新手求教

新手专区
标签: #<Tag:0x00007fb3d7ccd458>

(zhudan) #1

新手小白编程能力太差,求教几个代码问题,实在不知道以下几个问题代码该如何编写,求大佬指点。

1、如何在策略中实现当日涨停,取消卖单
2、预测待买入的股票中,某支股票如果当日跌停,则排除待买入订单,按顺序买别的股票,避免次日买入。
3、在 m3输入特征列表模块 里,如何过滤掉某个因子,再把其他因子导入训练模块? 比如close_0 , 因为自定义衍生因子需要close_0做参数,但是训练模块里,不想用close_0做训练。。


(iQuant) #2

您好,收到您的提问,已提交给策略工程师,会尽快为您回复。


(达达) #3

1.当日涨停取消卖单 目前只支持回测 不支持模拟交易
方法是在准备函数中获取涨跌停状态数据,同时在盘前处理函数中进行判断撤单
2、跌停的不买,需要在买入逻辑中进行当日是否跌停的判断,自定义一个计数器,通过循环控制
3、增加一个特征列表模块里面填入过滤的因子,把训练的因子单独放在m3中,只连m3的输出到训练,例如下面的例子增加了上市日期过滤

克隆策略

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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)\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n # 买入数量计数器buy_counts\n buy_counts = 0\n for i, instrument in enumerate(buy_instruments):\n # 如果买入的股票数量达到要求了就停止\n if buy_counts >= len(buy_cash_weights):\n break\n # 如果是股票状态st 或*st或暂停上市就不买入\n try:\n st_status_instrument = status_today_df[status_today_df.instrument==instrument]['st_status_0'].values[0]\n if st_status_instrument>0:\n continue\n except:\n pass\n # 如果昨日跌停就不买入\n try:\n status_instrument = status_today_df[status_today_df.instrument==instrument]['price_limit_status_1'].values[0]\n if status_instrument<2:\n continue\n except:\n pass\n cash = cash_for_buy * buy_cash_weights[buy_counts]\n sid = context.symbol(instrument)\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 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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 = 0.2\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":"before_trading_start","Value":"def bigquant_run(context, data):\n # 获取涨跌停状态数据\n df_price_limit_status=context.status_df.set_index('date')\n today=data.current_dt.strftime('%Y-%m-%d')\n # 得到当前未完成订单\n for orders in get_open_orders().values():\n # 循环,撤销订单\n for _order in orders:\n ins=str(_order.sid.symbol)\n try:\n #判断一下如果当日涨停,则取消卖单\n if df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status_0.ix[today]>2 and _order.amount<0:\n cancel_order(_order)\n print(today,'尾盘涨停取消卖单',ins) \n except:\n 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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年3月15日 12:00
    # 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:每日数据处理函数,每天执行一次
    def m4_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')]
        today = 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.portfolio.positions.items()}
        equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
    
        # 判断并记录持仓中st的股票,发现就卖出
        st_stock_list = []
        status_df = context.status_df
        status_today_df = status_df[status_df.date==today]
        for instrument in equities:
            try:
                st_status_instrument = status_today_df[status_today_df.instrument==instrument]['st_status_0'].values[0]
                # 如果股票状态变为了st 则卖出
                if st_status_instrument>0:
                    print(today,instrument,st_status_instrument)
                    # 指定一个limit_price,此时会以开盘价成交,这是由于初始化函数中改写了下单价格
                    context.order_target(context.symbol(instrument), 0, limit_price=1.0)
                    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')
     
        # 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:
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))
            for instrument in instruments:
                # 如果是st股票已经卖过了,就跳过
                if instrument in st_stock_list:
                    continue
                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)
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        # 买入数量计数器buy_counts
        buy_counts = 0
        for i, instrument in enumerate(buy_instruments):
            # 如果买入的股票数量达到要求了就停止
            if buy_counts >= len(buy_cash_weights):
                break
            # 如果是股票状态st 或*st或暂停上市就不买入
            try:
                st_status_instrument = status_today_df[status_today_df.instrument==instrument]['st_status_0'].values[0]
                if st_status_instrument>0:
                    continue
            except:
                pass
            # 如果昨日跌停就不买入
            try:
                status_instrument = status_today_df[status_today_df.instrument==instrument]['price_limit_status_1'].values[0]
                if status_instrument<2:
                    continue
            except:
                pass
            cash = cash_for_buy * buy_cash_weights[buy_counts]
            sid = context.symbol(instrument)
            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 and data.can_trade(sid):
                context.order_value(sid, cash)
                buy_counts += 1
    
