Differences

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--- personal:portfolio:portopt [2014/05/28 14:55]
antonello [Acknowledgements]
+++ 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 93: / Line 93: @@
 Call:
-  double solveport (const vector< vector <double> > &VAR, const vector<double> &MEANS, const double &alpha, vector<double> &x_h, int &errorcode)
+  double solveport (const vector< vector <double> > &VAR, const vector<double> &MEANS, const double &alpha, vector<double> &x_h, int &errorcode, string &errormessage, const double tollerance = 0.000001)
-== As a lib ising Python: ==
+== As a lib using Python: ==
   import portopt
-  results = portopt.solveport(var,means,alpha)
+  results = portopt.solveport(var,means,alpha,tolerance) # tolerance is optional, default to 0.000001
+  functioncost = results[0]
+  shares       = results[1]
+  errorcode    = results[2]
+  errormessage = results[3]
 === Options ===
@@ Line 106: / Line 110: @@
   -a  --alpha [alpha_coefficient]              Coefficient between production and risk in the linear indifference curves
   -f  --field-delimiter [field_delimiter]      Character to use as field delimiter (default: ';')
-  -s  --decimal-separator [decimal-separator]  Character to use as decimal delimiter (default: '.')''
+  -s  --decimal-separator [decimal-separator]  Character to use as decimal delimiter (default: '.')
+  -t  --tollerance [tolerance]                 A tolerance level to distinguish from zero (default: 0.000001)
 </code>
@@ Line 113: / Line 118: @@
   * Higher the alpha, lower the agent risk aversion;
   * Set a negative alpha to retrieve the portfolio with the lowest possible variance;
-  * Set alpha to zero to retrieve the portfolio with the highest mean, indipendently from variance (solution not guaranteed to be unique);
+  * 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: problem has no solutions, 3: internal solver error) in the errorcode parameter.
+  * 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 included