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# 本代码由可视化策略环境自动生成 2021年11月25日 10:01
# 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。


# 回测引擎：初始化函数，只执行一次
def m2_initialize_bigquant_run(context):
    # 加载预测数据

    context.pre_act = 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 m2_handle_data_bigquant_run(context, data):
    sid = symbol('000001.SZA')
    
    action = context.pre_act[context.pre_act['date'] == data.current_dt.strftime('%Y-%m-%d')]
    
    # 持仓
    cur_position = context.portfolio.positions[sid].amount 
    
    if len(action['pre_action'])>0:

        if int(action['pre_action'])==0 and  cur_position == 0:
            context.order(sid, 100)
            
        elif int(action['pre_action'])==1 and cur_position > 0:
            context.order_target_percent(sid, 0)

# 回测引擎：准备数据，只执行一次
def m2_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 m2_before_trading_start_bigquant_run(context, data):
    pass


m7 = M.instruments.v2(
    start_date='2012-01-01',
    end_date='2016-01-01',
    market='CN_STOCK_A',
    instrument_list="""000001.SZA
""",
    max_count=0
)

m8 = M.advanced_auto_labeler.v2(
    instruments=m7.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)
shift(close, -5) / shift(open, -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=True
)

m11 = 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
close_0/mean(close_0,5)
close_0/mean(close_0,10)
close_0/mean(close_0,20)
close_0/open_0
open_0/mean(close_0,5)
open_0/mean(close_0,10)
open_0/mean(close_0,20)"""
)

m9 = M.general_feature_extractor.v7(
    instruments=m7.data,
    features=m11.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m10 = M.derived_feature_extractor.v3(
    input_data=m9.data,
    features=m11.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m12 = M.join.v3(
    data1=m8.data,
    data2=m10.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m14 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='000001.SZA',
    max_count=0
)

m19 = M.advanced_auto_labeler.v2(
    instruments=m14.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)
shift(close, -5) / shift(open, -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=True
)

m17 = 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
close_0/mean(close_0,5)
close_0/mean(close_0,10)
close_0/mean(close_0,20)
close_0/open_0
open_0/mean(close_0,5)
open_0/mean(close_0,10)
open_0/mean(close_0,20)
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m14.data,
    features=m17.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m17.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m18 = M.join.v3(
    data1=m19.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m4 = M.dropnan.v1(
    input_data=m18.data
)

m5 = M.DQN_model.v10(
    data=m12.data,
    data_2=m4.data
)

m3 = M.dropnan.v1(
    input_data=m5.data_1
)

m2 = M.trade.v4(
    instruments=m14.data,
    options_data=m3.data,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    handle_data=m2_handle_data_bigquant_run,
    prepare=m2_prepare_bigquant_run,
    before_trading_start=m2_before_trading_start_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=''
)

[2021-11-24 22:16:05.043230] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-11-24 22:16:05.735476] INFO: moduleinvoker: instruments.v2 运行完成[0.685421s].

[2021-11-24 22:16:05.776856] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-11-24 22:16:07.309083] INFO: 自动标注(股票): 加载历史数据: 958 行

[2021-11-24 22:16:07.313345] INFO: 自动标注(股票): 开始标注 ..

[2021-11-24 22:16:08.159818] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.38293s].

[2021-11-24 22:16:08.175614] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-24 22:16:08.193267] INFO: moduleinvoker: 命中缓存

[2021-11-24 22:16:08.208196] INFO: moduleinvoker: input_features.v1 运行完成[0.032587s].

[2021-11-24 22:16:08.284857] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-11-24 22:16:09.612621] INFO: 基础特征抽取: 年份 2011, 特征行数=15

[2021-11-24 22:16:10.553664] INFO: 基础特征抽取: 年份 2012, 特征行数=233

[2021-11-24 22:16:11.312986] INFO: 基础特征抽取: 年份 2013, 特征行数=237

[2021-11-24 22:16:12.250419] INFO: 基础特征抽取: 年份 2014, 特征行数=244

[2021-11-24 22:16:13.219753] INFO: 基础特征抽取: 年份 2015, 特征行数=244

[2021-11-24 22:16:14.313256] WARNING: bigdatasource: No data in table features_CN_STOCK_A_G300! return is None!

[2021-11-24 22:16:14.315613] INFO: 基础特征抽取: 年份 2016, 特征行数=0

[2021-11-24 22:16:14.404045] INFO: 基础特征抽取: 总行数: 973

[2021-11-24 22:16:14.408871] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[6.12405s].

[2021-11-24 22:16:14.419511] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-11-24 22:16:14.679354] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 0.035s

[2021-11-24 22:16:14.687407] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 0.006s

[2021-11-24 22:16:14.693791] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 0.005s

[2021-11-24 22:16:14.696590] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.002s

[2021-11-24 22:16:14.703786] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 0.006s

[2021-11-24 22:16:14.710187] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 0.005s

[2021-11-24 22:16:14.718326] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 0.007s

[2021-11-24 22:16:14.788950] INFO: derived_feature_extractor: /y_2011, 15

[2021-11-24 22:16:14.861232] INFO: derived_feature_extractor: /y_2012, 233

[2021-11-24 22:16:14.931174] INFO: derived_feature_extractor: /y_2013, 237

[2021-11-24 22:16:15.002684] INFO: derived_feature_extractor: /y_2014, 244

[2021-11-24 22:16:15.076286] INFO: derived_feature_extractor: /y_2015, 244

[2021-11-24 22:16:15.247705] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.828173s].

