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# 本代码由可视化策略环境自动生成 2021年10月10日11:29
# 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 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
    context.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, context.stock_count)])
    context.stock_weights = [0.5/context.stock_count for k in range(context.stock_count)]  #半仓买入，每只股票等资金分配
    # 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'] = 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')]
    
    # 当日可用资金
    cash_for_buy = context.portfolio.cash
    
    #　获取当日买入列表,每天选取context.stock_count只
    buy_list = list(ranker_prediction.instrument[:context.stock_count])
    
    #　获取当前持仓
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                      for e, p in context.perf_tracker.position_tracker.positions.items()} 

    # 需要卖出的股票:已有持仓中不在买入列表的股票
    stock_to_sell = [ i for i in stock_hold_now if i not in buy_list ]
    stock_to_buy =  [ i for i in buy_list if i not in stock_hold_now ]
    
    # 卖出列表进行卖出操作
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) # 全部卖出
                # 如果是早盘买早盘卖，卖出的资金可以用于买股票，此时应将下面的注释打开，卖出股票时更新可用现金；
                # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用
                #cash_for_buy += stock_hold_now[instrument]
    
    # 买入列表执行买操作
    if len(stock_to_buy)>0:
        for instrument,weight in zip(stock_to_buy,context.stock_weights):
            sid = context.symbol(instrument) # 将标的转化为equity格式
            if data.can_trade(sid):
                context.order_target_value(sid, min(cash_for_buy,context.portfolio.portfolio_value*0.5)) # 买入
# 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='2010-01-01',
    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)

# 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
)

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
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=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=False,
    remove_extra_columns=False
)

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

m13 = M.dropnan.v1(
    input_data=m7.data
)

m4 = M.stock_ranker_train.v6(
    training_ds=m13.data,
    features=m3.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
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2020-01-01'),
    end_date=T.live_run_param('trading_date', '2021-09-30'),
    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=False,
    remove_extra_columns=False
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m8 = M.stock_ranker_predict.v5(
    model=m4.model,
    data=m14.data,
    m_lazy_run=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.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=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=''
)

[2021-10-10 07:33:12.358020] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-10 07:33:12.841658] INFO: moduleinvoker: 命中缓存

[2021-10-10 07:33:12.849417] INFO: moduleinvoker: instruments.v2 运行完成[0.493503s].

[2021-10-10 07:33:12.867273] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-10-10 07:33:46.883848] INFO: 自动标注(股票): 加载历史数据: 6299139 行

[2021-10-10 07:33:46.886756] INFO: 自动标注(股票): 开始标注 ..

[2021-10-10 07:34:25.182627] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[72.315364s].

[2021-10-10 07:34:25.190457] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-10-10 07:34:25.202714] INFO: moduleinvoker: 命中缓存

[2021-10-10 07:34:25.206286] INFO: moduleinvoker: input_features.v1 运行完成[0.01583s].

[2021-10-10 07:34:25.275857] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-10 07:34:27.796524] INFO: 基础特征抽取: 年份 2010, 特征行数=431567

[2021-10-10 07:34:30.684075] INFO: 基础特征抽取: 年份 2011, 特征行数=511455

[2021-10-10 07:34:33.704614] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2021-10-10 07:34:36.607026] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2021-10-10 07:34:39.684224] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2021-10-10 07:34:42.716223] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2021-10-10 07:34:46.117639] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2021-10-10 07:34:50.477938] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2021-10-10 07:34:54.912669] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2021-10-10 07:34:59.594476] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2021-10-10 07:34:59.750262] INFO: 基础特征抽取: 总行数: 6299144

[2021-10-10 07:34:59.885825] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[34.609991s].

[2021-10-10 07:34:59.897402] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-10 07:35:50.381076] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 12.602s

[2021-10-10 07:36:02.778311] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 12.395s

[2021-10-10 07:36:25.905352] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 23.125s

[2021-10-10 07:36:25.976949] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.069s

[2021-10-10 07:36:36.771827] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 10.793s

[2021-10-10 07:36:48.102877] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 11.329s

[2021-10-10 07:36:58.802135] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 10.698s

[2021-10-10 07:37:02.713218] INFO: derived_feature_extractor: /y_2010, 431567

[2021-10-10 07:37:06.382457] INFO: derived_feature_extractor: /y_2011, 511455

[2021-10-10 07:37:10.415749] INFO: derived_feature_extractor: /y_2012, 565675

[2021-10-10 07:37:14.676622] INFO: derived_feature_extractor: /y_2013, 564168

[2021-10-10 07:37:18.781891] INFO: derived_feature_extractor: /y_2014, 569948

[2021-10-10 07:37:23.098821] INFO: derived_feature_extractor: /y_2015, 569698

[2021-10-10 07:37:27.910705] INFO: derived_feature_extractor: /y_2016, 641546

[2021-10-10 07:37:33.396183] INFO: derived_feature_extractor: /y_2017, 743233

[2021-10-10 07:37:40.296228] INFO: derived_feature_extractor: /y_2018, 816987

[2021-10-10 07:37:47.082575] INFO: derived_feature_extractor: /y_2019, 884867

[2021-10-10 07:37:48.983134] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[169.085724s].

