Feb 1, 2013

Investment systems: a pragmatic text.

Investment / Trading systems are computer programs that send orders in the capital market. It's technology architecture is designed to support a decision system that sends signals to enter and exit on asset positions (buy and / or sell).

What ensures investment efficiency is the ability of these systems on making profit in the long run with the maximum possible security. The big question when we consider these systems is the ability to evaluate the performance and rank it as sufficient to ensure efficient management.

That is, put another way, how to separate systems that lack credibility on signals generating of which represent a significant result.
The line between this models classification can be tenuous, several models with varying degrees of advancement, may present near this classification.

While many models do not have a significant degree of robustness, and in fact many do not have, yet can be managed with relative efficiency. Many systems maintain a good performance in certain periods, even being composed of models with poor complexity in decision making or optimization methodologies that may not necessarily increase the actual performance [Backtesting: good guy or bad guy?].

The capital market is an environment where competitiveness and the search for implicit information is what motivates the interaction of speculative capital. There is no mathematical way to determine whether an investment system will always win, ie, it is impossible to say formally that a particular strategy is profitable in the long run.

However it is possible to evaluate the performance on different ways. You can change the operating conditions to assess the sensitivity and behavior in adverse situations and stressful situations. It is possible to evaluate* in depth the patterns of prices and financial returns (* evaluate means "to understand" or model the past). It is possible to classify certain time intervals and the scale of certain asset price as repetitive with some significant degree.

You can monitor a change of state. However the change of state (the state classified by the model, like specific volatility or return level) may be apparent, masked by noise, characteristic of financial data.

After performing all these steps and analyze the behavior of the system, a more realistic notion of risk is built. That is, we can win on knowing about repetitive patterns that present possibility of gain - buy cheap and sell expensive, such as arbitration and trend patterns.

These patterns, of course, are not repeated regularly. This characteristic (the fact of the lack of regularity / unpredictability with which the patterns are repeated) highlights that financial movements have some degree of deterministic or chaotic properties.

Finally, you can monitor several models simultaneously and manage them, looking if their behavior remains consistent with that expected, looking if the indicators used to measure their performance remains stable and finally determine your own limits of performance and risk.

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Rodrigo Sucupira Rodrigo Sucupira
Rodrigo is a Automation and Control Engineer - Escola Polit├ęcnica / USP. Interested on Financial Engineering, writes articles about Finance and Technology.
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