New Frontiers in the Forex market analysis

New Frontiers in the Forex market analysis

This type of article is one of the most fun for me to write because it's really just imagination through stupidity. Since 1990, I made a hobby of exploring new and different ideas for the analysis of markets, and this is a great opportunity to dust off some of my old notes, published some of these ideas, and maybe some comments on them. I am also looking forward to using some of the following concepts in my current research, the exchange rate behavior. So put on your "what if ..." hats and let's get started!

Market Models - Old and New

Most traders are familiar with two basic schools of market analysis that we call fundamental analysis and technical analysis. In 1970, members of the academic community proposed a new model on the market known as the "efficient market hypothesis." This is known as "random walk theory" and basically said that the first two schools of thought were wasting their time. In response to the random walk model, other academics put forth more recent theory of how markets work "Behavioral Finance". These are all examples of a comprehensive explanation of the factors that drive market prices. Here is a brief summary of market models, some of which are only in their infancy:

Background: Market prices are driven by events and material conditions in the real world, such as income, sales, management, natural disasters, weather, economic conditions, geopolitical tensions and so on.

Technical: Market prices are driven by what prices have done in the past. As traders observe the past and present price movements, their expectations about future prices can lead to feelings of greed and fear which in turn create buying and selling pressures.

Random walk: current market prices are efficient reflections of all known fundamental and technical information, so we can consider anything about future price movements. Factors that cause future price movement will be so different that such movements can only be random in nature.

Behavioral Finance: The prices are driven by human psychology which is not always rational. Traders may base expectations about price movements, risk and reward of wrong thinking, causing prices to behave in a non-random ways. Source and accidents are classic examples.

Chaos theory: The market prices of the non-linear dynamic system in which the results are re-introduced back into the system as inputs, triggering loops and complex behavior very sensitive dependence on small variations in conditions.

Fractal Geometry: Cost models are nested recursively, meaning that a model can be composed of several smaller similar or even identical patterns and so on through all time scales. Elliott Wave Theory is a classic example of this idea.

Emergent property Scott Pattern: I discuss this in more detail in other articles, but the idea is basically to identify the properties of price behavior stems from a combination of unique individual trading styles of current market participants. Analogy would be like a person's personality stems from a combination of individual neurons in the brain. This price behavior changes gradually over time in an evolutionary way in the same way that the organism's behavior changes over time due to changes in its internal structure and external pressures from its environment.

My apologies if I have neglected or grossly misrepresented any of various ways of explaining what makes the market tick.

Mechanical trading systems

Another topic that often grabs my interest is the design of mechanical trading systems based on money management rules. Some examples of such systems are "buy and hold", "dollar cost average," Robert Lichello the "Automatic Investment Management" (AIM) and all other systems that try to emotion out of trading through the application of rule-based system buying and selling. Such systems differ from other mechanical systems in which rules are based entirely on money management variables such as cash on hand, the average price per unit, total portfolio value and the current position value.

These types of mechanical systems, mainly designed for securities but not for exchange or futures market where cash management situation is very different. In exchange, unlike securities, not taking account of any currency that we own and that currency exchange for some security (ie stock dollars). FOREX involves simply leaving margin deposit and then using that as a basis for borrowing some larger amount of money and exchange it for another currency, or making a long one currency and short another at all times. This is quite different structure of foreign exchange market is a new challenge for design, use and understanding of classic money management based on mechanical systems.

Artificial intelligence and artificial life

What if you could design a neural network that could learn over time how to make consistent profitable buying and selling decisions based on data exchange scheme? Or if you want cellular automata (CA) could create one of those multi-dimensional format in which the symbols "revolve around" a conflict and combine in new ways creating emergent behavior. If awarded lucrative behavior and punished behavior unprofitable CA will eventually learn to behave like super Forex trader? What do you say? Why not harness the power of evolution using genetic programming? Well, let's create an environment full of trading programs that have to compete with each other to survive and reproduce offspring programs. After many generations of "nature - red in tooth and claw" We may end up with a group of very robust forex trading programs. They have earned their place in this virtual world where law is the president of "survival of the fittest."

