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In [3]:
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

    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{"name":"data","node_id":"-251"}],"cacheable":true,"seq_num":32,"comment":"","comment_collapsed":true},{"node_id":"-436","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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\ndef bigquant_run(input_1, input_2, input_3, saving_path=None):\n # 示例代码如下。在这里编写您的代码\n from sklearn.model_selection import train_test_split\n # train data \n train_data = input_1.read()\n x_train, x_val, y_train, y_val = train_test_split(train_data[\"x\"], train_data['y'], shuffle=True, random_state=2021)\n \n model = GATModel(d_feat=98, hidden_size=64, num_layers=2, dropout=0.1, base_model=\"GRU\")\n opt = torch.optim.Adam(model.parameters(), lr=1e-3)\n loss = nn.MSELoss()\n model.compile(optimizer=opt, loss=loss, device=\"cuda:0\")\n \n earlystop = EarlyStopping(patience=3)\n model.fit(x_train, y_train, validation_data=(x_val, y_val), batch_size=128, callbacks=[earlystop], epochs=100, verbose=1, num_workers=4)\n \n if saving_path:\n torch.save(model.state_dict(), saving_path)\n \n data_1 = DataSource.write_pickle(model)\n return Outputs(data_1=data_1, data_2=None, data_3=None)","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 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。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{\n \"saving_path\": 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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\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n pred_label = input_1.read_pickle()\n \n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:], 'instrument':df.instrument, 'date':df.date})\n df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])\n return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 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。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-2431"},{"name":"input_2","node_id":"-2431"},{"name":"input_3","node_id":"-2431"}],"output_ports":[{"name":"data_1","node_id":"-2431"},{"name":"data_2","node_id":"-2431"},{"name":"data_3","node_id":"-2431"}],"cacheable":false,"seq_num":41,"comment":"","comment_collapsed":true},{"node_id":"-25911","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":"5","type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"3","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"False","type":"Literal","bound_global_parameter":null},{"name":"window_along_col","value":"instrument","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-25911"},{"name":"features","node_id":"-25911"}],"output_ports":[{"name":"data","node_id":"-25911"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-293","module_id":"BigQuantSpace.fillnan.fillnan-v1","parameters":[{"name":"fill_value","value":"0.0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-293"},{"name":"features","node_id":"-293"}],"output_ports":[{"name":"data","node_id":"-293"}],"cacheable":true,"seq_num":36,"comment":"","comment_collapsed":true},{"node_id":"-2305","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = 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=0.0003, sell_cost=0.0013, 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 = 5\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.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.hold_days # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 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。\ndef bigquant_run(context, data):\n 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test_end_date = i+'-12'+'-31'\n# test_end_date = i+'-12'+'-31'\n parameters = {'m22.start_date':train_start_date,\n 'm22.end_date':train_end_date,\n 'm26.start_date':test_start_date,\n 'm26.end_date':test_end_date,\n }\n \n parameters_list.append({'parameters': parameters})\n print(len(parameters_list), parameters_list)\n\n def run(parameters):\n try:\n print(parameters)\n return g.run(parameters)\n except Exception as e:\n print('ERROR --------', e)\n return None\n \n results = T.parallel_map(run, parameters_list, max_workers=1, remote_run=False, silent=True)\n\n return 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    In [4]:
    # 本代码由可视化策略环境自动生成 2022年11月10日 18:44
# 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


g = T.Graph({

    'm22': 'M.instruments.v2',
    'm22.start_date': '2020-01-01',
    'm22.end_date': '2021-12-31',
    'm22.market': 'CN_STOCK_A',
    'm22.instrument_list': '',

    'm23': 'M.advanced_auto_labeler.v2',
    'm23.instruments': T.Graph.OutputPort('m22.data'),
    'm23.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)
""",
    'm23.start_date': '',
    'm23.end_date': '',
    'm23.benchmark': '000300.SHA',
    'm23.drop_na_label': True,
    'm23.cast_label_int': False,

