Understanding Random Reinforcement in Trading

By | September 28, 2019

Few Trading Facts

  1. 80% of all day traders quit within the first 1 year  of there trading most of them trade in Options and use brokers who give them High leverage of 10X
  2. Traders with having losses for 5 years or more continue to trade.

The last point suggests that traders even continue to trade when they receive a negative signal regarding their ability.

When traders first begin their trading journey they are generally not prepared, or even aware of how much of a psychological challenge trading really can be Most of them learn many hard lessons when they Bust there trading account.  They underestimate the psychological challenges of trading and fail to eliminate emotion from their trades.

Trading is not just about a strategy, Its more of a psychological game and having the right mindset.

They fail to trade with a defined system. When they have a defined system, they often take trades outside of their own, established rules.

Today we will discuss about an important psychological challenge   Random Reinforcement. Not many Traders are aware how dangerous random reinforcement can be to a trader’s mental integrity. In fact you may already be under attack by this psychological black hole this very minute and not even realize it.

Pavlov’s dogs Experiment

Let’s think of a person who is trying to train their pet dog. The owner wants the dog to sit down on his command. So every time the owner says ‘sit’ and the dog sits down, the owner gives the dog a treat. The owner has just positively reinforced the good behavior and will encourage the dog to continue to sit on command in the future.

Every time someone walks past the front of the house, the dog starts barking aggressively and is annoying the rest of the neighborhood. To train the dog to not do this, every time the dog barks inappropriately, the owner will spray the dog in the face with water, which the dog absolutely hates. The owner has negatively reinforced the bad behavior to discourage the dog from doing it in the future.

So by rewarding good deeds and punishing bad deeds the dog will learn how to behave appropriately in the future.

When the dog starts barking at people walking past, the owner might come out and spray water in the dogs face, or he might run out and give the dog a treat,depending on  what kind of stranger which Dog do not know.

What is this going to do to the dog? Well it’s going to stress the dog out to its limits, the dog won’t be able to tell what’s right and what’s wrong, and will probably develop anxiety problems around the owner.

This is random reinforcement at work here, because the owner is randomly responding to the dog’s behavior with a positive or negative response. The dog will be unable to operate effectively because it’s so confused by the responses from its owner.

From Trading Perceptive The market has a tendency to reward bad habits, while concurrently punishing positive behaviors, especially with a small sample set. Let’s take a theoretical example to display this principal.

Ram wants to leave his job and become a full time trader. He sets aside  starting capital of 10 Lakh, try to follow  “big names” on twitter. The Trading Guru was talking about Index looking bullish so Ram opens the chart and sees that price is rising fast. He buys, Volia in matter of Minutes he sells for a quick profit. He does this again for few more traders and make decent profit. Ram starts to feel confident that he is a talented trader.

What is “random reinforcement”?

So what is the problem? Ram is trading without a system or a plan and is being fooled into believing that a successful outcome on a few random trades is indicative of likely success moving forward. The market has rewarded his bad behavior. We know how this story ends — Ram continues to make impulsive trades and eventually loses his capital.

There is a flip side to this coin.

You have what you believe to be, an awesome trading system that produces profitable trading signal. You’ve back tested it and the results are great. You have even paper traded the system for a while and demonstrated positive results. So you’ve decide it is time to start trading with your live account. You open the account, deposit your capital and begin placing live trades, following your trade rules and never deviating from your trading plan.

5 trades later and you still haven’t had a successful trade. You can’t understand what went wrong and you start questioning the trading system and your approach with the markets and you start trading impulsive trades which are against the system.

The market has negatively rewarded you for your good behavior. You’ve stuck with your trading plan and been immensely disciplined, but the market slaps you in the face for it. No-one enjoys this experience. Yet every trader experiences it.

Through random reinforcement, the market has re-conditioned the way you approach trading by distracting you away from your original trading plan and you’ve allowed yourself to be manipulated into an impulsive, knee-jerk, high risk, revenge based trading approach. Do you see the problem here?

Through random reinforcement, the market has re-conditioned the way Ram approaches trading by distracting him away from his trading plan. He has allowed himself to be manipulated into an impulsive, high risk, revenge based trading approach.

Solution is to Stick to the Plan irrespective of trade Outcome

No more than 1% of a trading Capital should be at risk on any single trade — this is the key to sustaining multiple, consecutive losses. Suppose your capital is 5 lakh you should not risk more than 5K in any single trade.

A good trade should be defined as one where a trader planned their trade, traded their plan and managed their risk — those are all elements they can control. It is NOT defined by the outcome.

By developing a well-tested plan, traders can overcome the pitfalls of random reinforcement, eliminate emotion and impulse, and learn to be profitable.

One thought on “Understanding Random Reinforcement in Trading

Leave a Reply