克隆策略

    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0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-45"}],"output_ports":[{"name":"data","node_id":"-45"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-37","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-37"},{"name":"features","node_id":"-37"}],"output_ports":[{"name":"data","node_id":"-37"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-50","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-50"}],"output_ports":[{"name":"data","node_id":"-50"},{"name":"left_data","node_id":"-50"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-6837","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"yz_1 = where(mean(close_0, 5) >= mean(close_0, 10), 1, 0)\nyz_2 = where(mean(close_0, 10) >= mean(close_0, 20), 1, 0)\nyz_3 = where(close_0 >= mean(close_0, 5), 1, 0)\nyz_4 = where(high_0 >= ts_max(high_0, 5), 1, 0)\nyz_5 = where(zf1 <= 0.04, 1, 0)\nyz_6 = where((close_0 - ts_min(close_0,20))/ts_min(close_0,20) < 0.4, 1, 0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-6837"}],"output_ports":[{"name":"data","node_id":"-6837"}],"cacheable":true,"seq_num":20,"comment":"","comment_collapsed":true},{"node_id":"-6842","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-6842"},{"name":"features","node_id":"-6842"}],"output_ports":[{"name":"data","node_id":"-6842"}],"cacheable":true,"seq_num":23,"comment":"","comment_collapsed":true},{"node_id":"-6851","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-6851"}],"output_ports":[{"name":"data","node_id":"-6851"},{"name":"left_data","node_id":"-6851"}],"cacheable":true,"seq_num":24,"comment":"","comment_collapsed":true},{"node_id":"-773","module_id":"BigQuantSpace.standardlize.standardlize-v8","parameters":[{"name":"columns_input","value":"label","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-773"},{"name":"input_2","node_id":"-773"}],"output_ports":[{"name":"data","node_id":"-773"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-141","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":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 = 1\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 = 1\n context.options['hold_days'] = 1","type":"Literal","bound_global_parameter":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 \n benckmark_risk=context.benckmark_risk.loc[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\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) * 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 #--------------------------START:持有固定天数卖出(不含建仓期)---------------\n current_stopdays_stock = [] \n today = data.current_dt\n today_date = data.current_dt.strftime('%Y-%m-%d')\n # 不是建仓期(在前hold_days属于建仓期)\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n if len(equities)>0:\n for i in equities:\n sid = equities[i].sid # 交易标的\n stock_cost=equities[i].cost_basis # 成本价\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 total_of_profit = stock_market_price/stock_cost - 1 # 持仓收益\n highest_price_since_buy = data.history(context.symbol(i), 'close', hold_days, '1d').max() # 建仓以来的最高价\n # 日均收益小于 9.7% 就全部卖出\n if total_of_profit/hold_days < 0.97:\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n # 当日下跌 直接卖出\n if i not in current_stopdays_stock and highest_price_since_buy and highest_price_since_buy > stock_market_price:\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n #if len(current_stopdays_stock)>0: \n #print(today_date,'日均收益小于9.7% 就全部卖出',current_stopdays_stock)\n #-------------------------------END:持有固定天数卖出-------------------------- \n \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 for instrument in instruments:\n #防止多个止损条件同时满足,出现多次卖出产生空单\n if instrument not in current_stopdays_stock:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n else:\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_tmp = list(ranker_prediction.instrument)\n #防止卖出后再次买入\n buy_instruments=[k for k in buy_instruments_tmp if k not in current_stopdays_stock][: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 current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n #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\n # 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\n start_date= 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2021年9月13日 10:10
    # 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 m26_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 = 1
        # 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 = 1
        context.options['hold_days'] = 1
    # 回测引擎:每日数据处理函数,每天执行一次
    def m26_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    
        benckmark_risk=context.benckmark_risk.loc[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')]
    
        # 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) * 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()}
        
        #--------------------------START:持有固定天数卖出(不含建仓期)---------------
        current_stopdays_stock = [] 
        today = data.current_dt
        today_date = data.current_dt.strftime('%Y-%m-%d')
        # 不是建仓期(在前hold_days属于建仓期)
        if not is_staging:
            equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
            if len(equities)>0:
                for i in equities:
                    sid = equities[i].sid  # 交易标的
                    stock_cost=equities[i].cost_basis # 成本价
                    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
                    total_of_profit = stock_market_price/stock_cost - 1 # 持仓收益
                    highest_price_since_buy = data.history(context.symbol(i), 'close', hold_days, '1d').max() # 建仓以来的最高价
                    # 日均收益小于 9.7% 就全部卖出
                    if total_of_profit/hold_days < 0.97:
                        context.order_target_percent(sid, 0)
                        current_stopdays_stock.append(i)
                    # 当日下跌 直接卖出
                    if i not in current_stopdays_stock and highest_price_since_buy and highest_price_since_buy > stock_market_price:
                        context.order_target_percent(sid, 0)
                        current_stopdays_stock.append(i)
                #if len(current_stopdays_stock)>0:        
                    #print(today_date,'日均收益小于9.7% 就全部卖出',current_stopdays_stock)
        #-------------------------------END:持有固定天数卖出--------------------------    
        
