Examples using the technical analysis libraries (ta and talib_wrapper)

tia package (pure python)



In [1]:

    
import tia.analysis.ta as ta
import tia.analysis.model as model



In [2]:

    
secs = model.load_yahoo_stock(['MSFT', 'INTC'], start='1/1/2000')









    



C:\Anaconda\lib\site-packages\pandas\tseries\base.py:363: FutureWarning: using '-' to provide set differences with datetimelike Indexes is deprecated, use .difference()
  "use .difference()",FutureWarning)
C:\Anaconda\lib\site-packages\pandas\core\index.py:1196: FutureWarning: using '-' to provide set differences with Indexes is deprecated, use .difference()
  "use .difference()",FutureWarning)



In [3]:

    
pxs = secs.frame
pxs.head()



In [4]:

    
close = pxs.swaplevel(1, 0, axis=1).close
close.head()



In [5]:

    
# moving average calculation
ta.SMA(close, 10).tail()



In [6]:

    
# macd
ta.MACD(close).tail()









    Out[6]:






  
    
      
      INTC
      MSFT
    
    
      
      MACD_FAST
      MACD_SLOW
      MACD
      MACD_SIGNAL
      MACD_HIST
      MACD_FAST
      MACD_SLOW
      MACD
      MACD_SIGNAL
      MACD_HIST
    
    
      Date
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2015-04-20
      32.025370
      31.919124
      0.106247
      -0.172275
      0.278522
      41.886691
      41.936118
      -0.049427
      -0.254985
      0.205558
    
    
      2015-04-21
      32.087621
      31.956966
      0.130655
      -0.111689
      0.242344
      42.002585
      41.988257
      0.014328
      -0.201122
      0.215450
    
    
      2015-04-22
      32.181833
      32.012006
      0.169827
      -0.055386
      0.225213
      42.154495
      42.062460
      0.092034
      -0.142491
      0.234525
    
    
      2015-04-23
      32.207705
      32.037042
      0.170662
      -0.010176
      0.180839
      42.336880
      42.157093
      0.179787
      -0.078035
      0.257823
    
    
      2015-04-24
      32.188058
      32.040224
      0.147834
      0.021426
      0.126408
      43.188129
      42.580271
      0.607858
      0.059143
      0.548715



In [7]:

    
# rsi
ta.RSI(close, n=14).tail()



In [8]:

    
# determine when 50 crosses the 200
cross = ta.cross_signal(ta.sma(close.MSFT, 50), ta.sma(close.MSFT, 200))
cross.tail()









    Out[8]:





Date
2015-04-20   -1
2015-04-21   -1
2015-04-22   -1
2015-04-23   -1
2015-04-24   -1
dtype: float64

ta-lib wrapper

Provide ability to call ta-lib with DataFrame or Series which may contain NaN values.



In [9]:

    
import tia.analysis.talib_wrapper as talib



In [10]:

    
talib.MACD(close).tail()









    Out[10]:






  
    
      
      INTC
      MSFT
    
    
      
      MACD
      MACD_SIGNAL
      MACD_HIST
      MACD
      MACD_SIGNAL
      MACD_HIST
    
    
      Date
      
      
      
      
      
      
    
  
  
    
      2015-04-20
      0.106247
      -0.172275
      0.278522
      -0.049427
      -0.254985
      0.205558
    
    
      2015-04-21
      0.130655
      -0.111689
      0.242344
      0.014328
      -0.201122
      0.215450
    
    
      2015-04-22
      0.169827
      -0.055386
      0.225213
      0.092034
      -0.142491
      0.234525
    
    
      2015-04-23
      0.170662
      -0.010176
      0.180839
      0.179787
      -0.078035
      0.257823
    
    
      2015-04-24
      0.147834
      0.021426
      0.126408
      0.607858
      0.059143
      0.548715



In [11]:

    
# average true range
talib.ATR(pxs).tail()



In [12]:

    
talib.RSI(close).tail()

	INTC							MSFT
	open	high	low	close	volume	adj close	dvds	open	high	low	close	volume	adj close	dvds
Date
2000-01-03	83.26562	87.375	83.2500	87.0000	57710200	31.97828	NaN	117.3750	118.625	112.0000	116.5625	53228400	41.47075	NaN
2000-01-04	85.43750	87.875	82.2500	82.9375	51019600	30.48504	NaN	113.5625	117.125	112.2500	112.6250	54119000	40.06986	NaN
2000-01-05	83.00000	85.875	80.5000	83.6250	52389000	30.73774	NaN	111.1250	116.375	109.3750	113.8125	64059600	40.49235	NaN
2000-01-06	81.75000	83.375	78.1875	78.7500	55171200	28.94586	NaN	112.1875	113.875	108.3750	110.0000	54976600	39.13594	NaN
2000-01-07	77.50000	82.000	77.3750	82.0000	41096400	30.14045	NaN	108.6250	112.250	107.3125	111.4375	62013600	39.64737	NaN

	INTC	MSFT
Date
2000-01-03	87.0000	116.5625
2000-01-04	82.9375	112.6250
2000-01-05	83.6250	113.8125
2000-01-06	78.7500	110.0000
2000-01-07	82.0000	111.4375

	INTC	MSFT
Date
2015-04-20	31.986	41.851
2015-04-21	32.103	41.962
2015-04-22	32.242	42.119
2015-04-23	32.353	42.305
2015-04-24	32.368	42.920

	INTC	MSFT
Date
2015-04-20	57.489183	59.218833
2015-04-21	54.419580	56.288763
2015-04-22	56.662369	59.112946
2015-04-23	53.020355	61.772796
2015-04-24	50.332676	79.951610

	INTC	MSFT
Date
2015-04-20	0.762301	0.769888
2015-04-21	0.757137	0.759181
2015-04-22	0.752342	0.746383
2015-04-23	0.747889	0.750927
2015-04-24	0.727325	1.040146

	INTC					MSFT
	MACD_FAST	MACD_SLOW	MACD	MACD_SIGNAL	MACD_HIST	MACD_FAST	MACD_SLOW	MACD	MACD_SIGNAL	MACD_HIST
Date
2015-04-20	32.025370	31.919124	0.106247	-0.172275	0.278522	41.886691	41.936118	-0.049427	-0.254985	0.205558
2015-04-21	32.087621	31.956966	0.130655	-0.111689	0.242344	42.002585	41.988257	0.014328	-0.201122	0.215450
2015-04-22	32.181833	32.012006	0.169827	-0.055386	0.225213	42.154495	42.062460	0.092034	-0.142491	0.234525
2015-04-23	32.207705	32.037042	0.170662	-0.010176	0.180839	42.336880	42.157093	0.179787	-0.078035	0.257823
2015-04-24	32.188058	32.040224	0.147834	0.021426	0.126408	43.188129	42.580271	0.607858	0.059143	0.548715