If you want to check your credit, you look up your credit score. If you’re going to measure the length of a room, you use a tape measure. But how do you evaluate your portfolio’s performance? How can you determine if your portfolio return is reasonable for the level of risk?
Traders use many different metrics, statistics, and formulas to evaluate their portfolio’s performance. What do they mean, and which one matters most?
Today, we’ll unpack some of the popular ways to analyze and evaluate portfolio performance. As is typical in investing and trading, there’s no single way to do this that works perfectly for all investors. There’s no “one-size-fits-all” approach, and we would argue that the combination and comparison of different tools to evaluate performance is more constructive anyways.
Examining backtest results
To evaluate performance, we’re going to look at a backtest from the Backtester. You can assess a live trading account’s performance or use a backtester to examine historical performance.
Backtesting lets you test different combinations of position or portfolio allocations to optimize strategies and test your ideas before live trading.
The following example examines a strategy from the Backtester’s Discover tab. The strategy, an AAPL monthly short put spread, ranked well with a high return on risk while having a relatively small value at risk.
The tables below display a series of screenshots from the Backtester showing performance metrics for this monthly AAPL short put spread strategy.
The first set of statistics is pretty straightforward. Return on capital compares the total profit or loss to the starting capital to display the percentage return over the strategy’s life. In this example, $310,409 of profit on $100,000 of starting capital is a 310.4% return on capital.
Return on risk compares the overall profit and loss to the maximum amount of risk for the strategy. For this example, the overall profit and loss of $310,409 divided by 10% of the portfolio’s capital risked per position is 343.4%.
Overall profit or loss sums the outcome from all trades to give you the total dollar amount of profit or loss for a strategy. Value at risk is the amount of capital risked on each position.
The compounded annual growth rate is the geometric or compounded average return for a strategy. CAGR gives you the compounded annual return that yields the total return of a strategy. Annual CAGR puts the total return in an annual context and allows for performance comparison on a relative timeline--yearly returns.
Global or top-level stats like these give you a birds-eye view of how the strategy performed historically. These stats are helpful because they provide context to dollar amount returns.
For example, CAGR is more applicable for multi-year comparisons, and return on risk displays returns in light of the amount of capital risked to generate those returns.
Remember, “past performance is not indicative of future returns,” and you should consider the market environment during the backtested period.
The second set of statistics digs a little deeper in evaluating portfolio performance. These metrics help you understand a portfolio’s performance while also taking into account volatility. Strong performance is great, but if you cannot stomach the volatility along the way to those great returns, then what good is the strategy?
The Sharpe ratio compares a portfolio's return to its volatility. The higher the Sharpe ratio, the higher the return for a given level of volatility. Instead of comparing returns on an absolute basis, the Sharpe ratio puts returns into context, given the volatility experienced to earn the returns. Portfolio A may have lower returns than Portfolio B, but because Portfolio A has lower volatility than Portfolio B, Portfolio A's Sharpe ratio may be higher. A higher Sharpe ratio demonstrates that Portfolio A has a higher risk-adjusted return than Portfolio B. The Sharpe ratio is a standard performance measurement because it provides an apples-to-apples comparison for diverse portfolio compositions. A portfolio’s Sharpe ratio is often compared to the Sharpe ratio for an index or broad ETF, such as SPY.
The Sortino ratio is similar to the Sharpe ratio but focuses on negative volatility in a portfolio. In calculating the standard deviation for the Sortino ratio, only the returns that lie below the mean are used. Therefore, the Sortino ratio gives investors a way to evaluate investment return based on its downside risk only. For example, portfolios with high levels of performance tend to have a higher standard deviation of returns. However, if the returns are +10%, +30%, +15%, and +50%, the high standard deviation of returns is misrepresentative because the volatility has all been to the upside.
The Calmar ratio uses a portfolio’s maximum drawdown to measure its risk and compares it to the compounded annual rate of return. The Calmar ratio typically uses a lookback period and is updated monthly. The lookback period for the Calmar ratio may be a set number of years or, in this example, the life of the backtest. A high Calmar ratio equates to a lower risk of large drawdowns, while a low Calmar ratio indicates a higher risk of a large drawdown.
