克隆策略

In [8]:

# 本代码由可视化策略环境自动生成 2021年12月3日 14:27
# 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。


# 用户的自定义层需要写到字典中，比如
# {
#   "MyLayer": MyLayer
# }
m5_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 m13_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 = 1
    # 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 = 1
    context.hold_days = 5
# 回测引擎：每日数据处理函数，每天执行一次
def m13_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)
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    # 所拥有的仓位情况
    positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    
     #------------------------------------------止赢模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')
    positions_1 = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stopwin_stock = [] 
    if len(positions_1) > 0:
        for i in positions.keys():
            stock_cost = positions_1[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 赚3元就止赢
            if stock_market_price - stock_cost >= 5: 
                context.order_target_percent(context.symbol(i),0)
                cash_for_sell -= stock_hold_now[i]
                current_stopwin_stock.append(i)
                print('日期：',date,'股票：',i,'出现止盈状况')
    #-------------------------------------------止赢模块END---------------------------------------------
    
    #------------------------------------------止损模块START--------------------------------------------
    
    # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stoploss_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
            last_sale_date = positions[i].last_sale_date   # 上次交易日期
            delta_days = data.current_dt - last_sale_date  
            hold_days = delta_days.days # 持仓天数
            # 建仓以来的最高价
            highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()
            # 确定止损位置
            stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.25
            record('止损位置', stoploss_line)
            # 如果价格下穿止损位置
            if stock_market_price < stoploss_line:
                context.order_target_percent(context.symbol(i),0)
                cash_for_sell -= stock_hold_now[i]
                current_stoploss_stock.append(i)
                print('日期：', date , '股票：', i, '出现止损状况')
    #-------------------------------------------止损模块END--------------------------------------------------


    # 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
    stock_to_sell = []
    stock_to_sell = current_stopwin_stock + current_stoploss_stock
    if not is_staging and cash_for_sell > 0:
        if len(positions) > 0:
            for instrument in positions.keys():
                last_sale_date = positions[instrument].last_sale_date   #上次交易日期
                delta_days = data.current_dt - last_sale_date  
                hold_days = delta_days.days #持仓天数
                # 股票实行t+1制度，必须使持仓天数大于0
                if hold_days > 0:
                    equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
                    instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                        lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
                    # print('rank order for sell %s' % instruments)
                    for instrument1 in instruments:
                        context.order_target(context.symbol(instrument1), 0)
                        cash_for_sell -= positions_1[instrument1]
                        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 - stock_hold_now.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 - stock_hold_now.get(instrument, 0)
        if cash > 0:
            # 获取今天和过去两天的成交量
            volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')
            close_price = data.current(context.symbol(instrument), 'close')  #当收盘价
            high_price = data.current(context.symbol(instrument), 'high')  #当天最高价
            # 冲高回落的股票不能买
            if ((volume_since_buy[2]/volume_since_buy[1] < 2.5) or (high_price/close_price<1.05)) and volume_since_buy[2]/volume_since_buy[0] > 1:
                current_price = data.current(context.symbol(instrument), 'price')
                amount = math.floor(cash / current_price - cash / current_price % 100)
                context.order(context.symbol(instrument), amount)
                return
# 回测引擎：准备数据，只执行一次
def m13_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 m13_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2019-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_

# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(open, -1)-1

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=False
)

m33 = M.standardlize.v9(
    input_1=m2.data,
    standard_func='ZScoreNorm',
    columns_input='label'
)

m3 = M.input_features.v1(
    features="""close_0/mean(close_0,5)
close_0/mean(close_0,10)
close_0/mean(close_0,20)
close_0/open_0
open_0/mean(close_0,5)
open_0/mean(close_0,10)
open_0/mean(close_0,20)
return_5
return_10
avg_amount_0/avg_amount_5
rank_avg_amount_0/rank_avg_amount_5
rank_return_0
rank_return_5
rank_return_0/rank_return_5
pe_ttm_0








""",
    m_cached=False
)

m12 = M.input_features.v1(
    features_ds=m3.data,
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
close_0
high_1
open_0
low_0
st_status_0"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m12.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m12.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m31 = M.standardlize.v9(
    input_1=m16.data,
    input_2=m3.data,
    standard_func='ZScoreNorm',
    columns_input='[]'
)

m7 = M.join.v3(
    data1=m33.data,
    data2=m31.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m19 = M.filter.v3(
    input_data=m7.data,
    expr='st_status_0==0 and low_0>high_1 and close_0>open_0',
    output_left_data=False
)

