A modern market approach consists in not trying to predict based on the gut feeling, technical analysis or even fundamental analysis.
The idea is to try to predict not the market direction but the market state.
1. Cluster the market states:
All the successful traders have in common that they try to distinguish the market state.
A common approach is to use the relationship between the time of the day in the forex markets and the corresponding volatility.
2. Appropriate Use of an adapted system for the period
So when a market state is identified it is necessary to use an adapted system for this market state.
Traditionally there are:
2.1 Intra-day Trend following system
A Moving average strategy is the archetype of those systems.
Neural nets perform very bad as a trend following systems (with some exceptions for Neural Nets specially tuned for those conditions).
Digital filters with genetic optimization do much better
2.2 Intra-day Range Bound system
Oscillator based strategy is the archetype of those systems. Modern oscillators appears to be much smoother but still all the oscillators shares a collinearity.
The neural nets perform well in those conditions.
Statistical indicators performs very well too. In fact the phase space is so big under such conditions that it is more appropriate to model it statistical instruments. The Bollinger bands as a rudimentary statistical instrument is a classic player in the Intra-day range bound systems.
2.3 Break - out system after a contraction of the volatility
Those systems are particular. For example a Neural Net cannot never ever predict a break-out in the forex market.
How to distinguish between those market conditions:
Well the obvious reason is practice, practice, practice and experience, experience, experience
The human mind and the human perception abilities are much more suited to distinguish those states than any machine. For example an image recognition, the machines are still not capable of doing it for now.
There are some methods and some new ones that will be covered later.
My opinion is that the human mind is appropriate to distinguish between the market states. We believe that when a market state is identified it will continue for a while.
Second we use statistical instruments and methods to help us.
And third once a state is identified we use the appropriate tested methodology. For me a tested algorithmic system appropriate for a particular market state outperforms the average human trader.
So we should not try to predict the future. Even if we try to predict this will destroy our capacity of observing. This phenomenon happens because we select and choose to observe only what conforms our hypothesis.
When the prediction is useful?
In fact the prediction is useful when you manage your position once the position is open.
Example 1: Trend prediction
You open a trade in a trending environment for example after a break - out. Your setup was good (a setup is a setup) and once your position is opened and the stop loss is returned to zero, your prediction game starts.
Now you are trying to predict the market, what is going on, what will happen in order to manage a position.
Remember, this is like chess game. Your little position can be like a spawn that can turn up to be a queen, when it reaches the end of the chess board. And in order to be a queen you have to push it forward. And as in the Chess a group of spawns have a better chance to reach their goal, to become a queen.
And vice versa as Glenn Neely observes even if you have a very good prediction capability this will not help you in your trading (even if you are right maybe your timing was wrong, or the stop was hit just before your prediction come true). You need a good setup.
Example 2:
You have a setup in a ranging environment. You buy at an oversold level. Logically you predict that it will reverse when it goes up at an overbought level. And this is prediction after you have opened a position.
Conclusions:
There are two types of trading styles. Those strategies have advantages and disadvantages.
One trading style is based on the prediction: Elliott wave analysis is the best example.
The second trading style is based on a behavior of the market with statistical tools: the price action setups are one of the best examples. For example a break - out strategy in a period with high volatility.
A third strategy is a mixed strategy between a behavioral and prediction. You use your setups as a behavioral strategy and try to manage your winning positions by a prediction strategy.
The advantage of the behavioral strategies is the good win to loose ratio.
The advantage of a prediction strategy is the good R/R ratio.
A mixed strategy tries to combine both.
The idea is to try to predict not the market direction but the market state.
1. Cluster the market states:
All the successful traders have in common that they try to distinguish the market state.
A common approach is to use the relationship between the time of the day in the forex markets and the corresponding volatility.
2. Appropriate Use of an adapted system for the period
So when a market state is identified it is necessary to use an adapted system for this market state.
Traditionally there are:
2.1 Intra-day Trend following system
A Moving average strategy is the archetype of those systems.
Neural nets perform very bad as a trend following systems (with some exceptions for Neural Nets specially tuned for those conditions).
Digital filters with genetic optimization do much better
2.2 Intra-day Range Bound system
Oscillator based strategy is the archetype of those systems. Modern oscillators appears to be much smoother but still all the oscillators shares a collinearity.
The neural nets perform well in those conditions.
Statistical indicators performs very well too. In fact the phase space is so big under such conditions that it is more appropriate to model it statistical instruments. The Bollinger bands as a rudimentary statistical instrument is a classic player in the Intra-day range bound systems.
2.3 Break - out system after a contraction of the volatility
Those systems are particular. For example a Neural Net cannot never ever predict a break-out in the forex market.
How to distinguish between those market conditions:
Well the obvious reason is practice, practice, practice and experience, experience, experience
The human mind and the human perception abilities are much more suited to distinguish those states than any machine. For example an image recognition, the machines are still not capable of doing it for now.
There are some methods and some new ones that will be covered later.
My opinion is that the human mind is appropriate to distinguish between the market states. We believe that when a market state is identified it will continue for a while.
Second we use statistical instruments and methods to help us.
And third once a state is identified we use the appropriate tested methodology. For me a tested algorithmic system appropriate for a particular market state outperforms the average human trader.
So we should not try to predict the future. Even if we try to predict this will destroy our capacity of observing. This phenomenon happens because we select and choose to observe only what conforms our hypothesis.
When the prediction is useful?
In fact the prediction is useful when you manage your position once the position is open.
Example 1: Trend prediction
You open a trade in a trending environment for example after a break - out. Your setup was good (a setup is a setup) and once your position is opened and the stop loss is returned to zero, your prediction game starts.
Now you are trying to predict the market, what is going on, what will happen in order to manage a position.
Remember, this is like chess game. Your little position can be like a spawn that can turn up to be a queen, when it reaches the end of the chess board. And in order to be a queen you have to push it forward. And as in the Chess a group of spawns have a better chance to reach their goal, to become a queen.
And vice versa as Glenn Neely observes even if you have a very good prediction capability this will not help you in your trading (even if you are right maybe your timing was wrong, or the stop was hit just before your prediction come true). You need a good setup.
Example 2:
You have a setup in a ranging environment. You buy at an oversold level. Logically you predict that it will reverse when it goes up at an overbought level. And this is prediction after you have opened a position.
Conclusions:
There are two types of trading styles. Those strategies have advantages and disadvantages.
One trading style is based on the prediction: Elliott wave analysis is the best example.
The second trading style is based on a behavior of the market with statistical tools: the price action setups are one of the best examples. For example a break - out strategy in a period with high volatility.
A third strategy is a mixed strategy between a behavioral and prediction. You use your setups as a behavioral strategy and try to manage your winning positions by a prediction strategy.
The advantage of the behavioral strategies is the good win to loose ratio.
The advantage of a prediction strategy is the good R/R ratio.
A mixed strategy tries to combine both.
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