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克隆策略

提升信号精准度---预测信号分组过滤的方法：

***学会这个 你的策略 最少提升提升 211%

1.问题:

为何要对模型预测score得分 进行分组统计？

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原因①

很多时候，我们会发现 stockranker每天按照score得分排序排名大小值 来推送的股票，未必是最好的结果。 尤其是一些 风格不是很稳定的模型，stockranker每天出的信号也不是很稳定。 有可能今天 排名NO.1的股票是赚钱的，但是到了第二天 变成了排名NO.2的股票赚钱，第一名反而变成了亏钱 甚至说，有时候 第1名的股票赚钱，有时候是最后一名的股票赚钱。 在实盘过程中，这种轮动现象还会交替出现。

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基于这个现象，我们就会去研究一下模型的选股，想办法搞明白： stockranker根据这个score得分排序选出来的股票，在长期的回测中，他的赚钱效应到底是属于偶然的结果， 还是真的存在什么赚钱的规律。

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原因②

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stockranker的排序模式，是一种非常好的线性切割模式。 它可以按照不同的等级，对好股票 和坏股票，按照一定的分布 进行 排序，从中把 优质的股票筛选出来。

正是因为stockranker具备这样的特点，我们可以利用排序算法的优点，针对 我们的score信号 进行强中选强。

为了方便我们用来验证 ， 我们可以运用分组统计的方法，对我们的模型选股绩效进行验证。 通过自定义python模块按照一定的方式 进行分组统计，也可以很方便的看到 我们模型选股的一些细节。

2.方法：

在我们的输入特征列表中 预先加入一个因子：

未来五日股票的收益

return_5_day=(shift(close_0, -5)-shift(open_0, -1))/shift(open_0, -1)

该因子是用来 计算我们模型的score得分 IC值，以及验证模型选股未来收益的基础

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接下来，在m10自定义python模块中，我们可以很方便的调整我们的分组对象， 对模型的score得分信号进行分段划分。

---

分组 = 输入整数
区间1 = 输入整数
区间2 = 输入整数
区间3 = 输入整数
作用：

可以对我们的预测集股票score按 一定的区间划分 成为20个股票池 区间1 区间2 区间3 是分组的代号 求的是三个分组区间并集∪

---

例如： 我 想要 分组82组

接着 是选中 区间1=82 [区间分组 第82组（score值最大的那一端）]
                       +
区间2=81 [区间分组 第81组（score值最大的那一端）]
                       +
区间3=80 [区间分组 第80组（score值最大的那一端）]

最终结果我们选的就是头部的那几组 股票 传入 我们的引擎的 第20组 股票 第19组股票 和第18组股票的这三个得分区间

---

3.实验结果：

基准：模板策略-stockranker多因子选股 夏普比率 -0.93

---

分组后： 提升 211% 夏普比率 1.04

研究 发现，大部分模型，只要 取到分组头部的那一段score得分区间 进行过滤，基本上都可以对原有模型有一定的提升。 强中选强，矮个子里拔将军 就是这样的道理。 大家有兴趣可以多去尝试一下。

