初级课程里面的策略代码改了回测时间就报错了,大神给看看!

策略分享
标签: #<Tag:0x00007fc4cd34a3e8>

(developer) #1

https://i.bigquant.com/user/developer/lab/share/%E9%87%8F%E5%8C%96%E7%AD%96%E7%95%A5%E5%88%86%E4%BA%AB_%E5%8F%AF%E8%A7%86%E5%8C%96%E7%89%88%E6%9C%AC(vinjie)-Copy1.ipynb?_t=1584683089049


(developer) #2


(iQuant) #3

时间写错了吧
image


(developer) #4

@iQuant 日期改好还是报错 同样的错


(developer) #5
克隆策略

    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= 1\n else:\n context.datecont = 0\n \n positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n #大盘风控模块,读取风控数据\n today = data.current_dt.strftime('%Y-%m-%d')\n \n #----------------大盘风控模块,读取风控数据------------------\n risk = 0\n today = data.current_dt.strftime('%Y-%m-%d')\n bm_ret0=ranker_prediction.bm_ret0.values[0]\n bm_ret1=ranker_prediction.bm_ret1.values[0]\n bm_ret2=ranker_prediction.bm_ret2.values[0]\n bm_ret3=ranker_prediction.bm_ret3.values[0]\n bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]\n bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]\n bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]\n \n if bm_ret0 < 0.001:\n if bm_risk_v0 > 0:\n print(today,'大盘放量下跌,全仓卖出')\n risk = 1\n elif bm_ret1 < 0.001 and bm_ret2 < 0.002:\n print(today,'大盘连续下跌,全仓卖出')\n risk = 1\n if bm_ret3 < -0.02:\n print(today,'大盘三日下跌超过2%,全仓卖出')\n risk = 1\n if bm_ret0 > 0.01:\n if (bm_risk_v0 + bm_risk_v1) < 0:\n print(today,'大盘缩量上涨,全仓卖出')\n risk = 1\n \n if risk == 1:\n \n if len(positions)>0:\n # 全部卖出后返回\n for instrument in positions:\n if data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n return # 风控卖出后直接使用return结束当日交易,后续轮仓逻辑不再执行\n #---------------------大盘风控结束--------------------------------------\n \n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n \n #------------------------------------------卖出模块START--------------------------------------------\n if len(positions) > 0:\n for instrument in positions.keys():\n last_sale_date = positions[instrument].last_sale_date #上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days #持仓天数\n if hold_days >= 0:\n context.order_target(context.symbol(instrument), 0)\n #-------------------------------------------卖出模块END---------------------------------------------\n \n \n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n\n for i, instrument in enumerate(buy_instruments):\n try:\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if context.datecont == 1:\n # 获取今天和昨天的成交量\n volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')\n close_price = data.current(context.symbol(instrument), 'close') #当收盘价\n high_price = 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    In [7]:
    # 本代码由可视化策略环境自动生成 2020年3月23日 13:35
    # 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。
    
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 1
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        #context.stock_weights = (1,0)
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 1
        context.options['hold_days'] = 1
        #用于判断奇偶交易
        context.datecont = 0
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        if context.datecont == 0:
            context.datecont = 1
        else:
            context.datecont = 0
        
        positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        
        #大盘风控模块,读取风控数据
        today = data.current_dt.strftime('%Y-%m-%d')
        
         #----------------大盘风控模块,读取风控数据------------------
        risk = 0
        today = data.current_dt.strftime('%Y-%m-%d')
        bm_ret0=ranker_prediction.bm_ret0.values[0]
        bm_ret1=ranker_prediction.bm_ret1.values[0]
        bm_ret2=ranker_prediction.bm_ret2.values[0]
        bm_ret3=ranker_prediction.bm_ret3.values[0]
        bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]
        bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]
        bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]
        
