# Earnings Events Analysis
This section contains 6 examples for Events using the `onetick-py`.
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Each example is a self-contained script that can be run against the OneTick Cloud sample databases.
```default
# onetick-py WebAPI configuration for OneTick Cloud
import os
os.environ['OTP_WEBAPI'] = '1'
os.environ['OTP_HTTP_ADDRESS'] = 'https://rest.cloud.onetick.com'
os.environ['OTP_ACCESS_TOKEN_URL'] = 'https://cloud-auth.parent.onetick.com/realms/OMD/protocol/openid-connect/token'
os.environ['OTP_CLIENT_ID'] = '__FILL_IN__'
os.environ['OTP_CLIENT_SECRET'] = '__FILL_IN__'
```
Earnings announcements are recorded in the `EVENT` tick type
and can be combined with market data to analyze market reactions and trading patterns around these events.
## Event Data Sources
Earnings announcement events are available in the daily market data databases:
* `US_COMP_DAILY` / `EVENT` - US earnings events
## Event Types
The EVENT tick type records two primary event types, held in the `EVENT_TYPE` field:
* `EARNING_DATE` - Earnings announcement dates
* `COMPANY_CONFERENCE_CALL` - Scheduled conference call dates
These events are useful for analyzing market behavior around significant corporate announcements.
## Earnings Event History for Specified Symbol
Retrieve all earnings events recorded for a specific symbol within a time range.
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This shows all historical earnings announcements for a company.
```ipython3
import onetick.py as otp
data = otp.DataSource(db='US_COMP_DAILY', tick_type='EVENT')
result = otp.run(data,
start=otp.dt(2026, 1, 1),
end=otp.dt(2026, 6, 12),
timezone='America/New_York',
symbols='CSCO')
result
```
```myst-ansi
Time EVENT_TYPE EVENT_CONDITION OMDSEQ
0 2026-02-11 16:05:00 EARNING_DATE 2
1 2026-02-11 16:30:00 COMPANY_CONFERENCE_CALL 6
2 2026-05-13 16:05:00 EARNING_DATE 4
3 2026-05-13 16:30:00 COMPANY_CONFERENCE_CALL 11
```
## Earnings Event History for all US Companies across a time range
Retrieve all earnings events across every symbol in the database within a specified time range,
by merging across [`otp.Symbols`](https://docs.pip.distribution.sol.onetick.com/api/sources/symbols.html.md#onetick.py.Symbols), the equivalent of SQL's `SYMBOL_NAME LIKE '%'`.
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This provides a comprehensive view of earnings announcements during a particular period.
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`identify_input_ts` adds the `SYMBOL_NAME` of each tick to the output.
```ipython3
import onetick.py as otp
data = otp.DataSource(db='US_COMP_DAILY', tick_type='EVENT')
# Merge the events for every symbol in the database into a single stream.
# identify_input_ts adds the SYMBOL_NAME of each tick to the output.
data = otp.merge([data],
symbols=otp.Symbols(db='US_COMP_DAILY', for_tick_type='EVENT'),
identify_input_ts=True)
result = otp.run(data,
start=otp.dt(2024, 1, 1),
end=otp.dt(2024, 1, 5),
timezone='America/New_York')
result
```
```myst-ansi
Time EVENT_CONDITION EVENT_TYPE OMDSEQ \
0 2024-01-03 08:00:00 EARNING_DATE 0
1 2024-01-03 09:00:00 COMPANY_CONFERENCE_CALL 0
2 2024-01-03 16:05:00 EARNING_DATE 0
3 2024-01-03 16:05:00 EARNING_DATE 1
4 2024-01-03 16:06:00 EARNING_DATE 0
.. ... ... ... ...
17 2024-01-04 16:03:00 EARNING_DATE 0
18 2024-01-04 16:05:00 EARNING_DATE 0
19 2024-01-04 17:00:00 COMPANY_CONFERENCE_CALL 0
20 2024-01-04 17:00:00 COMPANY_CONFERENCE_CALL 1
21 2024-01-04 19:00:00 EARNING_DATE 0
SYMBOL_NAME TICK_TYPE
0 UNF EVENT
1 UNF EVENT
2 RGP EVENT
3 CALM EVENT
4 SLP EVENT
.. ... ...
17 FC EVENT
18 KRUS EVENT
19 KRUS EVENT
20 FC EVENT
21 STZ EVENT
[22 rows x 6 columns]
```
## Joining Daily Pricing to US Earning Events
Correlate daily pricing data with earnings events to analyze closing prices and volume
on earnings announcement dates.
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Earnings events (excluding conference calls) are joined to the daily price/volume record
for the same symbol with [`otp.join`](https://docs.pip.distribution.sol.onetick.com/api/functions/join.html.md#onetick.py.join), keyed on the date.
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As closing prices and events occur at different times of day,
both sides are truncated to the day with the
[`.dt.date_trunc`](https://docs.pip.distribution.sol.onetick.com/api/operation/dt/date_trunc.html.md#onetick.py.core.column_operations.accessors.dt_accessor._DtAccessor.date_trunc)
accessor before joining.
