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# 本代码由可视化策略环境自动生成 2021年8月9日 20:20
# 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
    df =  input_1.read_pickle()
    feature_len = len(input_2.read_pickle())
    
    
    df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
    
    data_1 = DataSource.write_pickle(df)
    return Outputs(data_1=data_1)

# 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

# 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 m8_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df =  input_1.read_pickle()
    feature_len = len(input_2.read_pickle())
    
    
    df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
    
    data_1 = DataSource.write_pickle(df)
    return Outputs(data_1=data_1)

# 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 m8_post_run_bigquant_run(outputs):
    return outputs

# 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

# 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 m42_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df =  input_1.read_pickle()
    feature_len = len(input_2.read_pickle())
    
    
    df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
    
    data_1 = DataSource.write_pickle(df)
    return Outputs(data_1=data_1)

# 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 m42_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m45_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 m45_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=0.0013, 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 = 0.5
    context.options['hold_days'] = 1

# 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
     
        #----------------------------START：持有固定交易日天数卖出---------------------------
    today = data.current_dt.strftime('%Y-%m-%d')
    # 不是建仓期（在前hold_days属于建仓期）
    if not is_staging:
        equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        for instrument in equities:
            sid = equities[instrument].sid  # 交易标的
            # 今天和上次交易的时间相隔hold_days就全部卖出
            dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])
            if  pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                context.order_target_percent(sid, 0)
                cash_for_buy += positions[instrument]
    #--------------------------------END:持有固定天数卖出--------------------------- 

    # 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='2019-01-01',
    end_date='2020-01-01',
    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, -2) / shift(open, -1)-1

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

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    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'
)

m35 = 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, -2) / shift(open, -1)-1

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

# # 将分数映射到分类，这里使用20个分类
# all_wbins(label, 20)

# 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
)

m47 = M.standardlize.v8(
    input_1=m35.data,
    columns_input='label'
)

m3 = M.input_features.v1(
    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

# 换手率特征
turn_0
turn_1
turn_2
turn_3
turn_4
turn_5
turn_6
turn_7
turn_8
turn_9

"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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=''
)

m7 = M.join.v3(
    data1=m13.data,
    data2=m14.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m26 = M.dl_convert_to_bin.v2(
    input_data=m7.data,
    features=m3.data,
    window_size=5,
    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', '2019-01-01'),
    end_date=T.live_run_param('trading_date', '2020-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m22 = M.chinaa_stock_filter.v1(
    input_data=m18.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m25 = M.standardlize.v8(
    input_1=m22.data,
    input_2=m3.data,
    columns_input=''
)

m27 = M.dl_convert_to_bin.v2(
    input_data=m25.data,
    features=m3.data,
    window_size=5,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m8 = M.cached.v3(
    input_1=m27.data,
    input_2=m3.data,
    run=m8_run_bigquant_run,
    post_run=m8_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m6 = M.dl_layer_input.v1(
    shape='10,5',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m10 = M.dl_layer_conv1d.v1(
    inputs=m6.data,
    filters=20,
    kernel_size='3',
    strides='1',
    padding='valid',
    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=''
)

m12 = M.dl_layer_maxpooling1d.v1(
    inputs=m10.data,
    pool_size=1,
    padding='valid',
    name=''
)

m32 = M.dl_layer_conv1d.v1(
    inputs=m12.data,
    filters=20,
    kernel_size='3',
    strides='1',
    padding='valid',
    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=''
)

m33 = M.dl_layer_maxpooling1d.v1(
    inputs=m32.data,
    pool_size=1,
    padding='valid',
    name=''
)

m28 = M.dl_layer_globalmaxpooling1d.v1(
    inputs=m33.data,
    name=''
)

m30 = M.dl_layer_dense.v1(
    inputs=m28.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=m30.data
)

m5 = M.dl_model_train.v1(
    input_model=m34.data,
    training_data=m4.data_1,
    optimizer='RMSprop',
    loss='mean_squared_error',
    metrics='mae',
    batch_size=256,
    epochs=4,
    custom_objects=m5_custom_objects_bigquant_run,
    n_gpus=0,
    verbose='1:输出进度条记录'
)

m11 = M.dl_model_predict.v1(
    trained_model=m5.data,
    input_data=m8.data_1,
    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=m22.data,
    run=m24_run_bigquant_run,
    post_run=m24_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m36 = M.join.v3(
    data1=m24.data_1,
    data2=m47.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m23 = M.input_features.v1(
    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

pred_label

"""
)

m29 = M.stock_ranker_train.v6(
    training_ds=m36.data,
    features=m23.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m38 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2021-08-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m46 = M.input_features.v1(
    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

