Calculate The Sharpe Ratio In Python For Your Trading Strategy

How To Calculate The Sharpe Ratio In Python For Your Trading Strategy

In this article, we will go through the Sharpe Ratio indicator, explain its meaning, its importance, and provide a practical example. In short, we show you how to calculate the Sharpe ratio in Python.

Related reading: –Algo trading strategies Python (Backtesting, Code, List, And Plenty of Coding Examples)

First, Let’s explain why volatility matters:

The volatility problem the Sharpe Ratio tries to solve

In any investment, it is important to evaluate the safety of your investment, because you want to preserve or grow your wealth.

In the chart below you see two simulated investments with 19.56% yearly return, the main difference is that investment 1 is safer.

Python Sharpe Ratio example

Why is number one safer?

It’s because the second investment has more ups and downs along the way. This is what we call volatility.

Imagine that on day 120, you need to withdraw capital from investment 2; this means that you will lose more than 80% of the initial value!

This is the essence of the concept of risk: the possibility that you can lose part or all of your investment. This is often referred to as sequence risk. Please read our take on sequence risk explained.

Think about this: if a financial instrument consistently makes a positive return every day, that instrument does not have any market risk, because every day you will know that the market value of that instrument is higher than the previous day.

Let’s suppose that you are hired to manage a $100 million portfolio, and around day 120, you need to write a report regarding the performance of the portfolio (see chart Investment 1). Perhaps you’ll be fired because of poor performance, but more likely is that investors will flee from your fun.

This implies that an investor needs to consider not only the total return from an investment but also its volatility.

What is the Sharpe Ratio?

The Sharpe Ratio is a financial indicator that measures how well an investment and its risk have performed over time.

There are two important parts in the formula: the first is excess return and the other is volatility. Let’s break down the indicator into an equation:

The excess return measures your investment over the risk free rate (), meanwhile the volatility is estimated as the standard deviation from the portfolio. Do not worry if you do not understand the Sharpe Ratio; we will explain it in more detail later on.

Calculating the Sharpe Ratio for S&P 500 and Russell 2000

Now is the moment to estimate the Sharpe Ratio and Total return for the Standard and Poor’s 500 (SPY 500) and the Russell 2000. We will use the SPDR S&P 500 ETF (SPY) as a proxy for the S&P 500 and the iShares Russell 2000 ETF (IWM) as a proxy for Russell 2000.

The Standard and Poor’s 500 is an index that tracks the performance of the biggest 500 companies in the United States, weighted by market capitalization. The Russell 2000 is an index that contains the 2000 smallest securities from the largest 3000 largest companies in the U.S stock market.

The Python code for the Sharpe Ratio

The first step is to import the Python libraries:

The second step is to set the risk-free rate, which in this article will be 3%:

Next, we need to download data for our Python test.

The start and end periods will be 2016-02-05 and 2021-02-01 respectively. We use daily bars:

The returns are estimated as the log difference from the closing price:

In the previous image, it was necessary to implement .dropna() to eliminate the first row that contains a NaN value; see line 12.

Next, we need to estimate the yearly mean return as np.mean(russell_returns) * 252, it is necessary to multiply by 252, because our mean return was on a daily basis. The code to obtain the yearly volatility is np.std(russell_returns) * np.sqrt(252), because the volatility increases with the square root of time:

The next step is to calculate the Sharpe Ratio expressed as in the equation further up in the article:

The total return is estimated like this: where (russell_close[-1]) and (russell_close[-1]) are the first and last element from the closing price:

In the next line, we need to obtain the same financial ratios for SPY. We use the previous code, and replace the old symbol with the new symbol SPY:

How to calculate the Sharpe Ratio in Python – result interpretation

The Sharpe Ratio for Russell 2000/IWM indicates that for each excess return () of 0.57% the volatility is 1%. This implies that to get a yearly excess return of 16.25% – 3% =13.25%, the volatility of that investment will be 23.29%.

For instance, let’s calculate the bands at one volatility interval:

Volatility + Excess Return =23.29% +13.25% = 36.54%

-Volatility + Excess Return = -23.29% +13.25% =-10.04%

So, in one year, annual (yearly) excess return could range between -10.04% and 36.54%.

Similar Posts