In [93]:
#这里可以查看一下我们的分组情况
m12.data_1.read().head(20)
Out[93]:
0 avg_amount_0 avg_amount_0/avg_amount_5 avg_amount_20 avg_amount_5 avg_amount_5/avg_amount_20 close_0 date instrument open_0 ... rank_return_0/rank_return_5 rank_return_10 rank_return_5 rank_return_5/rank_return_10 return_10 return_20 return_5 return_5_day score score区间
0 1 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 2 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 3 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 4 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 5 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
5 6 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
6 7 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
7 8 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
8 9 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
9 10 NaN NaN NaN NaN NaN NaN NaT NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
41 NaN 2.492043e+09 1.618309 1.527730e+09 1.539906e+09 1.007970 1549.000000 2022-03-09 000001.SZA 1611.676270 ... 1.724588 0.084023 0.126113 1.500933 0.852742 0.822341 0.869347 0.009168 0.372825 10
136 NaN 2.461708e+09 1.265871 1.793048e+09 1.944676e+09 1.084564 1987.734131 2021-07-27 000001.SZA 2111.967529 ... 3.858925 0.066286 0.030801 0.464668 0.839319 0.779631 0.837341 0.002241 0.600460 10
137 NaN 1.898563e+09 0.957154 1.771935e+09 1.983550e+09 1.119426 1986.614868 2021-07-28 000001.SZA 1997.807129 ... 9.406167 0.124258 0.082744 0.665910 0.840436 0.794539 0.861650 -0.005584 0.356463 10
138 NaN 1.566108e+09 0.787511 1.803022e+09 1.988680e+09 1.102971 2010.118530 2021-07-29 000001.SZA 2004.522461 ... 4.368133 0.124971 0.096671 0.773547 0.865125 0.793988 0.878240 -0.013393 0.356356 10
139 NaN 1.679747e+09 0.851379 1.810127e+09 1.972972e+09 1.089963 1979.899536 2021-07-30 000001.SZA 2005.641602 ... 2.806757 0.036986 0.066515 1.798363 0.818224 0.762500 0.868008 0.012472 0.553538 10
142 NaN 1.300164e+09 0.771609 1.760661e+09 1.685003e+09 0.957029 1993.330200 2021-08-04 000001.SZA 2019.072266 ... 0.847129 0.046480 0.324545 6.982500 0.864563 0.781826 1.002815 0.119209 0.351922 10
151 NaN 3.907217e+09 1.358691 2.336447e+09 2.875722e+09 1.230810 2763.281494 2022-03-09 000002.SZA 2891.843994 ... 0.870029 0.035434 0.153349 4.327744 0.822674 0.813218 0.876161 -0.097206 0.372241 10
154 NaN 1.852414e+09 0.777551 2.163932e+09 2.382371e+09 1.100945 2618.445068 2022-03-14 000002.SZA 2665.638916 ... 6.406694 0.056992 0.060330 1.058582 0.823861 0.759679 0.859968 0.063291 0.540863 10
155 NaN 3.215505e+09 1.261564 2.193701e+09 2.548823e+09 1.161883 2408.513916 2022-03-15 000002.SZA 2571.251221 ... 3.112787 0.039797 0.030463 0.765471 0.770833 0.731225 0.798274 0.156579 1.200930 10
156 NaN 3.180486e+09 1.221009 2.237063e+09 2.604802e+09 1.164385 2524.057373 2022-03-16 000002.SZA 2473.608887 ... 11.837349 0.054615 0.069162 1.266370 0.800310 0.782938 0.877759 0.091408 0.443836 10

20 rows × 28 columns

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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.portfolio.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.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\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\n # 3. 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    In [151]:
    # 本代码由可视化策略环境自动生成 2022年6月29日 16:26
# 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 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

# 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.portfolio.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.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-12-31',
    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：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.HIX',
    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=90
)

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
)

m6 = M.stock_ranker_train.v6(
    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,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m4 = M.input_features.v1(
    features_ds=m3.data,
    features="""
#未来五日股票的收益
return_5_day=(shift(close_0, -5)-shift(open_0, -1))/shift(open_0, -1)
#未来10日股票的收益
#return_10_day=(shift(close_0, -10)-shift(open_0, -1))/shift(open_0, -1)
"""
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2022-06-28'),
    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=90
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

m5 = M.join.v3(
    data1=m8.predictions,
    data2=m14.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m12 = M.score_group_filter.v3(
    input_1=m5.data,
    分组=82,
    区间1=82,
    区间2=82,
    区间3=82
)

m11 = M.sort.v4(
    input_ds=m12.data_1,
    sort_by='score',
    group_by='--',
    keep_columns='--',
    ascending=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m11.sorted_data,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_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='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)
    设置评估测试数据集，查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2e2da09cc97f48f3b1a3db40abe94827"}/bigcharts-data-end
    • 收益率43.5%
    • 年化收益率28.94%
    • 基准收益率-13.83%
    • 阿尔法0.36
    • 贝塔0.48
    • 夏普比率1.04
    • 胜率0.45
    • 盈亏比1.36
    • 收益波动率24.6%
    • 信息比率0.09
    • 最大回撤26.8%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-042e73d07c414969bf3c9d3581e9f765"}/bigcharts-data-end