Trade (回测/模拟)(trade)使用错误

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标签: #<Tag:0x00007fc4dcb2e370>

(suhanxue) #1
克隆策略

StockRanker多因子选股自用修改版

    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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 = 3\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 = 0.6\n context.options['hold_days'] = 1\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n #获取当日日期\n today_date = data.current_dt.strftime('%Y-%m-%d')\n \n #--------------------------------大盘风控模块,读取风控数据START-----------------------------\n benckmark_risk=context.benckmark_risk.ix[today_date].values[0]\n\n #当risk为1时,市场有风险,全部平仓,不再执行其它操作\n if benckmark_risk > 0:\n position_all = context.portfolio.positions.keys()\n for i in position_all:\n context.order_target(i, 0)\n print(today_date,'大盘风控止损触发,全仓卖出')\n return\n \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 #---------------------------------大盘风控模块END-------------------------------------------\n \n #------------------------------------------止损模块START--------------------------------------------\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n stoploss_stock = [] \n if len(equities) > 0:\n for i in equities.keys():\n stock_market_price = data.current(context.symbol(i), 'price') # 最新市场价格\n last_sale_date = equities[i].last_sale_date # 上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days # 持仓天数\n # 建仓以来的最高价\n highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()\n # 确定止损位置\n stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.08\n #record('止损位置', stoploss_line)\n # 如果价格下穿止损位置\n if stock_market_price < stoploss_line:\n context.order_target_percent(context.symbol(i), 0) \n stoploss_stock.append(i)\n if len(stoploss_stock)>0:\n print('日期:', today, '股票:', stoploss_stock, '出现跟踪止损状况')\n #-------------------------------------------止损模块END--------------------------------------------- \n \n # -------------------------股票配对交易策略START-----------------\n zscore_today =context.zscore.ix[today]\n #获取股票的列表\n stocklist=context.instruments\n # 转换成回测引擎所需要的symbol格式\n symbol_1 = context.symbol(stocklist[0]) \n symbol_2 = context.symbol(stocklist[1]) \n\n # 持仓\n cur_position_1 = context.portfolio.positions[symbol_1].amount\n cur_position_2 = context.portfolio.positions[symbol_2].amount\n \n # 交易逻辑\n # 如果zesore大于上轨(>1),则价差会向下回归均值,因此需要买入股票x,卖出股票y\n if zscore_today > 1 and cur_position_1 == 0 and data.can_trade(symbol_1) and data.can_trade(symbol_2): \n context.order_target_percent(symbol_2, 0)\n context.order_target_percent(symbol_1, 1)\n print(today, '全仓买入:',stocklist[0])\n \n # 如果zesore小于下轨(<-1),则价差会向上回归均值,因此需要买入股票y,卖出股票x\n elif zscore_today < -1 and cur_position_2 == 0 and data.can_trade(symbol_1) and data.can_trade(symbol_2): \n context.order_target_percent(symbol_1, 0) \n context.order_target_percent(symbol_2, 1)\n print(today, '全仓买入:',stocklist[1])\n \n #-----------------------------------股票配对交易策略END----------------------------------- \n\n \n \n \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 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    In [7]:
    # 本代码由可视化策略环境自动生成 2020年3月17日 13:37
    # 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 m4_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 = 3
        # 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 = 0.6
        context.options['hold_days'] = 1
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m4_handle_data_bigquant_run(context, data):
        #获取当日日期
        today_date = data.current_dt.strftime('%Y-%m-%d')
        
        #--------------------------------大盘风控模块,读取风控数据START-----------------------------
        benckmark_risk=context.benckmark_risk.ix[today_date].values[0]
    
        #当risk为1时,市场有风险,全部平仓,不再执行其它操作
        if benckmark_risk > 0:
            position_all = context.portfolio.positions.keys()
            for i in position_all:
                context.order_target(i, 0)
            print(today_date,'大盘风控止损触发,全仓卖出')
            return
        
            # 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')]
        #---------------------------------大盘风控模块END-------------------------------------------
        
        #------------------------------------------止损模块START--------------------------------------------
        equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        
        # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
        stoploss_stock = [] 
        if len(equities) > 0:
            for i in equities.keys():
                stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
                last_sale_date = equities[i].last_sale_date   # 上次交易日期
                delta_days = data.current_dt - last_sale_date  
                hold_days = delta_days.days # 持仓天数
                # 建仓以来的最高价
                highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()
                # 确定止损位置
                stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.08
                #record('止损位置', stoploss_line)
                # 如果价格下穿止损位置
                if stock_market_price < stoploss_line:
                    context.order_target_percent(context.symbol(i), 0)     
                    stoploss_stock.append(i)
            if len(stoploss_stock)>0:
                print('日期:', today, '股票:', stoploss_stock, '出现跟踪止损状况')
        #-------------------------------------------止损模块END---------------------------------------------   
        
        # -------------------------股票配对交易策略START-----------------
        zscore_today =context.zscore.ix[today]
        #获取股票的列表
        stocklist=context.instruments
        # 转换成回测引擎所需要的symbol格式
        symbol_1 = context.symbol(stocklist[0]) 
        symbol_2 = context.symbol(stocklist[1])  
    
        # 持仓
        cur_position_1 = context.portfolio.positions[symbol_1].amount
        cur_position_2 = context.portfolio.positions[symbol_2].amount
           
        # 交易逻辑
        # 如果zesore大于上轨(>1),则价差会向下回归均值,因此需要买入股票x,卖出股票y
        if zscore_today > 1 and cur_position_1 == 0 and data.can_trade(symbol_1) and data.can_trade(symbol_2):  
            context.order_target_percent(symbol_2, 0)
            context.order_target_percent(symbol_1, 1)
            print(today, '全仓买入:',stocklist[0])
            
