复制链接

克隆策略

In [5]:

# 本代码由可视化策略环境自动生成 2022年4月4日 19:57
# 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 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 = 5
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.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='2020-01-01',
    end_date='2021-12-31',
    market='CN_STOCK_A',
    instrument_list="""603123.SHA
002761.SZA
002037.SZA
002657.SZA
600056.SHA
000736.SZA
000965.SZA
600082.SHA
000014.SZA
000797.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)
where(label>0.5, NaN, label)
where(label<-0.5, NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=False
)

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
return_5-1
return_10-1
return_20-1
avg_amount_0/avg_amount_5-1
avg_amount_5/avg_amount_20-1
rank_avg_amount_0-rank_avg_amount_5
rank_avg_amount_5-rank_avg_amount_10
rank_return_0-rank_return_5
rank_return_5-rank_return_10
beta_csi300_30_0/10
beta_csi300_60_0/10
swing_volatility_5_0/swing_volatility_30_0-1
swing_volatility_30_0/swing_volatility_60_0-1
ta_atr_14_0/ta_atr_28_0-1
ta_sma_5_0/ta_sma_20_0-1
ta_sma_10_0/ta_sma_20_0-1
ta_sma_20_0/ta_sma_30_0-1
ta_sma_30_0/ta_sma_60_0-1
ta_rsi_14_0/100
ta_rsi_28_0/100
ta_cci_14_0/500
ta_cci_28_0/500
beta_industry_30_0/10
beta_industry_60_0/10
ta_sma(amount_0, 10)/ta_sma(amount_0, 20)-1
ta_sma(amount_0, 20)/ta_sma(amount_0, 30)-1
ta_sma(amount_0, 30)/ta_sma(amount_0, 60)-1
ta_sma(amount_0, 50)/ta_sma(amount_0, 100)-1
ta_sma(turn_0, 10)/ta_sma(turn_0, 20)-1
ta_sma(turn_0, 20)/ta_sma(turn_0, 30)-1
ta_sma(turn_0, 30)/ta_sma(turn_0, 60)-1
ta_sma(turn_0, 50)/ta_sma(turn_0, 100)-1
high_0/low_0-1
close_0/open_0-1
shift(close_0,1)/close_0-1
shift(close_0,2)/close_0-1
shift(close_0,3)/close_0-1
shift(close_0,4)/close_0-1
shift(close_0,5)/close_0-1
shift(close_0,10)/close_0-1
shift(close_0,20)/close_0-1
ta_sma(high_0-low_0, 5)/ta_sma(high_0-low_0, 20)-1
ta_sma(high_0-low_0, 10)/ta_sma(high_0-low_0, 20)-1
ta_sma(high_0-low_0, 20)/ta_sma(high_0-low_0, 30)-1
ta_sma(high_0-low_0, 30)/ta_sma(high_0-low_0, 60)-1
ta_sma(high_0-low_0, 50)/ta_sma(high_0-low_0, 100)-1
rank_avg_amount_5
rank_avg_turn_5
rank_volatility_5_0
rank_swing_volatility_5_0
rank_avg_mf_net_amount_5
rank_beta_industry_5_0
rank_return_5
rank_return_2
std(close_0,5)/std(close_0,20)-1
std(close_0,10)/std(close_0,20)-1
std(close_0,20)/std(close_0,30)-1
std(close_0,30)/std(close_0,60)-1
std(close_0,50)/std(close_0,100)-1
"""
)

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
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = 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'
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2022-01-01'),
    end_date=T.live_run_param('trading_date', '2022-04-03'),
    market='CN_STOCK_A',
    instrument_list="""603123.SHA
002761.SZA
002037.SZA
002657.SZA
600056.SHA
000736.SZA
000965.SZA
600082.SHA
000014.SZA
000797.SZA""",
    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
)

m14 = M.dl_convert_to_bin.v2(
    input_data=m18.data,
    features=m3.data,
    window_size=1,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m6 = M.dl_layer_input.v1(
    shape='59',
    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=0.9,
    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=''
)

m23 = M.dl_layer_dense.v1(
    inputs=m20.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=m13.data,
    optimizer='Adam',
    loss='mean_squared_error',
    metrics='mse',
    batch_size=10240,
    epochs=2,
    custom_objects=m5_custom_objects_bigquant_run,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录'
)

m11 = M.dl_model_predict.v1(
    trained_model=m5.data,
    input_data=m14.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=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'
)

[2022-04-04 19:53:43.290445] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-04 19:53:43.297679] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.299733] INFO: moduleinvoker: instruments.v2 运行完成[0.009293s].

