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Developing Trading Strategies with Genetic Algorithms

Hi there,

Here is a Project where Genetic Algorithms were used to develop a trading strategy by combining a fixed subset of signals chained by logical operators.

The project uses the genetic algorithm library GeneticSharp integrated with LEAN by James Smith.

The best out-of-sample trading strategy developed by the genetic algorithm showed a Sharpe Ratio of 2.28 in trading of EURUSD with 25 trades in the out-of-sample period of January – April 2017 (attached).

But more important that the results itself, are the layout of a framework flexible enough to test a wide range of strategies and the proof of concept of what is possible with two powerful open sources tools as Lean and GeneticSharp.

Also, the Lean-centric framework has two very strong advantages:

  • The training evaluation can be as complex as needed (including ask-bid spread, fees, commissions, slippage model, risk management, etc.) to enhances the training by exposing the individuals to realistic environments.
  • 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. Therefore, a profitable set up developed by the genetic algorithm can be tested in real time or put to trade immediately.

Here’s a kind of paper where is detailed the technical side of the implementation and the statistical analysis of the training session.

Hope you enjoy it!

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.



Cool James. I will take look. I might migrate what I'm doing to yours baseline implementation.I was just about to create an implementation for turtle soup strategy, better to start from a good basis

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Thanks Jay, I will take a look at that option.

James, here my version of your baseline project. I will try to merge with the changes I've done on the previous project. The code looks cleaner.

GIT GenericTree updates

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and of course, the current BB strategy implementation there must be only for trading ranges

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James, 

I updated some code with fixes and to make it compatible with QC 

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Thanks Erik. I'm going to try to integrate your changes as soon as I can. I can work without a pull request but you can go that route if you prefer. I'm giving genetic programming using this setup a lot of attention so feel free to suggest improvements or report any issues. The number one thing that helps me out is getting a third-party opinion on things. I have made quite a lot of changes to this and the genetic optimizer project and am getting fairly pleasing results.

In terms of an optimization rig, I have an old 4 slot server capable of 24 cores that I obtained for basically peanuts. I don't know how the costs stack up over time against cloud compute. I imagine it would even out after a few weeks of running 24/7 as long as power costs aren't too high.

For live hosting of trading algorithms +1 for digitalocean.

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James, I will check how to do a pull request. Not really familiar with that.

Next steps for me are the integration of additional signals in order of creating a few strategies. The additional signals I'm looking at is the Autochartist, integration with rest based NN services.

I will provide further feedback as soon as I progress using this framework, but immeditelly I think the configuration is rather verbose, I will change it into 2 steps, to make it more human friendly.

Thanks for this.

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I've merged the Bollinger and Channel breakout from your fork. Seems like a great idea to allow a survival period for the approximate coincidence of signals. I'm wondering whether this could use QuantConnect.Indicators.RollingWindow?

You're right that the Optimizer configuration is unwieldy for this level of complexity. I may address that sometime soon. In the meantime I'm using a few scripts and tools to shift json around.

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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