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# 本代码由可视化策略环境自动生成 2021年10月29日 09:14
# 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
    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, stock_count)])
    # 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'] = 1

# 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')]
    print (ranker_prediction)
    ranker_prediction = ranker_prediction.sort_values('pred_label', ascending=False)
 
    # 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.portfolio.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.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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='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/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(high, -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="""return_0
mean(close_0,5)
turn_0
covariance(close_0,high_0,10)"""
)

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.filter_stockcode.v2(
    input_1=m13.data,
    start='688'
)

m6 = M.filter_stockcode.v2(
    input_1=m4.data_1,
    start='3'
)

m8 = M.filtet_st_stock.v7(
    input_1=m6.data_1
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2020-01-01'),
    end_date=T.live_run_param('trading_date', '2021-10-28'),
    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=30
)

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
)

m10 = M.filter_stockcode.v2(
    input_1=m14.data,
    start='688'
)

m11 = M.filter_stockcode.v2(
    input_1=m10.data_1,
    start='3'
)

m12 = M.filtet_st_stock.v7(
    input_1=m11.data_1
)

m21 = M.xgboost.v1(
    training_ds=m8.data_1,
    features=m3.data,
    predict_ds=m12.data_1,
    num_boost_round=10,
    objective='排序(pairwise)',
    booster='gbtree',
    max_depth=6,
    key_cols='date,instrument',
    group_col='date',
    nthread=1,
    n_gpus=-1,
    other_train_parameters={}
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m21.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,
    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'
)

[2021-10-29 09:09:51.096074] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-29 09:09:51.117117] INFO: moduleinvoker: 命中缓存

[2021-10-29 09:09:51.120043] INFO: moduleinvoker: instruments.v2 运行完成[0.023945s].

[2021-10-29 09:09:51.132455] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-10-29 09:09:51.138408] INFO: moduleinvoker: 命中缓存

[2021-10-29 09:09:51.141101] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.008638s].

[2021-10-29 09:09:51.145989] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-10-29 09:09:51.191387] INFO: moduleinvoker: input_features.v1 运行完成[0.045387s].

[2021-10-29 09:09:51.202281] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-29 09:09:53.599494] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2021-10-29 09:09:56.517901] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2021-10-29 09:09:59.670246] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2021-10-29 09:10:03.199261] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2021-10-29 09:10:06.910945] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2021-10-29 09:10:07.024730] INFO: 基础特征抽取: 总行数: 3656331

[2021-10-29 09:10:07.034983] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[15.832685s].

[2021-10-29 09:10:07.043661] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-29 09:10:16.152957] INFO: derived_feature_extractor: 提取完成 mean(close_0,5), 2.915s

[2021-10-29 09:10:35.377664] INFO: derived_feature_extractor: 提取完成 covariance(close_0,high_0,10), 19.223s

[2021-10-29 09:10:36.263182] INFO: derived_feature_extractor: /y_2015, 569698

[2021-10-29 09:10:37.277766] INFO: derived_feature_extractor: /y_2016, 641546

[2021-10-29 09:10:38.463183] INFO: derived_feature_extractor: /y_2017, 743233

[2021-10-29 09:10:39.811599] INFO: derived_feature_extractor: /y_2018, 816987

[2021-10-29 09:10:41.255317] INFO: derived_feature_extractor: /y_2019, 884867

[2021-10-29 09:10:41.626712] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[34.583043s].

[2021-10-29 09:10:41.635313] INFO: moduleinvoker: join.v3 开始运行..

[2021-10-29 09:10:49.304704] INFO: join: /y_2015, 行数=560441/569698, 耗时=2.234827s

[2021-10-29 09:10:51.579029] INFO: join: /y_2016, 行数=637482/641546, 耗时=2.269472s

[2021-10-29 09:10:54.069498] INFO: join: /y_2017, 行数=738271/743233, 耗时=2.485697s

[2021-10-29 09:10:56.954553] INFO: join: /y_2018, 行数=813531/816987, 耗时=2.879636s

[2021-10-29 09:11:00.129877] INFO: join: /y_2019, 行数=873854/884867, 耗时=3.169554s

[2021-10-29 09:11:00.196247] INFO: join: 最终行数: 3623579

[2021-10-29 09:11:00.216637] INFO: moduleinvoker: join.v3 运行完成[18.581299s].

[2021-10-29 09:11:00.228076] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-29 09:11:00.794840] INFO: dropnan: /y_2015, 537574/560441

[2021-10-29 09:11:01.353918] INFO: dropnan: /y_2016, 637319/637482

[2021-10-29 09:11:01.977518] INFO: dropnan: /y_2017, 737424/738271

[2021-10-29 09:11:02.674363] INFO: dropnan: /y_2018, 813186/813531

[2021-10-29 09:11:03.559958] INFO: dropnan: /y_2019, 872757/873854

[2021-10-29 09:11:03.621739] INFO: dropnan: 行数: 3598260/3623579

[2021-10-29 09:11:03.632662] INFO: moduleinvoker: dropnan.v1 运行完成[3.404573s].

[2021-10-29 09:11:03.644668] INFO: moduleinvoker: filter_stockcode.v2 开始运行..

[2021-10-29 09:11:08.234379] INFO: moduleinvoker: filter_stockcode.v2 运行完成[4.589695s].

[2021-10-29 09:11:08.247234] INFO: moduleinvoker: filter_stockcode.v2 开始运行..

