Even your best trading ideas won’t always go as expected. When you are looking at a losing position, employ game theory to guide you.
Use a decision tree to help define possible scenarios for your losing positions
Consider a list of possible moves you could make if a position is losing money
A well-thought-out decision tree can keep your emotions in check
We’ve all been there. You’ve got the best-laid plans for your trades. Yet, you know in your heart you can’t win ‘em all. Sometimes even your most solid ideas go south. But you also know the importance of an exit strategy. When you get in, what are your chances of getting out with a profit? And if the trade goes against you, what’s your pain point on the downside? Do you have a time limit, a dollar limit, or both?
Game theory might conjure images of math PhDs crunching complex equations. But at a basic level, it’s just codifying what you do every trading day—infusing your decisions with logic, reasoning, and yep, a bit of math. It all starts with the “decision tree”—a map of a trade’s possible outcomes. This includes steps you might take the possible outcomes of those steps, and so on, right down to when you would want to close out the trade. Despite the name, decision trees take the decisions out of the decisions.
In game-theory geek-speak, the sequences of steps are called “nodes.” After building a decision tree, you play the game in reverse. You know the endpoint (end node) of each path in the sequence, so you could let the game play itself. It’s like a chess master who looks at the board and knows the best response to an opponent’s any move. That’s because the chess master has played the game in reverse, all the way back to that current spot in the game, from each possible node.
On the bright side, your trade’s life cycle won’t have as many possible paths as a chessboard. Your tree won’t involve thousands of nodes—just a few. Yet, the concept is the same: Build the scenarios and possibilities, and let things run their course. When decision time comes, consult the tree.
Let’s say you put on a trade in hopes of making a profit. Although dealing with profit is a part of any trade’s decision tree, let’s focus for now on losing positions—decisions that must be made when a trade goes against those profitable expectations. Or as a famous boxing champ once quipped, “Everybody has a plan until they get punched in the mouth.” Here’s how to build a decision tree for a trade that gut punches you.
Say you just bought a stock. Perhaps you spotted a nice trend setup and decided it was time to jump on it. Turns out you were wrong. You entered too early and find yourself in a losing position. Now what?
Because losses typically come in two varieties—either you lose a little, or you lose a lot—those become the first two nodes of the tree.
With the first set of nodes in place, you could build the next layer. For each of the two loss scenarios, consider these questions. First, how quickly did the stock move? Was it swift and severe or a slow bleed? Or, put another way, what’s the stock’s implied volatility (IV), and how does it compare to historical volatility (vol) levels? Second, what caused the loss? Was it a trend breakout or reversal that turned out to be a false positive (“head fake”)? Or is there news that could impact the company’s fundamentals, such as a scandal or “accounting irregularities”? In other words, does the reason for the adverse move change your overall objective?
Next, it’s time to build the final layer. Consider a list of possible next moves (see decision tree, below):
A WALK THROUGH THE LIFE OF A TRADE. Using a decision tree.
For illustrative purposes only.
You now have two scenarios outlined: small loss and big loss. And you have five ultimate choices for each scenario. But each one has different probabilities based on price action and vol. Now it’s time to review your goals and ask yourself whether owning the stock can get you across the finish line.
Using a decision tree can help you take the emotion out of the trading process, especially when you’re facing a losing trade. When the market goes against you, consider following the tree.
Want to take game theory to the next level? Try assigning dollar values to the outcomes of your scenarios. There are no guarantees or exact probabilities, but the market does offer some clues. For example, the IV of a stock is the market’s best guess of the stock’s potential price range. Historical vol is a measure of a stock’s actual variability over a specific time period. You can find both measures in the thinkorswim® platform from TD Ameritrade. Look at the Options Statistics at the bottom of the Trade tab. (For more on how to use these statistics, see thinkTank.) This can help you define the parameters—winner, small loser, big loser, and so forth—and assign an annualized level of vol.
For options positions, you can use options delta to approximate the likelihood of a position finishing in the money or out of the money. (For more on delta see Delta: A Little Goes a Long Way.)
Life would be more fun if all trades were winners, but that’s not the way it works. If you can define the possible scenarios, probabilities, and your best move at any point in time with a well-thought-out decision tree, you can keep your emotions in check. And let the “theoretical” game play itself out.
When planning an exit strategy the game theory way, build each node to the end, then play the game in reverse. Want to shift into reverse for real? Fire up the live trading screen on the thinkorswim platform and try out the thinkOnDemand backtesting tools. You can backtest stocks, options, futures, and forex, right down to the tick level, all the way back to December 2009.
Want to know how your trading strategy might’ve fared during the 2016 election? Make sure you’re using your virtual account (paperMoney®), then roll the calendar back to November 2016, place your simulated trades, and watch the action unfold.
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