Share New Research 
Hi, just wandering if anyone else was having this issue.
I know I'm not a programmer (I'm a finance guy) but developping an algo on QC is incredibly/slow painful for these reasons:
- Launching a backtest takes AT LEAST 20-30 seconds each time. And when there's a bug (which happens 99% of the time for a newbie like me) it gets really annoying
- The Research app only allows to test certain aspects but not all of the code. For instance, universe selection is not possible. Consequently, selecting a universe requires multiple backtests which brings me back to point #1
- The website itself is super slow.... as if the display was way too crowded. Im running on this setup
So I'm posting this in the hope that someone could tell me that I'm 100% wrong with a list of tricks and ways for me to do things better. Basically, I want someone to tell me I'm an idiot and prove to me why. Thanks in advance,
Jared Broad
Hi Stephane, sorry you feel like that. It will never fast enough for us, we are doing active research to make it faster. We are even posting bounties to open source contributors to improve the performance. We record the performance of every bit of code and are working to improve them.
There might be a specific issue with your algorithm that we can improve. Please feel free to send it through here and we can suggest optimizations.
What browser are you working on? It should be snappy in Chrome -- but we have noticed people with hundreds of backtests have issues even in Chrome. You can avoid this by not optimizing parameters via backtest.
It might not be much comfort but if the backtest spin up takes a while; remember to factor in the time you'd otherwise spend:
On average we're saving you about 2 years =D vs 30 sec backtest spin up. We'll get that to 5 sec, and we'll add debugging, it just takes time.
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.
Stephane b
Hi Jared and, as always, thanks a lot for taking the time to reply.
#1 Glad to hear that im not an (complete) idiot. In other words, I don't seem to be the only one in this situation.
#2 I completetly agree with you: building my own plateform would take MUCH longer than what im doing right now.
To answer your question: Im using Chrome.
In the end and don't get me wrong, as I once said, I believe QC has a superior product than many of your competitors and that it why I use it to code. I just wish there was a faster and more efficient way to develop (I dare say: Learn to code with LEAN). I think, in the end, being able to use the research app more broadly would probably end up freeing server power for you guys and speed things up for us... does that make sense ? I mean, I think we would all win with a faster/more efficient developement tool and test the final product with a few backtests.
Again, thanks a lot for your time and for building this plateform which I humbly think has a lot of potential.
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Jack Simonson
Hi Stephane,
Our engineers have updated LEAN and the system (specifically the versions of Mono and PythonNet) which has sped things up a bit, and we're making some front-end updates which we expect to result in performance improvements. You can track the progress of these changes and any updates on GitHub.
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Stephane b
hi Jack,
I just wanted to thank you for taking the time to update us on the progress. What you guys are doing is not simple but it is extremely appreciated.
Best,
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.
Muthu Kumar
In my case, the execution (backtest) seems to take forever (minutes). The deployment takes ~15s or more. Any ideas?
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Douglas Stridsberg
Muthu, I'm assuming you mean the initial warmup phase of the process? It could be down to the load on the backtesting nodes at the time you submitted the backtest. I find, in general, that live servers are a bit faster than backtest nodes.
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Muthu Kumar
Ok, it is still running. It was deployed around
10 | 23:53:53: Backtest deployed in 14.63 seconds
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Muthu Kumar
just terminated it. Please see what went wrong. New back tests taking longer time too. I was trying to log strike deltas for 2018. May be too much datapoints?
2 | 09:46:49:
Algorithm Id:(486cadc9af0c5a4e5935c8e7debefb9f) completed in 22255.59 seconds at 19k data points per second. Processing total of 421,425,028 data points.
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.
Artem Seredyuk
IMHO current problems of QC is not in backtest lags, it's bit annoying but I you can live with it. It's more like your number 2 - you use backtests one after another just to put 2-3 additional self.Debug() calls to better understand what's going on inside. And I clearly see what can help:
1. Live debugging obviously
2. Get more typical examples available. Becauce every newbie doing same things before he understands. You got code for screening QC500 universe but I spent hours to understans how to enumerate all securities from that universe on daily basis. I got couple solutions and got no idea what is more efficient (ActiveSecurities list, UniverseManager class, simply storing in self.symbols after fine filter). For some reasons I couldn't get historical data for that universe unless I call AddSecurity for every new symbol etc.
Don't get me wrong Jared, I'm not complaining. You guys doing work and I know you hiring to get it done faster -just some perspective from other end of the pipe.
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Stephane b
Artem: YES !!! I went through the exact same thing. I agree 100%
(I literally went through the exact same situation with the universe management and AddSecurity stuff)
I hope those comments are taken as constructive because, I repeat myself, I believe QC has a far better product than most of the competitors... we just want to be able to code on it efficiently and take full advantage of it.
thanks
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Douglas Stridsberg
Muthu you're processing nearly 500 billion data points, it's not going to get much faster than that I'm afraid. Perhaps it's worth looking at why you're using so much data and if your algo can be optimised to use less than that?
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Muthu Kumar
Didn't realize the options data resolution is Minute only. I was trying to get familiar with options related API
and logging strike, price, greeks etc. for 1 year time frame! :)
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Charles Naccio
One thing I can recommend from our experience is to code the final backtest in C# instead of Python. For our backtest, switching from Python to C# literally sped things up 40-50x; it was night and day difference.
