I am facing a peculiar issue in fetching data. This wasn't a problem earlier when I fetched data using the same code. The code below loops through a few different date ranges to fetch data for that date range. I transform the fetched data into data frames of each date range and then concat it to return one single data frame.
But when I see the excel sheet that I store the data in the dates that I have fetched the data for are arbitrary, it skips a lot of days in between, sometimes skips years. Starts not from the start date I have set and instead from some arbitrary date, I have given a snippet of the result below. Date starts from 2013 instead of requested 2011, it jumps from 2015 to 2018 and then 2019 without retrieving the complete data. Can anyone help here.
def hist_data():
dfs = []
frm = ['2015-07-12 09:15', '2020-01-01 09:15']
until = ['2020-12-31 15:30', '2022-11-17 15:30']
for x, y in zip(frm, until):
try:
historicParam = {
"exchange": "NSE",
"symboltoken": tokens,
"interval": 'ONE_DAY',
"fromdate": x,
"todate": y
}
dfs.append(pd.DataFrame(obj.getCandleData(historicParam)['data'],
columns=['Date', 'Open', 'High', 'Low', 'Close', 'Volume']))
except Exception as e:
print(f'Historic Api failed: {e}')
df = pd.concat(x for x in dfs)
df['Date'] = df['Date'].str.split('T').str[0]
return df
for tokens, symbols in zip(nifty_50['token'], nifty_50['symbol']):
stock_data = hist_data()
stock_data.to_csv(f'/Users/varadjoshi/Documents/Markets Data/Price Volume Data/Nifty 500 Stocks Data/{symbols}.csv', index=False)
time.sleep(1)
print(symbols)
Output in the excel sheet below
 code_text
 code_text
```26/06/15 169.99 170.44 163.28 167.57 2150489
29/06/15 166.51 166.84 160.28 165.81 1140046
30/06/15 164.73 170.95 164.12 169.44 1197561
01/07/15 168.08 170.29 166.03 166.81 792876
02/07/15 164.42 174.04 164.42 172.46 1866024
03/07/15 172.89 173.34 168.05 170.86 1611144
06/07/15 168.05 173.46 166 172.8 2376410
07/07/15 173.49 177.9 171.5 177.15 1906853
08/07/15 175.64 175.91 167.57 168.87 3483626
09/07/15 168.96 169.26 166.24 166.96 1521201
10/07/15 167.66 169.2 165.33 167.36 655030
26/12/18 283.81 289.77 277.8 286.35 3413076
27/12/18 288.95 292.58 277.65 280.03 5553470
28/12/18 280.55 289.8 280.4 287.83 1932203
31/12/18 291.04 291.25 285.38 286.32 1383559
01/01/19 288.23 288.35 282.79 285.2 986138
02/01/19 285.08 286.77 278.92 281.76 1550368
03/01/19 281.15 286.41 274.44 277.04 1653258
04/01/19 277.95 283.99 276.38 280.61 1745111
07/01/19 284.42 285.29 278.67 282.15 1675020
08/01/19 279.34 282.3 275.95 279.16 1713143
09/01/19 280.49 281.7 273.2 277.77 1790489
10/01/19 277.77 278.95 274.87 277.77 1632163
11/01/19 277.1 278.16 270.27 273.87 2006360
14/01/19 270.21 273.23 262.96 265.44 3997561
15/01/19 265.98 269.91 265.04 268.43 3103563
16/01/19 267.52 272.51 266.8 270.15 2311584
17/01/19 271.96 276.35 268.07 270.66 2779574
18/01/19 272.33 273.14 265.13 266.92 1582590