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In [1]:



    


import barchart
from barchart import getHistory, getQuote, CONFIG

API key setup





In [2]:



    


#barchart.API_KEY = 'YOURAPIKEY'

You can also set an environment variable using Bash

export BARCHART_API_KEY="YOURAPIKEY"

Cache queries

requests_cache is optional

use it to have a cache mechanism

a session parameter can be pass to functions





In [3]:



    


import datetime
import requests_cache
session = requests_cache.CachedSession(cache_name='cache',
    backend='sqlite', expire_after=datetime.timedelta(days=1))
#session = None # pass a None session to avoid caching queries

getQuote with ONE symbol





In [4]:



    


symbol = "^EURUSD"
quote = getQuote(symbol, session=session)
quote # quote is a dict



    


















    
Out[4]:








{'close': '',
 'dayCode': 'S',
 'exchange': 'BATS',
 'flag': '',
 'high': 30.82,
 'lastPrice': 30.66,
 'low': 30.44,
 'mode': 'i',
 'name': 'Eni S.P.A',
 'netChange': 0.36,
 'open': 30.52,
 'percentChange': 1.19,
 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 40, 10, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))),
 'symbol': 'E',
 'tradeTimestamp': datetime.datetime(2015, 9, 29, 15, 25, 10, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))),
 'unitCode': None,
 'volume': 12620}

getQuote with SEVERAL symbols





In [5]:



    


symbols = ["ZC*1", "IBM", "GOOGL" , "^EURUSD"]
quotes = getQuote(symbols, session=session)
quotes # quotes is a Pandas DataFrame
#print(quotes.dtypes)
#print(type(quotes['serverTimestamp'][0])) # should be a pandas.tslib.Timestamp



    


















    
Out[5]:










  
    

      
      close
      dayCode
      exchange
      flag
      high
      lastPrice
      low
      mode
      name
      netChange
      open
      percentChange
      serverTimestamp
      symbol
      tradeTimestamp
      unitCode
      volume
    

  
  
    

      0
      389
      S
      CBOT
      s
      389.50000
      389.00000
      385.00000
      d
      Corn
      2.25000
      387.2500
      0.58
      2015-09-29 05:00:00
      ZCZ15
      2015-09-29 05:00:00
      -1
      117311
    

    

      1
      
      S
      BATS
      
      142.54000
      141.97000
      141.17000
      i
      International Business Machines
      -0.55000
      142.0000
      -0.39
      2015-09-29 19:41:56
      IBM
      2015-09-29 20:26:56
      None
      167005
    

    

      2
      
      S
      BATS
      
      634.67000
      619.44000
      618.49000
      i
      Google Inc
      -4.81000
      625.7600
      -0.77
      2015-09-29 19:41:49
      GOOGL
      2015-09-29 20:26:49
      None
      129034
    

    

      3
      
      S
      FOREX
      
      1.12806
      1.12604
      1.11933
      i
      Euro Fx/U.S. Dollar
      0.00172
      1.1242
      0.15
      2015-09-29 19:42:04
      ^EURUSD
      2015-09-29 19:32:04
      5
      488052
    

  




















In [6]:



    


CONFIG.output_pandas = False
quotes = getQuote(symbols, session=session)
print(quotes) # quotes is a Pandas DataFrame
CONFIG.output_pandas = True



    


















    






