请教如何完善handle模块卖出订单


(luckychan) #1
克隆策略

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talib\n\ndef self_diy(df,volume_0): \n volume = [float(x) for x in df['volume_0']]\n df['volume_0']=(df['volume_0']>mean(volume,136)).astype(int)\n return df['volume_0']\n #return pd.rolling_apply(df['close_0'], 5,np.mean)\nbigquant_run = {\n 'self_diy': self_diy\n}\n\n#import talib #这是引入一个计算技术指标的库,可以查一下论坛 基本能满足你的常用技术指标定义需求\n#def self_diy(df,close_0): \n# close = [float(x) for x in df['close_0']]\n# df['condition']=(df['close_0']>talib.MA(np.array(close), timeperiod=5)).astype(int)\n# return df['condition']\n#上面这几句是定义你的自定义函数具体计算内容,也就是指标值condition,这里计算的是当收盘价>5日均线时就是1,否则就是0\n#m5_user_functions_bigquant_run = {\n# 'self_diy': self_diy\n#}\n#函数返回值会传给self_diy这个列","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-81","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 # 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 positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\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 for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > (max_cash_per_instrument - positions.get(instrument, 0))*1:\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))*1\n if cash > 0:\n #以下是整百买卖的代码\n current_price = data.current(context.symbol(instrument), 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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 = 2\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 = 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 = 0.5\n context.options['hold_days'] = 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    In [1]:
    # 本代码由可视化策略环境自动生成 2018年5月19日 15:22
    # 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='2010-01-01',
        end_date='2015-01-01',
        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/data_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/big_expr.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, -5) / 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
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m15 = 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
    #self_diy(volume_0)
    #market_cap_float_0/close_0*adjust_factor_0
    #mean(volume_0,136)
    st_status_0 #st状态
    market_cap_float_0/close_0*adjust_factor_0 #流通股数
    list_days_0 #上市天数
    """
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=100
    )
    
    import talib
    
    def self_diy(df,volume_0): 
        volume = [float(x) for x in df['volume_0']]
        df['volume_0']=(df['volume_0']>mean(volume,136)).astype(int)
        return df['volume_0']
       #return pd.rolling_apply(df['close_0'], 5,np.mean)
    m5_user_functions_bigquant_run = {
        'self_diy':  self_diy
    }
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        user_functions=m5_user_functions_bigquant_run
    )
    
    m16 = M.filter.v3(
        input_data=m5.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        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,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2017-01-01'),
        end_date=T.live_run_param('trading_date', '2018-05-15'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    import talib
    
    def self_diy(df,volume_0): 
        volume = [float(x) for x in df['volume_0']]
        df['volume_0']=(df['volume_0']>mean(volume,136)).astype(int)
        return df['volume_0']
       #return pd.rolling_apply(df['close_0'], 5,np.mean)
    m11_user_functions_bigquant_run = {
        'self_diy':  self_diy
    }
    
    #import talib #这是引入一个计算技术指标的库,可以查一下论坛 基本能满足你的常用技术指标定义需求
    #def self_diy(df,close_0): 
    #    close = [float(x) for x in df['close_0']]
    #    df['condition']=(df['close_0']>talib.MA(np.array(close), timeperiod=5)).astype(int)
    #    return df['condition']
    #上面这几句是定义你的自定义函数具体计算内容,也就是指标值condition,这里计算的是当收盘价>5日均线时就是1,否则就是0
    #m5_user_functions_m11_user_functions_bigquant_run = {
    #    'self_diy':  self_diy
    #}
    #函数返回值会传给self_diy这个列
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        user_functions=m11_user_functions_bigquant_run
    )
    
    m17 = M.filter.v3(
        input_data=m11.data,
        expr='list_days_0<180 and st_status_0==0 and market_cap_float_0/close_0*adjust_factor_0 <300000000',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m17.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_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')]
    
        # 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)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 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):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > (max_cash_per_instrument - positions.get(instrument, 0))*1:
                # 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))*1
            if cash > 0:
                #以下是整百买卖的代码
                current_price = data.current(context.symbol(instrument), 'price')
                amount = math.floor(cash / current_price / 100*1) * 100
                context.order(context.symbol(instrument), amount)
    
