# 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})