< Streaming Prices | Contents | Bonus Material II >

Bonus Material I

FOREX LABS ENDPOINTS

Note: According to the documentation materials in these section are under development. So it may change at any point in time or become invalid



In [1]:

    
import pandas as pd
%matplotlib inline
import seaborn as sns
import oandapy
import configparser

config = configparser.ConfigParser()
config.read('../config/config_v1.ini')
account_id = config['oanda']['account_id']
api_key = config['oanda']['api_key']

oanda = oandapy.API(environment="practice", 
                    access_token=api_key)

Obtaining Historical Economic Calendar



In [2]:

    
calendar = oanda.get_eco_calendar(instrument="EUR_USD", period=86400)



In [3]:

    
print(calendar)









    



[{'unit': '% m/m', 'timestamp': 1485783000, 'market': '0.5', 'actual': '0.5', 'previous': '0.2', 'title': 'Personal spending', 'impact': 2, 'forecast': '0.4', 'region': 'americas', 'currency': 'USD'}, {'unit': '% m/m', 'timestamp': 1485788400, 'market': '1.1', 'previous': '-2.5', 'title': 'Pending Home Sales', 'impact': 2, 'currency': 'USD', 'region': 'americas', 'actual': '1.6'}]



In [4]:

    
df = pd.DataFrame(calendar)
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='s')
df.tail(10)









    Out[4]:






  
    
      
      actual
      currency
      forecast
      impact
      market
      previous
      region
      timestamp
      title
      unit
    
  
  
    
      0
      0.5
      USD
      0.4
      2
      0.5
      0.2
      americas
      2017-01-30 13:30:00
      Personal spending
      % m/m
    
    
      1
      1.6
      USD
      NaN
      2
      1.1
      -2.5
      americas
      2017-01-30 15:00:00
      Pending Home Sales
      % m/m

Historical Position Ratios



In [5]:

    
response = oanda.get_historical_position_ratios(instrument='USD_JPY', 
                                                period=60)
print(response['data']['USD_JPY']['data'])









    



[[1485741600, 46.58, 114.4], [1485742801, 46.61, 114.44], [1485744001, 46.41, 114.41], [1485745201, 46.42, 114.34], [1485746400, 46.32, 114.32], [1485747600, 46.42, 114.37], [1485748801, 47.04, 114.42], [1485750001, 47.28, 114.46], [1485751200, 47.33, 114.5], [1485752400, 47.34, 114.5], [1485753601, 46.75, 114.44], [1485754800, 46.94, 114.51], [1485756001, 47.27, 114.59], [1485757200, 47.43, 114.63], [1485758400, 47.41, 114.59], [1485759601, 47.3, 114.58], [1485760800, 47.57, 114.69], [1485762001, 47.82, 114.72], [1485763200, 47.73, 114.7], [1485764400, 48.04, 114.89], [1485765601, 48.08, 114.82], [1485766801, 47.83, 114.81], [1485768001, 48, 114.72], [1485769200, 47.76, 114.7], [1485770401, 47.91, 114.69], [1485771601, 47.47, 114.66], [1485772800, 47.58, 114.65], [1485774001, 47.06, 114.59], [1485775200, 47.11, 114.62], [1485776400, 48.42, 114.69], [1485777601, 48.08, 114.68], [1485778801, 48.63, 114.66], [1485780001, 49.3, 114.73], [1485781201, 48.19, 114.61], [1485782401, 47.64, 114.47], [1485783600, 48.42, 114.51], [1485784800, 48.39, 114.5], [1485786001, 48.26, 114.39], [1485787201, 48.14, 114.32], [1485788401, 47.01, 114.28], [1485789601, 47.74, 114.03], [1485790801, 48.48, 113.87], [1485792001, 49.52, 113.86], [1485793201, 50.16, 113.88], [1485794400, 50.4, 113.79], [1485795601, 50.42, 113.86], [1485796801, 50.48, 113.72], [1485798000, 50.51, 113.57], [1485799200, 50.95, 113.6], [1485800400, 51.21, 113.66], [1485801600, 51.19, 113.68], [1485802801, 51.31, 113.62], [1485804001, 51.49, 113.7], [1485805201, 51.35, 113.73], [1485806401, 51.42, 113.68], [1485807600, 51.66, 113.57], [1485808800, 51.94, 113.56], [1485810001, 51.64, 113.71], [1485811201, 51.07, 113.76], [1485812401, 51.12, 113.8], [1485813601, 50.69, 113.78], [1485814801, 50.62, 113.77], [1485816001, 50.81, 113.7], [1485817201, 50.77, 113.74], [1485818401, 50.72, 113.79], [1485819600, 51.05, 113.76], [1485820801, 50.53, 113.68], [1485822000, 50.85, 113.67], [1485823200, 50.87, 113.7], [1485824401, 51.3, 113.77], [1485825601, 51.4, 113.8], [1485826801, 51.17, 113.73]]



