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实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.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 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# 本代码由可视化策略环境自动生成 2022年9月9日 15:07
# 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 import losses 
m35_user_loss_bigquant_run=losses.huber
from tensorflow.keras.callbacks import EarlyStopping

m35_earlystop_bigquant_run=EarlyStopping(monitor='val_mse', min_delta=0.001, patience=3)
# 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


m1 = M.instruments.v2(
    start_date='2012-01-01',
    end_date='2017-12-31',
    market='CN_STOCK_A',
    instrument_list="""
002674.SZA
603066.SHA
000536.SZA
002374.SZA
002537.SZA
002542.SZA
002207.SZA
002272.SZA
002277.SZA
600794.SHA
600793.SHA
600172.SHA
600428.SHA
002575.SZA
603022.SHA
001217.SZA
002654.SZA
002587.SZA
000059.SZA
002868.SZA
""",
    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
)

m13 = M.standardlize.v8(
    input_1=m2.data,
    columns_input='label'
)

m3 = M.input_features.v1(
    features="""close_0
open_0
high_0
low_0 
turn_0 
return_0
amount_0

close_1
open_1
high_1
low_1
turn_1
return_1
amount_1

 
close_2
open_2
high_2
low_2
turn_2
amount_2
return_2
 
close_3
open_3
high_3
low_3
turn_3
amount_3
return_3
 
close_4
open_4
high_4
low_4
turn_4
amount_4
return_4
 
mean(close_0, 5)
mean(open_0, 5)
mean(high_0, 5)
mean(low_0, 5)
mean(turn_0, 5)
mean(amount_0, 5)
mean(return_0, 5)
 
ts_max(close_0, 5)
ts_max(open_0, 5)
ts_max(high_0, 5)
ts_max(low_0, 5)
ts_max(turn_0, 5)
ts_max(amount_0, 5)
ts_max(return_0, 5)
 
ts_min(close_0, 5)
ts_min(open_0, 5)
ts_min(high_0, 5)
ts_min(low_0, 5)
ts_min(turn_0, 5)
ts_min(amount_0, 5)
ts_min(return_0, 5) 
 
std(close_0, 5)
std(open_0, 5)
std(high_0, 5)
std(low_0, 5)
std(turn_0, 5)
std(amount_0, 5)
std(return_0, 5)
 
ts_rank(close_0, 5)
ts_rank(open_0, 5)
ts_rank(high_0, 5)
ts_rank(low_0, 5)
ts_rank(turn_0, 5)
ts_rank(amount_0, 5)
ts_rank(return_0, 5)
 
decay_linear(close_0, 5)
decay_linear(open_0, 5)
decay_linear(high_0, 5)
decay_linear(low_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)"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m14 = M.standardlize.v8(
    input_1=m16.data,
    input_2=m3.data,
    columns_input=''
)

m21 = M.fillnan.v1(
    input_data=m14.data,
    features=m3.data,
    fill_value='0.0'
)

m7 = M.join.v3(
    data1=m13.data,
    data2=m21.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m26 = M.dl_convert_to_bin.v2(
    input_data=m7.data,
    features=m3.data,
    window_size=1,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m4 = M.cached.v3(
    input_1=m26.data,
    input_2=m3.data,
    run=m4_run_bigquant_run,
    post_run=m4_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2018-01-01'),
    end_date=T.live_run_param('trading_date', '2022-8-15'),
    market='CN_STOCK_A',
    instrument_list="""
002674.SZA
603066.SHA
000536.SZA
002374.SZA
002537.SZA
002542.SZA
002207.SZA
002272.SZA
002277.SZA
600794.SHA
600793.SHA
600172.SHA
600428.SHA
002575.SZA
603022.SHA
001217.SZA
002654.SZA
002587.SZA
000059.SZA
002868.SZA
""",
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m25 = M.standardlize.v8(
    input_1=m18.data,
    input_2=m3.data,
    columns_input=''
)

m22 = M.fillnan.v1(
    input_data=m25.data,
    features=m3.data,
    fill_value='0.0'
)

