Conquering the Caveman Brain: Your Key to Successful Trading and Investing!

Your "Caveman Brain" vs. Your Trading Account: Why Emotions Sabotage Your Plans!

The concept of the "Caveman Brain" refers to the primitive part of our brain, primarily the amygdala, which governs survival instincts and emotional responses. This region is designed for rapid threat detection, triggering the fight-or-flight response to keep us safe. However, in the modern world, this mechanism often reacts to non-physical threats, such as financial risks, leading to instinctive reactions that may be detrimental to trading and investing success.

During the caveman era, humans would venture out in groups to hunt, driven by the primal necessity to secure food for themselves and their families. The animals they pursued were often fierce, and more formidable the beast, more precise the caveman's ability to swiftly detect and respond to the speed of that beast was required. The job description was fastest detection of speed and fastest anticipation of any upcoming threat. Any delay or misjudgment would be met with the most severe punishment of all: death!

The Caveman’s Neural Network (cNN)

Now, let's consider a neural network tasked with the above requirement. What would it be like? I guess it shall be a series of two networks.

The task here is to detect speed or rate of change in object’s speed. The first network shall take input from visuals, sounds, touch and smell. It shall be tasked to detect the rate of change of these signals. Based on the severity of rate of change of input signal it shall pass on the output signal to next network. The next network would work on the severity of the signal received from first network, analyze context and then give a reactive output of fight or flee.

The reward and punishment system for this neural network during its training was highly immediate and binary. Immediate gain was given the highest reward, while immediate loss was given the highest punishment. In contrast, potential future gains and gradual declines were largely ignored, with no punishment assigned.

So, A caveman’s brain, which processes critical signals, would then consist of neurons trained perfectly to detect volatility, i.e., the rate of change in a signal received from various senses like eyes, ears, nose or touch. The higher the volatility, the stronger the signal confirmed by this neural network which shall then be used in next network to decide whether to fight of flee!

If an object like a hare or deer was crossing across the caveman, the network shall identify the motion of these objects as ‘Food’. If that object was tiger, it would then be classified as ‘Threat/ Danger’. If the men were in a group, they might choose fight as a response or flee depending on what the overall group did. But if the man was alone, the neural network would definitely give a flee response. This neural network holds highest priority or an automatic priority over all other networks put together.

We are born with this Hardware whose priority is survival. Our rational thinking network is only valid when things around us are still and we are not in them. The moment, this changes the caveman’s neural network is holding the command. It is the primary decision maker which only sees current situation.

How this Impacts Trading?

Let’s say you have a back tested strategy and insight around the tradable instrument. Your rational mind is in complete agreement with this. Now you put on the strategy to work and start watching how the trade goes.

As you are born with this hardware of caveman’s neural network (cNN), the volatility to this part of brain is food or threat. It shall decipher it as food or threat depending on the overall context at play.

Let’s, say the trading system/ strategy you initiated put a buy order at 21.25$ rate. You are watching the screen for past 2 hours and the price of ETF was trading at 21.9$ and then was gradually moving down. The price of the ETF eventually went to 21.3$, and then it sharply moved up to 21.9$ rate in less than 1 minute. A sharp move in price shall get you excited as it is detected as food. In this context, cNN shall treat it as a lost opportunity and assign a penalty for not able to capture the trade. This might impel you to override your strategy and put in a buy order at 21.9$ rate. Once you modify the order to 21.9$, the price moves to 21.95$ without you. You lose patience. Loss of patience happens due to, severe penalty assigned by cNN for inability to get the food. You then place the buy order at 22.1$ and get in. You are inside the trade. Now the context changes completely. cNN’s anticipation for getting the food has ended. cNN switches to alert mode to detect any move against your expectation. A sharp move in price of stocks or ETFs we are holding, which goes against our expected opinion shall be considered as threat!! Same volatility with different context gets treated as food or threat by our caveman system.

The ETF price starts to move down and hits your 21.25$ initial buy order level! You sell your trade in loss and protect yourself. All this happened for what? Did you need that food? Was that a real threat?

It is impossible for a rational mind to overcome this cNN as it is the hardware we are born with.

Machines at Work

The machines have learnt from the price action data, the statistics of the Caveman’s Brain in various timeframes. Machines can open hundred thousand positions simultaneously, which might help to diversify the risk i.e. keep the volatility under control such that it completely avoids the triggering of caveman’s brain response from the human operator who is running that machine!

The immediate threat response system (the Caveman’s Brain) is finely tuned to respond to rapid, high-volatility movements. Some investors might have their threshold for response to take some action at a -6% drop in portfolio value within a week, while others might tolerate a -21% decline over a month. However, if the volatility remains within these bounds tolerable by the respective human, and the asset's price declines gradually, the investor/trader may remain indifferent until the loss on net portfolio exceeds -80%. At that point, the caveman’s brain—still governing the investor’s/trader’s instincts—finally awakens, prompting a sell-off. This is the moment the machine, with lot of statistics and back test and real-time data of what exactly is happening, might execute a buy.

Overcoming the cNN defect for getting consistent Profitable Returns:

What we know about ourself now is that, we are very reactive to volatility in either direction. To understand your thresholds, you might consider doing intraday trading with position size that shall affect you but not destroy you. Let’s say you start with trading 1 quantity of 21$ rate ETF. You trade in and out all the day, burning your money giving brokerage and taxes. Next day you do 100 quantity same ETF trading. And a day after that you do 1000 quantity same ETF trading. You know how sudden the moves of 0.1$ are feeling now. If you are born rich, your quantities would be completely different.

This exercise might give you some idea about your thresholds. That I guess, would be the acceptable starting bounds of volatility for the intraday, trading machine you develop or a trading strategy you might run.

Once these bounds are known, you can reverse engineer the position size per trade. Let’s say the volatility of the ETF in a day is bounded by -3% to +3% and threshold of your cNN to get active is -100$ or +100$. That means, the position size of your trade in the ETF can never be more than 100/0.06 = 1666$. 3% of 1666$ is 49.98$. In a worst case scenario of motion of ETF from +3% bound to -3% bound, your net max loss level shall always be bounded below 100$.

Once the position size is found you run your super back tested trade strategy, a strategy where you have a very good insight which is accepted by the rational part of your brain. The cNN shall never interfere and eventually, it might happen that, you get consistent profitable trading outcomes.