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| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

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Finance APIs: FXCM
| FXCM Online | Account Login |

Evan Marie online: | EvanMarie.com| EvanMarie@Proton.me | Linked In | GitHub | Hugging Face | Mastadon | Jovian.ai | TikTok | CodeWars | Discord ⇨ ✨ EvanMarie ✨#6114 |

• Leader in online foreign exchange trading
• Stocks and CFD trading (contract for difference, highly levered financial instrument)
• Cryptocurrencies

Pros and Cons of FXCM

Pros:

• Completely free with a demo account, which does not allow live trading
  
• Large datasets available for currencies, commodities, and indexes
  
• High frequency real-time data
  
• You can stream the high frequency real-time data
  
• Bid and ask prices are available
  
• fxcmpy is a powerful and professional wrapper package for python
  

Cons:

• Must register and make a demo account
• marketing calls and emails to create a live account
• demo accounts are not available in all countries, for example, the United State
• No data for single stocks and bonds, no fundamentals
• Only provide indexes, currencies, etc

from helpers_mardi_gras import *
import fxcmpy
import_all()
plt.style.use('mardi_gras.mplstyle')

%%html
<style>
a:link {color: #FFB83C !important; font-weight: 600 !important;}
a:visited {color: #FFB83C !important; font-weight: 600 !important;}
</style>

FXCM: First Steps

api_key = ''
api = fxcmpy.fxcmpy(access_token = api_key, log_level = 'error')

# api is an fxcmpy object

api

<fxcmpy.fxcmpy.fxcmpy at 0x7fdcfaba2b90>

# api.get_instruments() - trading instruments

pretty('api.get_instruments')
help(api.get_instruments)

api.get_instruments

Help on method get_instruments in module fxcmpy.fxcmpy:

get_instruments() method of fxcmpy.fxcmpy.fxcmpy instance
    Return the tradeable instruments of FXCM as a list.

trading_instruments = pd.Series(api.get_instruments())

see(trading_instruments.sample(10), '10 samples of trading instrumentsv')

10 samples of trading instrumentsv

	0
266	BTC/USD
151	CON.de
240	SPOT.us
49	CAD/CHF
203	BIDU.hk
40	BILI.us
44	USD/TRY
180	BABA.us
72	FLAT.hk
269	LTC/USD

api.close()- disconnect from the server

# api.close()

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| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

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Downloading Historical Data
- get_candles()

pretty('api.get_candles()')
help(api.get_candles)

api.get_candles()

Help on method get_candles in module fxcmpy.fxcmpy:

get_candles(instrument='', offer_id=None, period='H1', number=10, start=None, end=None, with_index=True, columns=[], stop=None) method of fxcmpy.fxcmpy.fxcmpy instance
    Return historical data from the fxcm database as pandas.DataFrame.
    
    Arguments:
    
    instrument: string (default ''):
        the instrument for which data is requested. For a list of all
        available instruments for historical data, use
        get_instruments_for_candles().
        If the value is equal to '' (default), offer_id must have a value. 
        If both, instrument and offer_id are given, the value of instrument 
        is used.
    
    offer_id: integer (default None): 
        the id of the instrument for which data is requested as given in the
        offer trading table as given by get_offers(). If offer_id is equal 
        to None (default), the parameter instrument must have a value. 
        If both, instrument and offer_id are given, the value of instrument 
        is used.
    
    period: string,
        the granularity of the data. Must be one of
        'm1', 'm5', 'm15', 'm30', 'H1', 'H2', 'H3', 'H4', 'H6', 'H8',
        'D1', 'W1', or 'M1'.
    
    number: integer (default 10),
        the number of candles to receive.
    
    start: datetime.datetime, datetime.date or string (defaut None),
        the first date to receive data for. 
    
    end: datetime.datetime, datetime.date or string (default None),
        the last date to receive data for.
    
    with_index: boolean (default True),
        whether the column 'date' should server as index in the resulting
        pandas.DataFrame.
    
    columns: list (default list()),
        a list of column labels the result should include. If empty, all
        columns are returned.
    
        Available column labels are
    
        'date', 'bidopen', 'bidclose', 'bidhigh', 'bidlow',
        'askopen', 'askclose', 'askhigh', 'asklow', 'tickqty'.
    
        Also available is 'asks' as shortcut for all ask related columns
        and 'bids' for all bid related columns, respectively.
    
        The column 'date' is always included.
    
    Returns:
    
    A pandas DataFrame containing the requested data.

