Forex data mining
On my last post I described how to use the R statistical software in order to generate simple random financial data series. Today we are going to use these series in order to test the data-mining bias of an automatic system generation approach in order to determine what system characteristics are required to assert that a strategy is most likely not the result of spurious correlations.
Although I have approached the data mining bias problem on some Currency Trader Magazine articles particularly using random variables to test for only spurious correlationsthis approach — forex data mining random synthetic data — offers us a forex data mining dimension regarding the interaction of the OHLC variables within the system creation process, something that the other approach neglects. Within the next few paragraphs and posts I am going forex data mining share with you my experience with this testing procedure based on random data, as well as my conclusions regarding its use in Forex data mining my system generator program.
So what is a data-mining bias? When you create strategies in an automatic forex data mining you face the problem of not being able to know the probability that the generated strategy is the mere result of a spurious correlation that is achieved simply because your forex data mining process is able forex data mining get anything outside of the data.
Given infinite degrees forex data mining freedom, there is always a fit for every problem. However, as soon as you forex data mining the degrees of freedom on a data mining approach you reduce the data-mining bias to a smaller level.
If your data mining process only generates systems with two rules it will be impossible for it to extract all possible profits from the market and the amount of profit it will be able to extract from random data series will be limited significantly. The idea of using random time series is to be able to answer a simple question: What are the best statistical characteristics that my system can achieve if it can only generate spurious correlations?
Once you have the answer to this question — which is forex data mining data-mining bias — you will forex data mining able to evaluate whether the strategies you generate using real financial time series are bound to be significant. Your data-mining bias depends forex data mining the degrees of freedom of your data-mining process how many rules, how many possible shifts, which exit mechanisms, how many parameters, etc and it also forex data mining on the amount of data being used.
For this reason you need to repeat your analysis for any changes across these aspects. Data length is a very important part of the question. Whenever you have more data the data-mining bias is reduced because the probability to find a spurious correlation that is present across the whole data set is diminished. For this same reason the TF is also important, the lower the TF, the lower your data mining bias other problems — like broker dependency — start to be present as well so that must be taken into forex data mining.
To test for our data mining bias in Kantu we need to first select the conditions we want for the creation of trading strategies. Using different inputs or using different rules and shift limits changes the magnitude of our testing bias. In the examples I am going to use I have generated random data for 25 years of daily data and will be generating strategies using a maximum shift of 50, a maximum set of 3 price action based rules and a stop-loss for the exits of the trading strategy.
You can also forex data mining experiments using different system generation options, to see how the degrees of freedom you give your strategy changes your data-mining bias.
It is also important that you record the amount of attempts used to forex data mining the strategiesbecause the confidence in your data-mining bias measurement depends on how exhaustive your search of the logic space is. The amount of systems you need to generate to have good confidence depends on your degrees of freedom and grows exponentially as the possibility of more complex systems becomes larger.
If your logic space is in the order of trillion it will be extremely hard to come to any realistic conclusions regarding the data-mining bias. Another important aspect is to use more than one random data series for the tests. Doing several tests on different random data sets can increase your confidence regarding the possibility to generate certain results. 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.
Now that we have a predefined setup we can run our tests and see what we obtain, we can then compare these results to the system generation results of Kantu on a real financial data symbol and see whether we can obtain systems above our data forex data mining bias. I hope you enjoyed this article!
Then build all the permutations of the chunks. Thanks for your comment: In essence since I have so much fitting power I can forex data mining perform this experiment as many times as necessary with different building paradigms and get whatever result I desire.
I believe that this type of techniques designed for system optimization are not suited for system generation using data-mining with modern computer capacity. Generate a data-mining bias and simply see if you can come up with something that is better.
Thanks again for commenting: I could think of two ways somehow complying with your text. The data-mining bias exercise we did before using kantu see here and here gives a relatively small data-mining bias of 0. With a shift step of 5, the total number possible strategies I computed was The number was if the shift step is 4.
I believe I used the same formula that gave me the correct number in the example count in the manual of OpenKantu. This million value was for a shift forex data mining of 4, but the result is actually million I have now corrected the mistake within the post. Here is the calculation broken down:. I hope this answers your forex data mining. Thanks again for writing.
Thanks for the explanation. I agree with the method of your calculation. We should arrive at the same answer but I think each of us made a mistake:. So the number forex data mining valid rules for a single rule is With 6 different SL levels, the total number of rules using this calculation should be Mail will not be published required. Mechanical Forex Trading in the FX market using mechanical trading strategies.
Data-mining in Algorithmic Trading: Determining your data-mining bias through the use of random data. January 22nd, 8 Comments.
Posted in Articles Tags: Kantusystem development. Generating simple random financial time series. January 22, at 7: January 22, at January forex data mining, at 7: January 24, at Reducing your data-mining bias: Creating trading systems with limited data Forex data mining Forex says: March 17, at 3: January 9, at 7: January 10, at January 10, at 7: Leave a Reply Click here to cancel reply.
This is enforced with additional checks in the memory manager that prevent code pages from becoming writable or forex data mining being modified by forex data mining process itself. Although this is great for security, ACG introduces a serious complication: modern web browsers rely on Just-in-Time (JIT) compilers for best performance. As a result, they inherently rely on the ability to generate some amount of unsigned native code in a content forex data mining.
Enabling JIT compilers to work with ACG enabled is a non-trivial engineering task, but it is an investment that weve made for Microsoft Edge in the Windows 10 Creators Update.
To support this, we moved the JIT functionality of Chakra into a separate process that runs in its own isolated sandbox.
The review for each broker will include whether it offers a demo in the Key Details section. Both systems can be checked before making a deposit. This type of account allows the user to not just trial the broker, but also use the demo account to try a new trading strategy, or even back test a strategy based on past financial data.