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In [28]:

# 本代码由可视化策略环境自动生成 2023年2月21日 11:03
# 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 m64_run_bigquant_run(input_1, input_2, input_3, SW_type):
    feature_list = input_1.read()
    new_feature_list = []
    feature_jc_list = []
    for feature in feature_list:
        splitStrindex = feature.find("=")
        feature_key = feature[0:splitStrindex].strip()
        feature_value = feature[splitStrindex:-1].strip()
        feature_jc_list.append(feature_key)
        new_feature_list.append('n_'+feature_key+'=rank('+feature_key+')')
    print('因子个数：{}'.format(len(new_feature_list)))
    data_1 = DataSource.write_pickle(new_feature_list)
    data_2 = DataSource.write_pickle(feature_jc_list)
    return Outputs(data_1=data_1,data_2=data_2)

# 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 m64_post_run_bigquant_run(outputs):
    return outputs


g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2016-01-01',
    'm1.end_date': '2021-01-20',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.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, -2) / 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)
""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': False,

    'm13': 'M.standardlize.v8',
    'm13.input_1': T.Graph.OutputPort('m2.data'),
    'm13.columns_input': 'label',

    'm3': 'M.input_features.v1',
    'm3.features': '',

    'm40': 'M.input_features.v1',
    'm40.features_ds': T.Graph.OutputPort('m3.data'),
    'm40.features': """buy_cond_1 = 1#where(sum(price_limit_status_1==3, 5)>0, 1, 0)
buy_cond_2 = where(volume_0/volume_1>1, 1, 0)
buy_cond_3 = where(return_0>1.05, 1, 0)
buy_cond_4 = where(list_days_0>120, 1, 0)

e1 = mf_net_amount_l_0

#目标变量
y = shift(close_0,-2)/shift(open_0,-1)

#未来两日股票的收益
return_2_day=(shift(close_0, -2)-shift(open_0, -1))/shift(open_0, -1)
#未来三日股票的收益
return_3_day=(shift(close_0, -3)-shift(open_0, -1))/shift(open_0, -1)
""",

    'm15': 'M.general_feature_extractor.v7',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m40.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 121,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m40.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm5': 'M.chinaa_stock_filter.v1',
    'm5.input_data': T.Graph.OutputPort('m16.data'),
    'm5.index_constituent_cond': ['全部'],
    'm5.board_cond': ['上证主板', '深证主板', '创业板'],
    'm5.industry_cond': ['全部'],
    'm5.st_cond': ['正常'],
    'm5.delist_cond': ['非退市'],
    'm5.output_left_data': False,

    'm52': 'M.filter.v3',
    'm52.input_data': T.Graph.OutputPort('m5.data'),
    'm52.expr': 'buy_cond_1==1&buy_cond_2==1&buy_cond_3==1&buy_cond_4==1',
    'm52.output_left_data': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m52.data'),
    'm7.data2': T.Graph.OutputPort('m13.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm14': 'M.input_features.v1',
    'm14.features': '',

    'm41': 'M.features_append.v1',
    'm41.input_1': T.Graph.OutputPort('m3.data'),
    'm41.input_2': T.Graph.OutputPort('m14.data'),

    'm64': 'M.cached.v3',
    'm64.input_1': T.Graph.OutputPort('m41.data_1'),
    'm64.run': m64_run_bigquant_run,
    'm64.post_run': m64_post_run_bigquant_run,
    'm64.input_ports': '',
    'm64.params': """{
    'SW_type':'name_SW2'
}""",
    'm64.output_ports': 'data_1,data_2',

    'm21': 'M.fillnan.v1',
    'm21.input_data': T.Graph.OutputPort('m7.data'),
    'm21.features': T.Graph.OutputPort('m64.data_2'),
    'm21.fill_value': 'mean',
})

# g.run({})



def m8_run_bigquant_run(bq_graph, inputs):
    g = bq_graph
    parameters_list = []
    #原始因子
    features = [
    ]
    add_features = [
        'd1 = turn_0',
        'd2 = turn_5'
    ]
    i = 0
    for feature in add_features:
        parameters = {}
        temp = list(set(features)).copy()
        if isinstance(feature,list)==True:
            for x in feature:
                temp.append(x) 
        else:
            temp.append(feature)
        temp = list(set(temp))
        parameters['m3.features'] = '\n'.join(temp)
        parameters_list.append({'parameters': parameters})
        i+=1
  
    #每组因子进行回测
    def run(parameters):
        try:
            result = g.run(parameters)
            return result
        except Exception as e:
            print('ERROR --------', e)
            return None
    results = T.parallel_map(run, parameters_list, max_workers=1, remote_run=False, silent=True)
       
    return results

m8 = M.hyper_run.v1(
    run=m8_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

[2023-02-21 11:03:17.723552] INFO: AI: 开始并行运算, remote_run=False, workers=1 ..