# 换手率特征
turn_0
turn_1
turn_2
turn_3
turn_4
turn_5
turn_6
turn_7
turn_8
turn_9

"""
)

m39 = M.general_feature_extractor.v7(
    instruments=m38.data,
    features=m46.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m37 = M.derived_feature_extractor.v3(
    input_data=m39.data,
    features=m46.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=False
)

m43 = M.chinaa_stock_filter.v1(
    input_data=m37.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m40 = M.standardlize.v8(
    input_1=m43.data,
    input_2=m46.data,
    columns_input=''
)

m41 = M.dl_convert_to_bin.v2(
    input_data=m40.data,
    features=m46.data,
    window_size=5,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m42 = M.cached.v3(
    input_1=m41.data,
    input_2=m46.data,
    run=m42_run_bigquant_run,
    post_run=m42_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m44 = M.dl_model_predict.v1(
    trained_model=m5.data,
    input_data=m42.data_1,
    batch_size=1024,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录'
)

m45 = M.cached.v3(
    input_1=m44.data,
    input_2=m43.data,
    run=m45_run_bigquant_run,
    post_run=m45_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m31 = M.stock_ranker_predict.v5(
    model=m29.model,
    data=m45.data_1,
    m_lazy_run=False
)

m19 = M.trade.v4(
    instruments=m38.data,
    options_data=m31.predictions,
    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=100000,
    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'
)

[2021-08-09 20:09:47.662834] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-09 20:09:47.684169] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:09:47.686831] INFO: moduleinvoker: instruments.v2 运行完成[0.024002s].

[2021-08-09 20:09:47.694777] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-08-09 20:09:47.704070] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:09:47.708585] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.013806s].

[2021-08-09 20:09:47.714258] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-08-09 20:09:47.722379] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:09:47.724946] INFO: moduleinvoker: standardlize.v8 运行完成[0.010698s].

[2021-08-09 20:09:47.728869] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-08-09 20:09:47.735926] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:09:47.738265] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.009384s].

[2021-08-09 20:09:47.741139] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-08-09 20:09:47.747042] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:09:47.748570] INFO: moduleinvoker: standardlize.v8 运行完成[0.007434s].

[2021-08-09 20:09:47.751549] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-09 20:09:47.790545] INFO: moduleinvoker: input_features.v1 运行完成[0.038976s].

[2021-08-09 20:09:47.885412] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-09 20:09:52.752144] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2021-08-09 20:09:54.776378] INFO: 基础特征抽取: 年份 2020, 特征行数=0

[2021-08-09 20:09:54.844819] INFO: 基础特征抽取: 总行数: 884867

[2021-08-09 20:09:54.851142] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[6.965757s].

[2021-08-09 20:09:54.855838] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-08-09 20:10:02.264006] INFO: derived_feature_extractor: /y_2019, 884867

[2021-08-09 20:10:03.224661] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[8.36881s].

[2021-08-09 20:10:03.228147] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-08-09 20:10:11.396751] INFO: moduleinvoker: standardlize.v8 运行完成[8.168589s].

[2021-08-09 20:10:11.404742] INFO: moduleinvoker: join.v3 开始运行..

[2021-08-09 20:10:21.038834] INFO: join: /data, 行数=872757/883042, 耗时=5.658736s

[2021-08-09 20:10:21.138626] INFO: join: 最终行数: 872757

[2021-08-09 20:10:21.182577] INFO: moduleinvoker: join.v3 运行完成[9.777835s].

[2021-08-09 20:10:21.222137] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-08-09 20:11:15.147622] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[53.925509s].

[2021-08-09 20:11:15.156561] INFO: moduleinvoker: cached.v3 开始运行..

[2021-08-09 20:11:15.665867] INFO: moduleinvoker: cached.v3 运行完成[0.509302s].

[2021-08-09 20:11:15.670458] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-09 20:11:15.677312] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:11:15.680110] INFO: moduleinvoker: instruments.v2 运行完成[0.009648s].

[2021-08-09 20:11:15.691227] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-09 20:11:15.698544] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:11:15.700666] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009475s].

[2021-08-09 20:11:15.704459] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-08-09 20:11:15.710879] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:11:15.712754] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.008312s].

[2021-08-09 20:11:15.718096] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-08-09 20:11:23.225972] INFO: A股股票过滤: 过滤 /y_2019, 851308/0/883042

[2021-08-09 20:11:23.230070] INFO: A股股票过滤: 过滤完成, 851308 + 0

[2021-08-09 20:11:23.262953] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[7.544852s].

