可视化模板自定义指标过滤的实现


(iQuant) #1

很多朋友在使用平台时希望在交易时加入一些自定义的指标过滤,比如股价在120日均线之上等。在可视化模板中如何修改,以实现过滤功能呢?分为两种情况:

  • 第一种情况:
    如果只是在买卖操作中做风控系统过滤,可以通过trade模块的盘前函数进行指标定义,通过context传给主函数模块,在主函数中通过条件判断实现风险控制,这可以参考下面的案例:

https://i.bigquant.com/user/iquant/lab/share/%E6%94%B6%E8%97%8F%EF%BC%88%E7%A4%BE%E5%8C%BA%EF%BC%89%2F%E5%8F%AF%E8%A7%86%E5%8C%96%E7%AD%96%E7%95%A5-%E9%A3%8E%E6%8E%A7%E4%B8%AD%E7%9A%84%E8%87%AA%E5%AE%9A%E4%B9%89%E6%8C%87%E6%A0%87%E8%BF%87%E6%BB%A4.ipynb

  • 第二种情况:
    如果希望在训练中就加入过滤条件,回测也使用相同的过滤条件,则可以参考下面的框架。

在输入特征列表中定义需要计算的因子名称,这个名称必须是个函数名(自变量)的格式,例如计算MA需要close_0因子作为自变量,那么可以定义指标因子名称为MA(close_0)。要注意自变量要填写齐全,比如计算成本均线需要用到换手率,那么指标名字就要定义成:CYC(close_o,turn_0)。
此外,这个输入特征列表里面的自定义指标只是用作过滤使用,由于并没有传给训练模块,因此并不参与最终的模型训练,只做过滤使用。
在衍生数据抽取模块中需要定义指标的计算公式,定义方法可以参考策略。
由于函数名不能用过滤模块直接过滤,所以指标抽取后在自定义模块中进行数据的过滤。
可视化流程中分为左分支训练集和右分支预测集的特征提取,新增模块应在两个分支保持一致,代码也保持一致以保证训练和测试过程的一致性。

https://i.bigquant.com/user/iquant/lab/share/%E6%94%B6%E8%97%8F%EF%BC%88%E7%A4%BE%E5%8C%BA%EF%BC%89%2F%E5%8F%AF%E8%A7%86%E5%8C%96%E7%AD%96%E7%95%A5-%E8%87%AA%E5%AE%9A%E4%B9%89%E6%8C%87%E6%A0%87%E8%BF%87%E6%BB%A4.ipynb

最后在两种情况下,都需要注意抽取基础因子时尽量向前多取一些天数,以保证指标值或条件值不为Nan


如何以年线以上的票为股票池
如何过滤60天内没有涨停的股票
如何以年线以上的票为股票池
(baifx) #2

@iQuant :我做了一个自定义指标过滤,用到close_0 和 low_0,
用上面克隆后修改。
特征列表:
close_0
low_0
return_5

函数名定义为: self_diy(close_0, low_0)
自定义指标计算处:

import talib
def self_diy(df,close_0,low_0): 
    close = [float(x) for x in df['close_0']] 
    df['condition']=(df['close_0']>talib.MA(np.array(close), timeperiod=10) and df['low_0'] < talib.MA(np.array(close), timeperiod=10)).astype(int)
    return df['condition']
bigquant_run = {
    'self_diy':  self_diy
}

自定义指标过滤处:

 def bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df()
    df1=df[df['self_diy(close_0,low_0)']>0]
   。。。。。。

现在报错:

2018-05-25 15:45:46.879323 INFO: derived_feature_extractor: 提取失败 self_diy(close_0,low_0): The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
---------------------------------------------------------------------------_
KeyError Traceback (most recent call last)_
in ()_
89 instrument_col=‘instrument’,_
90 user_functions=m5_user_functions_bigquant_run,_
—> 91 m_cached=False_
92 )_
93 _
KeyError: “[‘self_diy(close_0,low_0)’] not in index”_

请问什么原因?