    
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        # 获取st状态和涨跌停状态
        
        context.status_df = D.features(instruments =context.instruments,start_date = context.start_date, end_date = context.end_date, 
                               fields=['st_status_0','price_limit_status_0','price_limit_status_1'])
    
    
    # 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 = 5
        # 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 = 0.2
        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)
    def m4_before_trading_start_bigquant_run(context, data):
        # 获取涨跌停状态数据
        df_price_limit_status=context.status_df.set_index('date')
        today=data.current_dt.strftime('%Y-%m-%d')
        # 得到当前未完成订单
        for orders in get_open_orders().values():
            # 循环,撤销订单
            for _order in orders:
                ins=str(_order.sid.symbol)
                try:
                    #判断一下如果当日涨停,则取消卖单
                    if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status_0.ix[today]>2 and _order.amount<0:
                        cancel_order(_order)
                        print(today,'尾盘涨停取消卖单',ins) 
                except:
                    continue
    
    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
    """
    )
    
    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
    list_days_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m5.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    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='list_days_0>120',
        output_left_data=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m10.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', '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=m5.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    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='list_days_0>120',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        initialize=m4_initialize_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=1000000,
        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=''
    )
    
    2015-01-13 尾盘涨停取消卖单 300380.SZA
    2015-01-13 尾盘涨停取消卖单 002657.SZA
    2015-01-20 尾盘涨停取消卖单 300391.SZA
    2015-03-02 尾盘涨停取消卖单 600338.SHA
    2015-03-05 尾盘涨停取消卖单 601113.SHA
    2015-04-02 尾盘涨停取消卖单 300261.SZA
    2015-04-03 尾盘涨停取消卖单 300345.SZA
    2015-04-07 尾盘涨停取消卖单 300266.SZA
    2015-04-24 600401.SHA 2
    2015-04-24 持仓出现st股/退市股 ['600401.SHA'] 进行卖出处理
    2015-04-28 尾盘涨停取消卖单 300390.SZA
    2015-04-29 尾盘涨停取消卖单 300390.SZA
    2015-05-20 尾盘涨停取消卖单 300373.SZA
    2015-05-27 尾盘涨停取消卖单 002625.SZA
    2015-06-01 尾盘涨停取消卖单 002261.SZA
    2015-06-01 尾盘涨停取消卖单 002197.SZA
    2015-06-03 尾盘涨停取消卖单 002169.SZA
    2015-06-12 尾盘涨停取消卖单 600559.SHA
    2015-06-30 尾盘涨停取消卖单 300359.SZA
    2015-07-09 尾盘涨停取消卖单 601718.SHA
    2015-07-09 尾盘涨停取消卖单 300254.SZA
    2015-07-09 尾盘涨停取消卖单 300327.SZA
    2015-07-09 尾盘涨停取消卖单 002046.SZA
    2015-07-09 尾盘涨停取消卖单 600386.SHA
    2015-07-09 尾盘涨停取消卖单 300379.SZA
    2015-07-09 尾盘涨停取消卖单 000897.SZA
    2015-07-09 尾盘涨停取消卖单 600455.SHA
    2015-07-09 尾盘涨停取消卖单 300243.SZA
    2015-07-09 尾盘涨停取消卖单 002096.SZA
    2015-07-10 尾盘涨停取消卖单 601718.SHA
    2015-07-10 尾盘涨停取消卖单 300254.SZA
    2015-07-10 尾盘涨停取消卖单 300379.SZA
