策略训练出错(全部采用财务因子)

策略分享
标签: #<Tag:0x00007fcbfc3add90>

(kilmjin) #1

请版主删帖!


(kilmjin) #2

我主要抽取的都是财务因子,因此有些数据相当长时间是不变的,是不是这个原因?


(达达) #3

去掉了因子的注释,好像可以跑

克隆策略

    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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年1月16日 14:01
    # 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:每日数据处理函数,每天执行一次
    def m4_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')]
    
        # 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.perf_tracker.position_tracker.positions.items()}
        #print(context.perf_tracker.position_tracker.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StockRanker预测的排序末位淘汰
        if True:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            #instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    #lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            instruments12 = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities )])
            position_prediction = ranker_prediction[ranker_prediction.instrument.isin(instruments12)]
            instruments = list(position_prediction.instrument[position_prediction.score <1.0])
            #instruments = list(position_prediction.instrument[position_prediction.score < 0.2])
            #print(instruments)
            #print(instruments12,list(ranker_prediction.date)[0])
            #print(context.has_unfinished_sell_order)
            # print('rank order for sell %s' % instruments)
            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StockRanker预测的排序,买入前面的stock_count只股票
        #buy_cash_weights = context.stock_weights
        rank_buy = ranker_prediction[ranker_prediction.score>2.3]
        #rank_buy = ranker_prediction[ranker_prediction.score>1.12]
        buy_instruments1 = list(rank_buy.instrument)
        #print(list(ranker_prediction.date)[0],ranker_prediction.score)
        #buy_instruments1 = list(ranker_prediction.instrument[ranker_prediction.score>1.16])
        buy_scores1 = list(rank_buy.score)
        buy_instruments = buy_instruments1[:np.where(len(buy_instruments1)>4,4,len(buy_instruments1))]
        buy_scores = buy_scores1[:len(buy_instruments)]
        buy_cash_weights = buy_scores/np.sum(buy_scores)
        #buy_cash_weights = T.norm([1/math.log(i+2) for i in range(0,len(buy_instruments))])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        if any(buy_instruments):
            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)
                today_price = data.current(symbol(instrument), ['amount', 'volume'])
                buy_price =today_price['amount'] / today_price['volume']
                buy_amount = int(round(cash/(buy_price*100)))
                #print(buy_amount)
            #context.order_lots(symbol(instrument),1)
                if buy_amount > 0: 
                    context.order_lots(symbol(instrument),buy_amount)
           # if cash > 0:
             #   context.order_value(context.symbol(instrument), cash)
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        pass
    # 回测引擎:初始化函数,只执行一次
    def m4_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 = 0.3
        context.options['hold_days'] = 5
    
    
    m1 = M.instruments.v2(
        start_date='2005-01-01',
        end_date='2016-01-01',
        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)
    #signedpower((shift(close, -5) / shift(open, -1)-1),log10(market_cap_float)/pe_ttm)
    #where((shift(close, -3) / shift(open, -1) > 0) & (correlation(close, amount, -3)>0),correlation(close, amount, -3) ,0)
    (shift(close, -90) / shift(open, -1)-1)*100
    # 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, 500)
    
    # 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
    fs_net_profit_yoy_0
    rank_fs_net_profit_yoy_0
    fs_net_profit_qoq_0
    rank_fs_net_profit_qoq_0
    fs_operating_revenue_yoy_0
    rank_fs_operating_revenue_yoy_0
    fs_operating_revenue_qoq_0
    rank_fs_operating_revenue_qoq_0
    fs_eps_yoy_0
    rank_fs_eps_yoy_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=120
    )
    
    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
    )
    
    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=30,
        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', '2016-01-02'),
        end_date=T.live_run_param('trading_date', '2019-01-14'),
        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=120
    )
    
    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=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        initialize=m4_initialize_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=''
    )
    