In [6]:

    
df = pd.DataFrame(response['data']['USD_JPY']['data'])
df.columns = ["Timestamp", "Long_pos_ratio", "exchange_rate"]
df["Timestamp"] = pd.to_datetime(df['Timestamp'], unit='s')
df









    Out[6]:






  
    
      
      Timestamp
      Long_pos_ratio
      exchange_rate
    
  
  
    
      0
      2017-01-30 02:00:00
      46.58
      114.40
    
    
      1
      2017-01-30 02:20:01
      46.61
      114.44
    
    
      2
      2017-01-30 02:40:01
      46.41
      114.41
    
    
      3
      2017-01-30 03:00:01
      46.42
      114.34
    
    
      4
      2017-01-30 03:20:00
      46.32
      114.32
    
    
      5
      2017-01-30 03:40:00
      46.42
      114.37
    
    
      6
      2017-01-30 04:00:01
      47.04
      114.42
    
    
      7
      2017-01-30 04:20:01
      47.28
      114.46
    
    
      8
      2017-01-30 04:40:00
      47.33
      114.50
    
    
      9
      2017-01-30 05:00:00
      47.34
      114.50
    
    
      10
      2017-01-30 05:20:01
      46.75
      114.44
    
    
      11
      2017-01-30 05:40:00
      46.94
      114.51
    
    
      12
      2017-01-30 06:00:01
      47.27
      114.59
    
    
      13
      2017-01-30 06:20:00
      47.43
      114.63
    
    
      14
      2017-01-30 06:40:00
      47.41
      114.59
    
    
      15
      2017-01-30 07:00:01
      47.30
      114.58
    
    
      16
      2017-01-30 07:20:00
      47.57
      114.69
    
    
      17
      2017-01-30 07:40:01
      47.82
      114.72
    
    
      18
      2017-01-30 08:00:00
      47.73
      114.70
    
    
      19
      2017-01-30 08:20:00
      48.04
      114.89
    
    
      20
      2017-01-30 08:40:01
      48.08
      114.82
    
    
      21
      2017-01-30 09:00:01
      47.83
      114.81
    
    
      22
      2017-01-30 09:20:01
      48.00
      114.72
    
    
      23
      2017-01-30 09:40:00
      47.76
      114.70
    
    
      24
      2017-01-30 10:00:01
      47.91
      114.69
    
    
      25
      2017-01-30 10:20:01
      47.47
      114.66
    
    
      26
      2017-01-30 10:40:00
      47.58
      114.65
    
    
      27
      2017-01-30 11:00:01
      47.06
      114.59
    
    
      28
      2017-01-30 11:20:00
      47.11
      114.62
    
    
      29
      2017-01-30 11:40:00
      48.42
      114.69
    
    
      ...
      ...
      ...
      ...
    