[2022-04-04 19:53:43.307592] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-04-04 19:53:43.313727] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.315158] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.007565s].

[2022-04-04 19:53:43.319226] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-04 19:53:43.326068] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.327859] INFO: moduleinvoker: input_features.v1 运行完成[0.008623s].

[2022-04-04 19:53:43.343692] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-04 19:53:43.349847] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.351194] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.007509s].

[2022-04-04 19:53:43.358244] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-04 19:53:43.364338] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.365706] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.007458s].

[2022-04-04 19:53:43.373560] INFO: moduleinvoker: join.v3 开始运行..

[2022-04-04 19:53:43.379553] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.380809] INFO: moduleinvoker: join.v3 运行完成[0.007244s].

[2022-04-04 19:53:43.395073] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-04 19:53:43.402472] INFO: moduleinvoker: 命中缓存

[2022-04-04 19:53:43.404018] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.008986s].

[2022-04-04 19:53:43.408954] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-04 19:53:43.448458] INFO: moduleinvoker: instruments.v2 运行完成[0.039481s].

[2022-04-04 19:53:43.465195] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-04 19:53:43.549829] WARNING: bigdatasource: factor [beta_industry_30_0] will deprecated，you can replace with [beta_industry1_30_0]

[2022-04-04 19:53:43.551387] WARNING: bigdatasource: factor [beta_industry_60_0] will deprecated，you can replace with [beta_industry1_60_0]

[2022-04-04 19:53:43.552470] WARNING: bigdatasource: factor [rank_beta_industry_5_0] will deprecated，you can replace with [rank_beta_industry1_5_0]

[2022-04-04 19:53:45.068927] INFO: 基础特征抽取: 年份 2022, 特征行数=585

[2022-04-04 19:53:45.097689] INFO: 基础特征抽取: 总行数: 585

[2022-04-04 19:53:45.102983] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[1.637803s].

[2022-04-04 19:53:45.112140] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-04 19:53:45.167616] INFO: derived_feature_extractor: 提取完成 return_5-1, 0.001s

[2022-04-04 19:53:45.170814] INFO: derived_feature_extractor: 提取完成 return_10-1, 0.001s

[2022-04-04 19:53:45.173536] INFO: derived_feature_extractor: 提取完成 return_20-1, 0.001s

[2022-04-04 19:53:45.176434] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5-1, 0.001s

[2022-04-04 19:53:45.179368] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20-1, 0.001s

[2022-04-04 19:53:45.181954] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0-rank_avg_amount_5, 0.001s

[2022-04-04 19:53:45.184607] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5-rank_avg_amount_10, 0.001s

[2022-04-04 19:53:45.187311] INFO: derived_feature_extractor: 提取完成 rank_return_0-rank_return_5, 0.001s

[2022-04-04 19:53:45.189775] INFO: derived_feature_extractor: 提取完成 rank_return_5-rank_return_10, 0.001s

[2022-04-04 19:53:45.192224] INFO: derived_feature_extractor: 提取完成 beta_csi300_30_0/10, 0.001s

[2022-04-04 19:53:45.194687] INFO: derived_feature_extractor: 提取完成 beta_csi300_60_0/10, 0.001s

[2022-04-04 19:53:45.197458] INFO: derived_feature_extractor: 提取完成 swing_volatility_5_0/swing_volatility_30_0-1, 0.001s

[2022-04-04 19:53:45.200080] INFO: derived_feature_extractor: 提取完成 swing_volatility_30_0/swing_volatility_60_0-1, 0.001s

[2022-04-04 19:53:45.202820] INFO: derived_feature_extractor: 提取完成 ta_atr_14_0/ta_atr_28_0-1, 0.001s