    'm4': 'M.standardlize.v12',
    'm4.input_1': T.Graph.OutputPort('m23.data'),
    'm4.standard_func': 'ZScoreNorm',
    'm4.columns_input': 'label',

    'm24': 'M.input_features.v1',
    'm24.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',
    'm27.instruments': T.Graph.OutputPort('m22.data'),
    'm27.features': T.Graph.OutputPort('m24.data'),
    'm27.start_date': '',
    'm27.end_date': '',
    'm27.before_start_days': 10,

    'm6': 'M.chinaa_stock_filter.v1',
    'm6.input_data': T.Graph.OutputPort('m27.data'),
    'm6.index_constituent_cond': ['全部'],
    'm6.board_cond': ['全部'],
    'm6.industry_cond': ['全部'],
    'm6.st_cond': ['全部'],
    'm6.delist_cond': ['全部'],
    'm6.output_left_data': False,

    'm28': 'M.derived_feature_extractor.v3',
    'm28.input_data': T.Graph.OutputPort('m6.data'),
    'm28.features': T.Graph.OutputPort('m24.data'),
    'm28.date_col': 'date',
    'm28.instrument_col': 'instrument',
    'm28.drop_na': True,
    'm28.remove_extra_columns': False,

    'm3': 'M.standardlize.v12',
    'm3.input_1': T.Graph.OutputPort('m28.data'),
    'm3.input_2': T.Graph.OutputPort('m24.data'),
    'm3.standard_func': 'ZScoreNorm',
    'm3.columns_input': '',

    'm35': 'M.fillnan.v1',
    'm35.input_data': T.Graph.OutputPort('m3.data'),
    'm35.features': T.Graph.OutputPort('m24.data'),
    'm35.fill_value': '0.0',

    'm25': 'M.join.v3',
    'm25.data1': T.Graph.OutputPort('m4.data'),
    'm25.data2': T.Graph.OutputPort('m35.data'),
    'm25.on': 'date,instrument',
    'm25.how': 'inner',
    'm25.sort': False,

    'm2': 'M.dl_convert_to_bin.v2',
    'm2.input_data': T.Graph.OutputPort('m25.data'),
    'm2.features': T.Graph.OutputPort('m24.data'),
    'm2.window_size': 5,
    'm2.feature_clip': 3,
    'm2.flatten': False,
    'm2.window_along_col': 'instrument',

    'm33': 'M.cached.v3',
    'm33.input_1': T.Graph.OutputPort('m2.data'),
    'm33.run': m33_run_bigquant_run,
    'm33.post_run': m33_post_run_bigquant_run,
    'm33.input_ports': '',
    'm33.params': """{
    "saving_path": "/home/bigquant/work/userlib/gats_model_1109.pt.csv"
}""",
    'm33.output_ports': '',

    'm26': 'M.instruments.v2',
    'm26.start_date': T.live_run_param('trading_date', '2022-01-01'),
    'm26.end_date': T.live_run_param('trading_date', '2022-10-31'),
    'm26.market': 'CN_STOCK_A',
    'm26.instrument_list': '',
    'm26.max_count': 0,

    'm29': 'M.general_feature_extractor.v7',
    'm29.instruments': T.Graph.OutputPort('m26.data'),
    'm29.features': T.Graph.OutputPort('m24.data'),
    'm29.start_date': '',
    'm29.end_date': '',
    'm29.before_start_days': 10,

    'm8': 'M.chinaa_stock_filter.v1',
    'm8.input_data': T.Graph.OutputPort('m29.data'),
    'm8.index_constituent_cond': ['全部'],
    'm8.board_cond': ['全部'],
    'm8.industry_cond': ['全部'],
    'm8.st_cond': ['全部'],
    'm8.delist_cond': ['全部'],
    'm8.output_left_data': False,

    'm30': 'M.derived_feature_extractor.v3',
    'm30.input_data': T.Graph.OutputPort('m8.data'),
    'm30.features': T.Graph.OutputPort('m24.data'),
    'm30.date_col': 'date',
    'm30.instrument_col': 'instrument',
    'm30.drop_na': True,
    'm30.remove_extra_columns': False,