[2021-08-09 20:11:23.267202] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-08-09 20:11:30.282797] INFO: moduleinvoker: standardlize.v8 运行完成[7.015587s].

[2021-08-09 20:11:30.295441] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-08-09 20:12:24.164997] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[53.869557s].

[2021-08-09 20:12:24.171596] INFO: moduleinvoker: cached.v3 开始运行..

[2021-08-09 20:12:24.626228] INFO: moduleinvoker: cached.v3 运行完成[0.454643s].

[2021-08-09 20:12:27.727822] INFO: moduleinvoker: dl_layer_input.v1 运行完成[3.096763s].

[2021-08-09 20:12:27.818470] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.086424s].

[2021-08-09 20:12:27.836607] INFO: moduleinvoker: dl_layer_maxpooling1d.v1 运行完成[0.012569s].

[2021-08-09 20:12:27.862466] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.021334s].

[2021-08-09 20:12:27.882572] INFO: moduleinvoker: dl_layer_maxpooling1d.v1 运行完成[0.013917s].

[2021-08-09 20:12:27.896202] INFO: moduleinvoker: dl_layer_globalmaxpooling1d.v1 运行完成[0.007527s].

[2021-08-09 20:12:27.918593] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.015116s].

[2021-08-09 20:12:27.949706] INFO: moduleinvoker: cached.v3 开始运行..

[2021-08-09 20:12:27.987738] INFO: moduleinvoker: cached.v3 运行完成[0.038024s].

[2021-08-09 20:12:27.992855] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.068626s].

[2021-08-09 20:12:28.002106] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2021-08-09 20:12:28.332536] INFO: dl_model_train: 准备训练，训练样本个数：872757，迭代次数：4

[2021-08-09 20:13:48.318274] INFO: dl_model_train: 训练结束，耗时：79.98s

[2021-08-09 20:13:48.355595] INFO: moduleinvoker: dl_model_train.v1 运行完成[80.353478s].

[2021-08-09 20:13:48.360898] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2021-08-09 20:13:52.610811] INFO: moduleinvoker: dl_model_predict.v1 运行完成[4.24994s].

[2021-08-09 20:13:52.620755] INFO: moduleinvoker: cached.v3 开始运行..

[2021-08-09 20:13:55.304537] INFO: moduleinvoker: cached.v3 运行完成[2.683781s].

[2021-08-09 20:13:55.309754] INFO: moduleinvoker: join.v3 开始运行..

[2021-08-09 20:14:03.841620] INFO: join: /data, 行数=842437/873854, 耗时=5.799665s

[2021-08-09 20:14:03.937964] INFO: join: 最终行数: 842437

[2021-08-09 20:14:03.975665] INFO: moduleinvoker: join.v3 运行完成[8.665927s].

[2021-08-09 20:14:03.979977] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-09 20:14:03.988251] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:14:03.991105] INFO: moduleinvoker: input_features.v1 运行完成[0.011133s].

[2021-08-09 20:14:04.000854] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2021-08-09 20:14:04.725210] INFO: StockRanker: 特征预处理 ..

[2021-08-09 20:14:04.849962] INFO: StockRanker: prepare data: training ..

[2021-08-09 20:14:13.906355] INFO: StockRanker训练: 49a0bb54 准备训练: 842437 行数

[2021-08-09 20:14:13.908288] INFO: StockRanker训练: AI模型训练，将在842437*1=84.24万数据上对模型训练进行20轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2021-08-09 20:14:14.127411] INFO: StockRanker训练: 正在训练 ..

[2021-08-09 20:14:14.188728] INFO: StockRanker训练: 任务状态: Pending

[2021-08-09 20:14:24.235623] INFO: StockRanker训练: 任务状态: Running

[2021-08-09 20:14:34.283334] INFO: StockRanker训练: 00:00:07.8272353, finished iteration 1

[2021-08-09 20:14:34.285721] INFO: StockRanker训练: 00:00:14.1044777, finished iteration 2

[2021-08-09 20:14:44.330048] INFO: StockRanker训练: 00:00:20.1108167, finished iteration 3

[2021-08-09 20:14:44.333298] INFO: StockRanker训练: 00:00:25.0107569, finished iteration 4

[2021-08-09 20:14:54.388034] INFO: StockRanker训练: 00:00:31.0836150, finished iteration 5

