AI可视化模板的交易控制

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新手专区
标签: #<Tag:0x00007faff74bad58> #<Tag:0x00007faff74bab50>

(达达) #1

平台很多新来的朋友对如何控制交易感觉不太容易上手,改版AI可视化模板后可以轻松实现交易控制。
标准AI模板的trade模块:
image

我们在做量化时经常需要的功能如下表所示:

关于仓位的控制如下表所示:

image

因子条件过滤/周期触发/止盈止损/大盘风控相关代码则可以参考下面的例子:

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 \n #大盘风控模块,以上证指数5日涨幅为例,如果大盘下跌较多,全部卖出并结束当日操作\n today_date = data.current_dt.strftime('%Y-%m-%d')\n benckmarch_prices = data.history(context.symbols('000001.SHA'), ['close'], 5, '1d')['close']\n benckmarch_control = benckmarch_prices[context.symbol('000001.SHA')][-1] / benckmarch_prices[context.symbol('000001.SHA')][0]\n if benckmarch_control < 0.998:\n position_all = context.portfolio.positions.keys()\n for i in position_all:\n context.order_target(i, 0)\n print('日期',today_date,'大盘风控止损触发')\n return\n \n #周期控制模块\n if context.trading_day_index%3!=0:#以3天换一次仓为例\n return\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 #---------------------------START:止赢止损模块(含建仓期)--------------------\n today_date = data.current_dt.strftime('%Y-%m-%d')\n positions_stop={e.symbol:p.cost_basis \n for e,p in context.portfolio.positions.items()}\n # 新建当日止赢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 current_stopwin_stock=[]\n current_stoploss_stock = [] \n if len(positions_stop)>0:\n for i in positions_stop.keys():\n stock_cost=positions_stop[i] \n stock_market_price=data.current(context.symbol(i),'price') \n # 赚3元且可以交易and not context.has_unfinished_sell_order(equities[i])\n if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):\n context.order_target_percent(context.symbol(i),0) \n current_stopwin_stock.append(i)\n #print('日期:',today_date,'股票:',i,'出现止盈状况')\n print(today_date,'止盈股票列表',current_stopwin_stock)\n # 亏1元就止损and not context.has_unfinished_sell_order(equities[i])\n if stock_market_price - stock_cost <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i): \n context.order_target_percent(context.symbol(i),0) \n current_stoploss_stock.append(i)\n #print('日期:',today_date,'股票:',i,'出现止损状况')\n print(today_date,'止损股票列表',current_stoploss_stock)\n #--------------------------END: 止赢止损模块-----------------------------\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 # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数,比如datetime.timedelta(5)\n if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n #print('日期:',today_date,'持有固定天数卖出股票',str(i))\n print(today_date,'固定天数卖出列表',current_stopdays_stock)\n #-------------------------------END:持有固定天数卖出--------------------------\n \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 # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n if instrument in current_stopwin_stock:\n continue\n if instrument in current_stoploss_stock:\n continue\n if instrument in current_stopdays_stock:\n continue\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 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    In [18]:
    # 本代码由可视化策略环境自动生成 2018年5月10日 21:11
    # 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, -1) / 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="""rank_avg_amount_5
    rank_avg_turn_5
    rank_volatility_5_0"""
    )
    
    m14 = M.input_features.v1(
        features_ds=m3.data,
        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
    st_status_0
    list_board_0
    """
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m14.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m14.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m15 = M.filter.v3(
        input_data=m5.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m15.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m12 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m12.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', '2017-01-01'),
        end_date=T.live_run_param('trading_date', '2017-3-31'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m14.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m14.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m16 = M.filter.v3(
        input_data=m11.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m16.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m13.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m17_handle_data_bigquant_run(context, data):
        
        #大盘风控模块,以上证指数5日涨幅为例,如果大盘下跌较多,全部卖出并结束当日操作
        today_date = data.current_dt.strftime('%Y-%m-%d')
        benckmarch_prices = data.history(context.symbols('000001.SHA'), ['close'], 5, '1d')['close']
        benckmarch_control = benckmarch_prices[context.symbol('000001.SHA')][-1] / benckmarch_prices[context.symbol('000001.SHA')][0]
        if benckmarch_control < 0.998:
            position_all = context.portfolio.positions.keys()
            for i in position_all:
                context.order_target(i, 0)
            print('日期',today_date,'大盘风控止损触发')
            return
        
