Bayesian Financial Models
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Bayesian Financial Models
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Re: Bayesian Financial Models
#2I really wanted to read this but for some reason it doesn't resize for mobile unfortunately. I'll have to bookmark it. It may also possibly be my phone which is not on its best legs.
Re: Bayesian Financial Models
#3The analogy incorrectly compares a binary outcome (cancer / no cancer) with a continuous variable (stock price returns, which may have skewed magnitudes depending on upside or downside). So while it is nominally correct about the formula for "stocks up" or "stocks down", it does not help for an actual trading situation where "stocks down" could have a negative return mean much larger than the corresponding positive mean return of "stocks up", for example.
Re: Bayesian Financial Models
#4The analogy incorrectly compares a binary outcome (cancer / no cancer) with a continuous variable (stock price returns, which may have skewed magnitudes depending on upside or downside). So while it is nominally correct about the formula for "stocks up" or "stocks down", it does not help for an actual trading situation where "stocks down" could have a negative return mean much larger than the corresponding positive m…
A trade is a binary outcome, trade wins or trade loses.
Re: Bayesian Financial Models
#5The analogy incorrectly compares a binary outcome (cancer / no cancer) with a continuous variable (stock price returns, which may have skewed magnitudes depending on upside or downside). So while it is nominally correct about the formula for "stocks up" or "stocks down", it does not help for an actual trading situation where "stocks down" could have a negative return mean much larger than the corresponding positive m…
A trade is a binary outcome, trade wins or trade loses.
That is useless in the real world. Trade loses is inevitably a larger absolute value P&L than trade wins. Trade wins frequency > trade loses frequency. Trade loses size > trade wins size. You need both and the analysis only does the former.
Re: Bayesian Financial Models
#6Earlier quoted context omitted.
A trade is a binary outcome, trade wins or trade loses.
That is useless in the real world. Trade loses is inevitably a larger absolute value P&L than trade wins. Trade wins frequency > trade loses frequency. Trade loses size > trade wins size. You need both and the analysis only does the former.
Bayes is about working with incomplete information, you don't need all the data, you just need a slight edge. Just because it's not a complete analysis of all necessary input doesn't make it useless. Beyond that, you can directly control your win and loss size with targets and stops, so they're not necessarily unknown, and you certainly know your win/loss ratio which of course you'd want to plug in, at least for money management reasons, especially if you're using any variant of the Kelly formula for position size.
Re: Bayesian Financial Models
#7Earlier quoted context omitted.
That is useless in the real world. Trade loses is inevitably a larger absolute value P&L than trade wins. Trade wins frequency > trade loses frequency. Trade loses size > trade wins size. You need both and the analysis only does the former.
Bayes is about working with incomplete information, you don't need all the data, you just need a slight edge. Just because it's not a complete analysis of all necessary input doesn't make it useless. Beyond that, you can directly control your win and loss size with targets and stops, so they're not necessarily unknown, and you certainly know your win/loss ratio which of course you'd want to plug in, at least for mone…
Trade win:lose ratio > 1 is an elementary mistake in algorithmic backtesting. Not knocking Bayes. Only saying the analysis is incomplete. Cancer/noCancer is not a good analogy.
Re: Bayesian Financial Models
#8Earlier quoted context omitted.
Bayes is about working with incomplete information, you don't need all the data, you just need a slight edge. Just because it's not a complete analysis of all necessary input doesn't make it useless. Beyond that, you can directly control your win and loss size with targets and stops, so they're not necessarily unknown, and you certainly know your win/loss ratio which of course you'd want to plug in, at least for mone…
Trade win:lose ratio > 1 is an elementary mistake in algorithmic backtesting. Not knocking Bayes. Only saying the analysis is incomplete. Cancer/noCancer is not a good analogy.
> Trade win:loss ratio > 1 is an elementary mistake in algorithmic backtesting
Who said anything about that? Perhaps I was unclear, it's not about the ratio, it's about the streaks, you need to understand patterns of your wins and losses as it's critical to applying money management. If you only win 30% of the time, you could still make a killing if you can withstand the losing streaks without much drawdown or if the winners are big enough.
Re: Bayesian Financial Models
#9The analogy incorrectly compares a binary outcome (cancer / no cancer) with a continuous variable (stock price returns, which may have skewed magnitudes depending on upside or downside). So while it is nominally correct about the formula for "stocks up" or "stocks down", it does not help for an actual trading situation where "stocks down" could have a negative return mean much larger than the corresponding positive m…
Reality is continuous. Human categories--like cancer--only have sharp edges because we draw them with an act of selective attention. The edge of our attention is discontinuous. Nothing else (that doesn't involved quantum mechanics or integer counting of attentionally-isolated objects) is.
"Cancer" is not a simple thing. Two people with "breast cancer" may have very similar or almost completely different diseases. As others here have pointed out, the magnitude and frequency of wins and losses matter even though trades are binary win/lose (which they can be because we've created an entire category of imaginary objects called dollars that can be counted).
Bayes rule is as applicable to any area of significant uncertainty, including win/loss magnitude. It is universal.
Re: Bayesian Financial Models
#10Earlier quoted context omitted.
Trade win:lose ratio > 1 is an elementary mistake in algorithmic backtesting. Not knocking Bayes. Only saying the analysis is incomplete. Cancer/noCancer is not a good analogy.
> Trade win:loss ratio > 1 is an elementary mistake in algorithmic backtesting Who said anything about that? Perhaps I was unclear, it's not about the ratio, it's about the streaks, you need to understand patterns of your wins and losses as it's critical to applying money management. If you only win 30% of the time, you could still make a killing if you can withstand the losing streaks without much drawdown or if the…
Obviously I agree. My simple point is that the OP is making claims about a binary outcome, which could lead one to infer that stock trading binary up/down outcomes are all that matter.