AI选股中回归、分类、排序算法的构建流程

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标签: #<Tag:0x00007f61484ef1a0>

(lpl22) #1

在阅读了学院关于可视化模板教程后,相信你已经掌握了平台上的模块使用方法。本文将以XGBoost模型为例,介绍回归、排序、分类的不同之处。在文末,你可以克隆该算法自行研究、学习

首先我们明确一下算法在机器学习中的地位。一般来说,机器学习有三个要素:数据、算法和模型

  • 数据是场景的描述,包括输入和输出。
  • 算法是得到模型的过程,狭义上说,特指机器学习算法,如传统线性回归、树和支持向量机以及深度学习;广义上说,从最初得到数据到最终确定模型中间的所有过程,即建模流程都可以看作算法,如分类、回归模型,搜索最优参数算法。
  • 模型是输入到输出的映射,即我们最后需要得到的特定法则,对场景给出相关预测。
    对应这三个要素,机器学习又可以分为三个步骤:特征工程、模型训练和模型融合。

这里我们讨论的回归、排序、分类,对应的是算法——模型训练的环节。我们以XGBoost为例,分别使用三种算法。

一.排序算法

模板策略在本文末尾供各位克隆、研究、修改。

在运行策略后输入下列代码,可查看预测集各个股票在不同时间内的评分

m20.predictions.read_df().head(10)
运行结果
prediction date instrument
550388 1.442220 2015-08-03 603023.SHA
310324 1.425380 2015-06-19 300312.SZA
245931 1.410372 2015-07-02 002759.SZA
1112466 1.400640 2016-01-28 600805.SHA
576750 1.394390 2016-01-28 000039.SZA
444539 1.384524 2015-06-18 600573.SHA
1159144 1.377003 2016-01-28 601567.SHA
1161512 1.376757 2016-01-28 601618.SHA
200413 1.373183 2015-07-28 002522.SZA
538663 1.372157 2015-08-25 601789.SHA

通过调用下列代码,可以查看各个特征、因子在选股时的表现。

m20.feature_gains()
运行结果
feature gain
0 return_20 258
10 return_5 213
3 avg_amount_5/avg_amount_20 177
1 avg_amount_0/avg_amount_5 167
8 rank_avg_amount_0/rank_avg_amount_5 159
2 rank_return_5 127
7 rank_avg_amount_5/rank_avg_amount_10 121
5 rank_return_5/rank_return_10 115
6 pe_ttm_0 112
12 rank_return_10 111
11 rank_return_0 110
4 return_10 101
9 rank_return_0/rank_return_5 97

二.分类算法

二分类

此时需要通过数据标注给股票分类(读者可以根据自己需求改变分类的方式):

# 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)>0,1,0)

同上,调用相同的代码,得到如下结论:

m20.feature_gains()
运行结果
feature gain
3 return_5 257
10 return_10 244
11 rank_return_10 240
2 pe_ttm_0 204
12 rank_avg_amount_5/rank_avg_amount_10 181
0 return_20 179
1 avg_amount_5/avg_amount_20 147
5 rank_return_5 114
6 rank_return_5/rank_return_10 49
4 rank_avg_amount_0/rank_avg_amount_5 42
7 avg_amount_0/avg_amount_5 36
9 rank_return_0 29
8 rank_return_0/rank_return_5 27
m20.predictions.read_df().head(10)
运行结果
prediction date instrument
0 0.897233 2015-01-05 000001.SZA
255422 0.992079 2015-01-05 300038.SZA
55849 0.963490 2015-01-05 000725.SZA
419995 0.996210 2015-01-05 600425.SHA
168334 0.944331 2015-01-05 002363.SZA
255586 0.993303 2015-01-05 300039.SZA
535311 0.901999 2015-01-05 601668.SHA
484856 0.978802 2015-01-05 600787.SHA
348936 0.780496 2015-01-05 600036.SHA
3107 0.960541 2015-01-05 000019.SZA

多分类

不同于二分类,此时模块m20XGBoost应选择多分类,同时数据标注处代码应将数据分为多个类别,例如:

where(cond, x, y):#如果cond为True,则为 x, 否则 y 
where(a &gt, b, c, d)
where(a &gt, b, where(a &gt; c, c, d), e)
all_wbins(s, bins), #按等宽做离散化,映射从0开始。bins可以是正整数,表示bins的数量;list,表示splits,e.g. [-2, 0, 2],小于-2的数据将被映射为0,大于2的被映射为3,中间的分别为1和2
all_cbins(s, bins), #按等频做离散化,映射从0开始。bins可以是正整数,表示bins的数量;list,表示每个bin里的数据比例

