策略无法产生新的交易

(bonjovy) #1
克隆策略

    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    In [15]:
    # 本代码由可视化策略环境自动生成 2019年11月25日 11:54
# 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 = 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 m19_handle_data_bigquant_run(context, data):
     # 相隔几天（hold_days）进行一下换仓
    if context.trading_day_index % context.hold_days!= 0:
        return    

    # 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')]

   # 目前持仓
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    # 权重
    buy_cash_weights = context.stock_weights
    # 今日买入股票列表
    stock_to_buy = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    # 持仓上限
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument

    # 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 = [equity.symbol for equity in context.portfolio.positions ]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式
        if data.can_trade(context.symbol(stock)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，
            #   即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return
    
    # 买入
    for i, instrument in enumerate(stock_to_buy):
        cash = context.portfolio.portfolio_value * 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:
            price = data.current(context.symbol(instrument), 'price')  # 最新价格
            stock_num = np.floor(cash/price/100)*100  # 向下取整
            context.order(context.symbol(instrument), stock_num) # 整百下单
  
# 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='2016-03-01',
    end_date='2019-11-01',
    market='CN_STOCK_A',
    instrument_list="""000001.SZA
000002.SZA
000009.SZA
000021.SZA
000027.SZA
000028.SZA
000031.SZA
000039.SZA
000043.SZA
000046.SZA
000060.SZA
000062.SZA
000063.SZA
000066.SZA
000069.SZA
000088.SZA
000089.SZA
000100.SZA
000166.SZA
000333.SZA
000338.SZA
000401.SZA
000402.SZA
000413.SZA
000423.SZA
000429.SZA
000623.SZA
000627.SZA
000629.SZA
000630.SZA
000636.SZA
000661.SZA
000671.SZA
000672.SZA
000681.SZA
000686.SZA
000688.SZA
000690.SZA
000703.SZA
000709.SZA
000712.SZA
000723.SZA
000728.SZA
000729.SZA
000732.SZA
000738.SZA
000739.SZA
000761.SZA
000768.SZA
000776.SZA
000778.SZA
000783.SZA
000786.SZA
000792.SZA
000799.SZA
000800.SZA
000807.SZA
000810.SZA
000813.SZA
000826.SZA
000830.SZA
000831.SZA
000839.SZA
000860.SZA
000869.SZA
000876.SZA
000878.SZA
000883.SZA
000898.SZA
000918.SZA
000932.SZA
000938.SZA
000960.SZA
000961.SZA
000963.SZA
000977.SZA
000983.SZA
000987.SZA
000988.SZA
000997.SZA
000998.SZA
000999.SZA
001979.SZA
002001.SZA
002007.SZA
002008.SZA
002010.SZA
002013.SZA
002019.SZA
002024.SZA
002027.SZA
002032.SZA
002036.SZA
002038.SZA
002044.SZA
002049.SZA
002074.SZA
002078.SZA
002080.SZA
002081.SZA
002092.SZA
002099.SZA
002100.SZA
002110.SZA
002120.SZA
002123.SZA
