[宽客学院]收益分析

宽客学院
收益分析
标签: #<Tag:0x00007f5bfe74f930> #<Tag:0x00007f5bfe74f7c8>

(iQuant) #1
作者:bigquant
阅读时间:15分钟
本文由BigQuant宽客学院推出,难度标签:☆☆☆

导语:本文主要介绍收益分析,包括风格因子收益分析和策略收益综合分析。

克隆策略

1.一个完整的可视化AI策略

    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    In [1]:
    # 本代码由可视化策略环境自动生成 2018年1月19日 20:29
    # 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。
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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)
    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
    )
    
    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_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m5.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', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_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()}
    
        # 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.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]))])))
            # 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 m12_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m12_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.options['hold_days'] = 5
    
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-01-19 20:25:03.809178] INFO: bigquant: instruments.v2 开始运行..
    [2018-01-19 20:25:03.854734] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:03.856364] INFO: bigquant: instruments.v2 运行完成[0.047229s].
    [2018-01-19 20:25:04.114716] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-01-19 20:25:04.163262] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.165146] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.050458s].
    [2018-01-19 20:25:04.180165] INFO: bigquant: input_features.v1 开始运行..
    [2018-01-19 20:25:04.305503] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.307381] INFO: bigquant: input_features.v1 运行完成[0.127228s].
    [2018-01-19 20:25:04.492875] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-01-19 20:25:04.511967] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.513959] INFO: bigquant: general_feature_extractor.v6 运行完成[0.021133s].
    [2018-01-19 20:25:04.537698] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-01-19 20:25:04.554513] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.555956] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.018292s].
    [2018-01-19 20:25:04.575845] INFO: bigquant: join.v3 开始运行..
    [2018-01-19 20:25:04.593888] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.595670] INFO: bigquant: join.v3 运行完成[0.019844s].
    [2018-01-19 20:25:04.611438] INFO: bigquant: dropnan.v1 开始运行..
    [2018-01-19 20:25:04.629223] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.631500] INFO: bigquant: dropnan.v1 运行完成[0.020069s].
    [2018-01-19 20:25:04.659457] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2018-01-19 20:25:04.698933] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.700259] INFO: bigquant: stock_ranker_train.v5 运行完成[0.040823s].
    [2018-01-19 20:25:04.707997] INFO: bigquant: instruments.v2 开始运行..
    [2018-01-19 20:25:04.721018] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.723178] INFO: bigquant: instruments.v2 运行完成[0.015154s].
    [2018-01-19 20:25:04.839327] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-01-19 20:25:04.847949] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.850120] INFO: bigquant: general_feature_extractor.v6 运行完成[0.010817s].
    [2018-01-19 20:25:04.862137] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-01-19 20:25:04.878071] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.879899] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.017759s].
    [2018-01-19 20:25:04.892002] INFO: bigquant: dropnan.v1 开始运行..
    [2018-01-19 20:25:04.900298] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.902373] INFO: bigquant: dropnan.v1 运行完成[0.010357s].
    [2018-01-19 20:25:04.924271] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-01-19 20:25:04.942961] INFO: bigquant: 命中缓存
    [2018-01-19 20:25:04.944532] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.020247s].
    [2018-01-19 20:25:05.054483] INFO: bigquant: backtest.v7 开始运行..
    [2018-01-19 20:25:05.062164] INFO: bigquant: 命中缓存
    
    • 收益率301.06%
    • 年化收益率104.88%
    • 基准收益率-6.33%
    • 阿尔法1.08
    • 贝塔0.93
    • 夏普比率2.44
    • 胜率0.629
    • 盈亏比0.915
    • 收益波动率41.58%
    • 信息比率3.71
    • 最大回撤49.98%
    [2018-01-19 20:25:08.002504] INFO: bigquant: backtest.v7 运行完成[2.948003s].
    