[2022-04-04 19:53:45.205356] INFO: derived_feature_extractor: 提取完成 ta_sma_5_0/ta_sma_20_0-1, 0.001s

[2022-04-04 19:53:45.207922] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/ta_sma_20_0-1, 0.001s

[2022-04-04 19:53:45.209826] INFO: derived_feature_extractor: 提取完成 ta_sma_20_0/ta_sma_30_0-1, 0.001s

[2022-04-04 19:53:45.211710] INFO: derived_feature_extractor: 提取完成 ta_sma_30_0/ta_sma_60_0-1, 0.001s

[2022-04-04 19:53:45.213657] INFO: derived_feature_extractor: 提取完成 ta_rsi_14_0/100, 0.001s

[2022-04-04 19:53:45.215584] INFO: derived_feature_extractor: 提取完成 ta_rsi_28_0/100, 0.001s

[2022-04-04 19:53:45.217475] INFO: derived_feature_extractor: 提取完成 ta_cci_14_0/500, 0.001s

[2022-04-04 19:53:45.219357] INFO: derived_feature_extractor: 提取完成 ta_cci_28_0/500, 0.001s

[2022-04-04 19:53:45.221158] INFO: derived_feature_extractor: 提取完成 beta_industry_30_0/10, 0.001s

[2022-04-04 19:53:45.258129] INFO: derived_feature_extractor: 提取完成 beta_industry_60_0/10, 0.001s

[2022-04-04 19:53:45.290079] INFO: derived_feature_extractor: 提取完成 ta_sma(amount_0, 10)/ta_sma(amount_0, 20)-1, 0.031s

[2022-04-04 19:53:45.321100] INFO: derived_feature_extractor: 提取完成 ta_sma(amount_0, 20)/ta_sma(amount_0, 30)-1, 0.029s

[2022-04-04 19:53:45.370025] INFO: derived_feature_extractor: 提取完成 ta_sma(amount_0, 30)/ta_sma(amount_0, 60)-1, 0.047s

[2022-04-04 19:53:45.400343] INFO: derived_feature_extractor: 提取完成 ta_sma(amount_0, 50)/ta_sma(amount_0, 100)-1, 0.029s

[2022-04-04 19:53:45.426078] INFO: derived_feature_extractor: 提取完成 ta_sma(turn_0, 10)/ta_sma(turn_0, 20)-1, 0.024s

[2022-04-04 19:53:45.464556] INFO: derived_feature_extractor: 提取完成 ta_sma(turn_0, 20)/ta_sma(turn_0, 30)-1, 0.037s

[2022-04-04 19:53:45.494372] INFO: derived_feature_extractor: 提取完成 ta_sma(turn_0, 30)/ta_sma(turn_0, 60)-1, 0.028s

[2022-04-04 19:53:45.524773] INFO: derived_feature_extractor: 提取完成 ta_sma(turn_0, 50)/ta_sma(turn_0, 100)-1, 0.029s

[2022-04-04 19:53:45.527563] INFO: derived_feature_extractor: 提取完成 high_0/low_0-1, 0.001s

[2022-04-04 19:53:45.529843] INFO: derived_feature_extractor: 提取完成 close_0/open_0-1, 0.001s

[2022-04-04 19:53:45.534102] INFO: derived_feature_extractor: 提取完成 shift(close_0,1)/close_0-1, 0.003s

[2022-04-04 19:53:45.538143] INFO: derived_feature_extractor: 提取完成 shift(close_0,2)/close_0-1, 0.003s

[2022-04-04 19:53:45.560962] INFO: derived_feature_extractor: 提取完成 shift(close_0,3)/close_0-1, 0.004s

[2022-04-04 19:53:45.565630] INFO: derived_feature_extractor: 提取完成 shift(close_0,4)/close_0-1, 0.003s

[2022-04-04 19:53:45.570138] INFO: derived_feature_extractor: 提取完成 shift(close_0,5)/close_0-1, 0.003s

[2022-04-04 19:53:45.574616] INFO: derived_feature_extractor: 提取完成 shift(close_0,10)/close_0-1, 0.003s

