2087 穿越牛熊基业长青的价值精选策略.py 量化交易策略代码

# 标题：穿越牛熊基业长青的价值精选策略（小资本也有大作为）
# 本策略请选择 python 2 下回测

'''
投资程序：
霍华．罗斯曼强调其投资风格在于为投资大众建立均衡、且以成长为导向的投资组合。选股方式偏好大型股，
管理良好且为领导产业趋势，以及产生实际报酬率的公司；不仅重视公司产生现金的能力，也强调有稳定成长能力的重要。
总市值大于等于50亿美元。
良好的财务结构。
较高的股东权益报酬。
拥有良好且持续的自由现金流量。
稳定持续的营收成长率。
优于比较指数的盈余报酬率。
'''

import pandas as pd
import numpy as np
from jqdata import *
from kuanke.wizard import *
# 初始化函数，设定基准等等
def initialize(context):
    # 设定沪深300
    set_benchmark('000300.XSHG')
    # 开启动态复权模式(真实价格)
    
    set_option('use_real_price', True)
    # 输出内容到日志 log.info()
    log.info('初始函数开始运行且全局只运行一次')
    # 过滤掉order系列API产生的比error级别低的log
    # log.set_level('order', 'error')
    #策略参数设置
    #操作的股票列表
    g.buy_list = []
    # 设置滑点
    set_slippage(FixedSlippage(0.0026))
    ### 股票相关设定 ###
    # 股票类每笔交易时的手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
    # 个股最大持仓比重
    g.security_max_proportion = 0.2
    # 最大建仓数量
    #g.max_hold_stocknum = 10
    g.max_hold_stocknum = 3
    # 单只最大买入股数或金额
    g.max_buy_value = None
    g.max_buy_amount = None
    # 委托类型
    g.order_style_str = 'by_cap_mean'
    g.order_style_value = 100
    # 每月第5个交易日进行操作
    # 开盘前运行
    run_monthly(before_market_open,10,time='before_open', reference_security='000300.XSHG') 
    # 开盘时运行
    run_monthly(market_open,10,time='open', reference_security='000300.XSHG')
    
## 开盘前运行函数     
def before_market_open(context):
    # 获取要操作的股票列表
    temp_list = get_stock_list(context)
    #排除恒瑞
    #if '600276.XSHG' in temp_list :
    #    temp_list.remove('600276.XSHG');
    
    # 去除周期行业
    g.industry_list = ["801010","801040","801050","801080","801110","801120","801130","801150","801160","801200","801230","801710","801720","801730","801740","801750","801760","801770","801780","801790"]
    temp_list = industry_filter(context, temp_list, g.industry_list)

    log.info('满足条件的股票有%s只'%len(temp_list))
    #按市值进行排序
    g.buy_list = get_check_stocks_sort(context,temp_list)
    print(("the list consist of ",g.buy_list));
    

## 开盘时运行函数
def market_open(context):
    #卖出不在买入列表中的股票
    sell(context,g.buy_list)
    #买入不在持仓中的股票，按要操作的股票平均资金
    buy(context,g.buy_list)
#交易函数 - 买入
def buy(context, buy_lists):
    # 获取最终的 buy_lists 列表
    Num = g.max_hold_stocknum - len(context.portfolio.positions)
    buy_lists = buy_lists[:Num]
    # 买入股票
    if len(buy_lists)>0:
        # 分配资金
        
        result = order_style(context,buy_lists,g.max_hold_stocknum, g.order_style_str, g.order_style_value)
        for stock in buy_lists:
            if len(context.portfolio.positions) < g.max_hold_stocknum : # 获取资金 amount = result[stock] # 判断个股最大持仓比重 #value = judge_security_max_proportion(context,stock,Cash,g.security_max_proportion) # 判断单只最大买入股数或金额 #amount = max_buy_value_or_amount(stock,value,g.max_buy_value,g.max_buy_amount) # 下单 #log.info('Cash: ' + str(Cash) + ' value:' +str(value)) cash = context.portfolio.available_cash log.info(stock +' 购买资金：' +str(amount) + ' 剩余资金：' + str(cash)) #order(stock, amount, MarketOrderStyle()) order_target_value(stock,amount) #半年平衡一次仓位避免单一持仓过大 #month = context.current_dt.month #if month == 6 or month == 12: # maxvalue = context.portfolio.total_value*g.security_max_proportion # for s in list(context.portfolio.positions.keys()): # if context.portfolio.positions[s].value > maxvalue :
        #            order_target_value(s,maxvalue)
    return

       
# 交易函数 - 出场
def sell(context, buy_lists):
    # 获取 sell_lists 列表
    hold_stock = list(context.portfolio.positions.keys())
    for s in hold_stock:
        #卖出不在买入列表中的股票
        if s not in buy_lists:
            order_target_value(s,0)   

#按市值进行排序   
#从大到小
def get_check_stocks_sort(context,check_out_lists):
    df = get_fundamentals(query(valuation.circulating_cap,valuation.pe_ratio,valuation.code).filter(valuation.code.in_(check_out_lists)),date=context.previous_date)
    #asc值为0，从大到小
    #df = df.sort('circulating_cap',ascending=0)
    #asc值为1，从小到大
    df = df.sort('circulating_cap',ascending=1)
    out_lists = list(df['code'].values)
    return out_lists
    

    
#去极值（分位数法）  
def winsorize(se):
    q = se.quantile([0.025, 0.975])
    if isinstance(q, pd.Series) and len(q) == 2:
        se[se < q.iloc[0]] = q.iloc[0] se[se > q.iloc[1]] = q.iloc[1]
    return se
    
#获取多期财务数据内容
def get_data(pool, periods):
    q = query(valuation.code, income.statDate, income.pubDate).filter(valuation.code.in_(pool))
    df = get_fundamentals(q)
    df.index = df.code
    stat_dates = set(df.statDate)
    stat_date_stocks = { sd:[stock for stock in df.index if df['statDate'][stock]==sd] for sd in stat_dates }

    def quarter_push(quarter):
        if quarter[-1]!='1':
            return quarter[:-1]+str(int(quarter[-1])-1)
        else:
            return str(int(quarter[:4])-1)+'q4'

    q = query(valuation.code,valuation.code,valuation.circulating_market_cap,balance.total_current_assets,balance.total_current_liability,\
    indicator.roe,cash_flow.net_operate_cash_flow,cash_flow.net_invest_cash_flow,indicator.inc_revenue_year_on_year,indicator.eps
              )

    stat_date_panels = { sd:None for sd in stat_dates }

    for sd in stat_dates:
        quarters = [sd[:4]+'q'+str(int(sd[5:7])/3)]
        for i in range(periods-1):
            quarters.append(quarter_push(quarters[-1]))
        nq = q.filter(valuation.code.in_(stat_date_stocks[sd]))
        pre_panel = { quarter:get_fundamentals(nq, statDate = quarter) for quarter in quarters }
        for thing in list(pre_panel.values()):
            thing.index = thing.code.values
        panel = pd.Panel(pre_panel)
        panel.items = list(range(len(quarters)))
        stat_date_panels[sd] = panel.transpose(2,0,1)

    final = pd.concat(list(stat_date_panels.values()), axis=2)

    return final.dropna(axis=2)

# 行业过滤
def industry_filter(context, security_list, industry_list):
    if len(industry_list) == 0:
        # 返回股票列表
        return security_list
    else:
        securities = []
        for s in industry_list:
            temp_securities = get_industry_stocks(s)
            securities += temp_securities
        security_list = [stock for stock in security_list if stock in securities]
        # 返回股票列表
        return security_list