看看我这个策略错误出在哪里

策略分享
新手专区
标签: #<Tag:0x00007f25a5398c90> #<Tag:0x00007f25a5398b00>

(tkyz) #1

https://i.bigquant.com/user/tkyz/lab/share/%E7%82%8E%E6%9F%B1AI133-Copy2.ipynb?_t=1548340620250


(iQuant) #2

取消滚动是跑不通的 原因在于您使用bar1d_CN_STOCK_A表抽取因子数据抽不到,
单独使用基础数据模块抽取下面的因子

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

是可以的 但是抽不到high low close open
你可以考虑把数据源抽取的数据和基础特征抽取的数据合并,也可以考虑直接使用基础特征抽取抽取high_0 low_0 close_0和 open_0

建议您先不加滚动训练模块跑通流程。另外仔细检查每个模块的输出是否是自己希望要的结果,可以右键模块查看结果,或者根据模块编号查询例如:

m1.data.read()

(tkyz) #3

还是老是出错,你能不能发个改好的给我?谢谢啦


(iQuant) #4

我没加滚动,跑通了 但不知道是不是你原本的意图 我是把atr和cond2以及mid作为因子连同

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

一起作为指定训练的因子 传给了Stockranker,如果你的本意没有想把mid作为因子训练的话请在m20中删掉不想参加训练的因子,如果你想把cond2作为过滤的话可以在m10和m11中把st_status_0==0改为st_status_0==0 and cond2>0,所以看你怎么想的了。最后你可以自己加上滚动模块。

克隆策略

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    In [9]:
    # 本代码由可视化策略环境自动生成 2019年1月25日 10:02
    # 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 m19_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]))])))
            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):
        pass
    
    # 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=0.0002, sell_cost=0.0012, 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)])
        #改为等权重配置
        context.stock_weights = [1 / stock_count 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'] = 1
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2011-01-01',
        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, -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
    )
    
    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
    
    """
    )
    
    m4 = M.input_features.v1(
        features_ds=m3.data,
        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
    high_0
    low_0
    close_0
    open_0
    st_status_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m20 = M.input_features.v1(
        features_ds=m3.data,
        features="""
    # 输入参与训练的自定义列
    atr
    mid
    cond2
    """
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2011-01-01'),
        end_date=T.live_run_param('trading_date', '2012-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = 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
    mid=mean(close_0,14)
    atr=ta_atr(high_0, low_0, close_0, 14)
    """
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m21 = 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
    cond2=(close_0>atr+mid) & (shift(close_0, 1) < shift(atr+mid, 1))
    """
    )
    
    m12 = M.derived_feature_extractor.v3(
        input_data=m16.data,
        features=m21.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m12.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m11 = M.filter.v3(
        input_data=m7.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.data,
        features=m20.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
    )
    
    m24 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m21.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m10 = M.filter.v3(
        input_data=m24.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m10.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_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=''
    )
    