(baifx) #3

抱歉, 找到原因了,是我pandas 出错,
逻辑运算 and 改为 & 即可。

def self_diy(df,close_0,low_0): 
    close = [float(x) for x in df['close_0']] 
    df['condition']=(df['close_0']>talib.MA(np.array(close), timeperiod=10) & df['low_0'] < talib.MA(np.array(close), timeperiod=10)).astype(int)
    return df['condition']
bigquant_run = {
    'self_diy':  self_diy
}

(smallsnow) #5

“可视化流程中分为左分支训练集和右分支预测集的特征提取,新增模块应在两个分支保持一致,代码也保持一致以保证训练和测试过程的一致性。”,我定义的一个选股函数,左右两个一样的,但是发现左边对训练集有效(m13.data),右边的选出来结果(m14.data)乱七八糟的,导致最终回测的股票的时间点全是不符合我筛选策略的。不知道什么原因


(smallsnow) #6

补充一下我的疑问,右侧分支的自定义函数,好像其计算结果并没有按照单个股票计算,而是多只股票混合计算了,是不是哪里 需要用groupby(‘instrument’)?
当我只对单个股票进行测试,回测的自定义函数结果是正确的,但是多个股票就股票之间干扰了


(iQuant) #7

收到您的提问,我们已提交给策略工程师,会尽快给您回复。


(达达) #8

试一下这个吧

克隆策略

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回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['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,按机器学习算法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 context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 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    In [173]:
    # 本代码由可视化策略环境自动生成 2019年1月30日 11:42
    # 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 m16_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        input_df = input_1.read_df().reset_index(drop=True)
        
        def cal(df):
            # 将价格数据转化成float类型
            close = [float(x) for x in df['close_0']]
            # 通过talib计算移动平均值(方法2)
            df['MA10'] = talib.MA(np.array(close), timeperiod=10)
            df['cond'] = (df['close_0']>df['MA10']).astype(int)
            df.drop('MA10',axis=1)
            return df
        # 计算指标条件
        result = input_df.groupby('instrument').apply(cal)
        # 过滤
        filter_result = result[result['cond']>0]
        # 输出
        data_1 = DataSource.write_df(filter_result)
        return Outputs(data_1=data_1, 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 m16_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m14_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        input_df = input_1.read_df().reset_index(drop=True)
        
        def cal(df):
            # 将价格数据转化成float类型
            close = [float(x) for x in df['close_0']]
            # 通过talib计算移动平均值(方法2)
            df['MA10'] = talib.MA(np.array(close), timeperiod=10)
            df['cond'] = (df['close_0']>df['MA10']).astype(int)
            df.drop('MA10',axis=1)
            return df
        # 计算指标条件
        result = input_df.groupby('instrument').apply(cal)
        # 过滤
        filter_result = result[result['cond']>0]
        # 输出
        data_1 = DataSource.write_df(filter_result)
        return Outputs(data_1=data_1, 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 m14_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 m5_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 m5_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m5_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='2012-01-01',
        end_date='2012-05-01',
        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
    close_0
    return_5/return_10""",
        m_cached=False
    )
    
    m4 = M.input_features.v1(
        features_ds=m3.data,
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    close_0
    """
    )
    
    m18 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=300
    )
    
    m10 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m16 = M.cached.v3(
        input_1=m10.data,
        run=m16_run_bigquant_run,
        post_run=m16_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data_1,
        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
    )
    
    m20 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m4.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m21 = M.derived_feature_extractor.v3(
        input_data=m20.data,
        features=m4.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m14 = M.cached.v3(
        input_1=m21.data,
        run=m14_run_bigquant_run,
        post_run=m14_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m15 = M.dropnan.v1(
        input_data=m14.data_1
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m15.data,
        m_lazy_run=False
    )
    
    m5 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m5_handle_data_bigquant_run,
        prepare=m5_prepare_bigquant_run,
        initialize=m5_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'
    )
    