[{'name': 'Corn', 'dayCode': 'S', 'close': 389, 'high': 389.5, 'open': 387.25, 'flag': 's', 'percentChange': 0.58, 'netChange': 2.25, 'unitCode': '-1', 'lastPrice': 389, 'symbol': 'ZCZ15', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 0, 0, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 117311, 'low': 385, 'exchange': 'CBOT', 'mode': 'd', 'serverTimestamp': datetime.datetime(2015, 9, 29, 0, 0, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}, {'name': 'International Business Machines', 'dayCode': 'S', 'close': '', 'high': 142.54, 'open': 142, 'flag': '', 'percentChange': -0.39, 'netChange': -0.55, 'unitCode': None, 'lastPrice': 141.97, 'symbol': 'IBM', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 15, 26, 56, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 167005, 'low': 141.17, 'exchange': 'BATS', 'mode': 'i', 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 41, 56, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}, {'name': 'Google Inc', 'dayCode': 'S', 'close': '', 'high': 634.67, 'open': 625.76, 'flag': '', 'percentChange': -0.77, 'netChange': -4.81, 'unitCode': None, 'lastPrice': 619.44, 'symbol': 'GOOGL', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 15, 26, 49, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 129034, 'low': 618.49, 'exchange': 'BATS', 'mode': 'i', 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 41, 49, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}, {'name': 'Euro Fx/U.S. Dollar', 'dayCode': 'S', 'close': '', 'high': 1.12806, 'open': 1.1242, 'flag': '', 'percentChange': 0.15, 'netChange': 0.00172, 'unitCode': '5', 'lastPrice': 1.12604, 'symbol': '^EURUSD', 'tradeTimestamp': datetime.datetime(2015, 9, 29, 14, 32, 4, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400))), 'volume': 488052, 'low': 1.11933, 'exchange': 'FOREX', 'mode': 'i', 'serverTimestamp': datetime.datetime(2015, 9, 29, 14, 42, 4, tzinfo=datetime.timezone(datetime.timedelta(-1, 68400)))}]

getHistory with ONE symbol





In [7]:



    


symbol = 'IBM'
startDate = datetime.date(year=2014, month=9, day=28)
history = getHistory(symbol, typ='daily', startDate=startDate, session=session)
history
#print(history.dtypes)
#print(type(history['timestamp'][0])) # should be a pandas.tslib.Timestamp
#print(type(history.index[0])) # should be a pandas.tslib.Timestamp
#print(type(history['tradingDay'][0])) # should be a pandas.tslib.Timestamp



    


















    
Out[7]:










  
    

      
      close
      high
      low
      open
      openInterest
      symbol
      tradingDay
      volume
    

    

      timestamp
      
      
      
      
      
      
      
      
    

  
  
    

      2014-09-29 04:00:00
      184.1057
      184.4163
      182.6300
      183.0086
      None
      IBM
      2014-09-29
      2415183
    

    

      2014-09-30 04:00:00
      184.2901
      185.2803
      183.6299
      184.1057
      None
      IBM
      2014-09-30
      3020653
    

    

      2014-10-01 04:00:00
      181.7077
      184.8435
      181.3388
      184.3678
      None
      IBM
      2014-10-01
      3835019
    

    

      2014-10-02 04:00:00
      181.4553
      182.2999
      180.8049
      182.1834
      None
      IBM
      2014-10-02
      2352246
    

    

      2014-10-03 04:00:00
      183.1640
      183.8435
      182.0863
      182.6203
      None
      IBM
      2014-10-03
      3163831
    

    

      2014-10-06 04:00:00
      183.5231
      185.3192
      183.2028
      184.3387
      None
      IBM
      2014-10-06
      2163230
    

    

      2014-10-07 04:00:00
      180.2903
      182.6300
      180.1253
      182.2902
      None
      IBM
      2014-10-07
      3084001
    

    

      2014-10-08 04:00:00
      183.8338
      184.0668
      180.1933
      180.5428
      None
      IBM
      2014-10-08
      3075040
    

    

      2014-10-09 04:00:00
      180.9796
      183.9697
      180.6593
      183.6008
      None
      IBM
      2014-10-09
      2704527
    

    

      2014-10-10 04:00:00
      180.5039
      182.2611
      179.6982
      180.4360
      None
      IBM
      2014-10-10
      5243318
    

    

      2014-10-13 04:00:00
      178.1643
      181.2029
      178.0672
      180.0768
      None
      IBM
      2014-10-13
      3708115
    

    

      2014-10-14 04:00:00
      178.4361
      180.3000
      178.2322
      179.4943
      None
      IBM
      2014-10-14
      4042885
    

    

      2014-10-15 04:00:00
      176.4459
      178.4264
      173.5335
      177.3099
      None
      IBM
      2014-10-15
      7104947
    

    

      2014-10-16 04:00:00
      174.5916
      176.1838
      173.4752
      174.5528
      None
      IBM
      2014-10-16
      5746296
    

