xgboost algorithm in python
xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
max_depth = 5, alpha = 10, n_estimators = 10)xgboost algorithm in python
xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
max_depth = 5, alpha = 10, n_estimators = 10)python XGBoost
# Create a placeholder to store the stock data
stock_data_dictionary = {}
for stock_name in stock_list:
# Get the data
df = data.get_data_yahoo(stock_name, start_date, end_date)
# Calculate the daily percent change
df['daily_pct_change'] = df['Adj Close'].pct_change()
# create the predictors
predictor_list = []
for r in range(10, 60, 5):
df['pct_change_'+str(r)] = df.daily_pct_change.rolling(r).sum()
df['std_'+str(r)] = df.daily_pct_change.rolling(r).std()
predictor_list.append('pct_change_'+str(r))
predictor_list.append('std_'+str(r))
# Target Variable
df['return_next_day'] = df.daily_pct_change.shift(-1)
df['actual_signal'] = np.where(df.return_next_day > 0, 1, -1)
df = df.dropna()
# Add the data to dictionary
stock_data_dictionary.update({stock_name: df})xgboost algorithm in python
params = {"objective":"reg:linear",'colsample_bytree': 0.3,'learning_rate': 0.1,
'max_depth': 5, 'alpha': 10}
cv_results = xgb.cv(dtrain=data_dmatrix, params=params, nfold=3,
num_boost_round=50,early_stopping_rounds=10,metrics="rmse", as_pandas=True, seed=123)Copyright © 2021 Codeinu
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