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How do you decide when an automated strategy is good enough to take live?
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How do you decide when an automated strategy is good enough to take live?

  #21 (permalink)
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That PNL is obviously over fitted. It is not reasonable that a system 20 bags and has a life span of that long. Of course falling apart out of sample is a classic symptom of over fitting.

IMO testing back to 2011 just makes no sense in general.

At the most I would use something like whatever the data providers back fill on ticks is, this is your DB. View it as a rolling window that the stale data falls out of. To me that kills two birds with one stone as far as data curation and dumping stale data.

If you start going back longer IMO you need some kind of regime switching classifier.


Last edited by centaurer; February 10th, 2019 at 08:39 AM.
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  #22 (permalink)
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centaurer View Post
That PNL is obviously over fitted. It is not reasonable that a system 20 bags and has a life span of that long. Of course falling apart out of sample is a classic symptom of over fitting.

IMO testing back to 2011 just makes no sense in general.

At the most I would use something like whatever the data providers back fill on ticks is, this is your DB. View it as a rolling window that the stale data falls out of. To me that kills two birds with one stone as far as data curation and dumping stale data.

If you start going back longer IMO you need some kind of regime switching classifier.



Centaurer, what is "out of sample data"? I use TradeStation and don't see it there. Any help on this would be appreciated.

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  #23 (permalink)
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SidewalkAerobics View Post
Centaurer, what is "out of sample data"? I use TradeStation and don't see it there. Any help on this would be appreciated.

Out of sample data is any data which is not used to develop and optimize a strategy. I'm not familiar with TradeStation, but in NT you can select a date range when running optimization. OOS data would be anything outside of the date range being used to optimize.

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  #24 (permalink)
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Typically for ES I want a minimum average trade profit of $50 (after 2 ticks slippage and commission if using market orders).

The short side seems pointless, why not just make it a long only strategy?
Nice amount of trades, but usually if you can get more trades without sacrificing average trade profit thats better (more statistically significant).

Personally I wouldn't trade this strategy, and if I did it would be in sim for 6 months. The equity curve's correlation coefficient isn't high enough for me.

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  #25 (permalink)
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SydAlgoTrader View Post
Typically for ES I want a minimum average trade profit of $50 (after 2 ticks slippage and commission if using market orders).

The short side seems pointless, why not just make it a long only strategy?

Agreed. This backtest was from a while back. I've since killed the shorts.


SydAlgoTrader View Post
Personally I wouldn't trade this strategy, and if I did it would be in sim for 6 months. The equity curve's correlation coefficient isn't high enough for me.

Can you elaborate on this a little? The correlation coefficient with what? The underlying index?

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  #26 (permalink)
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303Trader View Post

Can you elaborate on this a little? The correlation coefficient with what? The underlying index?


The correlation coefficient would be compared to the "ideal equity curve" (straight line between start and end of the equity curve)

higher correlation coefficient between the two = straighter equity curve.

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  #27 (permalink)
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SydAlgoTrader View Post
The correlation coefficient would be compared to the "ideal equity curve" (straight line between start and end of the equity curve)

higher correlation coefficient between the two = straighter equity curve.

A system that trades all the time - i.e not regime dependant, but outperforms in certain regimes, and underperforms in others (while still being good enough to not try and filter), would have a poor correlation coefficient. Likewise, a system that dribbles along in a near perfect linear line may not be worth trading as it would tie up trading margin for not much return.

I agree that it would be lovely to have a high correlation, but I wouldn't put much weight in this metric.

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  #28 (permalink)
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Just for a warm and fuzzy, I would compare a sample set of timeusing high order fill resolution (essentially places the trade on a 1 tick data series. Run the strategy normally for lets say the last 12 months, then rerun using high fill. Running the strategy on High fill from 2011 will take a long time. Compare the two results and if you are not getting similar results, then it might be back to the drawing board.

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  #29 (permalink)
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sixtyseven View Post
A system that trades all the time - i.e not regime dependant, but outperforms in certain regimes, and underperforms in others (while still being good enough to not try and filter), would have a poor correlation coefficient. Likewise, a system that dribbles along in a near perfect linear line may not be worth trading as it would tie up trading margin for not much return.

I completely disagree. To give an example just 2 days ago I was sorting through some short ES strategies with equity curves back to 2004 and correlation coefficients in excess of 0.95, strategies which are regime dependent (i.e do well in 2018, 2008 etc, but have marginal return in big bull years.

Anything with a CorrCoef of 0.99 say in back test is probably not going to survive live trading through. It'just a number like Sharpe.

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  #30 (permalink)
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SydAlgoTrader View Post
I completely disagree. To give an example just 2 days ago I was sorting through some short ES strategies with equity curves back to 2004 and correlation coefficients in excess of 0.95, strategies which are regime dependent (i.e do well in 2018, 2008 etc, but have marginal return in big bull years.

Anything with a CorrCoef of 0.99 say in back test is probably not going to survive live trading through. It'just a number like Sharpe.

You are quite right. I was poor at visualising equity curves vs correl. It's not something that I use.

I did just plot some out in excel now to get a feel for it, and it wasn't obvious that it's a useful metric. 50%+ drawdowns would still end up with >0.95 for example. I'll stfu now, as I'll likely not add anything useful given my obvious lack of experience with this metric.

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