    [2019-01-30 11:40:39.936887] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-30 11:40:39.953899] INFO: bigquant: 命中缓存
    [2019-01-30 11:40:39.954995] INFO: bigquant: instruments.v2 运行完成[0.01814s].
    [2019-01-30 11:40:39.968514] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-30 11:40:40.812736] INFO: 自动标注(股票): 加载历史数据: 167908 行
    [2019-01-30 11:40:40.814256] INFO: 自动标注(股票): 开始标注 ..
    [2019-01-30 11:40:41.492252] INFO: bigquant: advanced_auto_labeler.v2 运行完成[1.523717s].
    [2019-01-30 11:40:41.495075] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-30 11:40:41.501408] INFO: bigquant: input_features.v1 运行完成[0.00635s].
    [2019-01-30 11:40:41.504246] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-30 11:40:41.513398] INFO: bigquant: input_features.v1 运行完成[0.009162s].
    [2019-01-30 11:40:41.518358] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-30 11:40:41.522616] INFO: bigquant: 命中缓存
    [2019-01-30 11:40:41.523493] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005138s].
    [2019-01-30 11:40:41.526128] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-30 11:40:41.714487] INFO: derived_feature_extractor: 提取完成 return_5/return_10, 0.002s
    [2019-01-30 11:40:41.833959] INFO: derived_feature_extractor: /y_2011, 433943
    [2019-01-30 11:40:42.085119] INFO: derived_feature_extractor: /y_2012, 167908
    [2019-01-30 11:40:42.172416] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.646251s].
    [2019-01-30 11:40:42.176959] INFO: bigquant: cached.v3 开始运行..
    [2019-01-30 11:40:49.572819] INFO: bigquant: cached.v3 运行完成[7.395832s].
    [2019-01-30 11:40:49.580235] INFO: bigquant: join.v3 开始运行..
    [2019-01-30 11:40:49.876540] INFO: join: /data, 行数=94941/262505, 耗时=0.236813s
    [2019-01-30 11:40:49.901880] INFO: join: 最终行数: 94941
    [2019-01-30 11:40:49.904987] INFO: bigquant: join.v3 运行完成[0.324732s].
    [2019-01-30 11:40:49.912251] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-30 11:40:50.035313] INFO: dropnan: /data, 94903/94941
    [2019-01-30 11:40:50.043490] INFO: dropnan: 行数: 94903/94941
    [2019-01-30 11:40:50.049388] INFO: bigquant: dropnan.v1 运行完成[0.137095s].
    [2019-01-30 11:40:50.064952] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-30 11:40:50.288947] INFO: StockRanker: 特征预处理 ..
    [2019-01-30 11:40:50.313967] INFO: StockRanker: prepare data: training ..
    [2019-01-30 11:40:50.831755] INFO: StockRanker: sort ..
    [2019-01-30 11:40:52.255937] INFO: StockRanker训练: d623e5b2 准备训练: 94903 行数
    [2019-01-30 11:40:52.292947] INFO: StockRanker训练: 正在训练 ..
    [2019-01-30 11:41:32.938479] INFO: bigquant: stock_ranker_train.v5 运行完成[42.873528s].
    [2019-01-30 11:41:32.941665] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-30 11:41:32.947906] INFO: bigquant: 命中缓存
    [2019-01-30 11:41:32.950211] INFO: bigquant: instruments.v2 运行完成[0.008529s].
    [2019-01-30 11:41:32.958838] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-30 11:41:32.967819] INFO: bigquant: 命中缓存
    [2019-01-30 11:41:32.970125] INFO: bigquant: general_feature_extractor.v7 运行完成[0.011311s].
    [2019-01-30 11:41:32.972817] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-30 11:41:36.076638] INFO: derived_feature_extractor: 提取完成 return_5/return_10, 0.003s
    [2019-01-30 11:41:36.244169] INFO: derived_feature_extractor: /y_2015, 569698
    [2019-01-30 11:41:36.753444] INFO: derived_feature_extractor: /y_2016, 641546
    [2019-01-30 11:41:37.116121] INFO: bigquant: derived_feature_extractor.v3 运行完成[4.14326s].
    [2019-01-30 11:41:37.120300] INFO: bigquant: cached.v3 开始运行..
    [2019-01-30 11:41:47.274580] INFO: bigquant: cached.v3 运行完成[10.154254s].
    [2019-01-30 11:41:47.277994] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-30 11:41:47.903562] INFO: dropnan: /data, 647443/648271
    [2019-01-30 11:41:47.920599] INFO: dropnan: 行数: 647443/648271
    [2019-01-30 11:41:47.945222] INFO: bigquant: dropnan.v1 运行完成[0.667158s].
    [2019-01-30 11:41:47.957907] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-01-30 11:41:48.214814] INFO: StockRanker: prepare data: prediction ..
    [2019-01-30 11:41:54.224949] INFO: stock_ranker_predict: 准备预测: 647443 行
    [2019-01-30 11:41:54.226144] INFO: stock_ranker_predict: 正在预测 ..
    [2019-01-30 11:42:14.534931] INFO: bigquant: stock_ranker_predict.v5 运行完成[26.57702s].
    [2019-01-30 11:42:14.587458] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-30 11:42:14.592176] INFO: bigquant: biglearning backtest:V8.1.8
    [2019-01-30 11:42:14.593339] INFO: bigquant: product_type:stock by specified
    [2019-01-30 11:42:28.861344] INFO: bigquant: 读取股票行情完成:1990277
    [2019-01-30 11:42:49.385377] INFO: algo: TradingAlgorithm V1.4.5
    [2019-01-30 11:43:00.350450] INFO: algo: trading transform...
    [2019-01-30 11:43:11.387039] INFO: Performance: Simulated 488 trading days out of 488.
    [2019-01-30 11:43:11.388383] INFO: Performance: first open: 2015-01-05 09:30:00+00:00
    [2019-01-30 11:43:11.389252] INFO: Performance: last close: 2016-12-30 15:00:00+00:00
    