[2021-10-29 09:11:11.866012] INFO: moduleinvoker: filter_stockcode.v2 运行完成[3.618789s].

[2021-10-29 09:11:11.879995] INFO: moduleinvoker: filtet_st_stock.v7 开始运行..

[2021-10-29 09:11:19.474160] INFO: moduleinvoker: filtet_st_stock.v7 运行完成[7.594158s].

[2021-10-29 09:11:19.479578] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-29 09:11:19.488465] INFO: moduleinvoker: 命中缓存

[2021-10-29 09:11:19.489891] INFO: moduleinvoker: instruments.v2 运行完成[0.010317s].

[2021-10-29 09:11:19.509404] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-29 09:11:21.312238] INFO: 基础特征抽取: 年份 2019, 特征行数=81997

[2021-10-29 09:11:24.963543] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2021-10-29 09:11:28.781322] INFO: 基础特征抽取: 年份 2021, 特征行数=849445

[2021-10-29 09:11:28.859705] INFO: 基础特征抽取: 总行数: 1877403

[2021-10-29 09:11:28.865982] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[9.356626s].

[2021-10-29 09:11:28.873819] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-29 09:11:33.393860] INFO: derived_feature_extractor: 提取完成 mean(close_0,5), 1.320s

[2021-10-29 09:11:52.700267] INFO: derived_feature_extractor: 提取完成 covariance(close_0,high_0,10), 19.305s

[2021-10-29 09:11:52.925994] INFO: derived_feature_extractor: /y_2019, 81997

[2021-10-29 09:11:54.233846] INFO: derived_feature_extractor: /y_2020, 945961

[2021-10-29 09:11:55.598527] INFO: derived_feature_extractor: /y_2021, 849445

[2021-10-29 09:11:55.894939] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[27.0211s].

[2021-10-29 09:11:55.902926] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-29 09:11:56.051128] INFO: dropnan: /y_2019, 48304/81997

[2021-10-29 09:11:56.654061] INFO: dropnan: /y_2020, 942367/945961

[2021-10-29 09:11:57.153471] INFO: dropnan: /y_2021, 845804/849445

[2021-10-29 09:11:57.255601] INFO: dropnan: 行数: 1836475/1877403

[2021-10-29 09:11:57.265186] INFO: moduleinvoker: dropnan.v1 运行完成[1.362255s].

[2021-10-29 09:11:57.275587] INFO: moduleinvoker: filter_stockcode.v2 开始运行..

[2021-10-29 09:11:59.027991] INFO: moduleinvoker: filter_stockcode.v2 运行完成[1.752434s].

[2021-10-29 09:11:59.036830] INFO: moduleinvoker: filter_stockcode.v2 开始运行..

[2021-10-29 09:12:00.367210] INFO: moduleinvoker: filter_stockcode.v2 运行完成[1.330382s].

[2021-10-29 09:12:00.379104] INFO: moduleinvoker: filtet_st_stock.v7 开始运行..

[2021-10-29 09:12:03.096044] INFO: moduleinvoker: filtet_st_stock.v7 运行完成[2.716941s].

[2021-10-29 09:12:03.115003] INFO: moduleinvoker: xgboost.v1 开始运行..

[2021-10-29 09:13:19.666946] INFO: moduleinvoker: xgboost.v1 运行完成[76.551943s].

[2021-10-29 09:13:19.745031] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-10-29 09:13:19.750277] INFO: backtest: biglearning backtest:V8.5.0

[2021-10-29 09:13:19.751595] INFO: backtest: product_type:stock by specified

[2021-10-29 09:13:19.846013] INFO: moduleinvoker: cached.v2 开始运行..

[2021-10-29 09:13:19.855595] INFO: moduleinvoker: 命中缓存

[2021-10-29 09:13:19.857732] INFO: moduleinvoker: cached.v2 运行完成[0.011739s].

[2021-10-29 09:13:22.352064] INFO: algo: TradingAlgorithm V1.8.5

[2021-10-29 09:13:23.367552] INFO: algo: trading transform...

[2021-10-29 09:13:23.720791] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: KeyError: 'pred_label'

[2021-10-29 09:13:23.725892] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: KeyError: 'pred_label'

        prediction       date  instrument
40727     0.857901 2020-01-02  000018.SZA
648523    0.839375 2020-01-02  603178.SHA
727008    0.777343 2020-01-02  603880.SHA
733776    0.777343 2020-01-02  603920.SHA
692883    0.772815 2020-01-02  603608.SHA
...            ...        ...         ...
585386    0.200890 2020-01-02  601158.SHA
596838    0.189988 2020-01-02  601398.SHA
620208    0.164190 2020-01-02  601988.SHA
592221    0.157198 2020-01-02  601288.SHA
611487    0.141882 2020-01-02  601857.SHA

[2751 rows x 3 columns]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-13-0a6fdd3b6566> in <module>
    209 )
    210 
--> 211 m19 = M.trade.v4(
    212     instruments=m9.data,
    213     options_data=m21.predictions,

<ipython-input-13-0a6fdd3b6566> in m19_handle_data_bigquant_run(context, data)
     25         context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
     26     print (ranker_prediction)
---> 27     ranker_prediction = ranker_prediction.sort_values('pred_label', ascending=False)
     28 
     29     # 1. 资金分配

KeyError: 'pred_label'