    'm5': 'M.standardlize.v12',
    'm5.input_1': T.Graph.OutputPort('m30.data'),
    'm5.input_2': T.Graph.OutputPort('m24.data'),
    'm5.standard_func': 'ZScoreNorm',
    'm5.columns_input': '',

    'm36': 'M.fillnan.v1',
    'm36.input_data': T.Graph.OutputPort('m5.data'),
    'm36.features': T.Graph.OutputPort('m24.data'),
    'm36.fill_value': '0.0',

    'm32': 'M.dl_convert_to_bin.v2',
    'm32.input_data': T.Graph.OutputPort('m36.data'),
    'm32.features': T.Graph.OutputPort('m24.data'),
    'm32.window_size': 5,
    'm32.feature_clip': 3,
    'm32.flatten': False,
    'm32.window_along_col': 'instrument',

    'm10': 'M.cached.v3',
    'm10.input_1': T.Graph.OutputPort('m33.data_1'),
    'm10.input_2': T.Graph.OutputPort('m32.data'),
    'm10.run': m10_run_bigquant_run,
    'm10.post_run': m10_post_run_bigquant_run,
    'm10.input_ports': '',
    'm10.params': """{
    "saving_path": "/home/bigquant/work/userlib/gats_model_1109.pt.csv"
}""",
    'm10.output_ports': '',

    'm41': 'M.cached.v3',
    'm41.input_1': T.Graph.OutputPort('m10.data_1'),
    'm41.input_2': T.Graph.OutputPort('m30.data'),
    'm41.run': m41_run_bigquant_run,
    'm41.post_run': m41_post_run_bigquant_run,
    'm41.input_ports': '',
    'm41.params': '{}',
    'm41.output_ports': '',
    'm41.m_cached': False,

    'm1': 'M.trade.v4',
    'm1.instruments': T.Graph.OutputPort('m26.data'),
    'm1.options_data': T.Graph.OutputPort('m41.data_1'),
    'm1.start_date': '',
    'm1.end_date': '',
    'm1.initialize': m1_initialize_bigquant_run,
    'm1.handle_data': m1_handle_data_bigquant_run,
    'm1.prepare': m1_prepare_bigquant_run,
    'm1.before_trading_start': m1_before_trading_start_bigquant_run,
    'm1.volume_limit': 0.025,
    'm1.order_price_field_buy': 'open',
    'm1.order_price_field_sell': 'close',
    'm1.capital_base': 1000000,
    'm1.auto_cancel_non_tradable_orders': True,
    'm1.data_frequency': 'daily',
    'm1.price_type': '真实价格',
    'm1.product_type': '股票',
    'm1.plot_charts': True,
    'm1.backtest_only': False,
    'm1.benchmark': '000300.HIX',
})

# g.run({})


def m7_run_bigquant_run(bq_graph, inputs):
    test_years = ['2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022']
    parameters_list = []
     
    for i in test_years:
        train_start_date = str(int(i)-2) + '-01' + '-01'
        train_end_date = str(int(i)-1) + '-12' + '-31'
        test_start_date = i + '-01' + '-01'
        if i == test_years[-1]:
            test_end_date = i+'-10'+'-31'
        else:
            test_end_date  =  i+'-12'+'-31'
#         test_end_date  =  i+'-12'+'-31'
        parameters = {'m22.start_date':train_start_date,
                      'm22.end_date':train_end_date,
                      'm26.start_date':test_start_date,
                      'm26.end_date':test_end_date,
                     }
        
        parameters_list.append({'parameters': parameters})
    print(len(parameters_list), parameters_list)

    def run(parameters):
        try:
            print(parameters)
            return g.run(parameters)
        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


m7 = M.hyper_run.v1(
    run=m7_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

