Differences

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--- personal:portfolio:portopt [2014/06/06 10:22]
antonello [Usage]
+++ personal:portfolio:portopt [2016/02/15 11:02]
antonello [Usage]
@@ 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 81: / Line 81: @@
   g++ -std=c++0x -O -shared -Wl,-soname,_portopt.so -o _portopt.so QuadProg++.o Array.o anyoption.o portopt.o portopt_wrap.o
 (then please refer to the python example for usage)
+If you want to change the output library name (e.g. you want to create _portopt_p3.so for python3 alongside _portopt.so for python2), do it in the %module variable of portopt.i and in the -soname and -o options of the linking command (and don't forget to use the right python included directory in the compilation command).\\
+You can then load the correct module in your script with something like:
+  import sys
+  if sys.version_info < (3, 0):
+    import portopt
+  else:
+    import portopt_p3 as portopt
 ===== Usage =====
@@ Line 93: / Line 101: @@
 Call:
-  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)
+  double solveport (const vector< vector <double> > &VAR, const vector<double> &MEANS, const double &alpha, vector<double> &x_h, int &errorcode, string &errormessage, double &port_opt_mean, double &port_opt_var, const double tollerance = 0.000001)
 == 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]
   errorcode    = results[2]
   errormessage = results[3]
+  opt_mean     = results[4]
+  opt_var      = results[5]
 === Options ===
 <code>
-  -h  --help                                    Prints this help
+  -h  --help                                   Prints this help
   -v  --var-file [input_var_file_name]         Input file containing the variance/covariance matrix (relative path)
   -m  --means-file [input_means_file_name]     Input file containing the means vector (relative path)