# Put your drawdown into context (quickly) with the Calmar ratio

# Put your drawdown into context (quickly) with the Calmar ratio

Every investor faces drawdowns. But that doesn’t make it any easier to deal with. What helps is to put drawdowns into perspective by relating them to annualized returns.

The Calmar Ratio is perfect for this.

It considers both returns and risk providing perspective on drawdown. Traditional metrics like the Sharpe and Sortino ratios don’t consider drawdown, only volatility.

When I started trading, dealing with drawdowns was always a struggle. Finding a metric that balanced helped put them in perspective really helped. I always say no single metric paints the whole picture but the Calmar Ratio helped put things into focus.

By reading today’s newsletter, you’ll be able to use Python to calculate the Calmar Ratio.

Let’s go!

## Put your drawdown into context (quickly) with the Calmar ratio

The Calmar Ratio is a key tool for risk analysis in trading. It compares the compound annual growth rate (CAGR) to the maximum drawdown (MDD). The Calmar Ratio was invented by Terry W. Young.

It helps us understand how much return we are getting for each unit of risk.

To use the Calmar Ratio, you first calculate the CAGR, which measures annual growth, and then determine the MDD, which measures the largest drop from a peak to a trough. Finally, you divide the CAGR by the MDD to get the Calmar Ratio.

Professionals use the Calmar Ratio to perform risk assessment across different investment strategies. It assists in identifying robust strategies and making strategic adjustments to improve stability and performance. Regularly updating the Calmar Ratio ensures that trading strategies remain resilient under changing market conditions.

Let's see how it works with Python.

### Import necessary libraries and prepare sample data

First, we need to import the necessary libraries and create a portfolio.

```
1import empyrical as ep
2import yfinance as yf
3import warnings
4warnings.filterwarnings("ignore")
```

We begin by importing `empyrical`

which is designed for financial calculations and yFinance for downloading market prices.

Let’s create a sample portfolio with the Magnificent 7 tech stocks that are driving most the market returns.

```
1symbols = [
2 "AAPL",
3 "AMZN",
4 "GOOG",
5 "META",
6 "MSFT",
7 "NVDA",
8 "TSLA",
9]
10
11data = yf.download(symbols, start="2020-01-01")["Adj Close"]
12returns = data.pct_change().dropna()
13portfolio_returns = returns.sum(axis=1)
```

Using yFinance, download price data, compute returns, and aggregate them together to come up with portfolio returns.

### Compute the Calmar Ratio using Empyrical

Now, we will use the Empyrical library to compute the Calmar Ratio for the returns data.

```
1# Compute the Calmar Ratio using Empyrical
2calmar_ratio = ep.calmar_ratio(portfolio_returns)
3print(f"Calmar Ratio: {calmar_ratio}")
```

We use the `calmar_ratio`

function from the Empyrical library to compute the Calmar Ratio for our returns data. The function takes the returns as input and calculates the ratio.

The negative Calmar Ratio tells us this portfolio has a negative average annual return when compared to its maximum drawdown. In other words, for every unit of risk, the return is not only insufficient but actually negative.

Maybe not so magnificent after all!

### Your next steps

Now that you know how to compute the Calmar Ratio, try experimenting with your own portfolio returns. You can also explore other functions in the Empyrical library to perform more advanced financial calculations.