    2.风格因子收益分析

    风格因子收益分析,即将组合收益归因在各个因子上,因子为:

    • 市场因子,股票收益对基准组合(沪深300)的敏感度

    • 财务质量因子,可以整体反映出公司的财务质量。

    • 成长因子,通过财务指标反映出公司的成长性。

    • 股东因子,公司户均持股比例因子。

    • 杠杆因子,公司债务相对于其总资产的比例。

    • 流动性因子,交易的活跃程度和流动性。

    • 动量因子,股票价格变化的总体趋势特征。

    • 规模因子,公司总市值。

    • 价值因子,股票的投资价值。

    • 波动率因子,股票价格变化幅度的度量。

    In [3]:
    m12.factor_profit_analyze() # 查看某个风格因子的收益,鼠标点击该因子的图例(文字)即可。
    

    3.策略收益综合分析

    策略综合分析是对整个策略回测结果分析,主要包括 策略收益综合分析 和策略风险风险分析:

    • 回测相关指标,比如年化收益、收益波动率、夏普比率、最大回撤、偏度、峰度、索提纳比率、信息比率、贝塔、阿尔法

    • 最严重的前五次回撤,包括回撤时间段、回撤幅度、回撤天数

    • 日收益折线图

    • 月度收益率、年度收益率、月度收益分布图

    • 日收益茎叶图、周收益茎叶图、年收益茎叶图

    • 盈利最大前10股票

    • 多头市值与空头市值

    • 每日持仓股票数、每日杠杆、每日换手率、每日交易额

    In [4]:
    m12.pyfolio_full_tear_sheet()
    
    Entire data start date: 2015-01-05
    Entire data end date: 2016-12-30
    
    
    Backtest Months: 23
    
    Performance statistics Backtest
    cum_returns_final 3.01
    annual_return 1.05
    annual_volatility 0.42
    sharpe_ratio 1.94
    calmar_ratio 2.10
    stability_of_timeseries 0.68
    max_drawdown -0.50
    omega_ratio 1.39
    sortino_ratio 2.72
    skew -0.68
    kurtosis 1.09
    tail_ratio 0.88
    common_sense_ratio 1.80
    information_ratio -0.06
    alpha 0.72
    beta 0.03
    Worst Drawdown Periods net drawdown in % peak date valley date recovery date duration
    0 49.98 2015-06-12 2015-09-15 2016-11-01 363
    1 6.47 2016-11-17 2016-12-14 NaT NaN
    2 5.93 2015-05-27 2015-05-28 2015-06-01 4
    3 5.10 2015-02-03 2015-02-09 2015-02-16 10
    4 4.57 2015-05-04 2015-05-07 2015-05-11 6
    [-0.049 -0.115]
    
