无穷大问题

策略分享
标签: #<Tag:0x00007fcf654abcf8>

(xinyan) #1
克隆策略
In [26]:
m2.plot_label_counts()
m2.data.read_df()
Out[26]:
m:open instrument m:close date m:amount m:low m:high label
0 1419.222412 000001.SZA 1456.430420 2018-01-02 2.856544e+09 1416.033081 1480.881470 11
1 4318.086914 000002.SZA 4470.489746 2018-01-02 2.218503e+09 4318.086914 4529.528809 19
2 90.583481 000004.SZA 90.786674 2018-01-02 1.395100e+07 89.404961 91.396255 7
3 38.460552 000005.SZA 40.036045 2018-01-02 3.052976e+07 38.460552 41.704212 8
4 1459.619751 000001.SZA 1417.096191 2018-01-03 4.006221e+09 1403.276001 1473.439819 12
5 4462.251953 000002.SZA 4438.910645 2018-01-03 2.130250e+09 4425.180664 4637.996094 19
6 91.111786 000004.SZA 96.719917 2018-01-03 4.321842e+07 90.502205 97.085663 4
7 40.314072 000005.SZA 39.572666 2018-01-03 2.896679e+07 39.109283 40.314072 9
8 1416.033081 000001.SZA 1408.591431 2018-01-04 2.454544e+09 1395.834473 1421.348511 15
9 4497.949707 000002.SZA 4547.377930 2018-01-04 1.740602e+09 4407.331543 4603.670898 19
10 96.719917 000004.SZA 94.444153 2018-01-04 3.390855e+07 93.956490 96.841835 5
11 39.572666 000005.SZA 39.758018 2018-01-04 1.873218e+07 39.201962 40.128719 9
12 1404.339111 000001.SZA 1413.906860 2018-01-05 1.603289e+09 1397.960571 1419.222412 15
13 4528.155762 000002.SZA 4772.549805 2018-01-05 2.916788e+09 4503.441895 4926.326172 15
14 94.403511 000004.SZA 94.200317 2018-01-05 2.427331e+07 92.859245 95.378838 5
15 39.479988 000005.SZA 40.221397 2018-01-05 3.728694e+07 39.479988 41.240833 8
16 1408.591431 000001.SZA 1377.761963 2018-01-08 2.806099e+09 1367.131104 1412.843872 15
17 4820.604980 000002.SZA 4941.429199 2018-01-08 2.994516e+09 4820.604980 5074.609863 15
18 93.915848 000004.SZA 92.818611 2018-01-08 1.640891e+07 92.371582 93.915848 6
19 39.758018 000005.SZA 40.499424 2018-01-08 2.991882e+07 39.572666 40.870129 7
20 1377.761963 000001.SZA 1390.519043 2018-01-09 1.754316e+09 1373.509521 1403.276001 15
21 4892.000977 000002.SZA 4920.833984 2018-01-09 1.700948e+09 4798.636719 4957.904785 17
22 92.818611 000004.SZA 93.550102 2018-01-09 1.261873e+07 92.574776 94.037766 6
23 40.314072 000005.SZA 40.870129 2018-01-09 4.797905e+07 39.943371 42.167595 6
24 1386.266602 000001.SZA 1431.979370 2018-01-10 3.196056e+09 1373.509521 1434.105591 14
25 4887.881836 000002.SZA 4897.493164 2018-01-10 1.633035e+09 4874.151855 5038.912109 17
26 93.590744 000004.SZA 91.030510 2018-01-10 1.949045e+07 90.827316 93.590744 8
27 40.684776 000005.SZA 40.036045 2018-01-10 2.487597e+07 39.758018 40.962807 8
28 1425.600830 000001.SZA 1424.537842 2018-01-11 1.937494e+09 1410.717651 1444.736450 12
29 4881.017090 000002.SZA 4826.097168 2018-01-11 1.411860e+09 4742.343750 4912.596191 16
... ... ... ... ... ... ... ... ...
166 86.031960 000004.SZA 84.934715 2018-03-07 1.082907e+07 84.853439 86.316429 14
167 35.587597 000005.SZA 39.479988 2018-03-07 9.103273e+07 35.587597 39.479988 5
168 1281.020874 000001.SZA 1287.399414 2018-03-08 8.321537e+08 1270.390015 1291.651855 7
169 4585.821777 000002.SZA 4618.773926 2018-03-08 1.175142e+09 4510.306641 4632.503906 5
170 84.650246 000004.SZA 85.341103 2018-03-08 9.333708e+06 84.528328 85.584930 13
171 39.201962 000005.SZA 38.645905 2018-03-08 1.068524e+08 37.719143 39.294636 6
172 1291.651855 000001.SZA 1285.273315 2018-03-09 1.139044e+09 1273.579346 1296.967285 5
173 4650.353027 000002.SZA 4594.060059 2018-03-09 1.092070e+09 4577.583984 4655.844727 5
174 85.503654 000004.SZA 86.113235 2018-03-09 1.241671e+07 84.528328 86.316429 12
175 38.275200 000005.SZA 38.182526 2018-03-09 4.782130e+07 37.719143 38.460552 3
176 1291.651855 000001.SZA 1278.894775 2018-03-12 1.526643e+09 1270.390015 1293.777954 4
177 4605.043945 000002.SZA 4478.728027 2018-03-12 1.979204e+09 4441.656738 4624.265625 5
178 85.625572 000004.SZA 88.389000 2018-03-12 2.431713e+07 84.528328 88.510918 12
179 38.089848 000005.SZA 38.367878 2018-03-12 3.332755e+07 37.997173 38.367878 4
180 1279.957886 000001.SZA 1277.831665 2018-03-13 1.309543e+09 1275.705444 1299.093384 4
181 4474.608887 000002.SZA 4459.505859 2018-03-13 7.190932e+08 4444.402832 4508.933594 7
182 88.104530 000004.SZA 95.094368 2018-03-13 6.111602e+07 86.926010 97.045021 12
183 38.089848 000005.SZA 37.997173 2018-03-13 2.263152e+07 37.997173 38.553230 5
184 1273.579346 000001.SZA 1267.200806 2018-03-14 7.570908e+08 1257.633057 1275.705444 5
185 4454.013672 000002.SZA 4489.711914 2018-03-14 1.031092e+09 4421.062012 4524.036621 7
186 90.949234 000004.SZA 90.786674 2018-03-14 4.186381e+07 89.608154 93.428185 11
187 37.904495 000005.SZA 38.089848 2018-03-14 2.115350e+07 37.719143 38.367878 7
188 1253.380615 000001.SZA 1244.875977 2018-03-15 1.356879e+09 1239.560547 1259.759155 6
189 4462.251953 000002.SZA 4484.219727 2018-03-15 8.252596e+08 4449.895020 4544.631836 11
190 89.404961 000004.SZA 92.249664 2018-03-15 2.665578e+07 87.657501 92.981163 9
191 37.719143 000005.SZA 36.977737 2018-03-15 3.091500e+07 36.421680 37.904495 8
192 1245.939087 000001.SZA 1237.434326 2018-03-16 1.130784e+09 1237.434326 1259.759155 6
193 4473.235840 000002.SZA 4480.100586 2018-03-16 1.111490e+09 4462.251953 4578.957031 10
194 91.762001 000004.SZA 92.656052 2018-03-16 1.439860e+07 90.258377 92.737328 9
195 36.885059 000005.SZA 36.792385 2018-03-16 1.719968e+07 36.607033 37.163090 7

