复制链接
克隆策略

optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) median 夏普= 98特征 optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) median 特征变成105,夏普=0.29 optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.37 optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) 0 特征变成105,夏普=0.27 optimizers.Adam(lr=0.00025, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.28 optimizers.Adam(lr=0.00015, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.38,kerel—size=6,drop=0.1,batch=1024 optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.25,kerel—size=6 optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.21,kerel—size=6 optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.4,kerel—size=6,drop=0.2,batch=256 optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.37,kerel—size=2-5,drop=0.2,batch=256 optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.44/0.34,kerel—size=2-4,drop=0.2,batch=256,中证800,去除ST退市 optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.56,kerel—size=2-4,drop=0.2,batch=256,中证800,非退市含ST,去极3 optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.57,kerel—size=2-4,drop=0.2,batch=128,中证800,非退市含ST,去极3 optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.47,kerel—size=2-4,drop=0.2,batch=256,中证800,非退市含ST,去极3 optimizers.Adam(lr=0.0003, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.41,kerel—size=2-4,drop=0.2,batch=256,中证800,非退市含ST,去极3 optimizers.Adam(lr=0.00025, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False) mean 特征变成105,夏普=0.32,kerel—size=2-4,drop=0.2,batch=256,中证800,非退市含ST,去极3

In [1]:
import tensorflow as tf
gpus = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(gpus[0], True)
In [2]:
from tensorflow.keras import optimizers

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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=0.0003, 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 = 0.05\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 pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":"0.025","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"后复权","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000905.SHA","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-141"},{"name":"options_data","node_id":"-141"},{"name":"history_ds","node_id":"-141"},{"name":"benchmark_ds","node_id":"-141"},{"name":"trading_calendar","node_id":"-141"}],"output_ports":[{"name":"raw_perf","node_id":"-141"}],"cacheable":false,"seq_num":19,"comment":"","comment_collapsed":true},{"node_id":"-160","module_id":"BigQuantSpace.dl_layer_input.dl_layer_input-v1","parameters":[{"name":"shape","value":"105,1","type":"Literal","bound_global_parameter":null},{"name":"batch_shape","value":"","type":"Literal","bound_global_parameter":null},{"name":"dtype","value":"float32","type":"Literal","bound_global_parameter":null},{"name":"sparse","value":"False","type":"Literal","bound_global_parameter":null},{"name":"name","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"inputs","node_id":"-160"}],"output_ports":[{"name":"data","node_id":"-160"}],"cacheable":false,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-1540","module_id":"BigQuantSpace.dl_model_predict.dl_model_predict-v1","parameters":[{"name":"batch_size","value":"1024","type":"Literal","bound_global_parameter":null},{"name":"n_gpus","value":"1","type":"Literal","bound_global_parameter":null},{"name":"verbose","value":"2:每个epoch输出一行记录","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"trained_model","node_id":"-1540"},{"name":"input_data","node_id":"-1540"}],"output_ports":[{"name":"data","node_id":"-1540"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-2431","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):\n # 示例代码如下。在这里编写您的代码\n pred_label = input_1.read_pickle()\n df = input_2.read_df()\n df = pd.DataFrame({'pred_label':pred_label[:,0], '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":true,"seq_num":24,"comment":"","comment_collapsed":true},{"node_id":"-773","module_id":"BigQuantSpace.standardlize.standardlize-v8","parameters":[{"name":"columns_input","value":"label","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-773"},{"name":"input_2","node_id":"-773"}],"output_ports":[{"name":"data","node_id":"-773"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-778","module_id":"BigQuantSpace.standardlize.standardlize-v8","parameters":[{"name":"columns_input","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-778"},{"name":"input_2","node_id":"-778"}],"output_ports":[{"name":"data","node_id":"-778"}],"cacheable":true,"seq_num":25,"comment":"","comment_collapsed":true},{"node_id":"-251","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":"1","type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"5","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","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":"-251"},{"name":"features","node_id":"-251"}],"output_ports":[{"name":"data","node_id":"-251"}],"cacheable":true,"seq_num":27,"comment":"","comment_collapsed":true},{"node_id":"-3880","module_id":"BigQuantSpace.dl_model_init.dl_model_init-v1","parameters":[],"input_ports":[{"name":"inputs","node_id":"-3880"},{"name":"outputs","node_id":"-3880"}],"output_ports":[{"name":"data","node_id":"-3880"}],"cacheable":false,"seq_num":34,"comment":"","comment_collapsed":true},{"node_id":"-3895","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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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":"-3895"},{"name":"input_2","node_id":"-3895"},{"name":"input_3","node_id":"-3895"}],"output_ports":[{"name":"data_1","node_id":"-3895"},{"name":"data_2","node_id":"-3895"},{"name":"data_3","node_id":"-3895"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-3984","module_id":"BigQuantSpace.standardlize.standardlize-v8","parameters":[{"name":"columns_input","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-3984"},{"name":"input_2","node_id":"-3984"}],"output_ports":[{"name":"data","node_id":"-3984"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-7618","module_id":"BigQuantSpace.fillnan.fillnan-v1","parameters":[{"name":"fill_value","value":"mean","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-7618"},{"name":"features","node_id":"-7618"}],"output_ports":[{"name":"data","node_id":"-7618"}],"cacheable":true,"seq_num":21,"comment":"","comment_collapsed":true},{"node_id":"-7623","module_id":"BigQuantSpace.fillnan.fillnan-v1","parameters":[{"name":"fill_value","value":"mean","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-7623"},{"name":"features","node_id":"-7623"}],"output_ports":[{"name":"data","node_id":"-7623"}],"cacheable":true,"seq_num":22,"comment":"","comment_collapsed":true},{"node_id":"-6044","module_id":"BigQuantSpace.dl_convert_to_bin.dl_convert_to_bin-v2","parameters":[{"name":"window_size","value":"1","type":"Literal","bound_global_parameter":null},{"name":"feature_clip","value":"5","type":"Literal","bound_global_parameter":null},{"name":"flatten","value":"True","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":"-6044"},{"name":"features","node_id":"-6044"}],"output_ports":[{"name":"data","node_id":"-6044"}],"cacheable":true,"seq_num":26,"comment":"","comment_collapsed":true},{"node_id":"-759","module_id":"BigQuantSpace.dl_model_train.dl_model_train-v1","parameters":[{"name":"optimizer","value":"自定义","type":"Literal","bound_global_parameter":null},{"name":"user_optimizer","value":"optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False)","type":"Literal","bound_global_parameter":null},{"name":"loss","value":"mean_squared_error","type":"Literal","bound_global_parameter":null},{"name":"user_loss","value":"","type":"Literal","bound_global_parameter":null},{"name":"metrics","value":"mse","type":"Literal","bound_global_parameter":null},{"name":"batch_size","value":"512","type":"Literal","bound_global_parameter":null},{"name":"epochs","value":"10000","type":"Literal","bound_global_parameter":null},{"name":"earlystop","value":"from tensorflow.keras.callbacks import EarlyStopping\nbigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=5)","type":"Literal","bound_global_parameter":null},{"name":"custom_objects","value":"# 用户的自定义层需要写到字典中,比如\n# {\n# \"MyLayer\": MyLayer\n# }\nbigquant_run = { \n 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bigquant_run(bq_graph, inputs):\n \n test_years = ['2014','2015','2016','2017','2018','2019','2020','2021']\n# test_years = ['2020','2021']\n parameters_list = []\n \n for i in test_years:\n train_start_date = str(int(i) -3)+'-01'+'-01'\n train_end_date = str(int(i) - 1)+'-12'+'-31'\n test_start_date = i+'-01'+'-01'\n if i == test_years[-1]:\n test_end_date = i+'-10'+'-31'\n else:\n test_end_date = i+'-12'+'-31'\n \n parameters = {'m1.start_date':train_start_date,\n 'm1.end_date':train_end_date,\n 'm9.start_date':test_start_date,\n 'm9.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=4, remote_run=True, silent=False)\n results = T.parallel_map(run, parameters_list, max_workers=1, remote_run=False, 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2021年12月22日 19:14
    # 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 m4_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'], shuffle=False, test_size=0.2)
        data_1 = DataSource.write_pickle({'x': x_train, 'y': y_train})
        data_2 = DataSource.write_pickle({'x': x_val, 'y': y_val})
        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 m4_post_run_bigquant_run(outputs):
        return outputs
    
