AI+涨停板特征提取


(makn) #1
克隆策略
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#号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 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)\nshift(close, -5) / shift(open, -1)\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 将分数映射到分类,这里使用20个分类\nall_wbins(label, 20)\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, 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#号开始的表示注释\n# 多个特征,每行一个,可以包含基础特征和衍生特征\nrank_turn_3 #过去i个交易日的换手率 (turn_i) 排名,=从小到大排名序号/总数\nreturn_3\nreturn_10 #10日累计收益\nreturn_360 #360日累计收益\nreturn_360/market_cap_float_0 #360日累计收益与流通市值比\nwest_netprofit_ftm_0 #一致预测净利润(未来12个月)\nwest_eps_ftm_0\t#一致预测每股收益(未来12个月)\nwest_avgcps_ftm_0\t#一致预测每股现金流(未来12个月)\nlist_board_0 #所在版块\nta_sma_5_0 \nta_sma_10_0\nta_sma_20_0\nta_sma_30_0\nta_sma_60_0\nfs_eps_yoy_0\t#每股收益同比增长率\nrank_fs_cash_ratio_0\t#现金比率,升序百分比排名 \nfs_roe_ttm_0\t#净资产收益率 (TTM)\nfs_net_profit_yoy_0\t#归属母公司股东的净利润同比增长率\nta_macd_macd_12_26_9_0 #MACD\nswing_volatility_5_0 #波动率\nprice_limit_status_1+price_limit_status_2+price_limit_status_3+price_limit_status_4+price_limit_status_5+price_limit_status_6+price_limit_status_7+price_limit_status_8+price_limit_status_9+price_limit_status_10\navg_amount_0/avg_amount_5\nrank_avg_amount_0/rank_avg_amount_5\npe_ttm_0\nrank_avg_mf_net_amount_3\navg_mf_net_amount_3/market_cap_float_0\n# BOLL、成交量相关\n(avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 + 2 * std(close_0, 20)),5) / 5\n(avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 - 2 * std(close_0, 20)),5) / 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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 #------------------------------------------止损模块START--------------------------------------------\n positions = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}\n # 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\n current_stoploss_stock = [] # 将当天止损的股票整理到一个集合\n if len(positions) > 0:\n for i in positions.keys():\n stock_cost = positions[i] \n stock_market_price = data.current(context.symbol(i), 'price') \n # 亏5%就止损\n if (stock_market_price - stock_cost) / stock_cost <= -0.03: \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 #-------------------------------------------止损模块END---------------------------------------------\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 # 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 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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 = 15\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 = 0.2\n context.options['hold_days'] = 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    In [32]:
    # 本代码由可视化策略环境自动生成 2018年5月24日 10:41
    # 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=T.live_run_param('trading_date', '2010-01-01'),
        end_date=T.live_run_param('trading_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
    rank_turn_3 #过去i个交易日的换手率 (turn_i) 排名,=从小到大排名序号/总数
    return_3
    return_10 #10日累计收益
    return_360 #360日累计收益
    return_360/market_cap_float_0 #360日累计收益与流通市值比
    west_netprofit_ftm_0 #一致预测净利润(未来12个月)
    west_eps_ftm_0	#一致预测每股收益(未来12个月)
    west_avgcps_ftm_0	#一致预测每股现金流(未来12个月)
    list_board_0 #所在版块
    ta_sma_5_0 
    ta_sma_10_0
    ta_sma_20_0
    ta_sma_30_0
    ta_sma_60_0
    fs_eps_yoy_0	#每股收益同比增长率
    rank_fs_cash_ratio_0	#现金比率,升序百分比排名 
    fs_roe_ttm_0	#净资产收益率 (TTM)
    fs_net_profit_yoy_0	#归属母公司股东的净利润同比增长率
    ta_macd_macd_12_26_9_0 #MACD
    swing_volatility_5_0 #波动率
    price_limit_status_1+price_limit_status_2+price_limit_status_3+price_limit_status_4+price_limit_status_5+price_limit_status_6+price_limit_status_7+price_limit_status_8+price_limit_status_9+price_limit_status_10
    avg_amount_0/avg_amount_5
    rank_avg_amount_0/rank_avg_amount_5
    pe_ttm_0
    rank_avg_mf_net_amount_3
    avg_mf_net_amount_3/market_cap_float_0
    # BOLL、成交量相关
    (avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 + 2 * std(close_0, 20)),5) / 5
    (avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 - 2 * std(close_0, 20)),5) / 5
    
    """
    )
    
    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', ''),
        end_date=T.live_run_param('trading_date', ''),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date=T.live_run_param('trading_date', ''),
        end_date=T.live_run_param('trading_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
    )
    
    # 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):
        #------------------------------------------止损模块START--------------------------------------------
        positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
        # 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 = []  # 将当天止损的股票整理到一个集合
        if len(positions) > 0:
            for i in positions.keys():
                stock_cost = positions[i] 
                stock_market_price = data.current(context.symbol(i), 'price') 
                # 亏5%就止损
                if (stock_market_price - stock_cost) / stock_cost <= -0.03:   
                    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')
        #-------------------------------------------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()}
    