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A left outer join keeps every daily record, with `EVENT_TYPE` populated only on event days.
```ipython3
import onetick.py as otp
# Daily pricing, filtered to the composite (empty EXCHANGE).
day = otp.DataSource(db='US_COMP_DAILY', tick_type='DAY')
day = day.where(day['EXCHANGE'] == '')
day = day[['CLOSE', 'VOLUME']]
# Earnings events only, excluding the conference calls.
event = otp.DataSource(db='US_COMP_DAILY', tick_type='EVENT',
schema_policy='manual', schema={'EVENT_TYPE': str})
event = event.where(event['EVENT_TYPE'] == 'EARNING_DATE')
event = event[['EVENT_TYPE']]
# Truncate both sides to the day so the differing intraday times still match.
day['DAY_KEY'] = day['TIMESTAMP'].dt.date_trunc('day')
event['DAY_KEY'] = event['TIMESTAMP'].dt.date_trunc('day')
# Left outer join keeps every daily record, with EVENT_TYPE populated only on event days.
data = otp.join(day, event, on=day['DAY_KEY'] == event['DAY_KEY'], how='left_outer')
data = data[['CLOSE', 'VOLUME', 'EVENT_TYPE']]
result = otp.run(data,
start=otp.dt(2024, 1, 1),
end=otp.dt(2024, 4, 1),
timezone='America/New_York',
symbols='CSCO')
result
```
```myst-ansi
Time CLOSE VOLUME EVENT_TYPE
0 2024-01-02 20:15:00 50.51 20242939
1 2024-01-03 20:15:00 50.51 20303875
2 2024-01-04 20:15:00 50.08 18134121
3 2024-01-05 20:15:00 50.09 13989287
4 2024-01-08 20:15:00 50.46 18070293
.. ... ... ... ...
56 2024-03-22 20:15:00 49.78 15022861
57 2024-03-25 20:15:00 49.68 16191164
58 2024-03-26 20:15:00 49.55 13842923
59 2024-03-27 20:15:00 49.77 17230958
60 2024-03-28 20:15:00 49.91 18139735
[61 rows x 4 columns]
```
## Combining or Unioning Daily Pricing and US Earnings Events
Union daily price data with earnings events, creating a combined dataset that shows both daily price bars and
discrete event occurrences, using [`otp.merge`](https://docs.pip.distribution.sol.onetick.com/api/functions/merge.html.md#onetick.py.merge).
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This is useful for time series visualization and analysis spanning both continuous pricing and discrete events.
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The output schema is standardized across both branches:
setting `CLOSE` as [`otp.nan`](https://docs.pip.distribution.sol.onetick.com/api/types/nan.html.md#onetick.py.nan) and `VOLUME` as 0 for the event rows,
and `EVENT_TYPE` as an empty [`otp.string[40]`](https://docs.pip.distribution.sol.onetick.com/api/types/string.html.md#onetick.py.string) for the daily pricing rows.
```ipython3
import onetick.py as otp
# Daily pricing, filtered to the composite (empty EXCHANGE).
day = otp.DataSource(db='US_COMP_DAILY', tick_type='DAY')
day = day.where(day['EXCHANGE'] == '')
day = day[['CLOSE', 'VOLUME']]
day['EVENT'] = 0
day['EVENT_TYPE'] = otp.string[40]('')
# Earnings events only, shaped to the same schema as the daily pricing.
event = otp.DataSource(db='US_COMP_DAILY', tick_type='EVENT',
schema_policy='manual', schema={'EVENT_TYPE': otp.string[40]})
event = event.where(event['EVENT_TYPE'] == 'EARNING_DATE')
event['CLOSE'] = otp.nan
event['VOLUME'] = 0
event['EVENT'] = 1
event = event[['CLOSE', 'VOLUME', 'EVENT', 'EVENT_TYPE']]
# Union the two streams.
data = otp.merge([day, event])
result = otp.run(data,
start=otp.dt(2024, 1, 1),
end=otp.dt(2024, 4, 1),
timezone='America/New_York',
symbols='CSCO')
result
```
```myst-ansi
Time CLOSE VOLUME EVENT EVENT_TYPE
0 2024-01-02 20:15:00 50.51 20242939 0
1 2024-01-03 20:15:00 50.51 20303875 0
2 2024-01-04 20:15:00 50.08 18134121 0
3 2024-01-05 20:15:00 50.09 13989287 0
4 2024-01-08 20:15:00 50.46 18070293 0
.. ... ... ... ... ...
57 2024-03-22 20:15:00 49.78 15022861 0
58 2024-03-25 20:15:00 49.68 16191164 0
59 2024-03-26 20:15:00 49.55 13842923 0
60 2024-03-27 20:15:00 49.77 17230958 0
61 2024-03-28 20:15:00 49.91 18139735 0
[62 rows x 5 columns]
```
## Combining or Unioning 1 Minute Trade Bars and US Earnings Events
Merge pre-calculated 1 minute trade bar data with earnings events on the same day,
allowing analysis of intraday price movement patterns around earnings announcements,
using [`otp.merge`](https://docs.pip.distribution.sol.onetick.com/api/functions/merge.html.md#onetick.py.merge).
