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This is the code for my candlestick scanner. My aim is to scan multiple variables simultaneously however when my code runs it only results in a single column of false. If anyone knows how to scan multiple tickers at once it will help tremendously.

import datetime as dt 
import pandas_datareader as web
import pandas as pd

start = dt.datetime(2020,12,31)
end = dt.datetime.now()
Stock = ('ANZ.AX','APT.AX','FMG.AX')

df = web.DataReader(Stock, 'yahoo', start, end)

# Change data to omit volume and adjusted close (can change later to display volume)
data = df[['Open', 'High', 'Low', 'Close']]

for i in range(2, df.shape[0]):
    current = df.iloc[i, :]
    prev = df.iloc[i - 1, :]
    prev_2 = df.iloc[i - 2, :]

    realbody = abs(current['Open'] - current['Close'])
    candle_range = current['High'] - current['Low']

    idx = df.index[i]

    # Bullish engulfing
    df.loc[idx, 'Bullish Engulfing'] = (prev['Open'] > prev['Close']) & (current['Close'] > current['Open']) \
    & (current['High'] > prev['High']) & (current['Low'] < prev['Low'])

df.fillna(False, inplace=True)

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print(df['Bullish Engulfing'])

Resulted code:

Date
2020-12-30    False
2021-01-03    False
2021-01-05    False
Name: Bullish Engulfing, dtype: bool
Thomas Kimber
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1 Answers1

1

the issue is you have a multindex on the columns

import datetime as dt 
import pandas_datareader as web
import pandas as pd

start = dt.datetime(2020,12,31)
end = dt.datetime.now()
Stock = ('ANZ.AX','APT.AX','FMG.AX')

df = web.DataReader(Stock, 'yahoo', start, end)

df.columns

gives

MultiIndex([('Adj Close', 'ANZ.AX'),
            ('Adj Close', 'APT.AX'),
            ('Adj Close', 'FMG.AX'),
            (    'Close', 'ANZ.AX'),
            (    'Close', 'APT.AX'),
            (    'Close', 'FMG.AX'),
            (     'High', 'ANZ.AX'),
            (     'High', 'APT.AX'),
            (     'High', 'FMG.AX'),
            (      'Low', 'ANZ.AX'),
            (      'Low', 'APT.AX'),
            (      'Low', 'FMG.AX'),
            (     'Open', 'ANZ.AX'),
            (     'Open', 'APT.AX'),
            (     'Open', 'FMG.AX'),
            (   'Volume', 'ANZ.AX'),
            (   'Volume', 'APT.AX'),
            (   'Volume', 'FMG.AX')],
           names=['Attributes', 'Symbols'])

where in your code you go current = df.iloc[i, :] it is not giving you what you think as you still have a multi index

current = df.iloc[1, :]

for example yeilds

Attributes  Symbols
Adj Close   ANZ.AX     2.304000e+01
            APT.AX     1.190000e+02
            FMG.AX     2.480000e+01
Close       ANZ.AX     2.304000e+01
            APT.AX     1.190000e+02
            FMG.AX     2.480000e+01
High        ANZ.AX     2.314000e+01
            APT.AX     1.223000e+02
            FMG.AX     2.480000e+01
Low         ANZ.AX     2.276000e+01
            APT.AX     1.190000e+02
            FMG.AX     2.370000e+01
Open        ANZ.AX     2.276000e+01
            APT.AX     1.196800e+02
            FMG.AX     2.371000e+01
Volume      ANZ.AX     3.207879e+06
            APT.AX     9.625380e+05
            FMG.AX     6.402739e+06
Name: 2021-01-03 00:00:00, dtype: float64

so when you write back df.loc[idx, 'Bullish Engulfing'] this is not stock specific.

you would be better off with a groupby and doing it all stock by stock.

Pandas Multiindex Groupby on Columns will show you how to do that.

Paul Brennan
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