m27 = M.dl_convert_to_bin.v2(
    input_data=m22.data,
    features=m3.data,
    window_size=1,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m6 = M.dl_layer_input.v1(
    shape='98,1',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m37 = M.dl_layer_batchnormalization.v1(
    inputs=m6.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m43 = M.dl_layer_conv1d.v1(
    inputs=m37.data,
    filters=128,
    kernel_size='3',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m8 = M.dl_layer_batchnormalization.v1(
    inputs=m43.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m44 = M.dl_layer_conv1d.v1(
    inputs=m8.data,
    filters=128,
    kernel_size='3',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m41 = M.dl_layer_maxpooling1d.v1(
    inputs=m44.data,
    pool_size=2,
    padding='valid',
    name=''
)

m5 = M.dl_layer_dropout.v1(
    inputs=m41.data,
    rate=0.15,
    noise_shape='',
    name=''
)

m45 = M.dl_layer_conv1d.v1(
    inputs=m5.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m10 = M.dl_layer_batchnormalization.v1(
    inputs=m45.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m47 = M.dl_layer_conv1d.v1(
    inputs=m10.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m48 = M.dl_layer_batchnormalization.v1(
    inputs=m47.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m50 = M.dl_layer_conv1d.v1(
    inputs=m48.data,
    filters=256,
    kernel_size='3',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m51 = M.dl_layer_batchnormalization.v1(
    inputs=m50.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m53 = M.dl_layer_conv1d.v1(
    inputs=m51.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m46 = M.dl_layer_batchnormalization.v1(
    inputs=m53.data,
    axis=-1,
    momentum=0.99,
    epsilon=0.001,
    center=True,
    scale=True,
    beta_initializer='Zeros',
    gamma_initializer='Ones',
    moving_mean_initializer='Zeros',
    moving_variance_initializer='Ones',
    beta_regularizer='None',
    beta_regularizer_l1=0,
    beta_regularizer_l2=0,
    gamma_regularizer='None',
    gamma_regularizer_l1=0,
    gamma_regularizer_l2=0,
    beta_constraint='None',
    gamma_constraint='None',
    name=''
)

m49 = M.dl_layer_conv1d.v1(
    inputs=m46.data,
    filters=256,
    kernel_size='5',
    strides='1',
    padding='same',
    dilation_rate=1,
    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=''
)

m62 = M.dl_layer_add.v1(
    input1=m49.data,
    input2=m47.data,
    name=''
)

m38 = M.dl_layer_globalmaxpooling1d.v1(
    inputs=m62.data,
    name=''
)

m61 = M.dl_layer_dropout.v1(
    inputs=m38.data,
    rate=0.15,
    noise_shape='',
    name=''
)

m57 = M.dl_layer_dense.v1(
    inputs=m61.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=''
)

m34 = M.dl_model_init.v1(
    inputs=m6.data,
    outputs=m57.data
)

m35 = M.dl_model_train.v1(
    input_model=m34.data,
    training_data=m4.data_1,
    validation_data=m4.data_2,
    optimizer='自定义',
    user_optimizer=optimizers.Adam(lr=0.001),
    loss='mean_squared_error',
    user_loss=m35_user_loss_bigquant_run,
    metrics='mse',
    batch_size=512,
    epochs=5,
    earlystop=m35_earlystop_bigquant_run,
    custom_objects=m35_custom_objects_bigquant_run,
    n_gpus=1,
    verbose='2:每个epoch输出一行记录',
    m_cached=False
)

m11 = M.dl_model_predict.v1(
    trained_model=m35.data,
    input_data=m27.data,
    batch_size=512,
    n_gpus=1,
    verbose='2:每个epoch输出一行记录'
)

m24 = M.cached.v3(
    input_1=m11.data,
    input_2=m18.data,
    run=m24_run_bigquant_run,
    post_run=m24_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m24.data_1,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

[2022-09-09 15:05:01.633190] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-09 15:05:01.699773] INFO: moduleinvoker: instruments.v2 运行完成[0.066615s].

[2022-09-09 15:05:01.723887] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-09-09 15:05:03.838343] INFO: 自动标注(股票): 加载历史数据: 22243 行

[2022-09-09 15:05:03.840745] INFO: 自动标注(股票): 开始标注 ..

[2022-09-09 15:05:05.295059] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[3.57117s].

[2022-09-09 15:05:05.396967] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-09-09 15:05:06.847458] INFO: moduleinvoker: standardlize.v8 运行完成[1.45049s].

[2022-09-09 15:05:06.863246] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-09 15:05:06.973970] INFO: moduleinvoker: 命中缓存

[2022-09-09 15:05:06.976199] INFO: moduleinvoker: input_features.v1 运行完成[0.112969s].

