用k-近邻回归算法实现A股股票选股

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(lpl22) #1
克隆策略

    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    In [3]:
    # 本代码由可视化策略环境自动生成 2019年1月22日 17:26:44
    # 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 m6_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.hold_days # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 m6_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m6_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:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m6_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2014-01-01',
        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
    """
    )
    
    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
    )
    
    m19 = M.kneighbors_regressor.v1(
        training_ds=m13.data,
        features=m3.data,
        predict_ds=m14.data,
        n_neighbors=5,
        weights='uniform',
        algorithm='auto',
        leaf_size=30,
        metric='minkowski',
        key_cols='date,instrument',
        workers=1,
        other_train_parameters={}
    )
    
    m6 = M.trade.v4(
        instruments=m9.data,
        options_data=m19.predictions,
        start_date='',
        end_date='',
        handle_data=m6_handle_data_bigquant_run,
        prepare=m6_prepare_bigquant_run,
        initialize=m6_initialize_bigquant_run,
        before_trading_start=m6_before_trading_start_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=''
    )
    
    [2019-01-22 17:14:39.801450] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-22 17:14:39.807381] INFO: bigquant: 命中缓存
    [2019-01-22 17:14:39.808425] INFO: bigquant: instruments.v2 运行完成[0.053396s].
    [2019-01-22 17:14:39.861820] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-22 17:14:39.869438] INFO: bigquant: 命中缓存
    [2019-01-22 17:14:39.871174] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.060048s].
    [2019-01-22 17:14:39.919055] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-22 17:14:39.925980] INFO: bigquant: 命中缓存
    [2019-01-22 17:14:39.927664] INFO: bigquant: input_features.v1 运行完成[0.052989s].
    [2019-01-22 17:14:39.982469] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-22 17:14:42.951269] INFO: 基础特征抽取: 年份 2014, 特征行数=569948
    [2019-01-22 17:14:53.359036] INFO: 基础特征抽取: 年份 2015, 特征行数=0
    [2019-01-22 17:14:53.377099] INFO: 基础特征抽取: 总行数: 569948
    [2019-01-22 17:14:53.383549] INFO: bigquant: general_feature_extractor.v7 运行完成[13.446511s].
    [2019-01-22 17:14:53.648806] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-22 17:14:53.911762] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s
    [2019-01-22 17:14:53.916442] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.003s
    [2019-01-22 17:14:53.926214] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.004s
    [2019-01-22 17:14:53.930993] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.003s
    [2019-01-22 17:14:53.935080] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s
    [2019-01-22 17:14:53.945973] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.010s
    [2019-01-22 17:14:54.190937] INFO: derived_feature_extractor: /y_2014, 569948
    [2019-01-22 17:14:54.793007] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.406185s].
    [2019-01-22 17:14:54.840590] INFO: bigquant: join.v3 开始运行..
    [2019-01-22 17:14:56.552963] INFO: join: /y_2014, 行数=555191/569948, 耗时=1.304376s
    [2019-01-22 17:14:56.629256] INFO: join: 最终行数: 555191
    [2019-01-22 17:14:56.632332] INFO: bigquant: join.v3 运行完成[1.836712s].
    [2019-01-22 17:14:56.683083] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-22 17:14:57.583817] INFO: dropnan: /y_2014, 553408/555191
    [2019-01-22 17:14:57.599430] INFO: dropnan: 行数: 553408/555191
    [2019-01-22 17:14:57.652183] INFO: bigquant: dropnan.v1 运行完成[1.016191s].
    [2019-01-22 17:14:57.705093] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-22 17:14:57.713560] INFO: bigquant: 命中缓存
    [2019-01-22 17:14:57.714856] INFO: bigquant: instruments.v2 运行完成[0.059312s].
    [2019-01-22 17:14:57.796468] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-22 17:15:00.460586] INFO: 基础特征抽取: 年份 2015, 特征行数=569698
    [2019-01-22 17:15:03.229865] INFO: 基础特征抽取: 年份 2016, 特征行数=0
    [2019-01-22 17:15:03.246600] INFO: 基础特征抽取: 总行数: 569698
    [2019-01-22 17:15:03.249853] INFO: bigquant: general_feature_extractor.v7 运行完成[5.527245s].
    [2019-01-22 17:15:03.708659] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-22 17:15:03.969613] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s
    [2019-01-22 17:15:03.974293] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.003s
    [2019-01-22 17:15:03.977519] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.002s
    [2019-01-22 17:15:03.980576] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.002s
    [2019-01-22 17:15:03.983796] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.002s
    [2019-01-22 17:15:03.994204] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.009s
    [2019-01-22 17:15:04.326846] INFO: derived_feature_extractor: /y_2015, 569698
    [2019-01-22 17:15:05.167283] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.914141s].
    [2019-01-22 17:15:05.223454] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-22 17:15:05.977071] INFO: dropnan: /y_2015, 565146/569698
    [2019-01-22 17:15:05.994422] INFO: dropnan: 行数: 565146/569698
    [2019-01-22 17:15:06.037863] INFO: bigquant: dropnan.v1 运行完成[0.867462s].
    [2019-01-22 17:15:06.127260] INFO: bigquant: kneighbors_regressor.v1 开始运行..
    [2019-01-22 17:16:18.868525] INFO: bigquant: kneighbors_regressor.v1 运行完成[72.826816s].
    [2019-01-22 17:16:18.974662] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-22 17:16:18.977024] INFO: bigquant: biglearning backtest:V8.1.7
    [2019-01-22 17:16:18.978123] INFO: bigquant: product_type:stock by specified
    [2019-01-22 17:16:28.817389] INFO: bigquant: 读取股票行情完成:1285997
    [2019-01-22 17:16:41.052131] INFO: algo: TradingAlgorithm V1.4.4
    [2019-01-22 17:16:49.418336] INFO: algo: trading transform...
    [2019-01-22 17:16:53.305616] INFO: Performance: Simulated 244 trading days out of 244.
    [2019-01-22 17:16:53.307420] INFO: Performance: first open: 2015-01-05 09:30:00+00:00
    [2019-01-22 17:16:53.308410] INFO: Performance: last close: 2015-12-31 15:00:00+00:00
    
