Share New Research 
I am trying to define sma's for different symbols, and i would like to parameterize them individually because i do not wish each symbol to have sma's with the same parameters as the other symbosl. However, I am having rouble accessing my data in the OnData function. Mainly, the line "self.window.Add(data["EURUSD"])" is where i seem to be having running into issues. If i incorporate another symbol within the brackets (ex: ["EURUSD", "USDJPY"] )it throws an error saying "Object reference not set to an instance of an object". Is there an efficient way i can define multiple sma's each with its own parameters and then access them each individually in the OnData function? I also need the values to be stored in a rolling window so i can access specific indecies of each sma. Any help would really be greatly appreciated
Thank you!!
Jing Wu
Hi Johnny,
We developed a simple algorithm for Multiple Simple Condolidation, you can find this algortihm on Github
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Johnny VanNuland
Thank you for your response! That algorithm is very helpful and very similar to what i am trying to do but i still have some of the same questions as originally posted.. In that algorithim i see that we define an SMA within a roling window, and i also see that I can access specific bars from the SMA within the "symboldata" class in the "_init_function". However I would like to be able to refrence those specific index values in the OnData function as well. How can I access those SMA indicies within the OnData function?
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Michael Manus
i am joining the group with:
with Runtime Error: Cannot perform runtime binding on a null reference
Jared Broad
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Jared Broad
If its a bug report in the LEAN stack please report to support@quantconnect.com. We'll need a little more information -
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Johnny VanNuland
Jing Wu
With regards to the Multiple symbol consolidatino template posted earlier, how am i able to access the different SMA indicies from withiin the OnData function???
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Jing Wu
Hi Johnny,
You could use symbolData.SMA.Current.Value when the indicator is ready to use. For example
def OnData(self,data): for symbol in self.Data.keys(): symbolData = self.Data[symbol] if symbolData.IsReady() and symbolData.WasJustUpdated(self.Time): # print the SMA value for each symbol self.Log(str(symbol) + " SMA " + str(symbolData.SMA.Current.Value))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.
Johnny VanNuland
I am trying to access the index values from the SMA, not just the current value. When i attempt to do so, i get an error saying that is an unindexable object.
This is what i tried within the OnData function...
"
Currentsma = symbolData.SMA[0]
previoussma = symbolData.SMA[1]
"
Thank you again for your help
Jing Wu
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Jing Wu
Hi Johnny,
symbolData.SMA is a SimpleMovingAverage object not a list of indicator values. It only has the current value property. If you want to access the history value of the indicator, First add a rolling window attribute in SymbolData class. Each time you get a new indicator value in OnData(), add that value to the window, The window size depends on the periods of history you wanna keep. Please see the attached algorithm for an example
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Johnny VanNuland
Thank you for your help. Very much appreciated!
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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.
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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're just finishing interactive brokers live trading and then will launch it :) ` 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.
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