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克隆策略

In [1]:

import torch
import torch.nn as nn
from sklearn.model_selection import train_test_split

from bigmodels.models.gats import GATModel
from bigmodels.models.base import BaseModel
from bigmodels.callbacks import EarlyStopping

In [3]:

# 本代码由可视化策略环境自动生成 2022年11月10日 19:01
# 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 m33_run_bigquant_run(input_1, input_2, input_3, saving_path=None):
    # 示例代码如下。在这里编写您的代码
    from sklearn.model_selection import train_test_split
    # train data 
    train_data = input_1.read()
    x_train, x_val, y_train, y_val = train_test_split(train_data["x"], train_data['y'], shuffle=True, random_state=2021)
    
    model = GATModel(d_feat=98, hidden_size=64, num_layers=2, dropout=0.1, base_model="GRU")
    opt = torch.optim.Adam(model.parameters(), lr=1e-3)
    loss = nn.MSELoss()
    model.compile(optimizer=opt, loss=loss, device="cuda:0")
    
    earlystop = EarlyStopping(patience=3)
    model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=128, callbacks=[earlystop], epochs=100, verbose=1, num_workers=4)
    
    if saving_path:
        torch.save(model.state_dict(), saving_path)
    
    data_1 = DataSource.write_pickle(model)
    return Outputs(data_1=data_1, 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 m33_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 m10_run_bigquant_run(input_1, input_2, input_dpath=None):
    # 示例代码如下。在这里编写您的代码
    from sklearn.model_selection import train_test_split
    # test data
    test_data = input_2.read()
    x_test = test_data["x"]
    
    model = input_1.read()
    output = model.predict(x_test)
    data_1 = DataSource.write_pickle(output)
    return Outputs(data_1=data_1, 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 m10_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 m41_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[:], '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 m41_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m1_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.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m1_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.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 m1_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m1_before_trading_start_bigquant_run(context, data):
    pass


m22 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list=''
)

m23 = M.advanced_auto_labeler.v2(
    instruments=m22.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
)

m4 = M.standardlize.v12(
    input_1=m23.data,
    standard_func='ZScoreNorm',
    columns_input='label'
)

m24 = 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)"""
)

m27 = M.general_feature_extractor.v7(
    instruments=m22.data,
    features=m24.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m6 = M.chinaa_stock_filter.v1(
    input_data=m27.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['全部'],
    delist_cond=['全部'],
    output_left_data=False
)

m28 = M.derived_feature_extractor.v3(
    input_data=m6.data,
    features=m24.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m3 = M.standardlize.v12(
    input_1=m28.data,
    input_2=m24.data,
    standard_func='ZScoreNorm',
    columns_input=''
)

m35 = M.fillnan.v1(
    input_data=m3.data,
    features=m24.data,
    fill_value='0.0'
)

m25 = M.join.v3(
    data1=m4.data,
    data2=m35.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m2 = M.dl_convert_to_bin.v2(
    input_data=m25.data,
    features=m24.data,
    window_size=5,
    feature_clip=3,
    flatten=False,
    window_along_col='instrument'
)

m33 = M.cached.v3(
    input_1=m2.data,
    run=m33_run_bigquant_run,
    post_run=m33_post_run_bigquant_run,
    input_ports='',
    params="""{
    "saving_path": "/home/bigquant/work/userlib/gats_model_1109.pt.csv"
}""",
    output_ports=''
)

m26 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2021-12-31'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m29 = M.general_feature_extractor.v7(
    instruments=m26.data,
    features=m24.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m8 = M.chinaa_stock_filter.v1(
    input_data=m29.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['全部'],
    delist_cond=['全部'],
    output_left_data=False
)

m30 = M.derived_feature_extractor.v3(
    input_data=m8.data,
    features=m24.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m5 = M.standardlize.v12(
    input_1=m30.data,
    input_2=m24.data,
    standard_func='ZScoreNorm',
    columns_input=''
)

m36 = M.fillnan.v1(
    input_data=m5.data,
    features=m24.data,
    fill_value='0.0'
)

m32 = M.dl_convert_to_bin.v2(
    input_data=m36.data,
    features=m24.data,
    window_size=5,
    feature_clip=3,
    flatten=False,
    window_along_col='instrument'
)

m10 = M.cached.v3(
    input_1=m33.data_1,
    input_2=m32.data,
    run=m10_run_bigquant_run,
    post_run=m10_post_run_bigquant_run,
    input_ports='',
    params="""{
    "saving_path": "/home/bigquant/work/userlib/gats_model_1109.pt.csv"
}""",
    output_ports=''
)

m41 = M.cached.v3(
    input_1=m10.data_1,
    input_2=m30.data,
    run=m41_run_bigquant_run,
    post_run=m41_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports='',
    m_cached=False
)

m1 = M.trade.v4(
    instruments=m26.data,
    options_data=m41.data_1,
    start_date='',
    end_date='',
    initialize=m1_initialize_bigquant_run,
    handle_data=m1_handle_data_bigquant_run,
    prepare=m1_prepare_bigquant_run,
    before_trading_start=m1_before_trading_start_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'
)

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[2022-11-10 18:57:27.834684] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.022237s].