# hourly data by default

pretty("api.get_candles('EUR/USD')")
api.get_candles('EUR/USD').head(5).style.format(precision = 4)

api.get_candles('EUR/USD')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-02-10 12:00:00	1.0698	1.0682	1.0700	1.0681	1.0699	1.0684	1.0701	1.0682	13552
2023-02-10 13:00:00	1.0682	1.0695	1.0699	1.0681	1.0684	1.0696	1.0700	1.0682	22645
2023-02-10 14:00:00	1.0695	1.0696	1.0706	1.0686	1.0696	1.0698	1.0708	1.0687	24374
2023-02-10 15:00:00	1.0696	1.0676	1.0706	1.0674	1.0698	1.0678	1.0708	1.0675	37516
2023-02-10 16:00:00	1.0676	1.0668	1.0686	1.0667	1.0678	1.0669	1.0688	1.0669	17770

pretty("api.get_candles('USD/JPY')")
api.get_candles('USD/JPY').head(5).style.format(precision = 4)

api.get_candles('USD/JPY')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-02-10 12:00:00	130.8250	130.9250	131.0650	130.7510	130.8430	130.9440	131.0840	130.7650	17851
2023-02-10 13:00:00	130.9280	130.7100	130.9460	130.5720	130.9440	130.7280	130.9650	130.5880	34571
2023-02-10 14:00:00	130.7080	130.9960	131.0900	130.6220	130.7280	131.0100	131.1090	130.6420	38187
2023-02-10 15:00:00	131.0010	131.3200	131.3970	131.0010	131.0130	131.3390	131.4140	131.0130	44088
2023-02-10 16:00:00	131.3210	131.5020	131.5690	131.2910	131.3400	131.5170	131.5830	131.3080	24394

pretty("api.get_candles('GBP/USD')")
api.get_candles('GBP/USD').head(5).style.format(precision = 4)

api.get_candles('GBP/USD')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-02-10 12:00:00	1.2108	1.2091	1.2111	1.2086	1.2109	1.2092	1.2113	1.2087	21900
2023-02-10 13:00:00	1.2091	1.2111	1.2121	1.2088	1.2092	1.2113	1.2123	1.2090	34786
2023-02-10 14:00:00	1.2111	1.2111	1.2130	1.2106	1.2113	1.2113	1.2131	1.2107	38244
2023-02-10 15:00:00	1.2111	1.2075	1.2121	1.2069	1.2113	1.2077	1.2123	1.2071	50437
2023-02-10 16:00:00	1.2075	1.2059	1.2087	1.2058	1.2077	1.2060	1.2089	1.2060	28630

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| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

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Intraday Frequency
- period: string, the granularity of the data - Must be one of: 'm1', 'm5', 'm15', 'm30', 'H1', 'H2', 'H3', 'H4', 'H6', 'H8', 'D1', 'W1', or 'M1' - number: integer (default 10), the number of candles to receive.

# api = fxcmpy.fxcmpy(access_token = api_key, log_level = 'error')

pretty("api.get_candles('EUR/USD', period = 'D1'")
api.get_candles('EUR/USD', period = 'D1').head(5).style.format(precision = 4)

api.get_candles('EUR/USD', period = 'D1'

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-01-30 22:00:00	1.0865	1.0848	1.0913	1.0838	1.0869	1.0852	1.0914	1.0840	266787
2023-01-31 22:00:00	1.0848	1.0860	1.0874	1.0801	1.0852	1.0867	1.0876	1.0803	300547
2023-02-01 22:00:00	1.0860	1.0988	1.1001	1.0852	1.0867	1.0994	1.1002	1.0853	372545
2023-02-02 22:00:00	1.0987	1.0908	1.1032	1.0885	1.0994	1.0910	1.1034	1.0886	415139
2023-02-03 22:00:00	1.0908	1.0792	1.0940	1.0792	1.0911	1.0795	1.0941	1.0793	397630

pretty("api.get_candles('EUR/USD', period = 'm1')")
api.get_candles('EUR/USD', period = 'm1').head(5).style.format(precision = 4)

api.get_candles('EUR/USD', period = 'm1')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-02-10 21:48:00	1.0676	1.0677	1.0677	1.0675	1.0678	1.0679	1.0679	1.0677	43
2023-02-10 21:49:00	1.0677	1.0677	1.0677	1.0676	1.0679	1.0679	1.0679	1.0678	29
2023-02-10 21:50:00	1.0677	1.0677	1.0677	1.0676	1.0679	1.0679	1.0679	1.0678	95
2023-02-10 21:51:00	1.0677	1.0678	1.0678	1.0677	1.0679	1.0679	1.0679	1.0679	22
2023-02-10 21:52:00	1.0678	1.0678	1.0678	1.0677	1.0679	1.0679	1.0679	1.0679	32

import random
instrument = random.choice(trading_instruments)

pretty(f"api.get_candles('Copper', period = 'H3')")
api.get_candles('Copper', period = 'H3').head(5).style.format(precision = 4)

api.get_candles('Copper', period = 'H3')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-02-09 16:00:00	4.0646	4.0801	4.1096	4.0538	4.0688	4.0843	4.1144	4.0580	10767
2023-02-09 19:00:00	4.0800	4.0662	4.0841	4.0611	4.0842	4.0704	4.0883	4.0653	4955
2023-02-09 22:00:00	4.0718	4.0589	4.0718	4.0606	4.0760	4.0631	4.0760	4.0648	1065
2023-02-10 01:00:00	4.0648	4.0446	4.0717	4.0437	4.0690	4.0488	4.0759	4.0479	6980
2023-02-10 04:00:00	4.0449	4.0541	4.0629	4.0292	4.0491	4.0583	4.0671	4.0334	5969