        
        # 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]))])))
            for instrument in instruments:
                #防止多个止损条件同时满足,出现多次卖出产生空单
                if instrument not in current_stopdays_stock:
                    context.order_target(context.symbol(instrument), 0)
                    cash_for_sell -= positions[instrument]
                else:
                    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_tmp = list(ranker_prediction.instrument)
        #防止卖出后再次买入
        buy_instruments=[k for k in buy_instruments_tmp if k not in current_stopdays_stock][: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:
                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 m26_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=['000001.SZA'], 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-10%则设置风险状态risk为1,否则为0
        benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.1,0,0)
        #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']]
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2019-12-31',
        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 {{web_host_url}}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 <{{web_host_url}}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, -2) / 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
    )
    
    m14 = M.standardlize.v8(
        input_1=m2.data,
        columns_input='label'
    )
    
    m3 = M.input_features.v1(
        features="""#std(deal_number_0, 3)
    std(turn_0, 3)
    #std(volume_0, 3)
    #std(amount_0, 3)
    #std(return_0, 3)
    zf1 = 1*abs((close_0 - open_0)/close_1)
    close_0/mean(close_0, 5)
    #(high_0 - low_0)/close_1
    zf2 = (high_2 - low_2)/close_3 + (high_1 - low_1)/close_2 + (high_0 - low_0)/close_1
    close_0/open_0
    close_0/high_0
    close_0/((high_0 + low_0)/2)
    zf3 = (close_0 - ts_min(close_0,5))/ts_min(close_0,5)
    #zf4 = (close_0 - ts_min(close_0,10))/ts_min(close_0,10)
    #zf5 = (close_0 - ts_min(close_0,20))/ts_min(close_0,20)"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=250
    )
    
    m4 = M.chinaa_stock_filter.v1(
        input_data=m15.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板', '创业板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2020-01-01'),
        end_date=T.live_run_param('trading_date', '2021-08-31'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m18 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=250
    )
    
    m11 = M.chinaa_stock_filter.v1(
        input_data=m18.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板', '创业板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m19 = M.derived_feature_extractor.v3(
        input_data=m11.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m5 = M.input_features.v1(
        features="""yz_1 = where(mean(close_0, 5) >= mean(close_0, 10), 1, 0)
    yz_2 = where(mean(close_0, 10) >= mean(close_0, 20), 1, 0)
    yz_3 = where(close_0 >= mean(close_0, 5), 1, 0)
    yz_4 = where(high_0 >= ts_max(high_0, 5), 1, 0)
    yz_5 = where(zf1 <= 0.04, 1, 0)
    yz_6 = where((close_0 - ts_min(close_0,20))/ts_min(close_0,20) < 0.4, 1, 0)"""
    )
    
    m10 = M.derived_feature_extractor.v3(
        input_data=m19.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m12 = M.filter.v3(
        input_data=m10.data,
        expr='yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True',
        output_left_data=False
    )
    
    m22 = M.dropnan.v2(
        input_data=m12.data
    )
    
    m20 = M.input_features.v1(
        features="""yz_1 = where(mean(close_0, 5) >= mean(close_0, 10), 1, 0)
    yz_2 = where(mean(close_0, 10) >= mean(close_0, 20), 1, 0)
    yz_3 = where(close_0 >= mean(close_0, 5), 1, 0)
    yz_4 = where(high_0 >= ts_max(high_0, 5), 1, 0)
    yz_5 = where(zf1 <= 0.04, 1, 0)
    yz_6 = where((close_0 - ts_min(close_0,20))/ts_min(close_0,20) < 0.4, 1, 0)"""
    )
    
    m23 = M.derived_feature_extractor.v3(
        input_data=m16.data,
        features=m20.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m24 = M.filter.v3(
        input_data=m23.data,
        expr='yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True',
        output_left_data=False
    )
    
    m7 = M.join.v3(
        data1=m14.data,
        data2=m24.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m21 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m6 = M.filter.v3(
        input_data=m21.data,
        expr='date < "2018-01-01"',
        output_left_data=True
    )
    
    m17 = M.stock_ranker_train.v6(
        training_ds=m6.data,
        features=m3.data,
        test_ds=m6.left_data,
        learning_algorithm='排序',
        number_of_leaves=80,
        minimum_docs_per_leaf=1000,
        number_of_trees=60,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m17.model,
        data=m22.data,
        m_lazy_run=False
    )
    
    m26 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m26_initialize_bigquant_run,
        handle_data=m26_handle_data_bigquant_run,
        prepare=m26_prepare_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='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )