使用深度学习DNN构建选股模型

深度学习
策略分享
幻方
dnn
标签: #<Tag:0x00007f20ca731cd8> #<Tag:0x00007f20ca730f68> #<Tag:0x00007f20ca730888> #<Tag:0x00007f20ca730310>

(brantyz) #1

在某路演活动上看到的幻方用DNN来构建量化策略模型(PPT归属幻方,不便于分享,有兴趣的请自行联系幻方)。于是用了1个小时做了模型复现,在BigQuant模型上做深度学习很方便。

模型没有做任何调优,欢迎大家克隆优化,一些可以优化的方向:

  • 预测目标:可以修改数据标注,预测1天、3天、5天或者其他周期的收益作为标注
  • 训练数据:可以选择不同时段数据作为训练
  • 模型参数:训练迭代次数,学习率等等
  • 特征选择:幻方他们使用了apha101和国泰君安191里面的98个因子。需要注意的,特征值需要一些预处理,比如归一化,标准化,clip等。把特征值控制在-1到1左右
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3日 15:59
    # 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。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m24_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 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='2014-01-01',
        end_date='2017-12-31',
        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/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)-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))
    
    # 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)
    where(label>0.5, NaN, label)
    where(label<-0.5, NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=False
    )
    
    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_5-1
    return_10-1
    return_20-1
    avg_amount_0/avg_amount_5-1
    avg_amount_5/avg_amount_20-1
    rank_avg_amount_0-rank_avg_amount_5
    rank_avg_amount_5-rank_avg_amount_10
    rank_return_0-rank_return_5
    rank_return_5-rank_return_10
    beta_csi300_30_0/10
    beta_csi300_60_0/10
    swing_volatility_5_0/swing_volatility_30_0-1
    swing_volatility_30_0/swing_volatility_60_0-1
    ta_atr_14_0/ta_atr_28_0-1
    ta_sma_5_0/ta_sma_20_0-1
    ta_sma_10_0/ta_sma_20_0-1
    ta_sma_20_0/ta_sma_30_0-1
    ta_sma_30_0/ta_sma_60_0-1
    ta_rsi_14_0/100
    ta_rsi_28_0/100
    ta_cci_14_0/500
    ta_cci_28_0/500
    beta_industry_30_0/10
    beta_industry_60_0/10
    ta_sma(amount_0, 10)/ta_sma(amount_0, 20)-1
    ta_sma(amount_0, 20)/ta_sma(amount_0, 30)-1
    ta_sma(amount_0, 30)/ta_sma(amount_0, 60)-1
    ta_sma(amount_0, 50)/ta_sma(amount_0, 100)-1
    ta_sma(turn_0, 10)/ta_sma(turn_0, 20)-1
    ta_sma(turn_0, 20)/ta_sma(turn_0, 30)-1
    ta_sma(turn_0, 30)/ta_sma(turn_0, 60)-1
    ta_sma(turn_0, 50)/ta_sma(turn_0, 100)-1
    high_0/low_0-1
    close_0/open_0-1
    shift(close_0,1)/close_0-1
    shift(close_0,2)/close_0-1
    shift(close_0,3)/close_0-1
    shift(close_0,4)/close_0-1
    shift(close_0,5)/close_0-1
    shift(close_0,10)/close_0-1
    shift(close_0,20)/close_0-1
    ta_sma(high_0-low_0, 5)/ta_sma(high_0-low_0, 20)-1
    ta_sma(high_0-low_0, 10)/ta_sma(high_0-low_0, 20)-1
    ta_sma(high_0-low_0, 20)/ta_sma(high_0-low_0, 30)-1
    ta_sma(high_0-low_0, 30)/ta_sma(high_0-low_0, 60)-1
    ta_sma(high_0-low_0, 50)/ta_sma(high_0-low_0, 100)-1
    rank_avg_amount_5
    rank_avg_turn_5
    rank_volatility_5_0
    rank_swing_volatility_5_0
    rank_avg_mf_net_amount_5
    rank_beta_industry_5_0
    rank_return_5
    rank_return_2
    std(close_0,5)/std(close_0,20)-1
    std(close_0,10)/std(close_0,20)-1
    std(close_0,20)/std(close_0,30)-1
    std(close_0,30)/std(close_0,60)-1
    std(close_0,50)/std(close_0,100)-1
    """
    )
    
    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=True,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m10 = M.dl_convert_to_bin.v1(
        input_data=m7.data,
        features=m3.data,
        window_size=1
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-01-01'),
        end_date=T.live_run_param('trading_date', '2018-12-31'),
        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=True,
        remove_extra_columns=False
    )
    
    m12 = M.dl_convert_to_bin.v1(
        input_data=m18.data,
        features=m3.data,
        window_size=1
    )
    
    m6 = M.dl_layer_input.v1(
        shape='59',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m8 = M.dl_layer_dense.v1(
        inputs=m6.data,
        units=256,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m21 = M.dl_layer_dropout.v1(
        inputs=m8.data,
        rate=0.9,
        noise_shape='',
        name=''
    )
    
    m20 = M.dl_layer_dense.v1(
        inputs=m21.data,
        units=128,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m22 = M.dl_layer_dropout.v1(
        inputs=m20.data,
        rate=0.9,
        noise_shape='',
        name=''
    )
    
    m23 = M.dl_layer_dense.v1(
        inputs=m22.data,
        units=1,
        activation='linear',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m4 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m23.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m4.data,
        training_data=m10.data,
        optimizer='Adam',
        loss='mean_squared_error',
        metrics='mse',
        batch_size=10240,
        epochs=2,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m12.data,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m18.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m24.data_1,
        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'
    )
    
