可视化策略-随机森林

策略分享
算法可视化界面
标签: #<Tag:0x00007f8c65e3b9a0> #<Tag:0x00007f8c65e3b838>

(iQuant) #1
克隆策略

    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    In [2]:
    # 本代码由可视化策略环境自动生成 2017年10月9日 15:44
    # 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='2010-01-01',
        end_date='2015-01-01',
        market='CN_STOCK_A',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        start_date='2010-01-01',
        end_date='2015-01-01',
        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)
    """,
        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_15
    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
    """
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='2010-01-01',
        end_date='2015-01-01'
    )
    
    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
    )
    
    m15 = M.random_forest_train.v2(
        training_ds=m13.data,
        features=m3.data,
        n_estimators=10,
        max_features='auto',
        max_depth=30,
        min_samples_leaf=200,
        n_jobs=1,
        algo='classifier'
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01')
    )
    
    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
    )
    
    m16 = M.random_forest_predict.v2(
        model=m15.model,
        data=m14.data,
        date_col='date',
        instrument_col='instrument',
        sort=True
    )
    
    # 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')]
    
        # 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=m16.predictions,
        start_date='2015-01-01',
        end_date='2017-01-01',
        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',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-10-09 15:34:10.090236] INFO: bigquant: instruments.v2 开始运行..
    [2017-10-09 15:34:10.094175] INFO: bigquant: 命中缓存
    [2017-10-09 15:34:10.095467] INFO: bigquant: instruments.v2 运行完成[0.005251s].
    [2017-10-09 15:34:10.104405] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-10-09 15:34:10.107634] INFO: bigquant: 命中缓存
    [2017-10-09 15:34:10.108682] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004289s].
    [2017-10-09 15:34:10.113495] INFO: bigquant: input_features.v1 开始运行..
    [2017-10-09 15:34:10.117816] INFO: bigquant: input_features.v1 运行完成[0.004314s].
    [2017-10-09 15:34:10.124811] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-10-09 15:34:17.975591] INFO: general_feature_extractor: 年份 2010, 特征行数=431567
    [2017-10-09 15:34:27.158673] INFO: general_feature_extractor: 年份 2011, 特征行数=511455
    [2017-10-09 15:34:37.476396] INFO: general_feature_extractor: 年份 2012, 特征行数=565675
    [2017-10-09 15:34:47.599411] INFO: general_feature_extractor: 年份 2013, 特征行数=564168
    [2017-10-09 15:34:52.476504] INFO: general_feature_extractor: 年份 2014, 特征行数=569948
    [2017-10-09 15:34:58.707619] INFO: general_feature_extractor: 年份 2015, 特征行数=0
    [2017-10-09 15:34:58.724581] INFO: general_feature_extractor: 总行数: 2642813
    [2017-10-09 15:34:58.726980] INFO: bigquant: general_feature_extractor.v6 运行完成[48.602174s].
    [2017-10-09 15:34:58.734685] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-10-09 15:34:59.792047] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.007s
    [2017-10-09 15:34:59.800002] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.007s
    [2017-10-09 15:34:59.807943] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.007s
    [2017-10-09 15:34:59.816142] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.007s
    [2017-10-09 15:34:59.823191] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.006s
    [2017-10-09 15:34:59.834939] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.010s
    [2017-10-09 15:35:00.604951] INFO: derived_feature_extractor: /y_2010, 431567
    [2017-10-09 15:35:00.990536] INFO: derived_feature_extractor: /y_2011, 511455
    [2017-10-09 15:35:01.546326] INFO: derived_feature_extractor: /y_2012, 565675
    [2017-10-09 15:35:01.994821] INFO: derived_feature_extractor: /y_2013, 564168
    [2017-10-09 15:35:02.529759] INFO: derived_feature_extractor: /y_2014, 569948
    [2017-10-09 15:35:03.012218] INFO: bigquant: derived_feature_extractor.v2 运行完成[4.277467s].
