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context.perf_tracker.position_tracker.positions.items()}\n \n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n #equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n \n # 记录持仓中st的股票\n st_stock_list = []\n name_df = context.name_df\n name_today = name_df[name_df.date==today]\n for instrument in equities:\n name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]\n # 如果股票状态变为了st 则卖出\n if 'ST' in name_instrument or '退' in name_instrument:\n # 指定一个limit_price,此时会以开盘价成交,这是由于初始化函数中改写了下单价格\n context.order_target(context.symbol(instrument), 0, limit_price=1.0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n if st_stock_list!=[]:\n print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')\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 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    In [2]:
    # 本代码由可视化策略环境自动生成 2020年10月12日 17:03
    # 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 m1_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 = 0.9
        context.options['hold_days'] = 2
        
        #------------------------计算确定买卖限值----------------------------
        #分位数95%以上的才买,62%以上的不卖
        dpre = context.ranker_prediction.score
        context.pre_buy = dpre.quantile(q=0.998)
        context.pre_sell = dpre.quantile(q=0.965)
        
    # 回测引擎:每日数据处理函数,每天执行一次
    def m1_handle_data_bigquant_run(context, data):
         #------------------------START:加入下面if的两行代码到之前到主函数的最前部分-------------------
        # 相隔几天(以5天举例)运行一下handle_data函数
        # if context.trading_day_index % 2 != 0:
        #    return 
        #------------------------END:加上这两句代码在主函数就能实现隔几天运行---------------------
            
        # 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')]
        today = 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()}
        
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        #equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
        
       # 记录持仓中st的股票
        st_stock_list = []
        name_df = context.name_df
        name_today = name_df[name_df.date==today]
        for instrument in equities:
            name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
            # 如果股票状态变为了st 则卖出
            if 'ST' in name_instrument or '退' in name_instrument:
                # 指定一个limit_price,此时会以开盘价成交,这是由于初始化函数中改写了下单价格
                context.order_target(context.symbol(instrument), 0, limit_price=1.0)
                st_stock_list.append(instrument)
                cash_for_sell -= positions[instrument]
        if st_stock_list!=[]:
            print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')
            
    
        # 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]))])))
            
            # price_limit_status = context.price_limit_status
            # status_today = price_limit_status[price_limit_status.date==today]
            
            # 预测值大于某值时,虽然排在最后也不卖
            no_sell_instruments = ranker_prediction[ranker_prediction.score > context.pre_sell]
            no_sell_instruments = no_sell_instruments.instrument
            instruments = list(set(instruments) - (set(instruments) & set(no_sell_instruments)))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                 # 如果是st股票已经卖过了,就跳过
                if instrument in st_stock_list:
                    continue
                # 如果涨停就跳过股票----
                #status_instrument = status_today[status_today.instrument==instrument]['price_limit_status'].values[0]
                #if status_instrument>2:
                #    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)])
        dd = ranker_prediction[:len(buy_cash_weights)]
        # 当预测值大于一定的数值才买
        buy_instruments = [x for x in dd.instrument if dd[dd.instrument.isin([x])].score.iloc[0] > context.pre_buy ]
        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 m1_prepare_bigquant_run(context):
        # 获取股票名称 用于过滤st和退市股
        context.name_df = DataSource('instruments_CN_STOCK_A').read()
        # 获取涨跌停状态
        context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])
    
    
    m20 = M.instruments.v2(
        start_date='2015-01-01',
        end_date='2018-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m21 = M.advanced_auto_labeler.v2(
        instruments=m20.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, -10) / 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=False
    )
    
    m22 = M.input_features.v1(
        features="""avg_amount_5
    avg_turn_0/avg_turn_9
    beta_gem_5_0
    decay_linear(volume_0,5)
    fs_current_assets_0
    fs_free_cash_flow_0
    fs_gross_revenues_0
    fs_roe_0
    fs_selling_expenses_0
    fs_total_operating_costs_0
    group_sum(industry_sw_level1_0,pb_lf_0)
    in_sse50_0
    low_1/low_5
    mean(abs(close_0-mean(close_0,6)),6)
    mean(mf_net_amount_l_0,10)
    mean(mf_net_amount_l_0,5)
    mean(turn_0,10)
    rank_sh_holder_avg_pct_3m_chng_0
    return_0
    shift(low_0,5)
    ta_macd_macd_12_26_9_0
    ta_mom(return_0,5)
    ta_rsi_14_0
    ta_trix_28_0
    ts_argmax(high_0,60)
    avg_turn_20
    ta_macd_macdsignal_12_26_9_0
    rank_amount_10/rank_amount_30
    ta_mfi_14_0
    daily_return_10"""
    )
    
    m25 = M.general_feature_extractor.v7(
        instruments=m20.data,
        features=m22.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m25.data,
        features=m22.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m23 = M.join.v3(
        data1=m21.data,
        data2=m26.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m2 = M.dropnan.v2(
        input_data=m23.data
    )
    
    m17 = M.stock_ranker_train.v6(
        training_ds=m2.data,
        features=m22.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
    )
    
    m24 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2020-10-11'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m27 = M.general_feature_extractor.v7(
        instruments=m24.data,
        features=m22.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m28 = M.derived_feature_extractor.v3(
        input_data=m27.data,
        features=m22.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m33 = M.chinaa_stock_filter.v1(
        input_data=m28.data,
        index_constituent_cond=['全部'],
        board_cond=['上证主板', '深证主板'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m3 = M.dropnan.v2(
        input_data=m33.data
    )
    
    m13 = M.stock_ranker_predict.v5(
        model=m17.model,
        data=m3.data,
        m_lazy_run=False
    )
    
    m1 = M.trade.v4(
        instruments=m24.data,
        options_data=m13.predictions,
        start_date='',
        end_date='',
        initialize=m1_initialize_bigquant_run,
        handle_data=m1_handle_data_bigquant_run,
        prepare=m1_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=200000,
        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'
    )
    
    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-322100e42093424d9e9df9cd42ee81fe"}/bigcharts-data-end
    • 收益率1093.5%
    • 年化收益率330.54%
    • 基准收益率55.49%
    • 阿尔法1.31
    • 贝塔0.86
    • 夏普比率3.57
    • 胜率0.6
    • 盈亏比2.23
    • 收益波动率42.66%
    • 信息比率0.21
    • 最大回撤25.88%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2d243977106048c6a356ee087623008f"}/bigcharts-data-end

    (iQuant) #2

    您好,哪里有问题呢?