关于predict和train函数问题以及结果理解?

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标签: #<Tag:0x00007f20c3816038>

(PAYNE) #1

1,predicitons的返回数据如下:
image

这里的score,该怎么理解? 是上涨概率,还是收益率?胜率?…或者能给出更详细的score参数算法也好

2,如果用了M.stock_ranker_train函数后 ,如何可以看出训练处的模型的好坏?方差,偏差,如何用测试数据进行比较?

3,看文档上的predict函数已经更新到V5了,为什么我代码段只能用V2的,其它版本都报错呢?

M.stock_ranker_predict文档上的解释如下,别再引用文档了,解释不清楚还不更新……哎

下面引用文档
定义

M.stock_ranker_predict.v5(self, model, data)
股票排序机器学习模型预测。

参数:
model (字符串) – 模型。
data (DataSource) – 数据。
返回:
.predictions: 预测结果
返回类型:
Outputs

示例代码


(iQuant) #2

你好,平台上的StockRanker算法是排序学习算法,该算法借鉴了搜索引擎的推荐学习算法。
在衡量算法训练效果的时候,并不是采取传统机器学习算法的准确率、召回率等指标,而是采取的ndcg这个指标。
相关参考资料为:

在BigQuant上开发的AI策略,你也可以查看具体的ndcg数据,具体查看的策略如下,欢迎克隆:

1
2

克隆策略

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    In [7]:
    # 本代码由可视化策略环境自动生成 2017年10月26日 12:02
    # 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='2010-01-01',
        end_date='2015-01-01',
        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='2010-01-01',
        end_date='2015-01-01'
    )
    
    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
    )
    
    m17 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m13.data,
        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=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01')
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m15 = M.advanced_auto_labeler.v2(
        instruments=m9.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='2015-01-01',
        end_date='2017-01-01',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m16 = M.join.v3(
        data1=m15.data,
        data2=m11.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m16.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 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='2015-01-01',
        end_date='2017-01-01',
        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',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-10-26 11:56:07.511438] INFO: bigquant: instruments.v2 开始运行..
    [2017-10-26 11:56:07.515232] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.516281] INFO: bigquant: instruments.v2 运行完成[0.004911s].
    [2017-10-26 11:56:07.525262] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-10-26 11:56:07.528252] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.529455] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004215s].
    [2017-10-26 11:56:07.534193] INFO: bigquant: input_features.v1 开始运行..
    [2017-10-26 11:56:07.538944] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.540073] INFO: bigquant: input_features.v1 运行完成[0.005879s].
    [2017-10-26 11:56:07.557765] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-10-26 11:56:07.561159] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.562298] INFO: bigquant: general_feature_extractor.v6 运行完成[0.004601s].
    [2017-10-26 11:56:07.570381] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-10-26 11:56:07.572950] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.573854] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003483s].
    [2017-10-26 11:56:07.585831] INFO: bigquant: join.v3 开始运行..
    [2017-10-26 11:56:07.589243] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.590363] INFO: bigquant: join.v3 运行完成[0.004528s].
    [2017-10-26 11:56:07.683159] INFO: bigquant: dropnan.v1 开始运行..
    [2017-10-26 11:56:07.687277] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.688203] INFO: bigquant: dropnan.v1 运行完成[0.005071s].
    [2017-10-26 11:56:07.696733] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2017-10-26 11:56:07.700277] INFO: bigquant: 命中缓存
    [2017-10-26 11:56:07.701345] INFO: bigquant: stock_ranker_train.v5 运行完成[0.00461s].
    [2017-10-26 11:56:07.784666] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2017-10-26 11:56:30.630841] INFO: df2bin: prepare data: prediction ..
    [2017-10-26 11:57:11.841522] INFO: stock_ranker_predict: 准备预测: 2606084 行
    [2017-10-26 11:57:51.273253] INFO: bigquant: stock_ranker_predict.v5 运行完成[103.488576s].
    
    In [8]:
    # 训练集上的ndcg
    m17.ndcg.read_df()
    
    Out[8]:
    position ndcg
    0 1 98.799415
    1 2 98.790408
    2 3 98.804231
    3 4 98.833986
    4 5 98.843145
    5 6 98.847617
    6 7 98.855920
    7 8 98.864041
    8 9 98.875784
    9 10 98.880987
    In [6]:
    # 预测集上的ndcg
    m8.ndcg.read_df()
    
    Out[6]:
    position ndcg
    0 1 99.760270
    1 2 99.751839
    2 3 99.758931
    3 4 99.742668
    4 5 99.732110
    5 6 99.728098
    6 7 99.726054
    7 8 99.724436
    8 9 99.723888
    9 10 99.724123