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Please let me be sure I have this right. Your strategy is positively correlated with market; but the beta is non-zero. IS the beta positive or negative? if negative, your previous statement cannot be true; you have to mis-measuring something somewhere (happy to help)! If your beta is the right sign but <>1, then that just reflects the imperfect ability ...

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Assume we are in the Black Scholes for call option settings, and let’s ignore the dividend. For the implied vol, we can treat all other variables as constant, and focus on the price of the call option as a function of implied vol. $C\left( \sigma\right)=SN\left(d_1\right)-Xe^{-rT}N\left(d_2\right)$ Where: \$d_1=\frac{ln \frac{F}{X}}{\sigma \sqrt{T}}+\frac{...

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The option price should be superior than the intrinsic value of the option. In your case: 31590-29800=1790>1768.05. if you want to test the IV given by your algorithm you can use my website [https://www.valometrics.com]. it is a web platform coded using javascript that contains an IV calculator. please let me know for more information.

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I did not tested it by now, but Google released a library similar to quantlib written in TensorFlow. It may be worthwhile to test it (and to post here your views on it), because once you are in TF, you can easily distribute your computations over a grid of computers (including GCP or AWS) if you are a machine learning enthousiastic: use it to backprop a ...

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Let’s put it this way. Classic MV is still used, but its shortcomings are universally appreciated. In its favour, the process is logical, conceptually intuitive, and non-quants easily understand it. But the optimisation produces some very unintuitive results, no different to multicollinearity effects in regression analysis. That’s a harder one for the non-...

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The justification for that microprice is empirical, not theoretical. In most market I can think of, most of the time, if there are more orders and more size on the bid than the ask, then it's more likely that that BBO will tick up rather than down. And the greater the imbalance, the higher the probability of an uptick (and vice-versa for downticks). For ...

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Its called 'universal' because, unlike usual models trained on time series for a given stock/ contract, this model is trained on a POOLED data set (in this case 500 or so stocks) and is then shown to be applicable for forecasting any stock, including those not included in the training data. This is different from the usual approach where, say, you use time ...

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A lot of ht e liquidity disappears after close so for that reason alone it is more expensive. The spread is much wider. To find someone willing to trade on the close price after close is the same thing as trying to find someone willing to trade on the price from 1 pm at 2 pm.

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