    [2019-01-16 13:45:47.640145] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-16 13:45:47.653801] INFO: bigquant: 命中缓存
    [2019-01-16 13:45:47.654889] INFO: bigquant: instruments.v2 运行完成[0.014765s].
    [2019-01-16 13:45:47.658526] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-16 13:45:47.662834] INFO: bigquant: 命中缓存
    [2019-01-16 13:45:47.663786] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.005261s].
    [2019-01-16 13:45:47.666049] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-16 13:45:47.672754] INFO: bigquant: input_features.v1 运行完成[0.006695s].
    [2019-01-16 13:45:47.694581] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-16 13:45:47.699490] INFO: bigquant: 命中缓存
    [2019-01-16 13:45:47.700293] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005729s].
    [2019-01-16 13:45:47.704870] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-16 13:45:52.461909] INFO: derived_feature_extractor: /y_2005, 314357
    [2019-01-16 13:45:52.668495] INFO: derived_feature_extractor: /y_2006, 288040
    [2019-01-16 13:45:52.993765] INFO: derived_feature_extractor: /y_2007, 323371
    [2019-01-16 13:45:53.222290] INFO: derived_feature_extractor: /y_2008, 360328
    [2019-01-16 13:45:53.492296] INFO: derived_feature_extractor: /y_2009, 375308
    [2019-01-16 13:45:53.803440] INFO: derived_feature_extractor: /y_2010, 431567
    [2019-01-16 13:45:54.216391] INFO: derived_feature_extractor: /y_2011, 511455
    [2019-01-16 13:45:54.684730] INFO: derived_feature_extractor: /y_2012, 565675
    [2019-01-16 13:45:55.175003] INFO: derived_feature_extractor: /y_2013, 564168
    [2019-01-16 13:45:55.661919] INFO: derived_feature_extractor: /y_2014, 569948
    [2019-01-16 13:45:56.151639] INFO: derived_feature_extractor: /y_2015, 569698
    [2019-01-16 13:45:56.517709] INFO: bigquant: derived_feature_extractor.v3 运行完成[8.812806s].
    [2019-01-16 13:45:56.521585] INFO: bigquant: join.v3 开始运行..
    [2019-01-16 13:46:02.064729] INFO: join: /y_2005, 行数=312074/314357, 耗时=2.32907s
    [2019-01-16 13:46:04.253764] INFO: join: /y_2006, 行数=285727/288040, 耗时=2.178578s
    [2019-01-16 13:46:06.552764] INFO: join: /y_2007, 行数=320285/323371, 耗时=2.289233s
    [2019-01-16 13:46:08.830259] INFO: join: /y_2008, 行数=358644/360328, 耗时=2.265509s
    [2019-01-16 13:46:11.125941] INFO: join: /y_2009, 行数=373820/375308, 耗时=2.28386s
    [2019-01-16 13:46:13.508333] INFO: join: /y_2010, 行数=430858/431567, 耗时=2.369981s
    [2019-01-16 13:46:15.900207] INFO: join: /y_2011, 行数=510609/511455, 耗时=2.377892s
    [2019-01-16 13:46:18.409454] INFO: join: /y_2012, 行数=564427/565675, 耗时=2.493084s
    [2019-01-16 13:46:20.911476] INFO: join: /y_2013, 行数=562749/564168, 耗时=2.484487s
    [2019-01-16 13:46:23.399851] INFO: join: /y_2014, 行数=566410/569948, 耗时=2.470443s
    [2019-01-16 13:46:25.786184] INFO: join: /y_2015, 行数=314141/569698, 耗时=2.367414s
    [2019-01-16 13:46:25.953389] INFO: join: 最终行数: 4599744
    [2019-01-16 13:46:25.956003] INFO: bigquant: join.v3 运行完成[29.434385s].
    [2019-01-16 13:46:25.960027] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-16 13:46:26.252727] INFO: dropnan: /y_2005, 75391/312074
    [2019-01-16 13:46:26.474948] INFO: dropnan: /y_2006, 75902/285727
    [2019-01-16 13:46:26.771861] INFO: dropnan: /y_2007, 236621/320285
    [2019-01-16 13:46:27.127004] INFO: dropnan: /y_2008, 347995/358644
    [2019-01-16 13:46:27.557279] INFO: dropnan: /y_2009, 368102/373820
    [2019-01-16 13:46:28.010001] INFO: dropnan: /y_2010, 407616/430858
    [2019-01-16 13:46:28.527755] INFO: dropnan: /y_2011, 489583/510609
    [2019-01-16 13:46:29.110603] INFO: dropnan: /y_2012, 550813/564427
    [2019-01-16 13:46:29.714864] INFO: dropnan: /y_2013, 560150/562749
    [2019-01-16 13:46:30.332404] INFO: dropnan: /y_2014, 560160/566410
    [2019-01-16 13:46:30.676199] INFO: dropnan: /y_2015, 303733/314141
    [2019-01-16 13:46:30.702012] INFO: dropnan: 行数: 3976066/4599744