    8 [{'parameters': {'m22.start_date': '2013-01-01', 'm22.end_date': '2014-12-31', 'm26.start_date': '2015-01-01', 'm26.end_date': '2015-12-31'}}, {'parameters': {'m22.start_date': '2014-01-01', 'm22.end_date': '2015-12-31', 'm26.start_date': '2016-01-01', 'm26.end_date': '2016-12-31'}}, {'parameters': {'m22.start_date': '2015-01-01', 'm22.end_date': '2016-12-31', 'm26.start_date': '2017-01-01', 'm26.end_date': '2017-12-31'}}, {'parameters': {'m22.start_date': '2016-01-01', 'm22.end_date': '2017-12-31', 'm26.start_date': '2018-01-01', 'm26.end_date': '2018-12-31'}}, {'parameters': {'m22.start_date': '2017-01-01', 'm22.end_date': '2018-12-31', 'm26.start_date': '2019-01-01', 'm26.end_date': '2019-12-31'}}, {'parameters': {'m22.start_date': '2018-01-01', 'm22.end_date': '2019-12-31', 'm26.start_date': '2020-01-01', 'm26.end_date': '2020-12-31'}}, {'parameters': {'m22.start_date': '2019-01-01', 'm22.end_date': '2020-12-31', 'm26.start_date': '2021-01-01', 'm26.end_date': '2021-12-31'}}, {'parameters': {'m22.start_date': '2020-01-01', 'm22.end_date': '2021-12-31', 'm26.start_date': '2022-01-01', 'm26.end_date': '2022-10-31'}}]

    {'m22.start_date': '2013-01-01', 'm22.end_date': '2014-12-31', 'm26.start_date': '2015-01-01', 'm26.end_date': '2015-12-31'}

    epoch 1   |  train_loss 0.98893|  vall_loss 0.98235|  0:00:51s
epoch 2   |  train_loss 0.98098|  vall_loss 0.97554|  0:01:43s
epoch 3   |  train_loss 0.97530|  vall_loss 0.97126|  0:02:36s
epoch 4   |  train_loss 0.96945|  vall_loss 0.96689|  0:03:29s
epoch 5   |  train_loss 0.96281|  vall_loss 0.96559|  0:04:23s
epoch 6   |  train_loss 0.95637|  vall_loss 0.96433|  0:05:18s
epoch 7   |  train_loss 0.95064|  vall_loss 0.96268|  0:06:10s
epoch 8   |  train_loss 0.94479|  vall_loss 0.96100|  0:07:04s
epoch 9   |  train_loss 0.93921|  vall_loss 0.95782|  0:07:58s
epoch 10  |  train_loss 0.93458|  vall_loss 0.95814|  0:08:51s
epoch 11  |  train_loss 0.92947|  vall_loss 0.96044|  0:09:45s
epoch 12  |  train_loss 0.92644|  vall_loss 0.95881|  0:10:40s
early stop on epoch: 12
    • 收益率294.4%
    • 年化收益率312.55%
    • 基准收益率5.58%
    • 阿尔法3.24
    • 贝塔0.95
    • 夏普比率2.76
    • 胜率0.61
    • 盈亏比0.85
    • 收益波动率56.03%
    • 信息比率0.22
    • 最大回撤51.59%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-76ca59f44ebc4c37bd469f278f76b261"}/bigcharts-data-end
    {'m22.start_date': '2014-01-01', 'm22.end_date': '2015-12-31', 'm26.start_date': '2016-01-01', 'm26.end_date': '2016-12-31'}