    [2019-01-25 10:00:40.223131] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-25 10:00:40.227795] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:40.228785] INFO: bigquant: instruments.v2 运行完成[0.005681s].
    [2019-01-25 10:00:40.230863] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-25 10:00:40.234784] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:40.235791] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004926s].
    [2019-01-25 10:00:40.237477] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-25 10:00:40.240756] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:40.241451] INFO: bigquant: input_features.v1 运行完成[0.003975s].
    [2019-01-25 10:00:40.243108] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-25 10:00:40.246627] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:40.247630] INFO: bigquant: input_features.v1 运行完成[0.00452s].
    [2019-01-25 10:00:40.252404] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-25 10:00:40.257119] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:40.258080] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005649s].
    [2019-01-25 10:00:40.260544] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-25 10:00:40.268468] INFO: bigquant: input_features.v1 运行完成[0.007931s].
    [2019-01-25 10:00:40.270623] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-25 10:00:40.274180] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:40.274930] INFO: bigquant: instruments.v2 运行完成[0.004297s].
    [2019-01-25 10:00:40.282853] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-25 10:00:42.778312] INFO: 基础特征抽取: 年份 2011, 特征行数=511455
    [2019-01-25 10:00:44.639509] INFO: 基础特征抽取: 年份 2012, 特征行数=0
    [2019-01-25 10:00:44.650496] INFO: 基础特征抽取: 总行数: 511455
    [2019-01-25 10:00:44.652437] INFO: bigquant: general_feature_extractor.v7 运行完成[4.369582s].
    [2019-01-25 10:00:44.654276] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-25 10:00:44.657954] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:44.658699] INFO: bigquant: input_features.v1 运行完成[0.004426s].
    [2019-01-25 10:00:44.661196] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-25 10:00:44.665602] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:44.666389] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.00524s].
    [2019-01-25 10:00:44.668121] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-25 10:00:46.158620] INFO: derived_feature_extractor: 提取完成 mid=mean(close_0,14), 1.293s
    [2019-01-25 10:00:48.325106] INFO: derived_feature_extractor: 提取完成 atr=ta_atr(high_0, low_0, close_0, 14), 2.165s
    [2019-01-25 10:00:48.463300] INFO: derived_feature_extractor: /y_2011, 511455
    [2019-01-25 10:00:49.253767] INFO: bigquant: derived_feature_extractor.v3 运行完成[4.585608s].
    [2019-01-25 10:00:49.256713] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-25 10:00:49.261061] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:49.261870] INFO: bigquant: input_features.v1 运行完成[0.005174s].
    [2019-01-25 10:00:49.264500] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-25 10:00:49.268663] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:49.269448] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.00496s].
    [2019-01-25 10:00:49.272272] INFO: bigquant: join.v3 开始运行..
    [2019-01-25 10:00:49.276233] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:49.277008] INFO: bigquant: join.v3 运行完成[0.004752s].
    [2019-01-25 10:00:49.279771] INFO: bigquant: filter.v3 开始运行..
    [2019-01-25 10:00:49.283746] INFO: bigquant: 命中缓存
    [2019-01-25 10:00:49.284437] INFO: bigquant: filter.v3 运行完成[0.004679s].
    [2019-01-25 10:00:49.288050] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-25 10:00:49.876552] INFO: dropnan: /y_2010, 369275/369275
    [2019-01-25 10:00:49.887384] INFO: dropnan: 行数: 369275/369275
    [2019-01-25 10:00:49.909371] INFO: bigquant: dropnan.v1 运行完成[0.621282s].
    [2019-01-25 10:00:49.913145] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-25 10:00:50.328731] INFO: StockRanker: 特征预处理 ..
    [2019-01-25 10:00:50.683550] INFO: StockRanker: prepare data: training ..
    [2019-01-25 10:00:52.352446] INFO: StockRanker: sort ..
    [2019-01-25 10:00:56.656763] INFO: StockRanker训练: 09b475d6 准备训练: 369275 行数
    [2019-01-25 10:00:56.714037] INFO: StockRanker训练: 正在训练 ..
    [2019-01-25 10:02:17.903325] INFO: bigquant: stock_ranker_train.v5 运行完成[87.990156s].
    [2019-01-25 10:02:17.905906] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-25 10:02:18.214087] INFO: derived_feature_extractor: 提取完成 cond2=(close_0>atr+mid) & (shift(close_0, 1) < shift(atr+mid, 1)), 0.144s
    [2019-01-25 10:02:18.381633] INFO: derived_feature_extractor: /y_2011, 477289
    [2019-01-25 10:02:19.058557] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.152617s].
    [2019-01-25 10:02:19.061074] INFO: bigquant: filter.v3 开始运行..
    [2019-01-25 10:02:19.066402] INFO: filter: 使用表达式 st_status_0==0 过滤
    [2019-01-25 10:02:19.441491] INFO: filter: 过滤 /y_2011, 450053/0/477289
    [2019-01-25 10:02:19.465970] INFO: bigquant: filter.v3 运行完成[0.404844s].
    [2019-01-25 10:02:19.468772] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-25 10:02:20.026671] INFO: dropnan: /y_2011, 450053/450053
    [2019-01-25 10:02:20.039086] INFO: dropnan: 行数: 450053/450053
    [2019-01-25 10:02:20.058695] INFO: bigquant: dropnan.v1 运行完成[0.589882s].
    [2019-01-25 10:02:20.063024] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-01-25 10:02:20.419506] INFO: StockRanker: prepare data: prediction ..
    [2019-01-25 10:02:26.540424] INFO: stock_ranker_predict: 准备预测: 450053 行
    [2019-01-25 10:02:26.541665] INFO: stock_ranker_predict: 正在预测 ..
    [2019-01-25 10:02:46.860642] INFO: bigquant: stock_ranker_predict.v5 运行完成[26.797586s].
    [2019-01-25 10:02:46.890518] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-25 10:02:46.893529] INFO: bigquant: biglearning backtest:V8.1.6
    [2019-01-25 10:02:46.894428] INFO: bigquant: product_type:stock by specified
    [2019-01-25 10:02:54.991413] INFO: bigquant: 读取股票行情完成:989702
    [2019-01-25 10:03:04.999277] INFO: algo: TradingAlgorithm V1.4.2
    [2019-01-25 10:03:11.693782] INFO: algo: trading transform...
    [2019-01-25 10:03:16.474844] INFO: Performance: Simulated 244 trading days out of 244.
    [2019-01-25 10:03:16.476099] INFO: Performance: first open: 2011-01-04 09:30:00+00:00
    [2019-01-25 10:03:16.477037] INFO: Performance: last close: 2011-12-30 15:00:00+00:00
    
    • 收益率56.68%
    • 年化收益率59.0%
    • 基准收益率-25.01%
    • 阿尔法0.81
    • 贝塔1.06
    • 夏普比率1.56
    • 胜率0.56
    • 盈亏比1.02
    • 收益波动率30.8%
    • 信息比率0.23
    • 最大回撤24.66%
    [2019-01-25 10:03:18.048206] INFO: bigquant: backtest.v8 运行完成[31.157658s].
    

    (tkyz) #5

    感谢,辛苦了,有问题再问你


    (tkyz) #6


    加上过滤条件 cond2>0出现以上错误,不加没事


    (iQuant) #7


    然后

    原因是cond2是布尔型的 不应参与训练,你可以把cond2改为数值型的比如cond2=where(XXX,1,0),这样这个因子是数值的了就可以参与训练


    (tkyz) #8

    好的,谢谢