    

      2014-10-17 04:00:00
      176.7372
      177.5041
      174.9606
      175.9508
      None
      IBM
      2014-10-17
      4485296
    

    

      2014-10-20 04:00:00
      164.1651
      165.3592
      161.8254
      161.9710
      None
      IBM
      2014-10-20
      24120416
    

    

      2014-10-21 04:00:00
      158.4664
      161.8157
      156.9616
      161.5439
      None
      IBM
      2014-10-21
      21583480
    

    

      2014-10-22 04:00:00
      157.0684
      160.5828
      156.3986
      157.6606
      None
      IBM
      2014-10-22
      11424816
    

    

      2014-10-23 04:00:00
      157.4470
      158.0781
      156.8257
      157.3888
      None
      IBM
      2014-10-23
      7828667
    

    

      2014-10-24 04:00:00
      157.3499
      157.6994
      156.7383
      157.3499
      None
      IBM
      2014-10-24
      6852273
    

    

      2014-10-27 04:00:00
      157.1461
      158.1557
      157.0878
      157.2723
      None
      IBM
      2014-10-27
      5139385
    

    

      2014-10-28 04:00:00
      158.8256
      158.8256
      157.0781
      157.2335
      None
      IBM
      2014-10-28
      8134183
    

    

      2014-10-29 04:00:00
      158.6897
      159.8158
      158.0101
      159.5343
      None
      IBM
      2014-10-29
      4884136
    

    

      2014-10-30 04:00:00
      159.5537
      159.8158
      158.2625
      158.7285
      None
      IBM
      2014-10-30
      4013013
    

    

      2014-10-31 04:00:00
      159.6022
      160.7575
      158.8450
      160.5536
      None
      IBM
      2014-10-31
      5993202
    

    

      2014-11-03 05:00:00
      159.5634
      159.7381
      158.6120
      159.4566
      None
      IBM
      2014-11-03
      4833457
    

    

      2014-11-04 05:00:00
      157.9033
      159.5634
      157.5053
      159.5440
      None
      IBM
      2014-11-04
      4374668
    

    

      2014-11-05 05:00:00
      157.0975
      158.7673
      156.8451
      158.3693
      None
      IBM
      2014-11-05
      4228090
    

    

      2014-11-06 05:00:00
      157.8209
      157.8893
      156.4426
      157.6449
      None
      IBM
      2014-11-06
      4161393
    

    

      2014-11-07 05:00:00
      158.4171
      158.5540
      157.2246
      157.7818
      None
      IBM
      2014-11-07
      3575385
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      2015-08-17 04:00:00
      156.3100
      156.6900
      154.7000
      155.2000
      None
      IBM
      2015-08-17
      2249500
    

    

      2015-08-18 04:00:00
      156.0100
      156.5200
      155.2500
      155.5100
      None
      IBM
      2015-08-18
      2018300
    

    

      2015-08-19 04:00:00
      153.9400
      155.6700
      153.4100
      155.1500
      None
      IBM
      2015-08-19
      4206300
    

    

      2015-08-20 04:00:00
      152.6600
      153.9100
      152.5000
      152.7400
      None
      IBM
      2015-08-20
      4011500
    

    

      2015-08-21 04:00:00
      148.8500
      153.1900
      148.7000
      151.5000
      None
      IBM
      2015-08-21
      7362100
    

    

      2015-08-24 04:00:00
      143.4700
      147.7600
      142.3200
      143.4700
      None
      IBM
      2015-08-24
      10189600
    

    

      2015-08-25 04:00:00
      140.9600
      147.1100
      140.6200
      146.9400
      None
      IBM
      2015-08-25
      7073200
    

    

      2015-08-26 04:00:00
      146.7000
      146.9800
      142.1400
      144.0900
      None
      IBM
      2015-08-26
      6221700
    

    

      2015-08-27 04:00:00
      148.5400
      148.9700
      145.6600
      148.5000
      None
      IBM
      2015-08-27
      4976600
    

    

      2015-08-28 04:00:00
      147.9800
      148.2000
      147.1800
      147.7500
      None
      IBM
      2015-08-28
      4076200
    