[2022-04-04 19:53:45.578862] INFO: derived_feature_extractor: 提取完成 shift(close_0,20)/close_0-1, 0.003s

[2022-04-04 19:53:45.606249] INFO: derived_feature_extractor: 提取完成 ta_sma(high_0-low_0, 5)/ta_sma(high_0-low_0, 20)-1, 0.026s

[2022-04-04 19:53:45.659299] INFO: derived_feature_extractor: 提取完成 ta_sma(high_0-low_0, 10)/ta_sma(high_0-low_0, 20)-1, 0.051s

[2022-04-04 19:53:45.689847] INFO: derived_feature_extractor: 提取完成 ta_sma(high_0-low_0, 20)/ta_sma(high_0-low_0, 30)-1, 0.029s

[2022-04-04 19:53:45.717001] INFO: derived_feature_extractor: 提取完成 ta_sma(high_0-low_0, 30)/ta_sma(high_0-low_0, 60)-1, 0.026s

[2022-04-04 19:53:45.762865] INFO: derived_feature_extractor: 提取完成 ta_sma(high_0-low_0, 50)/ta_sma(high_0-low_0, 100)-1, 0.044s

[2022-04-04 19:53:45.774598] INFO: derived_feature_extractor: 提取完成 std(close_0,5)/std(close_0,20)-1, 0.010s

[2022-04-04 19:53:45.785360] INFO: derived_feature_extractor: 提取完成 std(close_0,10)/std(close_0,20)-1, 0.009s

[2022-04-04 19:53:45.796344] INFO: derived_feature_extractor: 提取完成 std(close_0,20)/std(close_0,30)-1, 0.009s

[2022-04-04 19:53:45.806736] INFO: derived_feature_extractor: 提取完成 std(close_0,30)/std(close_0,60)-1, 0.009s

[2022-04-04 19:53:45.818547] INFO: derived_feature_extractor: 提取完成 std(close_0,50)/std(close_0,100)-1, 0.010s

[2022-04-04 19:53:45.866923] INFO: derived_feature_extractor: /y_2022, 585

[2022-04-04 19:53:46.202676] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[1.090523s].

[2022-04-04 19:53:46.217048] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2022-04-04 19:53:46.296095] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.079044s].

[2022-04-04 19:53:46.305564] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.001592s].

[2022-04-04 19:53:46.324253] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.011986s].

[2022-04-04 19:53:46.335570] INFO: moduleinvoker: dl_layer_dropout.v1 运行完成[0.004551s].

[2022-04-04 19:53:46.353948] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.012196s].

[2022-04-04 19:53:46.370721] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.01001s].

[2022-04-04 19:53:46.414064] INFO: moduleinvoker: cached.v3 开始运行..

[2022-04-04 19:53:46.467004] INFO: moduleinvoker: cached.v3 运行完成[0.052926s].

[2022-04-04 19:53:46.469288] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.078075s].

[2022-04-04 19:53:46.473791] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2022-04-04 19:53:46.567914] INFO: dl_model_train: 准备训练，训练样本个数：3761，迭代次数：2

[2022-04-04 19:53:47.561024] INFO: dl_model_train: 训练结束，耗时：0.99s

[2022-04-04 19:53:47.591630] INFO: moduleinvoker: dl_model_train.v1 运行完成[1.117823s].

[2022-04-04 19:53:47.596303] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2022-04-04 19:53:47.734762] ERROR: moduleinvoker: module name: dl_model_predict, module version: v1, trackeback: AttributeError: 'int' object has no attribute 'assign'

During handling of the above exception, another exception occurred:

AttributeError: 'int' object has no attribute 'assign'

Epoch 1/2
1/1 - 1s - loss: 0.1363 - mse: 0.1363
Epoch 2/2
1/1 - 0s - loss: 0.1068 - mse: 0.1068

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
AttributeError: 'int' object has no attribute 'assign'

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
<ipython-input-5-59767be951b5> in <module>
    366 )
    367 
--> 368 m11 = M.dl_model_predict.v1(
    369     trained_model=m5.data,
    370     input_data=m14.data,

AttributeError: 'int' object has no attribute 'assign'