Code and Data | Academic

Slide Template

Fri, 01 Nov 2024 00:00:00 +0000

This is a Slide Template that I built using Georgia Tech Theme.

Overleaf Link: https://www.overleaf.com/read/dkwrmfbvkfqw#ba70e9
Google Drive Share (Zip File Download): https://drive.google.com/file/d/1a8v-MRjtBYyiVMaYIAeGQvl4QsFOJWeo/view?usp=sharing

Monetary Policy Shock Data Replication

Fri, 12 Jul 2024 00:00:00 +0000

Here I replicated two Monetary Shock/Surprise paper and enlarged their data to the end of 2023.

Kenneth N Kuttner.2001.“Monetary policy surprises and interest rates: Evidence from the Fed funds futures market”.Journal of Monetary Economics,47(3):523-544.https://doi.org/https://doi.org/10.1016/S0304-3932(01)00055-1
- The data from author’s website ends at 2019, I enlarged them to the end of year 2023. Due to the data limitation, there are several dates with some small error, but for the most of them, the results are accurate.
- The link to the replicated data (2012 to 2023): https://drive.google.com/file/d/10sQQwf8g5EtOwO6MrfoQckzJD2wcpCGU/view?usp=drive_link

Personal Package Introduction

Fri, 17 May 2024 00:00:00 +0000

This is the personal package I built for academic research convenience. I will update it regularly and add more useful functions. You can download it through pip install if you think it is helpful.

How to Setup
Functions
1. File Operation
2. Regression Result Convert

How to Setup

Please ignore the version number shown in the featured figure and use the following code to set it up.

pip install yiyangzhang0201
import yiyangzhang0201

Functions

File Operation

This part contains the functions related to file operations.

from yiyangzhang0201.fileop import *
get_subfolders(parent_path)
get_files(folder_names)
create_new_folder(path)
copy_file(file_path, new_fold)

The functions included are:

Get subfolders under a folder and return a list containing all the subfolders.
Get all the files under a folder and return a list containing all the files.
Create a new folder if it does not exist.
Copy a file to the given folder; if the folder does not exist, create it.

Regression Result Convert

This part contains the functions related to converting regression results from Python/Stata to an academic table form.

Stata to Latex

First, we need to use the pystata package to link the python to STATA. This function needs to be run in Notebook Environment.

!pip install pystata
import stata_setup
stata_setup.config("C:/Program Files/Stata18/", "mp")

 ___ ____ ____ ____ ____ ®
/__ / ____/ / ____/ 18.0
___/ / /___/ / /___/ MP—Parallel Edition
Statistics and Data Science Copyright 1985-2023 StataCorp LLC
StataCorp
4905 Lakeway Drive
College Station, Texas 77845 USA
800-STATA-PC https://www.stata.com
979-696-4600 stata@stata.com
Stata license: Unlimited-user 2-core network, expiring 8 Aug 2024
Serial number: 501809315448
Licensed to: Scheller College of Business
GT
Notes:
1. Unicode is supported; see help unicode_advice.
2. More than 2 billion observations are allowed; see help obs_advice.
3. Maximum number of variables is set to 5,000 but can be increased;
see help set_maxvar.

A successful link should show the above result in the notebook. You must edit your path and version to suit your STATA on the machine.

I will use following data as an example:

%%stata
sysuse auto, clear

code = ["""
reg mpg price
""",
"""
reg mpg price weight
""",
"""
reg mpg price i.foreign
""",
"""
reg mpg price weight i.foreign i.trunk
"""]
from yiyangzhang0201.regcov import stata_result_latex
stata_result_latex(code, fe_list = [[],[],['Foreign'], ['Foreign', 'Trunk']], title = 'Function Test')

The function stata_result_latex will generate:

Original STATA outputs in the cell output.
A file named “results.tex” contains the code for the academic format latex table.
Note that the code is a list containing all the regression functions you want to run. And the fe_list is a list that each element of it is also a list, which contains the fixed effect for the corresponding function.
The length of the code should match the length of fe_list.

Because the original STATA output is too long, I won’t show it here, I will just show the latex table generated, which is shown in the following picture.

Bloomberg API Usage

Thu, 16 May 2024 00:00:00 +0000

In building this Bloomberg Api Sample File, I refered to multiple resourses from XBBG example, Stack Overflows and Bloomberg Official API document. I updated all the package info and codes to the newest version. I hope this sample file can be helpful.

1 Prepare
1.1 Prepare Terminal Device
1.2 Prepare API environment
1.2.1 Easier to use package: XBBG
1.2.2 Raw API
2 Data Collection Examples
2.2 Bloomberg Data Point
2.3 Bloomberg Data Set
2.3.2 Dividend Data
2.3.3 Earning Data
2.4 Bloomberg Historical Data
2.4.2 Stock Data
2.4.3 Option Data
2.5 Bloomberg Intraday Bars
2.5.2 Stock Data
2.5.3 Option Data
2.6 Bloomberg Intraday Tick Data
2.6.2 Stock Tick Level Data
2.6.3 Option Tick Level Data
2.6.4 Get more Info of Options
3 Other Useful Piplines
3.2 Raw API
3.3 VWAP for Intraday Bar Data
3.4 Total Traded Volume for Certain Period
3.5 Subscription

Prepare

Prepare Terminal Device

This is about how to use Python API for bloomberg through google colab.

Sign to a device with bloomberg terminal.
Make sure the Jupyter is insalled on the device. If not, install anaconda or use pip install to install jupyter on the device.
After this step, open terminal or Anaconda Powershell Prompt, paste following code and run:

 jupyter notebook \
--NotebookApp.allow_origin='https://colab.research.google.com' \
--port=8888 \
--NotebookApp.port_retries=0

Note that after experiments, the way that install anaconda and run above code in Anaconda Powershell Prompt is one of the easiest ways to success. (At Scheller Trading Floor Bloomberg terminal). 4. After run above code, from the output " Or copy and paste one of these URLs:", copy and paste one of the urls shown to the colab $→$ Connect to a local runtime $→$ Backend Url

Now, we finished the basic settings to use this colab notebook to get data from bloomberg.

