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Update docs
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docs/source/plot.rst

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@@ -73,7 +73,7 @@ Example
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method_mu='hist' # Method to estimate expected returns based on historical data.
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method_cov='hist' # Method to estimate covariance matrix based on historical data.
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port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)
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port.assets_stats(method_mu=method_mu, method_cov=method_cov)
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mu = port.mu
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cov = port.cov
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port.factors_stats(method_mu=method_mu,
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method_cov=method_cov,
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feature_selection='stepwise',
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dict_risk=dict(stepwise='Forward'))
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stepwise='Forward')
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w3 = port.rp_optimization(model='FC', rm='MV', rf=0, b_f=None)
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# Estimate the risk parity portfolio for principal components
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port.factors = X
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port.factors_stats(method_mu=method_mu,
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method_cov=method_cov,
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feature_selection='PCR',
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dict_risk=dict(n_components=0.95))
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n_components=0.95)
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w4 = port.rp_optimization(model='FC', rm='MV', rf=0, b_f=None)
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docs/source/reports.rst

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method_mu='hist' # Method to estimate expected returns based on historical data.
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method_cov='hist' # Method to estimate covariance matrix based on historical data.
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port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)
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port.assets_stats(method_mu=method_mu, method_cov=method_cov)
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# Estimate the portfolio that maximizes the risk adjusted return ratio
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w = port.optimization(model='Classic', rm=rm, obj='Sharpe', rf=0.0, l=0, hist=True)

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