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| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

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Defining a Time Period
- by default, FXCM returns the 10 most recent time stamps - this section will cover the ways to specify other parameters

Defining time with number

# daily data, number = 10000 (maxiumum number possible)

api.get_candles('EUR/USD', 
				period = 'D1', 
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2001-09-09 21:00:00	0.91	0.90	0.91	0.90	0.91	0.90	0.91	0.90	0
2001-09-10 21:00:00	0.90	0.91	0.91	0.90	0.90	0.91	0.92	0.90	0
2001-09-11 21:00:00	0.91	0.91	0.92	0.90	0.91	0.91	0.92	0.90	0
2001-09-12 21:00:00	0.91	0.91	0.91	0.90	0.91	0.91	0.91	0.91	0
2001-09-13 21:00:00	0.91	0.92	0.92	0.91	0.91	0.92	0.93	0.91	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	1.08	1.07	1.08	1.07	1.08	1.07	1.08	1.07	293847
2023-02-06 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	406035
2023-02-07 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	267334
2023-02-08 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	285708
2023-02-09 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	316647

6213 rows × 9 columns

^ passed 10,000, but received 6,213, because that is the most available

Defining time with start & end (daily data)

api.get_candles('EUR/USD', 
				period = 'D1', 
				start = '2001-01-01', 
				end = '2023-02-10')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2001-09-09 21:00:00	0.91	0.90	0.91	0.90	0.91	0.90	0.91	0.90	0
2001-09-10 21:00:00	0.90	0.91	0.91	0.90	0.90	0.91	0.92	0.90	0
2001-09-11 21:00:00	0.91	0.91	0.92	0.90	0.91	0.91	0.92	0.90	0
2001-09-12 21:00:00	0.91	0.91	0.91	0.90	0.91	0.91	0.91	0.91	0
2001-09-13 21:00:00	0.91	0.92	0.92	0.91	0.91	0.92	0.93	0.91	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	1.08	1.07	1.08	1.07	1.08	1.07	1.08	1.07	293847
2023-02-06 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	406035
2023-02-07 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	267334
2023-02-08 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	285708
2023-02-09 22:00:00	1.07	1.07	1.08	1.07	1.07	1.07	1.08	1.07	316647

6213 rows × 9 columns

Getting hourly data

api.get_candles('EUR/USD', 
				period = 'H1', 
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2021-07-16 14:00:00	1.18	1.18	1.18	1.18	1.18	1.18	1.18	1.18	14956
2021-07-16 15:00:00	1.18	1.18	1.18	1.18	1.18	1.18	1.18	1.18	9403
2021-07-16 16:00:00	1.18	1.18	1.18	1.18	1.18	1.18	1.18	1.18	4586
2021-07-16 17:00:00	1.18	1.18	1.18	1.18	1.18	1.18	1.18	1.18	4946
2021-07-16 18:00:00	1.18	1.18	1.18	1.18	1.18	1.18	1.18	1.18	6476
...	...	...	...	...	...	...	...	...	...
2023-02-10 17:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	12466
2023-02-10 18:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	7181
2023-02-10 19:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	7876
2023-02-10 20:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	6375
2023-02-10 21:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	2782

10000 rows × 9 columns

Getting hourly data with start and end - was able to get more than 10,000 - this is the exact date range it would allow, no more - this is a trial and error process, how to maximize the time period

api.get_candles('EUR/USD', 
				period = 'H1', 
				start = '2021-05-28', 
				end = '2023-02-10')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2021-05-28 00:00:00	1.22	1.22	1.22	1.22	1.22	1.22	1.22	1.22	5405
2021-05-28 01:00:00	1.22	1.22	1.22	1.22	1.22	1.22	1.22	1.22	6388
2021-05-28 02:00:00	1.22	1.22	1.22	1.22	1.22	1.22	1.22	1.22	3819
2021-05-28 03:00:00	1.22	1.22	1.22	1.22	1.22	1.22	1.22	1.22	4609
2021-05-28 04:00:00	1.22	1.22	1.22	1.22	1.22	1.22	1.22	1.22	2541
...	...	...	...	...	...	...	...	...	...
2023-02-09 20:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	12351
2023-02-09 21:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	3114
2023-02-09 22:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	2487
2023-02-09 23:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	2019
2023-02-10 00:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	6311

