Trading Journal #3: Risk Management (part 2)
In the second part of our Risk Management series we discuss some important metrics for measuring your risk vs. your performance. Being able to evaluate your performance is key to managing positions.
(Before reading this, be sure to check out our previous post)
When building a risk management strategy, one of the most important factors is the ability to define and measure your risk and performance over time. Similar to how scientists need the ability to measure their experiments, we too need to be able to measure our portfolio and trades over time.
In this post, we will dive into several useful metrics that professionals use to measure their performance over time.
Note: none of this should be construed as financial advice. Do your own research, and practice proper risk management when investing.
First things first, we must discuss the concept of an index. An index is essentially a grouping of various signals, such as stock prices, that represent some kind of “average” (either statistically or metaphorically) of whatever you’re trying to measure.
Of course, there are indices in stock markets such as the Dow Jones, S&P500, Nasdaq, and many more. There even exists indices in crypto, such as the many tokens on FTX such as $ALT, $DEFI, and more. However, most traders tend to compare their trading performance against just buying and holding BTC over the long term. If you can outperform Bitcoin, you’re a better trader than most!
The purpose of the index is just that — determining if you can outperform the market. By comparing your performance to an index, you should be able to see periods in which you perform better or worse than the broader market. The ability to outperform the market is broadly referred to as alpha.
By finding these areas — particularly those of poor performance — you can better hone in on trades that went wrong, and adjust accordingly. We’ve mentioned before the importance of keeping a trading journal — this is one of those examples where being able to go back and reference past trades becomes critically important.
Furthermore, you can reference historical data to determine what sort of trade setups would have been better to take, whether you should have traded differently (or even not at all), or if there were external factors out of your control. No matter what, having an index to compare your performance against is a critical tool for traders.
The next step is to introduce some metrics that we’ll use to measure our performance against the risk that we take. There are many well-known options, among them the Sharpe Ratio (which we will discuss), but before we start diving into these we should mention that no single metric can tell you whether or not a trade, strategy, or portfolio is good or bad.
In the end, what we are looking for is a way to measure our success versus our risk over time. The Sharpe Ratio is a famous example. Developed by the economist William Sharpe, the Sharpe Ratio is a metric that evaluates portfolio performance compared to the total risk it assumes (Investopedia does an excellent job explaining how it works).
However, there’s a few problems with the Sharpe Ratio, particularly with respect to cryptocurrency trading:
- It assumes that the returns for the assets in the portfolio are normally distributed, meaning they fit a “bell curve.” This is highly unlikely, particularly in crypto, where volatility can be wild and certain asset classes (such as DeFi tokens) perform significantly better/worse than others.
- It compares against the risk-free rate, which in traditional markets is usually a U.S. Treasury rate — the United States has (almost) never defaulted on its debt, which makes bonds risk-free. However, no such investment in crypto is risk-free, including stablecoins (see UST and LUNA as recent examples). Smart contract bugs, hacks, economic attacks, and regulatory changes can affect the price of a stablecoin, and therefore cannot be considered “risk-free.”
As such, the Sharpe Ratio is less informative than we would like and therefore we need something better.
Enter the Sortino Ratio. The Sortino Ratio improves upon the Sharpe Ratio by focusing purely on the downside volatility. That is, it doesn’t take into account “good risk” and can therefore better describe the negative risk taken.
However, it still compares the returns against a risk-free rate, which in crypto is zero — there is no risk free investment in crypto. Therefore, you’re essentially dividing the return on the portfolio by the downside risk, creating a ratio similar to the Risk/Reward ratio.
This doesn’t help us much, particularly with numerous trades over time.
Instead, we’d like to find some kind of way to assess our performance versus while considering the risk we take per trade. This is where the Treynor Ratio comes in: the Treynor Ratio allows us to measure the performance of a portfolio (or series of trades) against each individual unit of risk taken. This is a lot closer to what we’re looking for.
The thing is, calculating the Treynor Ratio is a bit more complicated. It involves comparing our performance against beta risk, or the volatility of an asset as compared to the broader market as a whole.
Remember how we discussed using BTC as an index with which we compare our trading performance? This is where that index comes into play. Instead of using the risk-free rate (which is zero) we can instead compare our performance against the index itself. As such, we compare the volatility of our performance against the volatility of Bitcoin to get a better idea of the risk we’re taking.
If our Beta value is greater than 1, it means our trading performance is more volatile than the broader market. If Beta < 1, our returns are less volatile. And of course, if Beta = 1, it’s exactly the same. In this context, we want a low Beta as it means we’re more consistent than Bitcoin’s performance.
Beta is defined as such:
For our purposes, we can calculate the beta for each trade by calculating the covariance between the return on our trades versus the return on the overall market in the same period of time, and dividing it by the variance of the overall market for the same period of time. How do we calculate the covariance/variance for the overall market?
If this is starting to look like a lot of statistics, you may start to understand why professional traders and quantitative analysts tend to be very good at math.
DON’T WORRY — we’ll help you automate this process so your computer can automatically calculate this for you. The purpose of this post is to point out why we do this, so that when your computer does it for you, you can understand what the numbers actually mean. But what do they mean?
What does it all mean?
By calculating our Treynor Ratio, we’re able to discern two important things:
- When we take a risk by actively trading (either manually or with a trading bot), are we outperforming the market?
- When we trade, are the risks worth it compared to just investing in the index?
To wrap things up, let’s make an arbitrary example:
- The total return we make over a year of trading is 100% — that is, we double our money.
- In the same period, Bitcoin goes up by 80%.
- Our Beta value is 2, meaning our trading returns are more volatile than just holding BTC.
In this scenario, our Treynor Ratio is such:
In most scenarios, a Treynor Ratio of 10 is absurdly high. However, this is cryptocurrency we’re talking about and high volatility is part of the game. If your trading can outperform Bitcoin by 25% as it did in the above example, you’re doing an excellent job.
By calculating and understanding this information, you should be able to better inform yourself as to the risks you’re taking. If your Treynor Ratio is low, it’s probably because your Beta risk is high — you’re overtrading or taking too much risk. Slow down, trade less often, take less risk, and be more certain before entering trades.
In the addendum to this post we will include some example Python code as well as spreadsheet formulas that you can use to dive into your performance. I encourage you to really think about what each of these variables mean: what kind of Beta risk are you assuming when you trade? How can you minimize this? These are the questions that professional traders ask themselves.
All in all, you shouldn’t be using a single metric as a goal — instead, your goals as a trader are likely twofold:
- Don’t lose money
- Make more money than the index
By utilizing these metrics you should have a better understanding of your performance and be able to see your improvement over time. If you’re not improving over time, it may be worth reevaluating your strategy.
Stay tuned for Risk Management post #3, where we dive into the concept of hedging — allowing yourself to be prepared for volatility and being ready for the market to move in any direction.