Corporate Actions#

Adjusting prices for corporate actions#

We can use built-in functions to adjust price and volume for corporate actions.

We follow an example of a 1:3 split for WMT on Feb 26, 2024. First let’s review the unadjusted data: there appears to be a price jump.

import onetick.py as otp
import matplotlib

data = otp.DataSource('US_COMP_SAMPLE_DAILY', tick_type='DAY')
data = data[['CLOSE', 'VOLUME', 'EXCHANGE']]
data = data.where(data['EXCHANGE'] == '')
result = otp.run(data,
                 start=otp.dt(2024, 2, 10),
                 end=otp.dt(2024, 3, 10),
                 symbols='WMT')
result.plot(x='Time', y='CLOSE')

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<AxesSubplot: xlabel='Time'>

../../_images/8b8a8021965c60a6b169646724864dd8aad48de6fa2ba70bae845fe99df18862.png

Adding adjustment for corporate actions fixes this. The prices are adjusted to the level before or after the split depending on the value of the adjustement_date parameter. The next example illustrates this:

data = otp.DataSource('US_COMP_SAMPLE_DAILY', tick_type='DAY')
data = data[['CLOSE', 'VOLUME', 'EXCHANGE']]
data = data.where(data['EXCHANGE'] == '')
data['ORG_CLOSE'] = data['CLOSE']
data = data.corp_actions(fields='CLOSE', adjust_rule='PRICE', apply_split=True)
result = otp.run(data,
                 start=otp.dt(2024, 2, 10),
                 end=otp.dt(2024, 3, 10),
                 timezone='America/New_York',
                 symbols='WMT',
                 symbol_date=otp.dt.now())

result.plot(x='Time', y='CLOSE')

<AxesSubplot: xlabel='Time'>

../../_images/5a46ef00391f610f54629020cb273701a30b0a058825c1eb34c32cbda87f5ea8.png

Adjustment can be applied to sizes in a similar way:

data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
data = data[['CLOSE', 'VOLUME', 'EXCHANGE']]
data = data.where(data['EXCHANGE'] == '')
result = otp.run(data,
                 start=otp.dt(2024, 2, 10),
                 end=otp.dt(2024, 3, 10),
                 timezone='America/New_York',
                 symbols='WMT',
                 symbol_date=otp.dt.now())
result.plot(x='Time', y='VOLUME')

<AxesSubplot: xlabel='Time'>

../../_images/e56f668b91c147ca03cd1d4a190716ef3e227629dca3924368134be142ca55c9.png

data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
data = data[['CLOSE', 'VOLUME', 'EXCHANGE']]
data = data.where(data['EXCHANGE'] == '')
data['ORG_VOLUME'] = data['VOLUME']
data = data.corp_actions(fields='VOLUME', adjust_rule='SIZE', apply_split=True)
result = otp.run(data,
                 start=otp.dt(2024, 2, 10),
                 end=otp.dt(2024, 3, 10),
                 timezone='America/New_York',
                 symbols='WMT',
                 symbol_date=otp.dt.now())
result.plot(x='Time', y='VOLUME')

<AxesSubplot: xlabel='Time'>

../../_images/bb488c0150ebf378c2d425f2069d4d35c1e547943693b550e8ecfbef669edbf2.png

Retrieving Corporate actions for a symbol#

We can retrieve all corporate actions for the symbols of interest:

db = otp.databases()['US_COMP_SAMPLE_DAILY']
db.ref_data(
    ref_data_type='corp_actions',
    start=otp.dt(2024, 2, 10),
    end=otp.dt(2024, 3, 10),
    timezone='America/New_York',
    symbol='WMT',
    symbol_date=otp.dt.now(),
)

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	Time	MULTIPLICATIVE_ADJUSTMENT	ADDITIVE_ADJUSTMENT	ADJUSTMENT_TYPE
0	2024-02-26	0.333333	0.0	SPLIT