    
    # 回测引擎:准备数据,只执行一次
    def m12_prepare_bigquant_run(context):
        pass
    #    bm_price = D.history_data(['000300.SHA'], start_date=context.start_date , end_date=context.end_date, fields=['close'])
    #    #bm_price['sma'] = bm_price['close'].rolling(5).mean()
        #bm_price['lma'] = bm_price['close'].rolling(20).mean()
        #bm_price['gold_cross_status'] = bm_price['sma'] > bm_price['lma']
    #    bm_price['ma10'] = bm_price['close'].rolling(10).mean()    
    #    bm_price['ma10_status'] = bm_price['close'] > bm_price['ma10']
    #    bm_price['pos_percent'] = np.where(bm_price['ma10_status'],1,0)
    #    context.pos_df = bm_price[['date', 'pos_percent']].set_index('date')
    #    context.pos_df = hs300_pos11.bm_price
    
    # 回测引擎:初始化函数,只执行一次
    def m12_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 = 2
        # 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 = 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 = 0.5
        context.options['hold_days'] = 3
    
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=100000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='前复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-05-19 14:39:43.073983] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-19 14:39:43.123137] INFO: bigquant: 命中缓存
    [2018-05-19 14:39:43.124695] INFO: bigquant: instruments.v2 运行完成[0.050752s].
    [2018-05-19 14:39:43.182126] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-05-19 14:39:43.221923] INFO: bigquant: 命中缓存
    [2018-05-19 14:39:43.223382] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.041278s].
    [2018-05-19 14:39:43.230645] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-19 14:39:43.333925] INFO: bigquant: 命中缓存
    [2018-05-19 14:39:43.335403] INFO: bigquant: input_features.v1 运行完成[0.104743s].
    [2018-05-19 14:39:43.340807] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-19 14:39:43.462792] INFO: bigquant: input_features.v1 运行完成[0.121995s].
    [2018-05-19 14:39:43.479155] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-19 14:39:57.749097] INFO: 基础特征抽取: 年份 2009, 特征行数=104293
    [2018-05-19 14:40:09.070547] INFO: 基础特征抽取: 年份 2010, 特征行数=431567
    [2018-05-19 14:40:18.335302] INFO: 基础特征抽取: 年份 2011, 特征行数=511455
    [2018-05-19 14:40:31.984311] INFO: 基础特征抽取: 年份 2012, 特征行数=565675
    [2018-05-19 14:40:41.000102] INFO: 基础特征抽取: 年份 2013, 特征行数=564168
    [2018-05-19 14:40:51.080824] INFO: 基础特征抽取: 年份 2014, 特征行数=569948
    [2018-05-19 14:40:57.124837] INFO: 基础特征抽取: 年份 2015, 特征行数=0
    [2018-05-19 14:40:57.149721] INFO: 基础特征抽取: 总行数: 2747106
    [2018-05-19 14:40:57.151641] INFO: bigquant: general_feature_extractor.v6 运行完成[73.672464s].
    [2018-05-19 14:40:57.164450] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-19 14:40:58.338539] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.008s
    [2018-05-19 14:40:58.349221] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.009s
    [2018-05-19 14:40:58.353342] INFO: derived_feature_extractor: 提取完成 list_days_0 #上市天数, 0.003s
    [2018-05-19 14:40:58.365596] INFO: derived_feature_extractor: 提取完成 market_cap_float_0/close_0*adjust_factor_0 #流通股数, 0.011s
    [2018-05-19 14:40:58.372090] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.005s
    [2018-05-19 14:40:58.389476] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.016s
    [2018-05-19 14:40:58.399469] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.008s
    [2018-05-19 14:40:58.407434] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.007s
    [2018-05-19 14:40:58.409960] INFO: derived_feature_extractor: 提取完成 st_status_0 #st状态, 0.001s
    [2018-05-19 14:41:01.005870] INFO: derived_feature_extractor: /y_2009, 104293
    [2018-05-19 14:41:01.273568] INFO: derived_feature_extractor: /y_2010, 431567
    [2018-05-19 14:41:01.716806] INFO: derived_feature_extractor: /y_2011, 511455
    [2018-05-19 14:41:02.224106] INFO: derived_feature_extractor: /y_2012, 565675
    [2018-05-19 14:41:02.821177] INFO: derived_feature_extractor: /y_2013, 564168
    [2018-05-19 14:41:03.400704] INFO: derived_feature_extractor: /y_2014, 569948
    [2018-05-19 14:41:03.973318] INFO: bigquant: derived_feature_extractor.v2 运行完成[6.808858s].
    [2018-05-19 14:41:03.984641] INFO: bigquant: filter.v3 开始运行..
    [2018-05-19 14:41:03.990973] INFO: filter: 使用表达式 st_status_0==0 过滤
    [2018-05-19 14:41:04.137401] INFO: filter: 过滤 /y_2009, 96579/104293
    [2018-05-19 14:41:04.503302] INFO: filter: 过滤 /y_2010, 401771/431567
    [2018-05-19 14:41:04.909049] INFO: filter: 过滤 /y_2011, 482231/511455
    [2018-05-19 14:41:05.387941] INFO: filter: 过滤 /y_2012, 541178/565675
    [2018-05-19 14:41:05.987341] INFO: filter: 过滤 /y_2013, 550859/564168
    [2018-05-19 14:41:06.698225] INFO: filter: 过滤 /y_2014, 561221/569948
    [2018-05-19 14:41:06.723005] INFO: bigquant: filter.v3 运行完成[2.738358s].
    [2018-05-19 14:41:06.733860] INFO: bigquant: join.v3 开始运行..
    [2018-05-19 14:41:10.973018] INFO: join: /y_2009, 行数=0/96579, 耗时=1.639972s