m26 = M.dl_convert_to_bin.v2(
    input_data=m19.data,
    features=m3.data,
    window_size=1,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2020-01-01'),
    end_date=T.live_run_param('trading_date', '2020-11-19'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m12.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m12.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m34 = M.standardlize.v9(
    input_1=m18.data,
    input_2=m3.data,
    standard_func='ZScoreNorm',
    columns_input='[]'
)

m28 = M.filter.v3(
    input_data=m34.data,
    expr='st_status_0==0 and low_0>high_1+0.02 and close_0>open_0',
    output_left_data=False
)

m27 = M.dl_convert_to_bin.v2(
    input_data=m28.data,
    features=m3.data,
    window_size=1,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m6 = M.dl_layer_input.v1(
    shape='15',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m8 = M.dl_layer_dense.v1(
    inputs=m6.data,
    units=256,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m21 = M.dl_layer_dropout.v1(
    inputs=m8.data,
    rate=0.1,
    noise_shape='',
    name=''
)

m20 = M.dl_layer_dense.v1(
    inputs=m21.data,
    units=128,
    activation='relu',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m22 = M.dl_layer_dropout.v1(
    inputs=m20.data,
    rate=0.1,
    noise_shape='',
    name=''
)

m23 = M.dl_layer_dense.v1(
    inputs=m22.data,
    units=1,
    activation='linear',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='Zeros',
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    bias_regularizer='None',
    bias_regularizer_l1=0,
    bias_regularizer_l2=0,
    activity_regularizer='None',
    activity_regularizer_l1=0,
    activity_regularizer_l2=0,
    kernel_constraint='None',
    bias_constraint='None',
    name=''
)

m4 = M.dl_model_init.v1(
    inputs=m6.data,
    outputs=m23.data
)

m5 = M.dl_model_train.v1(
    input_model=m4.data,
    training_data=m26.data,
    optimizer='Adam',
    loss='mean_squared_error',
    metrics='mse',
    batch_size=1024,
    epochs=1,
    custom_objects=m5_custom_objects_bigquant_run,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录',
    m_cached=False
)

m11 = M.dl_model_predict.v1(
    trained_model=m5.data,
    input_data=m27.data,
    batch_size=1024,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录'
)

m24 = M.cached.v3(
    input_1=m11.data,
    input_2=m28.data,
    run=m24_run_bigquant_run,
    post_run=m24_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m13 = M.trade.v4(
    instruments=m9.data,
    options_data=m24.data_1,
    start_date='',
    end_date='',
    initialize=m13_initialize_bigquant_run,
    handle_data=m13_handle_data_bigquant_run,
    prepare=m13_prepare_bigquant_run,
    before_trading_start=m13_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

m25 = M.metrics_regression.v1(
    predictions=m11.data,
    explained_variance_score=True,
    mean_absolute_error=True,
    mean_squared_error=True,
    mean_squared_log_error=True,
    median_absolute_error=True,
    r2_score=True
)

m29 = M.metrics_classification.v1(
    predictions=m11.data
)

[2021-12-03 14:16:43.091526] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-03 14:16:43.098728] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:16:43.100942] INFO: moduleinvoker: instruments.v2 运行完成[0.00943s].

[2021-12-03 14:16:43.111476] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-12-03 14:16:43.122382] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:16:43.124115] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.012654s].

[2021-12-03 14:16:43.129867] INFO: moduleinvoker: standardlize.v9 开始运行..

[2021-12-03 14:16:43.140164] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:16:43.141863] INFO: moduleinvoker: standardlize.v9 运行完成[0.011988s].

[2021-12-03 14:16:43.146928] INFO: moduleinvoker: input_features.v1 开始运行..

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[2021-12-03 14:16:43.291332] INFO: moduleinvoker: input_features.v1 运行完成[0.066896s].

[2021-12-03 14:16:43.305586] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-03 14:16:43.312586] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:16:43.314569] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008983s].

[2021-12-03 14:16:43.323851] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-12-03 14:16:46.677596] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.006s

[2021-12-03 14:16:48.013341] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 1.334s

[2021-12-03 14:16:49.164235] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 1.149s

[2021-12-03 14:16:50.307257] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.142s

[2021-12-03 14:16:50.311917] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.003s

[2021-12-03 14:16:51.469910] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.157s

[2021-12-03 14:16:52.758599] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.287s

[2021-12-03 14:16:54.112639] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.352s

[2021-12-03 14:16:54.117460] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s

[2021-12-03 14:16:54.121568] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s

[2021-12-03 14:16:56.220830] INFO: derived_feature_extractor: /y_2018, 816987

[2021-12-03 14:16:58.576409] INFO: derived_feature_extractor: /y_2019, 884867

[2021-12-03 14:16:59.666853] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[16.342991s].

[2021-12-03 14:16:59.673242] INFO: moduleinvoker: standardlize.v9 开始运行..