[2021-08-09 20:15:04.432865] INFO: StockRanker训练: 00:00:37.6497336, finished iteration 6

[2021-08-09 20:15:04.435970] INFO: StockRanker训练: 00:00:43.3934068, finished iteration 7

[2021-08-09 20:15:14.480671] INFO: StockRanker训练: 00:00:48.7665587, finished iteration 8

[2021-08-09 20:15:14.482531] INFO: StockRanker训练: 00:00:54.6477511, finished iteration 9

[2021-08-09 20:15:24.529076] INFO: StockRanker训练: 00:01:00.0971929, finished iteration 10

[2021-08-09 20:15:24.530596] INFO: StockRanker训练: 00:01:05.1297740, finished iteration 11

[2021-08-09 20:15:34.575494] INFO: StockRanker训练: 00:01:11.0790381, finished iteration 12

[2021-08-09 20:15:34.577588] INFO: StockRanker训练: 00:01:16.5396373, finished iteration 13

[2021-08-09 20:15:44.624552] INFO: StockRanker训练: 00:01:21.5502429, finished iteration 14

[2021-08-09 20:15:44.626133] INFO: StockRanker训练: 00:01:27.1891453, finished iteration 15

[2021-08-09 20:15:54.674705] INFO: StockRanker训练: 00:01:32.4555979, finished iteration 16

[2021-08-09 20:16:04.722864] INFO: StockRanker训练: 00:01:37.7276103, finished iteration 17

[2021-08-09 20:16:04.724928] INFO: StockRanker训练: 00:01:43.3719653, finished iteration 18

[2021-08-09 20:16:14.770372] INFO: StockRanker训练: 00:01:49.9247217, finished iteration 19

[2021-08-09 20:16:14.772268] INFO: StockRanker训练: 00:01:55.8204193, finished iteration 20

[2021-08-09 20:16:14.774377] INFO: StockRanker训练: 任务状态: Succeeded

[2021-08-09 20:16:14.966779] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[130.965941s].

[2021-08-09 20:16:14.971202] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-09 20:16:14.983526] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:16:14.986104] INFO: moduleinvoker: instruments.v2 运行完成[0.014914s].

[2021-08-09 20:16:14.988782] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-09 20:16:14.996033] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:16:14.998342] INFO: moduleinvoker: input_features.v1 运行完成[0.009531s].

[2021-08-09 20:16:15.009080] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-09 20:16:19.786955] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2021-08-09 20:16:23.076267] INFO: 基础特征抽取: 年份 2021, 特征行数=595158

[2021-08-09 20:16:23.155090] INFO: 基础特征抽取: 总行数: 1541119

[2021-08-09 20:16:23.214322] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[8.205263s].

[2021-08-09 20:16:23.217761] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-08-09 20:16:33.023477] INFO: derived_feature_extractor: /y_2020, 945961

[2021-08-09 20:16:36.597738] INFO: derived_feature_extractor: /y_2021, 595158

[2021-08-09 20:16:37.379644] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[14.161864s].

[2021-08-09 20:16:37.387323] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-08-09 20:16:46.806770] INFO: A股股票过滤: 过滤 /y_2020, 868107/0/942457

[2021-08-09 20:16:51.791961] INFO: A股股票过滤: 过滤 /y_2021, 529411/0/592516

[2021-08-09 20:16:51.797330] INFO: A股股票过滤: 过滤完成, 1397518 + 0

[2021-08-09 20:16:51.827394] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[14.440097s].

[2021-08-09 20:16:51.832298] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-08-09 20:17:05.044254] INFO: moduleinvoker: standardlize.v8 运行完成[13.211953s].

[2021-08-09 20:17:05.055928] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-08-09 20:18:34.555010] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[89.499116s].

[2021-08-09 20:18:34.563133] INFO: moduleinvoker: cached.v3 开始运行..

[2021-08-09 20:18:35.492793] INFO: moduleinvoker: cached.v3 运行完成[0.929662s].

[2021-08-09 20:18:35.495996] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2021-08-09 20:18:42.676418] INFO: moduleinvoker: dl_model_predict.v1 运行完成[7.180416s].

[2021-08-09 20:18:42.685577] INFO: moduleinvoker: cached.v3 开始运行..

[2021-08-09 20:18:47.685243] INFO: moduleinvoker: cached.v3 运行完成[4.99968s].

[2021-08-09 20:18:47.693044] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-08-09 20:18:48.636669] INFO: StockRanker预测: /data ..

[2021-08-09 20:18:53.074507] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[5.38145s].