[2022-09-09 15:05:07.021769] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-09 15:05:08.506891] INFO: 基础特征抽取: 年份 2011, 特征行数=97

[2022-09-09 15:05:10.197038] INFO: 基础特征抽取: 年份 2012, 特征行数=3708

[2022-09-09 15:05:11.655450] INFO: 基础特征抽取: 年份 2013, 特征行数=3743

[2022-09-09 15:05:13.196941] INFO: 基础特征抽取: 年份 2014, 特征行数=3478

[2022-09-09 15:05:14.816694] INFO: 基础特征抽取: 年份 2015, 特征行数=3233

[2022-09-09 15:05:16.422443] INFO: 基础特征抽取: 年份 2016, 特征行数=3787

[2022-09-09 15:05:18.380109] INFO: 基础特征抽取: 年份 2017, 特征行数=4294

[2022-09-09 15:05:18.436223] INFO: 基础特征抽取: 总行数: 22340

[2022-09-09 15:05:18.441699] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[11.419941s].

[2022-09-09 15:05:18.553418] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-09-09 15:05:18.952963] INFO: derived_feature_extractor: 提取完成 mean(close_0, 5), 0.016s

[2022-09-09 15:05:18.973738] INFO: derived_feature_extractor: 提取完成 mean(low_0, 5), 0.018s

[2022-09-09 15:05:18.993689] INFO: derived_feature_extractor: 提取完成 mean(open_0, 5), 0.018s

[2022-09-09 15:05:19.013780] INFO: derived_feature_extractor: 提取完成 mean(high_0, 5), 0.018s

[2022-09-09 15:05:19.033996] INFO: derived_feature_extractor: 提取完成 mean(turn_0, 5), 0.018s

[2022-09-09 15:05:19.054659] INFO: derived_feature_extractor: 提取完成 mean(amount_0, 5), 0.018s

[2022-09-09 15:05:19.075435] INFO: derived_feature_extractor: 提取完成 mean(return_0, 5), 0.019s

[2022-09-09 15:05:19.096381] INFO: derived_feature_extractor: 提取完成 ts_max(close_0, 5), 0.018s

[2022-09-09 15:05:19.113913] INFO: derived_feature_extractor: 提取完成 ts_max(low_0, 5), 0.015s

[2022-09-09 15:05:19.133828] INFO: derived_feature_extractor: 提取完成 ts_max(open_0, 5), 0.018s

[2022-09-09 15:05:19.155122] INFO: derived_feature_extractor: 提取完成 ts_max(high_0, 5), 0.019s

[2022-09-09 15:05:19.173544] INFO: derived_feature_extractor: 提取完成 ts_max(turn_0, 5), 0.016s

[2022-09-09 15:05:19.190478] INFO: derived_feature_extractor: 提取完成 ts_max(amount_0, 5), 0.015s

[2022-09-09 15:05:19.209669] INFO: derived_feature_extractor: 提取完成 ts_max(return_0, 5), 0.017s

[2022-09-09 15:05:19.233632] INFO: derived_feature_extractor: 提取完成 ts_min(close_0, 5), 0.022s

[2022-09-09 15:05:19.250799] INFO: derived_feature_extractor: 提取完成 ts_min(low_0, 5), 0.015s

[2022-09-09 15:05:19.265627] INFO: derived_feature_extractor: 提取完成 ts_min(open_0, 5), 0.013s

[2022-09-09 15:05:19.279560] INFO: derived_feature_extractor: 提取完成 ts_min(high_0, 5), 0.012s

[2022-09-09 15:05:19.294065] INFO: derived_feature_extractor: 提取完成 ts_min(turn_0, 5), 0.013s

[2022-09-09 15:05:19.311053] INFO: derived_feature_extractor: 提取完成 ts_min(amount_0, 5), 0.015s

[2022-09-09 15:05:19.326245] INFO: derived_feature_extractor: 提取完成 ts_min(return_0, 5), 0.013s

[2022-09-09 15:05:19.343280] INFO: derived_feature_extractor: 提取完成 std(close_0, 5), 0.015s

[2022-09-09 15:05:19.366915] INFO: derived_feature_extractor: 提取完成 std(low_0, 5), 0.021s

[2022-09-09 15:05:19.389620] INFO: derived_feature_extractor: 提取完成 std(open_0, 5), 0.020s

[2022-09-09 15:05:19.411226] INFO: derived_feature_extractor: 提取完成 std(high_0, 5), 0.019s

