【模板案例】交易逻辑案例_ST和退市股处理

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标签: #<Tag:0x00007fcf62430dd0>

(iQuant) #1

9月24日Meetup 模板案例:

交易逻辑案例_ST和退市股处理

克隆策略
In [6]:
m11.sorted_data.read()
Out[6]:
date instrument name score position
0 2015-01-05 300391.SZA 康跃科技 2.104525 1
1 2015-01-05 300038.SZA 梅泰诺 2.022842 2
2 2015-01-05 300367.SZA 东方网力 1.935853 3
3 2015-01-05 300302.SZA 同有科技 1.867654 4
4 2015-01-05 300109.SZA 新开源 1.701520 5
5 2015-01-05 002382.SZA 蓝帆医疗 1.669649 6
6 2015-01-05 002363.SZA 隆基机械 1.653199 7
7 2015-01-05 002640.SZA 跨境通 1.653199 8
8 2015-01-05 002488.SZA 金固股份 1.636742 9
9 2015-01-05 600446.SHA 金证股份 1.636742 10
10 2015-01-05 002591.SZA 恒大高新 1.627838 11
11 2015-01-05 300155.SZA 安居宝 1.627838 12
12 2015-01-05 300353.SZA 东土科技 1.562968 13
13 2015-01-05 300369.SZA 绿盟科技 1.562968 14
14 2015-01-05 300365.SZA 恒华科技 1.517969 15
15 2015-01-05 002514.SZA 宝馨科技 1.480892 16
16 2015-01-05 300270.SZA 中威电子 1.463622 17
17 2015-01-05 300053.SZA 欧比特 1.452665 18
18 2015-01-05 002579.SZA 中京电子 1.451597 19
19 2015-01-05 300378.SZA 鼎捷软件 1.451597 20
20 2015-01-05 300096.SZA 易联众 1.396828 21
21 2015-01-05 300264.SZA 佳创视讯 1.384275 22
22 2015-01-05 300085.SZA 银之杰 1.378759 23
23 2015-01-05 300209.SZA 天泽信息 1.367901 24
24 2015-01-05 300271.SZA 华宇软件 1.351417 25
25 2015-01-05 300388.SZA 国祯环保 1.335674 26
26 2015-01-05 002341.SZA 新纶科技 1.314450 27
27 2015-01-05 300322.SZA 硕贝德 1.313935 28
28 2015-01-05 300130.SZA 新国都 1.307132 29
29 2015-01-05 300301.SZA 长方集团 1.307132 30
... ... ... ... ... ...
1202028 2016-12-30 000509.SZA 华塑控股 -0.243827 2759
1202029 2016-12-30 002259.SZA 升达林业 -0.243827 2760
1202030 2016-12-30 002300.SZA 太阳电缆 -0.243827 2761
1202031 2016-12-30 002545.SZA 东方铁塔 -0.243827 2762
1202032 2016-12-30 601231.SHA 环旭电子 -0.245331 2763
1202033 2016-12-30 300562.SZA 乐心医疗 -0.249599 2764
1202034 2016-12-30 000993.SZA 闽东电力 -0.256679 2765
1202035 2016-12-30 600007.SHA 中国国贸 -0.257956 2766
1202036 2016-12-30 002821.SZA 凯莱英 -0.289152 2767
1202037 2016-12-30 300556.SZA 丝路视觉 -0.296651 2768
1202038 2016-12-30 002761.SZA 多喜爱 -0.297541 2769
1202039 2016-12-30 603819.SHA 神力股份 -0.299839 2770
1202040 2016-12-30 002591.SZA 恒大高新 -0.304972 2771
1202041 2016-12-30 300417.SZA 南华仪器 -0.304972 2772
1202042 2016-12-30 600255.SHA 鑫科材料 -0.304972 2773
1202043 2016-12-30 603159.SHA 上海亚虹 -0.317824 2774
1202044 2016-12-30 002216.SZA 三全食品 -0.334994 2775
1202045 2016-12-30 300376.SZA 易事特 -0.345000 2776
1202046 2016-12-30 600209.SHA 罗顿发展 -0.350958 2777
1202047 2016-12-30 002823.SZA 凯中精密 -0.354920 2778
1202048 2016-12-30 002819.SZA 东方中科 -0.369633 2779
1202049 2016-12-30 600760.SHA *ST黑豹 -0.379873 2780
1202050 2016-12-30 300308.SZA 中际装备 -0.380328 2781
1202051 2016-12-30 603336.SHA 宏辉果蔬 -0.385016 2782
1202052 2016-12-30 600215.SHA 长春经开 -0.394826 2783
1202053 2016-12-30 600520.SHA *ST中发 -0.394826 2784
1202054 2016-12-30 300567.SZA 精测电子 -0.422985 2785
1202055 2016-12-30 000538.SZA 云南白药 -0.424848 2786
1202056 2016-12-30 002346.SZA 柘中股份 -0.425944 2787
1202057 2016-12-30 002822.SZA 中装建设 -0.436982 2788