        # 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:
                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 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 = 15
        # 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=T.live_run_param('trading_date', ''),
        end_date=T.live_run_param('trading_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
    )
    
    [2018-05-24 10:36:17.596860] INFO: bigquant: instruments.v2 开始运行..
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    [2018-05-24 10:36:17.741786] INFO: bigquant: input_features.v1 开始运行..
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    [2018-05-24 10:36:17.781708] INFO: bigquant: 命中缓存
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    [2018-05-24 10:36:17.793649] INFO: bigquant: join.v3 开始运行..
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    [2018-05-24 10:36:17.854034] INFO: bigquant: instruments.v2 开始运行..
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    [2018-05-24 10:36:17.924201] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-05-24 10:36:17.932494] INFO: bigquant: 命中缓存
    [2018-05-24 10:36:17.933682] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.009501s].
    [2018-05-24 10:36:17.966945] INFO: bigquant: backtest.v7 开始运行..
    [2018-05-24 10:36:17.970103] INFO: bigquant: 命中缓存
    
    • 收益率100.84%
    • 年化收益率35.32%
    • 基准收益率3.31%
    • 阿尔法0.33
    • 贝塔0.71
    • 夏普比率1.29
    • 胜率0.528
    • 盈亏比1.237
    • 收益波动率23.88%
    • 信息比率1.61
    • 最大回撤14.91%
    [2018-05-24 10:36:22.043659] INFO: bigquant: backtest.v7 运行完成[4.076659s].
    

    (小Q) #2

    小Q情不自禁地来点赞,17年、18年很多主观策略和传统量化策略表现都不好,这个策略走势不错。


    (luckychan) #3
    克隆策略
    In [ ]:
     
    

      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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_sh = D.history_data(['000001.SHA'], start_date=context.start_date , end_date=context.end_date, fields=['close'])\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 < 0.96:\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 #------------------------------------------止损模块START--------------------------------------------\n positions = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}\n # 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\n current_stoploss_stock = [] # 将当天止损的股票整理到一个集合\n if len(positions) > 0:\n for i in positions.keys():\n stock_cost = positions[i] \n stock_market_price = data.current(context.symbol(i), 'price') \n # 亏5%就止损\n if (stock_market_price - stock_cost) / stock_cost <= -0.03: \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 #-------------------------------------------止损模块END---------------------------------------------\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 # 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 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 context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n context.instruments = context.instruments + ['000300.SHA','000905.SHA','000001.SHA']\n pass\n","ValueType":"Literal","LinkedGlobalParameter":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 = 2\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 = 0.5\n context.options['hold_days'] = 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      In [6]:
      # 本代码由可视化策略环境自动生成 2018年5月27日 23:20
      # 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=T.live_run_param('trading_date', '2010-01-01'),
          end_date=T.live_run_param('trading_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
      rank_turn_3 #过去i个交易日的换手率 (turn_i) 排名,=从小到大排名序号/总数
      return_3
      return_10 #10日累计收益
      return_360 #360日累计收益
      return_360/market_cap_float_0 #360日累计收益与流通市值比
      west_netprofit_ftm_0 #一致预测净利润(未来12个月)
      west_eps_ftm_0	#一致预测每股收益(未来12个月)
      west_avgcps_ftm_0	#一致预测每股现金流(未来12个月)
      list_board_0 #所在版块
      ta_sma_5_0 
      ta_sma_10_0
      ta_sma_20_0
      ta_sma_30_0
      ta_sma_60_0
      fs_eps_yoy_0	#每股收益同比增长率
      rank_fs_cash_ratio_0	#现金比率,升序百分比排名 
      fs_roe_ttm_0	#净资产收益率 (TTM)
      fs_net_profit_yoy_0	#归属母公司股东的净利润同比增长率
      ta_macd_macd_12_26_9_0 #MACD
      swing_volatility_5_0 #波动率
      price_limit_status_1+price_limit_status_2+price_limit_status_3+price_limit_status_4+price_limit_status_5+price_limit_status_6+price_limit_status_7+price_limit_status_8+price_limit_status_9+price_limit_status_10
      avg_amount_0/avg_amount_5
      rank_avg_amount_0/rank_avg_amount_5
      pe_ttm_0
      rank_avg_mf_net_amount_3
      avg_mf_net_amount_3/market_cap_float_0
      # BOLL、成交量相关
      (avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 + 2 * std(close_0, 20)),5) / 5
      (avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 - 2 * std(close_0, 20)),5) / 5
      
      """
      )
      
      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', '2018-05-25'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=0
      )
      
      m10 = M.general_feature_extractor.v6(
          instruments=m9.data,
          features=m3.data,
          start_date=T.live_run_param('trading_date', ''),
          end_date=T.live_run_param('trading_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
      )
      
      # 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):
          
          #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_sh = D.history_data(['000001.SHA'], start_date=context.start_date , end_date=context.end_date, fields=['close'])
          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 < 0.96:
              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
          
