How do I lag a lot of columns without having to type each one manually?-CodePudding

I am trying to lag the daily returns for 450 stocks. I figured out how to put the daily returns into a dataframe, and I know how to lag individual columns, but I don't want to do it for each stock, and I would like to input the stock tickers once, and have the code automatically lag.

below is my code

import yfinance as yf
import pandas as pd

data = yf.download("MMM AOS ABT ABBV ABMD ACN ATVI ADM ADBE ADP AAP AES AFL A APD AKAM ALK ALB ARE ALGN ALLE LNT ALL GOOGL GOOG MO AMZN AMCR AMD AEE AAL AEP AXP AIG AMT AWK AMP ABC AME AMGN APH ADI ANSS AON APA AAPL AMAT APTV ACGL ANET AJG AIZ T ATO ADSK AZO AVB AVY BKR BALL BAC BBWI BAX BDX WRB BRK.B BBY BIO TECH BIIB BLK BK BA BKNG BWA BXP BSX BMY AVGO BR BRO BF.B CHRW CDNS CZR CPT CPB COF CAH KMX CCL CTLT CAT CBOE CBRE CDW CE CNC CNP CF CRL SCHW CHTR CVX CMG CB CHD CI CINF CTAS CSCO C CFG CLX CME CMS KO CTSH CL CMCSA CMA CAG COP ED STZ COO CPRT GLW CSGP COST CTRA CCI CSX CMI CVS DHI DHR DRI DVA DE DAL XRAY DVN DXCM FANG DLR DFS DISH DIS DG DLTR D DPZ DOV DTE DUK DXC EMN ETN EBAY ECL EIX EW EA ELV LLY EMR ENPH ETR EOG EPAM EQT EFX EQR ESS EL ETSY RE ES EXC EXPE EXPD EXR XOM FFIV FDS FAST FRT FDX FITB FRC FE FIS FISV FLT FMC F FTNT FTV FBHS BEN FCX GRMN IT GEN GNRC GD GE GIS GM GPC GILD GL GPN GS HAL HIG HAS HCA PEAK HSY HES HPE HLT HOLX HD HON HRL HST HWM HPQ HUM HBAN HII IBM IEX IDXX ITW ILMN INCY IR INTC ICE IP IPG IFF INTU ISRG IVZ INVH IQV IRM JBHT JKHY J JNJ JCI JPM JNPR K KEY KEYS KMB KIM KMI KLAC KHC KR LH LRCX LW LVS LDOS LEN LNC LYV LKQ LMT L LOW LUMN LYB MTB MRO MPC MKTX MAR MMC MLM MAS MA MTCH MKC MCD MCK MDT MRK META MET MTD MGM MCHP MU MSFT MAA MHK MOH TAP MDLZ MPWR MNST MCO MS MOS MSI MSCI NDAQ NTAP NFLX NEM NEE NKE NI NSC NTRS NOC NRG NUE NVDA NVR NXPI ORLY OXY ODFL OMC ON OKE ORCL PCAR PKG PARA PH PAYX PAYC PYPL PNR PEP PKI PFE PCG PM PSX PNW PXD PNC POOL PPG PPL PFG PG PGR PLD PRU PEG PSA PHM QRVO PWR QCOM DGX RL RJF RTX O REGN RF RSG RMD RHI ROK ROL ROP ROST RCL SPGI CRM SBAC SLB STX SEE SRE NOW SHW SBNY SPG SWKS SJM SNA SEDG SO LUV SWK SBUX STT STE SYK SIVB SYF SNPS SYY TMUS TROW TTWO TPR TRGP TGT TEL TDY TFX TSLA TXN TXT TMO TJX TSCO TT TDG TRV TRMB TFC TYL TSN USB UDR ULTA UNP UPS URI UNH UHS VLO VTR VRSN VRSK VZ VRTX VFC VTRS V VNO VMC WAB WBA WMT WBD WM WAT WEC WFC WST WDC WRK WY WHR WMB GWW WYNN XEL XYL YUM ZBRA ZBH ZION ZTS", start="2017-01-01",end="2022-01-01")

daily_returns = data['Adj Close'].pct_change()

df2 = pd.DataFrame(daily_returns)

df2['AOS_lag'] = df2['AOS'].shift(1)
df2['MMM_lag'] = df2['MMM'].shift(1)

And that is as far as I've gotten. I started to do df2['TICKER_lag'] = df2['TICKER'].shift(1) a few more times and realized there has to be a better way. I believe a for loop could work, but I am not sure how to construct one for this specific scenario.