Prepare API environment

Easier to use package: XBBG

To set up the Bloomberg API, we need to run the following codes:

import pandas as pd
import numpy as np
!pip install blpapi --index-url=https://bcms.bloomberg.com/pip/simple/
!pip install xbbg
from xbbg import blp, pipeline

Looking in indexes: https://bcms.bloomberg.com/pip/simple/
Requirement already satisfied: blpapi in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (3.24.4)
Requirement already satisfied: xbbg in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (0.7.5a4)
Requirement already satisfied: numpy>=1.15.0 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from xbbg) (1.26.4)
Requirement already satisfied: pandas>=1.0.0 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from xbbg) (2.1.4)
Requirement already satisfied: pyarrow>=1.0.1 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from xbbg) (14.0.2)
Requirement already satisfied: pytz>=2020.4 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from xbbg) (2023.3.post1)
Requirement already satisfied: ruamel.yaml>=0.15.0 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from xbbg) (0.17.21)
Requirement already satisfied: pytest in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from xbbg) (7.4.0)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from pandas>=1.0.0->xbbg) (2.8.2)
Requirement already satisfied: tzdata>=2022.1 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from pandas>=1.0.0->xbbg) (2023.3)
Requirement already satisfied: iniconfig in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from pytest->xbbg) (1.1.1)
Requirement already satisfied: packaging in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from pytest->xbbg) (23.1)
Requirement already satisfied: pluggy<2.0,>=0.12 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from pytest->xbbg) (1.0.0)
Requirement already satisfied: colorama in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from pytest->xbbg) (0.4.6)
Requirement already satisfied: six>=1.5 in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->xbbg) (1.16.0)

from numpy import unique

Test the version of the api

blp.__version__

'0.7.5a4'

A simple test to check the connection to bloomberg terminal.

blp.bdp('AAPL US Equity', flds=['Security_Name', 'Last_Price'])

	security_name	last_price
AAPL US Equity	Apple Inc	189.72

Raw API

This cell is for raw Bloomberg API setup

!pip install blpapi
import blpapi
from datetime import datetime

Requirement already satisfied: blpapi in c:\users\yzhang3654\appdata\local\anaconda3\lib\site-packages (3.24.4)

Data Collection Examples

In this section, I will show detailed examples and explainations to different functions of bloomberg api, for more detailed information of functions, you can use help command to check the parameters of each functions.

Bloomberg Data Point

BDP (Bloomberg data point): returns data to a single cell. It contains only one ticker and one field.

It is suitable for questions like What is the last price of google?
This is similar to excel function Formula Syntax: =BDP (“Security”, “Field”)

blp.bdp('GOOG US Equity', flds=['Security_Name', 'Last_Price'])

	security_name	last_price
GOOG US Equity	Alphabet Inc	173.88

blp.bdp('AAPL US Equity', flds=['Security_Name','Last_Price'])

	security_name	last_price
AAPL US Equity	Apple Inc	189.72

blp.bdp('6758 JP Equity', flds='Crncy_Adj_Mkt_Cap', Eqy_Fund_Crncy='USD')

	crncy_adj_mkt_cap
6758 JP Equity	104655.884038

blp.bdp(tickers='AAPL US Equity', flds=['Security_Name', 'GICS_Sector_Name','PX_last', 'HISTORICAL_MARKET_CAP']).head()

	security_name	gics_sector_name	px_last	historical_market_cap
AAPL US Equity	Apple Inc	Information Technology	189.72	2630106.39528

Bloomberg Data Set

BDS (Bloomberg Data Set) This formula returns multi-cell descriptive data. This is for bulk/multi cell data.

It is suitabl for questions like Retrieve the top 20 shareholders of Google
Formula Syntax: =BDS (“Security”, “Field”, “Optional Arguments”)

holders = blp.bds('GOOG US Equity', flds='Top_20_Holders_Public_Filings', cache=True)
holders.head()

	holder_name	portfolio_name	source	amount_held	percent_outstanding	latest_change	filing_date	institution_type	country	metro_area
GOOG US Equity	Vanguard Group Inc/T	n/a	ULT-AGG	411921073.0	7.33	932245.0	2024-03-31	Investment Advisor	United States	Philadelphia
GOOG US Equity	Page Lawrence E	n/a	Form 4	390272280.0	6.95	-277760.0	2022-04-19	Unclassified	n/a	Unclassified
GOOG US Equity	Brin Sergey Mikhailo	n/a	Form 4	367312400.0	6.54	-2601500.0	2023-05-11	Unclassified	n/a	Unclassified
GOOG US Equity	BlackRock Inc	n/a	13G	356934964.0	6.35	356934964.0	2023-12-31	Investment Advisor	United States	New York City/Southern CT/Northern NJ
GOOG US Equity	State Street Corp	n/a	ULT-AGG	190762518.0	3.40	387787.0	2024-03-31	Investment Advisor	United States	Boston

We can make some edit to the data to make the data more clear.

holders = blp.bds('AMZN US Equity', flds='All_Holders_Public_Filings', cache=True)
(
holders
.loc[:, ~holders.columns.str.contains(
f'holder_id|portfolio_name|change|number|'
f'metro|percent_of_portfolio|source'
)]
.rename(
index=lambda tkr: tkr.replace(' Equity', ''),
columns={
'holder_name_': 'holder',
'position_': 'position',
'filing_date__': 'filing_dt',
'percent_outstanding': 'pct_out',
'insider_status_': 'insider',
}
)
).head()

	holder	position	filing_dt	insider	pct_out	institution_type_	country
AMZN US	Bezos Jeffrey Preston	937773965.0	2024-03-04	Y	9.01	Unclassified	n/a
AMZN US	Vanguard Group Inc/The	785811114.0	2024-03-31	N-P	7.55	Investment Advisor	United States
AMZN US	BlackRock Inc	630188686.0	2023-12-31	N-P	6.06	Investment Advisor	United States
AMZN US	State Street Corp	351606995.0	2024-03-31	N-P	3.38	Investment Advisor	United States
AMZN US	FMR LLC	325059822.0	2024-03-31	N-P	3.12	Investment Advisor	United States

Dividend Data

Dividend data is built in a seperate function.

help(blp.dividend)

