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--- personal:portfolio:portopt [2014/05/28 12:55] – [Acknowledgements] antonello
+++ personal:portfolio:portopt [2016/02/15 10:19] – antonello
@@ Line 21: / Line 21: @@
 In portfolio theory agents attempts to maximise portfolio expected return for a given amount of portfolio risk, or equivalently to minimise risk for a given level of expected return.
-{{ :personal:portfolio:portfolio_graph.png?nolink |Theoretical framework}}
+{{ :personal:portfolio:portfolio_model.png?nolink |Theoretical framework}}
 The portfolio management can be portrayed graphically as in the above Figure, where the feasible set of variance-profitability combinations in enclosed by the blue curve and the B-D segment represents the efficient frontier, where no variance can be lowered at productivity's price or equivalently no productivity can be increased at price of increasing variance.
@@ 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 =====
+:!: Please notice that the API changed from version 1.1, with the introduction of the ''port_opt_mean'' and ''port_opt_var'' parameters (both by reference). For the old 1.1 call instructions see [[https://lobianco.org/antonello/personal:portfolio:portopt?rev=1441891152|here]].
 == Linux ==
   ./portopt [options]
@@ Line 93: / Line 105: @@
 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, double &port_opt_mean, double &port_opt_var, 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]
+  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)
   -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 132: @@
   * 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
@@ Line 127: / Line 147: @@
 You should have received a copy of the GNU Lesser General Public License along with PortOpt.  If not, see [[http://www.gnu.org/licenses]].
+===== Citations =====
+If you use this program or a derivative of it in an academic framework, please cite it!\\
+Please cite as:
+  * A. Dragicevic, A. Lobianco,  A. Leblois (2016), "[[http://dx.doi.org/10.1016/j.forpol.2015.12.010|Forest planning and productivity-risk trade-off through the Markowitz mean-variance model]]", Forest Policy and Economics, Volume 64, March 2016, Pages 25–34.
 ===== Acknowledgements =====