克隆策略

StockRanker多因子选股策略¶

In [3]:

# 本代码由可视化策略环境自动生成 2021年12月3日 14:27
# 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 m4_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

# 回测引擎：每日数据处理函数，每天执行一次
def m4_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 m4_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_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
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
"""
)

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
)

m5 = M.stock_ranker_train.v6(
    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,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=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', '2017-01-01'),
    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=60
)

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=m5.model,
    data=m14.data,
    m_lazy_run=False
)

m4 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    prepare=m4_prepare_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=''
)

[2021-12-03 14:07:45.606766] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-03 14:07:45.623197] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.625360] INFO: moduleinvoker: instruments.v2 运行完成[0.018604s].

[2021-12-03 14:07:45.672034] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-12-03 14:07:45.692501] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.696171] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.024142s].

[2021-12-03 14:07:45.701503] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-12-03 14:07:45.709264] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.710863] INFO: moduleinvoker: input_features.v1 运行完成[0.009365s].

[2021-12-03 14:07:45.735241] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-03 14:07:45.746562] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.748572] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.013352s].

[2021-12-03 14:07:45.758148] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-12-03 14:07:45.771753] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.773817] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.015668s].

[2021-12-03 14:07:45.786409] INFO: moduleinvoker: join.v3 开始运行..

[2021-12-03 14:07:45.799365] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.801561] INFO: moduleinvoker: join.v3 运行完成[0.015146s].

[2021-12-03 14:07:45.823060] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-12-03 14:07:45.837515] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:07:45.840526] INFO: moduleinvoker: dropnan.v1 运行完成[0.017523s].

[2021-12-03 14:07:45.856739] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2021-12-03 14:07:50.209133] INFO: StockRanker: 特征预处理 ..

[2021-12-03 14:07:54.626087] INFO: StockRanker: prepare data: training ..

[2021-12-03 14:07:59.022523] INFO: StockRanker: sort ..

[2021-12-03 14:08:35.132396] INFO: StockRanker训练: 558de4b0 准备训练: 2606084 行数

[2021-12-03 14:08:35.134364] INFO: StockRanker训练: AI模型训练，将在2606084*13=3387.91万数据上对模型训练进行20轮迭代训练。预计将需要11～21分钟。请耐心等待。

[2021-12-03 14:08:35.394790] INFO: StockRanker训练: 正在训练 ..

[2021-12-03 14:08:36.462522] INFO: StockRanker训练: 任务状态: Pending

[2021-12-03 14:08:56.570313] INFO: StockRanker训练: 任务状态: Running

[2021-12-03 14:09:16.726312] INFO: StockRanker训练: 00:00:18.1776080, finished iteration 1

[2021-12-03 14:09:36.873725] INFO: StockRanker训练: 00:00:28.6620942, finished iteration 2

[2021-12-03 14:09:46.941891] INFO: StockRanker训练: 00:00:39.7683642, finished iteration 3

[2021-12-03 14:09:57.029460] INFO: StockRanker训练: 00:00:50.6296207, finished iteration 4

[2021-12-03 14:10:07.102635] INFO: StockRanker训练: 00:01:02.0326242, finished iteration 5

[2021-12-03 14:10:17.178389] INFO: StockRanker训练: 00:01:13.5309133, finished iteration 6

[2021-12-03 14:10:27.297774] INFO: StockRanker训练: 00:01:27.2528718, finished iteration 7

[2021-12-03 14:10:47.442316] INFO: StockRanker训练: 00:01:44.3621922, finished iteration 8

[2021-12-03 14:11:07.577265] INFO: StockRanker训练: 00:02:00.4774278, finished iteration 9

[2021-12-03 14:11:17.651044] INFO: StockRanker训练: 00:02:16.7869692, finished iteration 10

[2021-12-03 14:11:37.843131] INFO: StockRanker训练: 00:02:32.7241669, finished iteration 11

[2021-12-03 14:11:47.933266] INFO: StockRanker训练: 00:02:49.4880190, finished iteration 12

[2021-12-03 14:12:08.891780] INFO: StockRanker训练: 00:03:05.7835239, finished iteration 13

[2021-12-03 14:12:29.055973] INFO: StockRanker训练: 00:03:22.0778267, finished iteration 14

[2021-12-03 14:12:39.130535] INFO: StockRanker训练: 00:03:38.5425803, finished iteration 15

[2021-12-03 14:12:59.216809] INFO: StockRanker训练: 00:03:56.3441409, finished iteration 16

[2021-12-03 14:13:19.349253] INFO: StockRanker训练: 00:04:14.3708323, finished iteration 17

[2021-12-03 14:13:39.466205] INFO: StockRanker训练: 00:04:32.5065165, finished iteration 18

[2021-12-03 14:13:49.509129] INFO: StockRanker训练: 00:04:50.7338281, finished iteration 19

[2021-12-03 14:14:09.627819] INFO: StockRanker训练: 00:05:08.9646023, finished iteration 20

[2021-12-03 14:14:09.629991] INFO: StockRanker训练: 任务状态: Succeeded

[2021-12-03 14:14:09.847360] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[383.990602s].

[2021-12-03 14:14:09.856420] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-03 14:14:09.872067] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:14:09.876663] INFO: moduleinvoker: instruments.v2 运行完成[0.020238s].

[2021-12-03 14:14:09.912695] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-03 14:14:11.862577] INFO: 基础特征抽取: 年份 2014, 特征行数=99861

[2021-12-03 14:14:15.103653] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2021-12-03 14:14:19.395727] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2021-12-03 14:14:21.215924] INFO: 基础特征抽取: 年份 2017, 特征行数=0

[2021-12-03 14:14:21.325873] INFO: 基础特征抽取: 总行数: 1311105

[2021-12-03 14:14:21.332272] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[11.419584s].