Help on function dividend in module xbbg.blp:
dividend(tickers, typ='all', start_date=None, end_date=None, **kwargs) -> pandas.core.frame.DataFrame
Bloomberg dividend / split history
Args:
tickers: list of tickers
typ: dividend adjustment type
`all`: `DVD_Hist_All`
`dvd`: `DVD_Hist`
`split`: `Eqy_DVD_Hist_Splits`
`gross`: `Eqy_DVD_Hist_Gross`
`adjust`: `Eqy_DVD_Adjust_Fact`
`adj_fund`: `Eqy_DVD_Adj_Fund`
`with_amt`: `DVD_Hist_All_with_Amt_Status`
`dvd_amt`: `DVD_Hist_with_Amt_Status`
`gross_amt`: `DVD_Hist_Gross_with_Amt_Stat`
`projected`: `BDVD_Pr_Ex_Dts_DVD_Amts_w_Ann`
start_date: start date
end_date: end date
**kwargs: overrides
Returns:
pd.DataFrame

blp.dividend('SPY US Equity', start_date='2020').head()

	dec_date	ex_date	rec_date	pay_date	dvd_amt	dvd_freq	dvd_type
SPY US Equity	2024-03-14	2024-03-15	2024-03-18	2024-04-30	1.594937	Quarter	Income
SPY US Equity	2023-12-14	2023-12-15	2023-12-18	2024-01-31	1.906073	Quarter	Income
SPY US Equity	2023-09-14	2023-09-15	2023-09-18	2023-10-31	1.583169	Quarter	Income
SPY US Equity	2023-06-15	2023-06-16	2023-06-20	2023-07-31	1.638367	Quarter	Income
SPY US Equity	2023-03-16	2023-03-17	2023-03-20	2023-04-28	1.506204	Quarter	Income

Earning Data

Earning data is built in a seperate funciton.

help(blp.earning)

Help on function earning in module xbbg.blp:
earning(ticker, by='Geo', typ='Revenue', ccy=None, level=None, **kwargs) -> pandas.core.frame.DataFrame
Earning exposures by Geo or Products
Args:
ticker: ticker name
by: [G(eo), P(roduct)]
typ: type of earning, start with `PG_` in Bloomberg FLDS - default `Revenue`
`Revenue` - Revenue of the company
`Operating_Income` - Operating Income (also named as EBIT) of the company
`Assets` - Assets of the company
`Gross_Profit` - Gross profit of the company
`Capital_Expenditures` - Capital expenditures of the company
ccy: currency of earnings
level: hierarchy level of earnings
Returns:
pd.DataFrame

But we don’t have access to this data. We need to get access to them from WRDS.

Bloomberg Historical Data

help(blp.bdh)

Help on function bdh in module xbbg.blp:
bdh(tickers, flds=None, start_date=None, end_date='today', adjust=None, **kwargs) -> pandas.core.frame.DataFrame
Bloomberg historical data
Args:
tickers: ticker(s)
flds: field(s)
start_date: start date
end_date: end date - default today
adjust: `all`, `dvd`, `normal`, `abn` (=abnormal), `split`, `-` or None
exact match of above words will adjust for corresponding events
Case 0: `-` no adjustment for dividend or split
Case 1: `dvd` or `normal|abn` will adjust for all dividends except splits
Case 2: `adjust` will adjust for splits and ignore all dividends
Case 3: `all` == `dvd|split` == adjust for all
Case 4: None == Bloomberg default OR use kwargs
**kwargs: overrides
Returns:
pd.DataFrame

Stock Data

blp.bdh(
tickers='SHCOMP Index', flds=['high', 'low', 'open', 'last_price'],
start_date='2022-11', end_date='2023', Per='W', Fill='P', Days='A',
).head()

	SHCOMP Index
	high	low	open	last_price
2022-11-04	3081.590	2885.089	2893.196	3070.796
2022-11-11	3117.742	3022.851	3062.859	3087.292
2022-11-18	3145.753	3074.498	3100.867	3097.243
2022-11-25	3118.120	3056.170	3078.057	3101.693
2022-12-02	3198.409	3034.705	3055.290	3156.144

Dividend / Split Adjustment

pd.concat([
blp.bdh(
'AAPL US Equity', 'Px_Last', '20140605', '20140610',
CshAdjNormal=True, CshAdjAbnormal=True, CapChg=True
).rename(columns={'Px_Last': 'Px_Adj'}),
blp.bdh(
'AAPL US Equity', 'Px_Last', '20140605', '20140610',
CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False
).rename(columns={'Px_Last': 'Px_Raw'}),
], axis=1)

	AAPL US Equity
	Px_Adj	Px_Raw
2014-06-05	20.450	647.35
2014-06-06	20.394	645.57
2014-06-09	20.720	93.70
2014-06-10	20.842	94.25

Option Data

blp.bdh('TSLA US Equity',['PX_LAST','IVOL_Delta','VOLUME_TOTAL_CALL'],datetime(2023,8,4),datetime(2023,8,19),
IVOL_DELTA_LEVEL='DELTA_LVL_25',
IVOL_MATURITY='MATURITY_0D',
IVOL_DELTA_PUT_OR_CALL='IVOL_CALL').head()

	TSLA US Equity
	PX_LAST	IVOL_Delta	VOLUME_TOTAL_CALL
2023-08-04	253.86	42.4726	1537802.0
2023-08-07	251.45	39.5239	1064559.0
2023-08-08	249.70	39.6448	641504.0
2023-08-09	242.19	41.8794	841410.0
2023-08-10	245.34	39.5430	1102788.0

Bloomberg Intraday Bars

Function to get previous day

cur_dt = pd.Timestamp('today', tz='America/New_York').date()
# Range of the last two business days including today
recent = pd.bdate_range(end=cur_dt, periods=2, tz='America/New_York')
# Getting the date just before today which is also a business day
# We convert it to date format to remove any timezone information for simplicity
pre_dt = recent[0].date()
pre_dt

datetime.date(2024, 5, 15)

help(blp.bdib)