The max drawdown looks at the peak to trough decline in account value to demonstrate the largest decline--or drawdown--in the account. The max drawdown of a strategy is an important statistic to consider because your tolerance for significant changes in account value may impact your ability to stick with a strategy during a period of sub-optimal performance. If you cannot stomach a 30% drawdown, perhaps you should consider a strategy with less volatility. A 30% drawdown (from $10,000 to $7,000) takes more than a 40% increase to get back to the account’s peak capital level (from $7,000 back to $10,000).
Win/Loss metrics and position averages
Win/loss metrics describe the total number of positions, the number of wins and losses, the largest dollar amount win and loss, and sequences of wins and losses. Positions averages display similar information as the win/loss metrics but on an average basis.
Many traders are comforted by a high win-rate. A win-rate above 50% means more of your trades make money than lose money. However, a high win-rate is not enough for a profitable trading strategy if your losses are significantly larger than your wins.
For example, a 60% win-rate strategy where the average winning trade earns $50, and the average losing trade loses $80 is a losing strategy overall. A strategy with a high win-rate can overcome a situation where losing trades are larger than winning trades, but high win-rate strategies must have sufficient opportunities (think the number of trades) for the probabilities to play out long-term. So, how do you balance win-rate and average profit and loss? This is where the profit factor comes in.
The profit factor divides the total amount of money gained by the total amount of money lost. The profit factor is important when used in conjunction with win-rate because it creates a more complete view of your trading performance view.
For example, if an investor has placed 200 trades with a win rate of 60% and averages $50 per winning trade and $50 per losing trade, their profit factor will be 1.5 with a net profit of $2,000 (120 x $50 = $6,000) / (80 x $50 = $4,000). Compare this with an investor who has a 60% win rate on 200 trades but a profit factor of 0.5 because they average $50 per winning trade, but $150 per losing trade (120 x $50 = $6,000) / (80 x $150 = $12,000). They will have a net loss of -$6,000, despite the same win-rate.
You can take the profit factor a step further. A calculation similar to expectancy, a concept popularized by Dr. Van Tharp, can link win-rate, average profit, and average loss to calculate the expected profit or loss for a series of trades.
Here's how it works: consider a portfolio with a 40% win-rate, an average win of $100, and an average loss of $50. On average, 40% of the trades yield a $100 profit, and 60% of the trades lose $50. (0.40 x $100) + (0.60 x -$50) = $10. The expected value of each trade in the system is a positive $10. What does this mean? Over the long run, the expected profit or loss from each trade taken in the strategy is $10. So, it is advantageous to take as many trades as possible that align with the strategy!
Here's the caveat: you must have enough occurrences to allow the long-term probabilities to work. You should not extrapolate data from just ten trades or assume that the distribution of every ten trades will be similar. That would be like taking ten hot days in the summer and expecting the rest of the year to have all the same hot, summer-like days when we know there will be cold and rainy days.
The backtest had 140 trades with a win/rate of 90.7%. However, at one point in the backtest, five straight trades resulted in a loss.
Sequence of returns is the risk that the outcome of any small sample of trades could be completely random. For example, a strategy with a win-rate of 70% is still vulnerable to multiple consecutive losses. A system with a high win-rate and strong profit factor must still be combined with sound risk management.
Key takeaways for evaluating portfolio performance
As we mentioned in the opening paragraphs, there’s no perfect solution for evaluating portfolio performance.
It takes a combination of metrics and perspectives to understand if a certain strategy or model allocation will work well for your needs.
Here are a couple of key takeaways we think you should remember.
First, trade a strategy that aligns with your personality. If losses significantly impact your emotions, a strategy with a high win-rate may be easier to stick with when the inevitable drawdowns occur. Perhaps the possibility of the big winner will help you endure a series of small losses, so a strategy with a large average profit relative to average loss is important.
Second, to evaluate a portfolio's performance, you must have a trading log or journal capturing each trade’s results. Systematically evaluating portfolio performance requires detailed record-keeping and a consistent process. Good record keeping is imperative for performance evaluation.
Finally, different portfolio evaluation metrics tell different parts of the story. No single metric paints the entire picture. An understanding of the various performance measurements can yield valuable insights. What if your win-rate improves by 5%? What if your average loss decreases from -$50 to -$40? Incremental, compounded improvements can make a significant impact on your portfolio performance.