        #周期控制模块
        if context.trading_day_index%3!=0:#以3天换一次仓为例
            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 1.5) * 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()} 
    
        #---------------------------START:止赢止损模块(含建仓期)--------------------
        today_date = data.current_dt.strftime('%Y-%m-%d')
        positions_stop={e.symbol:p.cost_basis 
        for e,p in context.portfolio.positions.items()}
        # 新建当日止赢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
        current_stopwin_stock=[]
        current_stoploss_stock = []   
        if len(positions_stop)>0:
            for i in positions_stop.keys():
                stock_cost=positions_stop[i]  
                stock_market_price=data.current(context.symbol(i),'price')  
                # 赚3元且可以交易and not context.has_unfinished_sell_order(equities[i])
                if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):
                    context.order_target_percent(context.symbol(i),0)      
                    current_stopwin_stock.append(i)
                    #print('日期:',today_date,'股票:',i,'出现止盈状况')
                print(today_date,'止盈股票列表',current_stopwin_stock)
                    # 亏1元就止损and not context.has_unfinished_sell_order(equities[i])
                if stock_market_price - stock_cost  <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):   
                    context.order_target_percent(context.symbol(i),0)     
                    current_stoploss_stock.append(i)
                    #print('日期:',today_date,'股票:',i,'出现止损状况')
                print(today_date,'止损股票列表',current_stoploss_stock)
        #--------------------------END: 止赢止损模块-----------------------------
        
        #--------------------------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
                    # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数,比如datetime.timedelta(5)
                    if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):
                        context.order_target_percent(sid, 0)
                        current_stopdays_stock.append(i)
                        #print('日期:',today_date,'持有固定天数卖出股票',str(i))
                print(today_date,'固定天数卖出列表',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]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                if instrument  in current_stopwin_stock:
                    continue
                if instrument  in current_stoploss_stock:
                    continue
                if instrument  in current_stopdays_stock:
                    continue
                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 m17_prepare_bigquant_run(context):
        context.instruments = context.instruments + ['000300.SHA','000905.SHA','000001.SHA']
    
    # 回测引擎:初始化函数,只执行一次
    def m17_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 = 2
        # 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:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m17_before_trading_start_bigquant_run(context, data):
        pass
    
