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# 本代码由可视化策略环境自动生成 2022年12月16日 14:57
# 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。


# 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 m30_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_2.read()
    x_train, x_val, y_train, y_val = train_test_split(data["x"], data['y'])
    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 m30_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
# }
m6_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 m2_run_bigquant_run(input_1, input_2, input_3):

    test_data = input_2.read_pickle()
    pred_label = input_1.read_pickle()
    pred_result = pred_label.reshape(pred_label.shape[0]) 
    dt = input_3.read_df()['date'][-1*len(pred_result):]
    pred_df = pd.Series(pred_result, index=dt)
    ds = DataSource.write_df(pred_df)
    
    return Outputs(data_1=ds)

# 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 m2_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m1_initialize_bigquant_run(context):
    # 加载预测数据
    context.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))
# 回测引擎：每日数据处理函数，每天执行一次
def m1_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    try:
        prediction = context.prediction[data.current_dt.strftime('%Y-%m-%d')]
    except KeyError as e:
        return
    
    instrument = context.instruments[0]
    sid = context.symbol(instrument)
    cur_position = context.portfolio.positions[sid].amount
    
    # 交易逻辑
    if prediction > 0.5 and cur_position == 0:
        context.order_target_percent(context.symbol(instrument), 1)
        print(data.current_dt, '买入！')
        
    elif prediction < 0.5 and cur_position > 0:
        context.order_target_percent(context.symbol(instrument), 0)
        print(data.current_dt, '卖出！')
    
# 回测引擎：准备数据，只执行一次
def m1_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 m1_before_trading_start_bigquant_run(context, data):
    pass


m3 = M.dl_layer_input.v1(
    shape='50,5',
    batch_shape='',
    dtype='float32',
    sparse=False,
    name=''
)

m13 = M.dl_layer_reshape.v1(
    inputs=m3.data,
    target_shape='50,5,1',
    name=''
)

m14 = M.dl_layer_conv2d.v1(
    inputs=m13.data,
    filters=32,
    kernel_size='3,5',
    strides='1,1',
    padding='valid',
    data_format='channels_last',
    dilation_rate='1,1',
    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=''
)

m15 = M.dl_layer_reshape.v1(
    inputs=m14.data,
    target_shape='48,32',
    name=''
)

m4 = M.dl_layer_lstm.v1(
    inputs=m15.data,
    units=32,
    activation='tanh',
    recurrent_activation='hard_sigmoid',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    recurrent_initializer='Orthogonal',
    bias_initializer='Ones',
    unit_forget_bias=True,
    kernel_regularizer='None',
    kernel_regularizer_l1=0,
    kernel_regularizer_l2=0,
    recurrent_regularizer='None',
    recurrent_regularizer_l1=0,
    recurrent_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',
    recurrent_constraint='None',
    bias_constraint='None',
    dropout=0,
    recurrent_dropout=0,
    return_sequences=False,
    implementation='2',
    name=''
)

m11 = M.dl_layer_dropout.v1(
    inputs=m4.data,
    rate=0.4,
    noise_shape='',
    name=''
)

m10 = M.dl_layer_dense.v1(
    inputs=m11.data,
    units=32,
    activation='tanh',
    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_dropout.v1(
    inputs=m10.data,
    rate=0.8,
    noise_shape='',
    name=''
)

m9 = M.dl_layer_dense.v1(
    inputs=m12.data,
    units=1,
    activation='sigmoid',
    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=''
)

m5 = M.dl_model_init.v1(
    inputs=m3.data,
    outputs=m9.data
)

m8 = M.input_features.v1(
    features="""(close_0/close_1-1)*10
(high_0/high_1-1)*10
(low_0/low_1-1)*10
(open_0/open_1-1)*10
(volume_0/volume_1-1)*10"""
)

m24 = M.instruments.v2(
    start_date='2015-07-02',
    end_date='2017-10-30',
    market='CN_STOCK_A',
    instrument_list='600009.SHA',
    max_count=0
)

m21 = M.advanced_auto_labeler.v2(
    instruments=m24.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/develop/datasource/deprecated/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/develop/bigexpr/usage.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)
where(shift(close, -10) / close -1>0,1,0)

