克隆策略

TabNet在量化选股中的应用

TabNet: Attentive Interpretable Tabular Learning

基于Tabnet模型的量化选股方案。抽取了98个量价因子，2010到2018年为数据训练TabNet模型，并将模型的预测结果应用在2018到2021年9月的数据上进行了回测。

TabNet核心参数

  • input_dim: 输入的特征数
  • n_steps: 决策的步数，通常为{3 ~ 10}
  • n_d: 预测阶段的特征数，通常为{8 ~ 64}
  • n_a: Attentive阶段的特征数，通常为{8 ~ 64}
  • gamma: Attentive中注意力更新的比例，通常为{1.0 ~ 2.0}
  • momentum: BN层的动量，通常为{0.0 ~ 1.0}

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context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.001, sell_cost=0.001, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 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\n stock_count = 20\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 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 = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\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. 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n 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    In [1]:
    # 本代码由可视化策略环境自动生成 2021年10月15日 10:00
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m12_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    from sklearn.model_selection import train_test_split
    data = input_1.read()
    x_train, x_val, y_train, y_val = train_test_split(data["x"], data['y'], random_state=2021)
    data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train.reshape(-1, 1)})
    data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val.reshape(-1, 1)})
    return Outputs(data_1=data_1, data_2=data_2, data_3=None)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m12_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m20_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    pred_label = input_1.read_pickle()
    df = input_2.read_df()
    df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
    df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
    return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m20_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m21_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=0.001, sell_cost=0.001, 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 = 20
    # 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 m21_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 m21_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2017-12-31',
    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/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)-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))

# 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=False
)

m17 = M.standardlize.v8(
    input_1=m2.data,
    columns_input='label'
)

m3 = M.input_features.v1(
    features="""close_0
open_0
high_0
low_0 
amount_0
turn_0 
return_0
 
close_1
open_1
high_1
low_1
return_1
amount_1
turn_1
 
close_2
open_2
high_2
low_2
amount_2
turn_2
return_2
 
close_3
open_3
high_3
low_3
amount_3
turn_3
return_3
 
close_4
open_4
high_4
low_4
amount_4
turn_4
return_4
 
mean(close_0, 5)
mean(low_0, 5)
mean(open_0, 5)
mean(high_0, 5)
mean(turn_0, 5)
mean(amount_0, 5)
mean(return_0, 5)
 
ts_max(close_0, 5)
ts_max(low_0, 5)
ts_max(open_0, 5)
ts_max(high_0, 5)
ts_max(turn_0, 5)
ts_max(amount_0, 5)
ts_max(return_0, 5)
 
ts_min(close_0, 5)
ts_min(low_0, 5)
ts_min(open_0, 5)
ts_min(high_0, 5)
ts_min(turn_0, 5)
ts_min(amount_0, 5)
ts_min(return_0, 5) 
 
std(close_0, 5)
std(low_0, 5)
std(open_0, 5)
std(high_0, 5)
std(turn_0, 5)
std(amount_0, 5)
std(return_0, 5)
 
ts_rank(close_0, 5)
ts_rank(low_0, 5)
ts_rank(open_0, 5)
ts_rank(high_0, 5)
ts_rank(turn_0, 5)
ts_rank(amount_0, 5)
ts_rank(return_0, 5)
 
decay_linear(close_0, 5)
decay_linear(low_0, 5)
decay_linear(open_0, 5)
decay_linear(high_0, 5)
decay_linear(turn_0, 5)
decay_linear(amount_0, 5)
decay_linear(return_0, 5)
 
correlation(volume_0, return_0, 5)
correlation(volume_0, high_0, 5)
correlation(volume_0, low_0, 5)
correlation(volume_0, close_0, 5)
correlation(volume_0, open_0, 5)
correlation(volume_0, turn_0, 5)
  
correlation(return_0, high_0, 5)
correlation(return_0, low_0, 5)
correlation(return_0, close_0, 5)
correlation(return_0, open_0, 5)
correlation(return_0, turn_0, 5)
 
correlation(high_0, low_0, 5)
correlation(high_0, close_0, 5)
correlation(high_0, open_0, 5)
correlation(high_0, turn_0, 5)
 
correlation(low_0, close_0, 5)
correlation(low_0, open_0, 5)
correlation(low_0, turn_0, 5)
 