    m17 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m17_handle_data_bigquant_run,
        prepare=m17_prepare_bigquant_run,
        initialize=m17_initialize_bigquant_run,
        before_trading_start=m17_before_trading_start_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',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-05-10 21:08:37.839623] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-10 21:08:37.907073] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:37.908815] INFO: bigquant: instruments.v2 运行完成[0.069221s].
    [2018-05-10 21:08:37.924101] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-05-10 21:08:37.928440] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:37.930155] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.006083s].
    [2018-05-10 21:08:37.943077] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-10 21:08:37.949617] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:37.951210] INFO: bigquant: input_features.v1 运行完成[0.008167s].
    [2018-05-10 21:08:37.958468] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-10 21:08:37.962075] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:37.969160] INFO: bigquant: input_features.v1 运行完成[0.010644s].
    [2018-05-10 21:08:38.004678] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-10 21:08:38.018018] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.019535] INFO: bigquant: general_feature_extractor.v6 运行完成[0.014877s].
    [2018-05-10 21:08:38.043595] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-10 21:08:38.064658] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.066505] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.022931s].
    [2018-05-10 21:08:38.079987] INFO: bigquant: filter.v3 开始运行..
    [2018-05-10 21:08:38.090857] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.092637] INFO: bigquant: filter.v3 运行完成[0.012672s].
    [2018-05-10 21:08:38.118016] INFO: bigquant: join.v3 开始运行..
    [2018-05-10 21:08:38.122344] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.124767] INFO: bigquant: join.v3 运行完成[0.006771s].
    [2018-05-10 21:08:38.158156] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-10 21:08:38.163767] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.165236] INFO: bigquant: dropnan.v1 运行完成[0.007115s].
    [2018-05-10 21:08:38.187092] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2018-05-10 21:08:38.194337] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.195919] INFO: bigquant: stock_ranker_train.v5 运行完成[0.008859s].
    [2018-05-10 21:08:38.209359] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-10 21:08:38.215701] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.217200] INFO: bigquant: instruments.v2 运行完成[0.007855s].
    [2018-05-10 21:08:38.260685] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-10 21:08:38.271142] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.287544] INFO: bigquant: general_feature_extractor.v6 运行完成[0.026844s].
    [2018-05-10 21:08:38.348434] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-10 21:08:38.377567] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.384722] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.036279s].
    [2018-05-10 21:08:38.417492] INFO: bigquant: filter.v3 开始运行..
    [2018-05-10 21:08:38.430330] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.436834] INFO: bigquant: filter.v3 运行完成[0.019333s].
    [2018-05-10 21:08:38.449245] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-10 21:08:38.463731] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.474343] INFO: bigquant: dropnan.v1 运行完成[0.025074s].
    [2018-05-10 21:08:38.513331] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-05-10 21:08:38.537190] INFO: bigquant: 命中缓存
    [2018-05-10 21:08:38.539165] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.025884s].
    [2018-05-10 21:08:38.647979] INFO: bigquant: backtest.v7 开始运行..
    [2018-05-10 21:08:39.020920] INFO: algo: set price type:backward_adjusted
    2017-01-06 止盈股票列表 []
    2017-01-06 止损股票列表 ['002829.SZA']
    2017-01-06 止盈股票列表 []
    2017-01-06 止损股票列表 ['002829.SZA', '002826.SZA']
    2017-01-06 固定天数卖出列表 ['002829.SZA', '002826.SZA']
    [2018-05-10 21:09:13.827043] INFO: Blotter: 2017-01-09 cancel order Equity(2166 [603218.SHA]) 
    日期 2017-01-11 大盘风控止损触发
    日期 2017-01-12 大盘风控止损触发
    日期 2017-01-13 大盘风控止损触发
    日期 2017-01-16 大盘风控止损触发
    日期 2017-01-17 大盘风控止损触发
    日期 2017-01-18 大盘风控止损触发
    日期 2017-01-19 大盘风控止损触发
    2017-01-24 止盈股票列表 []
    2017-01-24 止损股票列表 ['002826.SZA']
    2017-02-03 止盈股票列表 ['300580.SZA']
    2017-02-03 止损股票列表 []
    2017-02-03 固定天数卖出列表 ['300580.SZA']
    2017-02-08 止盈股票列表 ['300580.SZA']
    2017-02-08 止损股票列表 []
    2017-02-08 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-02-08 止损股票列表 []
    2017-02-08 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-02-08 止损股票列表 []
    2017-02-08 固定天数卖出列表 ['002824.SZA', '300427.SZA']
    2017-02-13 止盈股票列表 ['300580.SZA']
    2017-02-13 止损股票列表 []
    2017-02-13 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-02-13 止损股票列表 []
    2017-02-13 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-02-13 止损股票列表 ['300427.SZA']
    2017-02-16 止盈股票列表 ['300580.SZA']
    2017-02-16 止损股票列表 []
    2017-02-16 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-02-16 止损股票列表 []
    2017-02-16 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-02-16 止损股票列表 []
    日期 2017-02-17 大盘风控止损触发
    2017-02-21 止盈股票列表 ['300580.SZA']
    2017-02-21 止损股票列表 []
    2017-02-21 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-02-21 止损股票列表 []
    2017-02-21 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-02-21 止损股票列表 []
    2017-02-24 止盈股票列表 ['300580.SZA']
    2017-02-24 止损股票列表 []
    2017-02-24 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-02-24 止损股票列表 []
    2017-02-24 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-02-24 止损股票列表 []
    日期 2017-02-27 大盘风控止损触发
    日期 2017-02-28 大盘风控止损触发
    2017-03-01 止盈股票列表 ['300580.SZA']
    2017-03-01 止损股票列表 []
    2017-03-01 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-03-01 止损股票列表 []
    2017-03-01 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-03-01 止损股票列表 []
    日期 2017-03-02 大盘风控止损触发
    日期 2017-03-03 大盘风控止损触发
    日期 2017-03-06 大盘风控止损触发
    2017-03-09 止盈股票列表 ['300580.SZA']
    2017-03-09 止损股票列表 []
    2017-03-09 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-03-09 止损股票列表 []
    2017-03-09 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-03-09 止损股票列表 []
    日期 2017-03-10 大盘风控止损触发
    2017-03-14 止盈股票列表 ['300580.SZA']
    2017-03-14 止损股票列表 []
    2017-03-14 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-03-14 止损股票列表 []
    2017-03-14 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-03-14 止损股票列表 []
    2017-03-17 止盈股票列表 ['300580.SZA']
    2017-03-17 止损股票列表 []
    2017-03-17 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-03-17 止损股票列表 []
    2017-03-17 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-03-17 止损股票列表 []
    日期 2017-03-22 大盘风控止损触发
    2017-03-27 止盈股票列表 ['300580.SZA']
    2017-03-27 止损股票列表 []
    2017-03-27 止盈股票列表 ['300580.SZA', '002824.SZA']
    2017-03-27 止损股票列表 []
    2017-03-27 止盈股票列表 ['300580.SZA', '002824.SZA', '300427.SZA']
    2017-03-27 止损股票列表 []
    日期 2017-03-29 大盘风控止损触发
    日期 2017-03-30 大盘风控止损触发
    日期 2017-03-31 大盘风控止损触发
    [2018-05-10 21:09:14.764615] INFO: Performance: Simulated 59 trading days out of 59.
    [2018-05-10 21:09:14.769440] INFO: Performance: first open: 2017-01-03 01:30:00+00:00
    [2018-05-10 21:09:14.776813] INFO: Performance: last close: 2017-03-31 07:00:00+00:00
    [注意] 有 5 笔卖出是在多天内完成的。当日卖出股票超过了当日股票交易的2.5%会出现这种情况。
    