    Stress Events mean min max
    Fall2015 -0.74% -7.73% 6.38%
    New Normal 0.32% -8.35% 6.48%
    Top 10 long positions of all time max
    Equity(1790 [600446.SHA]) 26.42%
    Equity(1274 [600656.SHA]) 22.32%
    Equity(91 [300372.SZA]) 22.18%
    Equity(1286 [300380.SZA]) 21.28%
    Equity(2697 [600153.SHA]) 21.20%
    Equity(1683 [002240.SZA]) 21.12%
    Equity(2862 [300028.SZA]) 21.03%
    Equity(2987 [601010.SHA]) 20.96%
    Equity(1378 [002373.SZA]) 20.76%
    Equity(752 [300094.SZA]) 20.74%
    Top 10 short positions of all time max
    Top 10 positions of all time max
    Equity(1790 [600446.SHA]) 26.42%
    Equity(1274 [600656.SHA]) 22.32%
    Equity(91 [300372.SZA]) 22.18%
    Equity(1286 [300380.SZA]) 21.28%
    Equity(2697 [600153.SHA]) 21.20%
    Equity(1683 [002240.SZA]) 21.12%
    Equity(2862 [300028.SZA]) 21.03%
    Equity(2987 [601010.SHA]) 20.96%
    Equity(1378 [002373.SZA]) 20.76%
    Equity(752 [300094.SZA]) 20.74%
    All positions ever held max
    Equity(1790 [600446.SHA]) 26.42%
    Equity(1274 [600656.SHA]) 22.32%
    Equity(91 [300372.SZA]) 22.18%
    Equity(1286 [300380.SZA]) 21.28%
    Equity(2697 [600153.SHA]) 21.20%
    Equity(1683 [002240.SZA]) 21.12%
    Equity(2862 [300028.SZA]) 21.03%
    Equity(2987 [601010.SHA]) 20.96%
    Equity(1378 [002373.SZA]) 20.76%
    Equity(752 [300094.SZA]) 20.74%
    Equity(1729 [600634.SHA]) 20.71%
    Equity(1034 [002018.SZA]) 20.70%
    Equity(2461 [000670.SZA]) 20.70%
    Equity(1198 [002112.SZA]) 20.67%
    Equity(1657 [002208.SZA]) 20.63%
    Equity(1600 [600715.SHA]) 20.62%
    Equity(511 [002049.SZA]) 20.56%
    Equity(3011 [000796.SZA]) 20.55%
    Equity(2498 [300089.SZA]) 20.53%
    Equity(1964 [000693.SZA]) 20.52%
    Equity(1372 [601388.SHA]) 20.50%
    Equity(1994 [300063.SZA]) 20.32%
    Equity(1338 [600365.SHA]) 20.31%
    Equity(1636 [300242.SZA]) 20.23%
    Equity(460 [002703.SZA]) 20.18%
    Equity(1382 [600230.SHA]) 20.15%
    Equity(105 [600058.SHA]) 20.14%
    Equity(498 [300292.SZA]) 20.12%
    Equity(1469 [000809.SZA]) 20.10%
    Equity(781 [600654.SHA]) 20.07%
    ... ...
    Equity(1541 [300245.SZA]) 1.23%
    Equity(2890 [002445.SZA]) 1.22%
    Equity(1314 [600687.SHA]) 1.21%
    Equity(2423 [603988.SHA]) 1.19%
    Equity(2737 [300240.SZA]) 1.19%
    Equity(1568 [002305.SZA]) 1.07%
    Equity(2070 [300053.SZA]) 1.06%
    Equity(259 [300002.SZA]) 1.06%
    Equity(2911 [000158.SZA]) 1.04%
    Equity(2787 [000921.SZA]) 1.03%
    Equity(2133 [600589.SHA]) 1.01%
    Equity(2646 [600682.SHA]) 0.94%
    Equity(123 [000409.SZA]) 0.88%
    Equity(449 [600432.SHA]) 0.75%
    Equity(900 [603000.SHA]) 0.54%
    Equity(3030 [300115.SZA]) 0.43%
    Equity(650 [600433.SHA]) 0.36%
    Equity(717 [002454.SZA]) 0.35%
    Equity(2928 [002436.SZA]) 0.29%
    Equity(1270 [002095.SZA]) 0.18%
    Equity(1046 [000516.SZA]) 0.13%
    Equity(2702 [000829.SZA]) 0.11%
    Equity(2609 [002481.SZA]) 0.10%
    Equity(1478 [000502.SZA]) 0.09%
    Equity(1298 [002355.SZA]) 0.08%
    Equity(1860 [002127.SZA]) 0.05%
    Equity(567 [000821.SZA]) 0.02%
    Equity(1881 [600112.SHA]) 0.02%
    Equity(1822 [002307.SZA]) 0.00%
    Equity(2012 [600192.SHA]) 0.00%

    1199 rows × 1 columns

    导语:希望通过本文的介绍,读者可以对策略的收益进行分解,更好地认识策略。


       本文由BigQuant宽客学院推出,版权归BigQuant所有,转载请注明出处。

    【宽客学院】回测数据深入分析
    能否增加pyfolio库
    (a20180322) #2

    2016年12月因子收益分析中动量因子为-0.09请问如何解读,说明动量大的收益差吗?


    (大胡子) #3

    我觉得是这样的,方向为负嘛,应该是负相关。