002124.SZA
002127.SZA
002128.SZA
002129.SZA
002131.SZA
002138.SZA
002142.SZA
002146.SZA
002174.SZA
002179.SZA
002180.SZA
002191.SZA
002202.SZA
002203.SZA
002212.SZA
002217.SZA
002221.SZA
002223.SZA
002230.SZA
002233.SZA
002236.SZA
002237.SZA
002241.SZA
002242.SZA
002244.SZA
002262.SZA
002268.SZA
002271.SZA
002273.SZA
002281.SZA
002287.SZA
002294.SZA
002299.SZA
002302.SZA
002304.SZA
002311.SZA
002340.SZA
002368.SZA
002371.SZA
002372.SZA
002373.SZA
002384.SZA
002387.SZA
002396.SZA
002399.SZA
002408.SZA
002410.SZA
002411.SZA
002414.SZA
002419.SZA
002422.SZA
002429.SZA
002430.SZA
002439.SZA
002440.SZA
002444.SZA
002460.SZA
002461.SZA
002463.SZA
002466.SZA
002493.SZA
002600.SZA
002601.SZA
002602.SZA
002603.SZA
002607.SZA
002624.SZA
002626.SZA
002643.SZA
002648.SZA
002670.SZA
002673.SZA
002678.SZA
002690.SZA
002701.SZA
002714.SZA
002736.SZA
002739.SZA
002773.SZA
002797.SZA
002812.SZA
002821.SZA
002916.SZA
002926.SZA
002938.SZA
002939.SZA
300001.SZA
300003.SZA
300009.SZA
300012.SZA
300014.SZA
300017.SZA
300024.SZA
300027.SZA
300033.SZA
300070.SZA
300072.SZA
300078.SZA
300088.SZA
300113.SZA
300122.SZA
300124.SZA
300134.SZA
300136.SZA
300142.SZA
300144.SZA
300146.SZA
300168.SZA
300207.SZA
300212.SZA
300226.SZA
300271.SZA
300274.SZA
300294.SZA
300296.SZA
300308.SZA
300316.SZA
300326.SZA
300339.SZA
300347.SZA
300369.SZA
300383.SZA
300408.SZA
300413.SZA
300433.SZA
300463.SZA
300476.SZA
300482.SZA
300496.SZA
300498.SZA
300601.SZA
300661.SZA
300676.SZA
300699.SZA
300748.SZA
300760.SZA
300782.SZA
600000.SHA
600004.SHA
600007.SHA
600008.SHA
600009.SHA
600010.SHA
600011.SHA
600016.SHA
600018.SHA
600019.SHA
600021.SHA
600022.SHA
600023.SHA
600026.SHA
600027.SHA
600028.SHA
600029.SHA
600030.SHA
600031.SHA
600036.SHA
600037.SHA
600038.SHA
600039.SHA
600048.SHA
600060.SHA
600061.SHA
600062.SHA
600064.SHA
600066.SHA
600068.SHA
600072.SHA
600079.SHA
600089.SHA
600093.SHA
600094.SHA
600098.SHA
600100.SHA
600104.SHA
600109.SHA
600111.SHA
600118.SHA
600126.SHA
600132.SHA
600143.SHA
600160.SHA
600161.SHA
600166.SHA
600167.SHA
600170.SHA
600176.SHA
600177.SHA
600183.SHA
600188.SHA
600196.SHA
600201.SHA
600208.SHA
600216.SHA
600219.SHA
600221.SHA
600233.SHA
600236.SHA
600260.SHA
600266.SHA
600267.SHA
600271.SHA
600273.SHA
600276.SHA
600277.SHA
600282.SHA
600297.SHA
600298.SHA
600299.SHA
600307.SHA
600309.SHA
600317.SHA
600320.SHA
600323.SHA
600332.SHA
600340.SHA
600346.SHA
600348.SHA
600362.SHA
600369.SHA
600372.SHA
600373.SHA
600376.SHA
600377.SHA
600380.SHA
600383.SHA
600388.SHA
600392.SHA
600398.SHA
600406.SHA
600409.SHA
600410.SHA
600426.SHA
600436.SHA
600438.SHA
600446.SHA
600460.SHA
600466.SHA
600482.SHA
600483.SHA
600486.SHA
600487.SHA
600489.SHA
600491.SHA
600497.SHA
600498.SHA
600600.SHA
600604.SHA
600606.SHA
600612.SHA
600623.SHA
600633.SHA
600637.SHA
600639.SHA
600641.SHA
