vn.py
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d0650a4b2b
@ -102,8 +102,129 @@ def run_backtesting(
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## 参数优化
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vnpy提供2种参数优化的解决方案：穷举算法、遗传算法
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参数优化功能使用的是穷举算法，即多进程对所有参数组合进行回测，并输出最终解集。其操作流程如下：
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### 穷举算法
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穷举算法原理：
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- 输入需要优化的参数名、优化区间、优化步进，以及优化目标。
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```
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def add_parameter(
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self, name: str, start: float, end: float = None, step: float = None
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):
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""""""
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if not end and not step:
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self.params[name] = [start]
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return
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if start >= end:
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print("参数优化起始点必须小于终止点")
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return
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if step <= 0:
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print("参数优化步进必须大于0")
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return
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value = start
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value_list = []
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while value <= end:
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value_list.append(value)
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value += step
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self.params[name] = value_list
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def set_target(self, target_name: str):
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""""""
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self.target_name = target_name
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```
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- 形成全局参数组合, 数据结构为[{key: value, key: value}, {key: value, key: value}]。
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```
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def generate_setting(self):
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""""""
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keys = self.params.keys()
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values = self.params.values()
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products = list(product(*values))
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settings = []
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for p in products:
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setting = dict(zip(keys, p))
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settings.append(setting)
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return settings
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```
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- 遍历全局中的每一个参数组合：遍历的过程即运行一次策略回测，并且返回优化目标数值；然后根据目标数值排序，输出优化结果。
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```
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def run_optimization(self, optimization_setting: OptimizationSetting, output=True):
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""""""
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# Get optimization setting and target
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settings = optimization_setting.generate_setting()
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target_name = optimization_setting.target_name
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if not settings:
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self.output("优化参数组合为空，请检查")
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return
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if not target_name:
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self.output("优化目标未设置，请检查")
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return
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# Use multiprocessing pool for running backtesting with different setting
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pool = multiprocessing.Pool(multiprocessing.cpu_count())
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results = []
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for setting in settings:
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result = (pool.apply_async(optimize, (
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target_name,
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self.strategy_class,
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setting,
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self.vt_symbol,
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self.interval,
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self.start,
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self.rate,
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self.slippage,
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self.size,
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self.pricetick,
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self.capital,
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self.end,
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self.mode
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)))
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results.append(result)
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pool.close()
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pool.join()
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# Sort results and output
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result_values = [result.get() for result in results]
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result_values.sort(reverse=True, key=lambda result: result[1])
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if output:
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for value in result_values:
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msg = f"参数：{value[0]}, 目标：{value[1]}"
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self.output(msg)
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return result_values
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```
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注意：可以使用multiprocessing库来创建多进程实现并行优化。例如：若用户计算机是2核，优化时间为原来1/2；若计算机是10核，优化时间为原来1/10。
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穷举算法操作：
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- 点击“参数优化”按钮，会弹出“优化参数配置”窗口，用于设置优化目标（如最大化夏普比率、最大化收益回撤比）和设置需要优化的参数以及优化区间，如图。
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@ -118,6 +239,153 @@ def run_backtesting(
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### 遗传算法
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遗传算法原理：
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- 输入需要优化的参数名、优化区间、优化步进，以及优化目标；
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- 形成全局参数组合，该组合的数据结构是列表内镶嵌元组，即[[(key, value), (key, value)] , [(key, value), (key,value)]]，与穷举算法的全局参数组合的数据结构不同。这样做的目的是有利于参数间进行交叉互换和变异。
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```
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def generate_setting_ga(self):
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""""""
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settings_ga = []
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settings = self.generate_setting()
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for d in settings:
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param = [tuple(i) for i in d.items()]
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settings_ga.append(param)
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return settings_ga
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```
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- 形成个体：调用random()函数随机从全局参数组合中获取参数。
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```
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def generate_parameter():
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""""""
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return random.choice(settings)
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```
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- 定义个体变异规则: 即发生变异时，旧的个体完全被新的个体替代。
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```
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def mutate_individual(individual, indpb):
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""""""
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size = len(individual)
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paramlist = generate_parameter()
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for i in range(size):
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if random.random() < indpb:
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individual[i] = paramlist[i]
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return individual,
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```
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- 定义评估函数：入参的是个体，即[(key, value), (key, value)]形式的参数组合，然后通过dict()转化成setting字典，然后运行回测，输出目标优化数值，如夏普比率、收益回撤比。(注意，修饰器@lru_cache作用是缓存计算结果，避免遇到相同的输入重复计算，大大降低运行遗传算法的时间)
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```
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@lru_cache(maxsize=1000000)
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def _ga_optimize(parameter_values: tuple):
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""""""
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setting = dict(parameter_values)
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result = optimize(
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ga_target_name,
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ga_strategy_class,
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setting,
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ga_vt_symbol,
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ga_interval,
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ga_start,
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ga_rate,
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ga_slippage,
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ga_size,
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ga_pricetick,
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ga_capital,
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ga_end,
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ga_mode
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)
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return (result[1],)
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def ga_optimize(parameter_values: list):
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""""""
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return _ga_optimize(tuple(parameter_values))
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```
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- 运行遗传算法：调用deap库的算法引擎来运行遗传算法，其具体流程如下。
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1）先定义优化方向，如夏普比率最大化；
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2）然后随机从全局参数组合获取个体，并形成族群；
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3）对族群内所有个体进行评估（即运行回测），并且剔除表现不好个体；
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4）剩下的个体会进行交叉或者变异，通过评估和筛选后形成新的族群；（到此为止是完整的一次种群迭代过程）；
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5）多次迭代后，种群内差异性减少，整体适应性提高，最终输出建议结果。该结果为帕累托解集，可以是1个或者多个参数组合。
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注意：由于用到了@lru_cache, 迭代中后期的速度回提高非常多，因为很多重复的输入都避免了再次的回测，直接在内存中查询并且返回计算结果。
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```
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from deap import creator, base, tools, algorithms
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creator.create("FitnessMax", base.Fitness, weights=(1.0,))
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creator.create("Individual", list, fitness=creator.FitnessMax)
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......
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# Set up genetic algorithem
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toolbox = base.Toolbox()
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toolbox.register("individual", tools.initIterate, creator.Individual, generate_parameter)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("mate", tools.cxTwoPoint)
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toolbox.register("mutate", mutate_individual, indpb=1)
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toolbox.register("evaluate", ga_optimize)
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toolbox.register("select", tools.selNSGA2)
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total_size = len(settings)
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pop_size = population_size # number of individuals in each generation
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lambda_ = pop_size # number of children to produce at each generation
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mu = int(pop_size * 0.8) # number of individuals to select for the next generation
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cxpb = 0.95 # probability that an offspring is produced by crossover
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mutpb = 1 - cxpb # probability that an offspring is produced by mutation
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ngen = ngen_size # number of generation
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pop = toolbox.population(pop_size)
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hof = tools.ParetoFront() # end result of pareto front
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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np.set_printoptions(suppress=True)
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stats.register("mean", np.mean, axis=0)
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stats.register("std", np.std, axis=0)
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stats.register("min", np.min, axis=0)
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stats.register("max", np.max, axis=0)
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algorithms.eaMuPlusLambda(
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pop,
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toolbox,
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mu,
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lambda_,
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cxpb,
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mutpb,
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ngen,
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stats,
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halloffame=hof
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)
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# Return result list
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results = []
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for parameter_values in hof:
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setting = dict(parameter_values)
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target_value = ga_optimize(parameter_values)[0]
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results.append((setting, target_value, {}))
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return results
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```
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