10844 rows × 9 columns

Getting data by the minute

api.get_candles('EUR/USD', 
				period = 'm1', 
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-02-01 22:24:00	1.10	1.10	1.10	1.10	1.10	1.10	1.10	1.10	4
2023-02-01 22:25:00	1.10	1.10	1.10	1.10	1.10	1.10	1.10	1.10	4
2023-02-01 22:26:00	1.10	1.10	1.10	1.10	1.10	1.10	1.10	1.10	6
2023-02-01 22:27:00	1.10	1.10	1.10	1.10	1.10	1.10	1.10	1.10	4
2023-02-01 22:28:00	1.10	1.10	1.10	1.10	1.10	1.10	1.10	1.10	2
...	...	...	...	...	...	...	...	...	...
2023-02-10 21:53:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	33
2023-02-10 21:54:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	35
2023-02-10 21:55:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	102
2023-02-10 21:56:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	122
2023-02-10 21:57:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	72

10000 rows × 9 columns

Getting data by the minute with start and end - was able to get close to 16,000 records - this is again the exact date range it would allow, no more - this is more data than yfinance or Alpha Vantage allowed

api.get_candles('EUR/USD', 
				period = 'm1', 
				start = '2023-01-26', 
				end = '2023-02-10')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2023-01-26 00:00:00	1.09	1.09	1.09	1.09	1.09	1.09	1.09	1.09	73
2023-01-26 00:01:00	1.09	1.09	1.09	1.09	1.09	1.09	1.09	1.09	93
2023-01-26 00:02:00	1.09	1.09	1.09	1.09	1.09	1.09	1.09	1.09	149
2023-01-26 00:03:00	1.09	1.09	1.09	1.09	1.09	1.09	1.09	1.09	65
2023-01-26 00:04:00	1.09	1.09	1.09	1.09	1.09	1.09	1.09	1.09	68
...	...	...	...	...	...	...	...	...	...
2023-02-09 23:56:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	74
2023-02-09 23:57:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	21
2023-02-09 23:58:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	134
2023-02-09 23:59:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	53
2023-02-10 00:00:00	1.07	1.07	1.07	1.07	1.07	1.07	1.07	1.07	172

15804 rows × 9 columns

Selecting specific columns to import

api.get_candles('EUR/USD', 
				period = 'D1', 
				start = '2001-01-01', 
				end = '2023-02-10',
			   columns = ['bidclose', 'askclose'])

	bidclose	askclose
date
2001-09-09 21:00:00	0.90	0.90
2001-09-10 21:00:00	0.91	0.91
2001-09-11 21:00:00	0.91	0.91
2001-09-12 21:00:00	0.91	0.91
2001-09-13 21:00:00	0.92	0.92
...	...	...
2023-02-05 22:00:00	1.07	1.07
2023-02-06 22:00:00	1.07	1.07
2023-02-07 22:00:00	1.07	1.07
2023-02-08 22:00:00	1.07	1.07
2023-02-09 22:00:00	1.07	1.07

6213 rows × 2 columns

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| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

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Historical Prices for Stock Indexes

instruments = pd.Series(list(api.get_instruments()))
index_mask = instruments.str.contains(r'\d')
indexes_available = pd.Series(instruments.loc[index_mask]).reset_index()

head_tail_horz(indexes_available, 12, 'Indexes Available on FXCM')

Indexes Available on FXCM

head(12)

	index	0
0	11	EUSTX50
1	29	US2000
2	153	DB1.de
3	163	TUI1.de
4	223	A2M.au
5	226	Z1P.au
6	300	TOM2.nl
7	302	5USNote
8	303	10USNote
9	304	2USNote
10	309	FED30D
11	311	EURIBOR3M

   

tail(12)

	index	0
12	312	SONIA3M
13	322	AUS200
14	323	ESP35
15	324	FRA40
16	325	GER30
17	326	HKG33
18	327	JPN225
19	328	NAS100
20	331	SPX500
21	332	UK100
22	333	US30
23	336	CHN50

   

