How to trade this market better? Is this question really bugging you?
As a technical trader/investor, most of our energy is spent in knowing which is the best indicator, which is the best timeframe, which is the best symbols to trade. The key answer lies in Bayesian Thinking.
In this tutorial, I am going to keep the math outside and gonna take only the underlying philosophy behind the Bayesian math to take better decision making towards trading.
Bayes’ Theorem was used to crack the infamous Nazi Enigma code during World War II. Alan Turing, a British mathematician, used Bayes Theorem to assess the translations culled from the Enigma encryption machine used to crack the German messaging code. Naive Bayes Filters are popularly used in Email filters to predict/classify whether the given mail is Spam or not. It is used in many real-life scientific applications especially in an uncertain environment where decision making is critical.
Bayesian thinking is all about continuously updating our older belief about our trading system/indicators with the newer information about the markets (Events, News, Volatility, Trading Sentiment, How traders behave in a particular environment, Global Markets,..etc).
What is Bayes Theorem?
I am using here the example from the Veritasium channel to explain the philosophy behind Bayes theorem.
By continuously updating our belief based on the new evidence is where bayesian plays a major rule especially in an uncertain environment and thereby taking better decision making.
Now you might wonder how to make trading decisions when there are extreme events like coronavirus fears, Iran Missile Attack, North Korean tensions, Demonetization, Saudi Aramco Attack..etc, and many other extreme market events which impact the markets in a very short term.
As a trader, it is highly critical to take objective decisions during uncertain times because that could directly contradict your prior belief about events and more than that it could bring bigger damage to the portfolio if false positive belief got traded.
So Keeping a continuous track record of how markets reacted to events, how your indicators behaved during events provides you a fair idea of whether to trade the events or not. If yes to trade the event day then how one should prepare trading it. What are the likely scenarios? Such preparation helps you to see reduce the false positive in your decisions.
Market Profile, Orderflow and Bayesian Thinking
Both in Market Profile & Orderflow, we give more importance to the ongoing newer information than what happened in the past i.e focusing on what is happening right now. By reading the past data of Market Profile Charts & Orderflow one could predict what is likely to happen (prior belief). However by focusing on what is happening right now, one could adjust their prior belief and not be so rigid about the prior belief as the newer information is presenting a change in context (Posterior Belief) from the prior belief. Thereby End of the Day you look like less of an idiot when dealing with uncertain market conditions.
Bayesian thinking help traders especially when there are two contradicting opinions. For Example, think about multi-timeframe analysis, let say hourly charts are indicating a buy however, daily charts are indicating a clear sell. View from the daily charts directly contradicts with the view from the hourly charts and puts traders in a dilemma. In such a scenario focusing on what is happening right now helps traders to stick objectively, whether to take a trade decision based on hourly or to wait for some more time to observe newer information so that one could even align their context with the daily charts.
Truly I find the Bayesian approach Interesting in Market Profile & Orderflow studies and could be applicable in the field of technical analysis.
It helps traders to organize the data and build a trading framework where newer information changes the view of our prior belief and helps us to change the opinion when the dynamics of the market itself completely changing and thereby better decision making.