Help on function bdib in module xbbg.blp:
bdib(ticker: str, dt, session='allday', typ='TRADE', **kwargs) -> pandas.core.frame.DataFrame
Bloomberg intraday bar data
Args:
ticker: ticker name
dt: date to download
session: [allday, day, am, pm, pre, post]
typ: [TRADE, BID, ASK, BID_BEST, ASK_BEST, BEST_BID, BEST_ASK]
**kwargs:
ref: reference ticker or exchange
used as supplement if exchange info is not defined for `ticker`
batch: whether is batch process to download data
log: level of logs
Returns:
pd.DataFrame

Stock Data

blp.bdib('QQQ US Equity', dt=pre_dt, session='day').tail()

	QQQ US Equity
	open	high	low	close	volume	num_trds	value
2024-05-14 15:55:00-04:00	446.380	446.38	446.290	446.380	198977	1136	88809112.0
2024-05-14 15:56:00-04:00	446.382	446.46	446.340	446.380	281996	1457	125880752.0
2024-05-14 15:57:00-04:00	446.380	446.45	446.330	446.355	493353	2235	220232448.0
2024-05-14 15:58:00-04:00	446.350	446.38	446.275	446.305	335184	1392	149599264.0
2024-05-14 15:59:00-04:00	446.305	446.31	445.920	445.930	1255656	4408	560149184.0

Use session = ‘allday’ to get after hour trading data.

blp.bdib('QQQ US Equity', dt=pre_dt, session='allday').tail()

	QQQ US Equity
	open	high	low	close	volume	num_trds	value
2024-05-14 15:57:00-04:00	446.380	446.45	446.330	446.355	493353	2235	220232448.0
2024-05-14 15:58:00-04:00	446.350	446.38	446.275	446.305	335184	1392	149599264.0
2024-05-14 15:59:00-04:00	446.305	446.31	445.920	445.930	1255656	4408	560149184.0
2024-05-14 16:14:00-04:00	445.930	445.93	445.930	445.930	0	1	0.0
2024-05-14 16:29:00-04:00	445.930	445.93	445.930	445.930	0	1	0.0

Option Data

blp.bdib('SPX US 05/17/24 C5300 Index', dt=pre_dt, session='allday').tail()

	SPX US 05/17/24 C5300 Index
	open	high	low	close	volume	num_trds	value
2024-05-14 15:52:00-04:00	7.49	7.49	7.49	7.49	1	1	7.49
2024-05-14 15:56:00-04:00	7.30	7.30	7.30	7.30	2	2	14.60
2024-05-14 15:57:00-04:00	7.40	7.40	7.40	7.40	1	1	7.40
2024-05-14 15:58:00-04:00	7.50	7.50	7.50	7.50	3	2	22.50
2024-05-14 15:59:00-04:00	6.87	6.87	6.87	6.87	2	1	13.74

Need the ref parameter to adjust

blp.bdib('AAPL US 05/17/24 C185 Equity', dt=pre_dt, ref='IndexUS').tail()

	AAPL US 05/17/24 C185 Equity
	open	high	low	close	volume	num_trds	value
2024-05-14 15:55:00-04:00	3.10	3.13	3.10	3.10	12	6	37.23
2024-05-14 15:56:00-04:00	3.15	3.20	3.15	3.20	17	8	54.27
2024-05-14 15:57:00-04:00	3.27	3.29	3.25	3.25	110	7	359.54
2024-05-14 15:58:00-04:00	3.25	3.27	3.15	3.25	90	9	293.30
2024-05-14 15:59:00-04:00	3.20	3.22	3.10	3.10	65	11	205.23

blp.bdib(ticker='AAPL US 05/17/24 C185 Equity', dt=pre_dt, ref='IndexUS', fields=['delta', 'vega'])

	AAPL US 05/17/24 C185 Equity
	open	high	low	close	volume	num_trds	value
2024-05-14 09:31:00-04:00	3.77	3.90	3.35	3.35	881	66	3289.60
2024-05-14 09:32:00-04:00	3.35	3.45	3.35	3.35	406	70	1388.82
2024-05-14 09:33:00-04:00	3.35	3.50	3.25	3.25	156	46	532.71
2024-05-14 09:34:00-04:00	3.10	3.20	3.05	3.18	120	31	375.24
2024-05-14 09:35:00-04:00	3.30	3.30	3.10	3.26	78	31	250.54
...	...	...	...	...	...	...	...
2024-05-14 15:55:00-04:00	3.10	3.13	3.10	3.10	12	6	37.23
2024-05-14 15:56:00-04:00	3.15	3.20	3.15	3.20	17	8	54.27
2024-05-14 15:57:00-04:00	3.27	3.29	3.25	3.25	110	7	359.54
2024-05-14 15:58:00-04:00	3.25	3.27	3.15	3.25	90	9	293.30
2024-05-14 15:59:00-04:00	3.20	3.22	3.10	3.10	65	11	205.23

351 rows × 7 columns

Bloomberg Intraday Tick Data

help(blp.bdtick)

Help on function bdtick in module xbbg.blp:
bdtick(ticker, dt, session='allday', types=None, **kwargs) -> pandas.core.frame.DataFrame
Bloomberg tick data
Args:
ticker: ticker name
dt: date to download
session: [allday, day, am, pm, pre, post]
types: str or list, one or combinations of [
TRADE, AT_TRADE, BID, ASK, MID_PRICE,
BID_BEST, ASK_BEST, BEST_BID, BEST_ASK,
]
Returns:
pd.DataFrame

Stock Tick Level Data

blp.bdtick('TSLA US Equity', dt=pre_dt).tail()

	TSLA US Equity
	typ	value	volume	cond	exch
2024-05-14 09:51:59.977141-04:00	TRADE	177.57	3	R6,IS,OL	K
2024-05-14 09:51:59.979534-04:00	TRADE	177.58	19	Q,OL	D
2024-05-14 09:51:59.980286-04:00	TRADE	177.58	300	R6,IS	Z
2024-05-14 09:51:59.980587-04:00	TRADE	177.58	100	N	D
2024-05-14 09:51:59.986661-04:00	TRADE	177.58	1	Q,OL	D

blp.bdtick('AAPL US Equity', dt=pre_dt).tail()

