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But how much is it actually used in trading?

• SatvikBeri 5y ago
  Almost no one uses it directly – in practice, Kelly is too aggressive – but variations of it are quite common.
  • syntaxing 5y ago
    Would love to hear about the variations!
    • paulpauper 5y ago
      bet less than the formula recommends. depends on personal preferences
• dcolkitt 5y ago
  Kelly criterion is just the square of Sharpe ratio. Sharpe is used pretty extensively from a strategy selection and risk management perspective.
  • SpicyP 5y ago
    This is not correct. Kelly criterion is not the square of the sharpe ratio
    • afryer 5y ago
      https://ifta.org/public/files/journal/d_ifta_journal_11.pdf https://ifta.org/public/files/journal/d_ifta_journal_11.pdf Page 27: Vince(vi) and independently Thorpv(ii) provide a solution that satisfies the Kelly Criterion for the continuous finance case, often quoted in the financial community to the effect that “f should equal the expected excess return of the strategy divided by the expected variance of the excess return:” f = (m-r) / s^2 so it's Sharpe with variance instead of standard deviation in the denominator, correct?
      • dcolkitt 5y ago
        Yes, that's correct. An intuitive way to think about this is that Sharpe depends on the specific horizon you're using. E.g. annualized Sharpe will be sqrt(252) larger than daily Sharpe. It would not make sense to change the Kelly criterion based on a substitution of variables. In contrast variance, like returns, scales linearly with time horizon. Therefore the variance ratio is invariant to the time horizon.
  • arthurcolle 5y ago
    Please share this calculation, dying to know what kind of inter-universal Teichmüller space theoretic math you're using to come up with this.
    • afryer 5y ago
      https://news.ycombinator.com/item?id=27453365 https://news.ycombinator.com/item?id=27453365 Edward Thorpe and Ralph Vince both conclude that the Kelly Criterion in the continuous case is excess returns divided by variance, which is pretty close to the Sharpe Ratio, correct? Asking to understand better, not to be combative. Your comment made it seem like that formula is way off.
• fighterpilot 5y ago
  It's not practical when the outcome is distributed in a very weird and unknown way and each bet isn't really iid. Moreover you can find the same result with a simple grid search over bet sizes, obviating the need to estimate any parameters of the outcome distribution. It would be more useful in professional gambling where outcome distributions are more knowable.
• evo 5y ago
  It's useful as an upper limit--if you're leveraging past what Kelly suggests you're almost certainly overleveraged. In theory, Kelly is optimal--if you knew the exact probability density function of your returns, it would give you the right leverage to take. In practice, you're always playing with risks, some you're factoring into your models, some you're choosing not to because they're intractable, some you're not even aware of until they occur. The most basic premise--today's returns will be a function of hypotheses that I've derived from looking at past observations--is an approximation at best. This mismatch between model and reality can lead to expensive lessons learned when using the full Kelly model, so often traders will "half-kelly" or something like that, to incorporate the basic idea of risk scaling proposed by the Kelly model but with more safety margin.
  • 6gvONxR4sf7o 5y ago
    > In theory, Kelly is optimal... It's optimal for one utility function (log of future $), but that doesn't make it necessarily optimal for everything, right?
    • evo 5y ago
      That’s a great callout as well: it’s optimal for maximizing your expected growth in the long term, but does carry significant volatility. That’s fine for an emotionless immortal robot investor, but as we’re human. If we’re close to an investment goal, like saving a house downpayment or retiring, the calculus is quite different. Even outside that, loss aversion is a real thing and we’re likely happier trading some upside for being able to sleep at night.
    • LudwigNagasena 5y ago
      It is optimal for the growth of your wealth in the long run. The issue of utility only comes into play if you have to bet a finite amount of times and you don’t plan to or cannot live forever.
  • kqr 5y ago
    > This mismatch between model and reality can lead to expensive lessons learned when using the full Kelly model, so often traders will "half-kelly" or something like that, to incorporate the basic idea of risk scaling proposed by the Kelly model but with more safety margin. And to be clear, half-Kelly, quarter-Kelly, eighty-percent-Kelly and any other linear combination between wallet and full Kelly are actually still the only strategies that are growth optimal -- given the particular safety margin each corresponds to.
• rezahussain 5y ago
  I used it in live trading last year, I couldn't really make it work. I precalc my stoplosses + stopgains then use a simulation to get the win/loss probabilities on training data. What I observed is the kelly formula really prefers the tiny stoplosses, so when you sort your predictions by kelly score it will pick the ones with tiny stoplosses. What happened to me in the live test is the tiny stoplosses triggered, when a stopgain would have triggered later. I know someone is going to say "thats a problem with your stoplosses+stopgains OOS performance" and they are right, but OOS stoploss+stopgain calculation isn't trivial for me to calculate :\
  • aborsy 5y ago
    Where did you get the distribution of the real data from?
    • rezahussain 5y ago
      Yes, model predictions on volume+price data from 2019+2020
• Solstinox 5y ago
  Knowingly? Not often. You don't find long-run successful traders who don't knowingly/unknowingly use it or some variation of it.
• paulpauper 5y ago
  I am sure it is used but the challenge is calculating the necessary parameters