    
      42
      2017-01-30 16:00:01
      49.52
      113.86
    
    
      43
      2017-01-30 16:20:01
      50.16
      113.88
    
    
      44
      2017-01-30 16:40:00
      50.40
      113.79
    
    
      45
      2017-01-30 17:00:01
      50.42
      113.86
    
    
      46
      2017-01-30 17:20:01
      50.48
      113.72
    
    
      47
      2017-01-30 17:40:00
      50.51
      113.57
    
    
      48
      2017-01-30 18:00:00
      50.95
      113.60
    
    
      49
      2017-01-30 18:20:00
      51.21
      113.66
    
    
      50
      2017-01-30 18:40:00
      51.19
      113.68
    
    
      51
      2017-01-30 19:00:01
      51.31
      113.62
    
    
      52
      2017-01-30 19:20:01
      51.49
      113.70
    
    
      53
      2017-01-30 19:40:01
      51.35
      113.73
    
    
      54
      2017-01-30 20:00:01
      51.42
      113.68
    
    
      55
      2017-01-30 20:20:00
      51.66
      113.57
    
    
      56
      2017-01-30 20:40:00
      51.94
      113.56
    
    
      57
      2017-01-30 21:00:01
      51.64
      113.71
    
    
      58
      2017-01-30 21:20:01
      51.07
      113.76
    
    
      59
      2017-01-30 21:40:01
      51.12
      113.80
    
    
      60
      2017-01-30 22:00:01
      50.69
      113.78
    
    
      61
      2017-01-30 22:20:01
      50.62
      113.77
    
    
      62
      2017-01-30 22:40:01
      50.81
      113.70
    
    
      63
      2017-01-30 23:00:01
      50.77
      113.74
    
    
      64
      2017-01-30 23:20:01
      50.72
      113.79
    
    
      65
      2017-01-30 23:40:00
      51.05
      113.76
    
    
      66
      2017-01-31 00:00:01
      50.53
      113.68
    
    
      67
      2017-01-31 00:20:00
      50.85
      113.67
    
    
      68
      2017-01-31 00:40:00
      50.87
      113.70
    
    
      69
      2017-01-31 01:00:01
      51.30
      113.77
    
    
      70
      2017-01-31 01:20:01
      51.40
      113.80
    
    
      71
      2017-01-31 01:40:01
      51.17
      113.73
    
  

72 rows × 3 columns



In [7]:

    
df = df.set_index("Timestamp")



In [8]:

    
df['Long_pos_ratio'].plot();

Get Historical Spread



In [9]:

    
response = oanda.get_historical_spreads(instrument='GBP_USD', period=600)
print(response)









    



{'avg': [[1485820800, 2.95722], [1485821700, 2.763]], 'max': [[1485820800, 4], [1485821700, 3.8]], 'min': [[1485820800, 2.3], [1485821700, 2.3]]}

Commitments of Traders



In [10]:

    
response = oanda.get_commitments_of_traders(instrument='USD_CAD')
print(response['USD_CAD'][:10])









    



[{'unit': 'Contracts Of CAD 100,000', 'ncs': '15224', 'price': '1.000845', 'date': 1199768400, 'oi': '82454', 'ncl': '31648'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '15101', 'price': '1.010115', 'date': 1200373200, 'oi': '81827', 'ncl': '22866'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '16786', 'price': '1.02546', 'date': 1200978000, 'oi': '86378', 'ncl': '24463'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '21653', 'price': '1.019535', 'date': 1201582800, 'oi': '90907', 'ncl': '25948'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '20363', 'price': '1.000095', 'date': 1202187600, 'oi': '93574', 'ncl': '30603'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '22956', 'price': '0.999475', 'date': 1202792400, 'oi': '101187', 'ncl': '43391'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '21440', 'price': '1.005535', 'date': 1203397200, 'oi': '95614', 'ncl': '33387'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '24139', 'price': '1.002335', 'date': 1204002000, 'oi': '110073', 'ncl': '50323'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '23699', 'price': '0.994475', 'date': 1204606800, 'oi': '117986', 'ncl': '60105'}, {'unit': 'Contracts Of CAD 100,000', 'ncs': '21422', 'price': '0.992375', 'date': 1205208000, 'oi': '117606', 'ncl': '50353'}]

Orderbook



In [11]:

    
response = oanda.get_orderbook(instrument="EUR_USD", period=1)



In [12]:

    
pd.DataFrame(response)









    Out[12]:






  
    
      
      1485820801
      1485822000
      1485823200
      1485824401
      1485825601
      1485826801
    
  
  
    
      price_points
      {'1.087': {'pl': 0.2415, 'os': 0.2689, 'ps': 0...
      {'1.087': {'pl': 0.2415, 'os': 0.2724, 'ps': 0...
      {'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...
      {'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...
      {'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...
      {'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...
    
    
      rate
      1.0708
      1.0702
      1.0699
      1.0697
      1.0697
      1.0696



In [13]:

    
pd.DataFrame(response['1485820801']['price_points'])









    Out[13]:






  
    
      
      1.03
      1.0305
      1.031
      1.0315
      1.032
      1.0325
      1.033
      1.0335
      1.034
      1.0345
      ...
      1.106
      1.1065
      1.107
      1.1075
      1.108
      1.1085
      1.109
      1.1095
      1.11
      1.1105
    
