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Hello,
I am pretty novice when it comes to quantitative finance and the various disciplines it involves. I just did some code to try and check if EURJPY and USDCAD are cointegrated. Most of the code is taken from another cointegration example I found. I got a good p-value and the test seems to indicate stationarity. Does my code and testing look good? Do you agree with the test results that these pairs are cointegrated? Any help is appreciated. Thank you so much in advance!
Jing Wu
Hi Hugh, the null hypothesis in ADF test is the time series is not stationary. The more negative the statistics, the more likely we are to reject the null hypothesis. The test statistic value here is -3.32. To help determine the ADF statistic, we can refer to the look-up table. -3.32 is less than the value of -2.868 at 5%. This suggests that we can reject the null hypothesis with a significance level of less than 5%. Rejecting the null hypothesis means that the time series is stationary. Hope you enjoy your quant research in QC.
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Hugo Acuna
Jing Wu, thank you! Also, based off the stationary time series I have, how would I trade this spread? What does it mean when the spread is in negative values versus when it's in positive values?
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Jing Wu
There are multiple methods to trade the spread when there is a price divergence. You can refer to this algorithm
Besides, we've made a few pairs trading examples, please search "pairs trading" in the strategy library.
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Hugo Acuna
So when the Stationary series crosses into the positive side does that mean the pairs are getting closer or farther apart? And when the stationary series crosses into the negative side does that mean the pairs are getting closer or farther apart? Thank you!
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Jing Wu
If the spread deviates away from its long-term mean, the algorithm should short the stock which price is diverging up and go long the stock which price is diverging down. The position is closed when prices revert back.
For example, The spread series is spread = y - x*beta - alpha. If spread > pre-defined level, then price y is diverging up, price x is diverging down, long x and short y. If spread < pre-defined level, short x and long y.
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James Smith
I guess you're talking about Lean in general: Jared would be the best person to help. The thing I'm referring to is a tool that allows you to optimize algorithm parameters with a genetic learning pattern.
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James Smith
Edit: last comment should have been @Larry Smith
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JayJayD
Hi there,
Based in the awesome work made by James Smith and this tutorial, I implemented a project called LeanSTP, which is able to run multiple Lean instances in parallel.
The only significant difference respect to the work done by James is that the parallelization is outside the genetic algorithm environment, the output is saved as JSON and the Log file is saved for each run. The default folder is Public Documents
The core to configure the parameters lays in this method. In this the example, the algorithm runs some combinations of EMA periods and runs multiple instances of the ParameterizedAlgorithm.
But IMHO, optimizing backtests is a bad idea. Is relatively easy to find an optimal strategy, even for a pure random walk, and The higher the number of configurations tried, the greater is the probability that the backtest is overfit. I guess you can calibrate some parameters without hurting so much the out of sample performance.
Anyway, I hope someone find this helpful.
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James Smith
The papers you've highlighted are very interesting, and cross over into some of the research areas I've been tackling. I think the GeneticSharp library that my optimizer references uses a similar approach to the smart thread pool in order to achieve parallel backtests. It's useful to have a trimmed down execution model for just this task.
In terms of the concept of backtest overfitting in general, this is certainly a problem. I would not recommend trusting an optimal result from just in sample data: rather its advisable to take multiple in samples and/or validate against an out of sample set. It's for this purpose that I've recently completed work on an optimization Batcher, that will automate the process of walk forward testing across multiple periods.
On a more general note, I think whilst there is not a (proven) efficient alternative, backtesting against historical data produces the most useful results. The main area of concern for me is that Sharpe ratio alone does not sufficiently account for overfitting. It may be that a balanced compromise can be found in moderating Sharpe ratio by (for instance) the volatility of returns. This is one of the reasons that I'm trying optimizations against some of the other Lean statistics using the ConfiguredFitness class that's now been provided inthe optimizer project. It may be that customized fitness measures could be the the arena for genuine innovation in this field.
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.
JayJayD
I like the use of “multiple out of sample” periods. Do you have any idea of how to implement it? Thinking out loud, I imagine the following scenario: there are many periods of out of sample data (OOSD), say, a random month for each year in the optimization period). Then every time you run an OOD test, a month is selected randomly, or maybe many. Maybe in that way we can avoid (in part) the problem of systematic use of the OOAD, that ends up in more overfitting by incorporating the OOSD as in sample data, but in a second stage.
I know backtests are useful, the point is not to fool yourself.
Respect to the statisitcs and the fitness formula, I can’t agree more, is the truly art of the whole process... and the secret sauce ;)
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James Smith
Its been quite clearly established that the singular Sharpe ratio and naive in-sample backtesting are probably a pestilence at the level of an epidemic.
My personal position is that the accepted terms in use already precludes the possibility of a "holy grail". We’re drawn to such illusions by the bewitching flaws of language and the beguiling form of mathematical notation.
Either you can build firmer foundations on this unsteady ground, or you can dig deeper and discard the entire existing edifice.
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Ian Worthington
Is % profit not a good measure? I understand drawdown etc, but with forward-testing you've implemented, this would be my first choice. Money is the point right?
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Jared Broad
% profit can also be misleading as the order of returns matters.
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James Smith
Return can easily be misleading when an optimization fixates on a single rare event. This is less an issue when drawdown is taken into account.
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Ian Worthington
Got it, thanks guys :-)
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Thomas Groenhout
Any progress on running parallel backtest in the cloud?
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James Candan
I'm very new here and to trading in general, so please excuse my ignorance. Is this basically equivalent to MetaTrader4's ability to run a backtest with inputs set to a range (e.g. start, step, end) and run through each combination of that range to identify the settings with the best outcome? I was really hoping this was part of QC/Lean out of the box. Is running it on a local machine really that intensive that it should be done on a cloud instance instead? MT4's optimization didn't seem to be too slow as a desktop client. I know MT4 has a "genetic" optimization option that I haven't looked into yet. Does LeanOptimization not have a non-genetic optimization option? What am I missing?
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Thomas Groenhout
James Candan, I'm kind of new to QuantConnect, but as far as I can tell, it's not too hard to run LEAN locally. The problem is that you'll have to constantly download new data if you want to be backtesting on current data.
Everyone, please correct me if I'm wrong.
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Petter Hansson
^That's accurate. I've had to resort to doing some things locally (e.g. intraday charting) in cases where data is available for download.
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Drew Baker
Jared Broad you said we′rejustf∈ish∈g∫eractivebrokerslivetrad∈gandthenwilllaunchit〉 back in March of 2015. Did this ever get pushed out? I'd love to be able to run a backtest with a range of variables and see which works the best.
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Michael Manus
of course, check the main website and scroll down
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Drew Baker
Michael Manus I meant the optimzation features, not the interactive brokers features... I don't see anything about that on the home page.
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Michael Manus
oh sorry I misunderstood you
i think it got delayed because of the many problems of overfitting strategies and more urgent things
but it seems james is still working on it when i check the commits on the optimiztion project
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Jared Broad
Its a work in progress; we recently changed our cloud architecture to make this possible so we'll be adding it in 2018 -- please feel free to follow along with the dedicated project here. Our general motto is bugs before features so it has been delayed slightly.
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James Smith
"Can we actually run parameter optimizations in local"
Ben, I've provided some information regarding this previously in this thread.
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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