    2015-07-10 尾盘涨停取消卖单 300327.SZA
    2015-07-10 尾盘涨停取消卖单 600895.SHA
    2015-07-10 尾盘涨停取消卖单 600386.SHA
    2015-07-10 尾盘涨停取消卖单 002046.SZA
    2015-07-10 尾盘涨停取消卖单 300243.SZA
    2015-07-10 尾盘涨停取消卖单 002096.SZA
    2015-07-13 尾盘涨停取消卖单 300254.SZA
    2015-07-13 尾盘涨停取消卖单 600895.SHA
    2015-07-13 尾盘涨停取消卖单 300380.SZA
    2015-07-13 尾盘涨停取消卖单 601919.SHA
    2015-07-13 尾盘涨停取消卖单 000868.SZA
    2015-07-13 尾盘涨停取消卖单 300379.SZA
    2015-07-13 尾盘涨停取消卖单 002096.SZA
    2015-07-14 尾盘涨停取消卖单 300379.SZA
    2015-07-14 尾盘涨停取消卖单 300327.SZA
    2015-07-14 尾盘涨停取消卖单 600455.SHA
    2015-07-14 尾盘涨停取消卖单 300243.SZA
    2015-07-14 尾盘涨停取消卖单 002154.SZA
    2015-07-17 尾盘涨停取消卖单 002327.SZA
    2015-07-17 尾盘涨停取消卖单 002148.SZA
    2015-07-17 尾盘涨停取消卖单 002154.SZA
    2015-07-20 尾盘涨停取消卖单 601919.SHA
    2015-07-20 尾盘涨停取消卖单 300100.SZA
    2015-07-21 尾盘涨停取消卖单 600052.SHA
    2015-07-21 尾盘涨停取消卖单 000948.SZA
    2015-07-21 尾盘涨停取消卖单 002240.SZA
    2015-07-24 尾盘涨停取消卖单 002167.SZA
    2015-07-24 尾盘涨停取消卖单 000670.SZA
    2015-07-31 尾盘涨停取消卖单 600338.SHA
    2015-08-04 尾盘涨停取消卖单 600986.SHA
    2015-08-10 尾盘涨停取消卖单 300226.SZA
    2015-08-17 尾盘涨停取消卖单 002642.SZA
    2015-08-27 尾盘涨停取消卖单 300380.SZA
    2015-08-27 尾盘涨停取消卖单 300348.SZA
    2015-08-27 尾盘涨停取消卖单 002117.SZA
    2015-08-28 尾盘涨停取消卖单 002170.SZA
    2015-08-28 尾盘涨停取消卖单 002117.SZA
    2015-08-28 尾盘涨停取消卖单 000014.SZA
    2015-09-07 尾盘涨停取消卖单 300152.SZA
    2015-09-08 尾盘涨停取消卖单 300349.SZA
    2015-09-08 尾盘涨停取消卖单 300348.SZA
    2015-09-09 尾盘涨停取消卖单 300053.SZA
    2015-09-11 尾盘涨停取消卖单 600855.SHA
    2015-09-11 尾盘涨停取消卖单 002268.SZA
    2015-09-16 尾盘涨停取消卖单 600855.SHA
    2015-09-16 尾盘涨停取消卖单 002163.SZA
    2015-09-16 尾盘涨停取消卖单 600118.SHA
    2015-09-21 尾盘涨停取消卖单 601890.SHA
    2015-09-22 尾盘涨停取消卖单 601890.SHA
    2015-09-23 尾盘涨停取消卖单 601890.SHA
    2015-09-24 尾盘涨停取消卖单 601890.SHA
    2015-09-29 尾盘涨停取消卖单 002308.SZA
    2015-10-09 尾盘涨停取消卖单 002270.SZA
    2015-10-20 尾盘涨停取消卖单 600689.SHA
    2015-10-21 尾盘涨停取消卖单 600689.SHA
    2015-10-22 尾盘涨停取消卖单 600689.SHA
    2015-10-23 尾盘涨停取消卖单 600689.SHA
    2015-10-26 尾盘涨停取消卖单 600689.SHA
    2015-10-30 尾盘涨停取消卖单 600446.SHA
    2015-11-09 尾盘涨停取消卖单 300310.SZA
    2015-11-23 尾盘涨停取消卖单 300215.SZA
    2015-11-25 尾盘涨停取消卖单 000025.SZA
    2015-11-27 尾盘涨停取消卖单 000025.SZA
    2015-11-30 尾盘涨停取消卖单 000025.SZA
    2015-12-02 尾盘涨停取消卖单 600683.SHA
    2015-12-14 尾盘涨停取消卖单 300160.SZA
    2015-12-17 尾盘涨停取消卖单 600048.SHA
    2015-12-24 尾盘涨停取消卖单 300383.SZA
    2016-01-06 尾盘涨停取消卖单 002599.SZA
    2016-01-12 尾盘涨停取消卖单 002717.SZA
    2016-01-12 尾盘涨停取消卖单 300329.SZA
    2016-01-20 尾盘涨停取消卖单 002150.SZA
    2016-01-25 尾盘涨停取消卖单 300250.SZA
    2016-02-04 尾盘涨停取消卖单 300469.SZA
    2016-02-05 尾盘涨停取消卖单 300469.SZA
    2016-03-08 尾盘涨停取消卖单 000802.SZA
    2016-03-18 尾盘涨停取消卖单 300088.SZA
    2016-05-20 尾盘涨停取消卖单 002326.SZA
    2016-05-27 尾盘涨停取消卖单 002548.SZA
    2016-06-03 尾盘涨停取消卖单 300410.SZA
    2016-08-05 尾盘涨停取消卖单 000948.SZA
    2016-08-09 尾盘涨停取消卖单 600084.SHA
    2016-08-16 尾盘涨停取消卖单 000638.SZA
    2016-08-17 尾盘涨停取消卖单 000638.SZA
    2016-08-31 尾盘涨停取消卖单 000918.SZA
    2016-10-12 尾盘涨停取消卖单 000639.SZA
    2016-10-13 尾盘涨停取消卖单 000639.SZA
    2016-10-14 尾盘涨停取消卖单 000639.SZA
    2016-10-17 尾盘涨停取消卖单 000639.SZA
    2016-10-18 尾盘涨停取消卖单 000935.SZA
    2016-10-19 尾盘涨停取消卖单 000935.SZA
    2016-11-21 尾盘涨停取消卖单 601801.SHA
    2016-12-20 尾盘涨停取消卖单 603518.SHA
    