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Karthik Kailash INVESTOR
Charles Naccio can you share some more about the performance difference? Attaching a backtest from each would be ideal if it is possible. Were you doing a lot of heavy computation? I decided on sticking with Python because of the wider availability of libraries, but if the speedup is that dramatic I may need to rethink.
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Charles Naccio
I can't post examples of the algos, but what I can say is that we're trading ~2,000 stocks in our daily universe with minute level data. We basically monitor price action throughout the day on a large number of stocks, and trade those intraday that match certain patterns. At the end of each backtest, you'll notice in the logs that it says "Processed x datapoints in y time." The datapoints per second increased 40-50x for us. It was an incredible difference. Given the sandbox like environment that algos run in, it seems C# just runs way faster. For smaller algos, I don't think you'd notice nearly as much of a difference, and in that case sticking with Python probably makes the most sense.
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Pier-Olivier Marquis
It's not necessarily about the computational speed. The algorithm is counter-intuitive and, in my opinion, a mess. First of all, getting some stacktrace in C# that do not even tell you where in your code there is an issue is really annoying. Starting a backtest just to debug one variable is excruciatingly slow. Second of all, the universes are terribly inefficient. Quantopian could do a universe selection backtest on the entire U.S. equities in 20 minutes or less while Quantconnect takes hours. Ironically, Quantconnect always boasted that they were faster than Quantopian. Well, it doesn't matter if your algorithm has a poor design. At this point, it would probably be better to rewrite the thing in python and use modern data analysis libraries. The whole process doesn't make sense: register indicator, tradebar consolidator, check if the data is ready. These are all things that do not need to be and make it complicated to develop on this platform.
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.
Franky
A quant framework that I used in a quant community about 10 years ago could do around 20 backtests per second for 25 years of hourly data. Utilising a GPU increased it to 1000. Quantconnect can barely do 1. So yeah, it's definitely not built for performance.
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September
The whole process doesn't make sense: register indicator, tradebar consolidator, check if the data is ready. These are all things that do not need to be and make it complicated to develop on this platform.
So much this. I have never in any other backtester, whether my own DIY scripts or other platforms, had hair tearing frustration just trying to pass the OHLC values around between libraries. It's a completely trivial and common place thing to do, but on QC it's abstracted to the moon and back and there's no good documentation.
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Lars Klawitter
To Yuri's earlier point of there being a difference between backtests and live trading as to when the stop orders are placed:
I've been running the strategy (using a larger universe and shorter opening range as per my previous post) on IBKR paper trading (i.e. on an actual IBKR account, not via QC paper trading) and as Yuri suggested, the stop orders are at times placed within the same minute. Mostly 20-40 seconds after the entry:
in one case the stop order was placed the same instance as the entry:
I tried second resolution, but that seems impractical given the large universe size.
So this is my attempt at an artificially delayed stop order placement:
I'm not a C# coder, so I definitely don't know what I'm doing. Backtests with this code produce by and large comparable results with the original code, so I'll try paper trading next.
Would the above code change make sense to you?
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Yuri Lopukhov
So far I can see two issues:
I can't fix the first issue in C#, so I guess I will switch to Python version unless somebody else fixes the C# version and share it. Not sure if fixing it will improve results as well.
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Jack Pizza
define: “does really well with 1 minute” ….. 2000-2002 still utterly collapses fail to see well really well fits in….
or does really well when overfitting super hard?
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Quant Stratege
These backtests are not representative of live performance. When adding slippage, it can be significant at the open due to volatility, small-cap stocks, and using stop orders, making the results much less appealing.
Just add this line when adding securities:
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.
Lars Klawitter
You're right. I had previously only simulated very small constant slippages, but MarketImpactSlippage has quite a savage effect…
If it looks too good to be true…
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AleNoc
I noticed a difference between the backtest and the selection with live data (data provider QuantConnect). What data provider do you use for live trading?
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.
Evan lightner
I don't know where to start, I'll have to write a whole post and how I'm planning to implement this strategy. But first of all thank you @derek for sharing this (and of course for everything you've done with QC -its been a game changer for me).
But first of all, I just want to comment with one simple non-coding question….
Why are a good amount of people HATING on this strategy? I understand the backtest cherry pick , but for a bare bones boiler plate ( i messed around with some variables like holdings and percent risk and still got good results) , this is the best algo I've seen in a while, especially for being shared so freely - not to mention brand new piece of research in the community.
Is there just some sort of deep skepticism in the quant community at large I guess, inherently? I suppose that fits!
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.
Kevin Patterson
Does anyone else end up with very different symbols when running the exact same algo in backtesting vs live (IBKR paper trading)? For example when it is set to choose 20 symbols, I'll see maybe only half overlap –- and of course the ones going positive for the day in the backtest are the ones not picked up live 😅. Seems the relative volume calculations aren't exactly the same and it doesn't take much to move the symbols you get. This is my first foray in to large universe algo's, is this type discrepancy common with large universe backtests or is there settings to help make it line up better with live? S
Some folks were asking about sized nodes: after trying the python version on IBKR (with a few mods) an L1-2 node will make it through one day (it crashes after close though, so likely you need the next level up if you dont want to restart daily)
Thanks for all the python related posts, even if I don't end up trading it, the algo has been super helpful for learning more about the QC code and had some good recipes in it that I think would be helpful for any algorithm.
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.
Foren Power
The results are the same for the C code with universe size = 20 versus universe size = 1000. This seems like a possible issue.
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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