    from tensorflow.keras.callbacks import EarlyStopping
    m35_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.0001, patience=5)
    # 用户的自定义层需要写到字典中,比如
    # {
    #   "MyLayer": MyLayer
    # }
    m35_custom_objects_bigquant_run = {    
        
    }
    
    # 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 m24_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 m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 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=0.0003, 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 = 0.05
        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.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 m19_prepare_bigquant_run(context):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2017-12-31',
        '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, -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)
    """,
        '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': """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)
    
    delta(close_0, 5)
    delta(low_0, 5)
    delta(open_0, 5)
    delta(high_0, 5)
    delta(turn_0, 5)
    delta(amount_0, 5)
    delta(return_0, 5)""",
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 10,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m3.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': True,
        'm16.remove_extra_columns': False,
    
        'm5': 'M.chinaa_stock_filter.v1',
        'm5.input_data': T.Graph.OutputPort('m16.data'),
        'm5.index_constituent_cond': ['中证800'],
        'm5.board_cond': ['全部'],
        'm5.industry_cond': ['全部'],
        'm5.st_cond': ['全部'],
        'm5.delist_cond': ['非退市'],
        'm5.output_left_data': False,
    
        'm14': 'M.standardlize.v8',
        'm14.input_1': T.Graph.OutputPort('m5.data'),
        'm14.input_2': T.Graph.OutputPort('m3.data'),
        'm14.columns_input': '',
    
        'm10': 'M.winsorize.v6',
        'm10.input_data': T.Graph.OutputPort('m14.data'),
        'm10.features': T.Graph.OutputPort('m3.data'),
        'm10.columns_input': '',
        'm10.median_deviate': 3,
    
        'm21': 'M.fillnan.v1',
        'm21.input_data': T.Graph.OutputPort('m10.data'),
        'm21.features': T.Graph.OutputPort('m3.data'),
        'm21.fill_value': 'mean',
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m13.data'),
        'm7.data2': T.Graph.OutputPort('m21.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm26': 'M.dl_convert_to_bin.v2',
        'm26.input_data': T.Graph.OutputPort('m7.data'),
        'm26.features': T.Graph.OutputPort('m3.data'),
        'm26.window_size': 1,
        'm26.feature_clip': 5,
        'm26.flatten': True,
        'm26.window_along_col': 'instrument',
    
        'm4': 'M.cached.v3',
        'm4.input_1': T.Graph.OutputPort('m26.data'),
        'm4.input_2': T.Graph.OutputPort('m3.data'),
        'm4.run': m4_run_bigquant_run,
        'm4.post_run': m4_post_run_bigquant_run,
        'm4.input_ports': '',
        'm4.params': '{}',
        'm4.output_ports': '',
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2018-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2021-10-31'),
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm17': 'M.general_feature_extractor.v7',
        'm17.instruments': T.Graph.OutputPort('m9.data'),
        'm17.features': T.Graph.OutputPort('m3.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 10,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': True,
        'm18.remove_extra_columns': False,
    
        'm8': 'M.chinaa_stock_filter.v1',
        'm8.input_data': T.Graph.OutputPort('m18.data'),
        'm8.index_constituent_cond': ['中证800'],
        'm8.board_cond': ['全部'],
        'm8.industry_cond': ['全部'],
        'm8.st_cond': ['全部'],
        'm8.delist_cond': ['非退市'],
        'm8.output_left_data': False,
    
        'm25': 'M.standardlize.v8',
        'm25.input_1': T.Graph.OutputPort('m8.data'),
        'm25.input_2': T.Graph.OutputPort('m3.data'),
        'm25.columns_input': '',
    
        'm20': 'M.winsorize.v6',
        'm20.input_data': T.Graph.OutputPort('m25.data'),
        'm20.features': T.Graph.OutputPort('m3.data'),
        'm20.columns_input': '',
        'm20.median_deviate': 3,
    
        'm22': 'M.fillnan.v1',
        'm22.input_data': T.Graph.OutputPort('m20.data'),
        'm22.features': T.Graph.OutputPort('m3.data'),
        'm22.fill_value': 'mean',
    
        'm27': 'M.dl_convert_to_bin.v2',
        'm27.input_data': T.Graph.OutputPort('m22.data'),
        'm27.features': T.Graph.OutputPort('m3.data'),
        'm27.window_size': 1,
        'm27.feature_clip': 5,
        'm27.flatten': True,
        'm27.window_along_col': 'instrument',
    
        'm6': 'M.dl_layer_input.v1',
        'm6.shape': '105,1',
        'm6.batch_shape': '',
        'm6.dtype': 'float32',
        'm6.sparse': False,
        'm6.name': '',
    
