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In [2]:

# 本代码由可视化策略环境自动生成 2021年11月25日 13:53
# 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):
    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

# 回测引擎：初始化函数，只执行一次
def m31_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.hold_days = 5
# 回测引擎：每日数据处理函数，每天执行一次
def m31_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}
   
    
    #大盘风控模块，读取风控数据  
    #----------------大盘风控模块，读取风控数据------------------
    # risk表示是否遇到了下跌的情况，等于0否，等于1是
    risk = 0
    today = data.current_dt.strftime('%Y-%m-%d')
    # 利用上证指数的涨跌来看大盘的涨跌
    bm_ret0=ranker_prediction.bm_ret0.values[0]
    bm_ret1=ranker_prediction.bm_ret1.values[0]
    bm_ret2=ranker_prediction.bm_ret2.values[0]
    bm_ret3=ranker_prediction.bm_ret3.values[0]
    bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]
    bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]
    bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]
    if bm_ret0 < 0.001:
        if bm_risk_v0 > 0:
            print(today,'大盘放量下跌,全仓卖出')
            risk = 1
        elif bm_ret1 < 0.001 and bm_ret2 < 0.002:
            print(today,'大盘连续下跌,全仓卖出')
            risk = 1
        if bm_ret3 < -0.02:
            print(today,'大盘三日下跌超过2%,全仓卖出')
            risk = 1
    if bm_ret0 > 0.01:
        if (bm_risk_v0 + bm_risk_v1) < 0:
            print(today,'大盘缩量上涨,全仓卖出')
            risk = 1

    # 此时需要卖出手上所有的股票
    if risk == 1:
    # 手上还有仓位
        if len(positions)>0:
        # 全部卖出后返回
            for instrument in positions:
                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
                if data.can_trade(context.symbol(instrument)) and hold_days > 0:
                    context.order_target_percent(context.symbol(instrument), 0)
                    return 
                # 风控卖出后直接使用return结束当日交易，后续轮仓逻辑不再执行
        #---------------------大盘风控结束--------------------------------------


    # 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
    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 -= stock_hold_now[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 m31_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 m31_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2015-01-01',
    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, -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'
)

m3 = M.input_features.v1(
    features="""# 资产周转率
fs_operating_revenue_ttm_0/(fs_non_current_assets_0+fs_current_assets_0) 
# 总盈利/总资产
fs_total_profit_0/(fs_non_current_assets_0+ fs_current_assets_0) 
# 自营现金流/总资产
fs_free_cash_flow_0/(fs_non_current_assets_0+ fs_current_assets_0)
# 总收入/价格 
fs_operating_revenue_ttm_0/close_0
# 现金流/股数/股价
fs_free_cash_flow_0/(fs_common_equity_0/close_0)/close_0
# 营业收入 Sales to EV
fs_operating_revenue_ttm_0/fs_common_equity_0
#  EBITDA to EV 
fs_net_income_0/fs_common_equity_0


"""
)

m22 = 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=m22.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m22.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
)

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=m22.data,
    window_size=5,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m4 = M.cached.v3(
    input_1=m26.data,
    input_2=m22.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', '2020-01-01'),
    end_date=T.live_run_param('trading_date', '2021-11-19'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m22.data,
    start_date='',
    end_date='',
    before_start_days=0
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m22.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=''
)

m21 = M.filter.v3(
    input_data=m25.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=m21.data,
    features=m22.data,
    window_size=5,
    feature_clip=5,
    flatten=True,
    window_along_col='instrument'
)

m8 = M.cached.v3(
    input_1=m27.data,
    input_2=m22.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='12,5',
    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=20,
    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=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=m21.data,
    run=m24_run_bigquant_run,
    post_run=m24_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m20 = 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
ret_1=close/shift(close,1)
ret_3=close/shift(close,3)
volumepct_1=volume/shift(volume,1)
bm_ret0=ret_1
bm_ret1=shift(bm_ret0,1)
bm_ret2=shift(bm_ret0,2)
bm_ret3=ret_3
bm_risk_v0=volumepct_1
bm_risk_v1=shift(bm_risk_v0,1)
bm_risk_v2=shift(bm_risk_v0,2)"""
)

m23 = M.index_feature_extract.v3(
    input_1=m9.data,
    input_2=m20.data,
    before_days=100,
    index='000001.HIX'
)

m29 = M.join.v3(
    data1=m24.data_1,
    data2=m23.data_1,
    on='date',
    how='left',
    sort=False
)

m31 = M.trade.v4(
    instruments=m9.data,
    options_data=m29.data,
    start_date='',
    end_date='',
    initialize=m31_initialize_bigquant_run,
    handle_data=m31_handle_data_bigquant_run,
    prepare=m31_prepare_bigquant_run,
    before_trading_start=m31_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=''
)

[2021-11-25 13:51:35.252705] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-11-25 13:51:35.264717] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.266987] INFO: moduleinvoker: instruments.v2 运行完成[0.014294s].

