量化之神1

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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年1月25日 14:32
    # 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:每日数据处理函数,每天执行一次
    def m19_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')]
    
        # 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 m19_prepare_bigquant_run(context):
        pass
    
    # 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'] = 10
    
    
    m1 = M.instruments.v2(
        start_date='2000-01-01',
        end_date='2013-01-01',
        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 {{web_host_url}}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 <{{web_host_url}}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_10
    avg_turn_10
    avg_turn_5
    market_cap_float_0
    pe_ttm_0
    mf_net_amount_10
    rank_avg_mf_net_amount_5
    avg_mf_net_amount_10
    fs_net_profit_yoy_0
    sh_holder_avg_pct_3m_chng_0
    rank_sh_holder_avg_pct_3m_chng_0
    ta_sma_5_0
    ta_mom_10_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
    )
    
    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', '2013-01-01'),
        end_date=T.live_run_param('trading_date', '2019-01-23'),
        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=0
    )
    
    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
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_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.SHA'
    )
    
    [2019-01-25 14:06:09.337890] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-25 14:06:09.427283] INFO: bigquant: instruments.v2 运行完成[0.180825s].
    [2019-01-25 14:06:09.525267] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-25 14:06:18.353582] INFO: 自动标注(股票): 加载历史数据: 3170101 行
    [2019-01-25 14:06:18.354702] INFO: 自动标注(股票): 开始标注 ..
    [2019-01-25 14:06:24.100257] INFO: bigquant: advanced_auto_labeler.v2 运行完成[14.668386s].
    [2019-01-25 14:06:24.179874] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-25 14:06:24.189435] INFO: bigquant: input_features.v1 运行完成[0.0858s].
    [2019-01-25 14:06:24.281432] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-25 14:06:24.698580] INFO: 基础特征抽取: 年份 2000, 特征行数=0
    [2019-01-25 14:06:24.702229] INFO: 基础特征抽取: 年份 2001, 特征行数=0
    [2019-01-25 14:06:24.705737] INFO: 基础特征抽取: 年份 2002, 特征行数=0
    [2019-01-25 14:06:24.709312] INFO: 基础特征抽取: 年份 2003, 特征行数=0
    [2019-01-25 14:06:24.712839] INFO: 基础特征抽取: 年份 2004, 特征行数=0
    [2019-01-25 14:06:36.898764] INFO: 基础特征抽取: 年份 2005, 特征行数=314357
    [2019-01-25 14:06:49.695465] INFO: 基础特征抽取: 年份 2006, 特征行数=288040
    [2019-01-25 14:07:04.613087] INFO: 基础特征抽取: 年份 2007, 特征行数=323371
    [2019-01-25 14:07:17.324063] INFO: 基础特征抽取: 年份 2008, 特征行数=360328
    [2019-01-25 14:07:29.659259] INFO: 基础特征抽取: 年份 2009, 特征行数=375308
    [2019-01-25 14:07:42.357427] INFO: 基础特征抽取: 年份 2010, 特征行数=431567
    [2019-01-25 14:07:48.004020] INFO: 基础特征抽取: 年份 2011, 特征行数=511455
    [2019-01-25 14:07:56.529174] INFO: 基础特征抽取: 年份 2012, 特征行数=565675
    [2019-01-25 14:08:02.251764] INFO: 基础特征抽取: 年份 2013, 特征行数=0
    [2019-01-25 14:08:02.278325] INFO: 基础特征抽取: 总行数: 3170101
    [2019-01-25 14:08:02.280686] INFO: bigquant: general_feature_extractor.v7 运行完成[98.081694s].
