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.

TOC

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.

1. Sign to a device with bloomberg terminal.
2. Make sure the Jupyter is insalled on the device. If not, install anaconda or use pip install to install jupyter on the device.
3. 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'])

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'])

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

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

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

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()

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()

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()

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()

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)

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()

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()

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

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

Option Data

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

Need the ref parameter to adjust

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

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

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()

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

Option Tick Level Data

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

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

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()

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')

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')

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

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, 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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