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test_user
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Interesting twitter thread. https://twitter.com/RobinWigg/status/1331168066177294336 |
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Jrinne
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Thanks Philip, "greater" excess return. (0.039/ 0.25 - 1) * 100 = 56% Admittedly a medical way to look at this as in "people on statins have a .0001% chance of dying while those on placebo had a 0.00015% chance of dying" A fifty percent increase in deaths for the placebo group. Despite the obvious problems with this we keep talking that way. You ask a great question. One I did not really think about until posted this: is this significant in a "clinical" sense. But it is a good question and an obvious one. So soon after posting I ran these numbers: (1 + (0.0039 - 0.0025)) = 1.0014 1.0014^52 = 1.0755 or 7.55% Thank you for expanding on this. This is also something that is endlessly discussed in medicine. Should you take a statin? Should I go through the extra work of Boosting? So I like your way of looking at it. And perhaps 7.5% is the number we want. Meaningful? I think so. And I think one can do better with just a little more work. Especially with XGBoost. But I would be interested to see what others find with their ranking systems. And see if they think what they find is meaningful. This was meant as just a simple, first-look at Boosting that most people could do on their own (although even this is not exactly easy). For me personally, i have found much more meaningful numbers are possible. I think with Marco’s API releases and Steve’s sharing of code members can see how much more potential XGBoost might be able to provide for their own systems and not have to trust me on any of this. Thanks. Jim From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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Edit 8 times,
last edit by
Jrinne
at Nov 26, 2020 11:18:25 AM
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Quantonomics
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@Jrinne Did you take into account transaction costs + slippage? Because your portfolio turnover of 5000% is a true killer. I wouldn't be surprised if your returns were down the gutter / poor after properly taking into account transaction costs + slippage. |
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Edit 2 times,
last edit by
Quantonomics
at Nov 26, 2020 11:38:56 AM
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philjoe
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You're missing the point. Your P123 chart says you achieved 50% annual return. The Benchmark do 50% over the entire time. Your claiming a 0.39% weekly excess return. That doesn't equate to a 50% annual return. Add 0.39% weekly return to any benchmark you want (SPX, SP1500 Value, etc) you don't end up with a 50% annual return. Somethings wrong. Just to clarify what you've achieved, you used JASP's boosting technique to optimize weighting for three factors (which happen to be composites, but JASP only saw the three factors), which increased your weekly excess return from 0.25% to 0.39%? |
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Edit 1 times,
last edit by
philjoe
at Nov 26, 2020 11:46:21 AM
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Jrinne
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You're missing the point. Your P123 chart says you achieved 50% annual return. The Benchmark do 50% over the entire time. Your claiming a 0.39% weekly excess return. That doesn't equate to a 50% annual return. Add 0.39% weekly return to any benchmark you want (SPX, SP1500 Value, etc) you don't end up with a 50% annual return. Somethings wrong. Just to clarify what you've achieved, you used JASP's boosting technique to optimize weighting for three factors (which happen to be composites, but JASP only saw the three factors), which increased your weekly excess return from 0.25% to 0.39%? My apologies if I am misleading. So the tickers are every single trade in the P123—exactly 25 trades every week. For my study the excess returns are excess relative to the sim. I cannot stress enough how one has to get the noise of the market out of the data. So if one week ticker ABC happened to have 0.39% excess return as I use it here this that would be in addition to the return of the 25 stock model. Is that responsive at all? I have to go for a while. But please ask about anything. I am pretty sure that Boosting did about 7.5% better annualized with this simple model than if you picked stocks based on rank. I think I can explain that (or apprreciate the correction if I missed something). Jim From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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Edit 1 times,
last edit by
Jrinne
at Nov 26, 2020 12:13:36 PM
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philjoe
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Ahhhh ok so let me go again: You had a model that was decent and already did say a 40% annualized return. You then used JASP to tweak the weightings for 3 composites. The new tweaked model had an annualized return of 50%. Is that right? |
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Jrinne
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@Jrinne Did you take into account transaction costs + slippage? Because your portfolio turnover of 5000% is a true killer. I wouldn't be surprised if your returns were down the gutter / poor after properly taking into account transaction costs + slippage. This is not a sim for trading. This is a sim for getting data. Just as the API Marco will provide gives you factor ranks and returns. If Marco is smart he will not add any noise to that data with slippage. You/he will have to work out the slippage later. This is a method to get data and only to get data. Data used to train boosting (or TensorFlow). Jim From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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Edit 1 times,
last edit by
Jrinne
at Nov 26, 2020 12:56:24 PM
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Jrinne
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Quantinomics, The weekly trunover is intentional for collecting data for boosting. The target (label) should be over the same time-period. One ticker where the target (label) is 1 week’s return and another were the target was 6 week’s return does not work all. From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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Edit 2 times,
last edit by
Jrinne
at Nov 26, 2020 12:55:06 PM
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Jrinne
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Ahhhh ok so let me go again: You had a model that was decent and already did say a 40% annualized return. You then used JASP to tweak the weightings for 3 composites. The new tweaked model had an annualized return of 50%. Is that right? Yes. Exactly. And what I did with JASP was not a serious attempt. One can do better. Thank you Philip. From time to time you will encounter Luddites, who are beyond redemption. --de Prado, Marcos López on the topic of machine learning for financial applications |
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