600642.SHA
600643.SHA
600648.SHA
600649.SHA
600660.SHA
600663.SHA
600667.SHA
600673.SHA
600674.SHA
600682.SHA
600688.SHA
600690.SHA
600699.SHA
600702.SHA
600703.SHA
600704.SHA
600717.SHA
600718.SHA
600728.SHA
600729.SHA
600737.SHA
600739.SHA
600741.SHA
600748.SHA
600760.SHA
600763.SHA
600776.SHA
600779.SHA
600782.SHA
600787.SHA
600797.SHA
600801.SHA
600803.SHA
600808.SHA
600809.SHA
600811.SHA
600812.SHA
600816.SHA
600820.SHA
600823.SHA
600827.SHA
600837.SHA
600839.SHA
600848.SHA
600862.SHA
600863.SHA
600864.SHA
600867.SHA
600869.SHA
600871.SHA
600872.SHA
600873.SHA
600879.SHA
600884.SHA
600886.SHA
600887.SHA
600893.SHA
600900.SHA
600901.SHA
600909.SHA
600917.SHA
600919.SHA
600926.SHA
600970.SHA
600977.SHA
600998.SHA
600999.SHA
601000.SHA
601003.SHA
601006.SHA
601009.SHA
601012.SHA
601018.SHA
601021.SHA
601066.SHA
601088.SHA
601098.SHA
601099.SHA
601100.SHA
601106.SHA
601108.SHA
601111.SHA
601117.SHA
601118.SHA
601127.SHA
601128.SHA
601138.SHA
601139.SHA
601162.SHA
601163.SHA
601166.SHA
601168.SHA
601169.SHA
601179.SHA
601186.SHA
601198.SHA
601211.SHA
601216.SHA
601229.SHA
601231.SHA
601233.SHA
601238.SHA
601288.SHA
601318.SHA
601319.SHA
601328.SHA
601333.SHA
601336.SHA
601369.SHA
601377.SHA
601390.SHA
601398.SHA
601600.SHA
601601.SHA
601607.SHA
601608.SHA
601611.SHA
601618.SHA
601628.SHA
601633.SHA
601636.SHA
601668.SHA
601669.SHA
601688.SHA
601689.SHA
601699.SHA
601718.SHA
601727.SHA
601766.SHA
601788.SHA
601799.SHA
601800.SHA
601801.SHA
601808.SHA
601818.SHA
601838.SHA
601866.SHA
601872.SHA
601877.SHA
601878.SHA
601880.SHA
601881.SHA
601888.SHA
601898.SHA
601899.SHA
601901.SHA
601919.SHA
601928.SHA
601933.SHA
601939.SHA
601966.SHA
601969.SHA
601988.SHA
601989.SHA
601990.SHA
601991.SHA
601992.SHA
601997.SHA
601998.SHA
603000.SHA
603012.SHA
603019.SHA
603027.SHA
603077.SHA
603160.SHA
603198.SHA
603222.SHA
603260.SHA
603288.SHA
603328.SHA
603338.SHA
603369.SHA
603377.SHA
603444.SHA
603609.SHA
603707.SHA
603737.SHA
603799.SHA
603806.SHA
603816.SHA
603866.SHA
603868.SHA
603882.SHA
603883.SHA
603888.SHA
603899.SHA
603939.SHA
603986.SHA
603993.SHA""",
    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(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='2016-03-01',
    end_date='2019-11-01',
    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
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_10
avg_amount_0/avg_amount_10
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_avg_amount_0/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_20
rank_return_0/rank_return_5
rank_return_5/rank_return_10
rank_return_0/rank_return_10
pe_ttm_0
pe_lyr_0
pb_lf_0
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='2016-03-01',
    end_date='2019-11-01',
    before_start_days=20
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True
)