    [2019-01-03 15:57:42.324053] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-03 15:57:42.337648] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.338972] INFO: bigquant: instruments.v2 运行完成[0.014959s].
    [2019-01-03 15:57:42.345243] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-03 15:57:42.351350] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.353004] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.007779s].
    [2019-01-03 15:57:42.358356] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-03 15:57:42.364833] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.365887] INFO: bigquant: input_features.v1 运行完成[0.007548s].
    [2019-01-03 15:57:42.404545] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-03 15:57:42.408610] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.409595] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005065s].
    [2019-01-03 15:57:42.412852] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-03 15:57:42.416921] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.417741] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.004887s].
    [2019-01-03 15:57:42.426825] INFO: bigquant: join.v3 开始运行..
    [2019-01-03 15:57:42.432147] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.433227] INFO: bigquant: join.v3 运行完成[0.006371s].
    [2019-01-03 15:57:42.440369] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
    [2019-01-03 15:57:42.444856] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.445726] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.005369s].
    [2019-01-03 15:57:42.448055] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-03 15:57:42.452644] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.453692] INFO: bigquant: instruments.v2 运行完成[0.00563s].
    [2019-01-03 15:57:42.463259] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-03 15:57:42.467665] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.468775] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005594s].
    [2019-01-03 15:57:42.470919] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-03 15:57:42.474454] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.475621] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.004681s].
    [2019-01-03 15:57:42.480083] INFO: bigquant: dl_convert_to_bin.v1 开始运行..
    [2019-01-03 15:57:42.483569] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:42.484286] INFO: bigquant: dl_convert_to_bin.v1 运行完成[0.004211s].
    
    Using TensorFlow backend.
    
    [2019-01-03 15:57:44.471844] INFO: bigquant: cached.v3 开始运行..
    [2019-01-03 15:57:45.146342] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:45.147476] INFO: bigquant: cached.v3 运行完成[0.675639s].
    [2019-01-03 15:57:45.156946] INFO: bigquant: dl_model_train.v1 开始运行..
    [2019-01-03 15:57:45.162414] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:45.163508] INFO: bigquant: dl_model_train.v1 运行完成[0.006585s].
    [2019-01-03 15:57:45.166850] INFO: bigquant: dl_model_predict.v1 开始运行..
    [2019-01-03 15:57:45.170708] INFO: bigquant: 命中缓存
    DataSource(e2228dc40f2c11e998490a580a8102d4, v3)
    [2019-01-03 15:57:45.171619] INFO: bigquant: dl_model_predict.v1 运行完成[0.004777s].
    [2019-01-03 15:57:45.174690] INFO: bigquant: cached.v3 开始运行..
    [2019-01-03 15:57:45.177969] INFO: bigquant: 命中缓存
    [2019-01-03 15:57:45.178624] INFO: bigquant: cached.v3 运行完成[0.003926s].
    [2019-01-03 15:57:45.205879] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-03 15:57:45.207919] INFO: bigquant: biglearning backtest:V8.1.6
    [2019-01-03 15:57:45.208751] INFO: bigquant: product_type:stock by specified
    [2019-01-03 15:57:52.815140] INFO: bigquant: 读取股票行情完成:1655305
    [2019-01-03 15:58:08.513778] INFO: algo: TradingAlgorithm V1.4.2
    [2019-01-03 15:58:19.347972] INFO: algo: trading transform...
    [2019-01-03 15:58:22.674326] INFO: Performance: Simulated 243 trading days out of 243.
    [2019-01-03 15:58:22.675790] INFO: Performance: first open: 2018-01-02 09:30:00+00:00
    [2019-01-03 15:58:22.676833] INFO: Performance: last close: 2018-12-28 15:00:00+00:00
    
    • 收益率-27.54%
    • 年化收益率-28.4%
    • 基准收益率-25.31%
    • 阿尔法-0.15
    • 贝塔0.6
    • 夏普比率-1.53
    • 胜率0.41
    • 盈亏比0.92
    • 收益波动率22.16%
    • 信息比率-0.01
    • 最大回撤37.14%
    [2019-01-03 15:58:24.648708] INFO: bigquant: backtest.v8 运行完成[39.44282s].
    

    (www232000) #2

    请问训练迭代次数,学习率 在那个模块?我没找到


    (fsm) #3

    m5…


    (www232000) #4

    谢谢回复


    (www232000) #5

    开发者提供了量化新方向,不得不增加了仰望大牛的角度。


    (ingeno) #6

    幻方路演PPT里说用了深度学习。这个复现很有用,可以帮助大家快速实验起来。群里有人优化到 100% 年化收益。但对于用深度学习和人工特征工程,在日线数据上,个人仍然觉得后者更好。在tick等更大量的数据上,已经很难做人工特征工程的地方,深度学习可能会更有用武之地。


    (微观见涨停) #10

    看来大家都对深度学习选股策略感兴趣


    (lihaoyang) #11

    老哥方便加入群吗


    (iQuant) #12

    可以,添加客服微信:bigq100,小Q拉您入群


    (tkyz) #13

    这个DNN模型能不能用滚动训练优化,能发一个带滚动训练的吗?