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The output schema is standardized across both branches:
setting `LAST` as [`otp.nan`](https://docs.pip.distribution.sol.onetick.com/api/types/nan.html.md#onetick.py.nan) and `VOLUME` as 0 for the event rows,
and `EVENT_TYPE` as an empty [`otp.string[40]`](https://docs.pip.distribution.sol.onetick.com/api/types/string.html.md#onetick.py.string) for the bar rows.
```ipython3
import onetick.py as otp
# Pre-calculated 1 minute trade bars.
bars = otp.DataSource(db='US_COMP_BARS', tick_type='TRD_1M')
bars = bars[['LAST', 'VOLUME']]
bars['EVENT'] = 0
bars['EVENT_TYPE'] = otp.string[40]('')
# Earnings events only, shaped to the same schema as the bars.
event = otp.DataSource(db='US_COMP_DAILY', tick_type='EVENT',
schema_policy='manual', schema={'EVENT_TYPE': otp.string[40]})
event = event.where(event['EVENT_TYPE'] == 'EARNING_DATE')
event['LAST'] = otp.nan
event['VOLUME'] = 0
event['EVENT'] = 1
event = event[['LAST', 'VOLUME', 'EVENT', 'EVENT_TYPE']]
# Union the two streams.
data = otp.merge([bars, event])
result = otp.run(data,
start=otp.dt(2024, 2, 14),
end=otp.dt(2024, 2, 15),
timezone='America/New_York',
symbols='CSCO')
result
```
```myst-ansi
Time LAST VOLUME EVENT EVENT_TYPE
0 2024-02-14 05:08:00 49.74 400 0
1 2024-02-14 05:09:00 49.74 0 0
2 2024-02-14 05:10:00 49.74 0 0
3 2024-02-14 05:11:00 49.74 0 0
4 2024-02-14 05:12:00 49.74 0 0
.. ... ... ... ... ...
894 2024-02-14 20:01:00 47.63 0 0
895 2024-02-14 20:02:00 47.63 0 0
896 2024-02-14 20:03:00 47.63 0 0
897 2024-02-14 20:04:00 47.63 0 0
898 2024-02-14 20:05:00 47.63 0 0
[899 rows x 5 columns]
```
## Combining or Unioning Trades and US Earnings Events
Combine individual trade data with earnings events into a unified stream, creating a single data source that
captures both transaction-level activity and corporate announcements, using [`otp.merge`](https://docs.pip.distribution.sol.onetick.com/api/functions/merge.html.md#onetick.py.merge).
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The output schema is standardized across both branches:
setting `PRICE` as [`otp.nan`](https://docs.pip.distribution.sol.onetick.com/api/types/nan.html.md#onetick.py.nan) and `SIZE` as 0 for the event rows,
and `EVENT_TYPE` as an empty [`otp.string[40]`](https://docs.pip.distribution.sol.onetick.com/api/types/string.html.md#onetick.py.string) for the trade rows.
```ipython3
import onetick.py as otp
# Trades from the US Composite.
trd = otp.DataSource(db='US_COMP', tick_type='TRD')
trd = trd[['PRICE', 'SIZE']]
trd['EVENT'] = 0
trd['EVENT_TYPE'] = otp.string[40]('')
# Earnings events only, shaped to the same schema as the trades.
event = otp.DataSource(db='US_COMP_DAILY', tick_type='EVENT',
schema_policy='manual', schema={'EVENT_TYPE': otp.string[40]})
event = event.where(event['EVENT_TYPE'] == 'EARNING_DATE')
event['PRICE'] = otp.nan
event['SIZE'] = 0
event['EVENT'] = 1
event = event[['PRICE', 'SIZE', 'EVENT', 'EVENT_TYPE']]
# Union the two streams.
data = otp.merge([trd, event])
# get only first 1000 rows
data = data.limit(1000)
result = otp.run(data,
start=otp.dt(2024, 2, 14),
end=otp.dt(2024, 2, 15),
timezone='America/New_York',
symbols='CSCO')
result
```
```myst-ansi
Time PRICE SIZE EVENT EVENT_TYPE
0 2024-02-14 04:00:00.036667885 49.80 80 0
1 2024-02-14 04:00:01.123032110 49.80 78 0
2 2024-02-14 04:00:01.123032235 49.70 20 0
3 2024-02-14 04:00:01.124715858 49.70 2 0
4 2024-02-14 04:00:02.068007001 49.68 2 0
.. ... ... ... ... ...
995 2024-02-14 09:30:01.429521692 49.60 100 0
996 2024-02-14 09:30:01.429530498 49.60 10 0
997 2024-02-14 09:30:01.429579520 49.60 50 0
998 2024-02-14 09:30:01.429707081 49.60 100 0
999 2024-02-14 09:30:01.429708760 49.60 17 0
[1000 rows x 5 columns]
```