老师您好,我想运用自定义模块构造TALIB指标,可是这里报错了,您能帮我看看嘛?

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标签: #<Tag:0x00007fc4c7f457e8>

(woshisilvio) #1
克隆策略

    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context.perf_tracker.position_tracker.positions.items()}\n\n # 记录用于买入股票的可用现金\n cash_for_buy = context.portfolio.cash\n \n # 获取当日符合买入/卖出条件的股票列表\n try:\n buy_stock = context.daily_stock_buy[today] # 当日符合买入条件的股票\n except:\n buy_stock=[]\n try:\n sell_stock = context.daily_stock_sell[today] # 当日符合卖出条件的股票\n except:\n sell_stock = []\n\n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [i for i in stock_hold_now if i in sell_stock]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n stock_to_buy = [i for i in buy_stock if i not in stock_hold_now]\n # 卖出\n for instrument in stock_to_sell:\n # 如果该股票停牌,则没法成交。因此需要用can_trade方法检查下该股票的状态\n # 如果返回真值,则可以正常下单,否则会出错\n # 因为stock是字符串格式,我们用symbol方法将其转化成平台可以接受的形式:Equity格式\n if data.can_trade(context.symbol(instrument)):\n # order_target_percent是平台的一个下单接口,表明下单使得该股票的权重为0,即卖出全部股票,可参考回测文档\n context.order_target_percent(context.symbol(instrument), 0)\n # 由于是收盘卖出且开盘买入,因此买入时无需更新当日可用现金,如果是收盘买入开盘卖出则需更新可用现金\n # cash_for_buy += stock_hold_now[instrument]\n # 如果当天没有买入的股票,就返回\n if len(stock_to_buy) == 0:\n return\n \n # 买入\n for instrument in stock_to_buy:\n # 针对当日可用现金使用等资金比例下单买入,整百股数下单\n cash = cash_for_buy / len(stock_to_buy)\n if data.can_trade(context.symbol(instrument)):\n current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\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 = 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    In [4]:
    # 本代码由可视化策略环境自动生成 2020年3月16日 20:54
    # 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。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m5_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        prices=[float(x) for x in df['close_0']]
        import talib
        macd, signal, hist = talib.MACD(np.array(prices),12,26,9)#使用talib库计算指标值
        df['macd']=pd.Series(macd,index=df.index)
        df['signal']=pd.Series(signal,index=df.index) 
        #计算昨日macd和signal
        df['macd_1']=df['macd'].shift(1)
        df['signal_1']=df['signal'].shift(1)
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m5_post_run_bigquant_run(outputs):
        return outputs
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m10_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        prices=[float(x) for x in df['close_0']]
        import talib
        macd, signal, hist = talib.MACD(np.array(prices),12,26,9)#使用talib库计算指标值
        df['macd']=pd.Series(macd,index=df.index)
        df['signal']=pd.Series(signal,index=df.index) 
        #计算昨日macd和signal
        df['macd_1']=df['macd'].shift(1)
        df['signal_1']=df['signal'].shift(1)
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m10_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 = 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
    
    # 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):
        
        #------------------这里是测试--------------
       
        # 回测引擎:每日数据处理函数,每天执行一次
        today = data.current_dt.strftime('%Y-%m-%d') # 日期
        # 通过positions对象,使用列表生成式的方法获取目前持仓的股票列表和对应的最新市值
        stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 记录用于买入股票的可用现金
        cash_for_buy = context.portfolio.cash
        
        # 获取当日符合买入/卖出条件的股票列表
        try:
            buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
        except:
            buy_stock=[]
        try:
            sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
        except:
            sell_stock = []
    
