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