[2021-10-10 07:37:48.995635] INFO: moduleinvoker: join.v3 开始运行..

[2021-10-10 07:38:35.681615] INFO: join: /y_2010, 行数=431039/431567, 耗时=10.192686s

[2021-10-10 07:38:45.302161] INFO: join: /y_2011, 行数=510931/511455, 耗时=9.608763s

[2021-10-10 07:38:54.976638] INFO: join: /y_2012, 行数=564591/565675, 耗时=9.607235s

[2021-10-10 07:39:04.976036] INFO: join: /y_2013, 行数=563149/564168, 耗时=9.984301s

[2021-10-10 07:39:15.216209] INFO: join: /y_2014, 行数=567883/569948, 耗时=10.220279s

[2021-10-10 07:39:25.597330] INFO: join: /y_2015, 行数=560441/569698, 耗时=10.322189s

[2021-10-10 07:39:36.201844] INFO: join: /y_2016, 行数=637482/641546, 耗时=10.58868s

[2021-10-10 07:39:47.781438] INFO: join: /y_2017, 行数=738271/743233, 耗时=11.506013s

[2021-10-10 07:40:00.175576] INFO: join: /y_2018, 行数=813531/816987, 耗时=12.368538s

[2021-10-10 07:40:16.590830] INFO: join: /y_2019, 行数=873854/884867, 耗时=16.39236s

[2021-10-10 07:40:16.806366] INFO: join: 最终行数: 6261172

[2021-10-10 07:40:17.278145] INFO: moduleinvoker: join.v3 运行完成[148.282504s].

[2021-10-10 07:40:17.291824] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-10 07:40:19.516782] INFO: dropnan: /y_2010, 393041/431039

[2021-10-10 07:40:21.498337] INFO: dropnan: /y_2011, 505326/510931

[2021-10-10 07:40:23.782364] INFO: dropnan: /y_2012, 561393/564591

[2021-10-10 07:40:25.999878] INFO: dropnan: /y_2013, 562941/563149

[2021-10-10 07:40:28.698102] INFO: dropnan: /y_2014, 566246/567883

[2021-10-10 07:40:30.895087] INFO: dropnan: /y_2015, 558526/560441

[2021-10-10 07:40:33.523833] INFO: dropnan: /y_2016, 636022/637482

[2021-10-10 07:40:36.612484] INFO: dropnan: /y_2017, 733517/738271

[2021-10-10 07:40:40.381846] INFO: dropnan: /y_2018, 812101/813531

[2021-10-10 07:40:44.325918] INFO: dropnan: /y_2019, 870923/873854

[2021-10-10 07:40:44.502927] INFO: dropnan: 行数: 6200036/6261172

[2021-10-10 07:40:44.586366] INFO: moduleinvoker: dropnan.v1 运行完成[27.29455s].

[2021-10-10 07:40:44.602653] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2021-10-10 07:41:00.801956] INFO: StockRanker: 特征预处理 ..

[2021-10-10 07:41:14.999545] INFO: StockRanker: prepare data: training ..

[2021-10-10 07:41:30.292148] INFO: StockRanker: sort ..

[2021-10-10 07:44:39.383185] INFO: StockRanker训练: 524f2a9a 准备训练: 6200036 行数

[2021-10-10 07:44:39.385468] INFO: StockRanker训练: AI模型训练，将在6200036*7=4340.03万数据上对模型训练进行20轮迭代训练。预计将需要13～27分钟。请耐心等待。

[2021-10-10 07:44:39.571007] INFO: StockRanker训练: 正在训练 ..

[2021-10-10 07:44:39.623502] INFO: StockRanker训练: 任务状态: Pending

[2021-10-10 07:44:49.746488] INFO: StockRanker训练: 任务状态: Running

[2021-10-10 07:45:19.907086] INFO: StockRanker训练: 00:00:34.6109097, finished iteration 1

[2021-10-10 07:45:50.029424] INFO: StockRanker训练: 00:01:04.7397179, finished iteration 2

[2021-10-10 07:46:30.202525] INFO: StockRanker训练: 00:01:38.3676493, finished iteration 3

[2021-10-10 07:47:20.386082] INFO: StockRanker训练: 00:02:29.1629079, finished iteration 4

[2021-10-10 07:48:10.606170] INFO: StockRanker训练: 00:03:22.9121493, finished iteration 5

[2021-10-10 07:49:00.814162] INFO: StockRanker训练: 00:04:09.3700132, finished iteration 6

[2021-10-10 07:49:40.958758] INFO: StockRanker训练: 00:04:52.2395004, finished iteration 7

[2021-10-10 07:50:21.115025] INFO: StockRanker训练: 00:05:30.3937585, finished iteration 8

[2021-10-10 07:50:51.234230] INFO: StockRanker训练: 00:06:05.1037235, finished iteration 9

[2021-10-10 07:51:31.431987] INFO: StockRanker训练: 00:06:40.7506527, finished iteration 10

