ETFs

Learn more: Example notebooks

Environment

Setting up your environment takes three steps:

• Import the relevant internal and external libraries

• Configure the environment parameters

• Initialise the environment

import sigtech.framework as sig
from sigtech.framework.instruments.equities import ExchangeTradedFund

import pandas as pd
import seaborn as sns

sns.set(rc={'figure.figsize': (18, 6)})
sig.config.init();

Learn more: Setting up the environment

ETF instrument

We can list available ETFs in a similar way to indices, by calling get_names on the relevant class. In this case, it is done on the ExchangeTradedFund class:

Input:

etf_names = ExchangeTradedFund.get_names()
etf_names[:5]

Output:

['AGG UP EQUITY',
 'BIL UP EQUITY',
 'BND UP EQUITY',
 'BNDX UP EQUITY',
 'DBC UP EQUITY']

You can retrieve an individual ETF through the common object API:

Input:

agg = sig.obj.get('AGG UP EQUITY')
agg

Output:

AGG UP EQUITY <class 'sigtech.framework.instruments.equities.ExchangeTradedFund'>[139953121183952]

Another way to list instruments is by retrieving an instrument’s group directly:

Input:

agg.group().query_instrument_names()[:5]

Output:

['AGG UP EQUITY',
 'BIL UP EQUITY',
 'BND UP EQUITY',
 'BNDX UP EQUITY',
 'DBC UP EQUITY']

It is also possible to access an instrument’s exchange as an object in its own right:

Input:

exchange = sig.obj.get(agg.exchange().name)
exchange

Output:

NYSEARC(T) EXCHANGE GROUP <class 'sigtech.framework.instruments.base.Exchange'>[139953149826448]

From an exchange you can access market holiday calendar names, for example:

Input:

exchange.holidays

Output:

'NYSEARC(T) CALENDAR'

Calendars are objects that you can fetch and interrogate:

Input:

calendar = sig.obj.get(exchange.holidays)
calendar

Output:

NYSEARC(T) CALENDAR <class 'sigtech.framework.infra.cal.calendar.Calendar'>[139953149818576]

Input:

calendar.holidays[:5]

Output:

[datetime.date(1973, 1, 1),
 datetime.date(1973, 1, 25),
 datetime.date(1973, 1, 25),
 datetime.date(1973, 2, 19),
 datetime.date(1973, 4, 20)]

To query the time series data from an ETF instrument, use the following API:

Python:

agg.history()

Output:

2003-09-29    102.17
2003-09-30    102.70
2003-10-01    102.65
2003-10-02    102.49
2003-10-03    101.75
               ...
2021-02-23    115.51
2021-02-24    115.47
2021-02-25    114.41
2021-02-26    115.34
2021-03-01    115.09
Name: (LastPrice, EOD, AGG UP EQUITY), Length: 4385, dtype: float64

Other time series related to the specific instrument are available as well:

Input:

agg.history_fields

Output:

['OPEN', 'HIGH', 'LOW', 'LastPrice', 'Volume']

By passing in one of the different history_fields values as a parameter in the .history(), different times series can be queried:

df_agg = pd.concat({
    'High': agg.history('HIGH').loc['2020':],
    'Low': agg.history('LOW').loc['2020':]
}, axis=1)
df_agg.plot(title='AGG UP Equity High / Low');

volume_agg = agg.history('Volume').loc['2021':]
volume_agg.index = [x.date() for x in volume_agg.index]
volume_agg.plot(title='AGG UP EQUITY Volume', kind='bar');

Apart from the time series data, a specific instrument also holds static data:

Input:

agg.data_dict()

Output:

{'instrument_id': None,
 'data_source_all': [],
 'available_data_points': ['EOD'],
 'default_data_point': 'EOD',
 'price_factor': 1.0,
 'use_price_factor': True,
 'intraday_times': [],
 'intraday_tz_str': 'UTC',
 'currency': 'USD',
 'db_ticker': None,
 'db_sector': None,
 'exchange_code': None,
 'activity_fields': ['Volume'],
 'group_name': None,
 'description': None,
 'db_history_end_date': datetime.date(9999, 12, 31),
 'market_impact': 0.02,
 'issuer': 'NYSEARC(T)',
 'liquid_date': Timestamp('1677-09-21 00:12:43.145225'),
 'delist_date': datetime.date(9999, 12, 31),
 'eq_identifier': 'AGG UP',
 'company_name': 'iShares Core U.S. Aggregate Bond ETF',
 'country': 'US',
 'currency_multiplier': 1,
 'isin': 'US4642872265',
 'cusip': '464287226',
 'us_ric': 'AGG.P',
 'primary_bbg_ticker': 'AGG UP Equity',
 'composite_bbg_ticker': 'AGG US Equity',
 'bbg_unique_id': 'EQ0000000000737315',
 'primary_us_bbg_ticker': 'AGG UP Equity',
 'composite_us_bbg_ticker': 'AGG US Equity',
 'sec_code': '',
 'region_code': '1',
 'corporate_actions_override': None,
 'sec_master_id': None,
 'fungible_id': None,
 'company_id': None,
 'underlying_index_ticker': 'LBUSTRUU Index',
 'total_expenses': 0.05,
 'prime_history_field': 'LastPrice'}

To compare several ETFs, the following example shows that the platform is highly compatible with, and makes use of, the Python Pandas library:

ptf_etfs = ['SPY UP EQUITY', 'QQQ UP EQUITY', 'VEA UP EQUITY',
            'AGG UP EQUITY', 'VWO UP EQUITY']
etf_histories = pd.concat({
    x: sig.obj.get(x).history() for x in ptf_etfs
}, axis=1).dropna()
etf_histories.columns = [x.split('_')[0] for x in etf_histories.columns]
etf_histories.tail()

(etf_histories / etf_histories.iloc[0]).plot(title='ETF Performance');