[2023-02-21 11:03:17.730284] INFO: AI: [ParallelEx(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.

[2023-02-21 11:03:17.749689] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-21 11:03:17.765603] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.767846] INFO: moduleinvoker: instruments.v2 运行完成[0.018167s].

[2023-02-21 11:03:17.773901] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-21 11:03:17.790355] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.794742] INFO: moduleinvoker: input_features.v1 运行完成[0.020853s].

[2023-02-21 11:03:17.824019] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-21 11:03:17.837953] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.840530] INFO: moduleinvoker: input_features.v1 运行完成[0.016505s].

[2023-02-21 11:03:17.854694] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-02-21 11:03:17.861578] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.863427] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.008737s].

[2023-02-21 11:03:17.867490] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-21 11:03:17.884742] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.887937] INFO: moduleinvoker: input_features.v1 运行完成[0.020419s].

[2023-02-21 11:03:17.945886] INFO: moduleinvoker: features_append.v1 开始运行..

[2023-02-21 11:03:17.954638] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.957222] INFO: moduleinvoker: features_append.v1 运行完成[0.011515s].

[2023-02-21 11:03:17.964408] INFO: moduleinvoker: standardlize.v8 开始运行..

[2023-02-21 11:03:17.971734] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:17.975255] INFO: moduleinvoker: standardlize.v8 运行完成[0.010788s].

[2023-02-21 11:03:18.024652] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-02-21 11:03:18.034944] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.039224] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014574s].

[2023-02-21 11:03:18.055287] INFO: moduleinvoker: cached.v3 开始运行..

[2023-02-21 11:03:18.062669] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.064812] INFO: moduleinvoker: cached.v3 运行完成[0.009533s].

[2023-02-21 11:03:18.072481] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-02-21 11:03:18.082180] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.087076] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.014587s].

[2023-02-21 11:03:18.134819] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-02-21 11:03:18.143401] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.146369] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.01157s].

[2023-02-21 11:03:18.154781] INFO: moduleinvoker: filter.v3 开始运行..

[2023-02-21 11:03:18.161962] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.163597] INFO: moduleinvoker: filter.v3 运行完成[0.008819s].

[2023-02-21 11:03:18.173974] INFO: moduleinvoker: join.v3 开始运行..

[2023-02-21 11:03:18.191579] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.198429] INFO: moduleinvoker: join.v3 运行完成[0.024468s].

[2023-02-21 11:03:18.250017] INFO: moduleinvoker: fillnan.v1 开始运行..

[2023-02-21 11:03:18.258084] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.260666] INFO: moduleinvoker: fillnan.v1 运行完成[0.010669s].

[2023-02-21 11:03:18.262626] INFO: AI: [ParallelEx(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.5s remaining:    0.0s

[2023-02-21 11:03:18.269974] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-21 11:03:18.284675] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.288332] INFO: moduleinvoker: instruments.v2 运行完成[0.018352s].

[2023-02-21 11:03:18.301754] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-21 11:03:18.316045] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.322320] INFO: moduleinvoker: input_features.v1 运行完成[0.020573s].

[2023-02-21 11:03:18.341646] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-21 11:03:18.349049] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.350762] INFO: moduleinvoker: input_features.v1 运行完成[0.009161s].

[2023-02-21 11:03:18.359611] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-02-21 11:03:18.372143] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.387258] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.027603s].

[2023-02-21 11:03:18.416082] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-21 11:03:18.435632] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.439639] INFO: moduleinvoker: input_features.v1 运行完成[0.023603s].

[2023-02-21 11:03:18.452416] INFO: moduleinvoker: features_append.v1 开始运行..

[2023-02-21 11:03:18.459692] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.461758] INFO: moduleinvoker: features_append.v1 运行完成[0.009355s].

[2023-02-21 11:03:18.467931] INFO: moduleinvoker: standardlize.v8 开始运行..

[2023-02-21 11:03:18.477234] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.488640] INFO: moduleinvoker: standardlize.v8 运行完成[0.020679s].

[2023-02-21 11:03:18.549176] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-02-21 11:03:18.556120] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.558226] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009066s].

[2023-02-21 11:03:18.571059] INFO: moduleinvoker: cached.v3 开始运行..

[2023-02-21 11:03:18.584254] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.590001] INFO: moduleinvoker: cached.v3 运行完成[0.018935s].

[2023-02-21 11:03:18.643650] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-02-21 11:03:18.654894] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.658386] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.014771s].

[2023-02-21 11:03:18.667983] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-02-21 11:03:18.678142] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.687892] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.019876s].

[2023-02-21 11:03:18.737307] INFO: moduleinvoker: filter.v3 开始运行..

[2023-02-21 11:03:18.748433] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.751350] INFO: moduleinvoker: filter.v3 运行完成[0.014075s].

