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68 lines
3.6 KiB
68 lines
3.6 KiB
from WindPy import *
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from datetime import *
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from WindAlgo import *
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from scipy.stats import rankdata
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import numpy as np
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import pandas as pd
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from datetime import datetime
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import scipy.stats as stats
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import numpy.linalg as la
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w.start()
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alpha_data = alpha_m
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def initialize(context):
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context.capital = 10000000 # 回测的初始资金
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context.securities = w.wset("sectorconstituent", "date=20140101;windcode=000906.SH").Data[1]
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context.start_date = "20140104" # 回测开始时间
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context.end_date = "20190530" # 回测结束时间
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context.commission = 0.0003 # 手续费
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context.alpha_data = alpha_data
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context.benchmark = '000906.SH' # 设置回测基准
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def handle_data(bar_datetime, context, bar_data):
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pass
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def my_schedule1(bar_datetime, context, bar_data):
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bar_datetime_str = bar_datetime.strftime('%Y-%m-%d')
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stock800 = w.wset("sectorconstituent", "date="+bar_datetime_str+";windcode=000906.SH").Data[1]
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stocks = get_stocks(bar_datetime_str,stock800) # 获取筛选后的股票池
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## 提取因子数据法
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data = context.alpha_data.loc[bar_datetime_str].reset_index().set_index(['codes']).drop(columns = ['date']) #提取因子数据并调整结构
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stock_v = list(set(stocks) & set(data.index))
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data = data.loc[stock_v].dropna(axis=0) # 筛选出有效股票池对应数据
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profit = w.wss(stock_v, "fa_oigr_ttm,pe_ttm", tradeDate=bar_datetime_str, usedf=True)[1] # 加入财务因子
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profit.index = data.index
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data['1/PEG'] = profit['FA_OIGR_TTM']/profit['PE_TTM']
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data = data_process(data,bar_datetime_str) # 数据预处理
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weight = [] # 因子加权权重
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data = factor_sum(data,weight) # 因子打分
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## 回测中计算法
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# start_time = w.tdaysoffset(-5, bar_datetime_str,usedf=True).Data[0][0].strftime('%Y-%m-%d')
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# close = w.wsd(stocks,'close',start_time,bar_datetime_str,usedf = True)[1].reset_index().drop(columns = ['index'])
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# vwap = w.wsd(stocks,'vwap',start_time,bar_datetime_str,usedf = True)[1].reset_index().drop(columns = ['index'])
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# alpha = {'alpha':alpha42(vwap,close).iloc[-1]}
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# data = pd.DataFrame(alpha,index=stocks)
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data = data.sort_values([data.columns.values[-1]],ascending=False) # 按打分大小排序
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code_list = list(data[:round(len(stocks)/5)].index) # 选出top组股票
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wa.change_securities(code_list) # 改变证券池
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context.securities = code_list
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list_sell = list(wa.query_position().get_field('code')) # 获取当前仓位股票池
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for code in list_sell:
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if code not in code_list:
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volumn = wa.query_position()[code]['volume'] # 找到每个股票的持仓量
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res = wa.order(code,volumn,'sell',price='close', volume_check=False)
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def my_schedule2(bar_datetime, context,bar_data):
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buy_code_list=list(set(context.securities)-(set(context.securities)-set(list(bar_data.get_field('code')))))
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list_now = list(wa.query_position().get_field('code')) # 获取当前仓位股票池
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for code in buy_code_list:
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if code not in list_now:
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res = wa.order_percent(code,1/len(buy_code_list),'buy',price='close', volume_check=False) # 等权买入
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wa = BackTest(init_func = initialize, handle_data_func=handle_data) # 实例化回测对象
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wa.schedule(my_schedule1, "w", 0) # w表示在每周执行一次策略，0表示偏移，表示月初第一个交易日往后0天
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wa.schedule(my_schedule2, "w", 0)
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res = wa.run(show_progress=True) # 调用run()函数开始回测,show_progress可用于指定是否显示回测净值曲线图
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nav_df = wa.summary('nav') # 获取回测结果，回测周期内每一天的组合净值
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