    • 收益率46.0%
    • 年化收益率47.82%
    • 基准收益率5.58%
    • 阿尔法0.34
    • 贝塔0.69
    • 夏普比率1.3
    • 胜率0.6
    • 盈亏比0.9
    • 收益波动率31.52%
    • 信息比率0.1
    • 最大回撤28.11%
    [2019-01-22 17:16:55.084111] INFO: bigquant: backtest.v8 运行完成[36.154277s].
    

    可视化模板实现不同算法下选股策略的流程汇总
    (达达) #2

    对于预测结果需要排序处理一下,修改后如下:

    克隆策略

      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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)\n is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.hold_days\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 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\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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      In [7]:
      # 本代码由可视化策略环境自动生成 2019年11月6日 09:55
      # 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 m6_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 m6_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.hold_days # 是否在建仓期间(前 hold_days 天)
          cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 m6_prepare_bigquant_run(context):
          pass
      
      # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
      def m6_before_trading_start_bigquant_run(context, data):
          pass
      
      
      m1 = M.instruments.v2(
          start_date='2014-01-01',
          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
      """
      )
      
      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.kneighbors_classifier.v1(
          training_ds=m13.data,
          features=m3.data,
          predict_ds=m14.data,
          n_neighbors=5,
          weights='uniform',
          algorithm='auto',
          leaf_size=30,
          metric='minkowski',
          key_cols='date,instrument',
          workers=1,
          other_train_parameters={}
      )
      
      m5 = M.sort.v4(
          input_ds=m4.predictions,
          sort_by='pred_label',
          group_by='date',
          keep_columns='--',
          ascending=False
      )
      
      m6 = M.trade.v4(
          instruments=m9.data,
          options_data=m5.sorted_data,
          start_date='',
          end_date='',
          initialize=m6_initialize_bigquant_run,
          handle_data=m6_handle_data_bigquant_run,
          prepare=m6_prepare_bigquant_run,
          before_trading_start=m6_before_trading_start_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=''
      )
      
      • 收益率45.14%
      • 年化收益率46.92%
      • 基准收益率5.58%
      • 阿尔法0.36
      • 贝塔0.75
      • 夏普比率1.09
      • 胜率0.57
      • 盈亏比0.92
      • 收益波动率40.25%
      • 信息比率0.07
      • 最大回撤44.51%
      bigcharts-data-start/{"__id":"bigchart-842d395f4eb74196af1b2e26454e1606","__type":"tabs"}/bigcharts-data-end