Data mining algo forex
to the machen kleine kryptowährungen sinnvoll system generation results of Kantu on a real financial data symbol and see whether we can obtain systems above our. Below is how this process works in principle. A single random data set might have some quirks (especially if the data is not very long) so it is desirable to repeat the analysis on several different sets. You may repeat this procedure with your own results using your own synthesis algo.
Select a forex pair with relatively high volatility and several up and down trends.
For example, gbpusd is a good candidate for daily data.
Make sure that the out-of-sample is trend-less.
You do not want to have your synthesis algo fit to a structural trend, which is what happens in many cases.
I have had my fair share of data mining, backtesting data mined systems, forward test them, walk forward test them, put them through Monte Carlo stress tests, etc.
For the logic space I chose for these experiments (SL.5-3.0.5 steps, OC 3 rules, max shift 50 there are about 167 million possible strategies (shift step 4, see comments for a breakdown of this calculation) so we should generate at least this. Remember that this is more than three years after this system was machine designed with data sehr profitable forex Strategie ending more than 5 years ago. Text Mining - mining news or technical analysis for pattern recognition. If your logic space is in the order of 1000 trillion it will be extremely hard to come to any realistic conclusions regarding the data-mining bias. Although I have approached the data mining bias problem on some Currency Trader Magazine articles (particularly using random variables to test for only spurious correlations this approach using random synthetic data offers us a complete dimension regarding the interaction of the ohlc variables within the. Alternatives involves validating the algo that is doing the synthesis. The net return on the whole out-of-sample is a little more than.
Data mining algo forex
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