MyStrategy

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import walk_forward_optimization
import ccxt  # Quotex API library
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Set up Quotex API connection
exchange = ccxt.quotex({
    'apiKey': 'YOUR_API_KEY',
    'apiSecret': 'YOUR_API_SECRET',
})

# Load historical data
data = pd.read_csv('quotex_data.csv')

# Preprocess data
X = data.drop(['target'], axis=1)
y = data['target']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train machine learning model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Optimize model using walk-forward optimization
model.optimize(X_test, y_test, walk_forward_optimization)

# Create LSTM neural network model
lstm_model = Sequential()
lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
lstm_model.add(Dense(1, activation='sigmoid'))
lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train LSTM model
lstm_model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

# Generate trading signals using LSTM model
lstm_signals = lstm_model.predict(X_test)

# Combine signals from machine learning model and LSTM model
combined_signals = []
for i in range(len(signals)):
    if signals[i] == "buy" and lstm_signals[i] > 0.5:
        combined_signals.append("buy")
    elif signals[i] == "sell" and lstm_signals[i] < 0.5:
        combined_signals.append("sell")
    else:
        combined_signals.append("neutral")

# Implement risk management
risk_management = RiskManagement(STOP_LOSS, TAKE_PROFIT, MAX_DAILY_LOSS, MAX_CONSECUTIVE_LOSSES)
risk_management.monitor_risk(combined_signals)

# Execute trades based on combined signals
for signal in combined_signals:
    if signal == "buy":
        exchange.place_order("buy", FREQTRADE_AMOUNT)
    elif signal == "sell":
        exchange.place_order("sell", FREQTRADE_AMOUNT)

# Integrate with additional strategies
from freqtrade.strategy import IStrategy
from freqtrade.exchange import Exchange

class MyStrategy(IStrategy):
    def populate_indicators(self, dataframe: pd.DataFrame) -> pd.DataFrame:
        # Add additional indicators here
        dataframe['ema_50'] = dataframe['close'].ewm(span=50, adjust=False).mean()
        dataframe['rsi_14'] = talib.RSI(dataframe['close'], timeperiod=14)
        return dataframe

    def populate_buy_trend(self, dataframe: pd.DataFrame) -> pd.DataFrame:
        # Add additional buy trend conditions here
        dataframe.loc[(dataframe['close'] > dataframe['ema_50']) & (dataframe['rsi_14'] < 30), 'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: pd.DataFrame) -> pd.DataFrame:
        # Add additional sell trend conditions here
        dataframe.loc[(dataframe['close'] < dataframe['ema_50']) & (dataframe['rsi_14'] > 70), 'sell'] = 1
        return dataframe

my_strategy = MyStrategy(exchange)

# Combine with other AI trading bots
from stockhero import StockHero
from tickeron import Tickeron
from signalstack import SignalStack

stock_hero = StockHero()
tickeron = Tickeron()
signal_stack = SignalStack()

def combine_signals(signals):
    # Combine signals from multiple bots
    combined_signal = 0
    for signal in signals:
        if signal == "buy":
            combined_signal += 1
        elif signal == "sell":
            combined_signal -= 1
    return combined_signal

# Run the complete signal bot
while True:
    signals = []
    signals.append(my_strategy.check_buy_sell_signals())
    signals.append(stock_hero.get_signal())
    signals.append(tickeron.get_signal())
    signals.append(signal_stack.get_signal())
    combined_signal = combine_signals(signals)
    if combined_signal > 0:
        exchange.place_order("buy", FREQTRADE_AMOUNT)
    elif combined_signal < 0:
        exchange.place_order("sell", FREQTRADE_AMOUNT)
    time.sleep(60)  # Run every 1 minute