        if bm_ret0 < 0.001:
            if bm_risk_v0 > 0:
                print(today,'大盘放量下跌,全仓卖出')
                risk = 1
            elif bm_ret1 < 0.001 and bm_ret2 < 0.002:
                print(today,'大盘连续下跌,全仓卖出')
                risk = 1
            if bm_ret3 < -0.02:
                print(today,'大盘三日下跌超过2%,全仓卖出')
                risk = 1
        if bm_ret0 > 0.01:
            if (bm_risk_v0 + bm_risk_v1) < 0:
                print(today,'大盘缩量上涨,全仓卖出')
                risk = 1
                
        if risk == 1:
            
            if len(positions)>0:
                # 全部卖出后返回
                for instrument in positions:
                    if data.can_trade(context.symbol(instrument)):
                        context.order_target_percent(context.symbol(instrument), 0)
            return # 风控卖出后直接使用return结束当日交易,后续轮仓逻辑不再执行
        #---------------------大盘风控结束--------------------------------------
        
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        
       #------------------------------------------卖出模块START--------------------------------------------
        if len(positions) > 0:
            for instrument in positions.keys():
                last_sale_date = positions[instrument].last_sale_date   #上次交易日期
                delta_days = data.current_dt - last_sale_date  
                hold_days = delta_days.days #持仓天数
                if hold_days >= 0:
                    context.order_target(context.symbol(instrument), 0)
        #-------------------------------------------卖出模块END---------------------------------------------
        
        
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    
        for i, instrument in enumerate(buy_instruments):
            try:
                cash = cash_for_buy * buy_cash_weights[i]
                if cash > max_cash_per_instrument - positions.get(instrument, 0):
                    # 确保股票持仓量不会超过每次股票最大的占用资金量
                    cash = max_cash_per_instrument - positions.get(instrument, 0)
                if context.datecont == 1:
                    # 获取今天和昨天的成交量
                    volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')
                    close_price = data.current(context.symbol(instrument), 'close')  #当收盘价
                    high_price = data.current(context.symbol(instrument), 'high')  #当天最高价
                    if ((volume_since_buy[2]/volume_since_buy[1] < 2.5) or (high_price/close_price<1.05)) and volume_since_buy[2]/volume_since_buy[0] > 1:
                        current_price = data.current(context.symbol(instrument), 'price')
                        amount = math.floor(cash / current_price - cash / current_price % 100)
                        context.order(context.symbol(instrument), amount)
                        return
                    else:
                        print('today = ',today,'instrument = ',instrument)
            except:
                print('today = ',today,'instrument = ',instrument)
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    m1 = M.instruments.v2(
        start_date='2010-02-01',
        end_date='2015-05-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -2) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    rank_return_10/rank_return_30
    avg_turn_15/turn_0
    ta_cci_14_0
    return_0
    turn_5/avg_turn_10
    swing_volatility_10_0/swing_volatility_60_0
    rank_amount_10/rank_amount_30
    list_days_0
    mf_net_amount_xl_0
    avg_turn_5
    turn_0/avg_turn_5
    rank_return_20
    rank_return_5/rank_return_20
    avg_amount_5
    rank_avg_turn_5"""
    )
    
    m5 = M.input_features.v1(
        features_ds=m3.data,
        features="""# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    close_0
    high_1
    open_0
    low_0
    st_status_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m5.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m10 = M.filter.v3(
        input_data=m7.data,
        expr='st_status_0==0 and low_0>high_1 and close_0>open_0',
        output_left_data=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m10.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m13.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-10-23'),
        end_date=T.live_run_param('trading_date', '2020-03-19'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m5.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m11 = M.filter.v3(
        input_data=m18.data,
        expr='st_status_0==0 and low_0>high_1+0.02 and close_0>open_0',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m20 = M.input_features.v1(
        features="""# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    ret_1=close/shift(close,1)
    ret_3=close/shift(close,3)
    volumepct_1=volume/shift(volume,1)
    bm_ret0=ret_1
    bm_ret1=shift(bm_ret0,1)
    bm_ret2=shift(bm_ret0,2)
    bm_ret3=ret_3
    bm_risk_v0=volumepct_1
    bm_risk_v1=shift(bm_risk_v0,1)
    bm_risk_v2=shift(bm_risk_v0,2)"""
    )
    
    m6 = M.index_feature_extract.v3(
        input_1=m9.data,
        input_2=m20.data,
        before_days=100,
        index='000001.HIX'
    )
    
    m25 = M.join.v3(
        data1=m8.predictions,
        data2=m6.data_1,
        on='date',
        how='left',
        sort=False
    )
    
    m26 = M.sort.v4(
        input_ds=m25.data,
        sort_by='date,position',
        group_by='--',
        keep_columns='--',
        ascending=True
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m26.sorted_data,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-86f7dd4863f440539f996a03ef6cacb6"}/bigcharts-data-end
    today =  2015-11-24 instrument =  002073.SZA
    