[2022-09-09 15:05:19.431937] INFO: derived_feature_extractor: 提取完成 std(turn_0, 5), 0.019s

[2022-09-09 15:05:19.452820] INFO: derived_feature_extractor: 提取完成 std(amount_0, 5), 0.019s

[2022-09-09 15:05:19.473645] INFO: derived_feature_extractor: 提取完成 std(return_0, 5), 0.019s

[2022-09-09 15:05:19.574740] INFO: derived_feature_extractor: 提取完成 ts_rank(close_0, 5), 0.099s

[2022-09-09 15:05:19.646955] INFO: derived_feature_extractor: 提取完成 ts_rank(low_0, 5), 0.070s

[2022-09-09 15:05:19.742742] INFO: derived_feature_extractor: 提取完成 ts_rank(open_0, 5), 0.094s

[2022-09-09 15:05:19.810196] INFO: derived_feature_extractor: 提取完成 ts_rank(high_0, 5), 0.066s

[2022-09-09 15:05:19.879814] INFO: derived_feature_extractor: 提取完成 ts_rank(turn_0, 5), 0.068s

[2022-09-09 15:05:19.957537] INFO: derived_feature_extractor: 提取完成 ts_rank(amount_0, 5), 0.076s

[2022-09-09 15:05:20.022170] INFO: derived_feature_extractor: 提取完成 ts_rank(return_0, 5), 0.063s

[2022-09-09 15:05:20.120240] INFO: derived_feature_extractor: 提取完成 decay_linear(close_0, 5), 0.096s

[2022-09-09 15:05:20.198663] INFO: derived_feature_extractor: 提取完成 decay_linear(low_0, 5), 0.076s

[2022-09-09 15:05:20.283684] INFO: derived_feature_extractor: 提取完成 decay_linear(open_0, 5), 0.083s

[2022-09-09 15:05:20.334895] INFO: derived_feature_extractor: 提取完成 decay_linear(high_0, 5), 0.049s

[2022-09-09 15:05:20.389452] INFO: derived_feature_extractor: 提取完成 decay_linear(turn_0, 5), 0.053s

[2022-09-09 15:05:20.443051] INFO: derived_feature_extractor: 提取完成 decay_linear(amount_0, 5), 0.051s

[2022-09-09 15:05:20.501889] INFO: derived_feature_extractor: 提取完成 decay_linear(return_0, 5), 0.057s

[2022-09-09 15:05:20.686915] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, return_0, 5), 0.183s

[2022-09-09 15:05:20.863499] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, high_0, 5), 0.174s

[2022-09-09 15:05:21.056319] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, low_0, 5), 0.190s

[2022-09-09 15:05:21.205092] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, close_0, 5), 0.145s

[2022-09-09 15:05:21.362626] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, open_0, 5), 0.155s

[2022-09-09 15:05:21.522201] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, turn_0, 5), 0.157s

[2022-09-09 15:05:21.683066] INFO: derived_feature_extractor: 提取完成 correlation(return_0, high_0, 5), 0.158s

[2022-09-09 15:05:21.819023] INFO: derived_feature_extractor: 提取完成 correlation(return_0, low_0, 5), 0.134s

[2022-09-09 15:05:21.972440] INFO: derived_feature_extractor: 提取完成 correlation(return_0, close_0, 5), 0.152s

[2022-09-09 15:05:22.108705] INFO: derived_feature_extractor: 提取完成 correlation(return_0, open_0, 5), 0.134s

[2022-09-09 15:05:22.247168] INFO: derived_feature_extractor: 提取完成 correlation(return_0, turn_0, 5), 0.137s

[2022-09-09 15:05:22.378058] INFO: derived_feature_extractor: 提取完成 correlation(high_0, low_0, 5), 0.129s

[2022-09-09 15:05:22.526435] INFO: derived_feature_extractor: 提取完成 correlation(high_0, close_0, 5), 0.147s

[2022-09-09 15:05:22.685714] INFO: derived_feature_extractor: 提取完成 correlation(high_0, open_0, 5), 0.158s

[2022-09-09 15:05:22.928330] INFO: derived_feature_extractor: 提取完成 correlation(high_0, turn_0, 5), 0.239s

[2022-09-09 15:05:23.107360] INFO: derived_feature_extractor: 提取完成 correlation(low_0, close_0, 5), 0.177s

