Wookeun Lee - 11 months ago 72

Python Question

I am a beginner of python and pandas. I am having difficulty with making volatility adjusted moving average, so I need your help.

Volatility adjusted moving average is a kind of moving average, of which moving average period is not static, but dynamically adjusted according to volatility.

What I'd like to code is,

- Get stock data from yahoo finance (monthly close)
- Calculate monthly volatility X some constant --> use variables of dynamic moving average period
- Calculate dynamic moving average

I've tried this code, but only to fail. I don't know what the problem is. If you know the problem, or any better code suggestion, please let me know.

`import pandas as pd`

import matplotlib.pyplot as plt

import numpy as np

import pandas_datareader.data as web

def price(stock, start):

price = web.DataReader(name=stock, data_source='yahoo', start=start)['Adj Close']

price = price / price[0]

a = price.resample('M').last().to_frame()

a.columns = ['price']

return a

a = price('SPY','2000-01-01')

a['volperiod'] = round(a.rolling(12).std()*100)*2

for i in range(len(a.index)):

k = a['price'].rolling(int(a['volperiod'][i])).mean()

a['ma'][i] = k[i]

print(a)

Answer Source

first of all: you need to calculate `pct_change`

on `price`

to calculate `volatility`

of `returns`

*my solution*

```
def price(stock, start):
price = web.DataReader(name=stock, data_source='yahoo', start=start)['Adj Close']
return price.div(price.iat[0]).resample('M').last().to_frame('price')
a = price('SPY','2000-01-01')
v = a.pct_change().rolling(12).std().dropna().mul(200).astype(int)
def dyna_mean(x):
end = a.index.get_loc(x.name)
start = end - x.price
return a.price.iloc[start:end].mean()
pd.concat([a.price, v.price, v.apply(dyna_mean, axis=1)],
axis=1, keys=['price', 'vol', 'mean'])
```