# 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=True,
    user_functions={}
)

m22 = M.general_feature_extractor.v7(
    instruments=m24.data,
    features=m8.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m23 = M.derived_feature_extractor.v3(
    input_data=m22.data,
    features=m8.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m17 = M.join.v3(
    data1=m21.data,
    data2=m23.data,
    on='date',
    how='inner',
    sort=True
)

m19 = M.dropnan.v2(
    input_data=m17.data
)

m18 = M.standardlize.v8(
    input_1=m19.data,
    input_2=m8.data,
    columns_input=''
)

m25 = M.dl_convert_to_bin.v2(
    input_data=m18.data,
    features=m8.data,
    window_size=50,
    feature_clip=5,
    flatten=False,
    window_along_col=''
)

m30 = M.cached.v3(
    input_2=m25.data,
    run=m30_run_bigquant_run,
    post_run=m30_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m6 = M.dl_model_train.v1(
    input_model=m5.data,
    training_data=m30.data_1,
    validation_data=m30.data_2,
    optimizer='Adam',
    loss='binary_crossentropy',
    metrics='accuracy',
    batch_size=2048,
    epochs=10,
    custom_objects=m6_custom_objects_bigquant_run,
    n_gpus=0,
    verbose='1:输出进度条记录'
)

m28 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-02-11'),
    end_date=T.live_run_param('trading_date', '2019-09-01'),
    market='CN_STOCK_A',
    instrument_list='600009.SHA',
    max_count=0
)

m16 = M.general_feature_extractor.v7(
    instruments=m28.data,
    features=m8.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m26 = M.derived_feature_extractor.v3(
    input_data=m16.data,
    features=m8.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m29 = M.dropnan.v2(
    input_data=m26.data
)

m20 = M.standardlize.v8(
    input_1=m29.data,
    input_2=m8.data,
    columns_input=''
)

m27 = M.dl_convert_to_bin.v2(
    input_data=m20.data,
    features=m8.data,
    window_size=50,
    feature_clip=5,
    flatten=False,
    window_along_col=''
)

m7 = M.dl_model_predict.v1(
    trained_model=m6.data,
    input_data=m27.data,
    batch_size=10240,
    n_gpus=0,
    verbose='2:每个epoch输出一行记录'
)

m2 = M.cached.v3(
    input_1=m7.data,
    input_2=m27.data,
    input_3=m20.data,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m1 = M.trade.v4(
    instruments=m28.data,
    options_data=m2.data_1,
    start_date='',
    end_date='',
    initialize=m1_initialize_bigquant_run,
    handle_data=m1_handle_data_bigquant_run,
    prepare=m1_prepare_bigquant_run,
    before_trading_start=m1_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='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2022-12-16 14:56:44.680742] INFO: moduleinvoker: dl_layer_input.v1 运行完成[0.002056s].

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[2022-12-16 14:56:45.955099] INFO: dl_model_train: 准备训练，训练样本个数：426，迭代次数：10

[2022-12-16 14:57:02.211793] INFO: dl_model_train: 训练结束，耗时：16.25s

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[2022-12-16 14:57:07.852295] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-12-16 14:57:07.860896] INFO: backtest: biglearning backtest:V8.6.3

[2022-12-16 14:57:07.862831] INFO: backtest: product_type:stock by specified

[2022-12-16 14:57:07.963242] INFO: moduleinvoker: cached.v2 开始运行..

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[2022-12-16 14:57:07.986447] INFO: moduleinvoker: cached.v2 运行完成[0.023224s].

[2022-12-16 14:57:11.758328] INFO: backtest: algo history_data=DataSource(79fc12454c1448b9b8d6eb38c288bd28T)

[2022-12-16 14:57:11.760570] INFO: algo: TradingAlgorithm V1.8.8

[2022-12-16 14:57:12.890327] INFO: algo: trading transform...