	AAPL US Equity
	typ	value	volume	cond	exch
2024-05-14 16:00:00.519780-04:00	TRADE	445.88	30	FT,OL	Q
2024-05-14 16:00:00.644831-04:00	TRADE	445.87	800	FT,R6,IS	P
2024-05-14 16:00:00.645722-04:00	TRADE	445.87	800	FT,R6,IS	P
2024-05-14 16:00:00.831138-04:00	TRADE	445.88	100	FT	Q
2024-05-14 16:00:00.917392-04:00	TRADE	445.93	1168	FT,Q,AP	D

Option Tick Level Data

blp.bdtick('TSLA US 05/17/24 C480 Equity', dt=pre_dt, ref='IndexUS').tail()

	TSLA US 05/17/24 C480 Equity
	typ	value	volume	cond	exch
2024-05-14 09:37:16-04:00	TRADE	0.01	10	ET	B
2024-05-14 10:27:44-04:00	TRADE	0.01	1	ME	W
2024-05-14 11:02:07-04:00	TRADE	0.01	1	ME	O
2024-05-14 11:30:13-04:00	TRADE	0.01	2	ET	B
2024-05-14 11:33:13-04:00	TRADE	0.01	48	ET	B

blp.bdtick('SPX US 05/17/24 C5300 Index', dt=pre_dt, types=['TRADE', 'AT_TRADE', 'BID', 'ASK']).tail()

	SPX US 05/17/24 C5300 Index
	typ	value	volume	cond	exch
2024-05-14 12:00:00-04:00	ASK	3.4	968	B	O
2024-05-14 12:00:00-04:00	ASK	3.4	943	B	O
2024-05-14 12:00:00-04:00	ASK	3.4	975	B	O
2024-05-14 12:00:00-04:00	ASK	3.3	63	B	O
2024-05-14 12:00:00-04:00	ASK	3.4	1025	B	O

Get more Info of Options

Xbbg Package will not give us greek letter info, so we need to directy use Bloomberg API to get the data. We need to calculate them by ourselves.

Other Useful Piplines

Raw API

Here is an example to use raw Bloomberg API to

# Create a session
session = blpapi.Session()
if not session.start():
print("Failed to start session.")
exit()
if not session.openService("//blp/refdata"):
print("Failed to open service.")
exit()
service = session.getService("//blp/refdata")

# Create a request for intraday data
request = service.createRequest("IntradayBarRequest")
request.set("security", "SPX US 05/17/24 C5300 Index") # Update with actual option ticker
request.set("eventType", "TRADE") # Typically TRADE for tick data, or BID/ASK
request.set("interval", 60) # Bar interval in minutes, set as needed (e.g., 1 for 1 minute)
# Set the start and end times for the data request
start_time = datetime(2024, 5, 14, 9, 30) # Example: starting at market open
end_time = datetime(2024, 5, 14, 16, 0) # Example: ending at market close
request.set("startDateTime", start_time)
request.set("endDateTime", end_time)
# Send the request
print("Sending Request:", request)
session.sendRequest(request)

Sending Request: IntradayBarRequest = {
security = "SPX US 05/17/24 C5300 Index"
eventType = TRADE
interval = 60
startDateTime = 2024-05-14T09:30:00.000
endDateTime = 2024-05-14T16:00:00.000
}
<blpapi.internals.CorrelationId; proxy of <Swig Object of type 'blpapi_CorrelationId_t *' at 0x000002A8A2332CA0> >

try:
while True:
event = session.nextEvent()
if event.eventType() == blpapi.Event.PARTIAL_RESPONSE or event.eventType() == blpapi.Event.RESPONSE:
for msg in event:
print(msg)
if event.eventType() == blpapi.Event.RESPONSE:
# End the loop when all data has been received
break
finally:
session.stop()

CID: {[ valueType=AUTOGEN classId=0 value=16 ]}
RequestId: 0e176c76-e7cf-4633-905f-efbbe85c9844
IntradayBarResponse = {
barData = {
eidData[] = {
}
delayedSecurity = true
barTickData[] = {
barTickData = {
time = 2024-05-14T09:30:00.000
open = 5.300000
high = 5.300000
low = 5.000000
close = 5.000000
volume = 750
numEvents = 19
value = 3906.040000
}
barTickData = {
time = 2024-05-14T10:30:00.000
open = 5.350000
high = 5.440000
low = 5.350000
close = 5.440000
volume = 3
numEvents = 2
value = 16.140000
}
barTickData = {
time = 2024-05-14T11:30:00.000
open = 5.500000
high = 5.900000
low = 5.500000
close = 5.900000
volume = 51
numEvents = 3
value = 280.900000
}
barTickData = {
time = 2024-05-14T12:30:00.000
open = 3.100000
high = 4.210000
low = 3.100000
close = 4.210000
volume = 714
numEvents = 18
value = 2685.810000
}
barTickData = {
time = 2024-05-14T13:30:00.000
open = 4.300000
high = 5.920000
low = 4.300000
close = 5.800000
volume = 332
numEvents = 34
value = 1837.060000
}
barTickData = {
time = 2024-05-14T14:30:00.000
open = 4.900000
high = 4.900000
low = 3.740000
close = 4.500000
volume = 1114
numEvents = 39
value = 4795.790000
}
barTickData = {
time = 2024-05-14T15:30:00.000
open = 4.400000
high = 4.400000
low = 3.430000
close = 3.600000
volume = 205
numEvents = 14
value = 801.300000
}
}
}
}

VWAP for Intraday Bar Data

fx = blp.bdib('JPY Curncy', dt=pre_dt)
jp = pd.concat([
blp.bdib(ticker, dt=pre_dt, session='day')
for ticker in ['7974 JP Equity', '9984 JP Equity']
], axis=1)
jp.tail()