    [2019-01-25 14:08:02.752293] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-25 14:08:03.967874] INFO: derived_feature_extractor: /y_2005, 314357
    [2019-01-25 14:08:04.230315] INFO: derived_feature_extractor: /y_2006, 288040
    [2019-01-25 14:08:04.913699] INFO: derived_feature_extractor: /y_2007, 323371
    [2019-01-25 14:08:05.205661] INFO: derived_feature_extractor: /y_2008, 360328
    [2019-01-25 14:08:05.503662] INFO: derived_feature_extractor: /y_2009, 375308
    [2019-01-25 14:08:05.821699] INFO: derived_feature_extractor: /y_2010, 431567
    [2019-01-25 14:08:06.301064] INFO: derived_feature_extractor: /y_2011, 511455
    [2019-01-25 14:08:06.818530] INFO: derived_feature_extractor: /y_2012, 565675
    [2019-01-25 14:08:07.309539] INFO: bigquant: derived_feature_extractor.v3 运行完成[5.025365s].
    [2019-01-25 14:08:07.384504] INFO: bigquant: join.v3 开始运行..
    [2019-01-25 14:08:11.070853] INFO: join: /y_2005, 行数=313301/314357, 耗时=1.789732s
    [2019-01-25 14:08:12.511118] INFO: join: /y_2006, 行数=286870/288040, 耗时=1.429781s
    [2019-01-25 14:08:14.023693] INFO: join: /y_2007, 行数=320460/323371, 耗时=1.503303s
    [2019-01-25 14:08:15.807806] INFO: join: /y_2008, 行数=358815/360328, 耗时=1.771843s
    [2019-01-25 14:08:17.489707] INFO: join: /y_2009, 行数=374455/375308, 耗时=1.669898s
    [2019-01-25 14:08:19.996671] INFO: join: /y_2010, 行数=431016/431567, 耗时=2.474982s
    [2019-01-25 14:08:22.216126] INFO: join: /y_2011, 行数=510907/511455, 耗时=2.187484s
    [2019-01-25 14:08:24.750662] INFO: join: /y_2012, 行数=552375/565675, 耗时=2.516209s
    [2019-01-25 14:08:25.296344] INFO: join: 最终行数: 3148199
    [2019-01-25 14:08:25.298588] INFO: bigquant: join.v3 运行完成[17.985564s].
    [2019-01-25 14:08:25.385685] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-25 14:08:25.636962] INFO: dropnan: /y_2005, 0/313301
    [2019-01-25 14:08:25.834410] INFO: dropnan: /y_2006, 371/286870
    [2019-01-25 14:08:26.047936] INFO: dropnan: /y_2007, 433/320460
    [2019-01-25 14:08:26.296101] INFO: dropnan: /y_2008, 127/358815
    [2019-01-25 14:08:26.541815] INFO: dropnan: /y_2009, 210/374455
    [2019-01-25 14:08:26.878114] INFO: dropnan: /y_2010, 104016/431016
    [2019-01-25 14:08:27.351121] INFO: dropnan: /y_2011, 154922/510907
    [2019-01-25 14:08:28.202959] INFO: dropnan: /y_2012, 532546/552375
    [2019-01-25 14:08:28.252768] INFO: dropnan: 行数: 792625/3148199
    [2019-01-25 14:08:28.275121] INFO: bigquant: dropnan.v1 运行完成[2.972319s].
    [2019-01-25 14:08:28.361241] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-25 14:08:29.206267] INFO: StockRanker: 特征预处理 ..
    [2019-01-25 14:08:30.510555] INFO: StockRanker: prepare data: training ..
    [2019-01-25 14:08:33.127520] INFO: StockRanker: sort ..
    [2019-01-25 14:08:42.706283] INFO: StockRanker训练: a207d6d0 准备训练: 792625 行数
    [2019-01-25 14:08:42.761560] INFO: StockRanker训练: 正在训练 ..
    [2019-01-25 14:10:13.914611] INFO: bigquant: stock_ranker_train.v5 运行完成[105.632702s].
    [2019-01-25 14:10:13.995773] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-25 14:10:14.229566] INFO: bigquant: instruments.v2 运行完成[0.312449s].
    [2019-01-25 14:10:14.322757] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-25 14:11:28.381593] INFO: 基础特征抽取: 年份 2013, 特征行数=564168
    [2019-01-25 14:11:58.499894] INFO: 基础特征抽取: 年份 2014, 特征行数=569948