  
  
    
      ol
      0.7606
      0.8490
      0.1592
      0.7429
      0.9375
      0.8455
      0.7358
      1.2488
      0.7606
      0.1132
      ...
      0.0460
      0.0283
      0.0495
      0.0389
      0.0495
      0.0318
      0.0495
      0.0318
      0.1840
      0.0212
    
    
      os
      0.3538
      0.2016
      0.2158
      0.1840
      0.1981
      0.2052
      0.3219
      0.4493
      0.1769
      0.1274
      ...
      0.1663
      0.1132
      0.1592
      0.1203
      0.1521
      0.1132
      0.1309
      0.1840
      0.3219
      0.0884
    
    
      pl
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0186
      0.0000
      ...
      0.1115
      0.0929
      0.0929
      0.0557
      0.1115
      0.0743
      0.0557
      0.0929
      0.1486
      0.0557
    
    
      ps
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0000
      0.0372
      0.0372
      ...
      0.0000
      0.0186
      0.0000
      0.0000
      0.0372
      0.0000
      0.0000
      0.0000
      0.0186
      0.0372
    
  

4 rows × 162 columns

Auto Chartist



In [14]:

    
response = oanda.get_autochartist()



In [15]:

    
pd.DataFrame(response['signals'][:5])









    Out[15]:






  
    
      
      data
      id
      instrument
      meta
      type
    
  
  
    
      0
      {'patternendtime': 1485806400, 'points': {'res...
      452007805
      XPT_USD
      {'length': 78, 'historicalstats': {'symbol': {...
      chartpattern
    
    
      1
      {'patternendtime': 1485792000, 'points': {'res...
      452005692
      USB05Y_USD
      {'length': 50, 'historicalstats': {'symbol': {...
      chartpattern
    
    
      2
      {'patternendtime': 1485784800, 'points': {'res...
      452004362
      CAD_SGD
      {'length': 69, 'historicalstats': {'symbol': {...
      chartpattern
    
    
      3
      {'patternendtime': 1485784800, 'points': {'res...
      452004356
      XAU_SGD
      {'length': 28, 'historicalstats': {'symbol': {...
      chartpattern
    
    
      4
      {'patternendtime': 1485777600, 'points': {'res...
      452003304
      XAG_EUR
      {'length': 26, 'historicalstats': {'symbol': {...
      chartpattern

< Streaming Prices | Contents | Bonus Material II >

	actual	currency	forecast	impact	market	previous	region	timestamp	title	unit
0	0.5	USD	0.4	2	0.5	0.2	americas	2017-01-30 13:30:00	Personal spending	% m/m
1	1.6	USD	NaN	2	1.1	-2.5	americas	2017-01-30 15:00:00	Pending Home Sales	% m/m

