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(a20180322) #1
克隆策略

    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* 0.1\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 current_stoploss_stock.append(i)\n print('日期:', today, '股票:', i, '出现止损状况')\n #-------------------------------------------止损模块END---------------------------------------------\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StockRanker预测的排序末位淘汰\n if not is_staging 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2018年3月29日 17:31
    # 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。
    
    
    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>mean(close_0,5)
    
    """
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2012-01-01'),
        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
    )
    
    m15 = M.rolling_conf.v1(
        start_date='2010-01-01',
        end_date=T.live_run_param('trading_date', '2018-03-28'),
        rolling_update_days=100,
        rolling_min_days=730,
        rolling_max_days=0,
        rolling_count_for_live=1
    )
    
    m1 = M.instruments.v2(
        rolling_conf=m15.data,
        start_date='',
        end_date='',
        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
    )
    
    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=True
    )
    
    m16 = M.rolling_run.v1(
        run=m6.m_lazy_run,
        input_list=m15.data,
        param_name='rolling_input'
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m16.data,
        data=m14.data,
        m_lazy_run=True
    )
    
    m17 = M.rolling_run_predict.v1(
        predict=m8.m_lazy_run,
        model_param_name='model',
        data_param_name='data'
    )
    
    # 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):
        # 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')]
    
        
        #------------------------------------------跟踪止损止损模块START--------------------------------------------
        today = data.current_dt  
        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
        current_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 = today - last_sale_date  
                print('股票:', i, '今天: ', today, '上次交易日期: ', 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)     
                    current_stoploss_stock.append(i)
                    print('日期:', today, '股票:', i, '出现止损状况')
        #-------------------------------------------止损模块END---------------------------------------------
              
        # 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StockRanker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StockRanker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 m12_prepare_bigquant_run(context):
        pass
    
    # 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=m17.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=100000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-03-29 17:25:47.728595] INFO: bigquant: input_features.v1 开始运行..
    [2018-03-29 17:25:47.739376] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:47.741484] INFO: bigquant: input_features.v1 运行完成[0.012922s].
    [2018-03-29 17:25:47.768467] INFO: bigquant: instruments.v2 开始运行..
    [2018-03-29 17:25:47.785296] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:47.792623] INFO: bigquant: instruments.v2 运行完成[0.024168s].
    [2018-03-29 17:25:47.845707] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-03-29 17:25:47.856702] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:47.858460] INFO: bigquant: general_feature_extractor.v6 运行完成[0.012758s].
    [2018-03-29 17:25:47.992604] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-03-29 17:25:47.996794] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:47.998960] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.006368s].
    [2018-03-29 17:25:48.021829] INFO: bigquant: dropnan.v1 开始运行..
    [2018-03-29 17:25:48.028074] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:48.029934] INFO: bigquant: dropnan.v1 运行完成[0.008122s].
    [2018-03-29 17:25:48.050022] INFO: 滚动运行配置: 生成了 23 次滚动,第一次 {'start_date': '2010-01-01', 'end_date': '2011-12-31'},最后一次 {'start_date': '2010-01-01', 'end_date': '2018-01-08'}
    [2018-03-29 17:25:48.110549] INFO: bigquant: instruments.v2 开始运行..
    [2018-03-29 17:25:48.115791] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:48.117958] INFO: bigquant: instruments.v2 运行完成[0.007429s].
    [2018-03-29 17:25:48.144627] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-03-29 17:25:48.151158] INFO: bigquant: 命中缓存
    [2018-03-29 17:25:48.153035] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.008432s].
    [2018-03-29 17:25:48.173930] INFO: bigquant: general_feature_extractor.v6 开始运行..
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    (iQuant) #2

    您好,首先你得保证你得策略是可以正常回测的,能够看出历史回测曲线。
    然后,观察到您的因子有:mean(close_0,5)
    因此在基础特征列表的模块里,你需要设置“向前取数据天数”,比如为10。
    具体参数位置见下图:


    (a20180322) #3

    感谢支持,已经按要求更改可以回测,也加入模拟交易,请问如何验证是否会产生买入信号,还是要等到晚上?https://i.bigquant.com/user/a20180322/lab/share/test%2F3加止损滚动训练.ipynb