[2023-02-21 11:03:18.761898] INFO: moduleinvoker: join.v3 开始运行..

[2023-02-21 11:03:18.776703] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.781287] INFO: moduleinvoker: join.v3 运行完成[0.019326s].

[2023-02-21 11:03:18.827639] INFO: moduleinvoker: fillnan.v1 开始运行..

[2023-02-21 11:03:18.838117] INFO: moduleinvoker: 命中缓存

[2023-02-21 11:03:18.841695] INFO: moduleinvoker: fillnan.v1 运行完成[0.014079s].

[2023-02-21 11:03:18.844716] INFO: AI: [ParallelEx(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.1s remaining:    0.0s

[2023-02-21 11:03:18.848390] INFO: AI: [ParallelEx(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.1s finished

[2023-02-21 11:03:18.851357] INFO: moduleinvoker: hyper_run.v1 运行完成[1.128761s].

In [29]:

m8.result

Out[29]:

[{'m1': {'version': 'v3', 'data': DataSource(0fad65c8a5ec45cbaf6d4d13a4eb80d2T)},
  'm2': {'version': 'v3', 'cast_label_int': False, 'data': DataSource(6b048ed358574d6d9714101477a6ce63T), 'plot_label_counts': <bound method plot_label_counts of {...}>},
  'm13': {'version': 'v3', 'data': DataSource(5c413a52256e44a39c56d8c4b0249209T)},
  'm3': {'version': 'v3', 'data': DataSource(33e424ec859143a0af247da4de924fe6T)},
  'm40': {'version': 'v3', 'data': DataSource(45eea5c9ec924c958b2fa707761705c2T)},
  'm15': {'version': 'v3', 'data': DataSource(af2ac54204944968a21654d28c1cb7baT)},
  'm16': {'version': 'v3', 'data': DataSource(9d9c4488cc8344988587d2aacce29e78T)},
  'm5': {'version': 'v3', 'data': DataSource(2b51a703b40a4ac7a89f32ada0a80c85T), 'left_data': None, 'row_count': 4121103},
  'm52': {'version': 'v3', 'data': DataSource(cf65c7a122ee4e8982121c3f117cd89dT), 'left_data': None, 'row_count': 151150},
  'm7': {'version': 'v3', 'data': DataSource(71c13edcff1941c49b71bbbcd103b524T), 'rows': 130473},
  'm14': {'version': 'v3', 'data': DataSource(99f3e32db19c4fa38656b6a75132be9fT)},
  'm41': {'version': 'v3', 'data_1': DataSource(930ffb629cb34a46a1384334be70daa2T)},
  'm64': {'version': 'v3', 'data_1': DataSource(94d2a220af0f4e95a052108a3e2368d7T), 'data_2': DataSource(4a43b40d737a42f08146c980835758abT)},
  'm21': {'version': 'v3', 'data': DataSource(5d2f6058ab464af5b6ab1d75c587ac73T)}},
 {'m1': {'version': 'v3', 'data': DataSource(0fad65c8a5ec45cbaf6d4d13a4eb80d2T)},
  'm2': {'version': 'v3', 'cast_label_int': False, 'data': DataSource(6b048ed358574d6d9714101477a6ce63T), 'plot_label_counts': <bound method plot_label_counts of {...}>},
  'm13': {'version': 'v3', 'data': DataSource(5c413a52256e44a39c56d8c4b0249209T)},
  'm3': {'version': 'v3', 'data': DataSource(6791f679388c4ea7bbe712487738bebcT)},
  'm40': {'version': 'v3', 'data': DataSource(4ade7934dff0496e86ad75cfd8d1f661T)},
  'm15': {'version': 'v3', 'data': DataSource(69f610b2c70542df81b7e278ecf78ae9T)},
  'm16': {'version': 'v3', 'data': DataSource(54cd53ed73124b6ca76163e1ce6295a5T)},
  'm5': {'version': 'v3', 'data': DataSource(1130792c70f140f6bb95ebce694303a1T), 'left_data': None, 'row_count': 4121103},
  'm52': {'version': 'v3', 'data': DataSource(4888afd72c0d4e6cbae9215141ba06f2T), 'left_data': None, 'row_count': 151150},
  'm7': {'version': 'v3', 'data': DataSource(a9984b9a1df145e2a4d941b65f204becT), 'rows': 130473},
  'm14': {'version': 'v3', 'data': DataSource(99f3e32db19c4fa38656b6a75132be9fT)},
  'm41': {'version': 'v3', 'data_1': DataSource(2db88fc07c05473e8abd74bfe693a2adT)},
  'm64': {'version': 'v3', 'data_1': DataSource(5e6432ea53744f11a1309d6ea900dc92T), 'data_2': DataSource(efdbe532ea054222bd0933ea876af763T)},
  'm21': {'version': 'v3', 'data': DataSource(ecc02427550e482f86dc3f0760f9e7adT)}}]