1202058 rows × 5 columns

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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 = 3\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 = 0.6\n context.options['hold_days'] = 5\n\n from zipline.finance.slippage import SlippageModel\n class FixedPriceSlippage(SlippageModel):\n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price = data.current(order.asset, price_field)\n else:\n price = data.current(order.asset, self._price_field_buy)\n # 返回希望成交的价格和数量\n return (price, order.amount)\n # 设置price_field,默认是开盘买入,收盘卖出\n context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')\n context.set_slippage(us_equities=context.fix_slippage)","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 # 获取当前持仓\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n \n today = data.current_dt.strftime('%Y-%m-%d')\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 == today]\n \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 \n # 2. ST股和退市股的卖出\n stock_sold = [] # 记录卖出的股票,防止多次卖出出现空单\n \n #-------------------------- START: ST和退市股卖出 --------------------- \n st_stock_list = []\n for instrument in positions.keys():\n try:\n instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]\n # 如果股票状态变为了st或者退市 则卖出\n if 'ST' in instrument_name or '退' in instrument_name:\n if instrument in stock_sold:\n continue\n if data.can_trade(context.symbol(instrument)):\n context.order_target(context.symbol(instrument), 0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n except:\n continue\n if st_stock_list!=[]:\n print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理') \n stock_sold += st_stock_list\n\n #-------------------------- END: ST和退市股卖出 --------------------- \n \n \n # 3. 生成轮仓卖出订单: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 instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in positions)])))\n for instrument in instruments:\n # 如果资金够了就不卖出了\n if cash_for_sell <= 0:\n break\n #防止多个止损条件同时满足,出现多次卖出产生空单\n if instrument in stock_sold:\n continue\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n stock_sold.append(instrument)\n\n # 4. 生成轮仓买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n # 计算今日ST/退市的股票\n st_list = list(ranker_prediction[ranker_prediction.name.str.contains('ST')|ranker_prediction.name.str.contains('退')].instrument)\n # 计算所有禁止买入的股票池\n banned_list = stock_sold+st_list\n buy_cash_weights = context.stock_weights\n buy_instruments=[k for k in list(ranker_prediction.instrument) if k not in banned_list][: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):\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 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    In [3]:
    # 本代码由可视化策略环境自动生成 2020年9月24日 16:38
    # 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 m4_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 = 3
        # 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 = 0.6
        context.options['hold_days'] = 5
    
        from zipline.finance.slippage import SlippageModel
        class FixedPriceSlippage(SlippageModel):
            def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
                if order.limit is None:
                    price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                    price = data.current(order.asset, price_field)
                else:
                    price = data.current(order.asset, self._price_field_buy)
                # 返回希望成交的价格和数量
                return (price, order.amount)
        # 设置price_field,默认是开盘买入,收盘卖出
        context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
        context.set_slippage(us_equities=context.fix_slippage)
    # 回测引擎:每日数据处理函数,每天执行一次
    def m4_handle_data_bigquant_run(context, data):
        # 获取当前持仓
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
        
        today = data.current_dt.strftime('%Y-%m-%d')
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == today]
        
        
        # 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)
       
        
        # 2. ST股和退市股的卖出
        stock_sold = [] # 记录卖出的股票,防止多次卖出出现空单
        
        #-------------------------- START: ST和退市股卖出 ---------------------  
        st_stock_list = []
        for instrument in positions.keys():
            try:
                instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]
                # 如果股票状态变为了st或者退市 则卖出
                if 'ST' in instrument_name or '退' in instrument_name:
                    if instrument in stock_sold:
                        continue
                    if data.can_trade(context.symbol(instrument)):
                        context.order_target(context.symbol(instrument), 0)
                        st_stock_list.append(instrument)
                        cash_for_sell -= positions[instrument]
            except:
                continue
        if st_stock_list!=[]:
            print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')    
            stock_sold += st_stock_list
    
        #-------------------------- END: ST和退市股卖出 --------------------- 
        
        
        # 3. 生成轮仓卖出订单: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:
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in positions)])))
            for instrument in instruments:
                # 如果资金够了就不卖出了
                if cash_for_sell <= 0:
                    break
                #防止多个止损条件同时满足,出现多次卖出产生空单
                if instrument in stock_sold:
                    continue
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                stock_sold.append(instrument)
    
        # 4. 生成轮仓买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        # 计算今日ST/退市的股票
        st_list = list(ranker_prediction[ranker_prediction.name.str.contains('ST')|ranker_prediction.name.str.contains('退')].instrument)
        # 计算所有禁止买入的股票池
        banned_list = stock_sold+st_list
        buy_cash_weights = context.stock_weights
        buy_instruments=[k for k in list(ranker_prediction.instrument) if k not in banned_list][: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):
                # 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 cash > 0:
                context.order_value(context.symbol(instrument), cash)
        
    
    
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        pass
    def m4_before_trading_start_bigquant_run(context, data):
        pass
    
    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.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.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
    )
    
    m6 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m5 = M.stock_ranker_train.v6(
        training_ds=m6.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', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m10 = M.dropnan.v2(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m5.model,
        data=m10.data,
        m_lazy_run=False
    )
    
    m20 = M.input_features.v1(
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    name"""
    )
    
    m22 = M.use_datasource.v1(
        instruments=m9.data,
        features=m20.data,
        datasource_id='instruments_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m27 = M.join.v3(
        data1=m8.predictions,
        data2=m22.data,
        on='date,instrument',
        how='inner',
        sort=True
    )
    
    m11 = M.sort.v4(
        input_ds=m27.data,
        sort_by='position',
        group_by='date',
        keep_columns='--',
        ascending=True
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m11.sorted_data,
        start_date='',
        end_date='',
        initialize=m4_initialize_bigquant_run,
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        before_trading_start=m4_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000001,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
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