    [2018-05-19 14:41:13.450399] INFO: join: /y_2010, 行数=401478/401771, 耗时=2.473854s
    [2018-05-19 14:41:17.537630] INFO: join: /y_2011, 行数=481982/482231, 耗时=4.07186s
    [2018-05-19 14:41:23.820289] INFO: join: /y_2012, 行数=540654/541178, 耗时=6.259021s
    [2018-05-19 14:41:30.114272] INFO: join: /y_2013, 行数=550031/550859, 耗时=6.274581s
    [2018-05-19 14:41:35.709544] INFO: join: /y_2014, 行数=546785/561221, 耗时=5.572654s
    [2018-05-19 14:41:35.846551] INFO: join: 最终行数: 2520930
    [2018-05-19 14:41:35.849067] INFO: bigquant: join.v3 运行完成[29.115249s].
    [2018-05-19 14:41:35.862136] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-19 14:41:35.956692] INFO: dropnan: /y_2009, 0/0
    [2018-05-19 14:41:36.598414] INFO: dropnan: /y_2010, 394189/401478
    [2018-05-19 14:41:38.011202] INFO: dropnan: /y_2011, 475786/481982
    [2018-05-19 14:41:39.587942] INFO: dropnan: /y_2012, 537181/540654
    [2018-05-19 14:41:41.189559] INFO: dropnan: /y_2013, 550001/550031
    [2018-05-19 14:41:42.438758] INFO: dropnan: /y_2014, 545002/546785
    [2018-05-19 14:41:42.475618] INFO: dropnan: 行数: 2502159/2520930
    [2018-05-19 14:41:42.499742] INFO: bigquant: dropnan.v1 运行完成[6.637585s].
    [2018-05-19 14:41:42.517809] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2018-05-19 14:41:50.576368] INFO: df2bin: prepare bins ..
    [2018-05-19 14:41:53.824882] INFO: df2bin: prepare data: training ..
    [2018-05-19 14:42:16.378414] INFO: df2bin: sort ..
    [2018-05-19 14:42:48.558234] INFO: stock_ranker_train: b10590f8 准备训练: 2502159 行数
    [2018-05-19 14:45:01.632528] INFO: bigquant: stock_ranker_train.v5 运行完成[199.114736s].
    [2018-05-19 14:45:01.639926] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-19 14:45:01.642622] INFO: bigquant: 命中缓存
    [2018-05-19 14:45:01.643818] INFO: bigquant: instruments.v2 运行完成[0.003889s].
    [2018-05-19 14:45:01.654247] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-19 14:45:40.096431] INFO: 基础特征抽取: 年份 2017, 特征行数=743233
    [2018-05-19 14:45:53.302002] INFO: 基础特征抽取: 年份 2018, 特征行数=282356
    [2018-05-19 14:45:53.310838] INFO: 基础特征抽取: 总行数: 1025589
    [2018-05-19 14:45:53.315960] INFO: bigquant: general_feature_extractor.v6 运行完成[51.661691s].
    [2018-05-19 14:45:53.323993] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-19 14:45:53.922216] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.007s
    [2018-05-19 14:45:53.931100] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.008s
    [2018-05-19 14:45:53.933439] INFO: derived_feature_extractor: 提取完成 list_days_0 #上市天数, 0.001s
    [2018-05-19 14:45:53.943181] INFO: derived_feature_extractor: 提取完成 market_cap_float_0/close_0*adjust_factor_0 #流通股数, 0.009s
    [2018-05-19 14:45:53.954393] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.008s
    [2018-05-19 14:45:53.961448] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.006s
    [2018-05-19 14:45:53.969296] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.006s
    [2018-05-19 14:45:53.976999] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.006s
    [2018-05-19 14:45:53.979169] INFO: derived_feature_extractor: 提取完成 st_status_0 #st状态, 0.001s
    [2018-05-19 14:45:55.137173] INFO: derived_feature_extractor: /y_2017, 743233
    [2018-05-19 14:45:56.810537] INFO: derived_feature_extractor: /y_2018, 282356
    [2018-05-19 14:45:57.204164] INFO: bigquant: derived_feature_extractor.v2 运行完成[3.880133s].
    [2018-05-19 14:45:57.212116] INFO: bigquant: filter.v3 开始运行..
    [2018-05-19 14:45:57.216814] INFO: filter: 使用表达式 list_days_0<180 and st_status_0==0 and market_cap_float_0/close_0*adjust_factor_0 <300000000 过滤
    [2018-05-19 14:45:57.732939] INFO: filter: 过滤 /y_2017, 51261/743233
    [2018-05-19 14:45:57.855997] INFO: filter: 过滤 /y_2018, 11668/282356
    [2018-05-19 14:45:57.865508] INFO: bigquant: filter.v3 运行完成[0.653392s].
    [2018-05-19 14:45:57.872340] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-19 14:45:57.984544] INFO: dropnan: /y_2017, 42056/51261
    [2018-05-19 14:45:58.033924] INFO: dropnan: /y_2018, 10577/11668
    [2018-05-19 14:45:58.041655] INFO: dropnan: 行数: 52633/62929
    [2018-05-19 14:45:58.044255] INFO: bigquant: dropnan.v1 运行完成[0.171905s].
    [2018-05-19 14:45:58.059415] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-05-19 14:45:58.200658] INFO: df2bin: prepare data: prediction ..
    [2018-05-19 14:45:59.219097] INFO: stock_ranker_predict: 准备预测: 52633 行
    [2018-05-19 14:46:00.736862] INFO: bigquant: stock_ranker_predict.v5 运行完成[2.677401s].
    [2018-05-19 14:46:00.798492] INFO: bigquant: backtest.v7 开始运行..
    [2018-05-19 14:46:00.901696] INFO: algo: set price type:forward_adjusted
    [2018-05-19 14:46:39.985378] INFO: Performance: Simulated 331 trading days out of 331.
    [2018-05-19 14:46:39.986990] INFO: Performance: first open: 2017-01-03 01:30:00+00:00
    [2018-05-19 14:46:39.988381] INFO: Performance: last close: 2018-05-15 07:00:00+00:00
    