[2021-10-10 07:52:01.546747] INFO: StockRanker训练: 00:07:18.4031710, finished iteration 11

[2021-10-10 07:52:51.743348] INFO: StockRanker训练: 00:08:00.0097402, finished iteration 12

[2021-10-10 07:53:31.900096] INFO: StockRanker训练: 00:08:41.7828505, finished iteration 13

[2021-10-10 07:54:12.064633] INFO: StockRanker训练: 00:09:21.6425670, finished iteration 14

[2021-10-10 07:54:52.234036] INFO: StockRanker训练: 00:10:02.4694666, finished iteration 15

[2021-10-10 07:55:32.376397] INFO: StockRanker训练: 00:10:45.3221900, finished iteration 16

[2021-10-10 07:56:12.530180] INFO: StockRanker训练: 00:11:25.4651420, finished iteration 17

[2021-10-10 07:56:52.691826] INFO: StockRanker训练: 00:12:05.7959651, finished iteration 18

[2021-10-10 07:57:32.853344] INFO: StockRanker训练: 00:12:45.4174267, finished iteration 19

[2021-10-10 07:58:13.025196] INFO: StockRanker训练: 00:13:25.5423301, finished iteration 20

[2021-10-10 07:58:13.026813] INFO: StockRanker训练: 任务状态: Succeeded

[2021-10-10 07:58:13.211905] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[1048.609249s].

[2021-10-10 07:58:13.219521] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-10 07:58:13.231967] INFO: moduleinvoker: 命中缓存

[2021-10-10 07:58:13.233655] INFO: moduleinvoker: instruments.v2 运行完成[0.014151s].

[2021-10-10 07:58:13.256086] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-10 07:58:15.404618] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2021-10-10 07:58:17.181096] INFO: 基础特征抽取: 年份 2021, 特征行数=781988

[2021-10-10 07:58:17.256951] INFO: 基础特征抽取: 总行数: 1727949

[2021-10-10 07:58:17.306238] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[4.050217s].

[2021-10-10 07:58:17.313631] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-10 07:58:23.730529] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,5), 1.280s

[2021-10-10 07:58:25.052925] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,10), 1.321s

[2021-10-10 07:58:26.372919] INFO: derived_feature_extractor: 提取完成 close_0/mean(close_0,20), 1.318s

[2021-10-10 07:58:26.377624] INFO: derived_feature_extractor: 提取完成 close_0/open_0, 0.003s

[2021-10-10 07:58:27.686916] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,5), 1.308s

[2021-10-10 07:58:28.995508] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,10), 1.307s

[2021-10-10 07:58:30.313018] INFO: derived_feature_extractor: 提取完成 open_0/mean(close_0,20), 1.316s

[2021-10-10 07:58:33.095233] INFO: derived_feature_extractor: /y_2020, 945961

[2021-10-10 07:58:35.998007] INFO: derived_feature_extractor: /y_2021, 781988

[2021-10-10 07:58:36.753034] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[19.439395s].

[2021-10-10 07:58:36.761044] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-10 07:58:38.139546] INFO: dropnan: /y_2020, 867504/945961

[2021-10-10 07:58:39.308363] INFO: dropnan: /y_2021, 774790/781988

[2021-10-10 07:58:39.409987] INFO: dropnan: 行数: 1642294/1727949

[2021-10-10 07:58:39.438197] INFO: moduleinvoker: dropnan.v1 运行完成[2.677143s].

[2021-10-10 07:58:39.445753] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-10-10 07:58:41.875843] INFO: StockRanker预测: /y_2020 ..

[2021-10-10 07:58:47.099565] INFO: StockRanker预测: /y_2021 ..

[2021-10-10 07:58:56.604030] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[17.15827s].

[2021-10-10 07:58:56.699989] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-10-10 07:58:56.705529] INFO: backtest: biglearning backtest:V8.5.0

[2021-10-10 07:58:56.706663] INFO: backtest: product_type:stock by specified

[2021-10-10 07:58:57.396559] INFO: moduleinvoker: cached.v2 开始运行..

[2021-10-10 07:59:13.880183] INFO: backtest: 读取股票行情完成:2829909

[2021-10-10 07:59:21.877119] INFO: moduleinvoker: cached.v2 运行完成[24.480572s].

[2021-10-10 07:59:25.880035] INFO: algo: TradingAlgorithm V1.8.5

[2021-10-10 07:59:27.399883] INFO: algo: trading transform...

[2021-10-10 07:59:34.979564] INFO: Performance: Simulated 425 trading days out of 425.

[2021-10-10 07:59:34.981244] INFO: Performance: first open: 2020-01-02 09:30:00+00:00

[2021-10-10 07:59:34.982425] INFO: Performance: last close: 2021-09-30 15:00:00+00:00

[2021-10-10 07:59:42.925493] INFO: moduleinvoker: backtest.v8 运行完成[46.225506s].

[2021-10-10 07:59:42.927093] INFO: moduleinvoker: trade.v4 运行完成[46.294078s].

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bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f7321161d858409bbd740934ae2a349e"}/bigcharts-data-end