def optimize(in_file, out_file): proggy = json.loads(in_file.read()) proggy = opt.optimize(proggy) json.dump(proggy, out_file)
# factor1_sig = np.sqrt(factor_df[(factor1, factor1)].fillna(0).ix[pd.to_datetime(dayname)])
# factor2_sig = np.sqrt(factor_df[(factor2, factor2)].fillna(0).ix[pd.to_datetime(dayname)])
# print "Factor Correlation {}, {}: {}".format(factor1, factor2, factor_cov/(factor1_sig*factor2_sig))
except:
# print "No cov found for {} {}".format(factor1, factor2)
factor_cov = 0
opt.g_fcov[find1, find2] = factor_cov * horizon
opt.g_fcov[find2, find1] = factor_cov * horizon
find2 += 1
find1 += 1
try:
(target, dutil, eslip, dmu, dsrisk, dfrisk, costs,
dutil2) = opt.optimize()
except:
date_group.to_csv("problem.csv")
raise
optresults_df = pd.DataFrame(index=date_group.index,
columns=[
'target', 'dutil', 'eslip', 'dmu',
'dsrisk', 'dfrisk', 'costs', 'dutil2',
'traded'
])
optresults_df['target'] = target
optresults_df['dutil'] = dutil
optresults_df['eslip'] = eslip
optresults_df['dmu'] = dmu
optresults_df['dsrisk'] = dsrisk
def on_run(self):
start = time.time()
# Construct full distance map
positions = {self.namesToIDs[task.name] : task.startPosition() for task in self.tasks}
fullDistanceMap = opt.FullDistanceMap()
for taskInd, task in enumerate(self.tasks):
distanceMap = opt.DistanceMap()
for otherInd, other in enumerate(self.tasks):
distanceMap[otherInd] = numpy.linalg.norm(positions[otherInd] - positions[taskInd])
fullDistanceMap[taskInd] = distanceMap
# Construct optimizable tasks
possibleTasks = set()
optimizableTaskList = opt.OptimizableTaskList()
modes = {self.namesToIDs[task.name] : self.instances[task.name].possibleModes() for task in self.tasks}
for taskInd, task in enumerate(self.tasks):
if not self.taskExecutable[task.name]:
continue
modeList = opt.ModeList()
for modeInd, mode in enumerate(modes[taskInd]):
modeList.append(opt.Mode(modeInd, mode.expectedPoints, mode.expectedTime))
if len(modes[taskInd]) > 0:
possibleTasks.add(self.idsToNames[taskInd])
optimizableTaskList.append(opt.OptimizableTask(taskInd, modeList))
# Construct topological restrictions
topologicalRestrictionList = opt.TopologicalRestrictionList()
for restriction in self.restrictions:
if restriction.beforeTask in possibleTasks: # TODO FIXME better way, this doesn't really check completion status?
topologicalRestrictionList.append(opt.TopologicalRestriction(self.namesToIDs[restriction.beforeTask], self.namesToIDs[restriction.afterTask]))
remainingTime = (60. * 15) - (time.time() - self.initialization)
self.logv('Remaining time: {}s'.format(remainingTime))
res = opt.optimize(fullDistanceMap, remainingTime, optimizableTaskList, topologicalRestrictionList)
end = time.time()
self.logv('Generated, validated, and scored possible execution plans in {} seconds.'.format(end - start))
if len(res) > 0:
plan = []
for val in res:
plan.append(TaskPlan(self.idsToNames[val.id()], modes[val.id()][val.mode().id()]))
if self.lastPlan is None or not equivalent(plan, self.lastPlan):
self.logi('New optimal execution plan: {}.'.format(prettify(plan)))
self.lastPlan = plan
taskPlan = plan[0]
if taskPlan.taskName != self.lastTask or taskPlan.mode.name != self.lastMode:
self.instances[taskPlan.taskName] = self.tasks[self.namesToIDs[taskPlan.taskName]].cls()
self.logi('Reinstantiated task: {} due to reason: switched.'.format(taskPlan.taskName))
self.logi('Switched to: task {} with mode {}.'.format(colored(taskPlan.taskName, 'green', attrs = ['bold']), colored(taskPlan.mode.name, 'cyan', attrs = ['bold'])))
self.lastTask = taskPlan.taskName
self.lastMode = taskPlan.mode.name
assertModules(self.instances[taskPlan.taskName].desiredModules(), self.logi)
try:
execute(self.instances[taskPlan.taskName], taskPlan)
except Exception as e:
