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Your best bet at linking a company across ISIN or CUSIP changes is to go with their legal entity ID (LEI). That said, sometimes you need to actually read through the news to determine if the company is the same or if the company name and legal structure was changed slightly or more. Note also that CUSIPs do not generally change without reason, but CUSIPs do ...


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Hi: You can calculate the weights in the index of the two stocks. $w_{A}$ and $w_{B}$ and the weights of the stocks in the portfolio, $w^{\prime}_A$ and $w^{\prime}_B$. Then, the return contribution due to the mis-weighting, is $(w_{A} - w^{\prime}_{A}) R_{A} + (w_{B} - w^{\prime}_{B}) R_{B}$. Then, assuming you don't have a risk model such as Barra, you can ...


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Your shift is in the wrong direction. Do this: df.price = pd.to_numeric(df.price) df['logret'] = np.log(df.price/df.price.shift(1))


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Just by looking at the graphs, I'd say: Unit root Constant series Seasonality AR model No AC No AC


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There is a multitude of texts which answer this question the easiest and free source is Rob Hyndmans from Monash Universities online text on forecasting, https://otexts.com/fpp2/, the topic is covered in many time series books and econometric texts, another good general reference is by Galit Schmueli who ran a course on Future learn for free on Time series ...


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Position sizing, in and of itself, is insufficient to manage risk trading any financial market. You also need to be well informed about the expected ranges of prices on the particular instrument you are using. You can use aggressive position sizing, but you'd have to modify the martingale so that you are not strictly using 2x, 3x, etc on each level. ...


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I think I understand where you are going, please correct me if I'm wrong. I also think whatever you could do to transform the returns to Gaussian will be very complex or not really useful. In short, I'm not convinced this will be a fruitful approach. If you have a sample of returns you can always apply a scaling to individual observations to make them normal....


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