新建的策略报错 大神帮忙看看哪里错了“BigQuant needs data fields:['open', 'high', 'low', 'close', 'volume']”

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标签: #<Tag:0x00007fc83c4d4a70>

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克隆策略

    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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 # 记录持仓中st的股票\n try:\n st_stock_list = []\n name_df = context.name_df\n name_today = name_df[name_df.date==today]\n for instrument in equities:\n name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]\n # 如果股票状态变为了st 则卖出\n if 'ST' in name_instrument or '退' in name_instrument:\n # 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\n context.order_target(context.symbol(instrument), 0, limit_price=1.0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\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 except:\n pass\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 \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 # 3. 生成买入订单:按StockRanker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[:1])[:]\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in 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    In [1]:
    # 本代码由可视化策略环境自动生成 2020年2月12日 17:29
    # 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 m20_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 = 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.hold_days = 1
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m20_handle_data_bigquant_run(context, data):
        today = data.current_dt.strftime('%Y-%m-%d')
        
        
    
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
        #print(ranker_prediction)
        # 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()}
        # 记录持仓中st的股票
        try:
            st_stock_list = []
            name_df = context.name_df
            name_today = name_df[name_df.date==today]
            for instrument in equities:
                name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
                # 如果股票状态变为了st 则卖出
                if 'ST' in name_instrument or '退' in name_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]
            if st_stock_list!=[]:
                print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')
        except:
            pass
        # 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StockRanker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[:1])[:]
        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:
                current_price = data.current(context.symbol(instrument), 'price')
                amount = math.floor(cash / current_price / 100) * 100
                context.order(context.symbol(instrument), amount)
    # 回测引擎:准备数据,只执行一次
    def m20_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m20_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2007-01-01',
        end_date='2016-01-01',
        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:2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,
        user_functions={}
    )
    
    m4 = 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
    """
    )
    
    m3 = M.general_feature_extractor_vx1.v1(
        instruments=m1.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=240
    )
    
    m6 = M.derived_feature_extractor.v3(
        input_data=m3.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m6.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m8 = M.filter_delist_stock.v4(
        input_1=m7.data
    )
    
    m10 = M.dropnan.v1(
        input_data=m8.data_1
    )
    
    m9 = M.filter_stockcode.v2(
        input_1=m10.data,
        start='688'
    )
    
    m18 = M.stock_ranker_train.v6(
        training_ds=m9.data_1,
        features=m4.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.instruments.v2(
        start_date=T.live_run_param('trading_date', '2016-01-01'),
        end_date=T.live_run_param('trading_date', '2018-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m12 = M.general_feature_extractor_vx1.v1(
        instruments=m11.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=240
    )
    
    m14 = M.derived_feature_extractor.v3(
        input_data=m12.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m15 = M.filter_delist_stock.v4(
        input_1=m14.data
    )
    
    m17 = M.dropnan.v1(
        input_data=m15.data_1
    )
    
    m16 = M.filter_stockcode.v2(
        input_1=m17.data,
        start='688'
    )
    
    m19 = M.stock_ranker_predict.v5(
        model=m18.model,
        data=m16.data_1,
        m_lazy_run=False
    )
    
    m20 = M.trade.v4(
        instruments=m11.data,
        history_ds=m19.predictions,
        start_date='',
        end_date='',
        initialize=m20_initialize_bigquant_run,
        handle_data=m20_handle_data_bigquant_run,
        prepare=m20_prepare_bigquant_run,
        before_trading_start=m20_before_trading_start_bigquant_run,
        volume_limit=1,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=100000,
        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=''
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a1db6989e2cf441397af12170a775f73"}/bigcharts-data-end
    error_help error:  'NoneType' object has no attribute 'get'
    

    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

    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-1-0877b7eaa584> in <module>()
        261     plot_charts=True,
        262     backtest_only=False,
    --> 263     benchmark=''
        264 )
    
    Exception: BigQuant needs data fields:['open', 'high', 'low', 'close', 'volume']
    In [ ]:
     
    
    In [ ]:
     
    

    (Fengshu) #2

    找到了 有根线练错了 哈哈哈