Attached is the backtest with performance of the best out-of-sample individual. You will note the backtest shared from the QuantConnect platform is different from the one in the GitHub repository. This is because the use of the DynamicExpresso library, which for security reasons is not whitelisted in the QuantConnect platform. Fitness by Generations The fitness shows a typical pattern in genetic algorithms, that is, many generations of fitness stagnation until an innovation reaches new highs.
Figure 1Fitness by Generation 1 A compressed file with all the data analyzed in this section can be found here. Fitness by Individuals Figure 2 Fitness by Individuals The blanks in the plot corresponds to the cases where the drawdown is equal to zero, thus the fitness cannot be estimated.
From those cases, corresponds to cases where the individual was unable to generate signals; the remaining cases are consequence of some flaws in the experiment design and implementation. The reason for that decision was trying to keep the portfolio in positives values, even when many bad trades were made by the individuals some individuals made more than trades!
The problem with the low exposure is that the drawdowns are very small.
Second, the Drawdown statistic from the backtest results is parsed from a string in percentage format with two decimals; thus, the small drawdowns are rounded to zero. In other words, many individuals made trades but as the drawdown is rounded to zero, the GA ignore them in the Selection process. Some of those ignored individuals had good behavior in the in-sample period, the best one had an in-sample Sharpe Ratio of 1. In-sample vs Out of Sample Even when the statistic used as fitness is the Sterling Ratio, we will use the Sharpe Ratio as proxy for the out-of-sample performance.
There are two main reasons, Sharpe Ratio is one of the most widely statistic used as performance. Second, this work is not just a machine learning experiment, its goal is to test if the GA can develop profitable strategies as defined in this context.
From the individuals only with Sterling ratio greater than one were considered to run the out-of-sample analysis. For each one of the individuals four out-of-sample backtest were ran, all starting January 1st , but with different length, from one to four months. The Figure 3 compares the performance of the individual with the best in-sample fitness vs. The best out-of-sample Sharpe Ratio was 2.
However, this individual had an in-sample Sharpe Ratio of 0. From this plot seems that there is not a strong correlation, between the in-sample and the out-of-sample performance.
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The Figure 4 shows the Sharpe Ratio densities of the in-sample and the four out-of-sample periods. As expected, the out-of-sample kernel densities have a lower kurtosis and are positively skewed. However, the loss in the out-of-sample performance is not progressive. Figure 4 Sharpe Ratio in-sample vs. Here is clear the second out-of-sample period January — February have the worst performance and the following months the performance starts to improve slightly but fading out. This can mean some kind of short term edge for the strategies developed in this experiment.
Figure 5 Sharpe Ratio by out-of-sample Period length Finally, Figure 6 shows the in-sample Sterling Ration vs the out-of-sample Sharpe Ratio, the colors represents the out-of-sample length and the size the out-of-sample Sterling Ratio. Again, is clear the negative effect of February for all the strategies selected and the slight improvement in March and April.
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Figure 6 Out-of-Sample Sharpe Ratio vs. In other words, the GA can be used as optimizer to develop trading strategies, despite the very basic set of available tool it has standards technical indicators. Now, the poor correlation is expected, given the random nature of the genetic algorithms and the complexity of the experiment domain.
GA are noisy by definition. On the other hand, the low correlation raises questions about the robustness of the Sterling Ration as fitness. To test the if the Sterling Ratio is a good measure of fitness, a meta-analysis should be done. Many training sessions must be run and check if the in-sample fitness vs the out-of-sample performance shows the same behavior consistently. Conclusion The statistical analysis shows the feasibility of using genetic algorithm GA to develop a trading strategy by combining a fixed subset of signals chained by logical operators, despite the set of very basic dull signals used.
This could potentially be improved further by using a set of weak but more profitable signals. Although the Sterling Ratio shows some power as fitness, a meta-analysis should be done in order to address its robustness. The experiment has some flaws in design and implementation. One of these shortcomings, detailed in the results section, is the issue of low market exposure plus parsing the backtest statistics form fixed two decimal strings. This issue affected the Selection process itself, potentially hurting the GA outcomes.
In addition, just one training session was considered, to fully test the genetic algorithm power, multiple training session should be running. The QCAlgorithm used by the genetic algorithm to evaluate the individuals can be used to trade in live paper mode and even in real trade. Methods Citations.
Developed Trading Strategies by Genetic Algorithm -
Results Citations. Figures, Tables, and Topics from this paper. Genetic algorithm Foreign exchange service telecommunications Reinforcement learning Machine learning Mathematical optimization Sensor Protocols documentation Rule guideline. Citation Type. Has PDF. Publication Type. More Filters.
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