SPX500: daily data, last 10,000 days - returns price index and not total return index - this is the more commonly used index - price return - just the change in price - total return accounts for dividends and the reinvestment thereof

api.get_candles('SPX500', 
				period = 'D1', 
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2001-09-10 00:00:00	1,085.78	1,092.54	1,096.94	1,073.15	1,085.78	1,092.54	1,096.94	1,073.15	0
2001-09-17 00:00:00	1,092.54	1,038.77	1,092.54	1,037.46	1,092.54	1,038.77	1,092.54	1,037.46	0
2001-09-18 00:00:00	1,038.77	1,032.74	1,046.42	1,029.25	1,038.77	1,032.74	1,046.42	1,029.25	0
2001-09-19 00:00:00	1,032.74	1,016.10	1,038.91	984.62	1,032.74	1,016.10	1,038.91	984.62	0
2001-09-20 00:00:00	1,016.10	984.54	1,016.10	984.49	1,016.10	984.54	1,016.10	984.49	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	4,124.12	4,112.85	4,132.61	4,093.90	4,124.72	4,113.45	4,133.21	4,094.40	192597
2023-02-06 22:00:00	4,114.08	4,156.83	4,178.52	4,088.76	4,114.68	4,157.23	4,179.02	4,089.26	336170
2023-02-07 22:00:00	4,157.61	4,123.33	4,169.75	4,111.91	4,158.21	4,123.73	4,170.35	4,112.41	194720
2023-02-08 22:00:00	4,122.78	4,088.38	4,161.05	4,069.08	4,123.38	4,088.78	4,161.65	4,069.58	209966
2023-02-09 22:00:00	4,088.54	4,089.93	4,094.96	4,051.16	4,089.14	4,090.53	4,095.46	4,051.76	217261

5533 rows × 9 columns

Dow Jones Industrial Average: daily data, last 10,000 days

api.get_candles('US30', 
				period = 'D1', 
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2001-09-10 00:00:00	9,603.36	9,605.51	9,671.80	9,493.55	9,603.36	9,605.51	9,671.80	9,493.55	0
2001-09-17 00:00:00	9,580.32	8,920.70	9,580.32	8,883.40	9,580.32	8,920.70	9,580.32	8,883.40	0
2001-09-18 00:00:00	8,922.70	8,903.40	9,022.06	8,861.05	8,922.70	8,903.40	9,022.06	8,861.05	0
2001-09-19 00:00:00	8,903.54	8,759.13	8,945.47	8,480.21	8,903.54	8,759.13	8,945.47	8,480.21	0
2001-09-20 00:00:00	8,748.61	8,376.21	8,748.82	8,375.72	8,748.61	8,376.21	8,748.82	8,375.72	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	33,843.20	33,871.70	33,967.90	33,643.10	33,854.70	33,873.20	33,970.10	33,646.90	457177
2023-02-06 22:00:00	33,878.70	34,106.20	34,244.90	33,624.40	33,884.20	34,107.70	34,247.10	33,626.60	680590
2023-02-07 22:00:00	34,111.80	34,010.30	34,176.70	33,886.00	34,118.40	34,011.80	34,181.50	33,888.20	481016
2023-02-08 22:00:00	33,998.90	33,712.10	34,245.20	33,586.20	34,003.70	33,713.60	34,247.40	33,588.40	471943
2023-02-09 22:00:00	33,720.10	33,864.60	33,885.90	33,504.10	33,724.90	33,868.40	33,888.10	33,507.90	506106

5540 rows × 9 columns

GER30: hourly data, May 28, 2021 to current - this is a total return index, not price return

api.get_candles('GER30', 
				period = 'H1', 
				start = '2021-05-28', 
				end = '2023-02-10')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2021-05-28 00:00:00	15,428.93	15,462.56	15,471.50	15,458.56	15,430.03	15,469.07	15,473.07	15,460.97	320
2021-05-28 01:00:00	15,462.56	15,469.50	15,475.74	15,460.56	15,469.07	15,470.80	15,477.04	15,463.47	701
2021-05-28 02:00:00	15,469.50	15,465.67	15,470.74	15,456.56	15,470.80	15,466.97	15,473.07	15,459.07	583
2021-05-28 03:00:00	15,465.67	15,470.67	15,471.83	15,464.56	15,466.97	15,471.97	15,474.07	15,466.47	521
2021-05-28 04:00:00	15,470.67	15,478.83	15,479.74	15,461.45	15,471.97	15,480.13	15,481.04	15,462.75	413
...	...	...	...	...	...	...	...	...	...
2023-02-09 17:00:00	15,513.50	15,501.60	15,534.60	15,469.50	15,515.50	15,503.60	15,536.60	15,471.50	7646
2023-02-09 18:00:00	15,501.00	15,481.50	15,509.50	15,470.00	15,503.00	15,483.50	15,511.50	15,472.00	11274
2023-02-09 19:00:00	15,481.00	15,450.50	15,487.50	15,444.50	15,483.00	15,452.50	15,489.50	15,446.50	8850
2023-02-09 20:00:00	15,450.10	15,433.80	15,450.60	15,403.50	15,452.10	15,435.30	15,452.60	15,405.50	9611
2023-02-10 00:00:00	15,396.40	15,412.40	15,415.40	15,395.40	15,400.40	15,416.40	15,419.40	15,399.40	332

8920 rows × 9 columns

---

| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

---

Historical Prices for Commodities
- Oil, Gold, Silver, Copper, Wheat, Corn - FXCM is the best place for commodities information