此处我们作如下标注:

# #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)>1,2,where(shift(close, -5) / shift(open, -1)>0,1,0))

同时在m20XGBoost参数字典中写入:

#写入我们分类的个数
{'num_class':3}
m20.feature_gains()
运行结果
feature gain
9 return_5 841
12 return_10 701
6 return_20 583
0 rank_return_10 559
7 rank_return_5 478
1 pe_ttm_0 465
10 avg_amount_5/avg_amount_20 462
4 rank_avg_amount_5/rank_avg_amount_10 424
3 avg_amount_0/avg_amount_5 305
11 rank_return_5/rank_return_10 215
2 rank_avg_amount_0/rank_avg_amount_5 207
5 rank_return_0 158
8 rank_return_0/rank_return_5 129
m20.predictions.read_df().head(10)
运行结果
prediction date instrument
0 1.0 2015-01-05 000001.SZA
255422 2.0 2015-01-05 300038.SZA
55849 1.0 2015-01-05 000725.SZA
419995 1.0 2015-01-05 600425.SHA
168334 2.0 2015-01-05 002363.SZA
255586 1.0 2015-01-05 300039.SZA
535311 1.0 2015-01-05 601668.SHA
484856 1.0 2015-01-05 600787.SHA
348936 1.0 2015-01-05 600036.SHA
3107 2.0 2015-01-05 000019.SZA

三.回归算法

同上,调用相同的代码,得到如下结论:

m20.feature_gains()
运行结果
feature gain
0 return_20 261
10 return_5 254
1 return_10 220
2 rank_return_5 180
5 rank_return_10 156
3 avg_amount_5/avg_amount_20 128
4 avg_amount_0/avg_amount_5 127
6 pe_ttm_0 124
8 rank_avg_amount_0/rank_avg_amount_5 96
7 rank_avg_amount_5/rank_avg_amount_10 92
12 rank_return_5/rank_return_10 87
11 rank_return_0 61
9 rank_return_0/rank_return_5 59
m20.predictions.read_df().head(10)
运行结果
prediction date instrument
0 7.877901 2015-01-05 000001.SZA
255422 10.281054 2015-01-05 300038.SZA
55849 7.953732 2015-01-05 000725.SZA
419995 8.362499 2015-01-05 600425.SHA
168334 9.308661 2015-01-05 002363.SZA
255586 7.749955 2015-01-05 300039.SZA
535311 8.530932 2015-01-05 601668.SHA
484856 7.365503 2015-01-05 600787.SHA
348936 8.462595 2015-01-05 600036.SHA
3107 7.805900 2015-01-05 000019.SZA

四.三种算法的比较

通过使用不同的算法,平台可以实现不同的策略

  • 排序可以将股票按照某个特征排序,进而达到筛选股票的目的
  • 回归可以预测出股票收盘价等数据的走向,帮助选股
  • 分类可以筛选出某个特征达到一定值的股票,过滤掉表现不好的股票
克隆策略

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回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\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 = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\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 = 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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28日 18:00
    # 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 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()
    
        # 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)
    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
    """
    )
    
    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
    )
    
    m20 = M.xgboost.v1(
        training_ds=m13.data,
        features=m3.data,
        predict_ds=m14.data,
        num_boost_round=30,
        objective='排序(pairwise)',
        booster='gbtree',
        max_depth=6,
        key_cols='date,instrument',
        group_col='date',
        other_train_parameters={}
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m20.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'
    )
    
