{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-231:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-238:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-250:options_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-250:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-86:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-86:input_data","from_node_id":"-238:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# 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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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# 本代码由可视化策略环境自动生成 2023年1月14日 10:52
# 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。


import empyrical
import bottleneck as bn

#标准化
def standardize(df, columns):
    def handle(df1,columns):
        for col in columns:
            mean = bn.nanmean(df1[col], axis=0)
            std = bn.nanstd(df1[col], ddof=1, axis=0)
            df1[col] = (df1[col] - mean) / std
        return df1

    df[columns] = df.groupby("date").apply(handle, columns = columns)[columns]
    return df 

#计算函数，用到的列名也需要传入，例如close
#注意这里的df_total是整个数据集没有经过任何的groupby
def cal_fun(df_total, close, open,benchmark_close,benchmark_open):
    def get_label(df):
        #未来5日相对收益
        relative_r = df.close.shift(-5) / df.open.shift(-1) - df.benchmark_close.shift(-5) / df.benchmark_open.shift(-1)
        #每日相对收益
        relative_r_daily = df.close.shift(-1) / df.close - df.benchmark_close.shift(-1) / df.benchmark_close
        #滚动求5日的最大回撤
        max_dd = relative_r_daily.rolling(5).apply(lambda x:abs(empyrical.max_drawdown(x)))
        #偏移成未来5日
        max_dd1 = max_dd.shift(-5)                                           
        df['label'] = relative_r / max_dd1
        return df
    
    df1=df_total.groupby(["instrument"]).apply(get_label)
    df1['label'] = np.where(np.isinf(df1.label),np.nan,df1.label)
    df1 = standardize(df1,['label'])
    return df1['label']

    
m2_user_functions_bigquant_run = {
    'cal_fun':cal_fun
}

# 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>`_

#自定义函数实现
cal_fun(close, open, benchmark_close,benchmark_open)

# 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,
    user_functions=m2_user_functions_bigquant_run
)

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
)

m9 = 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
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

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
)

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

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    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'
)

[2023-01-13 14:43:06.337949] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-01-13 14:43:06.346481] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:43:06.348387] INFO: moduleinvoker: instruments.v2 运行完成[0.010453s].

[2023-01-13 14:43:06.365126] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-01-13 14:43:09.869120] INFO: 自动标注(股票): 加载历史数据: 2647809 行

[2023-01-13 14:43:09.871196] INFO: 自动标注(股票): 开始标注 ..

[2023-01-13 14:48:34.044815] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[327.679687s].

[2023-01-13 14:48:34.054257] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-01-13 14:48:34.062254] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:48:34.064206] INFO: moduleinvoker: input_features.v1 运行完成[0.009971s].

[2023-01-13 14:48:34.081131] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-01-13 14:48:34.087998] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:48:34.089701] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008599s].

[2023-01-13 14:48:34.099198] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-01-13 14:48:34.108268] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:48:34.110406] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011204s].

[2023-01-13 14:48:34.122531] INFO: moduleinvoker: join.v3 开始运行..

[2023-01-13 14:48:43.647818] INFO: join: /y_2017, 行数=0/193398, 耗时=1.280122s

[2023-01-13 14:48:47.004162] INFO: join: /y_2018, 行数=792072/816987, 耗时=3.353905s

[2023-01-13 14:48:50.600010] INFO: join: /y_2019, 行数=867293/884867, 耗时=3.584323s

[2023-01-13 14:48:54.495488] INFO: join: /y_2020, 行数=901015/945961, 耗时=3.886224s

[2023-01-13 14:48:54.675347] INFO: join: 最终行数: 2560380

[2023-01-13 14:48:54.702089] INFO: moduleinvoker: join.v3 运行完成[20.579553s].

[2023-01-13 14:48:54.715389] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-01-13 14:48:55.562622] INFO: dropnan: /y_2017, 0/0

[2023-01-13 14:48:58.876516] INFO: dropnan: /y_2018, 790408/792072

[2023-01-13 14:49:02.780622] INFO: dropnan: /y_2019, 864030/867293

[2023-01-13 14:49:06.875553] INFO: dropnan: /y_2020, 892822/901015

[2023-01-13 14:49:07.064494] INFO: dropnan: 行数: 2547260/2560380

[2023-01-13 14:49:07.083295] INFO: moduleinvoker: dropnan.v1 运行完成[12.3679s].

[2023-01-13 14:49:07.096918] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2023-01-13 14:49:17.462995] INFO: StockRanker: 特征预处理 ..

[2023-01-13 14:49:21.596950] INFO: StockRanker: prepare data: training ..

[2023-01-13 14:49:25.466088] INFO: StockRanker: sort ..