CodePudding user response：

Put all the stock symbols in a list, then loop over it.

symbols = ['MMM', 'AOS', 'ABT', 'ABBV', 'ABMD', 'ACN', 'ATVI', 'ADM', 'ADBE', 'ADP', 'AAP', 'AES', 'AFL', 'A', 'APD', 'AKAM', 'ALK', 'ALB', 'ARE', 'ALGN', 'ALLE', 'LNT', 'ALL', 'GOOGL', 'GOOG', 'MO', 'AMZN', 'AMCR', 'AMD', 'AEE', 'AAL', 'AEP', 'AXP', 'AIG', 'AMT', 'AWK', 'AMP', 'ABC', 'AME', 'AMGN', 'APH', 'ADI', 'ANSS', 'AON', 'APA', 'AAPL', 'AMAT', 'APTV', 'ACGL', 'ANET', 'AJG', 'AIZ', 'T', 'ATO', 'ADSK', 'AZO', 'AVB', 'AVY', 'BKR', 'BALL', 'BAC', 'BBWI', 'BAX', 'BDX', 'WRB', 'BRK.B', 'BBY', 'BIO', 'TECH', 'BIIB', 'BLK', 'BK', 'BA', 'BKNG', 'BWA', 'BXP', 'BSX', 'BMY', 'AVGO', 'BR', 'BRO', 'BF.B', 'CHRW', 'CDNS', 'CZR', 'CPT', 'CPB', 'COF', 'CAH', 'KMX', 'CCL', 'CTLT', 'CAT', 'CBOE', 'CBRE', 'CDW', 'CE', 'CNC', 'CNP', 'CF', 'CRL', 'SCHW', 'CHTR', 'CVX', 'CMG', 'CB', 'CHD', 'CI', 'CINF', 'CTAS', 'CSCO', 'C', 'CFG', 'CLX', 'CME', 'CMS', 'KO', 'CTSH', 'CL', 'CMCSA', 'CMA', 'CAG', 'COP', 'ED', 'STZ', 'COO', 'CPRT', 'GLW', 'CSGP', 'COST', 'CTRA', 'CCI', 'CSX', 'CMI', 'CVS', 'DHI', 'DHR', 'DRI', 'DVA', 'DE', 'DAL', 'XRAY', 'DVN', 'DXCM', 'FANG', 'DLR', 'DFS', 'DISH', 'DIS', 'DG', 'DLTR', 'D', 'DPZ', 'DOV', 'DTE', 'DUK', 'DXC', 'EMN', 'ETN', 'EBAY', 'ECL', 'EIX', 'EW', 'EA', 'ELV', 'LLY', 'EMR', 'ENPH', 'ETR', 'EOG', 'EPAM', 'EQT', 'EFX', 'EQR', 'ESS', 'EL', 'ETSY', 'RE', 'ES', 'EXC', 'EXPE', 'EXPD', 'EXR', 'XOM', 'FFIV', 'FDS', 'FAST', 'FRT', 'FDX', 'FITB', 'FRC', 'FE', 'FIS', 'FISV', 'FLT', 'FMC', 'F', 'FTNT', 'FTV', 'FBHS', 'BEN', 'FCX', 'GRMN', 'IT', 'GEN', 'GNRC', 'GD', 'GE', 'GIS', 'GM', 'GPC', 'GILD', 'GL', 'GPN', 'GS', 'HAL', 'HIG', 'HAS', 'HCA', 'PEAK', 'HSY', 'HES', 'HPE', 'HLT', 'HOLX', 'HD', 'HON', 'HRL', 'HST', 'HWM', 'HPQ', 'HUM', 'HBAN', 'HII', 'IBM', 'IEX', 'IDXX', 'ITW', 'ILMN', 'INCY', 'IR', 'INTC', 'ICE', 'IP', 'IPG', 'IFF', 