    

      2015-08-31 04:00:00
      147.8900
      148.4000
      146.2600
      147.3800
      None
      IBM
      2015-08-31
      4093000
    

    

      2015-09-01 04:00:00
      142.6800
      144.9800
      141.8500
      144.9100
      None
      IBM
      2015-09-01
      5272000
    

    

      2015-09-02 04:00:00
      145.0500
      145.0800
      143.1800
      144.7400
      None
      IBM
      2015-09-02
      4252000
    

    

      2015-09-03 04:00:00
      146.7800
      148.0300
      145.7700
      146.0500
      None
      IBM
      2015-09-03
      3603500
    

    

      2015-09-04 04:00:00
      143.7000
      145.4000
      143.3200
      144.5700
      None
      IBM
      2015-09-04
      4201000
    

    

      2015-09-08 04:00:00
      147.2300
      147.3400
      145.6600
      145.8600
      None
      IBM
      2015-09-08
      3933200
    

    

      2015-09-09 04:00:00
      145.0500
      149.0400
      144.8500
      148.7400
      None
      IBM
      2015-09-09
      3407700
    

    

      2015-09-10 04:00:00
      146.2000
      147.1600
      144.5100
      145.8500
      None
      IBM
      2015-09-10
      3461600
    

    

      2015-09-11 04:00:00
      147.3700
      147.5000
      145.6700
      145.7100
      None
      IBM
      2015-09-11
      3115000
    

    

      2015-09-14 04:00:00
      145.6500
      147.3700
      145.4100
      147.3700
      None
      IBM
      2015-09-14
      3226700
    

    

      2015-09-15 04:00:00
      147.5300
      147.9300
      145.7600
      146.6000
      None
      IBM
      2015-09-15
      2717000
    

    

      2015-09-16 04:00:00
      148.4100
      148.8900
      147.5400
      147.8400
      None
      IBM
      2015-09-16
      2799200
    

    

      2015-09-17 04:00:00
      148.1400
      149.6800
      147.3000
      148.1000
      None
      IBM
      2015-09-17
      4002900
    

    

      2015-09-18 04:00:00
      144.5100
      146.3800
      143.9800
      146.0500
      None
      IBM
      2015-09-18
      7975800
    

    

      2015-09-21 04:00:00
      146.4800
      146.9800
      144.9200
      145.3900
      None
      IBM
      2015-09-21
      3825000
    

    

      2015-09-22 04:00:00
      144.4300
      145.0600
      143.7700
      144.6200
      None
      IBM
      2015-09-22
      3564000
    

    

      2015-09-23 04:00:00
      143.6600
      144.5700
      142.7500
      144.2100
      None
      IBM
      2015-09-23
      2674200
    

    

      2015-09-24 04:00:00
      144.4100
      145.0700
      141.9500
      142.6000
      None
      IBM
      2015-09-24
      3280200
    

    

      2015-09-25 04:00:00
      145.4200
      146.2700
      144.5300
      145.5500
      None
      IBM
      2015-09-25
      3474400
    

    

      2015-09-28 04:00:00
      142.5200
      145.3800
      142.4700
      144.4200
      None
      IBM
      2015-09-28
      4317300
    

  



252 rows × 8 columns

getHistory with SEVERAL symbols





In [8]:



    


symbols = ["ZC*1", "IBM", "GOOGL" , "^EURUSD"]
histories = getHistory(symbols, typ='daily', startDate=startDate, session=session)
histories
#print(histories.dtypes)
#print(type(histories.index[0])) # should be a pandas.tslib.Timestamp
#print(type(histories['timestamp'][0])) # should be a pandas.tslib.Timestamp



    


















    
Out[8]:








<class 'pandas.core.panel.Panel'>
Dimensions: 8 (items) x 511 (major_axis) x 4 (minor_axis)
Items axis: close to volume
Major_axis axis: 2014-09-29 04:00:00 to 2015-09-29 05:00:00
Minor_axis axis: ZC*1 to ^EURUSD

















In [9]:



    


#histories.loc[:, :, "IBM"] #??

Content source: femtotrader/barchart-ondemand-client-python

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