    epoch 1   |  train_loss 0.98389|  vall_loss 0.97405|  0:00:57s
epoch 2   |  train_loss 0.97539|  vall_loss 0.97134|  0:01:55s
epoch 3   |  train_loss 0.96781|  vall_loss 0.96206|  0:02:52s
epoch 4   |  train_loss 0.96037|  vall_loss 0.95718|  0:03:50s
epoch 5   |  train_loss 0.95327|  vall_loss 0.95336|  0:04:49s
epoch 6   |  train_loss 0.94595|  vall_loss 0.94930|  0:05:46s
epoch 7   |  train_loss 0.93951|  vall_loss 0.94815|  0:06:44s
epoch 8   |  train_loss 0.93302|  vall_loss 0.94695|  0:07:42s
epoch 9   |  train_loss 0.92677|  vall_loss 0.94212|  0:08:41s
epoch 10  |  train_loss 0.92124|  vall_loss 0.94479|  0:09:37s
epoch 11  |  train_loss 0.91640|  vall_loss 0.94273|  0:10:36s
epoch 12  |  train_loss 0.91255|  vall_loss 0.94032|  0:11:35s
epoch 13  |  train_loss 0.90905|  vall_loss 0.93939|  0:12:34s
epoch 14  |  train_loss 0.90571|  vall_loss 0.94173|  0:13:32s
epoch 15  |  train_loss 0.90268|  vall_loss 0.94735|  0:14:31s
epoch 16  |  train_loss 0.90026|  vall_loss 0.93997|  0:15:30s
early stop on epoch: 16
    • 收益率27.17%
    • 年化收益率28.17%
    • 基准收益率-11.28%
    • 阿尔法0.52
    • 贝塔1.06
    • 夏普比率0.8
    • 胜率0.53
    • 盈亏比0.96
    • 收益波动率35.22%
    • 信息比率0.1
    • 最大回撤20.61%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2ec14431b0e04689841ae4005e0537ef"}/bigcharts-data-end
    {'m22.start_date': '2015-01-01', 'm22.end_date': '2016-12-31', 'm26.start_date': '2017-01-01', 'm26.end_date': '2017-12-31'}

    epoch 1   |  train_loss 0.98114|  vall_loss 0.97319|  0:01:08s
epoch 2   |  train_loss 0.97165|  vall_loss 0.96438|  0:02:18s
epoch 3   |  train_loss 0.96381|  vall_loss 0.96104|  0:03:30s
epoch 4   |  train_loss 0.95712|  vall_loss 0.95553|  0:04:40s
epoch 5   |  train_loss 0.94978|  vall_loss 0.95238|  0:05:49s
epoch 6   |  train_loss 0.94258|  vall_loss 0.94861|  0:06:57s
epoch 7   |  train_loss 0.93592|  vall_loss 0.94873|  0:08:06s
epoch 8   |  train_loss 0.93065|  vall_loss 0.94582|  0:09:17s
epoch 9   |  train_loss 0.92500|  vall_loss 0.94405|  0:10:28s
epoch 10  |  train_loss 0.92008|  vall_loss 0.94377|  0:11:36s
epoch 11  |  train_loss 0.91582|  vall_loss 0.94765|  0:12:45s
epoch 12  |  train_loss 0.91169|  vall_loss 0.94730|  0:13:53s
epoch 13  |  train_loss 0.90844|  vall_loss 0.93983|  0:15:00s
epoch 14  |  train_loss 0.90515|  vall_loss 0.94307|  0:16:11s
epoch 15  |  train_loss 0.90274|  vall_loss 0.94190|  0:17:20s
epoch 16  |  train_loss 0.89984|  vall_loss 0.94311|  0:18:22s
early stop on epoch: 16
    • 收益率3.05%
    • 年化收益率3.15%
    • 基准收益率21.78%
    • 阿尔法-0.11
    • 贝塔0.96
    • 夏普比率0.17
    • 胜率0.49
    • 盈亏比0.89
    • 收益波动率33.72%
    • 信息比率-0.02
    • 最大回撤31.02%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5687f41177124d39bc4778bc74ca03b8"}/bigcharts-data-end
    {'m22.start_date': '2016-01-01', 'm22.end_date': '2017-12-31', 'm26.start_date': '2018-01-01', 'm26.end_date': '2018-12-31'}

