I have this code:
import pandas as pd
import yfinance as yF
import datetime
from functools import reduce
def get_returns_change_period(tic,com,OHLC_COL1,OHLC_COL2):
df_Stock = yF.download(tickers = tic, period = "38y", interval = "1mo", prepost = False, repair = False)
df_Stock['MONTH'] = pd.to_datetime(df_Stock.index)
df_Stock = df_Stock.sort_values(by='MONTH')
df_Stock[com + ' % Change '+'3M'] = (((df_Stock[OHLC_COL1].shift(2) - df_Stock[OHLC_COL2]))/df_Stock[OHLC_COL2]) *100
df_Stock[com + ' % Change '+'3M'] = (df_Stock[com + ' % Change '+'3M'] * 100 )/df_Stock[OHLC_COL2]
df_Stock[com + ' % Change '+'2M'] = (((df_Stock[OHLC_COL1].shift(1) - df_Stock[OHLC_COL2]))/df_Stock[OHLC_COL2]) *100
df_Stock[com + ' % Change '+'2M'] = (df_Stock[com + ' % Change '+'2M'] * 100 )/df_Stock[OHLC_COL2]
df_Stock[com + ' % Change '+'M'] = (((df_Stock[OHLC_COL1].shift(0) - df_Stock[OHLC_COL2]))/df_Stock[OHLC_COL2]) *100
df_Stock[com + ' % Change '+'M'] = (df_Stock[com + ' % Change '+'M'] * 100 )/df_Stock[OHLC_COL2]
return df_Stock.filter(regex='MONTH|% Change')
Everything works as expected until this point.
get_returns_change_period('XOM','EXXON MOBIL','High','Open')
Now I am trying to Apply Lambda function to this function.
df_Industry = pd.DataFrame({'ID':['1', '2'], 'Ticker': ['AIN', 'TILE'], 'Company':['Albany International', 'Interface']})
df1 = df_Industry.apply(lambda x: get_returns_change_period(x.Ticker, x.Company, 'High','Open'), axis=1)
#df1 = df1.T
df1
The output has to be data frame which is merged result of multiple stocks which are returned by get_returns_change_period() function.
The problem/solution here is very similar to this one. But I am unable to reuse the solution given. Returns for multiple time periods are calculated and returned by the function here:
get_returns_change_period()
If you want to merge the dataframes computed by each return of the get_returns_change_period function, you can use the concat function of pandas in this way:
merged_df = pd.concat(df1.tolist(), axis=1)
Here's the output you get on a sample of your data (the last 5 observations):
| Date | MONTH | Albany International % Change 3M | Albany International % Change 2M | Albany International % Change M | MONTH | Interface % Change 3M | Interface % Change 2M | Interface % Change M |
|---|---|---|---|---|---|---|---|---|
| 2023-06-01 00:00:00 | 2023-06-01 00:00:00 | 10.1677 | 11.6873 | 14.0497 | 2023-06-01 00:00:00 | 271.476 | 235.975 | 400.949 |
| 2023-07-01 00:00:00 | 2023-07-01 00:00:00 | 1.14388 | 3.13983 | 5.61434 | 2023-07-01 00:00:00 | -92.5231 | 10.4251 | 140.739 |
| 2023-08-01 00:00:00 | 2023-08-01 00:00:00 | -1.31092 | 0.967072 | 0.92409 | 2023-08-01 00:00:00 | -90.5255 | 15.9752 | 90.5256 |
| 2023-09-01 00:00:00 | 2023-09-01 00:00:00 | 4.85091 | 4.80482 | 1.45181 | 2023-09-01 00:00:00 | -49.2854 | 15.8085 | 34.4068 |
| 2023-10-01 00:00:00 | 2023-10-01 00:00:00 | 15.2679 | 11.3361 | 2.08076 | 2023-10-01 00:00:00 | 77.0512 | 97.8758 | 15.6185 |
Let me know if this meets your requirements!