Estimating Amihud Illiquidity with Adjusted Prices and Trading Volume

# Estimating Amihud's illiquidity in Python


# Estimating Amihud's illiquidity in Python












I have found the following code in the book Python for Finance by Yuxing Yan, in page 267 for estimating Amihud's illiquidity

```
import numpy as np 
import statsmodels.api as sm 
from matplotlib.finance import quotes_historical_yahoo_ochl as getData 
begdate=(2013,10,1) 
enddate=(2013,10,30) 
ticker='IBM'                   # or WMT  
data= getData(ticker, begdate, enddate,asobject=True, adjusted=True) 
p=np.array(data.aclose) 
dollar_vol=np.array(data.volume*p) 
ret=np.array((p[1:] - p[:-1])/p[1:]) 
illiq=np.mean(np.divide(abs(ret),dollar_vol[1:])) 
print("Aminud illiq for =",ticker,illiq)
```

The matplotlib.finance has been deprecated.The new module does not support collection of financial data, so I found an other way to collect them:

```
import pandas as pd
import pandas_datareader.data as web
end = '2013-10-30'
start = '2013-10-1'
get_px = lambda x: web.DataReader(x, 'yahoo', start=start, end=end)['Adj Close']

symbols = ['IBM']
data = pd.DataFrame({sym:get_px(sym) for sym in symbols})
data = data.rename({'IBM': 'Adj Close'}, axis=1)
p1 = data
p = p1['Adj Close'].ravel()
```

So far so good.But I don't know from the original code what the `data.volume` does and how I can translate `dollar_vol=np.array(data.volume*p)` with the existing functions of any module in Python.

## Answer by Pleb (score 1, accepted)

https://quant.stackexchange.com/a/69539

From the documentation of matplotlib.finance (under `parse_yahoo_historical_ochl(...)`) it is specified that the dollars traded/dollar-volume is the unadjusted volume multiplied by the adjusted closing prices of the given ticker (At `quotes_yahoo_historical_ochl(...)` they refer to the above function in order to understand the output format):

> adjusted : bool If True (default) replace open, close, high, low prices with their adjusted values. The adjustment is by a scale factor, S = adjusted_close/close. Adjusted prices are actual prices multiplied by S. Volume is not adjusted as it is already backward split adjusted by Yahoo. If you want to compute dollars traded, multiply volume by the adjusted close, regardless of whether you choose adjusted = True|False.

Here, `data.volume` gets the corresponding "unadjusted" volume array from the `getData` output. If you want to replicate `dollar_vol` you need to get the backwards split-adjusted volume (eg. from Yahoo finance) for your corresponding ticker as it seems you already have the adjusted close prices.

If you do something a lá:

```
from pandas_datareader import data

IBM = data.DataReader("IBM", 
                       start='2013-10-1', 
                       end='2013-10-30', 
                       data_source='yahoo')

dollar_vol = IBM['Volume'] * IBM['Adj Close']
```

You should get the dollar_volume/dollars_traded as described in the documentation and in your first code snippet.