These are all examples of how ideas popularly known as AI and A-Life can be applied to forex trading. Using programming languages ​​such as LISP, I think it would be interesting to use neural networks, cellular automata, genetic programming environments and other techniques to create a rudimentary trading programs. These programs will be exposed to many sets of market data (probably daily charts), and over time will "learn" or "evolve" in commercial expert systems. This is not science fiction, but it is within the cognitive sciences.

Computer modeling of dynamic systems

What happens if the markets are in a deterministic way? In other words, I'm wondering if there is some kind of "physics" behind the price movements that are ultimately subject to the complex cause and effect relationships. Anyway, we know the cause and effect relationship certainly exists. I decided to buy a currency pair exchange, which causes me to place an order, which causes several offers to be affected in order to fill my order, which causes the bid and ask exchange pair to rise, causing some orders to stop is activated, which causes more buying, causing another price increase, causing some news services to take notice, causing several people to buy, which causes the price to become so big that people start to take profits causing me to sell. Whew!

Following all these relationships, where each event can be caused by and in turn causes several other events work in computer modeling. In fact, computer modeling or simulation is that we use to try to understand the behavior of any complex system can be described by a few simple rules.

We can use this technique to try to understand the dynamics of what happens in the Forex market as described above. We can also use it to describe what happens in the economy. We do not necessarily have to use cause and effect relationships. We work as a model of supply and demand levels of money, goods and labor in the economy and the level of prices of these items. The market could try to discover what factors led to equipping the purchase or sale of "pressures" and what factors can act as a catalyst for posting those pressures, causing catastrophic accidents or sudden market bubbles.

I very much modeling paper by simply drawing diagrams of how I think that some systems might work. This is different from computer modeling what I'm trying to simulate the behavior of the system. Instead, I often try to reduce the complexity of the system down to some very simple concepts to understand what the main driving force.

Here's an example. One of my favorite ways to reduce the complexity of each economy is to look at it as a simple set of elements and behaviors such as people apply to work on natural resources, which are producing goods that are or may be consumed or save, allowing them to raise their quality of life and continue the process. Believe it or not, are actually much diagramming before they realized that the economy can really be boiled down to:

1st People who want to live well, and
2nd Natural resources that enable them to do so.

Any other economic concepts such as manpower, money, wages and prices, etc. are just extensions of this basic model.

Another way I try to model the market is in terms of different types of participants and their behavior. For example I will divide the market "contrarians" who sell when the price goes up and buy when it goes down and "trend followers" who buy and sell when prices fall when. How different mixtures of trend followers and contrarians will affect the behavior of the overall market? Conversely, is there a way just by observing the behavior of the market that can tell if the market is dominated by trend followers or contrarians?

Of course none of these models may actually describe reality correctly. We need to remember that models are really tools to help us to suggest various hypotheses about the way things work. Then you can test these hypotheses scientifically.

Inventing new indicators

One of the most interesting coincidences (for me anyway) between market and natural sciences is the use of variables "P", "C" and "T". The chemistry and thermodynamics of these usually stand the pressure, volume and temperature of gas, and they are connected to one another in the manner described by law Boyle. The price pattern stand for the price, volume and time. Are they all related? What if we had evidence that cared how long it takes the average cost to move a certain amount. Is it taking longer to move up than to go down? Are there more volume associated with the down move from the moves? What is this indicator? Or an indicator that combines all three variables?

I read another blog a few weeks before merchant and they mention that they would like to set in motion an indicator of average price instead of the standard moving average which uses the means of prices. The discussion continued to speculate about how a regular moving average and moving average line will interact together. Crossover of the average means would be significant? I thought it was pretty interesting idea.

Coming up with new indicators is a common pastime for many traders, and I just wanted to give you an example of the thought process that could pass through when inventing a new one. Of course, there are more indicators than any man could ever use, but who knows ... Maybe next will give us new insight into the movement of prices that we've never had before. So, to keep inventing!

Conclusion

So that's it for our flights of fancy and romps through the imagination. Or is it just the beginning ....?



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