[2021-11-24 22:16:15.269789] INFO: moduleinvoker: join.v3 开始运行..

[2021-11-24 22:16:15.496117] INFO: join: /y_2011, 行数=0/15, 耗时=0.093194s

[2021-11-24 22:16:15.564576] INFO: join: /y_2012, 行数=233/233, 耗时=0.066536s

[2021-11-24 22:16:15.612574] INFO: join: /y_2013, 行数=237/237, 耗时=0.046428s

[2021-11-24 22:16:15.691593] INFO: join: /y_2014, 行数=244/244, 耗时=0.077285s

[2021-11-24 22:16:15.786466] INFO: join: /y_2015, 行数=239/244, 耗时=0.093089s

[2021-11-24 22:16:15.836219] INFO: join: 最终行数: 953

[2021-11-24 22:16:15.864428] INFO: moduleinvoker: join.v3 运行完成[0.594818s].

[2021-11-24 22:16:15.869909] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-11-24 22:16:15.944942] INFO: moduleinvoker: instruments.v2 运行完成[0.075014s].

[2021-11-24 22:16:15.955968] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-11-24 22:16:16.186490] INFO: 自动标注(股票): 加载历史数据: 244 行

[2021-11-24 22:16:16.193982] INFO: 自动标注(股票): 开始标注 ..

[2021-11-24 22:16:16.338659] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.382683s].

[2021-11-24 22:16:16.346165] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-24 22:16:16.360036] INFO: moduleinvoker: 命中缓存

[2021-11-24 22:16:16.363035] INFO: moduleinvoker: input_features.v1 运行完成[0.016892s].

[2021-11-24 22:16:16.382329] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-11-24 22:16:17.383572] INFO: 基础特征抽取: 年份 2015, 特征行数=14

[2021-11-24 22:16:18.458512] INFO: 基础特征抽取: 年份 2016, 特征行数=244

[2021-11-24 22:16:19.661059] WARNING: bigdatasource: No data in table features_CN_STOCK_A_G300! return is None!

[2021-11-24 22:16:19.663539] INFO: 基础特征抽取: 年份 2017, 特征行数=0

[2021-11-24 22:16:19.738135] INFO: 基础特征抽取: 总行数: 258

[2021-11-24 22:16:19.745070] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[3.362773s].

[2021-11-24 22:16:19.757489] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-11-24 22:16:19.861359] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 0.006s

[2021-11-24 22:16:19.868076] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 0.005s

[2021-11-24 22:16:19.873595] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 0.004s

[2021-11-24 22:16:19.875603] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.001s

[2021-11-24 22:16:19.880578] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 0.004s

[2021-11-24 22:16:19.886966] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 0.005s

[2021-11-24 22:16:19.893207] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 0.005s

[2021-11-24 22:16:19.952377] INFO: derived_feature_extractor: /y_2015, 14

[2021-11-24 22:16:20.005701] INFO: derived_feature_extractor: /y_2016, 244

[2021-11-24 22:16:20.102532] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.345039s].

[2021-11-24 22:16:20.113827] INFO: moduleinvoker: join.v3 开始运行..

[2021-11-24 22:16:20.283578] INFO: join: /y_2015, 行数=0/14, 耗时=0.047939s

[2021-11-24 22:16:20.345428] INFO: join: /y_2016, 行数=239/244, 耗时=0.060256s

[2021-11-24 22:16:20.443397] INFO: join: 最终行数: 239

[2021-11-24 22:16:20.464825] INFO: moduleinvoker: join.v3 运行完成[0.350994s].

[2021-11-24 22:16:20.480876] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-11-24 22:16:20.575375] INFO: dropnan: /y_2015, 0/0

[2021-11-24 22:16:20.629330] INFO: dropnan: /y_2016, 234/239

[2021-11-24 22:16:20.694283] INFO: dropnan: 行数: 234/239

[2021-11-24 22:16:20.699091] INFO: moduleinvoker: dropnan.v1 运行完成[0.218213s].

[2021-11-24 22:16:35.994077] INFO: moduleinvoker: DQN_model.v10 开始运行..

[2021-11-25 07:52:36.672299] ERROR: moduleinvoker: module name: DQN_model, module version: v10, trackeback: AttributeError: 'NoneType' object has no attribute 'drop'

episode: 0
total_reward: 165826.08728027344
episode: 1
total_reward: 284445.70819091797
episode: 2
total_reward: 286264.33239746094
episode: 3
total_reward: 286217.8956298828
episode: 4
total_reward: 286217.8956298828
9eefdfafe01e4979b871f746722b28caT not found.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-1-9320cd90e834> in <module>
    196 )
    197 
--> 198 m5 = M.DQN_model.v10(
    199     data=m12.data,
    200     data_2=m4.data

AttributeError: 'NoneType' object has no attribute 'drop'