US Oil: daily, max results

api.get_candles('USOil', 
				period = 'D1', 
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2001-09-10 00:00:00	27.95	27.63	28.00	27.57	27.95	27.63	28.00	27.57	0
2001-09-11 00:00:00	27.65	27.77	27.77	27.53	27.65	27.77	27.77	27.53	0
2001-09-17 00:00:00	29.00	28.81	29.25	28.30	29.00	28.81	29.25	28.30	0
2001-09-18 00:00:00	28.80	27.70	28.86	27.60	28.80	27.70	28.86	27.60	0
2001-09-19 00:00:00	27.67	26.72	27.95	25.80	27.67	26.72	27.95	25.80	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	73.22	74.42	74.50	72.23	73.26	74.45	74.54	72.27	132901
2023-02-06 22:00:00	74.53	77.57	77.57	74.33	74.59	77.60	77.61	74.37	140385
2023-02-07 22:00:00	77.45	78.47	78.55	77.07	77.51	78.50	78.59	77.11	118053
2023-02-08 22:00:00	78.42	77.64	78.83	76.49	78.46	77.67	78.86	76.53	113971
2023-02-09 22:00:00	77.66	79.81	80.31	77.45	77.70	79.84	80.35	77.49	133209

5525 rows × 9 columns

Gold in USD: daily data, max results

api.get_candles('XAU/USD', 
				period = 'D1',
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2001-09-09 21:00:00	273.25	271.75	273.60	270.95	273.25	271.75	273.60	270.95	0
2001-09-10 21:00:00	271.75	286.25	292.50	271.30	271.75	286.25	292.50	271.30	0
2001-09-11 21:00:00	286.75	278.25	292.50	276.75	286.75	278.25	292.50	276.75	0
2001-09-12 21:00:00	278.25	281.25	282.00	278.00	278.25	281.25	282.00	278.00	0
2001-09-13 21:00:00	281.25	285.55	290.00	279.00	281.25	285.55	290.00	279.00	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	1,860.83	1,867.24	1,881.23	1,860.36	1,861.89	1,867.54	1,881.55	1,861.36	439702
2023-02-06 22:00:00	1,866.84	1,871.79	1,884.31	1,864.85	1,868.56	1,872.09	1,884.68	1,865.22	484418
2023-02-07 22:00:00	1,872.39	1,875.35	1,886.23	1,868.91	1,873.94	1,875.65	1,886.59	1,869.28	359629
2023-02-08 22:00:00	1,875.36	1,861.70	1,890.17	1,858.78	1,875.98	1,862.00	1,890.53	1,859.11	375300
2023-02-09 22:00:00	1,861.18	1,865.26	1,872.07	1,852.59	1,862.72	1,865.91	1,872.45	1,852.98	409643

5560 rows × 9 columns

Siver in USD: daily data, max results

api.get_candles('XAG/USD', 
				period = 'D1',
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
1950-02-27 21:00:00	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0
1950-03-30 21:00:00	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0
1950-04-27 21:00:00	0.72	0.72	0.72	0.72	0.72	0.72	0.72	0.72	0
1950-05-30 21:00:00	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0
1950-09-28 21:00:00	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0
...	...	...	...	...	...	...	...	...	...
2023-02-05 22:00:00	22.22	22.07	22.60	22.07	22.27	22.34	22.63	22.16	126839
2023-02-06 22:00:00	22.05	22.16	22.44	22.03	22.36	22.20	22.48	22.07	155153
2023-02-07 22:00:00	22.11	22.27	22.52	22.11	22.47	22.37	22.55	22.20	108466
2023-02-08 22:00:00	22.05	21.96	22.58	21.89	22.38	22.00	22.61	21.93	118493
2023-02-09 22:00:00	21.94	21.98	22.29	21.64	22.06	22.04	22.33	21.85	134826

5601 rows × 9 columns

Natural Gas: hourly data, max results

api.get_candles('NGAS', 
				period = 'H1',
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2021-06-04 12:00:00	3.06	3.04	3.07	3.04	3.07	3.06	3.08	3.06	1214
2021-06-04 13:00:00	3.04	3.04	3.06	3.03	3.06	3.05	3.07	3.04	1464
2021-06-04 14:00:00	3.04	3.04	3.05	3.02	3.05	3.05	3.06	3.03	984
2021-06-04 15:00:00	3.04	3.04	3.05	3.03	3.05	3.05	3.06	3.04	784
2021-06-04 16:00:00	3.04	3.10	3.11	3.04	3.05	3.12	3.13	3.05	2243
...	...	...	...	...	...	...	...	...	...
2023-02-10 17:00:00	2.50	2.51	2.55	2.49	2.51	2.52	2.56	2.50	2761
2023-02-10 18:00:00	2.51	2.48	2.52	2.48	2.52	2.49	2.53	2.49	1673
2023-02-10 19:00:00	2.48	2.54	2.54	2.47	2.49	2.55	2.56	2.48	2900
2023-02-10 20:00:00	2.54	2.57	2.58	2.54	2.55	2.58	2.59	2.55	1342
2023-02-10 21:00:00	2.57	2.57	2.58	2.55	2.58	2.58	2.59	2.56	640