    • 收益率216.09%
    • 年化收益率81.18%
    • 基准收益率-6.33%
    • 阿尔法0.69
    • 贝塔1.09
    • 夏普比率1.44
    • 胜率0.6
    • 盈亏比0.92
    • 收益波动率47.11%
    • 信息比率0.14
    • 最大回撤49.33%

    (zhudan) #4

    比如在m5里输入 self_diy(close_0),在m16里输入self_diy()的表达式,比如五日均线之类,然后过滤出五日线以上的股票。我试了下,必须在m3里加入close_0,不然会报错,但是我并不想用close_0做训练,需要怎么解决呢?

    另外,能不能帮忙改一下代码,不要st股退市股的过滤了,只要在代码里加入 涨停取消卖单 支持回测就行,跌停取消第二天买单,不用换别的股票买,计数器什么的不懂啊 。。


    (达达) #5

    5日均线你不需要在m16用自定义函数的,直接mean(close_0,5)就可以,另外代码是模块化的,你简单如下修改删去st的判断可处理,就行了。用下面的代码替换掉主函数就行了。

    # 回测引擎:每日数据处理函数,每天执行一次
    def 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')]
        today = 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.portfolio.positions.items()}
        equities = {e.symbol: e for e, p in context.portfolio.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:
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))
            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):
            # 如果昨日跌停就不买入
            try:
                status_instrument = status_today_df[status_today_df.instrument==instrument]['price_limit_status_1'].values[0]
                if status_instrument<2:
                    continue
            except:
                pass
            cash = cash_for_buy * buy_cash_weights[buy_counts]
            sid = context.symbol(instrument)
            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 and data.can_trade(sid):
                context.order_value(sid, cash)