[2021-08-09 20:18:55.323741] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-08-09 20:18:55.330381] INFO: backtest: biglearning backtest:V8.5.0

[2021-08-09 20:18:55.332473] INFO: backtest: product_type:stock by specified

[2021-08-09 20:18:56.175046] INFO: moduleinvoker: cached.v2 开始运行..

[2021-08-09 20:18:56.188248] INFO: moduleinvoker: 命中缓存

[2021-08-09 20:18:56.190838] INFO: moduleinvoker: cached.v2 运行完成[0.015833s].

[2021-08-09 20:19:00.337297] INFO: algo: TradingAlgorithm V1.8.3

[2021-08-09 20:19:01.767512] INFO: algo: trading transform...

[2021-08-09 20:19:40.676721] INFO: Performance: Simulated 383 trading days out of 383.

[2021-08-09 20:19:40.679321] INFO: Performance: first open: 2020-01-02 09:30:00+00:00

[2021-08-09 20:19:40.682126] INFO: Performance: last close: 2021-07-30 15:00:00+00:00

[2021-08-09 20:19:50.582513] INFO: moduleinvoker: backtest.v8 运行完成[55.258778s].

[2021-08-09 20:19:50.586515] INFO: moduleinvoker: trade.v4 运行完成[57.502386s].

Epoch 1/4
   1/3410 [..............................] - ETA: 1:05:52 - loss: 1.0506 - mae: 0.6977  10/3410 [..............................] - ETA: 19s - loss: 1.0458 - mae: 0.7006      18/3410 [..............................] - ETA: 20s - loss: 1.0255 - mae: 0.6987  26/3410 [..............................] - ETA: 21s - loss: 1.0186 - mae: 0.6989  35/3410 [..............................] - ETA: 21s - loss: 1.0161 - mae: 0.6995  46/3410 [..............................] - ETA: 19s - loss: 1.0179 - mae: 0.7011  55/3410 [..............................] - ETA: 19s - loss: 1.0190 - mae: 0.7019  65/3410 [..............................] - ETA: 19s - loss: 1.0174 - mae: 0.7018  73/3410 [..............................] - ETA: 19s - loss: 1.0159 - mae: 0.7016  82/3410 [..............................] - ETA: 19s - loss: 1.0141 - mae: 0.7013  92/3410 [..............................] - ETA: 19s - loss: 1.0117 - mae: 0.7009 102/3410 [..............................] - ETA: 19s - loss: 1.0098 - mae: 0.7005 112/3410 [..............................] - ETA: 18s - loss: 1.0085 - mae: 0.7004 122/3410 [>.............................] - ETA: 18s - loss: 1.0074 - mae: 0.7003 133/3410 [>.............................] - ETA: 18s - loss: 1.0063 - mae: 0.7003 142/3410 [>.............................] - ETA: 18s - loss: 1.0055 - mae: 0.7002 151/3410 [>.............................] - ETA: 18s - loss: 1.0049 - mae: 0.7001 160/3410 [>.............................] - ETA: 18s - loss: 1.0044 - mae: 0.7001 170/3410 [>.............................] - ETA: 18s - loss: 1.0038 - mae: 0.7000 178/3410 [>.............................] - ETA: 18s - loss: 1.0034 - mae: 0.7000 188/3410 [>.............................] - ETA: 18s - loss: 1.0030 - mae: 0.7000 196/3410 [>.............................] - ETA: 18s - loss: 1.0028 - mae: 0.7001 205/3410 [>.............................] - ETA: 18s - loss: 1.0025 - mae: 0.7001 213/3410 [>.............................] - ETA: 18s - loss: 1.0023 - mae: 0.7001 222/3410 [>.............................] - ETA: 18s - loss: 1.0022 - 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Epoch 2/4
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Epoch 3/4
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Epoch 4/4
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mae: 0.6973

832/832 - 4s
DataSource(cf0fef96f2dd4ca6817b1fe8e55e4ed1T)

设置评估测试数据集，查看训练曲线
[视频教程]StockRanker训练曲线

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0bf3a6ded5ea47fd93b2d51c39026313"}/bigcharts-data-end

1365/1365 - 6s
DataSource(28a276d3ab2b4569af7d077d112e6785T)

收益率10.01%
年化收益率6.48%
基准收益率17.44%
阿尔法0.05
贝塔0.66
夏普比率0.28
胜率0.49
盈亏比1.17
收益波动率40.65%
信息比率0.0
最大回撤45.32%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-aec81ffff3074f9992674aa497285fd0"}/bigcharts-data-end