4. 指数对冲

Hedge

空仓操作

利用基本面选股，当大盘低于MA40空仓。

# coding=utf-8
import numpy as np
import talib as ta
import pandas as pd
import os
import rqalpha
from rqalpha.api import *
from fxdayu_data import DataAPI

DataAPI读取HS300指数

在init设置你需要的外部数据。

def init(context):
    DataAPI.set_file('D:/PycharmProjects/Quant_Alpha/Data_Manager/Valuation_Selection/config.py')
    df = DataAPI.candle('sh000001', 'D')
    df['MA_hedge'] = ta.abstract.MA(df, 40)
    context.df = df
    #读取excel选股表
    codes = pd.read_excel('Value_Selection.xlsx')
    codes.index = codes.pop('date')
    context.codes = codes

def find_pool(context, date):
    codes = context.codes.loc[date]
    stocks = codes.index[codes == True]
    return stocks
def handle_bar(context, bar_dict):
    if can_trade(context):
        sell(context)
        buy(context, bar_dict)
def buy(context, bar_dict):
    pool = find_pool(context, context.now)
    if pool is not None:
        stocks_len = len(pool)
    for stocks in context.portfolio.positions:
        if stocks not in pool:
            order_target_percent(stocks, 0)
    result = []
    for codes in pool:
        data_c = history_bars(codes, 25, '1d', 'close', adjust_type='post')
        ma = ta.MA(data_c, timeperiod=20)
        if len(ma) > 3 and context.df.close.loc[context.now] > context.df.MA100.loc[context.now]:
            if ma[-1] > ma[-2] and ma[-2] > ma[-3] and codes not in result and codes not in context.portfolio.positions:
                result.append(codes)
    if len(result):
        for r in result:
            order_target_percent(r, 1.0 / stocks_len)
def sell(context):
    sell_list = []
    for stocks in context.portfolio.positions:
        data_c = history_bars(stocks, 25, '1d', 'close', adjust_type='post')
        ma = ta.MA(data_c, timeperiod=20)
        if len(ma) > 3:
            if (ma[-1] < ma[-2] and ma[-2] < ma[-3]):
                sell_list.append(stocks)
    # print('sell_list:', sell_list)
    if len(sell_list):
        for s in sell_list:
            order_target_percent(s, 0)
def sell_all(context):
    for stock in context.portfolio.positions:
        order_target_percent(stock, 0)
#设置可否交易的布尔开关
def can_trade(context):
    if context.df.close.loc[context.now] < context.df.MA_hedge.loc[context.now]:
        sell_all(context)
        return False
    else:
        return True
config = {
    "base": {
        "start_date": "2010-06-01",
        "end_date": "2017-05-30",
        "frequency": "1d",
        "securities": ['stock'],
        "stock_starting_cash": 10000000,
        "benchmark": "000300.XSHG",
        "strategy_file_path": os.path.abspath(__file__)
    },
    "extra": {
        "log_level": "verbose",
    },
    "mod": {
        "sys_analyser": {
            "report_save_path": ".",
            "enabled": True,
            "plot": True
        },
        'sys_simulation': {'match_engine': 'next_bar'}
        }
    }
rqalpha.run_func(init=init, handle_bar=handle_bar, config=config)