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If by the middle part you refer to $$\bigg[1 + \bigg(\frac{E_T[f]}{\hat{\sigma}_T(f)}\bigg)^2 \bigg]^{-1},$$ then I believe that $E_T[f]$ is the mean of the excess factor returns and $\hat{\sigma}_T(f)$ is the standard deviation of the excess factor returns. As everything is scalar, it is just simple inverse. Python implementation shouldn't be too difficult.

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From the documentation of matplotlib.finance (under parse_yahoo_historical_ochl(...)) it is specified that the dollars traded/dollar-volume is the unadjusted volume multiplied by the adjusted closing prices of the given ticker (At quotes_yahoo_historical_ochl(...) they refer to the above function in order to understand the output format): adjusted : bool If ...

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I think approaches that you suggest are incompatible. Risk contribution (or risk parity/HRP) is portfolio construction method that takes the volatility and correlation of securities, along with a desired contribution to risk, and generates the weights of the portfolio. Black-Litterman is related to mean-variance optimization. In a simple form, it can take ...

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your Sigma matrix is 5x5 and not 10x10, try this A = pd.DataFrame( [np.random.randn(n) for i in range(5*n)], columns=[chr(65+i) for i in range(n)] ) it will work. [ADDITION following a remark] I assumed that you expected the portfolio to be of dimension 10 (because you write n=10;w = cp.Variable(n)), hence your covariance matrix should have ...

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The largest C# technical indicator library so far is at https://github.com/ooples/OoplesFinance.StockIndicators The full list of technical indicators is extremely large and they are all at https://ooples.github.io/OoplesFinance.StockIndicators/indicators There are over 350 unique technical indicators and it is by far the easiest to use. You can make an ...

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