	Timestamp	Long_pos_ratio	exchange_rate
0	2017-01-30 02:00:00	46.58	114.40
1	2017-01-30 02:20:01	46.61	114.44
2	2017-01-30 02:40:01	46.41	114.41
3	2017-01-30 03:00:01	46.42	114.34
4	2017-01-30 03:20:00	46.32	114.32
5	2017-01-30 03:40:00	46.42	114.37
6	2017-01-30 04:00:01	47.04	114.42
7	2017-01-30 04:20:01	47.28	114.46
8	2017-01-30 04:40:00	47.33	114.50
9	2017-01-30 05:00:00	47.34	114.50
10	2017-01-30 05:20:01	46.75	114.44
11	2017-01-30 05:40:00	46.94	114.51
12	2017-01-30 06:00:01	47.27	114.59
13	2017-01-30 06:20:00	47.43	114.63
14	2017-01-30 06:40:00	47.41	114.59
15	2017-01-30 07:00:01	47.30	114.58
16	2017-01-30 07:20:00	47.57	114.69
17	2017-01-30 07:40:01	47.82	114.72
18	2017-01-30 08:00:00	47.73	114.70
19	2017-01-30 08:20:00	48.04	114.89
20	2017-01-30 08:40:01	48.08	114.82
21	2017-01-30 09:00:01	47.83	114.81
22	2017-01-30 09:20:01	48.00	114.72
23	2017-01-30 09:40:00	47.76	114.70
24	2017-01-30 10:00:01	47.91	114.69
25	2017-01-30 10:20:01	47.47	114.66
26	2017-01-30 10:40:00	47.58	114.65
27	2017-01-30 11:00:01	47.06	114.59
28	2017-01-30 11:20:00	47.11	114.62
29	2017-01-30 11:40:00	48.42	114.69
...	...	...	...
42	2017-01-30 16:00:01	49.52	113.86
43	2017-01-30 16:20:01	50.16	113.88
44	2017-01-30 16:40:00	50.40	113.79
45	2017-01-30 17:00:01	50.42	113.86
46	2017-01-30 17:20:01	50.48	113.72
47	2017-01-30 17:40:00	50.51	113.57
48	2017-01-30 18:00:00	50.95	113.60
49	2017-01-30 18:20:00	51.21	113.66
50	2017-01-30 18:40:00	51.19	113.68
51	2017-01-30 19:00:01	51.31	113.62
52	2017-01-30 19:20:01	51.49	113.70
53	2017-01-30 19:40:01	51.35	113.73
54	2017-01-30 20:00:01	51.42	113.68
55	2017-01-30 20:20:00	51.66	113.57
56	2017-01-30 20:40:00	51.94	113.56
57	2017-01-30 21:00:01	51.64	113.71
58	2017-01-30 21:20:01	51.07	113.76
59	2017-01-30 21:40:01	51.12	113.80
60	2017-01-30 22:00:01	50.69	113.78
61	2017-01-30 22:20:01	50.62	113.77
62	2017-01-30 22:40:01	50.81	113.70
63	2017-01-30 23:00:01	50.77	113.74
64	2017-01-30 23:20:01	50.72	113.79
65	2017-01-30 23:40:00	51.05	113.76
66	2017-01-31 00:00:01	50.53	113.68
67	2017-01-31 00:20:00	50.85	113.67
68	2017-01-31 00:40:00	50.87	113.70
69	2017-01-31 01:00:01	51.30	113.77
70	2017-01-31 01:20:01	51.40	113.80
71	2017-01-31 01:40:01	51.17	113.73

	1485820801	1485822000	1485823200	1485824401	1485825601	1485826801
price_points	{'1.087': {'pl': 0.2415, 'os': 0.2689, 'ps': 0...	{'1.087': {'pl': 0.2415, 'os': 0.2724, 'ps': 0...	{'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...	{'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...	{'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...	{'1.087': {'pl': 0.2601, 'os': 0.2724, 'ps': 0...
rate	1.0708	1.0702	1.0699	1.0697	1.0697	1.0696

	1.03	1.0305	1.031	1.0315	1.032	1.0325	1.033	1.0335	1.034	1.0345	...	1.106	1.1065	1.107	1.1075	1.108	1.1085	1.109	1.1095	1.11	1.1105
ol	0.7606	0.8490	0.1592	0.7429	0.9375	0.8455	0.7358	1.2488	0.7606	0.1132	...	0.0460	0.0283	0.0495	0.0389	0.0495	0.0318	0.0495	0.0318	0.1840	0.0212
os	0.3538	0.2016	0.2158	0.1840	0.1981	0.2052	0.3219	0.4493	0.1769	0.1274	...	0.1663	0.1132	0.1592	0.1203	0.1521	0.1132	0.1309	0.1840	0.3219	0.0884
pl	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0186	0.0000	...	0.1115	0.0929	0.0929	0.0557	0.1115	0.0743	0.0557	0.0929	0.1486	0.0557
ps	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0372	0.0372	...	0.0000	0.0186	0.0000	0.0000	0.0372	0.0000	0.0000	0.0000	0.0186	0.0372

	data	id	instrument	meta	type
0	{'patternendtime': 1485806400, 'points': {'res...	452007805	XPT_USD	{'length': 78, 'historicalstats': {'symbol': {...	chartpattern
1	{'patternendtime': 1485792000, 'points': {'res...	452005692	USB05Y_USD	{'length': 50, 'historicalstats': {'symbol': {...	chartpattern
2	{'patternendtime': 1485784800, 'points': {'res...	452004362	CAD_SGD	{'length': 69, 'historicalstats': {'symbol': {...	chartpattern
3	{'patternendtime': 1485784800, 'points': {'res...	452004356	XAU_SGD	{'length': 28, 'historicalstats': {'symbol': {...	chartpattern
4	{'patternendtime': 1485777600, 'points': {'res...	452003304	XAG_EUR	{'length': 26, 'historicalstats': {'symbol': {...	chartpattern