Back to top

Image: Bigstock

Have an Options Trade Idea, Backtest It

Read MoreHide Full Article

Sophisticated option traders are always looking for an edge – a built in advantage that puts the odds of success on their trades.

One tool available to traders is the analysis of how a given strategy would have performed at one or more instances in the past – or “back testing.”

Not all that long ago, traders used to spend days and weeks analyzing historical data by hand, looking for clues that would point them toward profitable trades. Today, huge databases that can be queried easily and commercially available software tools allow even small individual traders to analyze historical data very efficiently.

Let’s take a quick look at two methods of back testing that tend to work well – and one that doesn’t.

Trade Optimization

This is a fairly simple form of back testing that is still quite effective. The basic concept is that the trader starts with a market thesis and then analyzes the past to see which trades have historically offered the best risk/return profiles in similar situations.

For example, if a trader was bearish on a particular stock, he might want to know what the best way is to express that short thesis in the markets.

Back testing would allow him to look at the relative effectiveness of various ways to take a short position and compare what the results would have been had he employed those strategies during various points in the past.

The analysis can be as simple of complicated as the trader desires. He might compare: selling the stock short, buying puts and/or selling calls at various amounts of moneyness, buying vertical put spreads, selling vertical call spreads, or an almost infinite number of other combinations.

Then he would pick the historical period(s) he wanted to use for the analysis. He might choose a random sample, or a continuously rolling period. He might choose specific periods in which the conditions that brought him to the short thesis were similar to the present, or he could “cherry-pick” times when he knows the stock price fell rapidly (as he is expecting again now) just to see which strategy is most profitable.

Finally, after the results are tallied, the trader could choose the strategy that best suits his capital and risk constraints. He might choose the strategy that was profitable the highest percentage of the time, or the strategy that offers the biggest payout when it is correct. He could also combine those into essentially an expected value for each strategy.

If he’s really being sensible, he will also look at the possibility for losses if he is incorrect and tailor his approach toward avoiding crippling drawdowns. It’s inevitable that every trader will take a few shots right on the chin. Keeping the size of the losses manageable is the key to living to trade another day.

Opportunity Notification (Screening)

This strategy is a bit more complicated to set up and can take some serious computing firepower to run, but it has the advantage that the trader doesn’t need to start with a directional hypothesis.
Assume our trader thinks that big moves in the implied volatilities of options tends to be followed but a pullback from extreme volatility levels as the markets digest the true possibilities for movement of the underlying.

He can search historical price data for instances in which the implied volatility of at-the-money options increased or decreased more than 30% in a single day, then compute the percentage of the time that implied vol reverted to some mean level.

Once again, the analysis can be as simple or as complicated as the trader wants to make it.  It could be a search across all stock symbols in the database, a specific list of symbols, or a single ticker. He can choose a specific date range. He might exclude expiration months that include a quarterly earnings report or filter out results in which the change in implied volatility was accompanied by an outsized move in the underlying security itself.

Once again, the number of combinations of parameters to use in the analysis is virtually infinite, and this is where a trader can create an edge for himself that provides risk-adjusted profits in excess of the risk-free rate – as known as “alpha.”

If the analysis confirms the theory about the volatility retracement, the trader can use the software to continuously monitor the markets in real time for opportunities to execute the trade.

Smoke and Mirrors

In both of the above examples, the trader started with a hypothesis about how the price of a security would move in a given circumstance and then consulted historical data to prove (or refute) his hypothesis. There is another way that traders sometimes use historical data that is fraught with hidden risks and is likely not only to be unprofitable in the long run, but actually puts the trader at risk for spectacular losses.

It’s the case when a trader looks at the current situation, then uses historical data to make a prediction about what is likely to happen next.

Example: The S&P 500 opens 25 points lower and then drops 25 more points in the first 15 minutes of trading. The trader scans 30 years of historical data and finds that in the 22 instances in which a move like that happened, 17 times the market closed down less than 20 points that day. Believing he has unearthed an opportunity for a trade with a win percentage of better than 75%, he buys S&P futures.

On the surface, this seems like a sound trading strategy – who wouldn’t take a .770 batting average on their trades, right? In reality, it’s probably a losing strategy, because it ignores a couple of important factors.

The first is that the external conditions may well be very different. The reasons for the decline are unique each time and the focus on price movement in a broad index is like staring at a single tree in the forest.

The analysis also ignores the magnitude of the losses when the trader is incorrect. It’s possible or even likely that although the trade is profitable 17 out of 22 times, the losses in the remaining 5 instances vastly overshadows the 17 small gains. Even though the trade has a positive win percentage, it’s a long-term money loser.

Finally, if the trader is a devoted disciple to the strategy, it’s perversely likely to lead him into terrible trades at the worst times.

In the above example, assume the market continues to head lower after the initial trade. The trader runs the analysis again and finds only 6 instances when the S&P declined 75 points in the first half of the trading day and 5 of them were followed by significant recoveries. The odds are actually getting better! The trader increases the size of his losing position. This is a violation of the most basic trading logic, but if he believes the original premise, he must logically believe it more now.
In a continued down trend that has been caused by aberrant external events – the kid we tend to see every 5-10 years in the broad markets – the trader will eventually lose everything following this strategy.

There’s an infamous money manager who has written books that include following (slightly more complicated) versions of this strategy. He has also lost the entire value of his fund…twice. You can look it up.

The real problem as I see it is that the trader didn’t start with a hypothesis and then ask history whether the resulting trade(s) would have been profitable during similar situations in the past, but rather reached for the possibility that history would show him what to do.

Back testing is a powerful tool, but you still need to do the hard work of creating a hypothesis, then act like a scientist and determine whether history suggest you’re likely to be correct. That’s how you create edge for yourself.


Want to apply this winning option strategy and others to your trading? Then be sure to check out our Zacks Options Trader service.

Interested in strategies with profit potential even in declining markets? Maybe our Short List Trader service is for you.

In-Depth Zacks Research for the Tickers Above

Normally $25 each - click below to receive one report FREE:

Cboe Global Markets, Inc. (CBOE) - free report >>

Published in