{"Description":"实验创建于3/23/2018","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-21:instruments","SourceOutputPortId":"-4:data"}],"ModuleNodes":[{"Id":"-4","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2018-03-22","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-4"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-4","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"Comment":"","CommentCollapsed":true},{"Id":"-21","ModuleId":"BigQuantSpace.trade.trade-v3","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按月调仓\n if context.trading_day_index % 20 != 0:\n return \n \n date = data.current_dt.strftime('%Y-%m-%d') # 日期\n # 整理出当天要买入的股票\n buy_industry = context.daily_buy_industry[date] \n stock_to_buy = []\n for ind in buy_industry:\n ind = int(ind[2:8]) # 转化为行业代码的数字格式\n stock_to_buy.append(context.daily_buy_stock[date][ind])\n stock_to_buy = sum(stock_to_buy, [])\n \n # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表\n stock_hold_now = [equity.symbol for equity in context.portfolio.positions]\n # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有\n no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]\n # 需要卖出的股票\n stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]\n \n # 卖出\n for stock in stock_to_sell:\n # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态\n # 如果返回真值，则可以正常下单，否则会出错\n # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式\n if data.can_trade(context.symbol(stock)):\n # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，\n # 即卖出全部股票，可参考回测文档\n context.order_target_percent(context.symbol(stock), 0)\n \n # 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# 本代码由可视化策略环境自动生成 2018年3月23日 14:41
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2018-03-22',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    # 按月调仓
    if context.trading_day_index % 20 != 0:
        return 
    
    date = data.current_dt.strftime('%Y-%m-%d') # 日期
    # 整理出当天要买入的股票
    buy_industry = context.daily_buy_industry[date]  
    stock_to_buy = []
    for ind in buy_industry:
        ind = int(ind[2:8]) # 转化为行业代码的数字格式
        stock_to_buy.append(context.daily_buy_stock[date][ind])
    stock_to_buy = sum(stock_to_buy, [])
    
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式
        if data.can_trade(context.symbol(stock)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，
            #   即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为weight大于0,因此是等权重买入
            context.order_target_percent(context.symbol(stock), weight)
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    start_date = context.start_date
    end_date = context.end_date
    # 获取目前的行业列表
    industry = list(set(D.history_data(D.instruments(), end_date, end_date, ['industry_sw_level1']).industry_sw_level1))
    # 获取行业指数的行情数据
    industry = ['SW'+str(j)+'.SHA' for j in industry]
    data = D.history_data(industry, start_date, end_date, ['close','name'])

    # 计算此处每日动量较高的行业
    ret_data = data.groupby('instrument').apply(calcu_ret)
    ret_data.reset_index(inplace=True, drop=True)
    ret_data['date'] = ret_data['date'].map(lambda x:x.strftime('%Y-%m-%d'))
    context.daily_buy_industry = pd.Series({dt:seek_head_industry(ret_data.set_index('date').ix[dt]) for dt in list(set(ret_data.date))})

    # 每个交易日 每个行业的优质股 
    # 优质股的确定依据是：净资产收益率 (TTM)、营业收入同比增长率、归属母公司股东的净利润同比增长率
    features_data = D.features(D.instruments(start_date, end_date), start_date, end_date, ['fs_roe_ttm_0', 'fs_operating_revenue_yoy_0', 'fs_net_profit_yoy_0', 'industry_sw_level1_0'])
    # 整理出每个行业的优质股票
    context.daily_buy_stock = features_data.groupby(['date', 'industry_sw_level1_0']).apply(seek_head_stock)

# 计算不同周期的动量
def calcu_ret(df):
    df = df.sort_values('date')
    for i in [42, 84, 126]: # 分别代表2月、4月、半年的动量
        df['ret_%s'%i] = df['close']/df['close'].shift(i)-1 
    return df

# 计算出得分
def seek_head_industry(df):
    for j in ['ret_42','ret_84','ret_126']:
        df['%s'%j] = df['%s'%j].rank(ascending=True) 
    df['score'] = 0.4*df['ret_42']+0.3*df['ret_84']+0.3*df['ret_126']  # 得分的权重分别为0.4、0.3、0.3
    result = df.sort_values('score', ascending=False)
    return list(result.instrument)[:3]  # 前3个行业

# 选出特定行业优质股票
def seek_head_stock(df):
    result = df.sort_values(['fs_roe_ttm_0', 'fs_net_profit_yoy_0', 'fs_operating_revenue_yoy_0'], ascending=False)
    return list(result.instrument[:10]) # 每个行业选10只股票
# 回测引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):
    # 手续费设置
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5)) 

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m3_before_trading_start_bigquant_run(context, data):
    pass

m3 = M.trade.v3(
    instruments=m1.data,
    start_date='',
    end_date='',
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    initialize=m3_initialize_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=1000000,
    benchmark='000300.SHA',
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    plot_charts=True,
    backtest_only=False,
    amount_integer=False
)

[2018-03-23 14:36:35.353946] INFO: bigquant: instruments.v2 开始运行..
[2018-03-23 14:36:35.361383] INFO: bigquant: 命中缓存
[2018-03-23 14:36:35.363330] INFO: bigquant: instruments.v2 运行完成[0.009448s].
[2018-03-23 14:36:35.399952] INFO: bigquant: backtest.v7 开始运行..
[2018-03-23 14:39:15.018858] INFO: algo: set price type:backward_adjusted
[2018-03-23 14:40:07.388117] INFO: Performance: Simulated 541 trading days out of 541.
[2018-03-23 14:40:07.389843] INFO: Performance: first open: 2016-01-04 01:30:00+00:00
[2018-03-23 14:40:07.391252] INFO: Performance: last close: 2018-03-22 07:00:00+00:00

[2018-03-23 14:40:10.819357] INFO: bigquant: backtest.v7 运行完成[215.419508s].