	7974 JP Equity							9984 JP Equity
	open	high	low	close	volume	num_trds	value	open	high	low	close	volume	num_trds	value
2024-05-15 14:54:00+09:00	8312.0	8313.0	8309.0	8312.0	9000	27	74797504.0	8378.0	8380.0	8377.0	8377.0	11900	43	99706496.0
2024-05-15 14:55:00+09:00	8312.0	8312.0	8309.0	8309.0	10000	30	83099400.0	8378.0	8384.0	8377.0	8379.0	43600	109	365335616.0
2024-05-15 14:56:00+09:00	8309.0	8312.0	8304.0	8307.0	24000	45	199409104.0	8379.0	8382.0	8377.0	8381.0	20000	73	167580800.0
2024-05-15 14:57:00+09:00	8308.0	8308.0	8301.0	8305.0	14200	39	117900800.0	8381.0	8381.0	8377.0	8380.0	19700	58	165051808.0
2024-05-15 14:58:00+09:00	8305.0	8309.0	8299.0	8309.0	42300	73	351199072.0	8378.0	8379.0	8369.0	8372.0	63600	78	532562688.0

prices = (
jp
.pipe(pipeline.get_series, col='close')
.pipe(pipeline.apply_fx, fx=fx)
.tz_convert('Asia/Tokyo')
)
prices.tail()

C:\Users\yzhang3654\AppData\Local\anaconda3\Lib\site-packages\xbbg\pipeline.py:118: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
add_fx.iloc[:, -1] = add_fx.iloc[:, -1].fillna(method='pad')

	7974 JP Equity	9984 JP Equity
2024-05-15 14:54:00+09:00	53.172979	53.588792
2024-05-15 14:55:00+09:00	53.167392	53.615306
2024-05-15 14:56:00+09:00	53.151193	53.624672
2024-05-15 14:57:00+09:00	53.145197	53.625136
2024-05-15 14:58:00+09:00	53.177600	53.580800

def vwap(data: pd.DataFrame, fx=None, name=None) -> pd.Series:
return pd.Series({
ticker: (
data[ticker][['close', 'volume']].prod(axis=1).sum()
if fx is None else (
data[ticker].close
.pipe(pipeline.apply_fx, fx)
.close
.mul(data[ticker].volume)
.sum()
)
) / data[ticker].volume.sum()
for ticker in data.columns.get_level_values(0).unique()
}, name=name)

VWAP in Local Currency

jp.pipe(vwap, name=jp.index[-1].date())

7974 JP Equity 8348.106662
9984 JP Equity 8382.766258
Name: 2024-05-15, dtype: float64

VWAP in USD

jp.pipe(vwap, fx=fx, name=jp.index[-1].date())

C:\Users\yzhang3654\AppData\Local\anaconda3\Lib\site-packages\xbbg\pipeline.py:118: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
add_fx.iloc[:, -1] = add_fx.iloc[:, -1].fillna(method='pad')
C:\Users\yzhang3654\AppData\Local\anaconda3\Lib\site-packages\xbbg\pipeline.py:118: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
add_fx.iloc[:, -1] = add_fx.iloc[:, -1].fillna(method='pad')
7974 JP Equity 53.369565
9984 JP Equity 53.595914
Name: 2024-05-15, dtype: float64

Total Traded Volume for Certain Period

Get total tradded volume of certain date between 0:00 to input time.

jp_hist = pd.concat([
pd.concat([
blp.bdib(ticker, dt=dt, session='day')
for ticker in ['7974 JP Equity', '9984 JP Equity']
], axis=1)
for dt in pd.bdate_range(end='today', periods=10)[:-1]
], sort=False)
unique(jp_hist.index.date)

array([datetime.date(2024, 5, 7), datetime.date(2024, 5, 8),
datetime.date(2024, 5, 9), datetime.date(2024, 5, 10),
datetime.date(2024, 5, 13), datetime.date(2024, 5, 14),
datetime.date(2024, 5, 15)], dtype=object)

def drop_zeros(data: pd.DataFrame) -> pd.DataFrame:
return (
data
.replace(0, np.nan)
.dropna(how='all')
.replace(np.nan, 0)
)

def traded_volume(data: pd.DataFrame, asof: str) -> pd.DataFrame:
return (
data
.pipe(pipeline.get_series, col='volume')
.between_time('0:00', asof)
.resample('B')
.sum()
.pipe(drop_zeros)
)

jp_hist.pipe(traded_volume, asof='10:00')

	7974 JP Equity	9984 JP Equity
2024-05-07 00:00:00+09:00	1286900	1996500
2024-05-08 00:00:00+09:00	5777300	1111200
2024-05-09 00:00:00+09:00	2213900	1464800
2024-05-10 00:00:00+09:00	3861900	2657500
2024-05-13 00:00:00+09:00	1040100	2537000
2024-05-14 00:00:00+09:00	2712600	5331800
2024-05-15 00:00:00+09:00	903600	2267000

jp_hist.pipe(traded_volume, asof='11:00')

	7974 JP Equity	9984 JP Equity
2024-05-07 00:00:00+09:00	1950200	2799900
2024-05-08 00:00:00+09:00	7407000	1627400
2024-05-09 00:00:00+09:00	3055800	1971500
2024-05-10 00:00:00+09:00	4686200	3460500
2024-05-13 00:00:00+09:00	1627400	3614300
2024-05-14 00:00:00+09:00	3468300	8610400
2024-05-15 00:00:00+09:00	1307300	3429100

Subscription

async for snap in blp.live(['ESA Index', 'NQA Index'], max_cnt=2):
print(snap)

