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stochastic_optimization.cc
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stochastic_optimization.cc
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#include "monte_carlo.h"
#include "stochastic_optimization.h"
static void make_delta (portfolio & p, int size)
{
for (int i = 0; i < size; i++) {
int r = rand () % 3;
switch (r) {
case 0:
p.proportions[i] = 1.0f;
break;
case 1:
p.proportions[i] = 0.8f;
break;
case 2:
p.proportions[i] = 1.2f;
break;
}
}
}
static void make_single_delta (portfolio & p, int index, int size, float factor)
{
index %= size;
for (int i = 0; i < size; i++)
p.proportions[i] = 0.f;
if (rand () % 2 == 0)
p.proportions[index] = 1.0f * factor;
else
p.proportions[index] = -1.0f * factor;
}
static void add (portfolio & p, portfolio & delta, int size)
{
for (int i = 0; i < size; i++)
p.proportions[i] += delta.proportions[i];
}
static void mul (portfolio & p, portfolio & delta, int size)
{
for (int i = 0; i < size; i++)
p.proportions[i] *= delta.proportions[i];
}
static void one (portfolio & p, int size)
{
for (int i = 0; i < size; i++)
p.proportions[i] = 1.f;
}
static float fitness_function (std::vector < data_series > &data, portfolio & p, float expectancy, float downsize_75_deviation, float goal)
{
float factor1 = 0.f;
float factor2 = 0.f;
float factor3 = 0.f;
/* The fitness function that evaluates a portfolio has 3 main components:
*
* 1. We want to try to get at least the user-specific expectancy. Anything
* above the goal is only a small win, anything below the goal is bad.
*
* 2. We want to minimize the risk, i.e. the downside deviation.
*
* 3. We try to get some diversification, i.e. don't put more than 100% in a
* single sector (keep in mind we are leveraged so we can go to a total of 300%)
*
* We sum all 3 components into a single metric, which is the touchy part: how
* much weight should be given to each is very much a personal preference.
* These should be reasonable (?) defaults.
*/
// 1. Maximize expectancy
if (expectancy < goal)
factor1 = (goal - expectancy) * 6.0f;
else
factor1 = expectancy * -0.01f;
// 2. Minimize downside 75 deviation
factor2 = downsize_75_deviation * 50.f;
// 3. Try to get some diversification
float sectors[NUM_SECTORS], total = 0.f;
for (unsigned s = 0; s < NUM_SECTORS; s++)
sectors[s] = 0.f;
for (unsigned t = 0; t < data.size (); t++) {
for (unsigned s = 0; s < NUM_SECTORS; s++) {
float v = p.proportions[t] * data[t].leverage * data[t].sector_proportions[s];
sectors[s] += v;
total += v;
}
}
for (unsigned s = 0; s < NUM_SECTORS; s++)
if (sectors[s] > 100.f)
factor3 += (sectors[s] - 100.f);
factor3 /= 100.f;
return -factor1 - factor2 - factor3;
}
void stochastic_optimization (std::vector < data_series > &historical_data, portfolio & p, bool refine, int days_back, float goal)
{
portfolio p_new;
portfolio delta;
int iteration = 0;
int size = historical_data.size ();
float fitness = -FLT_MAX;
float expectancy, standard_deviation, downside_deviation, downsize_75_deviation;
int num_rounds = 1 << 17;
int stagnate = 0;
monte_carlo m (historical_data, true);
p.print (historical_data);
m.run (p, expectancy, standard_deviation, downside_deviation, downsize_75_deviation, num_rounds, days_back);
fitness = fitness_function (historical_data, p, expectancy, downsize_75_deviation, goal);
if (!refine) {
// Initialize with ones
one (p, size);
p.normalize ();
// Coarse pass
m.run (p, expectancy, standard_deviation, downside_deviation, downsize_75_deviation, num_rounds, days_back);
fitness = fitness_function (historical_data, p, expectancy, downsize_75_deviation, goal);
float factor = 0.5f;
while (stagnate < 4000) {
make_single_delta (delta, iteration % size, size, factor);
do_it_again:
p_new = p;
add (p_new, delta, size);
p_new.normalize ();
p_new.max_proportions (historical_data);
m.run (p_new, expectancy, standard_deviation, downside_deviation, downsize_75_deviation, num_rounds, days_back);
float new_fitness = fitness_function (historical_data, p, expectancy, downsize_75_deviation, goal);
if (new_fitness > fitness) {
p = p_new;
fitness = new_fitness;
printf ("fitness now %f e = %f σ = %f σd = %f σd75 = %f \n", fitness, expectancy, standard_deviation, downside_deviation, downsize_75_deviation);
stagnate = 0;
p.print (historical_data);
goto do_it_again;
}
iteration++;
stagnate++;
if ((stagnate == 4000) && factor > 0.1f) {
factor /= 1.5f;
stagnate = 0;
printf ("new factor %f\n", factor);
}
}
printf ("============================\n");
}
// Fine pass
stagnate = 0;
num_rounds = 1 << 17;
while (stagnate < 10000) {
make_delta (delta, size);
do_it_again2:
p_new = p;
mul (p_new, delta, size);
p_new.normalize ();
p_new.max_proportions (historical_data);
m.run (p_new, expectancy, standard_deviation, downside_deviation, downsize_75_deviation, num_rounds, days_back);
float new_fitness = fitness_function (historical_data, p, expectancy, downsize_75_deviation, goal);
if (new_fitness > fitness) {
stagnate = 0;
p = p_new;
fitness = new_fitness;
printf ("fitness now %f e = %f σ = %f σd = %f σd75 = %f \n", fitness, expectancy, standard_deviation, downside_deviation, downsize_75_deviation);
p.print (historical_data);
goto do_it_again2;
}
iteration++;
stagnate++;
if ((stagnate == 5000) && (num_rounds < 1 << 20)) {
num_rounds *= 2;
stagnate = 0;
printf ("rounds %d\n", num_rounds);
m.run (p, expectancy, standard_deviation, downside_deviation, downsize_75_deviation, num_rounds, days_back);
fitness = fitness_function (historical_data, p, expectancy, downsize_75_deviation, goal);
}
}
p.print_as_file (historical_data);
for (int l = 0; l < 10; l++) {
m.run (p, expectancy, standard_deviation, downside_deviation, downsize_75_deviation, num_rounds, days_back);
printf ("e = %f σ = %f σd = %f σd75 = %f \n", expectancy, standard_deviation, downside_deviation, downsize_75_deviation);
}
}