    epoch 1   |  train_loss 0.98860|  vall_loss 0.98707|  0:01:09s
epoch 2   |  train_loss 0.98118|  vall_loss 0.98287|  0:02:23s
epoch 3   |  train_loss 0.97473|  vall_loss 0.97880|  0:03:41s
epoch 4   |  train_loss 0.96853|  vall_loss 0.97397|  0:05:01s
epoch 5   |  train_loss 0.96265|  vall_loss 0.97003|  0:06:19s
epoch 6   |  train_loss 0.95730|  vall_loss 0.96664|  0:07:39s
epoch 7   |  train_loss 0.95117|  vall_loss 0.96824|  0:08:57s
epoch 8   |  train_loss 0.94678|  vall_loss 0.96461|  0:10:15s
epoch 9   |  train_loss 0.94218|  vall_loss 0.96308|  0:11:34s
epoch 10  |  train_loss 0.93813|  vall_loss 0.96333|  0:12:49s
epoch 11  |  train_loss 0.93401|  vall_loss 0.96618|  0:14:04s
epoch 12  |  train_loss 0.93061|  vall_loss 0.96237|  0:15:21s
epoch 13  |  train_loss 0.92765|  vall_loss 0.96244|  0:16:40s
epoch 14  |  train_loss 0.92437|  vall_loss 0.96043|  0:18:00s
epoch 15  |  train_loss 0.92248|  vall_loss 0.95950|  0:19:18s
epoch 16  |  train_loss 0.91994|  vall_loss 0.96255|  0:20:30s
epoch 17  |  train_loss 0.91735|  vall_loss 0.96250|  0:21:40s
epoch 18  |  train_loss 0.91632|  vall_loss 0.96090|  0:22:51s
early stop on epoch: 18
    • 收益率7.05%
    • 年化收益率7.32%
    • 基准收益率-25.31%
    • 阿尔法0.47
    • 贝塔0.9
    • 夏普比率0.29
    • 胜率0.49
    • 盈亏比1.03
    • 收益波动率36.26%
    • 信息比率0.08
    • 最大回撤21.15%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-958265b916ff48fd93c1c4f41a1fb5a7"}/bigcharts-data-end
    {'m22.start_date': '2017-01-01', 'm22.end_date': '2018-12-31', 'm26.start_date': '2019-01-01', 'm26.end_date': '2019-12-31'}

    epoch 1   |  train_loss 0.98604|  vall_loss 0.98402|  0:01:21s
epoch 2   |  train_loss 0.97797|  vall_loss 0.97767|  0:02:41s
epoch 3   |  train_loss 0.97248|  vall_loss 0.97377|  0:04:00s
epoch 4   |  train_loss 0.96738|  vall_loss 0.97030|  0:05:20s
epoch 5   |  train_loss 0.96220|  vall_loss 0.96933|  0:06:38s
epoch 6   |  train_loss 0.95785|  vall_loss 0.96846|  0:07:59s
epoch 7   |  train_loss 0.95381|  vall_loss 0.96807|  0:09:16s
epoch 8   |  train_loss 0.95014|  vall_loss 0.96537|  0:10:37s
epoch 9   |  train_loss 0.94683|  vall_loss 0.96854|  0:11:59s
epoch 10  |  train_loss 0.94315|  vall_loss 0.96493|  0:13:15s
epoch 11  |  train_loss 0.94026|  vall_loss 0.96472|  0:14:33s
epoch 12  |  train_loss 0.93798|  vall_loss 0.96292|  0:15:53s
epoch 13  |  train_loss 0.93596|  vall_loss 0.96405|  0:17:13s
epoch 14  |  train_loss 0.93286|  vall_loss 0.96153|  0:18:32s
epoch 15  |  train_loss 0.93071|  vall_loss 0.96489|  0:19:48s
epoch 16  |  train_loss 0.92957|  vall_loss 0.96401|  0:21:08s
epoch 17  |  train_loss 0.92809|  vall_loss 0.96459|  0:22:28s
early stop on epoch: 17
    • 收益率41.18%
    • 年化收益率42.79%
    • 基准收益率36.07%
    • 阿尔法0.09
    • 贝塔0.91
    • 夏普比率1.29
    • 胜率0.5
    • 盈亏比1.25
    • 收益波动率28.63%
    • 信息比率0.02
    • 最大回撤21.0%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e727f9c2564b444e83cb40d736d2b7b8"}/bigcharts-data-end
    {'m22.start_date': '2018-01-01', 'm22.end_date': '2019-12-31', 'm26.start_date': '2020-01-01', 'm26.end_date': '2020-12-31'}