[2022-09-09 15:05:23.292033] INFO: derived_feature_extractor: 提取完成 correlation(low_0, open_0, 5), 0.183s

[2022-09-09 15:05:23.446773] INFO: derived_feature_extractor: 提取完成 correlation(low_0, turn_0, 5), 0.153s

[2022-09-09 15:05:23.594631] INFO: derived_feature_extractor: 提取完成 correlation(close_0, open_0, 5), 0.146s

[2022-09-09 15:05:23.746153] INFO: derived_feature_extractor: 提取完成 correlation(close_0, turn_0, 5), 0.149s

[2022-09-09 15:05:23.910125] INFO: derived_feature_extractor: 提取完成 correlation(open_0, turn_0, 5), 0.162s

[2022-09-09 15:05:24.036703] INFO: derived_feature_extractor: /y_2011, 97

[2022-09-09 15:05:24.141338] INFO: derived_feature_extractor: /y_2012, 3708

[2022-09-09 15:05:24.273456] INFO: derived_feature_extractor: /y_2013, 3743

[2022-09-09 15:05:24.374467] INFO: derived_feature_extractor: /y_2014, 3478

[2022-09-09 15:05:24.491013] INFO: derived_feature_extractor: /y_2015, 3233

[2022-09-09 15:05:24.677396] INFO: derived_feature_extractor: /y_2016, 3787

[2022-09-09 15:05:24.814080] INFO: derived_feature_extractor: /y_2017, 4294

[2022-09-09 15:05:24.963312] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[6.409863s].

[2022-09-09 15:05:24.974762] INFO: moduleinvoker: standardlize.v8 开始运行..

[2022-09-09 15:07:06.144324] INFO: moduleinvoker: standardlize.v8 运行完成[101.169531s].

[2022-09-09 15:07:06.155427] INFO: moduleinvoker: fillnan.v1 开始运行..

[2022-09-09 15:07:06.529339] INFO: moduleinvoker: fillnan.v1 运行完成[0.373882s].

[2022-09-09 15:07:06.540064] INFO: moduleinvoker: join.v3 开始运行..

[2022-09-09 15:07:07.029134] INFO: join: /data, 行数=22012/22257, 耗时=0.321142s

[2022-09-09 15:07:07.087230] INFO: join: 最终行数: 22012

[2022-09-09 15:07:07.096799] INFO: moduleinvoker: join.v3 运行完成[0.556723s].

[2022-09-09 15:07:07.119420] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-09-09 15:07:07.369722] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.25031s].

[2022-09-09 15:07:07.386808] INFO: moduleinvoker: cached.v3 开始运行..

[2022-09-09 15:07:07.604644] INFO: moduleinvoker: cached.v3 运行完成[0.217831s].

[2022-09-09 15:07:07.611699] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-09 15:07:07.633957] INFO: moduleinvoker: instruments.v2 运行完成[0.022255s].

[2022-09-09 15:07:07.649461] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-09 15:07:09.579723] INFO: 基础特征抽取: 年份 2017, 特征行数=102

[2022-09-09 15:07:11.906394] INFO: 基础特征抽取: 年份 2018, 特征行数=4375

[2022-09-09 15:07:14.267312] INFO: 基础特征抽取: 年份 2019, 特征行数=4625

[2022-09-09 15:07:16.673200] INFO: 基础特征抽取: 年份 2020, 特征行数=4611

[2022-09-09 15:07:19.416153] INFO: 基础特征抽取: 年份 2021, 特征行数=4679

[2022-09-09 15:07:21.579574] INFO: 基础特征抽取: 年份 2022, 特征行数=2970

[2022-09-09 15:07:21.617205] INFO: 基础特征抽取: 总行数: 21362

[2022-09-09 15:07:21.623507] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[13.974051s].

