蜗牛之路

四、知识

五、代码

# 导入函数库
import math
import talib as tl
import pandas as pd
import numpy as np
from datetime import timedelta

# 设定最大显示行数、列数为10000
pd.set_option('display.max_rows', 10000)

# 初始化函数，设定基准等等
def initialize(context):
    # 初始化此策略
    # 设置要操作的股票池为空，每天需要不停变化股票池
    set_universe([])
    
    # 设置手续费，买入时万分之三，卖出时万分之三加千分之一印花税, 每笔交易最低扣5块钱
    # set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))

    # 设置风险基准为沪深300指数
    g.riskbench = '000300.XSHG'
    
    # 设置基准对比为沪深300指数
    set_benchmark(g.riskbench)
    
    # 初始化当日买卖列表
    g.stock_buy = []
    g.stock_sell = []
    g.stock_hold = []
    
    # 初始化综合风险结果
    g.ris = 10
    g.ar = 1
    
    # 记录真实RSI_F和AR
    g.RIS_T = 0
    g.AR_T = 0
    
    # 初始化持仓比例
    g.hold_radio = 0
    g.hold_limit = 0
    
    # 仓位控制趋势
    g.hold_dir = 0
    
    # 初始化风险仓位表 risk_radio为基准持仓比例，risk_hold为是否可长期持仓标志
    d = {'risk_radio' : pd.Series([90, 75, 70, 45, 20], index = [50, 40, 30, 20, 10]),
        'risk_radio_limit' : pd.Series([100, 100, 100, 60, 40], index = [50, 40, 30, 20, 10])}
    g.con_df_risk = pd.DataFrame(d)
    
    # 初始化风险变化趋势数据
    g.risk_day1 = 0
    g.risk_day2 = 0
    g.risk_day3 = 0
    
    # 风险累积累值
    g.risk_count = 0
    
    # 初始化日期
    g.d_yesterday = ''
    g.d_today = ''
    
    # 初始化风险参数
    g.con_FAST_RSI = 20
    g.con_SLOW_RSI = 60
    g.con_AR_COUNT = 26 # 以4天为期间计算AR值（普通为26天），反应长期趋势
    
    # 去除创业版
    g.stock1 = get_index_stocks('399006.XSHE')
    g.stock2 = get_index_stocks('399012.XSHE')
    
    # 关闭部分log
    log.set_level('order', 'error')
   
    ## 运行函数（reference_security为运行时间的参考标的；传入的标的只做种类区分，因此传入'000300.XSHG'或'510300.XSHG'是一样的）
    # 开盘时运行
    run_monthly(handle,1)
    
    
def handle(context):
    industrys = ['A01','A02','A03','A04','A05','B06','B07','B08','B09','B11',
                'C13','C14','C15','C17','C18','C19','C20','C21','C22','C23',
                'C24','C25','C26','C27','C28','C29','C30','C31','C32','C33',
                'C34','C35','C36','C37','C38','C39','C40','C41','C42',
                'D44','D45','D46','E47','E48','E50','F51','F52',
                'G53','G54','G55','G56','G58','G59','H61','H62',
                'I63','I64','I65','J66','J67','J68','J69','K70','L71','L72',
                'M73','M74','N77','N78','P82','Q83','R85','R86','R87','S90']
    
    df_industrys =  pd.DataFrame(index = ['DH4', 'DT4', 'CHANGE', 'SORT_DH4' ,'SORT_DT4'])
    stocks = get_industry_stocks('A01')

    df_list = get_price(stocks, start_date = g.d_today, end_date = g.d_today, 
                        frequency = 'daily', fields = ['close', 'pre_close'],panel=False)
    df_close = history(4, unit='1d', field='close', security_list = stocks)
    df_close = df_close.append(df_list['close'])
    
    df_pre_close = history(4, unit='1d', field='pre_close', security_list = stocks)
    df_pre_close = df_pre_close.append(df_list['pre_close'])
    
    df_dustry = (df_close - df_pre_close) / df_pre_close
    df_dustry = df_dustry.T.sum() / len(stocks)    #df.T表示行与列对调，之前是日期，股票1，股票2这样的形式

    
    sum_DH4 = df_dustry.head(4).sum()       #取最近4天的总和
    sum_DT4 = df_dustry.tail(4).sum()       #取最近2天的，以及close,preclose(但这两个数为0？）

    df_dustry = pd.DataFrame([sum_DH4, sum_DT4, 0, 0, 0], 
                             index = ['DH4', 'DT4', 'CHANGE', 'SORT_DH4' ,'SORT_DT4'], 
                             columns = ['industry'])
    #concat函数是在pandas底下的方法，可以将数据根据不同的轴作简单的融合
    df_industrys = pd.concat([df_industrys, df_dustry], axis = 1, join_axes = [df_industrys.index])
    print(df_industrys)