主成分分析降维(PCA) + StockRanker

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标签: #<Tag:0x00007fa1a1ae2460>

(ykgong) #1

降维

很多机器学习的问题都会涉及到有着几千甚至数百万维的特征的训练实例。这不仅让训练过程变得非常缓慢,同时还很难找到一个很好的解,这种问题通常被称为维数灾难(curse of dimentionality)。

幸运的是,在现实生活中我们经常可以极大的降低特征维度,将一个十分棘手的问题转变成一个可以较为容易解决的问题。例如,对于 MNIST 图片集:图片四周边缘部分的像素几乎总是白的,因此你完全可以将这些像素从你的训练集中扔掉而不会丢失太多信息。同时,两个相邻的像素往往是高度相关的:如果你想要将他们合并成一个像素(比如取这两个像素点的平均值)你并不会丢失很多信息。


警告:降维肯定会丢失一些信息(这就好比将一个图片压缩成 JPEG 的格式会降低图像的质量),因此即使这种方法可以加快训练的速度,同时也会让你的系统表现的稍微差一点。降维会让你的工作流水线更复杂因而更难维护。所有你应该先尝试使用原始的数据来训练,如果训练速度太慢的话再考虑使用降维。在某些情况下,降低训练集数据的维度可能会筛选掉一些噪音和不必要的细节,这可能会让你的结果比降维之前更好(这种情况通常不会发生;它只会加快你训练的速度)。降维除了可以加快训练速度外,在数据可视化方面(或者 DataViz)也十分有用。降低特征维度到 2(或者 3)维从而可以在图中画出一个高维度的训练集,让我们可以通过视觉直观的发现一些非常重要的信息,比如聚类。


两种主要的降维方法:投影(projection)和流形学习(Manifold Learning),三种流行的降维技术:主成分分析(PCA),核主成分分析(Kernel PCA)和局部线性嵌入(LLE)。

主成分分析

sklearn使用奇异值分解(SVD)的标准矩阵分解技术,可以将训练集矩阵 X 分解为三个矩阵 U·Σ·V^T 的点积,其中 V^T 包含我们想要的所有主成分。

运行StockRanker前利用PCA降维

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2月15日 15:32
    # 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 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')]
    
        # 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 m19_prepare_bigquant_run(context):
        pass
    
    # 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.options['hold_days'] = 5
    
    
    m1 = M.instruments.v2(
        start_date='2015-01-01',
        end_date='2015-09-01',
        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://ppe.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://ppe.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
    """
    )
    
    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
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2016-01-01'),
        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
    )
    
    m4 = M.decomposition_pca.v1(
        training_ds=m13.data,
        features=m3.data,
        predict_ds=m14.data,
        n_components=1,
        whiten=False,
        other_train_parameters={}
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m4.transform_trainds,
        features=m4.pca_features,
        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
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m4.transform_predictds,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_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='000300.SHA'
    )
    
    [2019-02-15 15:32:00.434576] INFO: bigquant: instruments.v2 开始运行..
    [2019-02-15 15:32:00.447902] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.448875] INFO: bigquant: instruments.v2 运行完成[0.014349s].
    [2019-02-15 15:32:00.451768] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-02-15 15:32:00.455110] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.455917] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004152s].
    [2019-02-15 15:32:00.458205] INFO: bigquant: input_features.v1 开始运行..
    [2019-02-15 15:32:00.461276] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.461901] INFO: bigquant: input_features.v1 运行完成[0.0037s].
    [2019-02-15 15:32:00.479329] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-02-15 15:32:00.483053] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.483763] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004437s].
    [2019-02-15 15:32:00.487007] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-02-15 15:32:00.491099] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.491921] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.004893s].
    [2019-02-15 15:32:00.501634] INFO: bigquant: join.v3 开始运行..
    [2019-02-15 15:32:00.506003] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.506978] INFO: bigquant: join.v3 运行完成[0.00536s].
    [2019-02-15 15:32:00.509898] INFO: bigquant: dropnan.v1 开始运行..
    [2019-02-15 15:32:00.513445] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.514260] INFO: bigquant: dropnan.v1 运行完成[0.004364s].
    [2019-02-15 15:32:00.516062] INFO: bigquant: instruments.v2 开始运行..
    [2019-02-15 15:32:00.519168] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.520143] INFO: bigquant: instruments.v2 运行完成[0.0041s].
    [2019-02-15 15:32:00.525717] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-02-15 15:32:00.529728] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.530560] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004846s].
    [2019-02-15 15:32:00.532789] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-02-15 15:32:00.536491] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.537391] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.004592s].
    [2019-02-15 15:32:00.539431] INFO: bigquant: dropnan.v1 开始运行..
    [2019-02-15 15:32:00.542552] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.543228] INFO: bigquant: dropnan.v1 运行完成[0.003799s].
    [2019-02-15 15:32:00.809482] INFO: bigquant: decomposition_pca.v1 开始运行..
    [2019-02-15 15:32:00.846797] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.848164] INFO: bigquant: decomposition_pca.v1 运行完成[0.038755s].
    [2019-02-15 15:32:00.851723] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-02-15 15:32:00.892646] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.893943] INFO: bigquant: stock_ranker_train.v5 运行完成[0.04221s].
    [2019-02-15 15:32:00.897849] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-02-15 15:32:00.906373] INFO: bigquant: 命中缓存
    [2019-02-15 15:32:00.907435] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.009603s].
    [2019-02-15 15:32:00.929947] INFO: bigquant: backtest.v8 开始运行..
    [2019-02-15 15:32:00.992261] INFO: bigquant: 命中缓存
    
    • 收益率87.95%
    • 年化收益率91.87%
    • 基准收益率5.58%
    • 阿尔法0.62
    • 贝塔0.6
    • 夏普比率2.0
    • 胜率0.6
    • 盈亏比0.93
    • 收益波动率33.99%
    • 信息比率0.12
    • 最大回撤33.18%
    [2019-02-15 15:32:02.061243] INFO: bigquant: backtest.v8 运行完成[1.131284s].
    

    BigQuant常见问题和经验整理合计(1.0版本)
    深度学习前沿 | 利用GAN预测股价走势