Create Pach.py

Pach.py

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+import ccxt
+import pandas as pd
+import streamlit as st
+import xgboost as xgb
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+
+exchange = ccxt.mexc({
+    'apiKey': 'mx0vglbkoCOwmqV5tn',
+    'secret': 'c6d8cc8953fd405787ed54e3488ae0db',
+    'enableRateLimit': True,
+})
+
+def fetch_ohlcv_data(symbol, timeframe, limit):
+    return exchange.fetch_ohlcv(symbol, timeframe, since=None, limit=limit)
+
+def data_to_dataframe(data):
+    df = pd.DataFrame(data, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+    df.set_index('timestamp', inplace=True)
+    return df
+
+def prepare_dataset(df, lags):
+    X = pd.DataFrame()
+    y = pd.DataFrame()
+
+    for lag in range(1, lags + 1):
+        shifted_close = df['close'].shift(lag)
+        X[f'close_lag_{lag}'] = shifted_close
+
+    y = df['close'].shift(-1)
+    return X, y
+
+def train_xgboost_model(X_train, y_train):
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    params = {
+        'objective': 'reg:squarederror',
+        'eval_metric': 'rmse',
+    }
+
+    model = xgb.train(params, dtrain)
+    return model
+
+def predict_next_hour_price(df, model, lags):
+    X_test = pd.DataFrame()
+
+    for lag in range(1, lags + 1):
+        shifted_close = df['close'].shift(lag)
+        X_test[f'close_lag_{lag}'] = shifted_close
+
+    X_test = X_test.tail(1)
+    dtest = xgb.DMatrix(X_test)
+    next_hour_price = model.predict(dtest)
+    return next_hour_price
+
+def evaluate_prediction_accuracy(y_test, y_pred):
+    accuracy = r2_score(y_test, y_pred)
+    return accuracy
+
+def execute_trade(exchange, symbol, side, amount, leverage):
+    order = exchange.create_market_order(symbol, side, amount)
+
+    # Set leverage for the order
+    if leverage is not None and leverage != 1:
+        position = exchange.fetch_position(symbol)
+        if position is not None:
+            current_leverage = position['info']['leverage']
+            if current_leverage != leverage:
+                # Update leverage for the position
+                exchange.set_leverage(leverage, symbol)
+
+    return order
+
+symbol = 'BTC/USDT'
+timeframe = '1h'
+limit = 100
+lags = 12
+test_size = 0.3
+leverage = 5
+
+ohlcv_data = fetch_ohlcv_data(symbol, timeframe, limit)
+df = data_to_dataframe(ohlcv_data)
+
+if st.button("hoo"):
+    st.write(df)
+
+
+X, y = prepare_dataset(df, lags)
+
+# Replace missing values with the mean of the column
+X = X.fillna(X.mean())
+y = y.fillna(y.mean())
+
+#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+
+#model, scaler = train_xgboost_model(X_train, y_train)
+
+#y_pred = model.predict(X_test)
+
+# Inverse transform the predicted values to get the original scale
+#y_pred = scaler.inverse_transform(y_pred.reshape(-1, 1))
+
+#accuracy = evaluate_prediction_accuracy(y_test, y_pred)
+
+#X, y = prepare_dataset(df, lags)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+
+model = train_xgboost_model(X_train, y_train)
+y_pred = model.predict(X_test)
+accuracy = evaluate_prediction_accuracy(y_test, y_pred)
+
+if accuracy > 0.9:
+    latest_data = df.tail(lags)
+    predicted_price = predict_next_hour_price(latest_data, model, lags)
+    current_price = latest_data['close'].iloc[-1]
+
+    if predicted_price > current_price:
+        # Buy BTC
+        amount = 0.001  # Set the amount you want to trade
+        order = execute_trade(exchange, symbol, 'buy', amount, leverage)
+        print(f"Bought {amount} BTC at {current_price} with {leverage}x leverage")
+    elif predicted_price < current_price:
+        # Sell BTC
+        amount = 0.001  # Set the amount you want to trade
+        order = execute_trade(exchange, symbol, 'sell', amount, leverage)
+        print(f"Sold {amount} BTC at {current_price} with {leverage}x leverage")
+else:
+    print("Accuracy is low, not placing order.")