[2021-12-03 14:17:36.470256] INFO: moduleinvoker: standardlize.v9 运行完成[36.796984s].

[2021-12-03 14:17:36.484588] INFO: moduleinvoker: join.v3 开始运行..

[2021-12-03 14:17:45.770664] INFO: join: /data, 行数=1606689/1630286, 耗时=6.753425s

[2021-12-03 14:17:45.900224] INFO: join: 最终行数: 1606689

[2021-12-03 14:17:45.916390] INFO: moduleinvoker: join.v3 运行完成[9.431813s].

[2021-12-03 14:17:45.926188] INFO: moduleinvoker: filter.v3 开始运行..

[2021-12-03 14:17:45.956686] INFO: filter: 使用表达式 st_status_0==0 and low_0>high_1 and close_0>open_0 过滤

[2021-12-03 14:17:47.738095] INFO: filter: 过滤 /data, 20388/0/1606689

[2021-12-03 14:17:47.790664] INFO: moduleinvoker: filter.v3 运行完成[1.864456s].

[2021-12-03 14:17:47.811376] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-12-03 14:17:47.984962] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.173596s].

[2021-12-03 14:17:47.993184] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-03 14:17:48.004288] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:17:48.006025] INFO: moduleinvoker: instruments.v2 运行完成[0.012846s].

[2021-12-03 14:17:48.053213] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-03 14:17:48.065417] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:17:48.067329] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014134s].

[2021-12-03 14:17:48.076850] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-12-03 14:17:49.528268] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s

[2021-12-03 14:17:50.147986] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 0.618s

[2021-12-03 14:17:50.768533] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 0.619s

[2021-12-03 14:17:51.387959] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 0.618s

[2021-12-03 14:17:51.391453] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.002s

[2021-12-03 14:17:51.988205] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 0.596s

[2021-12-03 14:17:52.588315] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 0.598s

[2021-12-03 14:17:53.230244] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 0.640s

[2021-12-03 14:17:53.234446] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s

[2021-12-03 14:17:53.238734] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.002s

[2021-12-03 14:17:54.757610] INFO: derived_feature_extractor: /y_2020, 823415

[2021-12-03 14:17:55.617222] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[7.540354s].

[2021-12-03 14:17:55.624043] INFO: moduleinvoker: standardlize.v9 开始运行..

[2021-12-03 14:18:12.413748] INFO: moduleinvoker: standardlize.v9 运行完成[16.789691s].

[2021-12-03 14:18:12.423774] INFO: moduleinvoker: filter.v3 开始运行..

[2021-12-03 14:18:12.436382] INFO: filter: 使用表达式 st_status_0==0 and low_0>high_1+0.02 and close_0>open_0 过滤

[2021-12-03 14:18:13.140651] INFO: filter: 过滤 /data, 10056/0/745884

[2021-12-03 14:18:13.180948] INFO: moduleinvoker: filter.v3 运行完成[0.757137s].

[2021-12-03 14:18:13.206355] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-12-03 14:18:13.327681] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.121323s].

[2021-12-03 14:18:13.337156] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.001502s].

[2021-12-03 14:18:13.355486] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.010752s].

[2021-12-03 14:18:13.376000] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.004513s].

[2021-12-03 14:18:13.397279] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.013619s].

[2021-12-03 14:18:13.408664] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.003768s].

[2021-12-03 14:18:13.430238] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.010672s].

[2021-12-03 14:18:13.466759] INFO: moduleinvoker: cached.v3 开始运行..

[2021-12-03 14:18:13.482405] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:18:13.484902] INFO: moduleinvoker: cached.v3 运行完成[0.018159s].

[2021-12-03 14:18:13.487941] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.051689s].

[2021-12-03 14:18:13.493603] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2021-12-03 14:18:14.022188] INFO: dl_model_train: 准备训练，训练样本个数：20388，迭代次数：1

[2021-12-03 14:18:14.993129] INFO: dl_model_train: 训练结束，耗时：0.97s

[2021-12-03 14:18:15.034779] INFO: moduleinvoker: dl_model_train.v1 运行完成[1.541161s].

[2021-12-03 14:18:15.040766] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2021-12-03 14:18:15.285735] INFO: moduleinvoker: dl_model_predict.v1 运行完成[0.244959s].

[2021-12-03 14:18:15.306927] INFO: moduleinvoker: cached.v3 开始运行..

[2021-12-03 14:18:15.496241] INFO: moduleinvoker: cached.v3 运行完成[0.189335s].

[2021-12-03 14:18:15.586648] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-12-03 14:18:15.594256] INFO: backtest: biglearning backtest:V8.6.0

[2021-12-03 14:18:15.596155] INFO: backtest: product_type:stock by specified

[2021-12-03 14:18:15.681923] INFO: moduleinvoker: cached.v2 开始运行..