        'm37': 'M.dl_layer_batchnormalization.v1',
        'm37.inputs': T.Graph.OutputPort('m6.data'),
        'm37.axis': -1,
        'm37.momentum': 0.99,
        'm37.epsilon': 0.001,
        'm37.center': True,
        'm37.scale': True,
        'm37.beta_initializer': 'Zeros',
        'm37.gamma_initializer': 'Ones',
        'm37.moving_mean_initializer': 'Zeros',
        'm37.moving_variance_initializer': 'Ones',
        'm37.beta_regularizer': 'None',
        'm37.beta_regularizer_l1': 0,
        'm37.beta_regularizer_l2': 0,
        'm37.gamma_regularizer': 'None',
        'm37.gamma_regularizer_l1': 0,
        'm37.gamma_regularizer_l2': 0,
        'm37.beta_constraint': 'None',
        'm37.gamma_constraint': 'None',
        'm37.name': '',
    
        'm43': 'M.dl_layer_conv1d.v1',
        'm43.inputs': T.Graph.OutputPort('m37.data'),
        'm43.filters': 64,
        'm43.kernel_size': '2',
        'm43.strides': '1',
        'm43.padding': 'same',
        'm43.dilation_rate': 1,
        'm43.activation': 'relu',
        'm43.use_bias': True,
        'm43.kernel_initializer': 'glorot_uniform',
        'm43.bias_initializer': 'Zeros',
        'm43.kernel_regularizer': 'None',
        'm43.kernel_regularizer_l1': 0,
        'm43.kernel_regularizer_l2': 0,
        'm43.bias_regularizer': 'None',
        'm43.bias_regularizer_l1': 0,
        'm43.bias_regularizer_l2': 0,
        'm43.activity_regularizer': 'None',
        'm43.activity_regularizer_l1': 0,
        'm43.activity_regularizer_l2': 0,
        'm43.kernel_constraint': 'None',
        'm43.bias_constraint': 'None',
        'm43.name': '',
    
        'm44': 'M.dl_layer_conv1d.v1',
        'm44.inputs': T.Graph.OutputPort('m43.data'),
        'm44.filters': 64,
        'm44.kernel_size': '2',
        'm44.strides': '1',
        'm44.padding': 'same',
        'm44.dilation_rate': 1,
        'm44.activation': 'relu',
        'm44.use_bias': True,
        'm44.kernel_initializer': 'glorot_uniform',
        'm44.bias_initializer': 'Zeros',
        'm44.kernel_regularizer': 'None',
        'm44.kernel_regularizer_l1': 0,
        'm44.kernel_regularizer_l2': 0,
        'm44.bias_regularizer': 'None',
        'm44.bias_regularizer_l1': 0,
        'm44.bias_regularizer_l2': 0,
        'm44.activity_regularizer': 'None',
        'm44.activity_regularizer_l1': 0,
        'm44.activity_regularizer_l2': 0,
        'm44.kernel_constraint': 'None',
        'm44.bias_constraint': 'None',
        'm44.name': '',
    
        'm41': 'M.dl_layer_maxpooling1d.v1',
        'm41.inputs': T.Graph.OutputPort('m44.data'),
        'm41.pool_size': 2,
        'm41.padding': 'valid',
        'm41.name': '',
    
        'm45': 'M.dl_layer_conv1d.v1',
        'm45.inputs': T.Graph.OutputPort('m41.data'),
        'm45.filters': 128,
        'm45.kernel_size': '4',
        'm45.strides': '1',
        'm45.padding': 'same',
        'm45.dilation_rate': 1,
        'm45.activation': 'relu',
        'm45.use_bias': True,
        'm45.kernel_initializer': 'glorot_uniform',
        'm45.bias_initializer': 'Zeros',
        'm45.kernel_regularizer': 'None',
        'm45.kernel_regularizer_l1': 0,
        'm45.kernel_regularizer_l2': 0,
        'm45.bias_regularizer': 'None',
        'm45.bias_regularizer_l1': 0,
        'm45.bias_regularizer_l2': 0,
        'm45.activity_regularizer': 'None',
        'm45.activity_regularizer_l1': 0,
        'm45.activity_regularizer_l2': 0,
        'm45.kernel_constraint': 'None',
        'm45.bias_constraint': 'None',
        'm45.name': '',
    
        'm47': 'M.dl_layer_conv1d.v1',
        'm47.inputs': T.Graph.OutputPort('m45.data'),
        'm47.filters': 128,
        'm47.kernel_size': '4',
        'm47.strides': '1',
        'm47.padding': 'same',
        'm47.dilation_rate': 1,
        'm47.activation': 'relu',
        'm47.use_bias': True,
        'm47.kernel_initializer': 'glorot_uniform',
        'm47.bias_initializer': 'Zeros',
        'm47.kernel_regularizer': 'None',
        'm47.kernel_regularizer_l1': 0,
        'm47.kernel_regularizer_l2': 0,
        'm47.bias_regularizer': 'None',
        'm47.bias_regularizer_l1': 0,
        'm47.bias_regularizer_l2': 0,
        'm47.activity_regularizer': 'None',
        'm47.activity_regularizer_l1': 0,
        'm47.activity_regularizer_l2': 0,
        'm47.kernel_constraint': 'None',
        'm47.bias_constraint': 'None',
        'm47.name': '',
    
        'm48': 'M.dl_layer_batchnormalization.v1',
        'm48.inputs': T.Graph.OutputPort('m47.data'),
        'm48.axis': -1,
        'm48.momentum': 0.99,
        'm48.epsilon': 0.001,
        'm48.center': True,
        'm48.scale': True,
        'm48.beta_initializer': 'Zeros',
        'm48.gamma_initializer': 'Ones',
        'm48.moving_mean_initializer': 'Zeros',
        'm48.moving_variance_initializer': 'Ones',
        'm48.beta_regularizer': 'None',
        'm48.beta_regularizer_l1': 0,
        'm48.beta_regularizer_l2': 0,
        'm48.gamma_regularizer': 'None',
        'm48.gamma_regularizer_l1': 0,
        'm48.gamma_regularizer_l2': 0,
        'm48.beta_constraint': 'None',
        'm48.gamma_constraint': 'None',
        'm48.name': '',
    