[2022-12-16 14:57:13.282835] INFO: algo: handle_splits get splits [dt:2015-08-20 00:00:00+00:00] [asset:Equity(27 [600009.SHA]), ratio:0.9882234930992126]

[2022-12-16 14:57:13.284777] INFO: Position: position stock handle split[sid:27, orig_amount:48900, new_amount:49482.0, orig_cost:20.430083285176046, new_cost:20.1895, ratio:0.9882234930992126, last_sale_price:29.369998931884766]

[2022-12-16 14:57:13.286417] INFO: Position: after split: PositionStock(asset:Equity(27 [600009.SHA]), amount:49482.0, cost_basis:20.1895, last_sale_price:29.71999740600586)

[2022-12-16 14:57:13.287705] INFO: Position: returning cash: 21.5504

[2022-12-16 14:57:13.924852] INFO: algo: handle_splits get splits [dt:2016-08-18 00:00:00+00:00] [asset:Equity(27 [600009.SHA]), ratio:0.9852133393287659]

[2022-12-16 14:57:13.926966] INFO: Position: position stock handle split[sid:27, orig_amount:49482.0, new_amount:50224.0, orig_cost:20.1895, new_cost:19.891, ratio:0.9852133393287659, last_sale_price:28.65000343322754]

[2022-12-16 14:57:13.929149] INFO: Position: after split: PositionStock(asset:Equity(27 [600009.SHA]), amount:50224.0, cost_basis:19.891, last_sale_price:29.079999923706055)

[2022-12-16 14:57:13.931117] INFO: Position: returning cash: 18.7637

[2022-12-16 14:57:14.570908] INFO: algo: handle_splits get splits [dt:2017-08-24 00:00:00+00:00] [asset:Equity(27 [600009.SHA]), ratio:0.9882099032402039]

[2022-12-16 14:57:14.572836] INFO: Position: position stock handle split[sid:27, orig_amount:50224.0, new_amount:50823.0, orig_cost:19.891, new_cost:19.6565, ratio:0.9882099032402039, last_sale_price:36.8799934387207]

[2022-12-16 14:57:14.574768] INFO: Position: after split: PositionStock(asset:Equity(27 [600009.SHA]), amount:50823.0, cost_basis:19.6565, last_sale_price:37.31999969482422)

[2022-12-16 14:57:14.576468] INFO: Position: returning cash: 7.7658

[2022-12-16 14:57:15.221644] INFO: algo: handle_splits get splits [dt:2018-08-23 00:00:00+00:00] [asset:Equity(27 [600009.SHA]), ratio:0.9900703430175781]

[2022-12-16 14:57:15.223286] INFO: Position: position stock handle split[sid:27, orig_amount:50823.0, new_amount:51332.0, orig_cost:19.6565, new_cost:19.4613, ratio:0.9900703430175781, last_sale_price:57.83000946044922]

[2022-12-16 14:57:15.224653] INFO: Position: after split: PositionStock(asset:Equity(27 [600009.SHA]), amount:51332.0, cost_basis:19.4613, last_sale_price:58.40999984741211)

[2022-12-16 14:57:15.225813] INFO: Position: returning cash: 41.4216

[2022-12-16 14:57:15.791430] INFO: algo: handle_splits get splits [dt:2019-08-22 00:00:00+00:00] [asset:Equity(27 [600009.SHA]), ratio:0.9922354221343994]

[2022-12-16 14:57:15.793124] INFO: Position: position stock handle split[sid:27, orig_amount:51332.0, new_amount:51733.0, orig_cost:19.4613, new_cost:19.3102, ratio:0.9922354221343994, last_sale_price:84.34001159667969]

[2022-12-16 14:57:15.794636] INFO: Position: after split: PositionStock(asset:Equity(27 [600009.SHA]), amount:51733.0, cost_basis:19.3102, last_sale_price:85.0)

[2022-12-16 14:57:15.796246] INFO: Position: returning cash: 58.2171

[2022-12-16 14:57:15.814234] INFO: Performance: Simulated 1111 trading days out of 1111.

[2022-12-16 14:57:15.816052] INFO: Performance: first open: 2015-02-11 09:30:00+00:00

[2022-12-16 14:57:15.817505] INFO: Performance: last close: 2019-08-30 15:00:00+00:00

[2022-12-16 14:57:19.048473] INFO: moduleinvoker: backtest.v8 运行完成[11.196182s].

