2082 动态选择的etf轮动策略，无需先验知识，更具鲁棒性.py

# 标题：无需先验知识，动态选择的etf轮动策略，更具鲁棒性
# 标题：etf轮动final,动态选择etf候选

from jqdata import *
from pandas.core.frame import DataFrame
import talib

'''
原理：按成交量选出最近一月top10的etf基金，然后进行轮动
持仓原则：
    1、对泸深指数的成交量进行统计，如果连续6（lag）天成交量小于7（lag0)天成交量的，空仓处理（购买货币基金511880 银华日利或国债 511010 ）
    2、13个交易日内（lag1）涨幅大于1的，并且“均线差值”大于0的才进行考虑。
    3、对符合考虑条件的ETF的涨幅进行排序，买涨幅最高的三个。
'''
def initialize(context):
    set_params()
    set_variables()
    set_backtest()
    run_daily(ETFtrade1, time='11:30')
    run_daily(ETFtrade2, time='14:40')

#选择etf
def get_ETF():
    # 获得etf基金列表
    #df = get_all_securities(['etf', 'fjb'])
    df = get_all_securities(['etf'])
    p = 1
    i = 0  # 计数器初始化
    # 创建保持计算结果的DataFrame
    g.ETF = pd.DataFrame()
    for k, row in df.iterrows():
        security = k
        #分级B优化
        #if row['type'] == 'fjb':
        #    p = 3
        # 获取股票的收盘价
        volume = attribute_history(security,30, '1d', ('volume'))
        volume = volume['volume'].mean()*p
        #print(k,volume)
        # 计算前一收盘价与均值差值
        g.ETF.loc[i, '股票代码'] = security  # 把标的股票代码添加到DataFrame
        g.ETF.loc[i, '股票名称'] = get_security_info(security).display_name  # 把标的股票名称添加到DataFrame
        g.ETF.loc[i, '成交量'] = volume  # 把计算结果添加到DataFrame
        i = i + 1
    # 对计算结果表格进行从大到小排序
    g.ETF = g.ETF.fillna(-100)
    g.ETF.sort_values(by='成交量', ascending=False, inplace=True)  # 按照成交量排序
    g.ETF.reset_index(drop=True, inplace=True) # 重新设置索引

    #print(g.ETF[:15])
    return g.ETF['股票代码'].tolist()[:7]
#1 设置参数
def set_params():
    # 设置基准收益
    #set_benchmark('601318.XSHG')
    set_benchmark('000300.XSHG')
    g.use_dynamic_target_market = True #是否动态改变大盘热度参考指标
    g.target_market = '399975.XSHE'
    #g.target_market = '399001.XSHE'
    g.empty_keep_stock = '511880.XSHG'#闲时买入的标的
    #g.empty_keep_stock = '601318.XSHG'#闲时买入的标的
    g.signal = 'KEEP'  #交易信号初始化
    g.emotion_rate = 0 #市场热度
    g.emotion_p = 0   #大盘成交量均线突破天数
    g.emotion_n = 0   #大盘成交量均线跌破天数
    g.empty_day = 2  #空仓之后持续时间
    g.empty_min = 0  #最小值
    g.lag = 6  #大盘成交量连续跌破均线的天数，发出空仓信号
    g.lag0 = 7  #大盘成交量监控周期
    g.lag1 = 13  #比价均线周期
    g.lag2 = 13  #价格涨幅计算周期
    g.last = [] #持仓股票代码初始化
    g.buy = [] #购买股票列表
    g.clear = []
    g.df = pd.DataFrame()
    g.INDEXList = {
        '399001.XSHE':'150019.XSHE',#银华锐进
        #'399395.XSHE': '150197.XSHE',#有色B
        #'399905.XSHE':'159902.XSHE',#中小板指
        #'399975.XSHE':'150201.XSHE',#券商B
        #'399975.XSHE':'512880.XSHG',#证券ETF
        #'399632.XSHE':'159901.XSHE',#深100etf
        #'000010.XSHE':'162605.XSHE',#景顺鼎益
        #'399608.XSHE':'515000.XSHG',#科技100
        #'399006.XSHE':'515000.XSHG',#科技100
        '000016.XSHG':'510050.XSHG',#上证50
        '000010.XSHG':'510180.XSHG',#上证180
        '000015.XSHG':'510880.XSHG',#红利ETF
        '399324.XSHE':'159905.XSHE',#深红利
        '399006.XSHE':'159915.XSHE',#创业板
        #'399006.XSHE':'150153.XSHE',#创业板B
        #'000300.XSHG':'510300.XSHG',#沪深300
        '000905.XSHG':'510500.XSHG',#中证500   
        #'399673.XSHE':'159949.XSHE'#创业板50
    }
    #g.ETFList.append('150019.XSHE')