[2023-01-13 14:50:00.752505] INFO: StockRanker训练: 6032e81c 准备训练: 2547260 行数

[2023-01-13 14:50:00.754512] INFO: StockRanker训练: AI模型训练，将在2547260*13=3311.44万数据上对模型训练进行20轮迭代训练。预计将需要10～21分钟。请耐心等待。

[2023-01-13 14:50:00.989974] INFO: StockRanker训练: 正在训练 ..

[2023-01-13 14:50:01.052904] INFO: StockRanker训练: 任务状态: Pending

[2023-01-13 14:50:11.099413] INFO: StockRanker训练: 任务状态: Running

[2023-01-13 14:51:21.461795] INFO: StockRanker训练: 00:01:13.8963291, finished iteration 1

[2023-01-13 14:51:31.507963] INFO: StockRanker训练: 00:01:23.3063624, finished iteration 2

[2023-01-13 14:51:41.569657] INFO: StockRanker训练: 00:01:32.8330672, finished iteration 3

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[2023-01-13 14:52:01.660387] INFO: StockRanker训练: 00:01:52.3005441, finished iteration 5

[2023-01-13 14:52:11.706972] INFO: StockRanker训练: 00:02:03.3735834, finished iteration 6

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[2023-01-13 14:52:51.929834] INFO: StockRanker训练: 00:02:38.4960733, finished iteration 9

[2023-01-13 14:53:02.070017] INFO: StockRanker训练: 00:02:49.8407091, finished iteration 10

[2023-01-13 14:53:12.195133] INFO: StockRanker训练: 00:03:01.8571982, finished iteration 11

[2023-01-13 14:53:22.278187] INFO: StockRanker训练: 00:03:13.7841206, finished iteration 12

[2023-01-13 14:53:32.511440] INFO: StockRanker训练: 00:03:25.4425513, finished iteration 13

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[2023-01-13 14:54:02.848341] INFO: StockRanker训练: 00:03:49.7194581, finished iteration 15

[2023-01-13 14:54:12.908742] INFO: StockRanker训练: 00:04:01.7402489, finished iteration 16

[2023-01-13 14:54:22.953840] INFO: StockRanker训练: 00:04:13.7593589, finished iteration 17

[2023-01-13 14:54:33.025522] INFO: StockRanker训练: 00:04:26.2569376, finished iteration 18

[2023-01-13 14:54:53.127964] INFO: StockRanker训练: 00:04:38.8382253, finished iteration 19

[2023-01-13 14:55:03.181966] INFO: StockRanker训练: 00:04:50.7540539, finished iteration 20

[2023-01-13 14:55:03.183671] INFO: StockRanker训练: 任务状态: Succeeded

[2023-01-13 14:55:03.333952] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[356.237038s].

[2023-01-13 14:55:03.339875] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-01-13 14:55:03.350921] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:55:03.352762] INFO: moduleinvoker: instruments.v2 运行完成[0.01289s].

[2023-01-13 14:55:03.367817] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-01-13 14:55:03.376444] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:55:03.378625] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010833s].

[2023-01-13 14:55:03.386942] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-01-13 14:55:03.399608] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:55:03.401501] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.014568s].

[2023-01-13 14:55:03.416184] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-01-13 14:55:03.492169] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:55:03.494756] INFO: moduleinvoker: dropnan.v1 运行完成[0.078567s].

[2023-01-13 14:55:03.518420] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2023-01-13 14:55:04.294669] INFO: StockRanker预测: /y_2020 ..

[2023-01-13 14:55:06.559301] INFO: StockRanker预测: /y_2021 ..

[2023-01-13 14:55:10.189985] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[6.671587s].

[2023-01-13 14:55:13.917498] INFO: moduleinvoker: backtest.v8 开始运行..

[2023-01-13 14:55:13.931962] INFO: backtest: biglearning backtest:V8.6.3

[2023-01-13 14:55:13.933718] INFO: backtest: product_type:stock by specified

[2023-01-13 14:55:14.010072] INFO: moduleinvoker: cached.v2 开始运行..

[2023-01-13 14:55:14.022630] INFO: moduleinvoker: 命中缓存

[2023-01-13 14:55:14.025167] INFO: moduleinvoker: cached.v2 运行完成[0.015119s].

[2023-01-13 14:55:23.837282] INFO: backtest: algo history_data=DataSource(a929ac626e724fc5a53ed51cf775e582T)

[2023-01-13 14:55:23.839101] INFO: algo: TradingAlgorithm V1.8.9

[2023-01-13 14:55:26.362551] INFO: algo: trading transform...

[2023-01-13 14:55:35.722431] INFO: Performance: Simulated 243 trading days out of 243.

[2023-01-13 14:55:35.724322] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2023-01-13 14:55:35.725832] INFO: Performance: last close: 2021-12-31 15:00:00+00:00

[2023-01-13 14:55:39.775169] INFO: moduleinvoker: backtest.v8 运行完成[25.857673s].

[2023-01-13 14:55:39.776959] INFO: moduleinvoker: trade.v4 运行完成[29.572417s].

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