        'm50': 'M.dl_layer_conv1d.v1',
        'm50.inputs': T.Graph.OutputPort('m48.data'),
        'm50.filters': 128,
        'm50.kernel_size': '2',
        'm50.strides': '1',
        'm50.padding': 'same',
        'm50.dilation_rate': 1,
        'm50.activation': 'relu',
        'm50.use_bias': True,
        'm50.kernel_initializer': 'glorot_uniform',
        'm50.bias_initializer': 'Zeros',
        'm50.kernel_regularizer': 'None',
        'm50.kernel_regularizer_l1': 0,
        'm50.kernel_regularizer_l2': 0,
        'm50.bias_regularizer': 'None',
        'm50.bias_regularizer_l1': 0,
        'm50.bias_regularizer_l2': 0,
        'm50.activity_regularizer': 'None',
        'm50.activity_regularizer_l1': 0,
        'm50.activity_regularizer_l2': 0,
        'm50.kernel_constraint': 'None',
        'm50.bias_constraint': 'None',
        'm50.name': '',
    
        'm51': 'M.dl_layer_batchnormalization.v1',
        'm51.inputs': T.Graph.OutputPort('m50.data'),
        'm51.axis': -1,
        'm51.momentum': 0.99,
        'm51.epsilon': 0.001,
        'm51.center': True,
        'm51.scale': True,
        'm51.beta_initializer': 'Zeros',
        'm51.gamma_initializer': 'Ones',
        'm51.moving_mean_initializer': 'Zeros',
        'm51.moving_variance_initializer': 'Ones',
        'm51.beta_regularizer': 'None',
        'm51.beta_regularizer_l1': 0,
        'm51.beta_regularizer_l2': 0,
        'm51.gamma_regularizer': 'None',
        'm51.gamma_regularizer_l1': 0,
        'm51.gamma_regularizer_l2': 0,
        'm51.beta_constraint': 'None',
        'm51.gamma_constraint': 'None',
        'm51.name': '',
    
        'm53': 'M.dl_layer_conv1d.v1',
        'm53.inputs': T.Graph.OutputPort('m51.data'),
        'm53.filters': 128,
        'm53.kernel_size': '4',
        'm53.strides': '1',
        'm53.padding': 'same',
        'm53.dilation_rate': 1,
        'm53.activation': 'relu',
        'm53.use_bias': True,
        'm53.kernel_initializer': 'glorot_uniform',
        'm53.bias_initializer': 'Zeros',
        'm53.kernel_regularizer': 'None',
        'm53.kernel_regularizer_l1': 0,
        'm53.kernel_regularizer_l2': 0,
        'm53.bias_regularizer': 'None',
        'm53.bias_regularizer_l1': 0,
        'm53.bias_regularizer_l2': 0,
        'm53.activity_regularizer': 'None',
        'm53.activity_regularizer_l1': 0,
        'm53.activity_regularizer_l2': 0,
        'm53.kernel_constraint': 'None',
        'm53.bias_constraint': 'None',
        'm53.name': '',
    
        'm46': 'M.dl_layer_batchnormalization.v1',
        'm46.inputs': T.Graph.OutputPort('m53.data'),
        'm46.axis': -1,
        'm46.momentum': 0.99,
        'm46.epsilon': 0.001,
        'm46.center': True,
        'm46.scale': True,
        'm46.beta_initializer': 'Zeros',
        'm46.gamma_initializer': 'Ones',
        'm46.moving_mean_initializer': 'Zeros',
        'm46.moving_variance_initializer': 'Ones',
        'm46.beta_regularizer': 'None',
        'm46.beta_regularizer_l1': 0,
        'm46.beta_regularizer_l2': 0,
        'm46.gamma_regularizer': 'None',
        'm46.gamma_regularizer_l1': 0,
        'm46.gamma_regularizer_l2': 0,
        'm46.beta_constraint': 'None',
        'm46.gamma_constraint': 'None',
        'm46.name': '',
    
        'm49': 'M.dl_layer_conv1d.v1',
        'm49.inputs': T.Graph.OutputPort('m46.data'),
        'm49.filters': 128,
        'm49.kernel_size': '2',
        'm49.strides': '1',
        'm49.padding': 'same',
        'm49.dilation_rate': 1,
        'm49.activation': 'relu',
        'm49.use_bias': True,
        'm49.kernel_initializer': 'glorot_uniform',
        'm49.bias_initializer': 'Zeros',
        'm49.kernel_regularizer': 'None',
        'm49.kernel_regularizer_l1': 0,
        'm49.kernel_regularizer_l2': 0,
        'm49.bias_regularizer': 'None',
        'm49.bias_regularizer_l1': 0,
        'm49.bias_regularizer_l2': 0,
        'm49.activity_regularizer': 'None',
        'm49.activity_regularizer_l1': 0,
        'm49.activity_regularizer_l2': 0,
        'm49.kernel_constraint': 'None',
        'm49.bias_constraint': 'None',
        'm49.name': '',
    
        'm62': 'M.dl_layer_add.v1',
        'm62.input1': T.Graph.OutputPort('m49.data'),
        'm62.input2': T.Graph.OutputPort('m47.data'),
        'm62.name': '',
    
        'm38': 'M.dl_layer_globalmaxpooling1d.v1',
        'm38.inputs': T.Graph.OutputPort('m62.data'),
        'm38.name': '',
    
        'm61': 'M.dl_layer_dropout.v1',
        'm61.inputs': T.Graph.OutputPort('m38.data'),
        'm61.rate': 0.15,
        'm61.noise_shape': '',
        'm61.name': '',
    
        'm57': 'M.dl_layer_dense.v1',
        'm57.inputs': T.Graph.OutputPort('m61.data'),
        'm57.units': 1,
        'm57.activation': 'linear',
        'm57.use_bias': True,
        'm57.kernel_initializer': 'glorot_uniform',
        'm57.bias_initializer': 'Zeros',
        'm57.kernel_regularizer': 'None',
        'm57.kernel_regularizer_l1': 0,
        'm57.kernel_regularizer_l2': 0,
        'm57.bias_regularizer': 'None',
        'm57.bias_regularizer_l1': 0,
        'm57.bias_regularizer_l2': 0,
        'm57.activity_regularizer': 'None',
        'm57.activity_regularizer_l1': 0,
        'm57.activity_regularizer_l2': 0,
        'm57.kernel_constraint': 'None',
        'm57.bias_constraint': 'None',
        'm57.name': '',
    
        'm34': 'M.dl_model_init.v1',
        'm34.inputs': T.Graph.OutputPort('m6.data'),
        'm34.outputs': T.Graph.OutputPort('m57.data'),
    
        'm35': 'M.dl_model_train.v1',
        'm35.input_model': T.Graph.OutputPort('m34.data'),
        'm35.training_data': T.Graph.OutputPort('m4.data_1'),
        'm35.validation_data': T.Graph.OutputPort('m4.data_2'),
        'm35.optimizer': '自定义',
        'm35.user_optimizer': optimizers.Adam(lr=0.0002, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00, amsgrad=False),
        'm35.loss': 'mean_squared_error',
        'm35.metrics': 'mse',
        'm35.batch_size': 512,
        'm35.epochs': 10000,
        'm35.earlystop': m35_earlystop_bigquant_run,
        'm35.custom_objects': m35_custom_objects_bigquant_run,
        'm35.n_gpus': 1,
        'm35.verbose': '2:每个epoch输出一行记录',
        'm35.m_cached': False,
    