          #------------------------------------------止损模块START--------------------------------------------
          positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
          # 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 = []  # 将当天止损的股票整理到一个集合
          if len(positions) > 0:
              for i in positions.keys():
                  stock_cost = positions[i] 
                  stock_market_price = data.current(context.symbol(i), 'price') 
                  # 亏5%就止损
                  if (stock_market_price - stock_cost) / stock_cost <= -0.03:   
                      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')
          #-------------------------------------------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()}
      
          # 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:
                  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 m12_prepare_bigquant_run(context):
          context.instruments = context.instruments + ['000300.SHA','000905.SHA','000001.SHA']
          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 = 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 = 0.5
          context.options['hold_days'] = 2
      
      m12 = M.trade.v3(
          instruments=m9.data,
          options_data=m8.predictions,
          start_date=T.live_run_param('trading_date', ''),
          end_date=T.live_run_param('trading_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='后复权',
          plot_charts=True,
          backtest_only=False,
          amount_integer=False
      )
      
      [2018-05-27 23:05:33.528268] INFO: bigquant: instruments.v2 开始运行..
      [2018-05-27 23:05:33.579276] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.580299] INFO: bigquant: instruments.v2 运行完成[0.052055s].
      [2018-05-27 23:05:33.588240] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
      [2018-05-27 23:05:33.626830] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.627964] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.039725s].
      [2018-05-27 23:05:33.633332] INFO: bigquant: input_features.v1 开始运行..
      [2018-05-27 23:05:33.635941] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.636703] INFO: bigquant: input_features.v1 运行完成[0.003372s].
      [2018-05-27 23:05:33.647504] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2018-05-27 23:05:33.649862] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.650886] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003385s].
      [2018-05-27 23:05:33.658294] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2018-05-27 23:05:33.660852] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.661750] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003455s].
      [2018-05-27 23:05:33.669832] INFO: bigquant: join.v3 开始运行..
      [2018-05-27 23:05:33.672256] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.673036] INFO: bigquant: join.v3 运行完成[0.00321s].
      [2018-05-27 23:05:33.680326] INFO: bigquant: dropnan.v1 开始运行..
      [2018-05-27 23:05:33.682848] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.683672] INFO: bigquant: dropnan.v1 运行完成[0.003334s].
      [2018-05-27 23:05:33.693578] INFO: bigquant: stock_ranker_train.v5 开始运行..
      [2018-05-27 23:05:33.697678] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.698743] INFO: bigquant: stock_ranker_train.v5 运行完成[0.005139s].
      [2018-05-27 23:05:33.705099] INFO: bigquant: instruments.v2 开始运行..
      [2018-05-27 23:05:33.707953] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.709060] INFO: bigquant: instruments.v2 运行完成[0.003917s].
      [2018-05-27 23:05:33.719619] INFO: bigquant: general_feature_extractor.v6 开始运行..
      [2018-05-27 23:05:33.721738] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.722709] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003076s].
      [2018-05-27 23:05:33.729105] INFO: bigquant: derived_feature_extractor.v2 开始运行..
      [2018-05-27 23:05:33.731391] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.732174] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003064s].
      [2018-05-27 23:05:33.738955] INFO: bigquant: dropnan.v1 开始运行..
      [2018-05-27 23:05:33.741960] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.743093] INFO: bigquant: dropnan.v1 运行完成[0.004139s].
      [2018-05-27 23:05:33.757676] INFO: bigquant: stock_ranker_predict.v5 开始运行..
      [2018-05-27 23:05:33.766529] INFO: bigquant: 命中缓存
      [2018-05-27 23:05:33.767542] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.009906s].
      [2018-05-27 23:05:33.795449] INFO: bigquant: backtest.v7 开始运行..
      [2018-05-27 23:05:33.799123] INFO: bigquant: 命中缓存
      
      • 收益率461.07%
      • 年化收益率69.14%
      • 基准收益率8.0%
      • 阿尔法0.66
      • 贝塔0.64
      • 夏普比率1.66
      • 胜率0.569
      • 盈亏比1.06
      • 收益波动率38.91%
      • 信息比率1.83
      • 最大回撤27.04%
      [2018-05-27 23:05:36.433484] INFO: bigquant: backtest.v7 运行完成[2.638037s].
      

      优化一下,加个大盘风控,年化收益加倍,回撤减半。


      (wien) #4

      请问这个策略哪一块跟涨停板有关?没看出来呢


      (sensezeng) #5

      特征列表里面 有price_limit_status,表示涨跌停状态。


      (zhang785285157) #6

      模拟的时候为什么报错呢?


      (zhang785285157) #7


      请问一下,这个错误怎么改呢?


      (达达) #8

      回测没发现问题,昨晚服务器有点问题跑策略失败,再查查看


      (zhang785285157) #9

      一直这个错误,看他的意思是,nan是重复的,我把drop_na_label 参数关了,看看怎么样,那个duplicates 的参数,应该是python 里的函数,不知道怎么改那个参数


      (smallsnow) #10

      好像跟涨停没关系