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克隆策略

移动止损功能

版本v1.0

在开发策略时,经常使用个股的固定点位/百分比止盈止损功能。

本策略以买入后最高价下跌10%止损为例,介绍移动止损功能的实现步骤:

1、新建AI可视化模板策略

2、在回测/模拟模块m19的属性栏中进入“主函数”代码框,在函数体最前面插入移动止损的相关代码,详见策略

  • 初始化变量stoploss_stock用来记录移动止损卖出的股票,

  • 针对持仓的股票计算买入以来的最高价,并计算最新价相比最高价的回撤百分比,如果下跌超过10%就止损卖出,同时将股票添加到stoploss_stock变量:

3、在回测/模拟模块m19的属性栏中进入“主函数”代码框,在函数体中找到“# 2. 生成卖出订单”位置,

  • 在上方初始化 sell_stock 列表用来记录轮仓卖出的股票;

  • 在下方轮仓卖出代码 context.order_target(context.symbol(instrument), 0) 前加入判断语句:

    #如果已经移动止损卖出过则不再轮仓卖出,以防止出现空头持仓 if instrument in stoploss_stock:

      continue

4、在回测/模拟模块m19的属性栏中进入“主函数”代码框,在函数体中找到“# 3. 生成买入订单”位置,将原有的buy_instruments一行代码改为如下:

# 获取所有排序结果股票列表
buy_list = list(ranker_prediction.instrument)

# 不再买入已经移动止损和轮仓卖出的股票,以防止出现空头持仓
buy_instruments = [i for i in buy_list if i not in sell_stock + stoploss_stock][:len(buy_cash_weights)]

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    In [1]:
    # 本代码由可视化策略环境自动生成 2023年4月23日 22:31
    # 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 m19_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
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        
        today = data.current_dt.strftime('%Y-%m-%d')
        
        #------------------------------------------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 * 0.1
                #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---------------------------------------------    
        
        # 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()}
    
        sell_stock = []
        # 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:
                # 如果已经移动止损卖出过则不再轮仓卖出,以防止出现空头持仓
                if instrument in stoploss_stock:
                    continue
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                # 记录轮仓卖出的股票
                sell_stock.append(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_list = list(ranker_prediction.instrument)
        # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓
        buy_instruments = [i for i in buy_list if i not in sell_stock + stoploss_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:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-02',
        end_date='2010-05-01',
        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
    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
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    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
    )
    
    m5 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m5.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,
        plot_charts=True,
        ndcg_discount_base=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', '2015-05-01'),
        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=60
    )
    
    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.dropnan.v2(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m10.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_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.HIX'
    )
    
    m6 = M.strategy_ret_risk_analysis.v2(
        input_1=m19.raw_perf,
        analysis_flag='absolute',
        benchmark_index='000300.HIX',
        terms='long'
    )
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-95d13789a4e446a297b7dd8797944327"}/bigcharts-data-end
    日期: 2015-01-12 股票: ['000557.SZA', '600853.SHA', '600528.SHA'] 出现跟踪止损状况
    日期: 2015-01-19 股票: ['000935.SZA', '600428.SHA'] 出现跟踪止损状况
    日期: 2015-02-02 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-03 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-04 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-05 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-06 股票: ['002113.SZA', '002575.SZA'] 出现跟踪止损状况
    日期: 2015-02-09 股票: ['002113.SZA', '000791.SZA', '601727.SHA', '300100.SZA'] 出现跟踪止损状况
    日期: 2015-02-10 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-11 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-12 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-13 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-16 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-17 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-25 股票: ['002113.SZA', '600282.SHA'] 出现跟踪止损状况
    日期: 2015-02-26 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-02-27 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-02 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-03 股票: ['002113.SZA', '002712.SZA'] 出现跟踪止损状况
    日期: 2015-03-04 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-05 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-06 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-09 股票: ['002113.SZA', '002108.SZA'] 出现跟踪止损状况
    日期: 2015-03-10 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-11 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-12 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-13 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-16 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-17 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-18 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-19 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-20 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-23 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-24 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-25 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-26 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-27 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-03-30 股票: ['002113.SZA', '300389.SZA'] 出现跟踪止损状况
    日期: 2015-03-31 股票: ['002113.SZA'] 出现跟踪止损状况
    日期: 2015-04-14 股票: ['300261.SZA'] 出现跟踪止损状况
    日期: 2015-04-15 股票: ['300117.SZA', '002006.SZA', '300069.SZA'] 出现跟踪止损状况
    日期: 2015-04-16 股票: ['300019.SZA'] 出现跟踪止损状况
    日期: 2015-04-20 股票: ['002652.SZA', '300195.SZA'] 出现跟踪止损状况
    日期: 2015-04-28 股票: ['000513.SZA', '002004.SZA'] 出现跟踪止损状况
    
    • 收益率79.49%
    • 年化收益率561.78%
    • 基准收益率34.42%
    • 阿尔法3.13
    • 贝塔0.5
    • 夏普比率6.82
    • 胜率0.68
    • 盈亏比2.05
    • 收益波动率27.93%
    • 信息比率0.21
    • 最大回撤5.03%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-8a275515587b4b7180bf7c11f9273ed4"}/bigcharts-data-end
    ---------------------------------------------------------------------------
    AttributeError                            Traceback (most recent call last)
    <ipython-input-1-ff64f8b3cf2e> in <module>
        253 )
        254 
    --> 255 m6 = M.strategy_ret_risk_analysis.v2(
        256     input_1=m19.raw_perf,
        257     analysis_flag='absolute',
    
    AttributeError: 'NoneType' object has no attribute 'set_index'