'INTU', 'ISRG', 'IVZ', 'INVH', 'IQV', 'IRM', 'JBHT', 'JKHY', 'J', 'JNJ', 'JCI', 'JPM', 'JNPR', 'K', 'KEY', 'KEYS', 'KMB', 'KIM', 'KMI', 'KLAC', 'KHC', 'KR', 'LH', 'LRCX', 'LW', 'LVS', 'LDOS', 'LEN', 'LNC', 'LYV', 'LKQ', 'LMT', 'L', 'LOW', 'LUMN', 'LYB', 'MTB', 'MRO', 'MPC', 'MKTX', 'MAR', 'MMC', 'MLM', 'MAS', 'MA', 'MTCH', 'MKC', 'MCD', 'MCK', 'MDT', 'MRK', 'META', 'MET', 'MTD', 'MGM', 'MCHP', 'MU', 'MSFT', 'MAA', 'MHK', 'MOH', 'TAP', 'MDLZ', 'MPWR', 'MNST', 'MCO', 'MS', 'MOS', 'MSI', 'MSCI', 'NDAQ', 'NTAP', 'NFLX', 'NEM', 'NEE', 'NKE', 'NI', 'NSC', 'NTRS', 'NOC', 'NRG', 'NUE', 'NVDA', 'NVR', 'NXPI', 'ORLY', 'OXY', 'ODFL', 'OMC', 'ON', 'OKE', 'ORCL', 'PCAR', 'PKG', 'PARA', 'PH', 'PAYX', 'PAYC', 'PYPL', 'PNR', 'PEP', 'PKI', 'PFE', 'PCG', 'PM', 'PSX', 'PNW', 'PXD', 'PNC', 'POOL', 'PPG', 'PPL', 'PFG', 'PG', 'PGR', 'PLD', 'PRU', 'PEG', 'PSA', 'PHM', 'QRVO', 'PWR', 'QCOM', 'DGX', 'RL', 'RJF', 'RTX', 'O', 'REGN', 'RF', 'RSG', 'RMD', 'RHI', 'ROK', 'ROL', 'ROP', 'ROST', 'RCL', 'SPGI', 'CRM', 'SBAC', 'SLB', 'STX', 'SEE', 'SRE', 'NOW', 'SHW', 'SBNY', 'SPG', 'SWKS', 'SJM', 'SNA', 'SEDG', 'SO', 'LUV', 'SWK', 'SBUX', 'STT', 'STE', 'SYK', 'SIVB', 'SYF', 'SNPS', 'SYY', 'TMUS', 'TROW', 'TTWO', 'TPR', 'TRGP', 'TGT', 'TEL', 'TDY', 'TFX', 'TSLA', 'TXN', 'TXT', 'TMO', 'TJX', 'TSCO', 'TT', 'TDG', 'TRV', 'TRMB', 'TFC', 'TYL', 'TSN', 'USB', 'UDR', 'ULTA', 'UNP', 'UPS', 'URI', 'UNH', 'UHS', 'VLO', 'VTR', 'VRSN', 'VRSK', 'VZ', 'VRTX', 'VFC', 'VTRS', 'V', 'VNO', 'VMC', 'WAB', 'WBA', 'WMT', 'WBD', 'WM', 'WAT', 'WEC', 'WFC', 'WST', 'WDC', 'WRK', 'WY', 'WHR', 'WMB', 'GWW', 'WYNN', 'XEL', 'XYL', 'YUM', 'ZBRA', 'ZBH', 'ZION', 'ZTS']

data = yf.download(" ".join(symbols), start="2017-01-01",end="2022-01-01")
daily_returns = data['Adj Close'].pct_change()
df2 = pd.DataFrame(daily_returns)

for symbol in symbols:
    df2[f'{symbol}_lag'] = df2[symbol].shift(1)