    [2017-10-09 15:35:03.021487] INFO: bigquant: join.v3 开始运行..
    [2017-10-09 15:35:07.103675] INFO: join: /y_2010, 行数=431028/431567, 耗时=1.649131s
    [2017-10-09 15:35:08.853497] INFO: join: /y_2011, 行数=510922/511455, 耗时=1.734169s
    [2017-10-09 15:35:10.737983] INFO: join: /y_2012, 行数=564582/565675, 耗时=1.867379s
    [2017-10-09 15:35:12.597848] INFO: join: /y_2013, 行数=563132/564168, 耗时=1.838279s
    [2017-10-09 15:35:14.412243] INFO: join: /y_2014, 行数=555191/569948, 耗时=1.794616s
    [2017-10-09 15:35:14.506842] INFO: join: 最终行数: 2624855
    [2017-10-09 15:35:14.508541] INFO: bigquant: join.v3 运行完成[11.487068s].
    [2017-10-09 15:35:14.515030] INFO: bigquant: dropnan.v1 开始运行..
    [2017-10-09 15:35:15.133802] INFO: dropnan: /y_2010, 424081/431028
    [2017-10-09 15:35:15.879800] INFO: dropnan: /y_2011, 505020/510922
    [2017-10-09 15:35:16.543619] INFO: dropnan: /y_2012, 561281/564582
    [2017-10-09 15:35:17.338741] INFO: dropnan: /y_2013, 563103/563132
    [2017-10-09 15:35:18.163681] INFO: dropnan: /y_2014, 553511/555191
    [2017-10-09 15:35:18.181183] INFO: dropnan: 行数: 2606996/2624855
    [2017-10-09 15:35:18.204290] INFO: bigquant: dropnan.v1 运行完成[3.689232s].
    [2017-10-09 15:35:18.212919] INFO: bigquant: random_forest_train.v2 开始运行..
    [2017-10-09 15:39:31.705020] INFO: random_forest_train: 模型在训练集分数是:0.18
    [2017-10-09 15:39:31.725461] INFO: bigquant: random_forest_train.v2 运行完成[253.512526s].
    [2017-10-09 15:39:31.732900] INFO: bigquant: instruments.v2 开始运行..
    [2017-10-09 15:39:31.736469] INFO: bigquant: 命中缓存
    [2017-10-09 15:39:31.737489] INFO: bigquant: instruments.v2 运行完成[0.004625s].
    [2017-10-09 15:39:31.743528] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-10-09 15:39:37.654902] INFO: general_feature_extractor: 年份 2015, 特征行数=569698
    [2017-10-09 15:39:43.851494] INFO: general_feature_extractor: 年份 2016, 特征行数=641546
    [2017-10-09 15:39:51.692946] INFO: general_feature_extractor: 年份 2017, 特征行数=0
    [2017-10-09 15:39:51.711744] INFO: general_feature_extractor: 总行数: 1211244
    [2017-10-09 15:39:51.714523] INFO: bigquant: general_feature_extractor.v6 运行完成[19.970978s].
    [2017-10-09 15:39:51.722534] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-10-09 15:39:52.841708] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s
    [2017-10-09 15:39:52.845399] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.003s
    [2017-10-09 15:39:52.848812] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.002s
    [2017-10-09 15:39:52.852891] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.003s
    [2017-10-09 15:39:52.857334] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s
    [2017-10-09 15:39:52.861284] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.003s
    [2017-10-09 15:39:54.231979] INFO: derived_feature_extractor: /y_2015, 569698
    [2017-10-09 15:39:56.143105] INFO: derived_feature_extractor: /y_2016, 641546
    [2017-10-09 15:39:58.203954] INFO: bigquant: derived_feature_extractor.v2 运行完成[6.481393s].
    [2017-10-09 15:39:58.211486] INFO: bigquant: dropnan.v1 开始运行..
    [2017-10-09 15:39:58.900927] INFO: dropnan: /y_2015, 565355/569698
    [2017-10-09 15:39:59.599159] INFO: dropnan: /y_2016, 637125/641546
    [2017-10-09 15:39:59.617735] INFO: dropnan: 行数: 1202480/1211244
    [2017-10-09 15:39:59.641586] INFO: bigquant: dropnan.v1 运行完成[1.430065s].
    [2017-10-09 15:39:59.648068] INFO: bigquant: random_forest_predict.v2 开始运行..
    [2017-10-09 15:40:23.312799] INFO: bigquant: random_forest_predict.v2 运行完成[23.664699s].
    [2017-10-09 15:40:23.340106] INFO: bigquant: backtest.v7 开始运行..
    [2017-10-09 15:40:58.095101] INFO: Performance: Simulated 488 trading days out of 488.
    [2017-10-09 15:40:58.096241] INFO: Performance: first open: 2015-01-05 14:30:00+00:00
    [2017-10-09 15:40:58.097046] INFO: Performance: last close: 2016-12-30 20:00:00+00:00
    
    • 收益率43.32%
    • 年化收益率20.42%
    • 基准收益率-6.33%
    • 阿尔法0.23
    • 贝塔0.9
    • 夏普比率0.41
    • 收益波动率41.92%
    • 信息比率0.78
    • 最大回撤54.42%
    [2017-10-09 15:41:00.816023] INFO: bigquant: backtest.v7 运行完成[37.475879s].