        # 如果zesore小于下轨(<-1),则价差会向上回归均值,因此需要买入股票y,卖出股票x
        elif zscore_today < -1 and cur_position_2 == 0 and data.can_trade(symbol_1) and data.can_trade(symbol_2):  
            context.order_target_percent(symbol_1, 0)  
            context.order_target_percent(symbol_2, 1)
            print(today, '全仓买入:',stocklist[1])
     
        #-----------------------------------股票配对交易策略END-----------------------------------      
    
     
        
        
        # 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 m4_prepare_bigquant_run(context):
        #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
        # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间
        start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d')     
        benckmark_data=D.history_data(instruments=['000300.SHA'], start_date=start_date, end_date=context.end_date,fields=['close'])
        #计算指数5日涨幅
        benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(5)-1
        #计算大盘风控条件,如果5日涨幅小于-5%则设置风险状态risk为1,否则为0
        benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)
        #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
        benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        #设置日期为索引
        benckmark_data.set_index('date',inplace=True)
        #把风控序列输出给全局变量context.benckmark_risk
        context.benckmark_risk=benckmark_data[['risk']]
    
    
    def m4_before_trading_start_bigquant_run(context,data):    
        # 加载股票历史数据
        df = context.options['data'].read_df()
        df['date'] = df['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        today = data.current_dt.strftime('%Y-%m-%d')
        # 获取前240个自然日的数据
        start_date = (pd.to_datetime(data.current_dt)-datetime.timedelta(days=240)).strftime('%Y-%m-%d')
        stock_data = df[df.date <= today]
        #获取股票的列表,由于可能上市天数不同,对缺失值填充处理
        stocklist=context.instruments
        prices_df=pd.pivot_table(stock_data, values='close_0', index=['date'], columns=['instrument'])
        prices_df.fillna(method='ffill',inplace=True)
        
        x = prices_df[stocklist[0]] # 股票1
        y = prices_df[stocklist[1]] # 股票2
        
        # 线性回归两个股票的股价 y=ax+b
        from pyfinance import ols
        model = ols.OLS(y=y, x=x)
     
        def zscore(series):
            return (series - series.mean()) / np.std(series)
        
        # 计算 y-a*x 序列的zscore值序列
        zscore_calcu = zscore(y-model.beta*x)
        context.zscore=zscore_calcu
    
    m1 = M.instruments.v2(
        start_date='2015-01-01',
        end_date='2016-12-31',
        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, -7) / shift(open, -21)
    
    # 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
    close_0/adjust_factor_0
    
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='2016-01-01',
        end_date='2017-01-01',
        before_start_days=55
    )
    
    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
    )
    
    m12 = M.filter_delist_stock.v6(
        input_1=m7.data
    )
    
    m6 = M.filtet_st_stock.v7(
        input_1=m12.data_1
    )
    
    m11 = M.filter_stockcode.v2(
        input_1=m6.data_1,
        start='688'
    )
    
    m13 = M.dropnan.v1(
        input_data=m11.data_1
    )
    
    m5 = M.stock_ranker_train.v6(
        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,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2017-01-01'),
        end_date=T.live_run_param('trading_date', '2019-12-31'),
        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=55
    )
    
    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
    )
    
    m10 = M.filtet_st_stock.v7(
        input_1=m18.data
    )
    
    m19 = M.filter_delist_stock.v6(
        input_1=m10.data_1
    )
    
    m20 = M.filter_stockcode.v2(
        input_1=m19.data_1,
        start='688'
    )
    
    m14 = M.dropnan.v1(
        input_data=m20.data_1
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m5.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='2017-01-01',
        end_date='2018-12-31',
        initialize=m4_initialize_bigquant_run,
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        before_trading_start=m4_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=100000,
        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'
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2f78d146fb5f4683adc46d29d24f1653"}/bigcharts-data-end

    Trade (回测/模拟)(trade)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    KeyError                                  Traceback (most recent call last)
    <ipython-input-7-059b86abc9a4> in <module>()
        357     plot_charts=True,
        358     backtest_only=False,
    --> 359     benchmark='000300.SHA'
        360 )
    
    <ipython-input-7-059b86abc9a4> in m4_before_trading_start_bigquant_run(context, data)
        160     #获取股票的列表,由于可能上市天数不同,对缺失值填充处理
        161     stocklist=context.instruments
    --> 162     prices_df=pd.pivot_table(stock_data, values='close_0', index=['date'], columns=['instrument'])
        163     prices_df.fillna(method='ffill',inplace=True)
        164 
    
    KeyError: 'close_0'

    KeyError Traceback (most recent call last)
    in ()
    357 plot_charts=True,
    358 backtest_only=False,
    –> 359 benchmark=‘000300.SHA’
    360 )

    in m4_before_trading_start_bigquant_run(context, data)
    160 #获取股票的列表,由于可能上市天数不同,对缺失值填充处理
    161 stocklist=context.instruments
    –> 162 prices_df=pd.pivot_table(stock_data, values=‘close_0’, index=[‘date’], columns=[‘instrument’])
    163 prices_df.fillna(method=‘ffill’,inplace=True)
    164

    KeyError: ‘close_0’

    请问这个错误要如何修改


    (达达) #2

    出错在于你的盘前处理函数计算相关性,这个模版默认是对两只股票做计算的。并不适用于多只股票,您的具体思路是什么呢?


    (suhanxue) #4

    哦……因为我没什么基础,但是又对量化交易挺感兴趣所以就在自学,可能对策略的理解有误,感谢老师的耐心回答