methods function main(Algorithm,Problem) %% Parameter setting kappa = Algorithm.ParameterSet(0.05); %% Generate random population Population = Problem.Initialization(); %% Optimization while Algorithm.NotTerminated(Population) MatingPool = TournamentSelection(2,Problem.N,-CalFitness(Population.objs,kappa)); Offspring = OperatorGA(Problem,Population(MatingPool)); Population = EnvironmentalSelection([Population,Offspring],Problem.N,kappa); end end end end
时间: 2023-06-17 08:05:59 浏览: 67
这是一个遗传算法的主程序,其中Algorithm和Problem是输入的参数。该程序实现了基本的遗传算法框架,包括初始化种群、运行遗传算法迭代、进行选择和交叉操作、评估适应度、进行环境选择等步骤。
具体来说,该程序首先根据输入的Problem生成初始种群,然后在Algorithm规定的迭代次数内运行遗传算法。每次迭代中,程序通过锦标赛选择算子选择一定数量的个体作为交叉和变异的父代,然后通过交叉和变异算子生成一定数量的后代个体。接着,程序通过评估函数计算后代个体的适应度,然后通过环境选择算子选择出一定数量的个体作为下一代种群的成员。
需要注意的是,该程序中的kappa参数是用于控制选择压力的系数,具体作用是控制选择强度,防止算法过早陷入局部最优。
相关问题
classdef IBEA < ALGORITHM % <multi/many> <real/integer/label/binary/permutation> % Indicator-based evolutionary algorithm % kappa --- 0.05 --- Fitness scaling factor %------------------------------- Reference -------------------------------- % E. Zitzler and S. Kunzli, Indicator-based selection in multiobjective % search, Proceedings of the International Conference on Parallel Problem % Solving from Nature, 2004, 832-842. %------------------------------- Copyright -------------------------------- % Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for % research purposes. All publications which use this platform or any code % in the platform should acknowledge the use of "PlatEMO" and reference "Ye % Tian, Ran Cheng, Xingyi Zhang, and Yaochu Jin, PlatEMO: A MATLAB platform % for evolutionary multi-objective optimization [educational forum], IEEE % Computational Intelligence Magazine, 2017, 12(4): 73-87". %-------------------------------------------------------------------------- methods function main(Algorithm,Problem) %% Parameter setting kappa = Algorithm.ParameterSet(0.05); %% Generate random population Population = Problem.Initialization(); %% Optimization while Algorithm.NotTerminated(Population) MatingPool = TournamentSelection(2,Problem.N,-CalFitness(Population.objs,kappa)); Offspring = OperatorGA(Problem,Population(MatingPool)); Population = EnvironmentalSelection([Population,Offspring],Problem.N,kappa); end end end end
这是一个 MATLAB 中的 Indicator-based evolutionary algorithm (IBEA) 的实现。IBEA 是一种用于多目标优化的演化算法,其核心思想是通过定义适应度指标来指导个体的选择和进化。具体来说,该算法通过计算每个个体与种群中其他个体之间的距离来计算每个个体的适应度指标,并使用适应度指标来进行选择和进化。算法中的参数 kappa 是适应度缩放因子。算法的优化过程是通过在种群中进行竞标赛选择,使用遗传算子生成后代,然后通过环境选择来更新种群。这个实现是使用了 PlatEMO 平台。
import numpy as np import pandas as pd import talib def initialize(context): context.symbol = 'BTCUSDT' context.window_size = 5 context.deviation = 1 context.trade_size = 0.01 context.stop_loss = 0.05 context.take_profit = 0.1 schedule_function(rebalance, date_rules.every_day(), time_rules.market_open()) def rebalance(context, data): price = data.history(context.symbol, 'close', context.window_size + 1, '1d') signal = mean_reversion_signal(price, context.window_size, context.deviation) current_position = context.portfolio.positions[context.symbol].amount if signal[-1] == 1 and current_position <= 0: target_position_size = context.trade_size / data.current(context.symbol, 'close') order_target_percent(context.symbol, target_position_size) elif signal[-1] == -1 and current_position >= 0: order_target(context.symbol, 0) elif current_position > 0: current_price = data.current(context.symbol, 'close') stop_loss_price = current_price * (1 - context.stop_loss) take_profit_price = current_price * (1 + context.take_profit) if current_price <= stop_loss_price or current_price >= take_profit_price: order_target(context.symbol, 0) def moving_average(x, n): ma = talib.SMA(x, timeperiod=n) return ma def std_deviation(x, n): std = talib.STDDEV(x, timeperiod=n) return std def mean_reversion_signal(price, window_size, deviation): ma = moving_average(price, window_size) std = std_deviation(price, window_size) upper_band = ma + deviation * std lower_band = ma - deviation * std signal = np.zeros_like(price) signal[price > upper_band] = -1 # 卖出信号 signal[price < lower_band] = 1 # 买入信号 return signal ''' 运行回测 ''' start_date = pd.to_datetime('2019-01-01', utc=True) end_date = pd.to_datetime('2021-01-01', utc=True) results = run_algorithm( start=start_date, end=end_date, initialize=initialize, capital_base=10000, data_frequency='daily', bundle='binance' ) ''' 查看回测结果 ''' print(results.portfolio_value)运行有错误
在代码中有一行缺少了冒号,应该在第一行的末尾添加一个冒号。另外,还需要导入run_algorithm函数,可以使用如下语句进行导入:
from zipline import run_algorithm
下面是修改后的代码:
```
import numpy as np
import pandas as pd
import talib
from zipline import run_algorithm
def initialize(context):
context.symbol = 'BTCUSDT'
context.window_size = 5
context.deviation = 1
context.trade_size = 0.01
context.stop_loss = 0.05
context.take_profit = 0.1
schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())
def rebalance(context, data):
price = data.history(context.symbol, 'close', context.window_size + 1, '1d')
signal = mean_reversion_signal(price, context.window_size, context.deviation)
current_position = context.portfolio.positions[context.symbol].amount
if signal[-1] == 1 and current_position <= 0:
target_position_size = context.trade_size / data.current(context.symbol, 'close')
order_target_percent(context.symbol, target_position_size)
elif signal[-1] == -1 and current_position >= 0:
order_target(context.symbol, 0)
elif current_position > 0:
current_price = data.current(context.symbol, 'close')
stop_loss_price = current_price * (1 - context.stop_loss)
take_profit_price = current_price * (1 + context.take_profit)
if current_price <= stop_loss_price or current_price >= take_profit_price:
order_target(context.symbol, 0)
def moving_average(x, n):
ma = talib.SMA(x, timeperiod=n)
return ma
def std_deviation(x, n):
std = talib.STDDEV(x, timeperiod=n)
return std
def mean_reversion_signal(price, window_size, deviation):
ma = moving_average(price, window_size)
std = std_deviation(price, window_size)
upper_band = ma + deviation * std
lower_band = ma - deviation * std
signal = np.zeros_like(price)
signal[price > upper_band] = -1 # 卖出信号
signal[price < lower_band] = 1 # 买入信号
return signal
''' 运行回测 '''
start_date = pd.to_datetime('2019-01-01', utc=True)
end_date = pd.to_datetime('2021-01-01', utc=True)
results = run_algorithm(
start=start_date,
end=end_date,
initialize=initialize,
capital_base=10000,
data_frequency='daily',
bundle='binance'
)
''' 查看回测结果 '''
print(results.portfolio_value)
```
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