Python Guide to MT5 Automated Trading, Data & AI Integration

1. Introduction

2. Benefits of Using MT5 and Python

• Transition from manual processes to automation
• High‑speed processing of large data sets
• Integration with external AI
• Creating custom trading logic , among other essential features for modern FX trading

3. Environment Setup Procedure

Configuration Order and Overall Flow

1. Install the MetaTrader 5 (MT5) terminal Download and install the Windows 64‑bit version of MT5 from the official site or a broker’s website.
2. Launch the MT5 terminal and log in to an account Start MT5 and log in to a demo or live account.
3. Configure MT5 options In the “Tools → Options → Expert Advisors” tab, check “Allow automated trading” and “Allow DLL imports”.
4. Install Python (64‑bit version) Download and install the 64‑bit version from the official Python website.
5. Install required libraries Run the following in a command prompt or terminal. pip install MetaTrader5 pandas numpy
6. Run Python script (connection test) Once the environment is set up, use a sample script to verify the connection.

3.1 Installing the MT5 Terminal

3.2 MT5 Option Settings

1. Open “Tools → Options”
2. Select the “Expert Advisors” tab
3. Check “Allow automated trading” and “Allow DLL imports”

3.3 Preparing the Python Environment and Libraries

pip install MetaTrader5 pandas numpy

3.4 Points to Note When Integrating

• Ensure that the bitness (64‑bit) of Python and MT5 matches.
• Run the Python script while the MT5 terminal is running and logged in.
• Always start testing with a demo account.

4. Connecting to MT5 from Python

4.1 Considerations for the Execution Environment

• OS and Bitness MetaTrader 5 (MT5) is officially supported on Windows 64‑bit. Please also use the 64‑bit version of Python. Mismatched bitness can cause errors when importing the library or connecting.
• MT5 Terminal State Before running a Python script, you need to launch the MT5 terminal and log into an account. If the terminal is not running or you are not logged in, you will get a connection error.
• Recommended Environment · Windows 10/11 64‑bit · Python 3.8 or later (64‑bit) · Latest MetaTrader 5 terminal * Running on macOS or Linux is not officially supported, but it can work via a virtual environment (VirtualBox, Parallels, etc.). However, you are responsible for any issues.

4.2 Importing and Initializing the MT5 Library

import MetaTrader5 as mt5

# Connect to MT5 terminal
if not mt5.initialize():
    print("Failed to connect to MT5.")
    mt5.shutdown()
else:
    print("Successfully connected to MT5.")
    mt5.shutdown()

• Make sure the MT5 terminal is launched and you are logged into an account beforehand.
• Also verify that the Python used to run the sample script is the 64‑bit version.
• When the script finishes, always disconnect using mt5.shutdown().

4.3 Retrieving Account Information

import MetaTrader5 as mt5

mt5.initialize()

# Retrieve account information
account_info = mt5.account_info()
if account_info is None:
    print("Failed to retrieve account information.")
else:
    print(f"Account number: {account_info.login}")
    print(f"Balance: {account_info.balance}")

mt5.shutdown()

4.4 Troubleshooting Checklist

• MT5 terminal not launched / not logged in → Ensure the MT5 terminal is running and you are logged into an account.
• Python and MT5 bitness mismatch → Use matching 64‑bit versions. You can check with commands like python --version or python -c "import struct;print(struct.calcsize('P') * 8)" in the command prompt.
• Symbol name or order setting errors → Verify that the symbol is valid and tradable.

4.5 Note: Handling Timestamps

import pandas as pd
# Example: rates_df["time"] = pd.to_datetime(rates_df["time"], unit="s").dt.tz_localize("UTC").dt.tz_convert("Asia/Tokyo")

5. Retrieving Price Data and Order Processing

5.1 Tick & Bar (OHLC) Data Retrieval

import MetaTrader5 as mt5

mt5.initialize()

# Symbol specification (e.g., USDJPY)
symbol = "USDJPY"

# Retrieve latest tick data
tick = mt5.symbol_info_tick(symbol)
if tick:
    print(f"Bid: {tick.bid}, Ask: {tick.ask}, Time: {tick.time}")
else:
    print("Failed to retrieve tick data.")

mt5.shutdown()

import MetaTrader5 as mt5
import pandas as pd

mt5.initialize()

symbol = "USDJPY"
# Retrieve 100 bars of 1-hour data
rates = mt5.copy_rates_from_pos(symbol, mt5.TIMEFRAME_H1, 0, 100)

if rates is not None and len(rates) > 0:
    df = pd.DataFrame(rates)
    print(df.head())
else:
    print("Failed to retrieve candlestick data.")

mt5.shutdown()

5.2 Sample for Sending Orders

import MetaTrader5 as mt5

mt5.initialize()

symbol = "USDJPY"

# Create order parameters
order = {
    "action": mt5.TRADE_ACTION_DEAL,
    "symbol": symbol,
    "volume": 0.1,
    "type": mt5.ORDER_TYPE_BUY,
    "price": mt5.symbol_info_tick(symbol).ask,
    "deviation": 10,
    "magic": 234000,
    "comment": "python script order",
    "type_time": mt5.ORDER_TIME_GTC,
    "type_filling": mt5.ORDER_FILLING_IOC,
}

result = mt5.order_send(order)

if result.retcode == mt5.TRADE_RETCODE_DONE:
    print("The order completed successfully.")
else:
    print(f"Order error: {result.retcode}")

mt5.shutdown()

• When placing an order, regardless of whether it’s a live or demo account, you need sufficient margin in the account beforehand.
• Always test your order logic first on a demo account.
• Because spreads, lot limits, or broker specifications may prevent orders from being accepted, checking return codes and error messages is essential.

