Python launcher functioning but not displaying any output
Multi tool use
Python launcher functioning but not displaying any output
I am using Pandas to read a CSV file that consists of names of stocks. After that, I am creating a new list where stocks that fit certain algorithms are appended; however, it only works with 8 - 9 stocks at a time. Is there somewhere I am making the code very inefficient and that can be optimized?
Here is my code:
import datetime as dt
from dateutil.relativedelta import relativedelta
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import pandas_datareader.data as web
import numpy as np
import csv
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter, MonthLocator, YearLocator, DayLocator, WeekdayLocator
end = dt.date.today()
start_48 = end - relativedelta( years=4 )
ticker_names = [ 'GGP', 'XLU', 'AAPL', 'AMZN', 'NVDA', 'XLI', 'XLP', 'ROL', 'MU', 'CAKE', 'CHUBA', 'HCA', 'ADI', 'EA', 'WYNN' ]
new_list =
for each in ticker_names:
df_w = web.DataReader( each, 'morningstar', start_48, end )
file_name_w = each + 'weekly.csv'
df_w.to_csv( file_name_w )
df_w = pd.read_csv( file_name_w, parse_dates=True, index_col=0 )
df_w[ 'Date2' ] = df_w[ 'Date' ]
df_w['Date'] = pd.to_datetime(df_w['Date'])
df_w.set_index('Date', inplace=True)
df_w.sort_index(inplace=True)
def take_first(array_like):
return array_like[0]
def take_last(array_like):
return array_like[-1]
output_w = df_w.resample('W', # Weekly resample
how={'Date2': take_first,
'Open': take_first,
'High': 'max',
'Low': 'min',
'Close': take_last,
'Volume': 'sum'},
loffset=pd.offsets.timedelta(days=-6)) # to put the labels to Monday
df_w = output_w[['Date2','Open', 'High', 'Low', 'Close', 'Volume']]
alldays = DayLocator()# minor ticks on the days
allweeks = WeekdayLocator()
allmonths = MonthLocator()
yearFormatter = DateFormatter( '%b %Y' )
monthFormatter = DateFormatter( '%m' )
weekFormatter = DateFormatter('%b %d %Y') # e.g., Jan 12 2018
dayFormatter = DateFormatter('%d') # e.g., 12
df_w[ 'EMA_40' ] = df_w[ 'Close' ].ewm( span = 40, adjust=False ).mean()
df_w[ 'EMA_150' ] = df_w[ 'Close' ].ewm( span = 150, adjust=False ).mean()
df_w[ 'EMA_200' ] = df_w[ 'Close' ].ewm( span = 200, adjust=False ).mean()
df_w[ 'H_40' ] = ( df_w[ 'High' ] - df_w[ 'EMA_40' ] ) / df_w[ 'EMA_40' ]
df_w[ 'H_150' ] = ( df_w[ 'High' ] - df_w[ 'EMA_150' ] ) / df_w[ 'EMA_150' ]
df_w[ 'H_200' ] = ( df_w[ 'High' ] - df_w[ 'EMA_200' ] ) / df_w[ 'EMA_200' ]
df_w[ 'L_40' ] = ( df_w[ 'Low' ] - df_w[ 'EMA_40' ] ) / df_w[ 'EMA_40' ]
df_w[ 'L_150' ] = ( df_w[ 'Low' ] - df_w[ 'EMA_150' ] ) / df_w[ 'EMA_150' ]
df_w[ 'L_200' ] = ( df_w[ 'Low' ] - df_w[ 'EMA_200' ] ) / df_w[ 'EMA_200' ]
df_w[ 'C_40' ] = ( df_w[ 'Close' ] - df_w[ 'EMA_40' ] ) / df_w[ 'EMA_40' ]
df_w[ 'C_150' ] = ( df_w[ 'Close' ] - df_w[ 'EMA_150' ] ) / df_w[ 'EMA_150' ]
df_w[ 'C_200' ] = ( df_w[ 'Close' ] - df_w[ 'EMA_200' ] ) / df_w[ 'EMA_200' ]
df_w[ 'OP_40' ] = ( df_w[ 'Open' ] - df_w[ 'EMA_40' ] ) / df_w[ 'EMA_40' ]
df_w[ 'OP_150' ] = ( df_w[ 'Open' ] - df_w[ 'EMA_150' ] ) / df_w[ 'EMA_150' ]
df_w[ 'OP_200' ] = ( df_w[ 'Open' ] - df_w[ 'EMA_200' ] ) / df_w[ 'EMA_200' ]
df_w.dropna( inplace=True )
df_ohlc_w = df_w
final_file_name_w = each + 'weekly.csv'
df_ohlc_w.to_csv( final_file_name_w, index=False )
latest_ema_40 = df_w[ 'EMA_40' ].tail( 1 )
latest_ema_40 = max( latest_ema_40 )
latest_ema_150 = df_w[ 'EMA_150' ].tail( 1 )
latest_ema_150 = max( latest_ema_150 )
latest_ema_200 = df_w[ 'EMA_200' ].tail( 1 )
latest_ema_200 = max( latest_ema_200 )
max_h_40 = df_tail[ 'H_40' ].max()
max_h_150 = df_tail[ 'H_150' ].max()
max_h_200 = df_tail[ 'H_200' ].max()
min_h_40 = df_tail[ 'H_40' ].min()
min_h_150 = df_tail[ 'H_150' ].min()
min_h_200 = df_tail[ 'H_200' ].min()
last_close_40 = df_w[ 'C_40' ].tail( 1 )
last_close_40 = max( last_close_40 )
last_close_150 = df_w[ 'C_150' ].tail( 1 )
last_close_150 = max( last_close_150 )
last_close_200 = df_w[ 'C_200' ].tail( 1 )
last_close_200 = max( last_close_200 )
if ( ( max_h_40 - min_h_40 <= 0.15 ) or ( max_h_150 - min_h_150 <= 0.15 ) or ( max_h_200 - min_h_200 <= 0.15 ) ):
new_list.append( each )
elif ( abs( last_close_40 ) <= 0.08 or abs( last_close_150 ) <= 0.08 or abs( last_close_200 ) <= 0.08 ):
new_list.append( each )
print( new_list )
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