    Trade (回测/模拟)(trade)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    <ipython-input-7-0eecfd6a11e7> in <module>()
        335     plot_charts=True,
        336     backtest_only=False,
    --> 337     benchmark='000300.SHA'
        338 )
    
    <ipython-input-7-0eecfd6a11e7> in m19_handle_data_bigquant_run(context, data)
         41     risk = 0
         42     today = data.current_dt.strftime('%Y-%m-%d')
    ---> 43     bm_ret0=ranker_prediction.bm_ret0.values[0]
         44     bm_ret1=ranker_prediction.bm_ret1.values[0]
         45     bm_ret2=ranker_prediction.bm_ret2.values[0]
    
    IndexError: index 0 is out of bounds for axis 0 with size 0
    In [ ]:
    m6.data_1.read()
    

    (达达) #6

    部分日期的指数数据丢失,正在修复~ 可以先容错处理一下。

    克隆策略

      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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只\n stock_count = 1\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n #context.stock_weights = (1,0)\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 1\n context.options['hold_days'] = 1\n #用于判断奇偶交易\n context.datecont = 0\n","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 # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n if context.datecont == 0:\n context.datecont = 1\n else:\n context.datecont = 0\n \n positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n #大盘风控模块,读取风控数据\n today = data.current_dt.strftime('%Y-%m-%d')\n try:\n #----------------大盘风控模块,读取风控数据------------------\n risk = 0\n today = data.current_dt.strftime('%Y-%m-%d')\n bm_ret0=ranker_prediction.bm_ret0.values[0]\n bm_ret1=ranker_prediction.bm_ret1.values[0]\n bm_ret2=ranker_prediction.bm_ret2.values[0]\n bm_ret3=ranker_prediction.bm_ret3.values[0]\n bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]\n bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]\n bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]\n except:\n print(today,'缺失风控数据!')\n return\n if bm_ret0 < 0.001:\n if bm_risk_v0 > 0:\n print(today,'大盘放量下跌,全仓卖出')\n risk = 1\n elif bm_ret1 < 0.001 and bm_ret2 < 0.002:\n print(today,'大盘连续下跌,全仓卖出')\n risk = 1\n if bm_ret3 < -0.02:\n print(today,'大盘三日下跌超过2%,全仓卖出')\n risk = 1\n if bm_ret0 > 0.01:\n if (bm_risk_v0 + bm_risk_v1) < 0:\n print(today,'大盘缩量上涨,全仓卖出')\n risk = 1\n \n if risk == 1:\n \n if len(positions)>0:\n # 全部卖出后返回\n for instrument in positions:\n if data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n return # 风控卖出后直接使用return结束当日交易,后续轮仓逻辑不再执行\n #---------------------大盘风控结束--------------------------------------\n \n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n \n #------------------------------------------卖出模块START--------------------------------------------\n if len(positions) > 0:\n for instrument in positions.keys():\n last_sale_date = positions[instrument].last_sale_date #上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days #持仓天数\n if hold_days >= 0:\n context.order_target(context.symbol(instrument), 0)\n #-------------------------------------------卖出模块END---------------------------------------------\n \n \n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n\n for i, instrument in enumerate(buy_instruments):\n try:\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if context.datecont == 1:\n # 获取今天和昨天的成交量\n volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')\n close_price = 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      In [11]:
      # 本代码由可视化策略环境自动生成 2020年3月23日 14:12
      # 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。
      
      
      # 回测引擎:初始化函数,只执行一次
      def m19_initialize_bigquant_run(context):
          # 加载预测数据
          context.ranker_prediction = context.options['data'].read_df()
      
          # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
          context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
          # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
          # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
          stock_count = 1
          # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
          #context.stock_weights = (1,0)
          context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
          # 设置每只股票占用的最大资金比例
          context.max_cash_per_instrument = 1
          context.options['hold_days'] = 1
          #用于判断奇偶交易
          context.datecont = 0
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m19_handle_data_bigquant_run(context, data):
          # 按日期过滤得到今日的预测数据
          ranker_prediction = context.ranker_prediction[
              context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
      
          if context.datecont == 0:
              context.datecont = 1
          else:
              context.datecont = 0
          
          positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
          
          #大盘风控模块,读取风控数据
          today = data.current_dt.strftime('%Y-%m-%d')
          try:
               #----------------大盘风控模块,读取风控数据------------------
              risk = 0
              today = data.current_dt.strftime('%Y-%m-%d')
              bm_ret0=ranker_prediction.bm_ret0.values[0]
              bm_ret1=ranker_prediction.bm_ret1.values[0]
              bm_ret2=ranker_prediction.bm_ret2.values[0]
              bm_ret3=ranker_prediction.bm_ret3.values[0]
              bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]
              bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]
              bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]
          except:
              print(today,'缺失风控数据!')
              return
          if bm_ret0 < 0.001:
              if bm_risk_v0 > 0:
                  print(today,'大盘放量下跌,全仓卖出')
                  risk = 1
              elif bm_ret1 < 0.001 and bm_ret2 < 0.002:
                  print(today,'大盘连续下跌,全仓卖出')
                  risk = 1
              if bm_ret3 < -0.02:
                  print(today,'大盘三日下跌超过2%,全仓卖出')
                  risk = 1
          if bm_ret0 > 0.01:
              if (bm_risk_v0 + bm_risk_v1) < 0:
                  print(today,'大盘缩量上涨,全仓卖出')
                  risk = 1
                  
          if risk == 1:
              
              if len(positions)>0:
                  # 全部卖出后返回
                  for instrument in positions:
                      if data.can_trade(context.symbol(instrument)):
                          context.order_target_percent(context.symbol(instrument), 0)
              return # 风控卖出后直接使用return结束当日交易,后续轮仓逻辑不再执行
          #---------------------大盘风控结束--------------------------------------
          
          # 1. 资金分配
          # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
          # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
          is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
          cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
          cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
          cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
          
         #------------------------------------------卖出模块START--------------------------------------------
          if len(positions) > 0:
              for instrument in positions.keys():
                  last_sale_date = positions[instrument].last_sale_date   #上次交易日期
                  delta_days = data.current_dt - last_sale_date  
                  hold_days = delta_days.days #持仓天数
                  if hold_days >= 0:
                      context.order_target(context.symbol(instrument), 0)
          #-------------------------------------------卖出模块END---------------------------------------------
          
          
          # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
          buy_cash_weights = context.stock_weights
          buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
          max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
      
          for i, instrument in enumerate(buy_instruments):
              try:
                  cash = cash_for_buy * buy_cash_weights[i]
                  if cash > max_cash_per_instrument - positions.get(instrument, 0):
                      # 确保股票持仓量不会超过每次股票最大的占用资金量
                      cash = max_cash_per_instrument - positions.get(instrument, 0)
                  if context.datecont == 1:
                      # 获取今天和昨天的成交量
                      volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')
                      close_price = data.current(context.symbol(instrument), 'close')  #当收盘价
                      high_price = data.current(context.symbol(instrument), 'high')  #当天最高价
                      if ((volume_since_buy[2]/volume_since_buy[1] < 2.5) or (high_price/close_price<1.05)) and volume_since_buy[2]/volume_since_buy[0] > 1:
                          current_price = data.current(context.symbol(instrument), 'price')
                          amount = math.floor(cash / current_price - cash / current_price % 100)
                          context.order(context.symbol(instrument), amount)
                          return
                      else:
                          print('today = ',today,'instrument = ',instrument)
              except:
                  print('today = ',today,'instrument = ',instrument)
      # 回测引擎:准备数据,只执行一次
      def m19_prepare_bigquant_run(context):
          pass
      
      m1 = M.instruments.v2(
          start_date='2010-02-01',
          end_date='2015-05-01',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m2 = M.advanced_auto_labeler.v2(
          instruments=m1.data,
          label_expr="""# #号开始的表示注释
      # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
      # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
      #   添加benchmark_前缀,可使用对应的benchmark数据
      # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
      
      # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
      shift(close, -2) / shift(open, -1)
      
      # 极值处理:用1%和99%分位的值做clip
      clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
      
      # 将分数映射到分类,这里使用20个分类
      all_wbins(label, 20)
      
      # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
      where(shift(high, -1) == shift(low, -1), NaN, label)
      """,
          start_date='',
          end_date='',
          benchmark='000300.SHA',
          drop_na_label=True,
          cast_label_int=True
      )
      
      m3 = M.input_features.v1(
          features="""# #号开始的表示注释
      # 多个特征,每行一个,可以包含基础特征和衍生特征
      rank_return_10/rank_return_30
      avg_turn_15/turn_0
      ta_cci_14_0
      return_0
      turn_5/avg_turn_10
      swing_volatility_10_0/swing_volatility_60_0
      rank_amount_10/rank_amount_30
      list_days_0
      mf_net_amount_xl_0
      avg_turn_5
      turn_0/avg_turn_5
      rank_return_20
      rank_return_5/rank_return_20
      avg_amount_5
      rank_avg_turn_5"""
      )
      
      m5 = M.input_features.v1(
          features_ds=m3.data,
          features="""# #号开始的表示注释
      # 多个特征,每行一个,可以包含基础特征和衍生特征
      close_0
      high_1
      open_0
      low_0
      st_status_0"""
      )
      
      m15 = M.general_feature_extractor.v7(
          instruments=m1.data,
          features=m5.data,
          start_date='',
          end_date='',
          before_start_days=60
      )
      
      m16 = M.derived_feature_extractor.v3(
          input_data=m15.data,
          features=m5.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m7 = M.join.v3(
          data1=m2.data,
          data2=m16.data,
          on='date,instrument',
          how='inner',
          sort=False
      )
      
      m10 = M.filter.v3(
          input_data=m7.data,
          expr='st_status_0==0 and low_0>high_1 and close_0>open_0',
          output_left_data=False
      )
      
      m13 = M.dropnan.v1(
          input_data=m10.data
      )
      
      m4 = M.stock_ranker_train.v6(
          training_ds=m13.data,
          features=m3.data,
          learning_algorithm='排序',
          number_of_leaves=30,
          minimum_docs_per_leaf=1000,
          number_of_trees=20,
          learning_rate=0.1,
          max_bins=1023,
          feature_fraction=1,
          data_row_fraction=1,
          ndcg_discount_base=1,
          m_lazy_run=False
      )
      
      m9 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2015-10-23'),
          end_date=T.live_run_param('trading_date', '2020-03-19'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m17 = M.general_feature_extractor.v7(
          instruments=m9.data,
          features=m5.data,
          start_date='',
          end_date='',
          before_start_days=60
      )
      
      m18 = M.derived_feature_extractor.v3(
          input_data=m17.data,
          features=m5.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m11 = M.filter.v3(
          input_data=m18.data,
          expr='st_status_0==0 and low_0>high_1+0.02 and close_0>open_0',
          output_left_data=False
      )
      
      m14 = M.dropnan.v1(
          input_data=m11.data
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m4.model,
          data=m14.data,
          m_lazy_run=False
      )
      
      m20 = M.input_features.v1(
          features="""# #号开始的表示注释
      # 多个特征,每行一个,可以包含基础特征和衍生特征
      ret_1=close/shift(close,1)
      ret_3=close/shift(close,3)
      volumepct_1=volume/shift(volume,1)
      bm_ret0=ret_1
      bm_ret1=shift(bm_ret0,1)
      bm_ret2=shift(bm_ret0,2)
      bm_ret3=ret_3
      bm_risk_v0=volumepct_1
      bm_risk_v1=shift(bm_risk_v0,1)
      bm_risk_v2=shift(bm_risk_v0,2)"""
      )
      
