The Real Reason You Move Your Stop

Trader's Mindset

Sep 09

Today, the S&P 500 took out both sides of the prior day range and presented opportunity for some large losses. Here are the real reasons that you move your stop and how to prevent it:

  1. Markets relentlessly target the optimal stop. Over time, traders recognize that markets often reverse at stops. The only way to see what happens is to give the trade more room– more room equals bigger losses. This line of thinking opens up possibility for cognitive bias. If additional room is needed then that should be clearly recognized upfront.
  2. The trader seeks the optimal but does not factor in the cost or constraints. The optimal stop changes as the market unfolds but tends to “jump” and become larger. Markets have tendencies to move in certain ranges. I think traders intuitively know this. If a trader could go back in time and change their position size, changing the stop loss would probably work very well. But, that’s impossible. The result is the trader identifies the new optimal but to take advantage of it must either exceed their risk limit or give up a trade that can likely recover.

    A real example is one of my systems has at least 2 optimal stop settings. I have seen the market clearly target both of them. One is much larger then the smaller one. The point is that if I put the trade on with leverage for the smaller stop– widening to the larger stop might be optimal but to take advantage then I would exceed my risk limit.
  3. The trader is not really clear about what they are trying to do or the wavelength they are trading. A trader might be generally bullish but without a defined plan can lead to larger losses then anticipated.
  4. Trader only considers growing profit potential and not risk. Volatile markets increase both reward and risk. If a trader sees the market as likely to mean revert, as the market moves away from value then the reward grows. The risk is that the market is really trending away from prior value.
  5. Compound errors, surprise, and loss aversion. It often happens as a result of multiple mistakes or errors. A trader enters a trade with a mindset that they will only lose $X on a trade. An order entry mistake or stop loss is blown and now the trader is down more then $X. Because the trader never considered to be down more then $X, the trader may not be psychologically prepared. At some point most people will quit trading.

The following can help prevent moving the stop loss:

  1. Clearly define your trades. Thinking about trades statistically, means falsifiable/quantifiable. Statistical trading can only work if the stop is adhered too. Be on guard against general or non-well defined plans.
  2. Clearly recognize when trades may not be “statistical” and try to structure them appropriately, i.e using options or with larger purposeful predefined stops.
  3. Control position risk through size.
  4. Use daily loss limits.
  5. Use external broker risk control.
  6. Mentally rehearse unexpected scenarios and make a plan.
  7. Switch tactics. If you are trying to get into a bigger trade but having trouble with the stop loss, move to the options market or reduce size.

For example, yesterday I was reading the market very accurately. However, I was thinking about a general bullish trade I wanted to put on at small size. This led to unclear trade and combined with some other factors, I took a larger loss then I really needed to take.

When the market traded down to the lower 2870’s, in addition to some trades I took based on my quantitative signals, I recognized I still wanted to express this general bullish thesis. But, that I didn’t have a good stop in mind. As such, I opened a risk limited 2-day call spread on the ES 2980-2990.

The key is recognition of proper tactics for the trade type you are structuring. And, that should be based on your prediction and how you want to formulate it.

About the Author

The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at