        'm11': 'M.dl_model_predict.v1',
        'm11.trained_model': T.Graph.OutputPort('m35.data'),
        'm11.input_data': T.Graph.OutputPort('m27.data'),
        'm11.batch_size': 1024,
        'm11.n_gpus': 1,
        'm11.verbose': '2:每个epoch输出一行记录',
    
        'm24': 'M.cached.v3',
        'm24.input_1': T.Graph.OutputPort('m11.data'),
        'm24.input_2': T.Graph.OutputPort('m22.data'),
        'm24.run': m24_run_bigquant_run,
        'm24.post_run': m24_post_run_bigquant_run,
        'm24.input_ports': '',
        'm24.params': '{}',
        'm24.output_ports': '',
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m24.data_1'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.volume_limit': 0.025,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 1000000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '后复权',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000905.SHA',
    })
    
    # g.run({})
    
    
    def m12_run_bigquant_run(bq_graph, inputs):
         
        test_years = ['2014','2015','2016','2017','2018','2019','2020','2021']
    #     test_years = ['2020','2021']
        parameters_list = []
         
        for i in test_years:
            train_start_date =  str(int(i) -3)+'-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'
            
            parameters = {'m1.start_date':train_start_date,
                          'm1.end_date':train_end_date,
                          'm9.start_date':test_start_date,
                          'm9.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=4, remote_run=True, silent=False)
        results = T.parallel_map(run, parameters_list, max_workers=1, remote_run=False, silent=False)
    
        return results
    
    
    m12 = M.hyper_run.v1(
        run=m12_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    8 [{'parameters': {'m1.start_date': '2011-01-01', 'm1.end_date': '2013-12-31', 'm9.start_date': '2014-01-01', 'm9.end_date': '2014-12-31'}}, {'parameters': {'m1.start_date': '2012-01-01', 'm1.end_date': '2014-12-31', 'm9.start_date': '2015-01-01', 'm9.end_date': '2015-12-31'}}, {'parameters': {'m1.start_date': '2013-01-01', 'm1.end_date': '2015-12-31', 'm9.start_date': '2016-01-01', 'm9.end_date': '2016-12-31'}}, {'parameters': {'m1.start_date': '2014-01-01', 'm1.end_date': '2016-12-31', 'm9.start_date': '2017-01-01', 'm9.end_date': '2017-12-31'}}, {'parameters': {'m1.start_date': '2015-01-01', 'm1.end_date': '2017-12-31', 'm9.start_date': '2018-01-01', 'm9.end_date': '2018-12-31'}}, {'parameters': {'m1.start_date': '2016-01-01', 'm1.end_date': '2018-12-31', 'm9.start_date': '2019-01-01', 'm9.end_date': '2019-12-31'}}, {'parameters': {'m1.start_date': '2017-01-01', 'm1.end_date': '2019-12-31', 'm9.start_date': '2020-01-01', 'm9.end_date': '2020-12-31'}}, {'parameters': {'m1.start_date': '2018-01-01', 'm1.end_date': '2020-12-31', 'm9.start_date': '2021-01-01', 'm9.end_date': '2021-10-31'}}]
    
    {'m1.start_date': '2011-01-01', 'm1.end_date': '2013-12-31', 'm9.start_date': '2014-01-01', 'm9.end_date': '2014-12-31'}
    
    Epoch 1/10000
    868/868 - 38s - loss: 1.2617 - mse: 1.2617 - val_loss: 0.8824 - val_mse: 0.8824
    Epoch 2/10000
    868/868 - 20s - loss: 0.8887 - mse: 0.8887 - val_loss: 0.8798 - val_mse: 0.8798
    Epoch 3/10000
    868/868 - 20s - loss: 0.8702 - mse: 0.8702 - val_loss: 0.8833 - val_mse: 0.8833
    Epoch 4/10000
    868/868 - 20s - loss: 0.8644 - mse: 0.8644 - val_loss: 0.8830 - val_mse: 0.8830
    Epoch 5/10000
    868/868 - 20s - loss: 0.8619 - mse: 0.8619 - val_loss: 0.8815 - val_mse: 0.8815
    Epoch 6/10000
    868/868 - 20s - loss: 0.8602 - mse: 0.8602 - val_loss: 0.8916 - val_mse: 0.8916
    Epoch 7/10000
    868/868 - 164s - loss: 0.8588 - mse: 0.8588 - val_loss: 0.8769 - val_mse: 0.8769
    Epoch 8/10000
    868/868 - 90s - loss: 0.8576 - mse: 0.8576 - val_loss: 0.8855 - val_mse: 0.8855
    Epoch 9/10000
    868/868 - 35s - loss: 0.8571 - mse: 0.8571 - val_loss: 0.8789 - val_mse: 0.8789
    Epoch 10/10000
    868/868 - 151s - loss: 0.8558 - mse: 0.8558 - val_loss: 0.8799 - val_mse: 0.8799
    Epoch 11/10000
    868/868 - 90s - loss: 0.8551 - mse: 0.8551 - val_loss: 0.8762 - val_mse: 0.8762
    Epoch 12/10000
    868/868 - 20s - loss: 0.8539 - mse: 0.8539 - val_loss: 0.8800 - val_mse: 0.8800
    Epoch 13/10000
    868/868 - 20s - loss: 0.8532 - mse: 0.8532 - val_loss: 0.8753 - val_mse: 0.8753
    Epoch 14/10000
    868/868 - 20s - loss: 0.8518 - mse: 0.8518 - val_loss: 0.8764 - val_mse: 0.8764
    Epoch 15/10000
    868/868 - 20s - loss: 0.8507 - mse: 0.8507 - val_loss: 0.8806 - val_mse: 0.8806
    Epoch 16/10000
    868/868 - 20s - loss: 0.8492 - mse: 0.8492 - val_loss: 0.8742 - val_mse: 0.8742
    Epoch 17/10000
    868/868 - 20s - loss: 0.8477 - mse: 0.8477 - val_loss: 0.8815 - val_mse: 0.8815
    Epoch 18/10000
    868/868 - 20s - loss: 0.8466 - mse: 0.8466 - val_loss: 0.8775 - val_mse: 0.8775
    Epoch 19/10000
    868/868 - 20s - loss: 0.8448 - mse: 0.8448 - val_loss: 0.8784 - val_mse: 0.8784
    Epoch 20/10000
    868/868 - 20s - loss: 0.8426 - mse: 0.8426 - val_loss: 0.8764 - val_mse: 0.8764
    Epoch 21/10000
    868/868 - 20s - loss: 0.8408 - mse: 0.8408 - val_loss: 0.8779 - val_mse: 0.8779
    
