2
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.
2
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 ...
1
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 ...
1
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 ...
1
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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