    [2019-01-16 13:46:30.713386] INFO: bigquant: dropnan.v1 运行完成[4.753338s].
    [2019-01-16 13:46:30.721781] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-16 13:46:33.592505] INFO: StockRanker: 特征预处理 ..
    [2019-01-16 13:46:36.153170] INFO: StockRanker: prepare data: training ..
    [2019-01-16 13:46:40.326649] INFO: StockRanker: sort ..
    [2019-01-16 13:47:22.417905] INFO: StockRanker训练: 12f03a26 准备训练: 3976066 行数
    [2019-01-16 13:47:22.472993] INFO: StockRanker训练: 正在训练 ..
    [2019-01-16 13:58:50.760972] INFO: bigquant: stock_ranker_train.v5 运行完成[740.039209s].
    [2019-01-16 13:58:50.763798] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-16 13:58:50.768665] INFO: bigquant: 命中缓存
    [2019-01-16 13:58:50.769536] INFO: bigquant: instruments.v2 运行完成[0.005719s].
    [2019-01-16 13:58:50.776117] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-16 13:58:52.365016] INFO: 基础特征抽取: 年份 2015, 特征行数=190352
    [2019-01-16 13:58:54.505029] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
    [2019-01-16 13:58:58.826980] INFO: 基础特征抽取: 年份 2017, 特征行数=743233
    [2019-01-16 13:59:03.254703] INFO: 基础特征抽取: 年份 2018, 特征行数=816987
    [2019-01-16 13:59:03.858692] INFO: 基础特征抽取: 年份 2019, 特征行数=31999
    [2019-01-16 13:59:03.910938] INFO: 基础特征抽取: 总行数: 2424117
    [2019-01-16 13:59:03.913830] INFO: bigquant: general_feature_extractor.v7 运行完成[13.137708s].
    [2019-01-16 13:59:03.916510] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-16 13:59:04.678366] INFO: derived_feature_extractor: /y_2015, 190352
    [2019-01-16 13:59:04.930782] INFO: derived_feature_extractor: /y_2016, 641546
    [2019-01-16 13:59:05.595225] INFO: derived_feature_extractor: /y_2017, 743233
    [2019-01-16 13:59:06.366632] INFO: derived_feature_extractor: /y_2018, 816987
    [2019-01-16 13:59:07.155303] INFO: derived_feature_extractor: /y_2019, 31999
    [2019-01-16 13:59:07.232380] INFO: bigquant: derived_feature_extractor.v3 运行完成[3.315855s].
    [2019-01-16 13:59:07.234667] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-16 13:59:07.428202] INFO: dropnan: /y_2015, 189343/190352
    [2019-01-16 13:59:07.994500] INFO: dropnan: /y_2016, 629016/641546
    [2019-01-16 13:59:08.795959] INFO: dropnan: /y_2017, 712314/743233
    [2019-01-16 13:59:09.726639] INFO: dropnan: /y_2018, 797203/816987
    [2019-01-16 13:59:09.821685] INFO: dropnan: /y_2019, 31050/31999
    [2019-01-16 13:59:09.860810] INFO: dropnan: 行数: 2358926/2424117
    [2019-01-16 13:59:09.864004] INFO: bigquant: dropnan.v1 运行完成[2.629306s].
    [2019-01-16 13:59:09.868122] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-01-16 13:59:11.465825] INFO: StockRanker: prepare data: prediction ..
    [2019-01-16 13:59:36.171395] INFO: stock_ranker_predict: 准备预测: 2358926 行
    [2019-01-16 13:59:36.172472] INFO: stock_ranker_predict: 正在预测 ..
    [2019-01-16 14:00:16.710753] INFO: bigquant: stock_ranker_predict.v5 运行完成[66.842618s].
    [2019-01-16 14:00:16.745623] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-16 14:00:16.747742] INFO: bigquant: biglearning backtest:V8.1.6
    [2019-01-16 14:00:16.748634] INFO: bigquant: product_type:stock by specified
    [2019-01-16 14:00:31.026190] INFO: bigquant: 读取股票行情完成:3056587
    [2019-01-16 14:01:00.113881] INFO: algo: TradingAlgorithm V1.4.2
    [2019-01-16 14:01:11.946839] INFO: algo: trading transform...
    [2019-01-16 14:01:24.070692] INFO: Performance: Simulated 740 trading days out of 740.
    [2019-01-16 14:01:24.071857] INFO: Performance: first open: 2016-01-04 09:30:00+00:00
    [2019-01-16 14:01:24.072763] INFO: Performance: last close: 2019-01-14 15:00:00+00:00
    
    • 收益率-44.59%
    • 年化收益率-18.21%
    • 基准收益率-17.78%
    • 阿尔法-0.13
    • 贝塔0.83
    • 夏普比率-0.74
    • 胜率0.37
    • 盈亏比0.85
    • 收益波动率26.36%
    • 信息比率-0.03
    • 最大回撤62.45%
    [2019-01-16 14:01:27.255353] INFO: bigquant: backtest.v8 运行完成[70.509702s].