标注加个括号,回测结果就不一样啦,按道理应该是一样的才对,怎么回事?

新手专区
标签: #<Tag:0x00007fcc1286b100>

(tkyz) #1

标注where((shift(close, -5) / shift(open, -1)-1)>0.2, 1, 0) 和 where(shift(close, -5) / shift(open, -1)-1>0.2, 1, 0) 是一样的吗?怎么回测结果不一样?

克隆策略

    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    In [2]:
    # 本代码由可视化策略环境自动生成 2019年1月31日 19:55
    # 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]))])))
            # 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().sort_values('classes_prob_0',ascending=False)
    
        # 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
    
    
    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)
    where((shift(close, -5) / shift(open, -1)-1)>0.2, 1, 0)
    
    # 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
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    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
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m6 = M.random_forest_classifier.v1(
        training_ds=m13.data,
        features=m3.data,
        predict_ds=m14.data,
        iterations=10,
        feature_fraction=1,
        max_depth=30,
        min_samples_per_leaf=200,
        key_cols='date,instrument',
        workers=1,
        other_train_parameters={}
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m6.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='000300.SHA'
    )
    
    [2019-01-31 19:49:23.119102] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-31 19:49:23.124470] INFO: bigquant: 命中缓存
    [2019-01-31 19:49:23.125425] INFO: bigquant: instruments.v2 运行完成[0.006366s].
    [2019-01-31 19:49:23.127944] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-31 19:49:38.021485] INFO: 自动标注(股票): 加载历史数据: 2642813 行
    [2019-01-31 19:49:38.022996] INFO: 自动标注(股票): 开始标注 ..
    [2019-01-31 19:49:42.866003] INFO: bigquant: advanced_auto_labeler.v2 运行完成[19.738028s].
    [2019-01-31 19:49:42.868238] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-31 19:49:42.873128] INFO: bigquant: 命中缓存
    [2019-01-31 19:49:42.874468] INFO: bigquant: input_features.v1 运行完成[0.006232s].
    [2019-01-31 19:49:42.880213] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-31 19:49:42.884029] INFO: bigquant: 命中缓存
    [2019-01-31 19:49:42.884887] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004676s].
    [2019-01-31 19:49:42.887177] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-31 19:49:42.891321] INFO: bigquant: 命中缓存
    [2019-01-31 19:49:42.892132] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.004943s].
    [2019-01-31 19:49:42.894701] INFO: bigquant: join.v3 开始运行..
    [2019-01-31 19:49:45.985664] INFO: join: /y_2010, 行数=431045/431567, 耗时=2.175008s
    [2019-01-31 19:49:48.283163] INFO: join: /y_2011, 行数=510937/511455, 耗时=2.283486s
    [2019-01-31 19:49:50.744096] INFO: join: /y_2012, 行数=564597/565675, 耗时=2.434042s
    [2019-01-31 19:49:53.255329] INFO: join: /y_2013, 行数=563157/564168, 耗时=2.491507s
    [2019-01-31 19:49:56.849321] INFO: join: /y_2014, 行数=568076/569948, 耗时=3.575922s
    [2019-01-31 19:49:56.974496] INFO: join: 最终行数: 2637812
    [2019-01-31 19:49:56.976592] INFO: bigquant: join.v3 运行完成[14.081882s].
    [2019-01-31 19:49:56.978652] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-31 19:49:57.647718] INFO: dropnan: /y_2010, 423756/431045
    [2019-01-31 19:49:58.343375] INFO: dropnan: /y_2011, 504741/510937
    [2019-01-31 19:49:59.114216] INFO: dropnan: /y_2012, 561124/564597
    [2019-01-31 19:49:59.902740] INFO: dropnan: /y_2013, 563127/563157
    [2019-01-31 19:50:00.739985] INFO: dropnan: /y_2014, 566227/568076
    [2019-01-31 19:50:00.763385] INFO: dropnan: 行数: 2618975/2637812
    [2019-01-31 19:50:00.797863] INFO: bigquant: dropnan.v1 运行完成[3.819171s].
    [2019-01-31 19:50:00.800633] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-31 19:50:00.805911] INFO: bigquant: 命中缓存
    [2019-01-31 19:50:00.807093] INFO: bigquant: instruments.v2 运行完成[0.006428s].
    [2019-01-31 19:50:00.812527] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-31 19:50:00.816321] INFO: bigquant: 命中缓存
    [2019-01-31 19:50:00.817348] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004816s].
    [2019-01-31 19:50:00.819396] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-31 19:50:00.823243] INFO: bigquant: 命中缓存
    [2019-01-31 19:50:00.824006] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.004607s].
    [2019-01-31 19:50:00.826320] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-31 19:50:00.829795] INFO: bigquant: 命中缓存
    [2019-01-31 19:50:00.830503] INFO: bigquant: dropnan.v1 运行完成[0.004174s].
    [2019-01-31 19:50:00.832660] INFO: bigquant: random_forest_classifier.v1 开始运行..
    [2019-01-31 19:53:04.976019] INFO: bigquant: random_forest_classifier.v1 运行完成[184.143304s].
    [2019-01-31 19:53:05.054134] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-31 19:53:05.056514] INFO: bigquant: biglearning backtest:V8.1.7
    [2019-01-31 19:53:05.057474] INFO: bigquant: product_type:stock by specified
    [2019-01-31 19:53:30.763389] INFO: bigquant: 读取股票行情完成:1990277
    [2019-01-31 19:54:01.809059] INFO: algo: TradingAlgorithm V1.4.5
    [2019-01-31 19:54:12.413427] INFO: algo: trading transform...
    [2019-01-31 19:54:31.759700] INFO: Performance: Simulated 488 trading days out of 488.
    [2019-01-31 19:54:31.761101] INFO: Performance: first open: 2015-01-05 09:30:00+00:00
    [2019-01-31 19:54:31.762206] INFO: Performance: last close: 2016-12-30 15:00:00+00:00
    
    • 收益率68.85%
    • 年化收益率31.06%
    • 基准收益率-6.33%
    • 阿尔法0.32
    • 贝塔0.98
    • 夏普比率0.86
    • 胜率0.59
    • 盈亏比0.91
    • 收益波动率35.23%
    • 信息比率0.13
    • 最大回撤35.44%
    [2019-01-31 19:54:37.462635] INFO: bigquant: backtest.v8 运行完成[92.40855s].
    
    In [2]:
    m6.predictions.read_df().head()
    
    Out[2]:
    classes_prob_0 classes_prob_1 pred_label date instrument
    0 0.981958 0.018042 0 2015-01-05 000001.SZA
    1 0.991570 0.008430 0 2015-01-06 000001.SZA
    2 0.991561 0.008439 0 2015-01-07 000001.SZA
    3 0.998941 0.001059 0 2015-01-08 000001.SZA
    4 0.997309 0.002691 0 2015-01-09 000001.SZA

    (达达) #2

    不是标注的问题,是因为你修改了标注模块,缓存就失效了,模型就重新训练了。
    随机森林每次模型训练结果不同。你可以尝试什么都不该,把缺失值处理模块的缓存勾掉,就会重新运算重新训练模型,可以发现结果也不一样。
    您可以使用DataSource.write_pickle()记录你满意的模型,然后复用。


    (tkyz) #3

    好的,怪不得每次回测都不一样