self.namesToExceptionCounts[taskPlan.taskName] += 1
if self.namesToExceptionCounts[taskPlan.taskName] < self.maxExceptionCount:
self.logw('Task {} threw exception: {}! Exception {} of {} before that task is killed!'.format(taskPlan.taskName, \
e, self.namesToExceptionCounts[taskPlan.taskName], self.maxExceptionCount))
else:
self.loge('Task {} threw exception: {}! Task has reached exception threshold, will no longer be attempted!'.format( \
taskPlan.taskName, e))
self.taskExecutable[taskPlan.taskName] = False
else:
self.finish()
factor_cov = factor_df[(factor1, factor2)].fillna(0).ix[pd.to_datetime(dayname)]
# factor1_sig = np.sqrt(factor_df[(factor1, factor1)].fillna(0).ix[pd.to_datetime(dayname)])
# factor2_sig = np.sqrt(factor_df[(factor2, factor2)].fillna(0).ix[pd.to_datetime(dayname)])
# print "Factor Correlation {}, {}: {}".format(factor1, factor2, factor_cov/(factor1_sig*factor2_sig))
except:
# print "No cov found for {} {}".format(factor1, factor2)
factor_cov = 0
opt.g_fcov[ find1, find2 ] = factor_cov * horizon
opt.g_fcov[ find2, find1 ] = factor_cov * horizon
find2 += 1
find1 += 1
try:
(target, dutil, eslip, dmu, dsrisk, dfrisk, costs, dutil2) = opt.optimize()
except:
date_group.to_csv("problem.csv")
raise
optresults_df = pd.DataFrame(index=date_group.index, columns=['target', 'dutil', 'eslip', 'dmu', 'dsrisk', 'dfrisk', 'costs', 'dutil2', 'traded'])
optresults_df['target'] = target
optresults_df['dutil'] = dutil
optresults_df['eslip'] = eslip
optresults_df['dmu'] = dmu
optresults_df['dsrisk'] = dsrisk
optresults_df['dfrisk'] = dfrisk
optresults_df['costs'] = costs
optresults_df['dutil2'] = dutil2
# pnl_df.ix[ date_group.index, 'target'] = optresults_df['target']
factor1, factor2)].fillna(0).ix[pd.to_datetime(dayname)]
# factor1_sig = np.sqrt(factor_df[(factor1, factor1)].fillna(0).ix[pd.to_datetime(dayname)])
# factor2_sig = np.sqrt(factor_df[(factor2, factor2)].fillna(0).ix[pd.to_datetime(dayname)])
# print("Factor Correlation {}, {}: {}".format(factor1, factor2, factor_cov/(factor1_sig*factor2_sig)))
except:
# print("No cov found for {} {}".format(factor1, factor2))
factor_cov = 0
opt.g_fcov[find1, find2] = factor_cov * horizon
opt.g_fcov[find2, find1] = factor_cov * horizon
find2 += 1
find1 += 1
try:
(target, dutil, eslip, dmu, dsrisk, dfrisk, costs, dutil2, vol,
price) = opt.optimize()
except:
date_group.to_csv("problem.csv")
raise
optresults_df = pd.DataFrame(index=date_group.index,
columns=[
'target', 'dutil', 'eslip', 'dmu',
'dsrisk', 'dfrisk', 'costs', 'dutil2',
'traded'
])
optresults_df['target'] = target
optresults_df['dutil'] = dutil
optresults_df['eslip'] = eslip
optresults_df['dmu'] = dmu
optresults_df['dsrisk'] = dsrisk
try:
factor_cov = factor_df[(factor1, factor2)].fillna(0).ix[pd.to_datetime(dayname)]
# factor1_sig = np.sqrt(factor_df[(factor1, factor1)].fillna(0).ix[pd.to_datetime(dayname)])
# factor2_sig = np.sqrt(factor_df[(factor2, factor2)].fillna(0).ix[pd.to_datetime(dayname)])
# print("Factor Correlation {}, {}: {}".format(factor1, factor2, factor_cov/(factor1_sig*factor2_sig)))
except:
# print("No cov found for {} {}".format(factor1, factor2))
factor_cov = 0
opt.g_fcov[find1, find2] = factor_cov * horizon
opt.g_fcov[find2, find1] = factor_cov * horizon
find2 += 1
find1 += 1
try:
(target, dutil, eslip, dmu, dsrisk, dfrisk, costs, dutil2, vol, price) = opt.optimize()
except:
date_group.to_csv("problem.csv")
raise
optresults_df = pd.DataFrame(index=date_group.index,
columns=['target', 'dutil', 'eslip', 'dmu', 'dsrisk', 'dfrisk', 'costs', 'dutil2',
'traded'])
optresults_df['target'] = target
optresults_df['dutil'] = dutil
optresults_df['eslip'] = eslip
optresults_df['dmu'] = dmu
optresults_df['dsrisk'] = dsrisk
optresults_df['dfrisk'] = dfrisk
optresults_df['costs'] = costs
optresults_df['dutil2'] = dutil2