[2022-09-09 15:07:21.633963] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-09-09 15:07:22.036248] INFO: derived_feature_extractor: 提取完成 mean(close_0, 5), 0.013s

[2022-09-09 15:07:22.051350] INFO: derived_feature_extractor: 提取完成 mean(low_0, 5), 0.013s

[2022-09-09 15:07:22.067459] INFO: derived_feature_extractor: 提取完成 mean(open_0, 5), 0.014s

[2022-09-09 15:07:22.082328] INFO: derived_feature_extractor: 提取完成 mean(high_0, 5), 0.013s

[2022-09-09 15:07:22.102851] INFO: derived_feature_extractor: 提取完成 mean(turn_0, 5), 0.018s

[2022-09-09 15:07:22.120924] INFO: derived_feature_extractor: 提取完成 mean(amount_0, 5), 0.016s

[2022-09-09 15:07:22.138698] INFO: derived_feature_extractor: 提取完成 mean(return_0, 5), 0.016s

[2022-09-09 15:07:22.156701] INFO: derived_feature_extractor: 提取完成 ts_max(close_0, 5), 0.016s

[2022-09-09 15:07:22.173740] INFO: derived_feature_extractor: 提取完成 ts_max(low_0, 5), 0.015s

[2022-09-09 15:07:22.191051] INFO: derived_feature_extractor: 提取完成 ts_max(open_0, 5), 0.015s

[2022-09-09 15:07:22.207407] INFO: derived_feature_extractor: 提取完成 ts_max(high_0, 5), 0.015s

[2022-09-09 15:07:22.224662] INFO: derived_feature_extractor: 提取完成 ts_max(turn_0, 5), 0.016s

[2022-09-09 15:07:22.242083] INFO: derived_feature_extractor: 提取完成 ts_max(amount_0, 5), 0.016s

[2022-09-09 15:07:22.259026] INFO: derived_feature_extractor: 提取完成 ts_max(return_0, 5), 0.015s

[2022-09-09 15:07:22.275235] INFO: derived_feature_extractor: 提取完成 ts_min(close_0, 5), 0.014s

[2022-09-09 15:07:22.294679] INFO: derived_feature_extractor: 提取完成 ts_min(low_0, 5), 0.018s

[2022-09-09 15:07:22.317203] INFO: derived_feature_extractor: 提取完成 ts_min(open_0, 5), 0.021s

[2022-09-09 15:07:22.334075] INFO: derived_feature_extractor: 提取完成 ts_min(high_0, 5), 0.015s

[2022-09-09 15:07:22.352087] INFO: derived_feature_extractor: 提取完成 ts_min(turn_0, 5), 0.016s

[2022-09-09 15:07:22.371632] INFO: derived_feature_extractor: 提取完成 ts_min(amount_0, 5), 0.018s

[2022-09-09 15:07:22.389486] INFO: derived_feature_extractor: 提取完成 ts_min(return_0, 5), 0.016s

[2022-09-09 15:07:22.408543] INFO: derived_feature_extractor: 提取完成 std(close_0, 5), 0.017s

[2022-09-09 15:07:22.431012] INFO: derived_feature_extractor: 提取完成 std(low_0, 5), 0.020s

[2022-09-09 15:07:22.448247] INFO: derived_feature_extractor: 提取完成 std(open_0, 5), 0.015s

[2022-09-09 15:07:22.464222] INFO: derived_feature_extractor: 提取完成 std(high_0, 5), 0.014s

[2022-09-09 15:07:22.481489] INFO: derived_feature_extractor: 提取完成 std(turn_0, 5), 0.015s

[2022-09-09 15:07:22.499054] INFO: derived_feature_extractor: 提取完成 std(amount_0, 5), 0.015s

[2022-09-09 15:07:22.518921] INFO: derived_feature_extractor: 提取完成 std(return_0, 5), 0.018s

[2022-09-09 15:07:22.587937] INFO: derived_feature_extractor: 提取完成 ts_rank(close_0, 5), 0.067s

[2022-09-09 15:07:22.666556] INFO: derived_feature_extractor: 提取完成 ts_rank(low_0, 5), 0.077s

[2022-09-09 15:07:22.741410] INFO: derived_feature_extractor: 提取完成 ts_rank(open_0, 5), 0.073s

[2022-09-09 15:07:22.813062] INFO: derived_feature_extractor: 提取完成 ts_rank(high_0, 5), 0.070s

[2022-09-09 15:07:22.889939] INFO: derived_feature_extractor: 提取完成 ts_rank(turn_0, 5), 0.075s

[2022-09-09 15:07:22.973611] INFO: derived_feature_extractor: 提取完成 ts_rank(amount_0, 5), 0.082s

[2022-09-09 15:07:23.059134] INFO: derived_feature_extractor: 提取完成 ts_rank(return_0, 5), 0.084s

[2022-09-09 15:07:23.129274] INFO: derived_feature_extractor: 提取完成 decay_linear(close_0, 5), 0.068s