[2022-11-10 18:57:27.852913] INFO: moduleinvoker: cached.v3 开始运行..

[2022-11-10 18:57:27.868733] INFO: moduleinvoker: 命中缓存

[2022-11-10 18:57:27.871525] INFO: moduleinvoker: cached.v3 运行完成[0.018633s].

[2022-11-10 18:57:27.890161] INFO: moduleinvoker: cached.v3 开始运行..

[2022-11-10 18:57:44.285914] INFO: moduleinvoker: cached.v3 运行完成[16.395786s].

[2022-11-10 18:57:44.539270] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-11-10 18:57:44.569727] INFO: backtest: biglearning backtest:V8.6.3

[2022-11-10 18:57:44.571886] INFO: backtest: product_type:stock by specified

[2022-11-10 18:57:44.859801] INFO: moduleinvoker: cached.v2 开始运行..

[2022-11-10 18:57:44.878957] INFO: moduleinvoker: 命中缓存

[2022-11-10 18:57:44.881532] INFO: moduleinvoker: cached.v2 运行完成[0.021758s].

[2022-11-10 18:57:57.944268] INFO: backtest: algo history_data=DataSource(35b982f92d154635b49d3cd0e204ebdfT)

[2022-11-10 18:57:57.946325] INFO: algo: TradingAlgorithm V1.8.8

[2022-11-10 18:58:01.124176] INFO: algo: trading transform...

[2022-11-10 18:58:05.296296] INFO: algo: handle_splits get splits [dt:2021-05-21 00:00:00+00:00] [asset:Equity(4849 [600176.SHA]), ratio:0.8637632131576538]

[2022-11-10 18:58:05.298662] INFO: Position: position stock handle split[sid:4849, orig_amount:2500, new_amount:2894.0, orig_cost:18.409999849634865, new_cost:15.9019, ratio:0.8637632131576538, last_sale_price:15.470000267028809]

[2022-11-10 18:58:05.300877] INFO: Position: after split: PositionStock(asset:Equity(4849 [600176.SHA]), amount:2894.0, cost_basis:15.9019, last_sale_price:17.910001754760742)

[2022-11-10 18:58:05.302888] INFO: Position: returning cash: 4.8225

[2022-11-10 18:58:05.462139] INFO: algo: handle_splits get splits [dt:2021-05-27 00:00:00+00:00] [asset:Equity(2287 [002455.SZA]), ratio:0.985358715057373]

[2022-11-10 18:58:05.464243] INFO: Position: position stock handle split[sid:2287, orig_amount:9200, new_amount:9336.0, orig_cost:6.889999896301878, new_cost:6.7891, ratio:0.985358715057373, last_sale_price:6.7300004959106445]

[2022-11-10 18:58:05.466092] INFO: Position: after split: PositionStock(asset:Equity(2287 [002455.SZA]), amount:9336.0, cost_basis:6.7891, last_sale_price:6.830000400543213)

[2022-11-10 18:58:05.467917] INFO: Position: returning cash: 4.7198

[2022-11-10 18:58:05.505167] INFO: algo: handle_splits get splits [dt:2021-05-28 00:00:00+00:00] [asset:Equity(5809 [603799.SHA]), ratio:0.9978138208389282]

[2022-11-10 18:58:05.507164] INFO: Position: position stock handle split[sid:5809, orig_amount:2100, new_amount:2104.0, orig_cost:93.82238188649038, new_cost:93.6173, ratio:0.9978138208389282, last_sale_price:91.28998565673828]

[2022-11-10 18:58:05.509018] INFO: Position: after split: PositionStock(asset:Equity(5809 [603799.SHA]), amount:2104.0, cost_basis:93.6173, last_sale_price:91.48999786376953)

[2022-11-10 18:58:05.510710] INFO: Position: returning cash: 54.8685

[2022-11-10 18:58:05.780030] INFO: algo: handle_splits get splits [dt:2021-06-07 00:00:00+00:00] [asset:Equity(2819 [688636.SHA]), ratio:0.9984304308891296]

