# Sharpe Ratio, Sorino Ratio and Calmar Ratio

We are going to examine the deficiencies of Sharpe ratio, and how we can complement it with Sorino Ratio and Calmar Ratio to gain a clearer picture of the performance of a portfolio.

In this short story, we are going to examine the deficiencies of Sharpe ratio, and how we can complement it with Sorino Ratio and Calmar Ratio to gain a clearer picture of the performance of a portfolio.

In portfolio performance analysis, sharpe ratio is the usually the first number that people look at. However, it does not tell us the whole story (nothing does…). So, let’s spend some time looking at a few more metrics that can be very helpful at times.

### Sharpe Ratio Revisited

Sharpe ratio is the ratio of average return divided by the standard deviation of returns annualized. We had an introduction to it in a previous story.

Let’s take a look at it again with a test price time series.

``````import pandas as pd
import numpy as np

from pandas.tseries.offsets import BDay

def daily_returns(prices):

res = (prices/prices.shift(1) - 1.0)[1:]
res.columns = ['return']

return res

def sharpe(returns, risk_free=0):

def test_price1():

start_date = pd.Timestamp(2020, 1, 1) + BDay()

len = 100

bdates = [start_date + BDay(i) for i in range(len)]
price = [10.0 + i/10.0 for i in range(len)]

return pd.DataFrame(data={'date': bdates,
'price1': price}).set_index('date')

def test_price2():

start_date = pd.Timestamp(2020, 1, 1) + BDay()

len = 100

bdates = [start_date + BDay(i) for i in range(len)]
price = [10.0 + i/10.0 for i in range(len)]

price[40:60] = [price[40] for i in range(20)]

return pd.DataFrame(data={'date': bdates,
'price2': price}).set_index('date')

def test_price3():

start_date = pd.Timestamp(2020, 1, 1) + BDay()

len = 100

bdates = [start_date + BDay(i) for i in range(len)]
price = [10.0 + i/10.0 for i in range(len)]

price[40:60] = [price[40] - i/10.0 for i in range(20)]

return pd.DataFrame(data={'date': bdates,
'price3': price}).set_index('date')

def test_price4():

start_date = pd.Timestamp(2020, 1, 1) + BDay()

len = 100

bdates = [start_date + BDay(i) for i in range(len)]
price = [10.0 + i/10.0 for i in range(len)]

price[40:60] = [price[40] - i/8.0 for i in range(20)]

return pd.DataFrame(data={'date': bdates,
'price4': price}).set_index('date')

price1 = test_price1()
return1 = daily_returns(price1)

price2 = test_price2()
return2 = daily_returns(price2)

price3 = test_price3()
return3 = daily_returns(price3)

price4 = test_price4()
return4 = daily_returns(price4)

print('price1')
print(f'sharpe: {sharpe(return1)}')

print('price2')
print(f'sharpe: {sharpe(return2)}')

print('price3')
print(f'sharpe: {sharpe(return3)}')

print('price4')
print(f'sharpe: {sharpe(return4)}')
``````

## Data Visualization With Python | Data Visualization | Python For Data Science

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