    epoch 1   |  train_loss 0.98626|  vall_loss 0.98235|  0:01:26s
epoch 2   |  train_loss 0.97941|  vall_loss 0.97690|  0:02:54s
epoch 3   |  train_loss 0.97383|  vall_loss 0.97595|  0:04:19s
epoch 4   |  train_loss 0.96913|  vall_loss 0.97210|  0:05:43s
epoch 5   |  train_loss 0.96499|  vall_loss 0.96921|  0:07:10s
epoch 6   |  train_loss 0.96053|  vall_loss 0.96964|  0:08:42s
epoch 7   |  train_loss 0.95625|  vall_loss 0.96849|  0:10:12s
epoch 8   |  train_loss 0.95273|  vall_loss 0.96583|  0:11:40s
epoch 9   |  train_loss 0.94839|  vall_loss 0.96734|  0:13:08s
epoch 10  |  train_loss 0.94552|  vall_loss 0.96566|  0:14:38s
epoch 11  |  train_loss 0.94275|  vall_loss 0.96495|  0:16:09s
epoch 12  |  train_loss 0.94120|  vall_loss 0.96438|  0:17:37s
epoch 13  |  train_loss 0.93854|  vall_loss 0.96689|  0:19:04s
epoch 14  |  train_loss 0.93662|  vall_loss 0.96701|  0:20:33s
epoch 15  |  train_loss 0.93498|  vall_loss 0.96347|  0:22:01s
epoch 16  |  train_loss 0.93344|  vall_loss 0.96499|  0:23:32s
epoch 17  |  train_loss 0.93222|  vall_loss 0.96324|  0:25:01s
epoch 18  |  train_loss 0.93109|  vall_loss 0.96547|  0:26:29s
epoch 19  |  train_loss 0.93012|  vall_loss 0.96337|  0:27:57s
epoch 20  |  train_loss 0.92928|  vall_loss 0.96510|  0:29:25s
early stop on epoch: 20
    • 收益率79.06%
    • 年化收益率82.96%
    • 基准收益率27.21%
    • 阿尔法0.48
    • 贝塔1.04
    • 夏普比率1.67
    • 胜率0.5
    • 盈亏比1.36
    • 收益波动率38.88%
    • 信息比率0.08
    • 最大回撤22.75%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-7b7e870c84e4424f8a7eee8ac46059c7"}/bigcharts-data-end
    {'m22.start_date': '2019-01-01', 'm22.end_date': '2020-12-31', 'm26.start_date': '2021-01-01', 'm26.end_date': '2021-12-31'}

    epoch 1   |  train_loss 0.98917|  vall_loss 0.98904|  0:01:32s
epoch 2   |  train_loss 0.98402|  vall_loss 0.98544|  0:03:07s
epoch 3   |  train_loss 0.97990|  vall_loss 0.98404|  0:04:49s
epoch 4   |  train_loss 0.97618|  vall_loss 0.97960|  0:06:31s
epoch 5   |  train_loss 0.97288|  vall_loss 0.97891|  0:08:14s
epoch 6   |  train_loss 0.96947|  vall_loss 0.97660|  0:09:56s
epoch 7   |  train_loss 0.96627|  vall_loss 0.97526|  0:11:37s
epoch 8   |  train_loss 0.96269|  vall_loss 0.97394|  0:13:17s
epoch 9   |  train_loss 0.96024|  vall_loss 0.97503|  0:14:59s
epoch 10  |  train_loss 0.95705|  vall_loss 0.97345|  0:16:40s
epoch 11  |  train_loss 0.95393|  vall_loss 0.97250|  0:18:25s
epoch 12  |  train_loss 0.95144|  vall_loss 0.97657|  0:20:09s
epoch 13  |  train_loss 0.94917|  vall_loss 0.97144|  0:21:48s
epoch 14  |  train_loss 0.94658|  vall_loss 0.97335|  0:23:26s
epoch 15  |  train_loss 0.94470|  vall_loss 0.97093|  0:25:06s
epoch 16  |  train_loss 0.94337|  vall_loss 0.97345|  0:26:39s
epoch 17  |  train_loss 0.94237|  vall_loss 0.97120|  0:28:09s
epoch 18  |  train_loss 0.94066|  vall_loss 0.97121|  0:29:41s
early stop on epoch: 18
    • 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%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c57338fdaac74d668c4514f6fd346b75"}/bigcharts-data-end
    {'m22.start_date': '2020-01-01', 'm22.end_date': '2021-12-31', 'm26.start_date': '2022-01-01', 'm26.end_date': '2022-10-31'}