{'TICKER': 'NQA Index', 'FIELD': 'LAST_PRICE', 'MKTDATA_EVENT_TYPE': 'SUMMARY', 'MKTDATA_EVENT_SUBTYPE': 'INITPAINT', 'BID': 18726.75, 'ASK': 18727.25, 'BEST_BID': 18726.75, 'BEST_ASK': 18727.25, 'BID_ALL_SESSION': 18726.75, 'ASK_ALL_SESSION': 18727.25, 'BID_SIZE_ALL_SESSIONS_RT': 2, 'ASK_SIZE_ALL_SESSIONS_RT': 5, 'IND_BID_FLAG': False, 'IND_ASK_FLAG': False, 'ASK_SIZE_TDY': 5, 'BID_SIZE_TDY': 2, 'BID_TDY': 18726.75, 'PRICE_LAST_BID_RT': 18726.75, 'ASK_TDY': 18727.25, 'PRICE_LAST_ASK_RT': 18727.25, 'BID_DIR': -1, 'ASK_DIR': 1, 'BID2': 18726.75, 'ASK2': 18727.25, 'ASK_SIZE': 5, 'BID_SIZE': 2, 'BID_ALL_SESSION_TDY_RT': 18726.75, 'ASK_ALL_SESSION_TDY_RT': 18727.25, 'CURRENT_SESSION_RT': 2, 'PX_OPEN_ALL_WITH_SWITCHOVER_RT': 18693.25, 'PX_CLOSE_ALL_WITH_SWITCHOVER_RT': 18691.75, 'LAST_PRICE_ALL_SESSION_DIR_RT': 1, 'SIMP_LAST_PX_ALL_SESS_DIR_RT': 1, 'ID_BB_SEC_NUM_SRC': 2078932528452, 'LAST_PRICE': 18727.0, 'LAST2_TRADE': 18727.25, 'SIZE_LAST_TRADE': 1, 'LAST_TRADE': 18727.0, 'VOLUME': 23392, 'HIGH': 18738.75, 'LOW': 18687.5, 'OPEN': 18693.25, 'ALL_PRICE_SIZE': 1, 'ALL_PRICE_COND_CODE': 'OR', 'OPEN_TDY': 18693.25, 'PRICE_OPEN_RT': 18693.25, 'VOLUME_TDY': 23392, 'LAST_PRICE_TDY': 18727.0, 'HIGH_TDY': 18738.75, 'PRICE_HIGH_RT': 18738.75, 'LOW_TDY': 18687.5, 'PRICE_LOW_RT': 18687.5, 'RT_OPEN_INTEREST': 250772, 'MID': 18727.0, 'MID_TDY': 18727.0, 'RT_EXCH_MARKET_STATUS': 'ACTV', 'MIN_LIMIT': 17390.0, 'DOWN_LIMIT': 17390.0, 'MAX_LIMIT': 19993.5, 'UP_LIMIT': 19993.5, 'THEO_PRICE': 18693.25, 'RT_TRADING_PERIOD': 'TRAD', 'LAST_ALL_SESSIONS': 18727.0, 'PREV_CLOSE_VALUE_REALTIME': 18691.75, 'TRADING_DT_REALTIME': datetime.date(2024, 5, 16), 'RT_OPEN_INT_DT': datetime.date(2024, 5, 15), 'PREV_TRADING_DT_REALTIME': datetime.date(2024, 5, 15), 'PRICE_CHANGE_1Y_NET_RT': 4716.0, 'PRICE_CHANGE_1Y_PCT_RT': 33.65999984741211, 'CLOSING_PRICE_1Y_AGO_RT': 14011.0, 'PX_SETTLE_ACTUAL_RT': 18691.75, 'OPEN_TRADE_PRICE_REALTIME': 18693.25, 'LOW_PRICE_AM_SESSION': 18228.75, 'LAST_PRICE_AM_SESSION': 18286.0, 'PX_SETTLE_INTRADAY_REALTIME': 18691.75, 'ZERO_BID_PRICE_INDICATOR_RT': False, 'ZERO_ASK_PRICE_INDICATOR_RT': False, 'PX_SETTLE_LAST_DT_RT': datetime.date(2024, 5, 15), 'IN_AUCTION_RT': False, 'RT_API_MACHINE': 'apipubx0@md1p-rr-006', 'PX_HIGH_BID_RT': 18738.5, 'PX_LOW_ASK_RT': 18688.25, 'VOLUME_AM_SESSION_RT': 404413, 'LAST_PRICE_COND_CODE_RT': 'TSUM', 'ALL_PRICE': 18727.0, 'RT_EXCH_TRADE_STATUS': True, 'RT_QUOTE_STATUS': True, 'RT_SIMP_SEC_STATUS': 'TRAD', 'VOLUME_THEO': 0, 'SIZE_LAST_TRADE_TDY': 1, 'MID2': 18727.0, 'MID_DIR': 1, 'RT_PRICING_SOURCE': 'EX', 'OFF_BOOK_VOLUME_REALTIME': 216, 'TRADE_SIZE_ALL_SESSIONS_RT': 1, 'ASK_CHANGE': 32.25, 'BID_CHANGE': 45.5, 'SPREAD_BA': 0.5, 'OPEN_CC_BLOOMBERG_RT': 'ORT', 'PRICE_52_WEEK_HIGH_RT': 18738.75, 'PRICE_52_WEEK_HIGH_DATE_RT': datetime.date(2024, 5, 16), 'PRICE_52_WEEK_LOW_RT': 14537.0, 'PRICE_52_WEEK_LOW_DATE_RT': datetime.date(2023, 10, 26), 'BLOOMBERG_CLOSE_PRICE_RT': 18691.75, 'PRICE_PREVIOUS_CLOSE_RT': 18691.75, 'PRICE_LAST_RT': 18727.0, 'LAST_TRADE_PRICE_TODAY_RT': 18727.0, 'PREVIOUS_TOTAL_VOLUME_RT': 596819, 'HIGH_TRADE_PRICE_RT': 18738.75, 'LOW_TRADE_PRICE_RT': 18687.5, 'SETTLEMENT_TYPE_RT': 'SETT', 'PER_TRADE_VWAP_TURNOVER_RT': 437534703.0, 'PER_TRADE_VWAP_VOLUME_RT': 23372.0, 'PER_TRADE_VWAP_REALTIME': 18720.4648, 'AUCTION_TYPE_REALTIME': 'OPEN', 'TRADING_HALT_REASON_TYPE_RT': 'NONE', 'PREVIOUS_CLOSE_BID_PRICE_RT': 18681.25, 'PREVIOUS_CLOSE_ASK_PRICE_RT': 18695.0, 'LAST_CONTINUOUS_TRADE_PRICE_RT': 18727.0, 'BLOOMBERG_CLOSE_DATE_RT': datetime.date(2024, 5, 15), 'LAST_TRADE_CANCELED_IND_RT': False, 'OPEN_TRADE_PRICE_TODAY_RT': 18693.25, 'HIGH_TRADE_PRICE_TODAY_RT': 18738.75, 'LOW_TRADE_PRICE_TODAY_RT': 18687.5, 'UNADJUSTED_PREV_LAST_PRICE_RT': 18691.75, 'ADJUSTED_PREV_LAST_PRICE_RT': 