[2022-12-16 14:57:19.050310] INFO: moduleinvoker: trade.v4 运行完成[15.458139s].

WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU

Epoch 1/10
1/1 [==============================] - ETA: 0s - loss: 1.0047 - accuracy: 0.48361/1 [==============================] - 14s 14s/step - loss: 1.0047 - accuracy: 0.4836 - val_loss: 0.6935 - val_accuracy: 0.5493
Epoch 2/10
1/1 [==============================] - ETA: 0s - loss: 0.9787 - accuracy: 0.51411/1 [==============================] - 0s 195ms/step - loss: 0.9787 - accuracy: 0.5141 - val_loss: 0.6937 - val_accuracy: 0.5493
Epoch 3/10
1/1 [==============================] - ETA: 0s - loss: 0.9244 - accuracy: 0.52581/1 [==============================] - 0s 219ms/step - loss: 0.9244 - accuracy: 0.5258 - val_loss: 0.6947 - val_accuracy: 0.5493
Epoch 4/10
1/1 [==============================] - ETA: 0s - loss: 0.8787 - accuracy: 0.55161/1 [==============================] - 0s 221ms/step - loss: 0.8787 - accuracy: 0.5516 - val_loss: 0.6963 - val_accuracy: 0.5493
Epoch 5/10
1/1 [==============================] - ETA: 0s - loss: 0.8617 - accuracy: 0.54691/1 [==============================] - 0s 191ms/step - loss: 0.8617 - accuracy: 0.5469 - val_loss: 0.6976 - val_accuracy: 0.5493
Epoch 6/10
1/1 [==============================] - ETA: 0s - loss: 0.9454 - accuracy: 0.49771/1 [==============================] - 0s 223ms/step - loss: 0.9454 - accuracy: 0.4977 - val_loss: 0.6983 - val_accuracy: 0.5493
Epoch 7/10
1/1 [==============================] - ETA: 0s - loss: 0.9612 - accuracy: 0.50941/1 [==============================] - 0s 217ms/step - loss: 0.9612 - accuracy: 0.5094 - val_loss: 0.6994 - val_accuracy: 0.5493
Epoch 8/10
1/1 [==============================] - ETA: 0s - loss: 0.8815 - accuracy: 0.54691/1 [==============================] - 0s 218ms/step - loss: 0.8815 - accuracy: 0.5469 - val_loss: 0.7005 - val_accuracy: 0.5493
Epoch 9/10
1/1 [==============================] - ETA: 0s - loss: 0.9096 - accuracy: 0.47891/1 [==============================] - 0s 199ms/step - loss: 0.9096 - accuracy: 0.4789 - val_loss: 0.7012 - val_accuracy: 0.5493
Epoch 10/10
1/1 [==============================] - ETA: 0s - loss: 0.9282 - accuracy: 0.52581/1 [==============================] - 0s 207ms/step - loss: 0.9282 - accuracy: 0.5258 - val_loss: 0.7012 - val_accuracy: 0.5493

1/1 - 1s
DataSource(d77b6ef9aba6447d914d8134d00b6c9aT)

2015-02-11 15:00:00+00:00 买入！

收益率334.9%
年化收益率39.57%
基准收益率11.52%
阿尔法0.42
贝塔0.96
夏普比率0.98
胜率1.0
盈亏比0.0
收益波动率38.83%
信息比率0.07
最大回撤40.62%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-ab4810558beb480eb5da674b862f7e04"}/bigcharts-data-end

In [2]:

# 方法一：手动绘制曲线
from matplotlib import pyplot as plt

train_loss = m6.data.read()["history"]["loss"]
val_loss = m6.data.read()["history"]["val_loss"]

plt.plot(train_loss, label="train")
plt.plot(val_loss, label="validation")
plt.legend()
plt.show()

In [3]:

train_acc = m6.data.read()["history"]["accuracy"]
val_acc = m6.data.read()["history"]["val_accuracy"]

plt.plot(train_acc, label="train")
plt.plot(val_acc, label="validation")
plt.legend()
plt.show()