correlation(close_0, open_0, 5)
correlation(close_0, turn_0, 5)

correlation(open_0, turn_0, 5)"""
)

m6 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m7 = M.derived_feature_extractor.v3(
    input_data=m6.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m13 = M.standardlize.v8(
    input_1=m7.data,
    input_2=m3.data,
    columns_input='[]'
)

m14 = M.fillnan.v1(
    input_data=m13.data,
    features=m3.data,
    fill_value='0.0'
)

m4 = M.join.v3(
    data1=m17.data,
    data2=m14.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m10 = M.dl_convert_to_bin.v2(
    input_data=m4.data,
    features=m3.data,
    window_size=1,
    feature_clip=3,
    flatten=True,
    window_along_col='instrument'
)

m12 = M.cached.v3(
    input_1=m10.data,
    run=m12_run_bigquant_run,
    post_run=m12_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m18 = M.dl_models_tabnet_train.v1(
    training_data=m12.data_1,
    validation_data=m12.data_2,
    input_dim=98,
    n_steps=3,
    n_d=32,
    n_a=32,
    gamma=1.3,
    momentum=0.02,
    batch_size=5120,
    virtual_batch_size=512,
    epochs=100,
    num_workers=4,
    device_name='auto:自动调用GPU',
    verbose='1:输出进度条记录'
)

m5 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2021-09-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m8 = M.general_feature_extractor.v7(
    instruments=m5.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m9 = M.derived_feature_extractor.v3(
    input_data=m8.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m16 = M.standardlize.v8(
    input_1=m9.data,
    input_2=m3.data,
    columns_input='[]'
)

m15 = M.fillnan.v1(
    input_data=m16.data,
    features=m3.data,
    fill_value='0.0'
)

m11 = M.dl_convert_to_bin.v2(
    input_data=m15.data,
    features=m3.data,
    window_size=1,
    feature_clip=3,
    flatten=True,
    window_along_col='instrument'
)

m19 = M.dl_models_tabnet_predict.v1(
    trained_model=m18.data,
    input_data=m11.data,
    m_cached=False
)

m20 = M.cached.v3(
    input_1=m19.data,
    input_2=m9.data,
    run=m20_run_bigquant_run,
    post_run=m20_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m21 = M.trade.v4(
    instruments=m5.data,
    options_data=m20.data_1,
    start_date='',
    end_date='',
    initialize=m21_initialize_bigquant_run,
    handle_data=m21_handle_data_bigquant_run,
    prepare=m21_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='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

m22 = M.strategy_turn_analysis.v1(
    raw_perf=m21.raw_perf
)