196 rows × 8 columns

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    In [27]:
    # 本代码由可视化策略环境自动生成 2018年4月4日 10:24
    # 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。
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2018-04-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=4
    )
    
    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, -10) / 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='2018-01-01',
        end_date='2018-04-01',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""#上影线策略特征
    #成交量
    amount_0/avg_amount_5
    
    """
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='2018-01-01',
        end_date='2018-04-01',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m5.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        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,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2014-01-01'),
        end_date=T.live_run_param('trading_date', '2015-04-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=4
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date='2018-01-01',
        end_date='2018-04-01',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_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 m12_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m12_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
    
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-04-04 10:24:25.559796] INFO: bigquant: instruments.v2 开始运行..
    [2018-04-04 10:24:25.563005] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.564225] INFO: bigquant: instruments.v2 运行完成[0.004474s].
    [2018-04-04 10:24:25.575472] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-04-04 10:24:25.578686] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.579550] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004137s].
    [2018-04-04 10:24:25.588552] INFO: bigquant: input_features.v1 开始运行..
    [2018-04-04 10:24:25.594224] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.595690] INFO: bigquant: input_features.v1 运行完成[0.007193s].
    [2018-04-04 10:24:25.609516] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-04-04 10:24:25.611904] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.612683] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003185s].
    [2018-04-04 10:24:25.619252] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-04-04 10:24:25.621507] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.622629] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.00338s].
    [2018-04-04 10:24:25.629457] INFO: bigquant: join.v3 开始运行..
    [2018-04-04 10:24:25.635793] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.636773] INFO: bigquant: join.v3 运行完成[0.00731s].
    [2018-04-04 10:24:25.644414] INFO: bigquant: dropnan.v1 开始运行..
    [2018-04-04 10:24:25.646742] INFO: bigquant: 命中缓存
    [2018-04-04 10:24:25.647594] INFO: bigquant: dropnan.v1 运行完成[0.003183s].
    [2018-04-04 10:24:25.655845] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2018-04-04 10:24:25.674880] INFO: stock_ranker_train: 4b47895e 准备训练: 196 行数
    
    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-27-5b95788967e7> in <module>()
         84     max_bins=1023,
         85     feature_fraction=1,
    ---> 86     m_lazy_run=False
         87 )
         88 
    
    Exception: output ranker not generated
    We cannot build a tree with gain = 负无穷大

    (iQuant) #2

    我看了下你的训练集的 证券代码列表模块,见下:

    可以看出一共几天的数据,就四只股票,这样的数据量太小,模型构建失败。
    ai相关的模型更适合在大数据上发掘规律和模式,建议股票池设为全市场。