    • 收益率173.24%
    • 年化收益率68.05%
    • 基准收益率-6.33%
    • 阿尔法0.59
    • 贝塔1.1
    • 夏普比率1.38
    • 胜率0.62
    • 盈亏比0.93
    • 收益波动率42.06%
    • 信息比率0.16
    • 最大回撤48.68%
    [2019-01-30 11:43:15.222149] INFO: bigquant: backtest.v8 运行完成[60.634679s].
    

    (smallsnow) #9

    谢谢技术指导,可以了!通过这个例子学会了一些东西


    (smallsnow) #10

    我把这个例子和模板(普通策略)结合起来,在前面输入TALIB公式的股票筛选,在特征列表里输入buy_condition 和sell_condition买卖条件,不知道为什么,buyconditon生效,但是sell_condition条件不起作用,导致买入的股票无法卖掉,不知道哪里错了,请帮忙看看,谢谢!
    代码如下:
    import talib
    def m10_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df().reset_index(drop=True)
    def cal(df):
    # 将价格数据转化成float类型
    close = [float(x) for x in df[‘close_0’]]

        df['RSI6'] = talib.RSI( np.array(close),timeperiod=6)
        df['RSI6_1'] = df['RSI6'].shift(1)
        df['RSI12']= talib.RSI( np.array(close),timeperiod=12)
        df['RSI12_1'] = df['RSI12'].shift(1)
        df['RSI24']= talib.RSI( np.array(close),timeperiod=24)
        df['RSI24_1'] = df['RSI24'].shift(1)
        df['cond']=((df['RSI6']<50) & (df['RSI6_1']<df['RSI12_1'])  & (df['RSI6_1']<df['RSI24_1']) & (df['RSI6']>df['RSI12']) & (df['RSI6']>df['RSI24']) ).astype(int)
        return df
    