6. Simple Automated Trading Strategy Example

6.1 Overview of the Golden Cross Strategy

• Buy when the short-term moving average (e.g., 5 periods) crosses the long-term moving average (e.g., 25 periods) from below to above
• Conversely, sell when the short-term crosses the long-term from above to below

6.2 Implementation Example in Python

import MetaTrader5 as mt5
import pandas as pd

# Connect to MT5
mt5.initialize()
symbol = "USDJPY"

# Retrieve the most recent 100 one-hour bars
rates = mt5.copy_rates_from_pos(symbol, mt5.TIMEFRAME_H1, 0, 100)
df = pd.DataFrame(rates)

# Calculate moving averages
df["ma_short"] = df["close"].rolling(window=5).mean()
df["ma_long"] = df["close"].rolling(window=25).mean()

# Golden cross detection (using the last 2 bars)
cross = (
    df["ma_short"].iloc[-2] < df["ma_long"].iloc[-2] and df["ma_short"].iloc[-1] > df["ma_long"].iloc[-1]
)

if cross:
    # Place a buy order only when the signal occurs
    order = {
        "action": mt5.TRADE_ACTION_DEAL,
        "symbol": symbol,
        "volume": 0.1,
        "type": mt5.ORDER_TYPE_BUY,
        "price": mt5.symbol_info_tick(symbol).ask,
        "deviation": 10,
        "magic": 234001,
        "comment": "golden cross buy",
        "type_time": mt5.ORDER_TIME_GTC,
        "type_filling": mt5.ORDER_FILLING_IOC,
    }
    result = mt5.order_send(order)
    if result.retcode == mt5.TRADE_RETCODE_DONE:
        print("Golden cross buy order has been placed.")
    else:
        print(f"Order error: {result.retcode}")
else:
    print("Currently, the golden cross conditions are not met.")

mt5.shutdown()

6.3 Practical Tips

• In live trading, incorporating entry/exit (settlement) conditions and risk management mechanisms makes it safer.
• To run the script regularly, you can automate it using Windows Task Scheduler or cron (Linux).
• More advanced strategies and complex signal detections can also be flexibly extended with Python.

7. Application Example: Integration with AI/Machine Learning

7.1 Creating Trade Signals with AI Models

1. Retrieve historical price data from MT5
2. Calculate features (e.g., moving averages, Bollinger Bands, etc.) using pandas or numpy
3. Output signals such as “up/down” from the AI model
4. Automatically place orders with mt5.order_send based on the signals

7.2 Strategy Optimization with Walk‑Forward Analysis

• Train the AI model on data from a given period
• Validate predictive performance on the most recent unseen data
• Retrain the model when new data becomes available
• By repeating this cycle, you can develop a strategy that remains robust against future unseen data

7.3 The Scope of Applications Is Unlimited

• Development of High‑Precision Predictive Logic You can evolve existing EA logic with AI, or use deep learning to learn patterns, among other diverse approaches.
• Utilizing External Data You can incorporate data outside MT5, such as economic calendars, news, or social media, into your trading strategies.
• Automated Tuning and Optimization Using Python’s optimization tools, you can achieve an “evolutionary EA” through automatic parameter search.

8. Risk Management and Considerations

8.1 Conduct Sufficient Testing on a Demo Account

8.2 Pay Attention to Spreads, Slippage, and Broker Differences

• Spreads and Slippage Even when placing orders from Python, spreads can widen and slippage (deviation from the execution price) can occur during rapid market moves. Extra caution is needed especially during economic releases or low‑liquidity periods.
• Broker‑Specific Specification Differences Since MT5 trading specifications vary between FX brokers, verify in advance details such as the minimum tradable lot size, maximum number of orders, and available symbols. When using the API, there may be cases where the server’s specifications prevent certain orders.

8.3 Backtesting and Logic Verification

• Backtesting: Conduct thorough verification using historical data
• Walk‑forward analysis: Validate real performance on out‑of‑sample data
• Risk management: Setting limits on maximum loss and stop‑loss orders, among other safeguards, is also crucial.

8.4 Don’t Forget System Monitoring and Maintenance

• Email notifications for system anomalies or errors
• Automatic log saving and backup
• Automatic recovery procedures on server restarts, etc.

8.5 Legal and Compliance Considerations

9. Summary and Next Steps

Examples of Next Steps

• Develop more complex technical indicators and proprietary strategies
• Enhance signals by creating and deploying your own AI and machine learning models
• Integrate with external notification services such as Slack or LINE
• Scale operations across multiple accounts and currency pairs

10. FAQ (Frequently Asked Questions)

Q1. Is MT5 free to use?

Q2. Can I work with multiple currency pairs?

Q3. What should I do if orders fail or produce errors?

Q4. Are Python and MT5 easy to use in a Japanese environment?

Q5. How easily can AI models be integrated?

Q6. I can’t connect due to library errors or version mismatches. What should I do?

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