    [2019-01-25 14:12:02.919809] INFO: 基础特征抽取: 年份 2015, 特征行数=569698
    [2019-01-25 14:12:07.672432] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
    [2019-01-25 14:12:40.117552] INFO: 基础特征抽取: 年份 2017, 特征行数=743233
    [2019-01-25 14:13:20.600573] INFO: 基础特征抽取: 年份 2018, 特征行数=816987
    [2019-01-25 14:13:22.527011] INFO: 基础特征抽取: 年份 2019, 特征行数=56911
    [2019-01-25 14:13:22.571946] INFO: 基础特征抽取: 总行数: 3962491
    [2019-01-25 14:13:22.575620] INFO: bigquant: general_feature_extractor.v7 运行完成[188.340482s].
    [2019-01-25 14:13:22.660235] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-25 14:13:25.429467] INFO: derived_feature_extractor: /y_2013, 564168
    [2019-01-25 14:13:25.964816] INFO: derived_feature_extractor: /y_2014, 569948
    [2019-01-25 14:13:26.596188] INFO: derived_feature_extractor: /y_2015, 569698
    [2019-01-25 14:13:27.075502] INFO: derived_feature_extractor: /y_2016, 641546
    [2019-01-25 14:13:27.703632] INFO: derived_feature_extractor: /y_2017, 743233
    [2019-01-25 14:13:28.988823] INFO: derived_feature_extractor: /y_2018, 816987
    [2019-01-25 14:13:29.989535] INFO: derived_feature_extractor: /y_2019, 56911
    [2019-01-25 14:13:30.062390] INFO: bigquant: derived_feature_extractor.v3 运行完成[7.483487s].
    [2019-01-25 14:13:30.165200] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-25 14:13:30.818982] INFO: dropnan: /y_2013, 561846/564168
    [2019-01-25 14:13:31.401543] INFO: dropnan: /y_2014, 562905/569948
    [2019-01-25 14:13:31.974280] INFO: dropnan: /y_2015, 548302/569698
    [2019-01-25 14:13:32.626507] INFO: dropnan: /y_2016, 625793/641546
    [2019-01-25 14:13:33.570111] INFO: dropnan: /y_2017, 699282/743233
    [2019-01-25 14:13:34.658897] INFO: dropnan: /y_2018, 791700/816987
    [2019-01-25 14:13:34.763830] INFO: dropnan: /y_2019, 55016/56911
    [2019-01-25 14:13:34.796314] INFO: dropnan: 行数: 3844844/3962491
    [2019-01-25 14:13:34.799597] INFO: bigquant: dropnan.v1 运行完成[4.733937s].
    [2019-01-25 14:13:34.878821] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-01-25 14:13:38.229697] INFO: StockRanker: prepare data: prediction ..
    [2019-01-25 14:14:25.266734] INFO: stock_ranker_predict: 准备预测: 3844844 行
    [2019-01-25 14:14:25.268001] INFO: stock_ranker_predict: 正在预测 ..
    [2019-01-25 14:16:06.524123] INFO: bigquant: stock_ranker_predict.v5 运行完成[151.720971s].
    [2019-01-25 14:16:06.642603] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-25 14:16:06.644865] INFO: bigquant: biglearning backtest:V8.1.7
    [2019-01-25 14:16:06.645625] INFO: bigquant: product_type:stock by specified
    [2019-01-25 14:16:43.765262] INFO: bigquant: 读取股票行情完成:4881716
    [2019-01-25 14:17:36.168669] INFO: algo: TradingAlgorithm V1.4.4
    [2019-01-25 14:17:49.830973] INFO: algo: trading transform...
    [2019-01-25 14:18:23.758731] INFO: Performance: Simulated 1474 trading days out of 1474.
    [2019-01-25 14:18:23.760225] INFO: Performance: first open: 2013-01-04 09:30:00+00:00
    [2019-01-25 14:18:23.761178] INFO: Performance: last close: 2019-01-23 15:00:00+00:00
    
    • 收益率372.0%
    • 年化收益率30.38%
    • 基准收益率24.5%
    • 阿尔法0.25
    • 贝塔0.73
    • 夏普比率0.99
    • 胜率0.56
    • 盈亏比1.07
    • 收益波动率27.9%
    • 信息比率0.07
    • 最大回撤32.83%
    [2019-01-25 14:18:30.838947] INFO: bigquant: backtest.v8 运行完成[144.26865s].