Cannot calculate MACD via python pandas

Question

I made a script to get stock information for a stock list. For the shares involved (group in group) I need to calculate the MACD.

In order not to mix the price for one stock with another, I use the pandas group.

# -*- coding: utf-8 -*-

import pandas as pd
from pandas.io.data import DataReader
import numpy as np
import time
from io import StringIO

runstart = time.time()     # Start script timer

stocklist = ['nflx','mmm'] 
tickers =   []
days_backtest=102  # MA98 kræver 102 d for at virke!
end = pd.Timestamp.utcnow()
start = end - days_backtest * pd.tseries.offsets.BDay()

    # Fetch stockinfo
def GetStock(stocklist, start, end, csv_file_all='alltickers_ohlc.csv'):
    '''
    Fetches stock-info for analysis of each ticker in stocklist
    '''
    print('\nGetting Stock-info from Yahoo-Finance')
    for ticker in stocklist:
        r = DataReader(ticker, "yahoo",
                       start = start, end = end)
        # add a symbol column
        r['Ticker'] = ticker
        tickers.append(r)
    # concatenate all the dfs
    df_all = pd.concat(tickers)

    # add col without space in adj close
    df_all['Adj_Close'] = df_all['Adj Close']
    #define df with the columns that i need             These can be put back in df_all
    df_all = df_all[['Ticker','Adj_Close','Volume']] #'Adj Close','Open','High','Low',

    # round to 2 dig.
#    df_all['Open'] = np.round(df_all['Open'], decimals=2)
#    df_all['High'] = np.round(df_all['High'], decimals=2)
#    df_all['Low'] = np.round(df_all['Low'], decimals=2)
#    df_all['Adj Close'] = np.round(df_all['Adj Close'], decimals=2)
    df_all['Adj_Close'] = np.round(df_all['Adj_Close'], decimals=2)

#    # Test the first 3 rows of each group for 'Difference' col transgress groups...
#    df_all_test = df_all.groupby('Ticker').head(27).reset_index().set_index('Date')
#    print ('\n df_all_test (27d summary from df) (Output)\n',df_all_test,'\n')

    # saving to a csv #
    df_all.reset_index().sort(['Ticker', 'Date'], ascending=[1,1]).set_index('Ticker').to_csv(csv_file_all, date_format='%Y/%m/%d')
#    df_all.sort_index(inplace=True)   # Sorts rows from date, mingling tickers - not wanted
    print('=========  Picked up new stockinfo   (df_all) \n')
#    print ('df_all.tail (Input)\n',df_all.tail(6),'\n')
    print(70 * '-')
#    print(df_all)

    return df_all

def moving_average(group, n=9, type='simple'):
    """
    compute an n period moving average.
    type is 'simple' | 'exponential'
    """
    group = np.asarray(df_['Adj_Close'])
    if type == 'simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()

    a = np.convolve(group, weights, mode='full')[:len(group)]
    a[:n] = a[n]
    return a
#    return pd.DataFrame({'MCD_Sign':a})

def moving_average_convergence(group, nslow=26, nfast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = moving_average(group, nslow, type='exponential')
    emafast = moving_average(group, nfast, type='exponential')
#    return emaslow, emafast, emafast - emaslow

    return pd.DataFrame({'emaSlw': emaslow,
                     'emaFst': emafast, 
                     'MACD': emafast - emaslow})


if __name__ == '__main__':

    ### Getstocks
    df_all = GetStock(stocklist, start, end)
    ### Sort DF
    df_all.reset_index().sort(['Ticker', 'Date'], ascending=[1,1]).set_index('Ticker')

    ### groupby screeener (filtering to only rel ticker group)
    df_ = df_all.set_index('Ticker', append=True)
    ''' Calculating all the KPIs via groupby (filtering pr ticker)'''
    grouped = df_.groupby(level=1).Adj_Close    
    nslow = 26
    nfast = 12
    nema = 9

    df_[['emaSlw', 'emaFst', 'MACD']] = df_.groupby(level=1).Adj_Close.apply(moving_average_convergence)
    df_['MCD_Sign'] = df_.groupby(level=1).Adj_Close.apply(moving_average)

    print ('(Output df)\n',df_,'\n')

    df = df_.reset_index('Ticker')    
    # Test the last row of each group for new numbers pr group...
    df_test = df.groupby('Ticker').tail(1).reset_index().set_index('Date')
    print ('df_test (summary from df) (Output)\n',df_test,'\n')

Obviously, I am not getting any column results for all MACD numbers. So somewhere the calculation goes south. I have no idea what is going wrong ...

Output line pr-ticker:

df_test (summary from df) (Output)
            Ticker  Adj_Close   Volume  emaSlw  emaFst  MACD MCD_Sign
Date                                                                
2016-07-07   nflx      95.10  9902700     NaN     NaN   NaN      NaN
2016-07-07    mmm     174.87  1842300     NaN     NaN   NaN      NaN 

Any of you guys ... the tip !?

Answer 1

, , . . , , .

moving_average :

def moving_average(group, n=9):
    sma = pd.rolling_mean(group, n)
    return sma

moving_average_convergance :

def moving_average_convergence(group, nslow=26, nfast=12):
    emaslow = pd.ewma(group, span=nslow, min_periods=1)
    emafast = pd.ewma(group, span=nfast, min_periods=1)
    result = pd.DataFrame({'MACD': emafast-emaslow, 'emaSlw': emaslow, 'emaFst': emafast})
    return result

N.B. "MACD", , , , DataFrame .

, :

df_[['emaSlw', 'emaFst', 'MACD']] = df_.groupby(level=1).Adj_Close.apply(moving_average_convergence)

df_[['MACD', 'emaSlw', 'emaFst']] = df_.groupby(level=1).Adj_Close.apply(moving_average_convergence)

.