    184/184 - 2s
    DataSource(9ac72664c3e54268967e510754b0df5dT)
    
    • 收益率87.4%
    • 年化收益率90.8%
    • 基准收益率39.01%
    • 阿尔法0.36
    • 贝塔1.02
    • 夏普比率3.05
    • 胜率0.59
    • 盈亏比1.47
    • 收益波动率20.99%
    • 信息比率0.3
    • 最大回撤8.74%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c2412f36819e4584aae8c5810a7b9717"}/bigcharts-data-end
    {'m1.start_date': '2012-01-01', 'm1.end_date': '2014-12-31', 'm9.start_date': '2015-01-01', 'm9.end_date': '2015-12-31'}
    
    Epoch 1/10000
    868/868 - 26s - loss: 1.2537 - mse: 1.2537 - val_loss: 0.8574 - val_mse: 0.8574
    Epoch 2/10000
    868/868 - 20s - loss: 0.8890 - mse: 0.8890 - val_loss: 0.8452 - val_mse: 0.8452
    Epoch 3/10000
    868/868 - 20s - loss: 0.8718 - mse: 0.8718 - val_loss: 0.8399 - val_mse: 0.8399
    Epoch 4/10000
    868/868 - 20s - loss: 0.8669 - mse: 0.8669 - val_loss: 0.8383 - val_mse: 0.8383
    Epoch 5/10000
    868/868 - 20s - loss: 0.8637 - mse: 0.8637 - val_loss: 0.8429 - val_mse: 0.8429
    Epoch 6/10000
    868/868 - 20s - loss: 0.8621 - mse: 0.8621 - val_loss: 0.8475 - val_mse: 0.8475
    Epoch 7/10000
    868/868 - 20s - loss: 0.8612 - mse: 0.8612 - val_loss: 0.8385 - val_mse: 0.8385
    Epoch 8/10000
    868/868 - 20s - loss: 0.8601 - mse: 0.8601 - val_loss: 0.8409 - val_mse: 0.8409
    Epoch 9/10000
    868/868 - 20s - loss: 0.8597 - mse: 0.8597 - val_loss: 0.8398 - val_mse: 0.8398
    
    174/174 - 2s
    DataSource(b1d0c730020f4795904bbb8137bec358T)
    
    • 收益率87.06%
    • 年化收益率90.94%
    • 基准收益率43.12%
    • 阿尔法0.29
    • 贝塔1.13
    • 夏普比率1.46
    • 胜率0.62
    • 盈亏比0.85
    • 收益波动率51.66%
    • 信息比率0.16
    • 最大回撤50.2%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-54318571988c44d2963c6258433d2552"}/bigcharts-data-end
    {'m1.start_date': '2013-01-01', 'm1.end_date': '2015-12-31', 'm9.start_date': '2016-01-01', 'm9.end_date': '2016-12-31'}
    
    Epoch 1/10000
    848/848 - 334s - loss: 1.2440 - mse: 1.2440 - val_loss: 0.8354 - val_mse: 0.8354
    Epoch 2/10000
    848/848 - 20s - loss: 0.8632 - mse: 0.8632 - val_loss: 0.8370 - val_mse: 0.8370
    Epoch 3/10000
    848/848 - 20s - loss: 0.8449 - mse: 0.8449 - val_loss: 0.8338 - val_mse: 0.8338
    Epoch 4/10000
    848/848 - 20s - loss: 0.8395 - mse: 0.8395 - val_loss: 0.8303 - val_mse: 0.8303
    Epoch 5/10000
    848/848 - 20s - loss: 0.8364 - mse: 0.8364 - val_loss: 0.8332 - val_mse: 0.8332
    Epoch 6/10000
    848/848 - 20s - loss: 0.8347 - mse: 0.8347 - val_loss: 0.8337 - val_mse: 0.8337
    Epoch 7/10000
    848/848 - 20s - loss: 0.8334 - mse: 0.8334 - val_loss: 0.8259 - val_mse: 0.8259
    Epoch 8/10000
    848/848 - 20s - loss: 0.8326 - mse: 0.8326 - val_loss: 0.8277 - val_mse: 0.8277
    Epoch 9/10000
    848/848 - 20s - loss: 0.8321 - mse: 0.8321 - val_loss: 0.8276 - val_mse: 0.8276
    Epoch 10/10000
    848/848 - 20s - loss: 0.8314 - mse: 0.8314 - val_loss: 0.8278 - val_mse: 0.8278
    Epoch 11/10000
    848/848 - 20s - loss: 0.8303 - mse: 0.8303 - val_loss: 0.8392 - val_mse: 0.8392
    Epoch 12/10000
    848/848 - 20s - loss: 0.8293 - mse: 0.8293 - val_loss: 0.8263 - val_mse: 0.8263
    
    183/183 - 2s
    DataSource(a91f28c210a34750893879b34a0fd5a7T)
    
    • 收益率6.35%
    • 年化收益率6.57%
    • 基准收益率-17.78%
    • 阿尔法0.31
    • 贝塔0.99
    • 夏普比率0.27
    • 胜率0.55
    • 盈亏比0.94
    • 收益波动率32.72%
    • 信息比率0.13
    • 最大回撤20.02%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-7a64af9187384a7fbe5df11753061c7b"}/bigcharts-data-end
    {'m1.start_date': '2014-01-01', 'm1.end_date': '2016-12-31', 'm9.start_date': '2017-01-01', 'm9.end_date': '2017-12-31'}
    