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
)

m6 = M.stock_ranker_train.v5(
    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,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2016-03-01'),
    end_date=T.live_run_param('trading_date', '2019-11-22'),
    market='CN_STOCK_A',
    instrument_list="""000050.SZA
000156.SZA
000157.SZA
000158.SZA
000415.SZA
000425.SZA
000503.SZA
000513.SZA
000538.SZA
000539.SZA
000540.SZA
000547.SZA
000555.SZA
000559.SZA
000563.SZA
000568.SZA
000581.SZA
000596.SZA
000598.SZA
000625.SZA
000651.SZA
000656.SZA
000725.SZA
000735.SZA
000750.SZA
000825.SZA
000858.SZA
000895.SZA
000935.SZA
000951.SZA
000959.SZA
000975.SZA
002025.SZA
002050.SZA
002051.SZA
002056.SZA
002065.SZA
002075.SZA
002085.SZA
002152.SZA
002153.SZA
002156.SZA
002157.SZA
002185.SZA
002195.SZA
002250.SZA
002252.SZA
002352.SZA
002353.SZA
002385.SZA
002405.SZA
002415.SZA
002450.SZA
002456.SZA
002458.SZA
002465.SZA
002475.SZA
002500.SZA
002506.SZA
002507.SZA
002508.SZA
002511.SZA
002531.SZA
002537.SZA
002555.SZA
002557.SZA
002558.SZA
002563.SZA
002572.SZA
002594.SZA
002597.SZA
002653.SZA
002675.SZA
002705.SZA
300015.SZA
300058.SZA
300059.SZA
300115.SZA
300251.SZA
300253.SZA
300285.SZA
300315.SZA
300357.SZA
300450.SZA
300454.SZA
300529.SZA
300558.SZA
300595.SZA
300750.SZA
600015.SHA
600025.SHA
600050.SHA
600056.SHA
600085.SHA
600115.SHA
600150.SHA
600153.SHA
600155.SHA
600157.SHA
600185.SHA
600195.SHA
600252.SHA
600256.SHA
600258.SHA
600305.SHA
600315.SHA
600325.SHA
600350.SHA
600352.SHA
600415.SHA
600435.SHA
600500.SHA
600507.SHA
600516.SHA
600518.SHA
600519.SHA
600521.SHA
600522.SHA
600528.SHA
600529.SHA
600535.SHA
600536.SHA
600546.SHA
600547.SHA
600548.SHA
600549.SHA
600566.SHA
600567.SHA
600570.SHA
600572.SHA
600575.SHA
600578.SHA
600580.SHA
600582.SHA
600583.SHA
600584.SHA
600585.SHA
600588.SHA
600597.SHA
600598.SHA
600635.SHA
600655.SHA
600675.SHA
600685.SHA
600705.SHA
600745.SHA
600754.SHA
600755.SHA
600795.SHA
600835.SHA
600845.SHA
600850.SHA
600875.SHA
600885.SHA
600895.SHA
600958.SHA
600959.SHA
601005.SHA
601155.SHA
601158.SHA
601225.SHA
601519.SHA
601555.SHA
601857.SHA
601958.SHA
601985.SHA
603156.SHA
603259.SHA
603456.SHA
603501.SHA
603515.SHA
603517.SHA
603568.SHA
603589.SHA
603658.SHA
603659.SHA
603858.SHA
603885.SHA
""",
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='2016-03-01',
    end_date='2019-11-01',
    before_start_days=20
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True
)

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

m8 = M.stock_ranker_predict.v5(
    model=m6.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='vwap_2',
    order_price_field_sell='open',
    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='00300.SHA'
)
    设置测试数据集，查看训练迭代过程的NDCG
    bigcharts-data-start/{"__id":"bigchart-d681a248dadc40a9b66dfd114834c7e9","__type":"tabs"}/bigcharts-data-end
    • 收益率238.01%
    • 年化收益率40.01%
    • 基准收益率0.0%
    • 阿尔法0
    • 贝塔0
    • 夏普比率1.38
    • 胜率0.52
    • 盈亏比1.29
    • 收益波动率24.36%
    • 信息比率0.09
    • 最大回撤22.77% 
    bigcharts-data-start/{"__id":"bigchart-240bd15b262e4122abf2cb1118d20d22","__type":"tabs"}/bigcharts-data-end

    策略建好后，11月21日之前的数据正常，但是22日之后的交易出了问题，主要有几个问题：
    1、一只股票策略运行的时候提示停牌，实际上没有停牌；
    2、没有产生买入的股票，只有卖出的股票，导致22日之后一直空仓。
    (focus777) #2

    m9和m1日期有部分重合了。m1是训练集，m9是测试集。不能重合 否则会有过拟合嫌疑

    (bonjovy) #3

    谢谢指导！

    (liujianliuku) #4

    问题是他为什么提示停牌，但实际却没有停牌呢