        # 需要卖出的股票:已有持仓中符合卖出条件的股票
        stock_to_sell = [i for i in stock_hold_now if i in sell_stock]
        # 需要买入的股票:没有持仓且符合买入条件的股票
        stock_to_buy = [i for i in buy_stock if i not in stock_hold_now]
        # 卖出
        for instrument in stock_to_sell:
            # 如果该股票停牌,则没法成交。因此需要用can_trade方法检查下该股票的状态
            # 如果返回真值,则可以正常下单,否则会出错
            # 因为stock是字符串格式,我们用symbol方法将其转化成平台可以接受的形式:Equity格式
            if data.can_trade(context.symbol(instrument)):
                # order_target_percent是平台的一个下单接口,表明下单使得该股票的权重为0,即卖出全部股票,可参考回测文档
                context.order_target_percent(context.symbol(instrument), 0)
                # 由于是收盘卖出且开盘买入,因此买入时无需更新当日可用现金,如果是收盘买入开盘卖出则需更新可用现金
                # cash_for_buy += stock_hold_now[instrument]
        # 如果当天没有买入的股票,就返回
        if len(stock_to_buy) == 0:
            return
        
        # 买入
        for instrument in stock_to_buy:
            # 针对当日可用现金使用等资金比例下单买入,整百股数下单
            cash = cash_for_buy / len(stock_to_buy)
            if data.can_trade(context.symbol(instrument)):
                current_price = data.current(context.symbol(instrument), 'price')
                amount = math.floor(cash / current_price / 100) * 100
                context.order(context.symbol(instrument), amount)
        # 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.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:
            equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
            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):
            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 m19_prepare_bigquant_run(context):
         # 加载预测数据
        df = context.options['data'].read_df()
    
        # 函数:求满足开仓条件的股票列表
        def open_pos_con(df):
            return list(df[df['buy_condition']>0].instrument)
    
        # 函数:求满足平仓条件的股票列表
        def close_pos_con(df):
            return list(df[df['sell_condition']>0].instrument)
    
        # 每日买入股票的数据框
        context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
        # 每日卖出股票的数据框
        context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
    
    
    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/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, -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
    close_0/adjust_factor_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m5 = M.cached.v3(
        input_1=m15.data,
        run=m5_run_bigquant_run,
        post_run=m5_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    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=60
    )
    
    m10 = M.cached.v3(
        input_1=m17.data,
        run=m10_run_bigquant_run,
        post_run=m10_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = 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
    
    buy_condition=where((macd_1<signal_1)&(macd>signal_1),1,0)
    sell_condition=where((macd_1>signal_1)&(macd<signal_1),1,0)"""
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m5.data_1,
        features=m6.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
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.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
    )
    
    m11 = 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
    
    buy_condition=where((macd_1<signal_1)&(macd>signal_1),1,0)
    sell_condition=where((macd_1>signal_1)&(macd<signal_1),1,0)"""
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m10.data_1,
        features=m11.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        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'
    )
    

    StockRanker训练(GBDT)(stock_ranker_train)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-4-a81d537c6684> in <module>()
        289     data_row_fraction=1,
        290     ndcg_discount_base=1,
    --> 291     m_lazy_run=False
        292 )
        293 
    
    ValueError: max() arg is an empty sequence

    StockRanker训练(GBDT)(stock_ranker_train)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-4-a81d537c6684> in <module>() 289 data_row_fraction=1, 290 ndcg_discount_base=1, --> 291 m_lazy_run=False 292 ) 293 ValueError: max() arg is an empty sequence


    (达达) #2

    第一种情况,如果你想把macd作为因子,那直接定义因子就好了,macd的条件到底是大于还是小于交给算法去学习,解放脑力和调参。比如直接定义因子macd_1/signal_1和
    macd/signal_1,那么机器会去考虑股票的这两个数据怎么个条件才能让未来收益率最高。
    第二种情况,你就是单纯的想看看满足macd条件的这些股票,然后训练一下这些股票收益率和close_0/adjust_factor_0这个因子的关系,那你用过滤模块做一下条件过滤后再给模型训练。
    第三种情况,你想在排序的基础上叠加一个条件过滤。
    不知道你具体要做什么。