10000 rows × 9 columns

Soy Futures: hourly data, max results

api.get_candles('SOYF', 
				period = 'H1',
				number = 10000)

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2020-12-30 14:00:00	1,289.12	1,292.39	1,294.68	1,286.11	1,290.05	1,293.50	1,295.89	1,287.24	12147
2020-12-30 15:00:00	1,292.39	1,295.60	1,297.93	1,289.84	1,293.50	1,296.33	1,299.09	1,290.87	13485
2020-12-30 16:00:00	1,295.60	1,297.48	1,304.25	1,294.06	1,296.33	1,298.20	1,305.39	1,295.22	13203
2020-12-30 17:00:00	1,297.48	1,293.25	1,297.95	1,289.76	1,298.20	1,293.99	1,298.97	1,290.86	9320
2020-12-30 18:00:00	1,293.25	1,303.45	1,303.93	1,292.36	1,293.99	1,304.42	1,304.89	1,293.53	8445
...	...	...	...	...	...	...	...	...	...
2023-02-10 15:00:00	1,520.26	1,529.11	1,530.26	1,516.99	1,521.52	1,530.37	1,531.52	1,518.25	5645
2023-02-10 16:00:00	1,529.00	1,521.86	1,529.51	1,519.49	1,530.26	1,523.12	1,530.77	1,520.75	4305
2023-02-10 17:00:00	1,521.88	1,528.88	1,529.26	1,521.01	1,523.14	1,530.14	1,530.52	1,522.27	3010
2023-02-10 18:00:00	1,529.01	1,531.26	1,532.26	1,527.24	1,530.27	1,532.52	1,533.52	1,528.50	2371
2023-02-10 19:00:00	1,531.12	1,532.76	1,533.26	1,530.99	1,532.38	1,534.02	1,534.52	1,532.25	1063

10000 rows × 9 columns

---

| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

---

Historical Prices for Cryptocurrencies
- they used to have Ethereum and a few others, but those no longer work

Bitcoin: daily data, start and end dates

api.get_candles('BTC/USD', 
				period = 'D1', 
				start = '2002-01-01', 
				end = '2023-02-10')

	bidopen	bidclose	bidhigh	bidlow	askopen	askclose	askhigh	asklow	tickqty
date
2017-12-15 22:00:00	19,900.00	19,900.00	19,900.00	19,900.00	19,901.00	19,901.00	19,901.00	19,901.00	0
2017-12-18 22:00:00	19,900.00	19,705.00	20,040.00	19,300.00	19,901.00	19,706.00	20,041.00	19,301.00	0
2017-12-19 22:00:00	19,705.00	18,555.00	19,340.00	19,310.00	19,706.00	18,556.00	19,341.00	19,311.00	0
2017-12-20 22:00:00	18,555.00	17,275.00	17,300.00	17,275.00	18,556.00	17,276.00	17,301.00	17,276.00	0
2017-12-21 22:00:00	17,275.00	15,680.00	16,330.00	15,915.00	17,276.00	15,681.00	16,331.00	15,916.00	0
...	...	...	...	...	...	...	...	...	...
2023-02-06 22:00:00	22,975.30	22,893.80	23,133.50	22,621.50	23,015.30	22,933.80	23,173.50	22,661.50	50536
2023-02-07 22:00:00	22,894.30	23,177.80	23,330.50	22,621.50	22,934.30	23,217.80	23,370.50	22,661.50	68856
2023-02-08 22:00:00	23,178.30	22,939.40	23,430.50	22,654.50	23,218.30	22,979.40	23,470.50	22,690.00	59518
2023-02-09 22:00:00	22,939.00	21,839.30	22,992.20	21,678.20	22,979.00	21,879.30	23,032.20	21,718.20	83253
2023-02-10 22:00:00	21,839.80	21,530.10	21,939.10	21,458.40	21,879.80	21,564.10	21,979.10	21,498.40	67891

1345 rows × 9 columns

---

| Top | Historical Data | Intraday Frequency | Defining Time Period | Stock Indexes | Commodities Cryptocurrencies Real-Time Data

---

High Frequency Real-Time Data
- FXCM supports high frequency real-time data for some instruments - this is something that usually costs a great deal for a subscription to access

api.subscribe_market_data()

help(api.subscribe_market_data)

Help on method subscribe_market_data in module fxcmpy.fxcmpy:

subscribe_market_data(symbol='', add_callbacks=()) method of fxcmpy.fxcmpy.fxcmpy instance
    Stream the prices of an instrument.
    
