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import ccxt
import pandas as pd
import xgboost as xgb
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
import time
exchange = ccxt.mexc({
'apiKey': 'YOUR_API_KEY',
'secret': 'YOUR_SECRET_KEY',
'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
symbol = 'BTC/USDT'
timeframe = '1h'
limit = 100000
lags = 12
test_size = 0.3
# Fetch initial data
ohlcv_data = fetch_ohlcv_data(symbol, timeframe, limit)
df = data_to_dataframe(ohlcv_data)
# Prepare dataset and train the model
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)
while True:
# Fetch new data every hour
ohlcv_data = fetch_ohlcv_data(symbol, timeframe, limit)
df = data_to_dataframe(ohlcv_data)
# Make prediction
latest_data = df.tail(lags)
predicted_price = predict_next_hour_price(latest_data, model, lags)
# Print predicted next hour price
print(f"Predicted next hour price: ${predicted_price[0]}")
# Sleep for 1 hour (3600 seconds)
time.sleep(3600)
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