    • 收益率1.73%
    • 年化收益率7.58%
    • 基准收益率4.41%
    • 阿尔法-0.01
    • 贝塔0.25
    • 夏普比率0.06
    • 胜率0.6
    • 盈亏比1.457
    • 收益波动率55.44%
    • 信息比率-0.23
    • 最大回撤22.17%
    [2018-05-10 21:09:16.672121] INFO: bigquant: backtest.v7 运行完成[38.024125s].
    

    默认的股票都是按比例购买的,我想等额资金交易该怎么设置?
    【宽客学院】策略止盈止损
    帮个忙,股票卖出问题
    【宽客学院】策略止盈止损
    (yilong10) #2

    把代码放入对应模块了 还是显示出错

    克隆策略

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stock_market_price=data.current(context.symbol(i),'price') \n # 赚3元且可以交易\n if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):\n context.order_target_percent(context.symbol(i),0) \n current_stopwin_stock.append(i)\n print('日期:',date,'股票:',i,'出现止盈状况')\n # 亏1元就止损\n if stock_market_price - stock_cost <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]): \n context.order_target_percent(context.symbol(i),0) \n current_stoploss_stock.append(i)\n print('日期:',date,'股票:',i,'出现止损状况')\n #--------------------------END: 止赢止损模块-----------------------------\n\n #--------------------------START:持有固定天数卖出(不含建仓期)---------------\n current_stopdays_stock = [] \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 # 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      In [1]:
      # 本代码由可视化策略环境自动生成 2018年4月24日 11:48
      # 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='',
          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
      """
      )
      
      m4 = M.general_feature_extractor.v6(
          instruments=m1.data,
          features=m3.data,
          start_date='',
          end_date='',
          before_start_days=0
      )
      
      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
      )
      
      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='',
          end_date='',
          before_start_days=0
      )
      
      m11 = M.derived_feature_extractor.v2(
          input_data=m10.data,
          features=m3.data,
          date_col='date',
          instrument_col='instrument'
      )
      
      m14 = M.dropnan.v1(
          input_data=m11.data
      )
      
      m8 = M.stock_ranker_predict.v5(
          model=m6.model,
          data=m14.data,
          m_lazy_run=False
      )
      
      #Trading模块主函数(以当日数据计算,次日执行)
       # 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):
          #周期控制
          if context.trading_day_index%3!=0:#以3天换一次仓为例
              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 1.5) * 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()}
      
          #未成交订单列表(之前所有未成交的订单记录)
          equities_tracker= {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
          date = data.current_dt.strftime('%Y-%m-%d') 
          #date用于输出print买卖日期,
          #注意多个卖出操作累加会让仓位变为负值!要记录卖出的股票并做判断
         
