如何应用此函数并在python中的dataframe的新列中分配计算值？

Question

我希望计算选项希腊增量值，并在dataframe的新列中分配计算值。这是我的代码below_

#!pip install mibian

import requests
import json
import pandas as pd
import mibian
import time

session = requests.Session() # Create request session object

url1 = "https://www.nseindia.com/option-chain?type=currency"
url = "https://www.nseindia.com/api/option-chain-currency?symbol=USDINR"

headers = {
    "user-agent": "Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 
    (KHTML, like Gecko) Chrome/99.0.4844.74 Mobile Safari/537.36",
    "accept-encoding": "gzip, deflate, br",
    "accept-language": "en-US,en;q=0.9"
    }

request = session.get(url1, headers=headers)
cookies = dict(request.cookies)

response = session.get(url, headers=headers, cookies=cookies).text
data = json.loads(response)

exp_list = data['records']['expiryDates']
# print(exp_list)
exp_date = exp_list[0]
# print("Expiry Date: " +exp_date)

ce = {}
pe = {}
n = 0
m = 0

for i in data['records']['data']:
    if i['expiryDate'] == exp_date:
        try:
            ce[n] = i['CE']
            n = n+1
        except:
            pass
        try:
            pe[m] = i['PE']
            m = n+1
        except:
            pass


intrestRate = 10
daysToExpiry = 1

def call_delta(a, b, c, d, e):
  # BS([underlyingPrice, strikePrice, interestRate, daysToExpiration], volatility=x, 
    callPrice=y, putPrice=z)
  c = mibian.BS([a, b, c, d], volatility=e)
  return c.callDelta

def call_theta(a, b, c, d, e):
  # BS([underlyingPrice, strikePrice, interestRate, daysToExpiration], volatility=x, 
    callPrice=y, putPrice=z)
  c = mibian.BS([a, b, c, d], volatility=e)
  return c.callTheta

def call_vega(a, b, c, d, e):
  # BS([underlyingPrice, strikePrice, interestRate, daysToExpiration], volatility=x, 
    callPrice=y, putPrice=z)
  c = mibian.BS([a, b, c, d], volatility=e)
  return c.vega


ce_df = pd.DataFrame.from_dict(ce).transpose()
ce_df = ce_df.drop(["underlying", "identifier", "openInterest", "changeinOpenInterest", 
"pchangeinOpenInterest", "totalTradedVolume", "change", "pChange", "totalBuyQuantity", 
"totalSellQuantity", "bidQty", "bidprice", "askQty", "askPrice"], axis=1)
ce_df = ce_df.rename(columns = {'strikePrice':'strike', 'expiryDate':'ce_expiryDate', 
'impliedVolatility':'ce_impliedVolatility', 'lastPrice':'ce_lastPrice', 
'underlyingValue':'ce_underlyingValue'})
ce_df["strike"] = ce_df["strike"].astype(float) # Change strike column dataType of ce_df from 
object to float
ce_df["ce_impliedVolatility"] = ce_df["ce_impliedVolatility"].astype(float) # Change 
ce_impliedVolatility column dataType of ce_df from object to float
ce_df["ce_lastPrice"] = ce_df["ce_lastPrice"].astype(float) # Change ce_lastPrice column 
dataType of ce_df from object to float
ce_df["ce_underlyingValue"] = ce_df["ce_underlyingValue"].astype(float) # Change 
ce_underlyingValue column dataType of ce_df from object to float
ce_df['ce_expiryDate'] = pd.to_datetime(ce_df['ce_expiryDate']) # Change dataType of 
ce_expiryDate column into datetime format

ce_df["ce_delta"] = ce_df.apply(call_delta(ce_df["ce_underlyingValue"], ce_df["strike"], 
int(intrestRate), int(daysToExpiry), ce_df["ce_impliedVolatility"]))

print(ce_df.head())
print(ce_df.info())

当我运行这段代码时，它会抛出错误，比如

TypeError: cannot convert the series to <class 'float'>

有人能告诉我，请告诉我如何解决这个问题，并在一个新的数据分配计算值。还有别的办法吗？

Answer 1

更改以下代码行，

ce_df["ce_delta"] = ce_df.apply(call_delta(ce_df["ce_underlyingValue"], ce_df["strike"], 
int(intrestRate), int(daysToExpiry), ce_df["ce_impliedVolatility"]))

至

ce_df["ce_delta"] = ce_df.apply(call_delta, axis=1)

和

def call_delta(a, b, c, d, e):
  # BS([underlyingPrice, strikePrice, interestRate, daysToExpiration], volatility=x, 
    callPrice=y, putPrice=z)
  c = mibian.BS([a, b, c, d], volatility=e)
  return c.callDelta

至

def _call_delta(a, b, c, d, e):
  # BS([underlyingPrice, strikePrice, interestRate, daysToExpiration], volatility=x, callPrice=y, putPrice=z)
  c = mibian.BS([a, b, c, d], volatility=e)
  return c.callDelta

def call_delta(row):
  return _call_delta(row['ce_underlyingValue'], row['strike'], int(intrestRate), int(daysToExpiry), row['ce_impliedVolatility'])