    g.IdxList=dict(zip(g.INDEXList.values(),g.INDEXList.keys()))

    stocks_info = "\n股票池:\n"
    for security in g.INDEXList:
        s_info = get_security_info(security)
        stocks_info+="【%s】%s 上市时间:%s\n"%(s_info.code,s_info.display_name,s_info.start_date)
    log.info(stocks_info)
#设置中间变量
def set_variables():
    return

#设置回测条件
def set_backtest():
    set_option("avoid_future_data", True)
    set_option('use_real_price', True) #用真实价格交易
    log.set_level('order', 'error')


'''
=================================================
每天开盘前
=================================================
'''
#每天开盘前要做的事情
def before_trading_start(context):
    set_slip_fee(context) 

# 根据不同的时间段设置滑点与手续费
def set_slip_fee(context):
    # 将滑点设置为0
    set_slippage(FixedSlippage(0)) 
    # 设置手续费
    #set_commission(PerTrade(buy_cost=0.00005, sell_cost=0.00005, min_cost=0))
    set_order_cost(OrderCost(open_tax=0, close_tax=0, open_commission=0.0003, close_commission=0.0003,close_today_commission=0, min_commission=5), type='fund')


'''
=================================================
每日交易时
=================================================
''' 
def ETFtrade1(context):
    g.ETFList = get_ETF()
    #print(g.ETFList, g.INDEXList.values)
    #g.ETFList.extend([etf for etf in g.INDEXList.values()])
    g.signal = get_signal(context)
    
def ETFtrade2(context):
    if g.signal == 'sell_the_stocks':
        g.empty_day += 1
        for stock in context.portfolio.positions.keys():
            if (stock == g.empty_keep_stock):
                continue
            log.info("正在卖出 %s" % stock)
            order_target_value(stock, 0)
    elif g.signal == 'KEEP':
        g.empty_day += 1
        log.info("交易信号:持仓不变")
    elif g.signal == 'BUY':
        if g.empty_day < g.empty_min: log.info("空仓时间未到:持仓不变") return g.empty_day = 0 if g.empty_keep_stock in context.portfolio.positions.keys(): order_target_value(g.empty_keep_stock, 0) g.last = list(context.portfolio.positions.keys()) g.buy.sort() g.last.sort() ratio = len(g.buy) cash = context.portfolio.total_value/ratio for code in g.last:#先进行清仓处理，如果持仓股票不在购买清单中 if code not in g.buy: log.info("正在清空 %s" % code) order_target_value(code,0) g.clear.append(code) for code in g.clear:#从g.last删除已经卖出的标的 g.last.remove(code) g.clear = [] #清除临时列表 for code in g.last:#如果持仓在购买清单中判断调仓 if code in g.buy: positions_dict = context.portfolio.positions for position in list(positions_dict.values()): if position.value/cash > 1.5:
                        log.info("正在调仓 %s" % position.security)
                        order_target_value(position.security,cash)
        for code in g.buy:
            if code not in g.last:
                log.info("正在买入 %s" % code)
                order_value(code,cash)
                g.last.append(code)
        g.buy = []
        current_returns = 100*context.portfolio.returns
        log.info("当前收益：%.2f%%，当前持仓%s",current_returns,g.last)
    if len(context.portfolio.positions)==0:
            order_target_value(g.empty_keep_stock, context.portfolio.available_cash)


# 大盘行情监控函数
def EmotionMonitor(context):
    try:
        volume = attribute_history(g.target_market,100, '1d', ('volume'))['volume'].values
        v_ma_lag0 = talib.MA(volume,g.lag0)
        vol = volume / v_ma_lag0 - 1
        g.emotion_rate = round(vol[-1] * 100,2)
        for i in range(30):
            if vol[-1]>=0:
                if vol[-1-i]<0: return 1 if (i >= 3) else 0
            else:
                if vol[-1-i]>=0:
                    return -1 if (i >= g.lag) else 0
    except:
        return 1