    Epoch 1/10000
    847/847 - 26s - loss: 1.2176 - mse: 1.2176 - val_loss: 0.6702 - val_mse: 0.6702
    Epoch 2/10000
    847/847 - 20s - loss: 0.8348 - mse: 0.8348 - val_loss: 0.6637 - val_mse: 0.6637
    Epoch 3/10000
    847/847 - 20s - loss: 0.8174 - mse: 0.8174 - val_loss: 0.6626 - val_mse: 0.6626
    Epoch 4/10000
    847/847 - 21s - loss: 0.8110 - mse: 0.8110 - val_loss: 0.6595 - val_mse: 0.6595
    Epoch 5/10000
    847/847 - 21s - loss: 0.8083 - mse: 0.8083 - val_loss: 0.6606 - val_mse: 0.6606
    Epoch 6/10000
    847/847 - 21s - loss: 0.8064 - mse: 0.8064 - val_loss: 0.6596 - val_mse: 0.6596
    Epoch 7/10000
    847/847 - 20s - loss: 0.8049 - mse: 0.8049 - val_loss: 0.6585 - val_mse: 0.6585
    Epoch 8/10000
    847/847 - 21s - loss: 0.8035 - mse: 0.8035 - val_loss: 0.6588 - val_mse: 0.6588
    Epoch 9/10000
    847/847 - 20s - loss: 0.8018 - mse: 0.8018 - val_loss: 0.6591 - val_mse: 0.6591
    Epoch 10/10000
    847/847 - 21s - loss: 0.8005 - mse: 0.8005 - val_loss: 0.6583 - val_mse: 0.6583
    Epoch 11/10000
    847/847 - 20s - loss: 0.7991 - mse: 0.7991 - val_loss: 0.6610 - val_mse: 0.6610
    Epoch 12/10000
    847/847 - 21s - loss: 0.7972 - mse: 0.7972 - val_loss: 0.6612 - val_mse: 0.6612
    Epoch 13/10000
    847/847 - 21s - loss: 0.7960 - mse: 0.7960 - val_loss: 0.6570 - val_mse: 0.6570
    Epoch 14/10000
    847/847 - 21s - loss: 0.7936 - mse: 0.7936 - val_loss: 0.6598 - val_mse: 0.6598
    Epoch 15/10000
    847/847 - 21s - loss: 0.7917 - mse: 0.7917 - val_loss: 0.6590 - val_mse: 0.6590
    Epoch 16/10000
    847/847 - 21s - loss: 0.7898 - mse: 0.7898 - val_loss: 0.6614 - val_mse: 0.6614
    Epoch 17/10000
    847/847 - 21s - loss: 0.7875 - mse: 0.7875 - val_loss: 0.6616 - val_mse: 0.6616
    Epoch 18/10000
    847/847 - 21s - loss: 0.7852 - mse: 0.7852 - val_loss: 0.6626 - val_mse: 0.6626
    
    182/182 - 3s
    DataSource(3e9168e4624844fd952b623c7b334973T)
    
    • 收益率16.52%
    • 年化收益率17.1%
    • 基准收益率-0.2%
    • 阿尔法0.18
    • 贝塔1.03
    • 夏普比率0.87
    • 胜率0.53
    • 盈亏比1.16
    • 收益波动率16.25%
    • 信息比率0.19
    • 最大回撤8.63%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-55133178b1aa4acdb4343045f932aa17"}/bigcharts-data-end
    {'m1.start_date': '2015-01-01', 'm1.end_date': '2017-12-31', 'm9.start_date': '2018-01-01', 'm9.end_date': '2018-12-31'}
    
    Epoch 1/10000
    844/844 - 334s - loss: 1.1682 - mse: 1.1682 - val_loss: 0.7852 - val_mse: 0.7852
    Epoch 2/10000
    844/844 - 19s - loss: 0.7890 - mse: 0.7890 - val_loss: 0.7722 - val_mse: 0.7722
    Epoch 3/10000
    844/844 - 19s - loss: 0.7729 - mse: 0.7729 - val_loss: 0.7844 - val_mse: 0.7844
    Epoch 4/10000
    844/844 - 19s - loss: 0.7672 - mse: 0.7672 - val_loss: 0.7698 - val_mse: 0.7698
    Epoch 5/10000
    844/844 - 19s - loss: 0.7643 - mse: 0.7643 - val_loss: 0.7830 - val_mse: 0.7830
    Epoch 6/10000
    844/844 - 20s - loss: 0.7620 - mse: 0.7620 - val_loss: 0.7851 - val_mse: 0.7851
    Epoch 7/10000
    844/844 - 20s - loss: 0.7609 - mse: 0.7609 - val_loss: 0.7736 - val_mse: 0.7736
    Epoch 8/10000
    844/844 - 20s - loss: 0.7592 - mse: 0.7592 - val_loss: 0.7697 - val_mse: 0.7697
    Epoch 9/10000
    844/844 - 20s - loss: 0.7574 - mse: 0.7574 - val_loss: 0.7826 - val_mse: 0.7826
    
    183/183 - 2s
    DataSource(e2c1da1bd04640499f905e463384abc8T)
    
    • 收益率-22.22%
    • 年化收益率-22.94%
    • 基准收益率-33.32%
    • 阿尔法0.15
    • 贝塔0.96
    • 夏普比率-1.07
    • 胜率0.48
    • 盈亏比0.88
    • 收益波动率24.3%
    • 信息比率0.14
    • 最大回撤30.03%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-eabc160e31a9488d81209bbe22dd0754"}/bigcharts-data-end
    {'m1.start_date': '2016-01-01', 'm1.end_date': '2018-12-31', 'm9.start_date': '2019-01-01', 'm9.end_date': '2019-12-31'}
    
    Epoch 1/10000
    860/860 - 364s - loss: 1.1989 - mse: 1.1989 - val_loss: 0.8017 - val_mse: 0.8017
    Epoch 2/10000
    860/860 - 20s - loss: 0.8153 - mse: 0.8153 - val_loss: 0.7980 - val_mse: 0.7980
    Epoch 3/10000
    860/860 - 20s - loss: 0.7972 - mse: 0.7972 - val_loss: 0.7998 - val_mse: 0.7998
    Epoch 4/10000
    860/860 - 20s - loss: 0.7917 - mse: 0.7917 - val_loss: 0.7945 - val_mse: 0.7945
    Epoch 5/10000
    860/860 - 20s - loss: 0.7890 - mse: 0.7890 - val_loss: 0.7965 - val_mse: 0.7965
    Epoch 6/10000
    860/860 - 20s - loss: 0.7875 - mse: 0.7875 - val_loss: 0.7941 - val_mse: 0.7941
    Epoch 7/10000
    860/860 - 20s - loss: 0.7860 - mse: 0.7860 - val_loss: 0.7944 - val_mse: 0.7944
    Epoch 8/10000
    860/860 - 20s - loss: 0.7850 - mse: 0.7850 - val_loss: 0.7943 - val_mse: 0.7943
    Epoch 9/10000
    860/860 - 20s - loss: 0.7843 - mse: 0.7843 - val_loss: 0.7922 - val_mse: 0.7922
    Epoch 10/10000
    860/860 - 20s - loss: 0.7833 - mse: 0.7833 - val_loss: 0.7924 - val_mse: 0.7924
    Epoch 11/10000
    860/860 - 20s - loss: 0.7822 - mse: 0.7822 - val_loss: 0.7920 - val_mse: 0.7920
    Epoch 12/10000
    860/860 - 20s - loss: 0.7813 - mse: 0.7813 - val_loss: 0.7923 - val_mse: 0.7923
    Epoch 13/10000
    860/860 - 20s - loss: 0.7807 - mse: 0.7807 - val_loss: 0.7915 - val_mse: 0.7915
    Epoch 14/10000
    860/860 - 20s - loss: 0.7796 - mse: 0.7796 - val_loss: 0.7933 - val_mse: 0.7933
    Epoch 15/10000
    860/860 - 20s - loss: 0.7786 - mse: 0.7786 - val_loss: 0.7928 - val_mse: 0.7928
    Epoch 16/10000
    860/860 - 20s - loss: 0.7768 - mse: 0.7768 - val_loss: 0.7925 - val_mse: 0.7925
    Epoch 17/10000
    860/860 - 20s - loss: 0.7756 - mse: 0.7756 - val_loss: 0.7919 - val_mse: 0.7919
    Epoch 18/10000
    860/860 - 20s - loss: 0.7740 - mse: 0.7740 - val_loss: 0.7937 - val_mse: 0.7937
    
