Hi Everyone. I'm working on a HFT pairs trading. I finished the prototype of it, and it looks promising. The Algorithm has a 3-month warming up peroid so there are no trades. I will continue to adjust the opening threshould, stop loss parameter and closing threshoud. I will also explore some good pairs to make the the algorithm trade at a high frequency.
I will update the algorithm here. Anyone thought or feedbacks are welcome.
Petter Hansson
The alpha seems good enough as it has both a high win rate and large wins, a few things I can think of however:
1) Few trades always makes me scared that what I'm observing is just some random fluctuation. Of course, since you're building on statistical methods this is less of a concern than for someone trying e.g. hand coded pattern recognition.
2) Due to aforementioned high win per trade, it's again probably manageable, however: Setting a simple slippage model is advisable (e.g. ConstantSlippageModel) to account for bid-ask spread slippage. I just checked one of the symbols you have and the bid-ask spread was approximately 0.5% of share price.
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JayJayD
Hi there Yan Xiaowei
Great work!
As Peter noticed, there are few trades, 24 in 9 months for minute data is too low. Is the cointegration criteria too high? Maybe more stocks are needed, by the way, what is the rationality behind the stocks selection?
Finally, from the implementation perspective, maybe you can use a Schedule event to tests correlation/cointegration every three months.
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Jared Broad
It looks like it trades more in highly volatile periods; perhaps you could add in more volatile stocks to encourage more trading.
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Yan Xiaowei
Hi Peter JayJay and Jared,
Thank you for your suggestions! Here are my thoughts about the issue you pointed out:
1. Few trades are caused by the number of stocks in the universe. I can defenitely select more stocks to make up more pairs, but it takes 6 second on average to conduct ADF test on a single pair -- I'm thinking about using a lower resolution such as 5min or 10min to speed up the ADF test so that I can have more pairs.
2. If we use a lower resolution, the bid-ask spread might not be a big problem -- although we will still overestimate the profit. I will check that after the algorithm is finished.
3. All of the stocks are U.S. bank equity. I randomly deleted some of them to make the list shorter for test purpose. I plan to select stocks in traditional industries such as energy, retails, manufactory and bank. The reason is the companies in the traditional industries are not that distinguishable, thus they are likely to have a stable high correlation.
4. The strategy first use 3-month history data to select pairs, and then use a rolling window to conduct correlation and cointegration test very month.
I will write the 3-month warmup period into Initialize step, and try to use a lower resolution data. Hopefully we can have more stocks so that we can trade at a higher frequency. I will also update it here.
Again, thank you for your feedbacks!
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Yan Xiaowei
Hi everyone,
I updated the algorithm to make it more flexible. Now you can change the resolution of the data by changing the parameter self.data_resolution. For example, 5 means aggregating the data into 5-minute resolution.
I also updated the method of using SetHoldings, which is wrong previously. In python you need to write 1.0/n to make it a float, otherwise it would be 0.
This algorithm is now flexible enough and it defenitely has a large room for improment. Feel free to play with it, try it on your target industry and find the best parameter for your own version.
Again, any thought or feedback are welcome!
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DrStrangeLove1955
Suggestion for intra day trading use futures. You get atleast 10:1 leverage using futures. Principal pairs I trade Silver (SI) & Gold(GC) also Russel(TF or RTY) & SP Futures(ES). I have found that (for these) you only need to trade the more volatile instrument of each pair. For Silver/Gold pair I only trade the silver contract where most of the price movement occurs, Gold is simply used as indicator.. This method used to be called Unipair Trading.
One other issue parameters have to be optimized every two weeks due to changing market character. Lean does not have this feature yet. Algorithm can be made self optimizing but for me was not worth the effort.
I would post the code but its in EasyLanguage not Lean.
Link to old (2004) out of print book. Still useful for ideas.
https://cp.sync.com/dl/476749430#ww9t7ymc-6xzwhbvq-9xpgbhua-83t8wnfp
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Rohit Nagendra
Hi Everyone,
When I cloned the second algorithm in this thread and run a backtest I keep getting the following errors:
Runtime Error: KeyNotFoundException : 'YDKN' wasn't found in the Slice object, likely because there was no-data at this moment in time and it wasn't possible to fillforward historical data. Please check the data exists before accessing it with data.ContainsKey("YDKN")
Backtest Handled Error: Order Error: id: 33, Insufficient buying power to complete order (Value:85697.0479), Reason: Id: 33, Initial Margin: 42854.453966259, Free Margin: 734.77068555175
Could anyone help me shine some light as to why this happening and how to fix it?
Any help would be much appriciated!
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Hugo Acuna
Hello, how do I implement this strategy using forex pairs? Thank you
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Alexandre Catarino
Rohit Nagendra, please rerun the algorithm.
The KeyNotFoundException error is gone, but the insufficient buying power is still there. It means that we need to improve the logic to place orders in that algorithm.
Hugo Acuna, please check out the Pairs Trading-Copula vs Cointegration tutorial. For further complex strategies, you can take a look at QuantConnect's Strategy Library.
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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′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.
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