leftyer/vnpy
1
0
Fork 0
mirror of https://gitee.com/vnpy/vnpy.git synced 2025-12-06 11:29:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Mod] proivde extra parameters in run_ga_optimization

Browse Source
This commit is contained in:
vn.py
2025-06-16 20:45:31 +08:00
parent d8e91a3f00
commit be617cd7ca
1 changed files with 20 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
vnpy/trader/optimize.py
View File

@@ -8,7 +8,7 @@ from multiprocessing.context import BaseContext
from multiprocessing.managers import DictProxy
from _collections_abc import dict_keys, dict_values, Iterable
from tqdm import tqdm # type: ignore
from tqdm import tqdm
from deap import creator, base, tools, algorithms # type: ignore
from .locale import _
@@ -134,20 +134,23 @@ def run_ga_optimization(
optimization_setting: OptimizationSetting,
key_func: KEY_FUNC,
max_workers: int | None = None,
population_size: int = 100,
ngen_size: int = 30,
pop_size: int = 100, # population size: number of individuals in each generation
ngen: int = 30, # number of generations: number of generations to evolve
mu: int | None = None, # mu: number of individuals to select for the next generation
lambda_: int | None = None, # lambda: number of children to produce at each generation
cxpb: float = 0.95, # crossover probability: probability that an offspring is produced by crossover
mutpb: float | None = None, # mutation probability: probability that an offspring is produced by mutation
indpb: float = 1.0, # independent probability: probability for each gene to be mutated
output: OUTPUT_FUNC = print,
indpb: float = 1,
cxpb: float = 0.95,
) -> list[tuple]:
"""Run genetic algorithm optimization"""
# Define functions for generate parameter randomly
buf: list[dict] = optimization_setting.generate_settings()
settings: list[list[tuple]] = [list(d.items()) for d in buf]
settings: list[dict] = optimization_setting.generate_settings()
parameter_tuples: list[list[tuple]] = [list(d.items()) for d in settings]
def generate_parameter() -> list:
""""""
return choice(settings)
return choice(parameter_tuples)
def mutate_individual(individual: list, indpb: float) -> tuple:
""""""
@@ -180,14 +183,17 @@ def run_ga_optimization(
key_func
)
total_size: int = len(settings)
pop_size: int = population_size # number of individuals in each generation
lambda_: int = pop_size # number of children to produce at each generation
mu: int = int(pop_size * 0.8) # number of individuals to select for the next generation
# Set default values for DEAP parameters if not specified
if mu is None:
mu = int(pop_size * 0.8)
mutpb: float = 1 - cxpb # probability that an offspring is produced by mutation
ngen: int = ngen_size # number of generation
if lambda_ is None:
lambda_ = pop_size
if mutpb is None:
mutpb = 1.0 - cxpb
total_size: int = len(parameter_tuples)
pop: list = toolbox.population(pop_size)
# Run ga optimization