          #---------------------START:大盘风控(含建仓期)--------------------------
          date = data.current_dt.strftime('%Y-%m-%d')
          positions_all = [equity.symbol for equity in context.portfolio.positions]
          # 满足空仓条件
          if context.pos.ix[date].pos_percent == 0:	
              if len(positions_all)>0:
              	# 全部卖出后返回
                  for i in positions_all:
                      if data.can_trade(context.symbol(i)):
                          context.order_target_percent(context.symbol(i), 0)
                          print('风控执行',date)
                  return
                          #运行风控后当日结束,不再执行后续的买卖订单
          #------------------------END:大盘风控(含建仓期)---------------------------
      
          #---------------------------START:止赢止损模块(含建仓期)--------------------
          positions_stop={e.symbol:p.cost_basis 
          for e,p in context.portfolio.positions.items()}
          # 新建当日止赢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
              current_stopwin_stock=[]
              current_stoploss_stock = []   
              if len(positions_stop)>0:
                  for i in positions_stop.keys():
                      stock_cost=positions_stop[i]  
                      stock_market_price=data.current(context.symbol(i),'price')  
                      # 赚3元且可以交易
                      if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):
                          context.order_target_percent(context.symbol(i),0)      
                          current_stopwin_stock.append(i)
                          print('日期:',date,'股票:',i,'出现止盈状况')
                      # 亏1元就止损
                      if stock_market_price - stock_cost  <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):   
                          context.order_target_percent(context.symbol(i),0)     
                          current_stoploss_stock.append(i)
                          print('日期:',date,'股票:',i,'出现止损状况')
          #--------------------------END: 止赢止损模块-----------------------------
      
          #--------------------------START:持有固定天数卖出(不含建仓期)---------------
           current_stopdays_stock = [] 
          # 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
                      # 今天和上次交易的时间相隔hold_days就全部卖出
                      if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and 
                      data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):
                          context.order_target_percent(sid, 0)
                          current_stopdays_stock .append(equities.keys(i))
          #-------------------------------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]))])))
              # print('rank order for sell %s' % instruments)
              for instrument in instruments:
                  if instrument  in current_stopwin_stock:
                      continue
                  if instrument  in current_stoploss_stock:
                      continue
                  if instrument  in current_stopdays_stock:
                      continue
                  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)
      #Trading模块数据准备函数
      #回测引擎:准备数据,只执行一次
      def m12_prepare_bigquant_run(context):
      #pass #做风控计算时删除此行
      bm_price = D.history_data([000300.SHA], start_date=2013-01-01 , end_date=2017-08-10, fields=[close])
      bm_price[sma] = bm_price[close].rolling(5).mean()
      bm_price[lma] = bm_price[close].rolling(32).mean()
      bm_price[gold_cross_status] = bm_price[sma] > bm_price[lma]
      bm_price[pos_percent] = np.where(bm_price[gold_cross_status],1,0)
      #这里根据金叉状态将大盘分为持仓和空仓两种状态
      pos_df = bm_price[[date, pos_percent]].set_index(date)
      #根据benchmark指数的长短周期均线状态生成一个风控序列pos_df,持仓为1,空仓为0
      context.pos=pos_df
      #通过context把计算的每日仓位数据pos_df传给handle,信号以当日价格计算,次日执行
      # 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='',
          end_date='',
          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',
          price_type='后复权',
          plot_charts=True,
          backtest_only=False,
          amount_integer=False
      )
      
        File "<tokenize>", line 194
          current_stopdays_stock = []
          ^
      IndentationError: unindent does not match any outer indentation level
      


      (达达) #3

      不好意思,之前的代码格式有问题,已经更新了,您可以克隆研究


      在AI模型中去掉ST股票
      在AI模型中去掉ST股票
      (wicked_code) #4

      直接克隆策略会报错


      (yangziriver) #5

      image 为什么系统不认‘000001.SHA’呢?


      (iQuant) #6

      是“000001.SZA”哈,详细内容可参考文档板块。


      (yangziriver) #7

      在这个策略中是000001.SHA 上证指数
      image
      在QQ群中我被告知,000001.SHA已经不能作为股票标的了。我改用了另外的策略来进行风控。谢谢!