      m6 = M.index_feature_extract.v3(
          input_1=m9.data,
          input_2=m20.data,
          before_days=100,
          index='000001.HIX'
      )
      
      m25 = M.join.v3(
          data1=m8.predictions,
          data2=m6.data_1,
          on='date',
          how='left',
          sort=False
      )
      
      m26 = M.sort.v4(
          input_ds=m25.data,
          sort_by='date,position',
          group_by='--',
          keep_columns='--',
          ascending=True
      )
      
      m19 = M.trade.v4(
          instruments=m9.data,
          options_data=m26.sorted_data,
          start_date='',
          end_date='',
          initialize=m19_initialize_bigquant_run,
          handle_data=m19_handle_data_bigquant_run,
          prepare=m19_prepare_bigquant_run,
          volume_limit=0.025,
          order_price_field_buy='open',
          order_price_field_sell='close',
          capital_base=1000000,
          auto_cancel_non_tradable_orders=True,
          data_frequency='daily',
          price_type='真实价格',
          product_type='股票',
          plot_charts=True,
          backtest_only=False,
          benchmark='000300.SHA'
      )
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-56baa837d79641459d93ebc31c107e18"}/bigcharts-data-end
      today =  2015-11-24 instrument =  002073.SZA
      2016-01-07 缺失风控数据!
      2016-01-14 缺失风控数据!
      today =  2016-01-26 instrument =  002127.SZA
      2016-03-29 缺失风控数据!
      today =  2016-05-06 instrument =  603012.SHA
      2016-05-09 缺失风控数据!
      
      today =  2016-07-27 instrument =  600113.SHA
      today =  2016-11-23 instrument =  300125.SZA
      today =  2017-01-11 instrument =  000892.SZA
      today =  2017-05-03 instrument =  300107.SZA
      today =  2017-05-05 instrument =  002373.SZA
      today =  2017-06-22 instrument =  600619.SHA
      today =  2017-09-14 instrument =  002251.SZA
      today =  2017-09-28 instrument =  300578.SZA
      today =  2017-12-04 instrument =  300494.SZA
      today =  2017-12-06 instrument =  002201.SZA
      today =  2018-04-11 instrument =  600727.SHA
      today =  2018-05-23 instrument =  603767.SHA
      today =  2018-06-21 instrument =  601500.SHA
      today =  2018-08-28 instrument =  603920.SHA
      today =  2018-09-21 instrument =  300071.SZA
      today =  2018-09-28 instrument =  603029.SHA
      today =  2018-10-19 instrument =  300501.SZA
      today =  2018-11-20 instrument =  300543.SZA
      today =  2018-12-04 instrument =  002703.SZA
      today =  2018-12-20 instrument =  002243.SZA
      today =  2019-01-03 instrument =  300555.SZA
      today =  2019-01-15 instrument =  300340.SZA
      today =  2019-01-17 instrument =  300302.SZA
      today =  2019-04-25 instrument =  002866.SZA
      
      today =  2019-07-22 instrument =  300150.SZA
      today =  2019-08-13 instrument =  002481.SZA
      today =  2019-08-23 instrument =  300242.SZA
      today =  2019-10-24 instrument =  000567.SZA
      today =  2019-12-23 instrument =  601901.SHA
      
      • 收益率639.5%
      • 年化收益率59.84%
      • 基准收益率1.83%
      • 阿尔法0.62
      • 贝塔0.89
      • 夏普比率1.03
      • 胜率0.55
      • 盈亏比1.13
      • 收益波动率59.81%
      • 信息比率0.07
      • 最大回撤52.4%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f2918f75eca4455fb5d8aea31b97292f"}/bigcharts-data-end
      In [10]:
      df = m26.sorted_data.read()
      df[df.date=='2016-01-07']
      
      Out[10]:
      date bm_ret0 bm_ret1 bm_ret2 bm_ret3 bm_risk_v0 bm_risk_v1 bm_risk_v2 instrument score position