18691.75, 'BASE_PRICE_ENABLED_RT': False, 'PREVIOUS_BLOOMBERG_CLOSE_PX_RT': 18691.75, 'PREVIOUS_LAST_TRADE_PRICE_RT': 18694.5, 'NORMALIZED_PRICE_REALTIME': 18255.0, 'OFF_BOOK_VOLUME_DATE_RT': datetime.date(2024, 5, 13), 'NUM_TRADES_RT': 16706, 'BLOOMBERG_CLOSE_METHOD_RT': 'SETTLE', 'LAST_TICK_DIRECTION_RT': 3, 'DECIMAL_SETTLEMENT_PRICE_RT': 18691.75, 'CHG_NET_REG_SES_PRV_RG_SES_CL_RT': 35.25, 'CHG_PCT_REG_SES_PRV_RG_SES_CL_RT': 0.18858587344684152, 'EXCHANGE_FIXING_PRICE_RT': 18691.85, 'LAST_FIXING_PRICE_DATE_RT': datetime.date(2024, 5, 15), 'IS_DELAYED_STREAM': True, 'EXCH_CODE_LAST_ALL_SESSION_RT': '', 'LOC_SRC_FOR_LST_TRD_ALL_SESS_RT': '', 'CHG_NET_MTD_RT': 1155.75, 'CHG_PCT_MTD_RT': 6.5775, 'CHG_NET_QTD_RT': 252.0, 'CHG_PCT_QTD_RT': 1.364, 'CHG_NET_YTD_RT': 1495.0, 'CHG_PCT_YTD_RT': 8.6757, 'CHG_NET_1M_RT': 845.75, 'CHG_PCT_1M_RT': 4.7298, 'CHG_NET_3M_RT': 748.5, 'CHG_PCT_3M_RT': 4.1633, 'REALTIME_2_DAY_CHANGE_NET': 312.0, 'REALTIME_2_DAY_CHANGE_PERCENT': 1.6943, 'REALTIME_5_DAY_CHANGE_NET': 512.5, 'REALTIME_5_DAY_CHANGE_PERCENT': 2.8137, 'RT_PX_AS_PCT_INTRADAY_RANGE': 77.0732, 'REALTIME_PERCENT_BID_ASK_SPREAD': 0.0027000000000043656, 'RT_THEORETICAL_PCT_CHG_FROM_LAST': -0.18019999999999925, 'CHG_NET_1D_ALL_FROM_REGULAR_RT': 35.25, 'CHG_PCT_1D_ALL_FROM_REGULAR_RT': 0.1886, 'PX_CHG_NET_1D_ALL_SWITCHOVER_RT': 35.25, 'PX_CHG_PCT_1D_ALL_SWITCHOVER_RT': 0.1886, 'CHG_PCT_VOLUME_REG_SES_CLOSE': 0.0, 'API_RULES_VERSION': 201411210, 'PREV_SES_LAST_PRICE': 18691.75, 'RT_PX_CHG_NET_1D': 35.25, 'RT_PX_CHG_PCT_1D': 0.18860000371932983, 'PRICE_CHANGE_ON_DAY_RT': 35.25, 'NET_CHANGE_ON_DAY_TODAY_RT': 35.25, 'PERCENT_CHANGE_ON_DAY_TODAY_RT': 0.1886, 'LAST_UPDATE_BID_RT': datetime.time(0, 38, 42), 'LAST_UPDATE_ASK_RT': datetime.time(0, 38, 58), 'RT_TIME_OF_TRADE': datetime.time(0, 38, 58), 'TIME': datetime.time(0, 38, 58), 'BID_ASK_TIME': datetime.time(0, 38, 58), 'SES_START': datetime.time(18, 0), 'SES_END': datetime.time(17, 0), 'ACT_TIME_LAST_SETTLE_RECEIVED_RT': datetime.time(19, 0, 16), 'LAST_TRADE_RECEIVED_TIME_RT': datetime.time(0, 38, 58), 'PRICE_LAST_TIME_RT': datetime.time(0, 38, 58), 'BLOOMBERG_CLOSE_TIME_RT': datetime.time(19, 0, 16), 'FIXING_PRICE_TIME_RT': datetime.time(16, 0, 5), 'TIME_AUCTION_CALL_CONCLUSION_RT': datetime.time(17, 59, 30), 'THEORETICAL_TIME_TODAY_RT': datetime.time(17, 59, 56), 'LAST_BID_TIME_TODAY_REALTIME': datetime.time(0, 38, 42, 997000), 'LAST_ASK_TIME_TODAY_REALTIME': datetime.time(0, 38, 58, 848000), 'LAST_PRICE_TIME_TODAY_REALTIME': datetime.time(0, 38, 58, 802000), 'LAST_TRADE_PRICE_TIME_TODAY_RT': datetime.time(0, 38, 58, 802000), 'SUB_SEC_TM_AUCT_CALL_CNCLSN_RT': datetime.time(17, 59, 30), 'LAST_UPDATE_ALL_SESSIONS_RT': datetime.time(0, 38, 58), 'ACTUAL_TM_FIRST_SETTLEMT_RCVD_RT': datetime.time(16, 1, 8)}
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Colab Link

This post was created with Colab. The orginal files can be found at https://colab.research.google.com/drive/1PAGHVgH5Hp2MjuEtFeYUEW5GxIsULX7g?usp=sharing

Code and Data | Academic

Slide Template

Monetary Policy Shock Data Replication

Personal Package Introduction

Table of contents

How to Setup

Functions

File Operation

Regression Result Convert

Stata to Latex

Bloomberg API Usage

TOC

Prepare

Prepare Terminal Device

Prepare API environment

Easier to use package: XBBG

Raw API

Data Collection Examples

Bloomberg Data Point

Bloomberg Data Set

Dividend Data

Earning Data

Bloomberg Historical Data

Stock Data

Option Data

Bloomberg Intraday Bars

Stock Data

Option Data

Bloomberg Intraday Tick Data

Stock Tick Level Data

Option Tick Level Data

Get more Info of Options

Other Useful Piplines

Raw API

VWAP for Intraday Bar Data

Total Traded Volume for Certain Period

Subscription

Colab Link