[2021-12-03 14:18:15.697753] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:18:15.700149] INFO: moduleinvoker: cached.v2 运行完成[0.018251s].

[2021-12-03 14:18:17.169359] INFO: algo: TradingAlgorithm V1.8.5

[2021-12-03 14:18:17.646912] INFO: algo: trading transform...

[2021-12-03 14:18:24.659579] INFO: Performance: Simulated 213 trading days out of 213.

[2021-12-03 14:18:24.661592] INFO: Performance: first open: 2020-01-02 09:30:00+00:00

[2021-12-03 14:18:24.663089] INFO: Performance: last close: 2020-11-19 15:00:00+00:00

[2021-12-03 14:18:29.116560] INFO: moduleinvoker: backtest.v8 运行完成[13.529982s].

[2021-12-03 14:18:29.119277] INFO: moduleinvoker: trade.v4 运行完成[13.614852s].

[2021-12-03 14:18:29.160071] ERROR: moduleinvoker: module name: metrics_regression, module version: v1, trackeback: tables.exceptions.HDF5ExtError: HDF5 error back trace

  File "H5F.c", line 509, in H5Fopen
    unable to open file
  File "H5Fint.c", line 1400, in H5F__open
    unable to open file
  File "H5Fint.c", line 1700, in H5F_open
    unable to read superblock
  File "H5Fsuper.c", line 411, in H5F__super_read
    file signature not found

End of HDF5 error back trace

Unable to open/create file '/var/app/data/bigquant/datasource/user/v3/b/64/b6440073d2b44d97bedfe1664c7a05f6T'

20/20 - 1s - loss: 1.7187 - mse: 1.7187

10/10 - 0s
DataSource(b6440073d2b44d97bedfe1664c7a05f6T)

日期： 2020-02-17 股票： 600596.SHA 出现止盈状况
日期： 2020-02-21 股票： 300065.SZA 出现止盈状况
日期： 2020-03-18 股票： 000975.SZA 出现止损状况
日期： 2020-04-07 股票： 002020.SZA 出现止盈状况
日期： 2020-04-09 股票： 000713.SZA 出现止盈状况
日期： 2020-04-16 股票： 603603.SHA 出现止损状况
日期： 2020-05-14 股票： 300498.SZA 出现止损状况
日期： 2020-05-21 股票： 600895.SHA 出现止盈状况
日期： 2020-06-04 股票： 300691.SZA 出现止盈状况
日期： 2020-06-12 股票： 002186.SZA 出现止盈状况
日期： 2020-07-02 股票： 601636.SHA 出现止盈状况
日期： 2020-07-03 股票： 600036.SHA 出现止盈状况
日期： 2020-07-09 股票： 600258.SHA 出现止盈状况
日期： 2020-07-09 股票： 300773.SZA 出现止盈状况
日期： 2020-07-13 股票： 300001.SZA 出现止盈状况
日期： 2020-07-14 股票： 300707.SZA 出现止盈状况
日期： 2020-07-14 股票： 002851.SZA 出现止盈状况
日期： 2020-07-20 股票： 600370.SHA 出现止盈状况
日期： 2020-07-20 股票： 000157.SZA 出现止盈状况
日期： 2020-10-14 股票： 601606.SHA 出现止损状况
日期： 2020-10-16 股票： 000001.SZA 出现止盈状况
日期： 2020-10-26 股票： 600277.SHA 出现止损状况
日期： 2020-10-30 股票： 603115.SHA 出现止损状况

收益率5.28%
年化收益率6.27%
基准收益率20.3%
阿尔法-0.09
贝塔0.72
夏普比率0.25
胜率0.69
盈亏比0.58
收益波动率25.1%
信息比率-0.05
最大回撤16.37%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d61538a53a3e4d6eb28f482ae34d7c7c"}/bigcharts-data-end

---------------------------------------------------------------------------
HDF5ExtError                              Traceback (most recent call last)
<ipython-input-8-8320c1f43a20> in <module>
    465 )
    466 
--> 467 m25 = M.metrics_regression.v1(
    468     predictions=m11.data,
    469     explained_variance_score=True,

HDF5ExtError: HDF5 error back trace

  File "H5F.c", line 509, in H5Fopen
    unable to open file
  File "H5Fint.c", line 1400, in H5F__open
    unable to open file
  File "H5Fint.c", line 1700, in H5F_open
    unable to read superblock
  File "H5Fsuper.c", line 411, in H5F__super_read
    file signature not found

End of HDF5 error back trace

Unable to open/create file '/var/app/data/bigquant/datasource/user/v3/b/64/b6440073d2b44d97bedfe1664c7a05f6T'