[2022-11-10 18:58:05.782474] INFO: Position: position stock handle split[sid:2819, orig_amount:400, new_amount:400.0, orig_cost:107.30000426712486, new_cost:107.1316, ratio:0.9984304308891296, last_sale_price:101.80995178222656]

[2022-11-10 18:58:05.784518] INFO: Position: after split: PositionStock(asset:Equity(2819 [688636.SHA]), amount:400.0, cost_basis:107.1316, last_sale_price:101.97000122070312)

[2022-11-10 18:58:05.786296] INFO: Position: returning cash: 64.0196

[2022-11-10 18:58:06.312834] INFO: algo: handle_splits get splits [dt:2021-06-25 00:00:00+00:00] [asset:Equity(3848 [688068.SHA]), ratio:0.9916601777076721]

[2022-11-10 18:58:06.315183] INFO: Position: position stock handle split[sid:3848, orig_amount:800, new_amount:806.0, orig_cost:172.9049913250761, new_cost:171.463, ratio:0.9916601777076721, last_sale_price:178.35008239746094]

[2022-11-10 18:58:06.316848] INFO: Position: after split: PositionStock(asset:Equity(3848 [688068.SHA]), amount:806.0, cost_basis:171.463, last_sale_price:179.85000610351562)

[2022-11-10 18:58:06.318580] INFO: Position: returning cash: 129.8331

[2022-11-10 18:58:06.358536] INFO: algo: handle_splits get splits [dt:2021-06-28 00:00:00+00:00] [asset:Equity(4413 [605117.SHA]), ratio:0.9930909872055054]

[2022-11-10 18:58:06.493812] INFO: algo: handle_splits get splits [dt:2021-07-01 00:00:00+00:00] [asset:Equity(3080 [688518.SHA]), ratio:0.9936071634292603]

[2022-11-10 18:58:06.495971] INFO: Position: position stock handle split[sid:3080, orig_amount:3000, new_amount:3019.0, orig_cost:18.139999680862598, new_cost:18.024, ratio:0.9936071634292603, last_sale_price:18.650007247924805]

[2022-11-10 18:58:06.498069] INFO: Position: after split: PositionStock(asset:Equity(3080 [688518.SHA]), amount:3019.0, cost_basis:18.024, last_sale_price:18.770000457763672)

[2022-11-10 18:58:06.500071] INFO: Position: returning cash: 5.6305

[2022-11-10 18:58:06.593218] INFO: algo: handle_splits get splits [dt:2021-07-06 00:00:00+00:00] [asset:Equity(3899 [688682.SHA]), ratio:0.9951218962669373]

[2022-11-10 18:58:06.595114] INFO: Position: position stock handle split[sid:3899, orig_amount:300, new_amount:301.0, orig_cost:129.80000390371012, new_cost:129.1668, ratio:0.9951218962669373, last_sale_price:163.1999969482422]

[2022-11-10 18:58:06.596683] INFO: Position: after split: PositionStock(asset:Equity(3899 [688682.SHA]), amount:301.0, cost_basis:129.1668, last_sale_price:164.0)

[2022-11-10 18:58:06.598308] INFO: Position: returning cash: 76.8027

[2022-11-10 18:58:06.927394] INFO: algo: handle_splits get splits [dt:2021-07-19 00:00:00+00:00] [asset:Equity(3036 [301002.SZA]), ratio:0.986262321472168]

[2022-11-10 18:58:06.930294] INFO: Position: position stock handle split[sid:3036, orig_amount:600, new_amount:608.0, orig_cost:92.00000041535877, new_cost:90.7361, ratio:0.986262321472168, last_sale_price:89.74000549316406]

[2022-11-10 18:58:06.933268] INFO: Position: after split: PositionStock(asset:Equity(3036 [301002.SZA]), amount:608.0, cost_basis:90.7361, last_sale_price:90.98999786376953)

[2022-11-10 18:58:06.935484] INFO: Position: returning cash: 32.0748

[2022-11-10 18:58:11.342096] INFO: Performance: Simulated 243 trading days out of 243.

[2022-11-10 18:58:11.343890] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2022-11-10 18:58:11.345441] INFO: Performance: last close: 2021-12-31 15:00:00+00:00

[2022-11-10 18:58:15.546473] INFO: moduleinvoker: backtest.v8 运行完成[31.007198s].

[2022-11-10 18:58:15.548703] INFO: moduleinvoker: trade.v4 运行完成[31.228939s].

收益率68.16%
年化收益率71.43%
基准收益率-5.2%
阿尔法0.81
贝塔0.45
夏普比率1.72
胜率0.48
盈亏比1.42
收益波动率32.81%
信息比率0.12
最大回撤14.8%

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