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--- personal:portfolio:portopt [2014/06/06 10:22]
antonello [Usage]
+++ personal:portfolio:portopt [2015/03/30 14:20]
antonello [Theorical Background]
@@ Line 54: / Line 54: @@
 \end{equation}
-where $x_i$ is the share of the asset $i$, $p_i$ is its productivity and hence $\sum_i {x_i p_i}$ is the overall portfolio productivity and $\sum_i { \sum_j { x_i x_j \sigma_{i,j}}}$ its variance.
+where $x_i$ is the share of the asset $i$, $p_i$ is its productivity, $\sigma_{i,j}$ is the covariance between assets $i$ and $j$ and hence $\sum_i {x_i p_i}$ is the overall portfolio productivity and $\sum_i { \sum_j { x_i x_j \sigma_{i,j}}}$ is its variance.
 As the only quadratic term arises when $i=j$ and $\sigma_{i,j}$ being the variance is always positive, the problem is convex and hence easily numerically solved.
@@ Line 97: / Line 97: @@
 == As a lib using Python: ==
   import portopt
-  results = portopt.solveport(var,means,alpha,tollerance) # tollerance is optional, default to 0.000001
+  results = portopt.solveport(var,means,alpha,tolerance) # tolerance is optional, default to 0.000001
   functioncost = results[0]
   shares       = results[1]
@@ Line 120: / Line 120: @@
   * Set alpha to zero to retrieve the portfolio with the highest mean, independently from variance (solution not guaranteed to be unique);
   * Assets shares are returned in the x_h vector, eventual error code (0: all fine, 1: input data error, 2: no solutions, 3: didn't solve, 4: solver internal error) in the errorcode parameter.
-  * Use option "tollerance" with two l up to version 1.1
+  * Use option "tollerance" with two l up to version 1.1 included