Manager Support: Clean initialization and cleanup with with statements; Mt5DataClient applies Mt5Config automatically
• Time Series Ready: OHLCV data with proper datetime indexing
• Robust Error Handling: Custom exceptions with detailed MT5 failure messages
• Direct Order Primitives: order_check / order_send wrappers with DataFrame/dict conversions

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

from datetime import datetime
from pdmt5 import Mt5DataClient, Mt5Config, parse_timeframe

# 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=parse_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 (initialize only)
• 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

MT5 Constant Parsing

pdmt5 is the canonical source for parsing MT5 constants shared by CLI, HTTP API, and other application layers. The constants module does not import MetaTrader5, so it can be used for request validation and JSON schema generation without a live terminal.

from pdmt5 import (
    list_copy_ticks_names,
    list_copy_ticks_values,
    list_order_type_names,
    list_order_type_values,
    list_timeframe_names,
    list_timeframe_values,
    parse_copy_ticks,
    parse_order_type,
    parse_timeframe,
)

parse_timeframe("TIMEFRAME_M1")  # 1
parse_timeframe("M1")  # 1
parse_copy_ticks("COPY_TICKS_ALL")  # -1
parse_copy_ticks("ALL")  # -1
parse_order_type("ORDER_TYPE_BUY")  # 0
parse_order_type("BUY")  # 0

# Integer values are accepted only when they belong to the requested family.
parse_timeframe(16385)  # TIMEFRAME_H1
parse_order_type(16385)  # raises ValueError

# Use these helpers to build Pydantic JSON schema enums.
timeframe_schema = {
    "anyOf": [
        {"type": "string", "enum": list_timeframe_names()},
        {"type": "integer", "enum": list_timeframe_values()},
    ],
}
copy_ticks_schema = {
    "anyOf": [
        {"type": "string", "enum": list_copy_ticks_names()},
        {"type": "integer", "enum": list_copy_ticks_values()},
    ],
}
order_type_schema = {
    "anyOf": [
        {"type": "string", "enum": list_order_type_names()},
        {"type": "integer", "enum": list_order_type_values()},
    ],
}

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

from datetime import datetime
from pdmt5 import Mt5DataClient, parse_timeframe

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=parse_timeframe("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
from pdmt5 import parse_copy_ticks

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=parse_copy_ticks("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)

Order Check and Send

with Mt5DataClient(config=config) as client:
    # Validate an order request without sending
    request = {
        "action": 1,  # TRADE_ACTION_DEAL
        "symbol": "EURUSD",
        "volume": 0.1,
        "type": 0,  # ORDER_TYPE_BUY
        "type_filling": 1,
        "type_time": 0,
    }
    check = client.order_check_as_dict(request=request)
    print(f"Check retcode: {check['retcode']}")

    # Send the order to the trade server
    result = client.order_send_as_dict(request=request)
    print(f"Send retcode: {result['retcode']}")

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 100% branch coverage enforcement
• Documentation: Google-style docstrings

Error Handling

The package provides detailed error information:

from pdmt5 import Mt5RuntimeError, parse_timeframe

try:
    with Mt5DataClient(config=config) as client:
        data = client.copy_rates_from(
            "INVALID", parse_timeframe("H1"), datetime.now(), 100
        )
except Mt5RuntimeError as e:
    print(f"MT5 Error: {e}")
    print("Inspect the exception message for the MT5 status details.")

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