[2021-11-25 13:51:35.276581] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-11-25 13:51:35.285048] INFO: moduleinvoker: 命中缓存

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[2021-11-25 13:51:35.294261] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-11-25 13:51:35.308573] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.311108] INFO: moduleinvoker: standardlize.v8 运行完成[0.016843s].

[2021-11-25 13:51:35.318853] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-25 13:51:35.335574] INFO: moduleinvoker: 命中缓存

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[2021-11-25 13:51:35.356631] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.358609] INFO: moduleinvoker: input_features.v1 运行完成[0.01232s].

[2021-11-25 13:51:35.372501] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-11-25 13:51:35.379762] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.382017] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.00956s].

[2021-11-25 13:51:35.389659] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-11-25 13:51:35.398366] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.400067] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010406s].

[2021-11-25 13:51:35.405035] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-11-25 13:51:35.413372] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.415073] INFO: moduleinvoker: standardlize.v8 运行完成[0.010036s].

[2021-11-25 13:51:35.429932] INFO: moduleinvoker: join.v3 开始运行..

[2021-11-25 13:51:35.440794] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.442687] INFO: moduleinvoker: join.v3 运行完成[0.012792s].

[2021-11-25 13:51:35.452746] INFO: moduleinvoker: filter.v3 开始运行..

[2021-11-25 13:51:35.460851] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.462416] INFO: moduleinvoker: filter.v3 运行完成[0.009681s].

[2021-11-25 13:51:35.474331] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-11-25 13:51:35.487074] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.488717] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.014395s].

[2021-11-25 13:51:35.502296] INFO: moduleinvoker: cached.v3 开始运行..

[2021-11-25 13:51:35.508612] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.510141] INFO: moduleinvoker: cached.v3 运行完成[0.007863s].

[2021-11-25 13:51:35.515907] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-11-25 13:51:35.525155] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.529727] INFO: moduleinvoker: instruments.v2 运行完成[0.013802s].

[2021-11-25 13:51:35.547866] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-11-25 13:51:35.562033] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.564423] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.01657s].

[2021-11-25 13:51:35.572280] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-11-25 13:51:35.579153] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.581199] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.008914s].

[2021-11-25 13:51:35.586706] INFO: moduleinvoker: standardlize.v8 开始运行..

[2021-11-25 13:51:35.596904] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.599238] INFO: moduleinvoker: standardlize.v8 运行完成[0.012524s].

[2021-11-25 13:51:35.608649] INFO: moduleinvoker: filter.v3 开始运行..

[2021-11-25 13:51:35.615619] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.617828] INFO: moduleinvoker: filter.v3 运行完成[0.009176s].

[2021-11-25 13:51:35.640755] INFO: moduleinvoker: dl_convert_to_bin.v2 开始运行..

[2021-11-25 13:51:35.648930] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.650607] INFO: moduleinvoker: dl_convert_to_bin.v2 运行完成[0.00988s].

[2021-11-25 13:51:35.666063] INFO: moduleinvoker: cached.v3 开始运行..

[2021-11-25 13:51:35.675669] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:35.677413] INFO: moduleinvoker: cached.v3 运行完成[0.011387s].

[2021-11-25 13:51:35.684927] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.00186s].

[2021-11-25 13:51:35.707677] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.017035s].

[2021-11-25 13:51:35.725635] INFO: moduleinvoker: dl_layer_maxpooling1d.v1 运行完成[0.012211s].

[2021-11-25 13:51:35.773802] INFO: moduleinvoker: dl_layer_conv1d.v1 运行完成[0.024549s].

[2021-11-25 13:51:35.792272] INFO: moduleinvoker: dl_layer_maxpooling1d.v1 运行完成[0.011066s].

[2021-11-25 13:51:35.806318] INFO: moduleinvoker: dl_layer_globalmaxpooling1d.v1 运行完成[0.006597s].

[2021-11-25 13:51:35.833123] INFO: moduleinvoker: dl_layer_dense.v1 运行完成[0.020113s].

[2021-11-25 13:51:35.871186] INFO: moduleinvoker: cached.v3 开始运行..

[2021-11-25 13:51:35.918034] INFO: moduleinvoker: cached.v3 运行完成[0.046822s].

[2021-11-25 13:51:35.924165] INFO: moduleinvoker: dl_model_init.v1 运行完成[0.082039s].

[2021-11-25 13:51:35.929896] INFO: moduleinvoker: dl_model_train.v1 开始运行..

[2021-11-25 13:51:36.113885] INFO: dl_model_train: 准备训练，训练样本个数：45005，迭代次数：20

[2021-11-25 13:51:56.072308] INFO: dl_model_train: 训练结束，耗时：19.96s

[2021-11-25 13:51:56.112453] INFO: moduleinvoker: dl_model_train.v1 运行完成[20.182548s].