    • 收益率-8.97%
    • 年化收益率-6.91%
    • 基准收益率18.55%
    • 阿尔法-0.21
    • 贝塔0.99
    • 夏普比率-0.31
    • 胜率0.582
    • 盈亏比0.985
    • 收益波动率36.42%
    • 信息比率-0.61
    • 最大回撤44.52%
    [2018-05-19 14:46:42.087707] INFO: bigquant: backtest.v7 运行完成[41.289227s].
    

    #===========================
    上面样例中增加一个上市时间小于180天的过滤条件,运行后发现如果持仓中的股票上市时间超过180天,那么它就不会出现在预测集内,那么它也不会生成卖出订单,就会一直在持仓内不交易 。能否完善一下这里的模块,使持仓内的股票能按hold_days的持股天数后就卖出。

    谢谢!


    #2

    我之前也遇到过这样的问题,要修改下买卖交易逻辑,参考:自定义买入卖出

    #----------------------------START:持有固定天数卖出---------------------------
        today = data.current_dt
        # 不是建仓期(在前hold_days属于建仓期)
        if not is_staging:
            equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
            for instrument in equities:
    #             print('last_sale_date: ', equities[instrument].last_sale_date)
                sid = equities[instrument].sid  # 交易标的
                # 今天和上次交易的时间相隔hold_days就全部卖出
                if today-equities[instrument].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(sid, 0)
        #--------------------------------END:持有固定天数卖出--------------------------- 
    

    (luckychan) #3

    是这样,回测正常了,谢谢!