[2022-09-09 15:07:23.198195] INFO: derived_feature_extractor: 提取完成 decay_linear(low_0, 5), 0.067s

[2022-09-09 15:07:23.262363] INFO: derived_feature_extractor: 提取完成 decay_linear(open_0, 5), 0.060s

[2022-09-09 15:07:23.332103] INFO: derived_feature_extractor: 提取完成 decay_linear(high_0, 5), 0.067s

[2022-09-09 15:07:23.409648] INFO: derived_feature_extractor: 提取完成 decay_linear(turn_0, 5), 0.075s

[2022-09-09 15:07:23.479736] INFO: derived_feature_extractor: 提取完成 decay_linear(amount_0, 5), 0.068s

[2022-09-09 15:07:23.546229] INFO: derived_feature_extractor: 提取完成 decay_linear(return_0, 5), 0.062s

[2022-09-09 15:07:23.721884] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, return_0, 5), 0.174s

[2022-09-09 15:07:23.902675] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, high_0, 5), 0.179s

[2022-09-09 15:07:24.070068] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, low_0, 5), 0.165s

[2022-09-09 15:07:24.243047] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, close_0, 5), 0.171s

[2022-09-09 15:07:24.416871] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, open_0, 5), 0.171s

[2022-09-09 15:07:24.598481] INFO: derived_feature_extractor: 提取完成 correlation(volume_0, turn_0, 5), 0.180s

[2022-09-09 15:07:24.831801] INFO: derived_feature_extractor: 提取完成 correlation(return_0, high_0, 5), 0.231s

[2022-09-09 15:07:25.004682] INFO: derived_feature_extractor: 提取完成 correlation(return_0, low_0, 5), 0.171s

[2022-09-09 15:07:25.221145] INFO: derived_feature_extractor: 提取完成 correlation(return_0, close_0, 5), 0.213s

[2022-09-09 15:07:25.445855] INFO: derived_feature_extractor: 提取完成 correlation(return_0, open_0, 5), 0.223s

[2022-09-09 15:07:25.608156] INFO: derived_feature_extractor: 提取完成 correlation(return_0, turn_0, 5), 0.160s

[2022-09-09 15:07:25.753818] INFO: derived_feature_extractor: 提取完成 correlation(high_0, low_0, 5), 0.144s

[2022-09-09 15:07:25.912173] INFO: derived_feature_extractor: 提取完成 correlation(high_0, close_0, 5), 0.156s

[2022-09-09 15:07:26.089863] INFO: derived_feature_extractor: 提取完成 correlation(high_0, open_0, 5), 0.176s

[2022-09-09 15:07:26.254588] INFO: derived_feature_extractor: 提取完成 correlation(high_0, turn_0, 5), 0.163s

[2022-09-09 15:07:26.415307] INFO: derived_feature_extractor: 提取完成 correlation(low_0, close_0, 5), 0.159s

[2022-09-09 15:07:26.561865] INFO: derived_feature_extractor: 提取完成 correlation(low_0, open_0, 5), 0.145s

[2022-09-09 15:07:26.738166] INFO: derived_feature_extractor: 提取完成 correlation(low_0, turn_0, 5), 0.174s

[2022-09-09 15:07:26.903112] INFO: derived_feature_extractor: 提取完成 correlation(close_0, open_0, 5), 0.163s

[2022-09-09 15:07:27.048723] INFO: derived_feature_extractor: 提取完成 correlation(close_0, turn_0, 5), 0.144s

[2022-09-09 15:07:27.216903] INFO: derived_feature_extractor: 提取完成 correlation(open_0, turn_0, 5), 0.166s

[2022-09-09 15:07:27.334105] INFO: derived_feature_extractor: /y_2017, 102

[2022-09-09 15:07:27.436699] INFO: derived_feature_extractor: /y_2018, 4375

[2022-09-09 15:07:27.551387] INFO: derived_feature_extractor: /y_2019, 4625

[2022-09-09 15:07:27.668433] INFO: derived_feature_extractor: /y_2020, 4611

[2022-09-09 15:07:27.773165] INFO: derived_feature_extractor: /y_2021, 4679

[2022-09-09 15:07:27.956018] INFO: derived_feature_extractor: /y_2022, 2970

[2022-09-09 15:07:28.046223] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[6.412256s].

[2022-09-09 15:07:28.053659] INFO: moduleinvoker: standardlize.v8 开始运行..