[2021-11-25 13:51:56.121513] INFO: moduleinvoker: dl_model_predict.v1 开始运行..

[2021-11-25 13:51:56.610137] INFO: moduleinvoker: dl_model_predict.v1 运行完成[0.488679s].

[2021-11-25 13:51:56.634047] INFO: moduleinvoker: cached.v3 开始运行..

[2021-11-25 13:51:56.838372] INFO: moduleinvoker: cached.v3 运行完成[0.204433s].

[2021-11-25 13:51:56.845532] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-25 13:51:56.857977] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:56.859971] INFO: moduleinvoker: input_features.v1 运行完成[0.014463s].

[2021-11-25 13:51:56.874141] INFO: moduleinvoker: index_feature_extract.v3 开始运行..

[2021-11-25 13:51:56.887175] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:56.888845] INFO: moduleinvoker: index_feature_extract.v3 运行完成[0.014721s].

[2021-11-25 13:51:56.899198] INFO: moduleinvoker: join.v3 开始运行..

[2021-11-25 13:51:57.086558] INFO: join: /data, 行数=12931/12931, 耗时=0.083451s

[2021-11-25 13:51:57.113187] INFO: join: 最终行数: 12931

[2021-11-25 13:51:57.120916] INFO: moduleinvoker: join.v3 运行完成[0.221675s].

[2021-11-25 13:51:57.192739] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-11-25 13:51:57.204170] INFO: backtest: biglearning backtest:V8.6.0

[2021-11-25 13:51:57.205986] INFO: backtest: product_type:stock by specified

[2021-11-25 13:51:57.312188] INFO: moduleinvoker: cached.v2 开始运行..

[2021-11-25 13:51:57.322746] INFO: moduleinvoker: 命中缓存

[2021-11-25 13:51:57.329813] INFO: moduleinvoker: cached.v2 运行完成[0.017636s].

[2021-11-25 13:52:00.577407] INFO: algo: TradingAlgorithm V1.8.5

[2021-11-25 13:52:01.590626] INFO: algo: trading transform...

[2021-11-25 13:52:06.373674] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: IndexError: index 0 is out of bounds for axis 0 with size 0

[2021-11-25 13:52:06.380198] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: IndexError: index 0 is out of bounds for axis 0 with size 0

Epoch 1/20
176/176 - 2s - loss: 1.7236 - mae: 0.9365
Epoch 2/20
176/176 - 1s - loss: 1.6863 - mae: 0.9245
Epoch 3/20
176/176 - 1s - loss: 1.6820 - mae: 0.9231
Epoch 4/20
176/176 - 1s - loss: 1.6774 - mae: 0.9220
Epoch 5/20
176/176 - 1s - loss: 1.6754 - mae: 0.9210
Epoch 6/20
176/176 - 1s - loss: 1.6751 - mae: 0.9205
Epoch 7/20
176/176 - 1s - loss: 1.6737 - mae: 0.9199
Epoch 8/20
176/176 - 1s - loss: 1.6722 - mae: 0.9195
Epoch 9/20
176/176 - 1s - loss: 1.6719 - mae: 0.9196
Epoch 10/20
176/176 - 1s - loss: 1.6718 - mae: 0.9196
Epoch 11/20
176/176 - 1s - loss: 1.6678 - mae: 0.9183
Epoch 12/20
176/176 - 1s - loss: 1.6692 - mae: 0.9184
Epoch 13/20
176/176 - 1s - loss: 1.6688 - mae: 0.9187
Epoch 14/20
176/176 - 1s - loss: 1.6675 - mae: 0.9182
Epoch 15/20
176/176 - 1s - loss: 1.6661 - mae: 0.9179
Epoch 16/20
176/176 - 1s - loss: 1.6659 - mae: 0.9175
Epoch 17/20
176/176 - 1s - loss: 1.6666 - mae: 0.9177
Epoch 18/20
176/176 - 1s - loss: 1.6657 - mae: 0.9179
Epoch 19/20
176/176 - 1s - loss: 1.6651 - mae: 0.9175
Epoch 20/20
176/176 - 1s - loss: 1.6653 - mae: 0.9177

13/13 - 0s
DataSource(c6e074ad2b79430a80fb2af8a44f2788T)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-2-5d70a1b40603> in <module>
    527 )
    528 
--> 529 m31 = M.trade.v4(
    530     instruments=m9.data,
    531     options_data=m29.data,

<ipython-input-2-5d70a1b40603> in m31_handle_data_bigquant_run(context, data)
     96     today = data.current_dt.strftime('%Y-%m-%d')
     97     # 利用上证指数的涨跌来看大盘的涨跌
---> 98     bm_ret0=ranker_prediction.bm_ret0.values[0]
     99     bm_ret1=ranker_prediction.bm_ret1.values[0]
    100     bm_ret2=ranker_prediction.bm_ret2.values[0]

IndexError: index 0 is out of bounds for axis 0 with size 0