    epoch 1   |  train_loss 0.99214|  vall_loss 0.98630|  0:01:48s
epoch 2   |  train_loss 0.98838|  vall_loss 0.98395|  0:03:32s
epoch 3   |  train_loss 0.98492|  vall_loss 0.98220|  0:05:17s
epoch 4   |  train_loss 0.98172|  vall_loss 0.98070|  0:07:02s
epoch 5   |  train_loss 0.97826|  vall_loss 0.97875|  0:08:48s
epoch 6   |  train_loss 0.97515|  vall_loss 0.97768|  0:10:34s
epoch 7   |  train_loss 0.97189|  vall_loss 0.97709|  0:12:21s
epoch 8   |  train_loss 0.96834|  vall_loss 0.97514|  0:14:06s
epoch 9   |  train_loss 0.96608|  vall_loss 0.97492|  0:15:53s
epoch 10  |  train_loss 0.96339|  vall_loss 0.97605|  0:17:39s
epoch 11  |  train_loss 0.96097|  vall_loss 0.97315|  0:19:25s
epoch 12  |  train_loss 0.95872|  vall_loss 0.97279|  0:21:11s
epoch 13  |  train_loss 0.95651|  vall_loss 0.97525|  0:23:00s
epoch 14  |  train_loss 0.95457|  vall_loss 0.97404|  0:24:46s
epoch 15  |  train_loss 0.95347|  vall_loss 0.97531|  0:26:32s
early stop on epoch: 15
    • 收益率-7.77%
    • 年化收益率-9.78%
    • 基准收益率-28.98%
    • 阿尔法0.37
    • 贝塔0.88
    • 夏普比率-0.22
    • 胜率0.45
    • 盈亏比1.16
    • 收益波动率34.18%
    • 信息比率0.08
    • 最大回撤26.29%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4927a14a97b84bff848c28fdf5f5df0c"}/bigcharts-data-end
    In [5]:
    df = pd.DataFrame() 
for i in range(len(m7.result)):
    tmp = m7.result[i]['m1'].raw_perf.read()
    df = df.append(tmp[['returns','benchmark_period_return']])
    
import empyrical

def get_stats(returns, benchmark_period_return):
    return_ratio  = empyrical.cum_returns_final(returns)
    annual_return_ratio  = empyrical.annual_return(returns)
    sharp_ratio = empyrical.sharpe_ratio(returns,0.035/252)
    return_volatility = empyrical.annual_volatility(returns)
    max_drawdown  = empyrical.max_drawdown(returns)
    benchmark_returns = (benchmark_period_return+1)/(benchmark_period_return+1).shift(1)-1
    alpha, beta =empyrical.alpha_beta_aligned(returns, benchmark_returns)
    
    return {
      'return_ratio': return_ratio,
      'annual_return_ratio': annual_return_ratio,
      'beta': beta,
      'alpha': alpha,
      'sharp_ratio': sharp_ratio,
      'return_volatility': return_volatility,
      'max_drawdown': max_drawdown,
      '收益回测比': abs(annual_return_ratio / max_drawdown)
    }
d=get_stats(df['returns'], df['benchmark_period_return'])
df1=pd.DataFrame.from_dict(d,orient='index')
df1.T
    Out[5]:
    return_ratio annual_return_ratio beta alpha sharp_ratio return_volatility max_drawdown 收益回测比
    0 20.693397 0.503003 0.554732 0.619133 1.173869 0.378963 -0.515877 0.975044
    In [6]:
    T.plot((df['returns']+1).cumprod())