    [2019-01-28 16:24:42.422467] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-28 16:24:42.434800] INFO: bigquant: 命中缓存
    [2019-01-28 16:24:42.436283] INFO: bigquant: instruments.v2 运行完成[0.01384s].
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    [2019-01-28 16:24:42.447409] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.452164] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-28 16:24:42.458288] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.473725] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-28 16:24:42.481569] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.494079] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.499280] INFO: bigquant: join.v3 开始运行..
    [2019-01-28 16:24:42.505799] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.510056] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-28 16:24:42.515563] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.519915] INFO: bigquant: instruments.v2 开始运行..
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    [2019-01-28 16:24:42.549281] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-28 16:24:42.555673] INFO: bigquant: 命中缓存
    [2019-01-28 16:24:42.557094] INFO: bigquant: general_feature_extractor.v7 运行完成[0.007811s].
    [2019-01-28 16:24:42.560659] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-28 16:24:42.567051] INFO: bigquant: 命中缓存
    [2019-01-28 16:24:42.568230] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.00759s].
    [2019-01-28 16:24:42.571140] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-28 16:24:42.577041] INFO: bigquant: 命中缓存
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    [2019-01-28 16:24:42.581621] INFO: bigquant: xgboost.v1 开始运行..
    [16:25:12] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:25:28] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:25:46] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:26:02] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:26:19] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:26:35] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:26:51] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:27:07] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:27:24] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:27:40] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:27:58] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:28:15] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:28:31] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:28:48] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:29:04] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
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    [16:29:54] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:30:10] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 118 extra nodes, 0 pruned nodes, max_depth=6
    [16:30:27] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6
    [16:30:44] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6
    [16:31:02] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:31:19] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6
    [16:31:37] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 114 extra nodes, 0 pruned nodes, max_depth=6
    [16:31:53] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 120 extra nodes, 0 pruned nodes, max_depth=6
    [16:32:10] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 124 extra nodes, 0 pruned nodes, max_depth=6
    [16:32:27] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:32:43] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [16:33:01] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 122 extra nodes, 0 pruned nodes, max_depth=6
    [16:33:19] /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 126 extra nodes, 0 pruned nodes, max_depth=6
    [2019-01-28 16:33:27.286447] INFO: bigquant: xgboost.v1 运行完成[524.704796s].
    [2019-01-28 16:33:27.302367] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-28 16:33:27.304609] INFO: bigquant: biglearning backtest:V8.1.7
    [2019-01-28 16:33:27.305521] INFO: bigquant: product_type:stock by specified
    [2019-01-28 16:33:42.775059] INFO: bigquant: 读取股票行情完成:1990277
    [2019-01-28 16:34:05.106943] INFO: algo: TradingAlgorithm V1.4.5
    [2019-01-28 16:34:15.853533] INFO: algo: trading transform...
    [2019-01-28 16:34:29.720732] INFO: Performance: Simulated 488 trading days out of 488.
    [2019-01-28 16:34:29.722059] INFO: Performance: first open: 2015-01-05 09:30:00+00:00
    [2019-01-28 16:34:29.723007] INFO: Performance: last close: 2016-12-30 15:00:00+00:00
    
    • 收益率419.55%
    • 年化收益率134.18%
    • 基准收益率-6.33%
    • 阿尔法0.92
    • 贝塔0.92
    • 夏普比率2.24
    • 胜率0.64
    • 盈亏比0.95
    • 收益波动率40.49%
    • 信息比率0.21
    • 最大回撤50.32%
    [2019-01-28 16:34:33.286362] INFO: bigquant: backtest.v8 运行完成[65.983968s].
    
    In [15]:
    m20.feature_gains()
    
    Out[15]:
    feature gain
    0 return_20 258
    10 return_5 213
    3 avg_amount_5/avg_amount_20 177
    1 avg_amount_0/avg_amount_5 167
    8 rank_avg_amount_0/rank_avg_amount_5 159
    2 rank_return_5 127
    7 rank_avg_amount_5/rank_avg_amount_10 121
    5 rank_return_5/rank_return_10 115
    6 pe_ttm_0 112
    12 rank_return_10 111
    11 rank_return_0 110
    4 return_10 101
    9 rank_return_0/rank_return_5 97
    In [13]:
    m20.predictions.read_df().head(10)
    
    Out[13]:
    prediction date instrument
    550388 1.442220 2015-08-03 603023.SHA
    310324 1.425380 2015-06-19 300312.SZA
    245931 1.410372 2015-07-02 002759.SZA
    1112466 1.400640 2016-01-28 600805.SHA
    576750 1.394390 2016-01-28 000039.SZA
    444539 1.384524 2015-06-18 600573.SHA
    1159144 1.377003 2016-01-28 601567.SHA
    1161512 1.376757 2016-01-28 601618.SHA
    200413 1.373183 2015-07-28 002522.SZA
    538663 1.372157 2015-08-25 601789.SHA

    【宽客学院】AI策略开发
    (yangziriver) #2

    此处有误
    image
    调用的代码应该是:
    image
    另外,结尾处的链接也不能用了
    https://i.bigquant.com/user/lpl22/lab/share/%E5%8F%AF%E8%A7%86%E5%8C%96%E7%AD%96%E7%95%A5-AI%E9%80%89%E8%82%A11-Clone1-Copy1-Clone5-Copy2.ipynb


    (iQuant) #3

    您好,已经修改了


    (yangziriver) #4

    好的,谢谢!