    # 计算指标条件
    result = df.groupby('instrument').apply(cal)
    # 过滤
    filter_result = result[result['cond']>0]
    # 输出
    data_1 = DataSource.write_df(filter_result)
    return Outputs(data_1=data_1, 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 m10_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 m9_handle_data_bigquant_run(context, data):
    # 回测引擎:每日数据处理函数,每天执行一次
    today = data.current_dt.strftime(’%Y-%m-%d’) # 日期
    # 通过positions对象,使用列表生成式的方法获取目前持仓的股票列表和对应的最新市值
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
    for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 记录用于买入股票的可用现金
    cash_for_buy = context.portfolio.cash
    
    # 获取当日符合买入/卖出条件的股票列表
    try:
        buy_stock = context.daily_stock_buy[today][:3]  # 当日符合买入条件的股票,限定3股
    except:
        buy_stock=[]
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock = []
    
    # 需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [i for i in stock_hold_now if i in sell_stock]
    # 需要买入的股票:没有持仓且符合买入条件的股票
    stock_to_buy = [i for i in buy_stock if i not in stock_hold_now]
    # 卖出
    for instrument in stock_to_sell:
        # 如果该股票停牌,则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值,则可以正常下单,否则会出错
        # 因为stock是字符串格式,我们用symbol方法将其转化成平台可以接受的形式:Equity格式
        if data.can_trade(context.symbol(instrument)):
            # order_target_percent是平台的一个下单接口,表明下单使得该股票的权重为0,即卖出全部股票,可参考回测文档
            context.order_target_percent(context.symbol(instrument), 0)
            # 由于是收盘卖出且开盘买入,因此买入时无需更新当日可用现金,如果是收盘买入开盘卖出则需更新可用现金
            # cash_for_buy += stock_hold_now[instrument]
    # 如果当天没有买入的股票,就返回
    if len(stock_to_buy) == 0:
        return
    
    # 买入
    for instrument in stock_to_buy:
        # 针对当日可用现金使用等资金比例下单买入,整百股数下单
        cash = cash_for_buy / len(stock_to_buy)
        if data.can_trade(context.symbol(instrument)):
            current_price = data.current(context.symbol(instrument), 'price')
            amount = math.floor(cash / current_price / 100) * 100
            context.order(context.symbol(instrument), amount)
    

    回测引擎:准备数据,只执行一次

    def m9_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options[‘data’].read_df()

    # 函数:求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)
    
    # 函数:求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)
    
    # 每日买入股票的数据框
    context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
    

    回测引擎:初始化函数,只执行一次

    def m9_initialize_bigquant_run(context):

    # 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))
    

    m1 = 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

    high_0
    low_0
    close_0
    “”"
    )

    m2 = M.instruments.v2(
    start_date=T.live_run_param(‘trading_date’, ‘2018-01-01’),
    end_date=T.live_run_param(‘trading_date’, ‘2019-01-25’),
    market=‘CN_STOCK_A’,
    instrument_list=""“000021.SZA
    000025.SZA”"",
    max_count=0
    )

    m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date=’’,
    end_date=’’,
    before_start_days=100
    )

    m10 = M.cached.v3(
    input_1=m7.data,
    run=m10_run_bigquant_run,
    post_run=m10_post_run_bigquant_run,
    input_ports=’’,
    params=’{}’,
    output_ports=’’
    )

    m11 = 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

    buy_condition =where((RSI6<50) & (RSI6>RSI12) & (RSI6>RSI24) & (RSI6_1<RSI12_1) & (RSI6_1<RSI24_1) ,1,0)
    sell_condition=where((RSI6>70) ,1,0)
    “”"
    )

    m8 = M.derived_feature_extractor.v3(
    input_data=m10.data_1,
    features=m11.data,
    date_col=‘date’,
    instrument_col=‘instrument’,
    drop_na=False,
    remove_extra_columns=False
    )

    m6 = M.dropnan.v1(
    input_data=m8.data
    )

    m9 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.data,
    start_date=’’,
    end_date=’’,
    handle_data=m9_handle_data_bigquant_run,
    prepare=m9_prepare_bigquant_run,
    initialize=m9_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=’’
    )


    (smallsnow) #11

    不好意思原因找到了,是被我自己的语句 filter_result = result[result[‘cond’]>0]过滤掉了卖出条件作用的对象集合。