    Device used : cuda

    epoch 0  | loss: 1.02933 | val_0_mse: 0.99521 |  0:02:13s
epoch 1  | loss: 0.99095 | val_0_mse: 0.99387 |  0:04:30s
epoch 2  | loss: 0.98998 | val_0_mse: 0.99277 |  0:06:52s
epoch 3  | loss: 0.98929 | val_0_mse: 0.99185 |  0:09:50s
epoch 4  | loss: 0.98827 | val_0_mse: 0.99178 |  0:12:34s
epoch 5  | loss: 0.98711 | val_0_mse: 0.99059 |  0:15:22s
epoch 6  | loss: 0.98646 | val_0_mse: 0.99011 |  0:18:24s
epoch 7  | loss: 0.98594 | val_0_mse: 0.98967 |  0:21:08s
epoch 8  | loss: 0.9858  | val_0_mse: 0.99058 |  0:23:49s
epoch 9  | loss: 0.98506 | val_0_mse: 0.98855 |  0:26:29s
epoch 10 | loss: 0.98473 | val_0_mse: 0.98899 |  0:29:19s
epoch 11 | loss: 0.98426 | val_0_mse: 0.98775 |  0:32:05s
epoch 12 | loss: 0.98417 | val_0_mse: 0.98779 |  0:34:40s
epoch 13 | loss: 0.98327 | val_0_mse: 0.98879 |  0:37:25s
epoch 14 | loss: 0.9833  | val_0_mse: 0.9871  |  0:40:00s
epoch 15 | loss: 0.98304 | val_0_mse: 0.98784 |  0:44:27s
epoch 16 | loss: 0.98241 | val_0_mse: 0.98724 |  0:48:30s
epoch 17 | loss: 0.98222 | val_0_mse: 0.98849 |  0:52:23s
epoch 18 | loss: 0.98194 | val_0_mse: 0.98602 |  0:56:38s
epoch 19 | loss: 0.98206 | val_0_mse: 0.98557 |  1:01:30s
epoch 20 | loss: 0.98123 | val_0_mse: 0.98592 |  1:06:31s
epoch 21 | loss: 0.98038 | val_0_mse: 0.98547 |  1:10:54s
epoch 22 | loss: 0.97999 | val_0_mse: 0.99245 |  1:15:11s
epoch 23 | loss: 0.97945 | val_0_mse: 0.98462 |  1:19:09s
epoch 24 | loss: 0.97909 | val_0_mse: 0.98395 |  1:23:11s
epoch 25 | loss: 0.97867 | val_0_mse: 0.98432 |  1:27:27s
epoch 26 | loss: 0.97825 | val_0_mse: 0.98472 |  1:31:55s
epoch 27 | loss: 0.97796 | val_0_mse: 0.98359 |  1:36:03s
epoch 28 | loss: 0.97756 | val_0_mse: 0.98389 |  1:40:29s
epoch 29 | loss: 0.9771  | val_0_mse: 0.98402 |  1:44:06s
epoch 30 | loss: 0.97731 | val_0_mse: 0.98474 |  1:47:54s
epoch 31 | loss: 0.9771  | val_0_mse: 0.9834  |  1:51:35s
epoch 32 | loss: 0.97626 | val_0_mse: 0.9836  |  1:55:25s
epoch 33 | loss: 0.97596 | val_0_mse: 0.98371 |  1:59:32s
epoch 34 | loss: 0.97575 | val_0_mse: 0.98391 |  2:04:19s
epoch 35 | loss: 0.97538 | val_0_mse: 0.98367 |  2:08:06s
epoch 36 | loss: 0.97678 | val_0_mse: 0.99854 |  2:11:30s
epoch 37 | loss: 0.97521 | val_0_mse: 0.98431 |  2:14:20s
epoch 38 | loss: 0.97473 | val_0_mse: 0.98489 |  2:17:22s
epoch 39 | loss: 0.97435 | val_0_mse: 0.98332 |  2:20:12s
epoch 40 | loss: 0.97421 | val_0_mse: 0.98577 |  2:23:02s
epoch 41 | loss: 0.97399 | val_0_mse: 0.98488 |  2:26:21s
epoch 42 | loss: 0.97369 | val_0_mse: 0.98932 |  2:29:33s
epoch 43 | loss: 0.97339 | val_0_mse: 0.98469 |  2:33:09s
epoch 44 | loss: 0.97316 | val_0_mse: 0.98526 |  2:37:29s
epoch 45 | loss: 0.97301 | val_0_mse: 0.98524 |  2:41:15s
epoch 46 | loss: 0.97529 | val_0_mse: 0.98503 |  2:44:50s
epoch 47 | loss: 0.97487 | val_0_mse: 0.98367 |  2:48:25s
epoch 48 | loss: 0.97323 | val_0_mse: 0.985   |  2:51:40s
epoch 49 | loss: 0.97276 | val_0_mse: 0.98524 |  2:54:19s

Early stopping occurred at epoch 49 with best_epoch = 39 and best_val_0_mse = 0.98332
Best weights from best epoch are automatically used!

    Device used : cuda
    • 收益率55.96%
    • 年化收益率13.36%
    • 基准收益率20.8%
    • 阿尔法0.1
    • 贝塔0.76
    • 夏普比率0.5
    • 胜率0.51
    • 盈亏比1.11
    • 收益波动率25.38%
    • 信息比率0.03
    • 最大回撤28.66%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f01bf287b0bd4412bbcdd443bc487971"}/bigcharts-data-end
    In [2]:
    # 输出predict
predict_df = m20.data_1.read()
predict_df.head()
    Out[2]:
    pred_label instrument date
    4 0.000000 000001.SZA 2017-12-28
    5 0.015592 000001.SZA 2017-12-29
    10 -0.005317 000002.SZA 2017-12-28
    11 -0.005753 000002.SZA 2017-12-29
    16 0.032249 000004.SZA 2017-12-28
    In [4]:
    predict_df.to_csv("tabnet_predict.csv")
    In [ ]: