senset/vnpy
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update backtesting.py

Browse Source
This commit is contained in:
1122455801 2019-05-08 14:30:14 +08:00
parent 9622d10186
commit 7c8ebbbbc7
1 changed files with 28 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

41
vnpy/app/cta_strategy/backtesting.py
View File

@ -6,7 +6,6 @@ from functools import lru_cache
from time import time
import multiprocessing
import random
import math
import numpy as np
import matplotlib.pyplot as plt
@ -85,6 +84,15 @@ class OptimizationSetting:
settings.append(setting)
return settings
def generate_setting_ga(self):
""""""
settings_ga = []
settings = self.generate_setting()
for d in settings:
param = [tuple(i) for i in d.items()]
settings_ga.append(param)
return settings_ga
class BacktestingEngine:
@ -520,10 +528,10 @@ class BacktestingEngine:
return result_values
def run_ga_optimization(self, optimization_setting: OptimizationSetting, output=True):
def run_ga_optimization(self, optimization_setting: OptimizationSetting, population_size=100, ngen_size=30, output=True):
""""""
# Get optimization setting and target
settings = optimization_setting.generate_setting()
settings = optimization_setting.generate_setting_ga()
target_name = optimization_setting.target_name
if not settings:
@ -537,7 +545,16 @@ class BacktestingEngine:
# Define parameter generation function
def generate_parameter():
""""""
return list(random.choice(settings).values())
return random.choice(settings)
def mutate_individual(individual, indpb):
""""""
size = len(individual)
paramlist = generate_parameter()
for i in range(size):
if random.random() < indpb:
individual[i] = paramlist[i]
return individual,
# Create ga object function
global ga_target_name
@ -573,18 +590,18 @@ class BacktestingEngine:
toolbox.register("individual", tools.initIterate, creator.Individual, generate_parameter)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutUniformInt, low=4, up=40, indpb=1)
toolbox.register("mutate", mutate_individual, indpb=1)
toolbox.register("evaluate", ga_optimize)
toolbox.register("select", tools.selNSGA2)
total_size = len(settings)
pop_size = int(pow(total_size, 1 / math.e)) # number of individuals in each generation
pop_size = population_size # number of individuals in each generation
lambda_ = pop_size # number of children to produce at each generation
mu = int(pop_size * 0.8) # number of individuals to select for the next generation
cxpb = 0.95 # probability that an offspring is produced by crossover
mutpb = 1 - cxpb # probability that an offspring is produced by mutation
ngen = 30 # number of generation
ngen = ngen_size # number of generation
pop = toolbox.population(pop_size)
hof = tools.ParetoFront() # end result of pareto front
@ -629,10 +646,9 @@ class BacktestingEngine:
# Return result list
results = []
parameter_keys = list(ga_setting.keys())
for parameter_values in hof:
setting = dict(zip(parameter_keys, parameter_values))
setting = dict(parameter_values)
target_value = ga_optimize(parameter_values)[0]
results.append((setting, target_value, {}))
@ -1094,10 +1110,9 @@ def optimize(
@lru_cache(maxsize=1000000)
def _ga_optimizae(parameter_values: tuple):
def _ga_optimize(parameter_values: tuple):
""""""
parameter_keys = list(ga_setting.keys())
setting = dict(zip(parameter_keys, parameter_values))
setting = dict(parameter_values)
result = optimize(
ga_target_name,
@ -1119,7 +1134,7 @@ def _ga_optimizae(parameter_values: tuple):
def ga_optimize(parameter_values: list):
""""""
return _ga_optimizae(tuple(parameter_values))
return _ga_optimize(tuple(parameter_values))
@lru_cache(maxsize=10)