From 539e7aca1eef30082c9dbf2fbd9e555bc46b0429 Mon Sep 17 00:00:00 2001 From: 1122455801 Date: Wed, 8 May 2019 22:18:56 +0800 Subject: [PATCH] Update cta_backtester.md --- docs/cta_backtester.md | 270 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 269 insertions(+), 1 deletion(-) diff --git a/docs/cta_backtester.md b/docs/cta_backtester.md index 5f2a43f4..c6e492f9 100644 --- a/docs/cta_backtester.md +++ b/docs/cta_backtester.md @@ -102,8 +102,129 @@ def run_backtesting(   ## 参数优化 +vnpy提供2种参数优化的解决方案：穷举算法、遗传算法 -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： + +  + +### 穷举算法 + +穷举算法原理： +- 输入需要优化的参数名、优化区间、优化步进，以及优化目标。 +``` + def add_parameter( + self, name: str, start: float, end: float = None, step: float = None + ): + """""" + if not end and not step: + self.params[name] = [start] + return + + if start >= end: + print("参数优化起始点必须小于终止点") + return + + if step <= 0: + print("参数优化步进必须大于0") + return + + value = start + value_list = [] + + while value <= end: + value_list.append(value) + value += step + + self.params[name] = value_list + + def set_target(self, target_name: str): + """""" + self.target_name = target_name +``` + +  + + +- 形成全局参数组合, 数据结构为[{key: value, key: value}, {key: value, key: value}]。 +``` + def generate_setting(self): + """""" + keys = self.params.keys() + values = self.params.values() + products = list(product(*values)) + + settings = [] + for p in products: + setting = dict(zip(keys, p)) + settings.append(setting) + + return settings +``` +  + + +- 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。 +``` + def run_optimization(self, optimization_setting: OptimizationSetting, output=True): + """""" + # Get optimization setting and target + settings = optimization_setting.generate_setting() + target_name = optimization_setting.target_name + + if not settings: + self.output("优化参数组合为空，请检查") + return + + if not target_name: + self.output("优化目标未设置，请检查") + return + + # Use multiprocessing pool for running backtesting with different setting + pool = multiprocessing.Pool(multiprocessing.cpu_count()) + + results = [] + for setting in settings: + result = (pool.apply_async(optimize, ( + target_name, + self.strategy_class, + setting, + self.vt_symbol, + self.interval, + self.start, + self.rate, + self.slippage, + self.size, + self.pricetick, + self.capital, + self.end, + self.mode + ))) + results.append(result) + + pool.close() + pool.join() + + # Sort results and output + result_values = [result.get() for result in results] + result_values.sort(reverse=True, key=lambda result: result[1]) + + if output: + for value in result_values: + msg = f"参数：{value[0]}, 目标：{value[1]}" + self.output(msg) + + return result_values +``` + + + + +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。 + +  + + +穷举算法操作： - 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。 @@ -118,6 +239,153 @@ def run_backtesting( ![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/cta_backtester/optimize_result.png) +  + +### 遗传算法 + +遗传算法原理： + +- 输入需要优化的参数名、优化区间、优化步进，以及优化目标； + +- 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。 +``` + 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 +``` + +  + + +- 形成个体：调用random()函数随机从全局参数组合中获取参数。 +``` + def generate_parameter(): + """""" + return random.choice(settings) +``` + +  + + +- 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。 +``` + 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, +``` + +  + + +- 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) +``` +@lru_cache(maxsize=1000000) +def _ga_optimize(parameter_values: tuple): + """""" + setting = dict(parameter_values) + + result = optimize( + ga_target_name, + ga_strategy_class, + setting, + ga_vt_symbol, + ga_interval, + ga_start, + ga_rate, + ga_slippage, + ga_size, + ga_pricetick, + ga_capital, + ga_end, + ga_mode + ) + return (result[1],) + + +def ga_optimize(parameter_values: list): + """""" + return _ga_optimize(tuple(parameter_values)) +``` + +  + +- 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。 +1）先定义优化方向，如夏普比率最大化； +2）然后随机从全局参数组合获取个体，并形成族群； +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； +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）； +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。 + +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。 +``` +from deap import creator, base, tools, algorithms +creator.create("FitnessMax", base.Fitness, weights=(1.0,)) +creator.create("Individual", list, fitness=creator.FitnessMax) + ...... + # Set up genetic algorithem + toolbox = base.Toolbox() + 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", mutate_individual, indpb=1) + toolbox.register("evaluate", ga_optimize) + toolbox.register("select", tools.selNSGA2) + + total_size = len(settings) + 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 = ngen_size # number of generation + + pop = toolbox.population(pop_size) + hof = tools.ParetoFront() # end result of pareto front + + stats = tools.Statistics(lambda ind: ind.fitness.values) + np.set_printoptions(suppress=True) + stats.register("mean", np.mean, axis=0) + stats.register("std", np.std, axis=0) + stats.register("min", np.min, axis=0) + stats.register("max", np.max, axis=0) + + algorithms.eaMuPlusLambda( + pop, + toolbox, + mu, + lambda_, + cxpb, + mutpb, + ngen, + stats, + halloffame=hof + ) + + # Return result list + results = [] + + for parameter_values in hof: + setting = dict(parameter_values) + target_value = ga_optimize(parameter_values)[0] + results.append((setting, target_value, {})) + + return results +``` + + + +