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MQL5 Göstergeler Komut dosyaları Strategy optimization Strategy modules Python

İş Gereklilikleri

import MetaTrader5 as mt5
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from backtrader import Cerebro

# Connect to MetaTrader 5
mt5.initialize()

# Function to fetch historical data from MT5
def get_historical_data(symbol, timeframe, num_bars):
    rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, num_bars)
    df = pd.DataFrame(rates)
    df['time'] = pd.to_datetime(df['time'], unit='s')
    return df

# Fetch XAUUSD data
symbol = "XAUUSD"
data = get_historical_data(symbol, mt5.TIMEFRAME_M1, 10000)

# Prepare features for the AI model
X = np.array([data['open'], data['close'], data['high'], data['low']]).T
y = np.array([1 if data['close'][i] > data['open'][i] else 0 for i in range(len(data))])

# Split data for AI model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train a Random Forest model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Make predictions on test data
predictions = model.predict(X_test)

# Add Moving Average Strategy to DataFrame (EMA12 and EMA26)
data['EMA_12'] = data['close'].ewm(span=12, adjust=False).mean()
data['EMA_26'] = data['close'].ewm(span=26, adjust=False).mean()
data['Signal'] = np.where(data['EMA_12'] > data['EMA_26'], 1, -1)

# Risk management (stop loss, take profit)
def place_trade(symbol, order_type, volume, stop_loss, take_profit):
    ask_price = mt5.symbol_info_tick(symbol).ask
    bid_price = mt5.symbol_info_tick(symbol).bid
    if order_type == "buy":
        mt5.order_send(symbol=symbol, action=mt5.TRADE_ACTION_DEAL, order_type=mt5.ORDER_TYPE_BUY, volume=volume, price=ask_price, sl=stop_loss, tp=take_profit)
    elif order_type == "sell":
        mt5.order_send(symbol=symbol, action=mt5.TRADE_ACTION_DEAL, order_type=mt5.ORDER_TYPE_SELL, volume=volume, price=bid_price, sl=stop_loss, tp=take_profit)

# Example trade (modify parameters as needed)
place_trade(symbol, "buy", 0.1, 5, 10)

# Backtesting Setup with Backtrader
class MyStrategy(bt.Strategy):
    def __init__(self):
        self.ema12 = bt.indicators.ExponentialMovingAverage(self.data.close, period=12)
        self.ema26 = bt.indicators.ExponentialMovingAverage(self.data.close, period=26)

    def next(self):
        if self.ema12 > self.ema26:
            self.buy(size=1)
        elif self.ema12 < self.ema26:
            self.sell(size=1)

# Backtest function
def run_backtest(data):
    cerebro = Cerebro()
    cerebro.addstrategy(MyStrategy)
    # Add your data feed here
    cerebro.run()

# Convert MT5 data to Backtrader data feed format if needed
# run_backtest(data)

# Shutdown MT5 connection
mt5.shutdown()

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