    Arguments:
    
    symbol:  string,
        the symbol of the instrument in question as given by
        get_instruments().
    
    add_callbacks: list of callables,
        all methods in that list will be called for every incoming dataset
        of the instrument. Such a method has to accept two positional
        arguments, data and dataframe, say. The first should be a json like
        object with the new price data received by the stream and the
        second should be a Pandas DataFrame with the collected price data
        as given by get_prices().

Subscribe to EUR/USD exchange data

api.subscribe_market_data('EUR/USD')

api.get_subscribed_symbols() - get the current open subscriptions

help(api.get_subscribed_symbols)

Help on method get_subscribed_symbols in module fxcmpy.fxcmpy:

get_subscribed_symbols() method of fxcmpy.fxcmpy.fxcmpy instance
    Returns a list of symbols for the subscribed instruments.

api.get_subscribed_symbols()

['EUR/USD']

api.get_last_price() - get the last reported price for a symbol

help(api.get_last_price)

Help on method get_last_price in module fxcmpy.fxcmpy:

get_last_price(symbol) method of fxcmpy.fxcmpy.fxcmpy instance
    Return the last prices of a given subscribed instrument.
    
    Arguments:
    
    symbol: string,
        the symbol of the instrument as given by get_instruments().

pretty('The most recent price data for EUR/USD')
api.get_last_price('EUR/USD')

The most recent price data for EUR/USD

Bid    1.07
Ask    1.07
High   1.08
Low    1.07
Name: 2023-02-10 21:57:59.544000, dtype: float64

api.get_prices() - get all available data for a subscription since the subscription was started

help(api.get_prices)

Help on method get_prices in module fxcmpy.fxcmpy:

get_prices(symbol) method of fxcmpy.fxcmpy.fxcmpy instance
    Return the prices of a given subscribed instrument.
    
    Arguments:
    
    symbol: string,
        the symbol of the instrument as given by get_instruments().

api.get_prices('EUR/USD')

	Bid	Ask	High	Low
2023-02-10 21:57:59.544	1.07	1.07	1.08	1.07

Subscribing to Bitcoin also, because Euro is not updating

api.subscribe_market_data('BTC/USD')

api.get_last_price('EUR/USD')

Bid    1.07
Ask    1.07
High   1.08
Low    1.07
Name: 2023-02-10 21:57:59.544000, dtype: float64

api.get_prices('BTC/USD')

	Bid	Ask	High	Low
2023-02-10 21:58:30.932	21530	21564	23,470.50	21,458.25

Stream live data:

import time
count = 0
pretty('Streaming EUR/USD data:')
while count < 10:
	time.sleep(1)
	print(api.get_last_price('EUR/USD').name, api.get_last_price('EUR/USD').Ask)
	count += 1

Streaming EUR/USD data:

2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803
2023-02-10 21:57:59.544000 1.06803

pretty('time.sleep()')
help(time.sleep)

time.sleep()

Help on built-in function sleep in module time:

sleep(...)
    sleep(seconds)
    
    Delay execution for a given number of seconds.  The argument may be
    a floating point number for subsecond precision.

Function to stream the current ask price

def stream_data(ticker, delay = 1, reps = 10):
	
	api.subscribe_market_data(ticker)
	import time
	
	pretty(f'Streaming {ticker} data:')
	count = 0
	while count < reps:
		time.sleep(delay)
		
		price = api.get_last_price(ticker).Ask
		timestamp = api.get_last_price(ticker).name
		pretty(f"{price:,.3f}", timestamp, fontsize = 3)
		
		count += 1
	api.subscribe_market_data(ticker)
	pretty(f'Transmission complete.')

stream_data('EUR/USD', delay = 1, reps = 3)

Streaming EUR/USD data:

2023-02-10 21:57:59.544000
1.068

2023-02-10 21:57:59.544000
1.068

2023-02-10 21:57:59.544000
1.068

Transmission complete.

stream_data('BTC/USD', delay = 1, reps = 3)

Streaming BTC/USD data:

2023-02-10 21:58:30.932000
21,564.000

2023-02-10 21:58:30.932000
21,564.000

2023-02-10 21:58:30.932000
21,564.000

Transmission complete.

Unsubscribe from market data

api.get_subscribed_symbols()

['EUR/USD', 'BTC/USD']

stream_data('LCattleF', delay = 1, reps = 3)

Streaming LCattleF data:

2023-02-10 19:04:55.811000
164.110

2023-02-10 19:04:55.811000
164.110

2023-02-10 19:04:55.811000
164.110

Transmission complete.

api.unsubscribe_market_data('EUR/USD')
api.unsubscribe_market_data('BTC/USD')

api.close()

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