max_features='sqrt', n_estimators=800,
random_state=42))])
Balanced Accuracy Score (Overall):
0.5281114056513132
Balanced Crosstab Rank (Overall):
Predicted +/-1% 1-5% Down 1-5% Up 5+% Down 5+% Up
Actual
+/-1% 57 18 51 1 2
1-5% Down 26 69 27 7 0
1-5% Up 28 12 157 1 7
5+% Down 1 12 3 12 0
5+% Up 0 0 26 0 23
'''
aapl_reg = cl.prepare_classical('AAPL') #instantiate the object
import_df = aapl_reg.get_prepare_stock_data()
dataset = aapl_reg.process_data(import_df)
dataset = dataset.drop(['adj close', 'day', 'ticker'], axis=1)
df_reshape = aapl_reg.reshape_dataset(np.array(dataset), 1)
X, y = aapl_reg.to_supervised_classical(df_reshape, 15, 5)
X_classical = pd.DataFrame(aapl_reg.reshape_X_classical(
X)) #Reshapes X into 1 row and all columns for the features
y_classical = aapl_reg.reshape_y_classical(
y, n_out=5) #Reshapes y to calculate % change
nday_chg, intraday_max = aapl_reg.get_chg_pc(y_classical)
nday_chg_label = pd.DataFrame.from_records(
def instantiate(ticker):
'''instantiate 2 class objects for a specific ticker'''
return cps.yfinance_scrape (ticker), cl.prepare_classical (ticker)