Create Posis.py

Posis.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',
+    }
+
+    try:
+        model = xgb.train(params, dtrain)
+    except xgb.core.XGBoostError as e:
+        print(f"Error training XGBoost model: {e}")
+        return None
+
+    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)
+
+    try:
+        next_hour_price = model.predict(dtest)
+    except xgb.core.XGBoostError as e:
+        print(f"Error making prediction with XGBoost model: {e}")
+        return None
+
+    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):
+    order = exchange.create_market_order(symbol, side, amount)
+    return order
+
+symbol = 'BTC/USDT'
+timeframe = '1h'
+limit = 100
+lags = 12
+test_size = 0.3
+
+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)
+
+if X.isnull().values.any() or y.isnull().values.any():
+    print("Error: Null values found in X or y")
+else:
+    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)
+
+    if model is None:
+        print("Error: XGBoost model could not be trained")
+    else:
+        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)
+
+            if predicted_price is None:
+                print("Error: XGBoost model could not make prediction")
+            else:
+                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)
+                    print(f"Bought {amount} BTC at {current_price}")
+                elif predicted_price < current_price:
+                    # Sell BTC
+                    amount = 0.001  # Set the amount you want to trade
+                    order = execute_trade(exchange, symbol, 'sell', amount)
+                    print(f"Sold {amount} BTC at {current_price}")
+        else:
+            print("Accuracy is low, not placing order.")