Genetic optimization. Application in TradeStation environment.: Building the trading systems
Building the trading systems
There are several approaches to building the trading systems:
- General approach;
- Smart “economic” approach.
General Approach to building trading systems consists of the following steps:
Begin loop
Selection of following parameters:
- Asset.
- Time interval.
- Concept (trend following for instance, or pattern recogniton, etc.).
- Formalization of a concept in terms of rules and indicators, etc.
- Possible set of indicator parameters values.
- Rules of signal generation.
- Type of orders (market, stop, limit) with values of parameters (if any).
- Optimization of parameters on data series “in sample”.
- Results estimation by NetProfit, ProfitFactor and other criteria for each set of parameters and selection of best parameter
set.
- If result is satisfactory then proceed to loading and testing on “out of
sample” data.
- If out of sample results differ from in sample results but is still in admissible limits then exit loop or go to begin of loop
otherwise.
End of loop
It is possible to include in loop the additional parameters:
- Position size computation algorithm.
- Computation of asset’s share in portfolio of portfolio optimization.
|
When using such approach we in fact define our solutions randomly and time costs are huge. Optimal solution of this task by enumeration of possibilities is not possible.
“Economic” approach,
“Let the man think and let the machine work”…
Selection of following parameters:
- Possible set of assets.
- Possible set of time intervals.
- Possible set of concepts.
- Possible set of indicators that are hypothetically capable for formalization of these concepts.
- Possible set of indicator parameters values.
- Possible set of elementary rules that form the signal generation rules.
- Possible variants of orders (with parameters).
- Definition of optimization criterion of any complexity and with any limitations.
- For our tasks basic is monotonous growth of Equity.
- Definition of Genetic Algorithm parameters.
- Definition of out of sample time interval.
- Launch of TS GO – Genetic optimizer.
|
By the means of genetic optimization algorithm we can get the set of optimal (according to our criterion) trading systems very quickly. When using such approach our task reduces to selection of reasonable rules variants, parameters alternatives and reasonable optimization criterion.
As it often mentioned, estimation of a system or a portfolio by some criterion like “NetProfit” frequently lead to “overfitting”. Using of indirect criteria which we can create solves this problem.
We can let genetic optimizer to have possibility to select parameters and algorithms for building trading systems.
|