    191/191 - 2s
    DataSource(b1031b4cc5e74e29bf207079cdf0074aT)
    
    • 收益率40.58%
    • 年化收益率42.16%
    • 基准收益率26.38%
    • 阿尔法0.15
    • 贝塔0.84
    • 夏普比率1.64
    • 胜率0.52
    • 盈亏比1.38
    • 收益波动率21.0%
    • 信息比率0.08
    • 最大回撤14.69%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6a673292d98a488faf63f58aede9c6b3"}/bigcharts-data-end
    {'m1.start_date': '2017-01-01', 'm1.end_date': '2019-12-31', 'm9.start_date': '2020-01-01', 'm9.end_date': '2020-12-31'}
    
    Epoch 1/10000
    876/876 - 25s - loss: 1.2181 - mse: 1.2181 - val_loss: 0.7709 - val_mse: 0.7709
    Epoch 2/10000
    876/876 - 21s - loss: 0.8644 - mse: 0.8644 - val_loss: 0.7665 - val_mse: 0.7665
    Epoch 3/10000
    876/876 - 21s - loss: 0.8479 - mse: 0.8479 - val_loss: 0.7677 - val_mse: 0.7677
    Epoch 4/10000
    876/876 - 21s - loss: 0.8425 - mse: 0.8425 - val_loss: 0.7623 - val_mse: 0.7623
    Epoch 5/10000
    876/876 - 21s - loss: 0.8396 - mse: 0.8396 - val_loss: 0.7625 - val_mse: 0.7625
    Epoch 6/10000
    876/876 - 21s - loss: 0.8379 - mse: 0.8379 - val_loss: 0.7634 - val_mse: 0.7634
    Epoch 7/10000
    876/876 - 21s - loss: 0.8365 - mse: 0.8365 - val_loss: 0.7629 - val_mse: 0.7629
    Epoch 8/10000
    876/876 - 21s - loss: 0.8354 - mse: 0.8354 - val_loss: 0.7653 - val_mse: 0.7653
    Epoch 9/10000
    876/876 - 21s - loss: 0.8348 - mse: 0.8348 - val_loss: 0.7625 - val_mse: 0.7625
    
    191/191 - 3s
    DataSource(3cbfbdfa0bb44dc9ad6c2b7b3fb52b16T)
    
    • 收益率27.38%
    • 年化收益率28.53%
    • 基准收益率20.87%
    • 阿尔法0.08
    • 贝塔0.86
    • 夏普比率1.02
    • 胜率0.54
    • 盈亏比1.11
    • 收益波动率24.74%
    • 信息比率0.03
    • 最大回撤16.32%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-03b44236cbf740bbb445a91e4a43d13f"}/bigcharts-data-end
    {'m1.start_date': '2018-01-01', 'm1.end_date': '2020-12-31', 'm9.start_date': '2021-01-01', 'm9.end_date': '2021-10-31'}
    
    Epoch 1/10000
    890/890 - 25s - loss: 1.1766 - mse: 1.1766 - val_loss: 0.7595 - val_mse: 0.7595
    Epoch 2/10000
    890/890 - 20s - loss: 0.8475 - mse: 0.8475 - val_loss: 0.7534 - val_mse: 0.7534
    Epoch 3/10000
    890/890 - 21s - loss: 0.8317 - mse: 0.8317 - val_loss: 0.7559 - val_mse: 0.7559
    Epoch 4/10000
    890/890 - 20s - loss: 0.8264 - mse: 0.8264 - val_loss: 0.7513 - val_mse: 0.7513
    Epoch 5/10000
    890/890 - 21s - loss: 0.8236 - mse: 0.8236 - val_loss: 0.7515 - val_mse: 0.7515
    Epoch 6/10000
    890/890 - 21s - loss: 0.8222 - mse: 0.8222 - val_loss: 0.7519 - val_mse: 0.7519
    Epoch 7/10000
    890/890 - 21s - loss: 0.8215 - mse: 0.8215 - val_loss: 0.7599 - val_mse: 0.7599
    Epoch 8/10000
    890/890 - 21s - loss: 0.8206 - mse: 0.8206 - val_loss: 0.7518 - val_mse: 0.7518
    Epoch 9/10000
    890/890 - 21s - loss: 0.8198 - mse: 0.8198 - val_loss: 0.7519 - val_mse: 0.7519
    
    157/157 - 2s
    DataSource(3825f005cee24b619e3dc5321324623bT)
    
    In [ ]:
    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,
        }
    df = pd.DataFrame() 
    for i in range(len(m12.result)):
        tmp = m12.result[i]['m19'].raw_perf.read()
        train_loss = m12.result[i]['m35'].data.read()["history"]["loss"]
        val_loss = m12.result[i]['m35'].data.read()["history"]["val_loss"]
        train_acc = m12.result[i]['m35'].data.read()["history"]["mse"]
        val_acc = m12.result[i]['m35'].data.read()["history"]["val_mse"]
        T.plot(pd.DataFrame({'train':train_loss,'validation':val_loss}), title ='LOSS')
        T.plot(pd.DataFrame({'train':train_acc,'validation':val_acc}), title ='MSE')
        m12.result[i]['m19'].display()
        df = df.append(tmp[['returns','benchmark_period_return']])
    result=get_stats(df['returns'], df['benchmark_period_return'])
    T.plot((df['returns']+1).cumprod())
    print(result)