Python × MT5 で機械学習FX自動売買を始める入門ガイド

pip install MetaTrader5 pandas numpy scikit-learn xgboost lightgbm matplotlib

import MetaTrader5 as mt5

if not mt5.initialize():
    print("MT5の初期化に失敗しました:", mt5.last_error())
    quit()

print("MT5バージョン:", mt5.version())
mt5.shutdown()

import MetaTrader5 as mt5
import pandas as pd
from datetime import datetime

mt5.initialize()

# USDJPY の日足データを2年分取得
rates = mt5.copy_rates_range(
    "USDJPY",
    mt5.TIMEFRAME_H1,           # 1時間足
    datetime(2024, 1, 1),
    datetime(2026, 6, 1)
)

df = pd.DataFrame(rates)
df['time'] = pd.to_datetime(df['time'], unit='s')
df.set_index('time', inplace=True)

mt5.shutdown()
print(df.head())

def create_features(df):
    df = df.copy()
    
    # リターン（対数差分）
    df['return_1h'] = df['close'].pct_change(1)
    df['return_4h'] = df['close'].pct_change(4)
    df['return_24h'] = df['close'].pct_change(24)
    
    # 移動平均からの乖離率
    df['ma20'] = df['close'].rolling(20).mean()
    df['ma50'] = df['close'].rolling(50).mean()
    df['ma_dev_20'] = (df['close'] - df['ma20']) / df['ma20']
    
    # ボラティリティ（ATR相当）
    df['high_low_range'] = (df['high'] - df['low']) / df['close']
    df['volatility_20'] = df['return_1h'].rolling(20).std()
    
    # 目的変数: 次の1時間のリターンが正なら1
    df['target'] = (df['close'].shift(-1) > df['close']).astype(int)
    
    return df.dropna()

df_features = create_features(df)

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import classification_report
import numpy as np

# 特徴量と目的変数の定義
feature_cols = ['return_1h', 'return_4h', 'return_24h',
                'ma_dev_20', 'high_low_range', 'volatility_20']

X = df_features[feature_cols]
y = df_features['target']

# 時系列分割（過去80%で訓練、直近20%でテスト）
split_idx = int(len(X) * 0.8)
X_train, X_test = X.iloc[:split_idx], X.iloc[split_idx:]
y_train, y_test = y.iloc[:split_idx], y.iloc[split_idx:]

# モデル訓練
model = GradientBoostingClassifier(
    n_estimators=100,
    learning_rate=0.05,
    max_depth=3,          # 浅めにして過学習を抑制
    random_state=42
)
model.fit(X_train, y_train)

# 評価
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

import MetaTrader5 as mt5

def send_order(symbol, direction, lot_size=0.01):
    """
    direction: 1=買い, 0=売り
    """
    mt5.initialize()
    
    price = mt5.symbol_info_tick(symbol).ask if direction == 1 else mt5.symbol_info_tick(symbol).bid
    order_type = mt5.ORDER_TYPE_BUY if direction == 1 else mt5.ORDER_TYPE_SELL
    
    request = {
        "action": mt5.TRADE_ACTION_DEAL,
        "symbol": symbol,
        "volume": lot_size,
        "type": order_type,
        "price": price,
        "deviation": 10,          # スリッページ許容（ポイント）
        "magic": 123456,          # EA識別番号
        "comment": "ML_signal",
        "type_time": mt5.ORDER_TIME_GTC,
        "type_filling": mt5.ORDER_FILLING_IOC,
    }
    
    result = mt5.order_send(request)
    
    if result.retcode != mt5.TRADE_RETCODE_DONE:
        print(f"注文エラー: {result.retcode}, {result.comment}")
    else:
        print(f"注文成功: {result.order}")
    
    mt5.shutdown()
    return result