[2021-12-03 14:14:21.343231] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-12-03 14:14:23.952963] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.005s

[2021-12-03 14:14:23.959305] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.005s

[2021-12-03 14:14:23.964548] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s

[2021-12-03 14:14:23.968952] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.003s

[2021-12-03 14:14:23.973776] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s

[2021-12-03 14:14:23.978357] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.003s

[2021-12-03 14:14:24.786220] INFO: derived_feature_extractor: /y_2014, 99861

[2021-12-03 14:14:25.922936] INFO: derived_feature_extractor: /y_2015, 569698

[2021-12-03 14:14:27.612918] INFO: derived_feature_extractor: /y_2016, 641546

[2021-12-03 14:14:28.204026] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[6.860788s].

[2021-12-03 14:14:28.214970] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-12-03 14:14:28.473591] INFO: dropnan: /y_2014, 99326/99861

[2021-12-03 14:14:29.255509] INFO: dropnan: /y_2015, 565146/569698

[2021-12-03 14:14:30.172032] INFO: dropnan: /y_2016, 636912/641546

[2021-12-03 14:14:30.286646] INFO: dropnan: 行数: 1301384/1311105

[2021-12-03 14:14:30.297367] INFO: moduleinvoker: dropnan.v1 运行完成[2.08239s].

[2021-12-03 14:14:30.310046] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-12-03 14:14:30.620639] INFO: StockRanker预测: /y_2014 ..

[2021-12-03 14:14:31.168918] INFO: StockRanker预测: /y_2015 ..

[2021-12-03 14:14:32.548778] INFO: StockRanker预测: /y_2016 ..

[2021-12-03 14:14:35.683317] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[5.373254s].

[2021-12-03 14:14:37.901607] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-12-03 14:14:37.908166] INFO: backtest: biglearning backtest:V8.6.0

[2021-12-03 14:14:37.909799] INFO: backtest: product_type:stock by specified

[2021-12-03 14:14:38.041033] INFO: moduleinvoker: cached.v2 开始运行..

[2021-12-03 14:14:38.057867] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:14:38.059811] INFO: moduleinvoker: cached.v2 运行完成[0.018801s].

[2021-12-03 14:14:40.223470] INFO: algo: TradingAlgorithm V1.8.5

[2021-12-03 14:14:41.345351] INFO: algo: trading transform...

[2021-12-03 14:14:58.063761] INFO: Performance: Simulated 488 trading days out of 488.

[2021-12-03 14:14:58.065467] INFO: Performance: first open: 2015-01-05 09:30:00+00:00

[2021-12-03 14:14:58.066747] INFO: Performance: last close: 2016-12-30 15:00:00+00:00

[2021-12-03 14:15:04.327155] INFO: moduleinvoker: backtest.v8 运行完成[26.425552s].

[2021-12-03 14:15:04.329521] INFO: moduleinvoker: trade.v4 运行完成[28.634378s].

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-00a46a6068334fab9629440150b8ec9c"}/bigcharts-data-end

收益率318.76%
年化收益率109.5%
基准收益率-6.33%
阿尔法1.24
贝塔0.93
夏普比率1.93
胜率0.62
盈亏比0.94
收益波动率41.31%
信息比率0.18
最大回撤47.62%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3fb0700809504943af5b8771139d31a4"}/bigcharts-data-end

In [6]:

m8.predictions.read()

Out[6]:

	date	instrument	score	position
0	2014-11-03	600097.SHA	1.500834	1
1	2014-11-03	300306.SZA	1.397000	2
2	2014-11-03	000736.SZA	1.161524	3
3	2014-11-03	600207.SHA	1.116308	4
4	2014-11-03	002110.SZA	1.017822	5
...	...	...	...	...
1301379	2016-12-30	600520.SHA	-0.394826	2784
1301380	2016-12-30	300567.SZA	-0.422985	2785
1301381	2016-12-30	000538.SZA	-0.424848	2786
1301382	2016-12-30	002346.SZA	-0.425943	2787
1301383	2016-12-30	002822.SZA	-0.436981	2788

1301384 rows × 4 columns

In [15]:

print(m8.start_date)
print(m8.end_date)
print(m8.instruments)

2014-11-03
2016-12-30
['603067.SHA', '300114.SZA', '000025.SZA', '002141.SZA', '600148.SHA', '002039.SZA', '600038.SHA', '002391.SZA', '300072.SZA', '002674.SZA', '000976.SZA', '603366.SHA', '002689.SZA', '002541.SZA', '600428.SHA', '000922.SZA', '600186.SHA', '600622.SHA', '600486.SHA', '603339.SHA', '600207.SHA', '601016.SHA', '600475.SHA', '600495.SHA', '600010.SHA', '002157.SZA', '603843.SHA', '300196.SZA', '601390.SHA', '600487.SHA', '002349.SZA', '600227.SHA', '300438.SZA', '600016.SHA', '300021.SZA', '300490.SZA', '603099.SHA', '601966.SHA', '002594.SZA', '300488.SZA', '002789.SZA', '300257.SZA', '300133.SZA', '300078.SZA', '300191.SZA', '002768.SZA', '002528.SZA', '002437.SZA', '000830.SZA', '002343.SZA', '600015.SHA', '603866.SHA', '600200.SHA', '600483.SHA', '300083.SZA', '300428.SZA', '600006.SHA', '300309.SZA', '601608.SHA', '002315.SZA', '603616.SHA', '002722.SZA', '603090.SHA', '300028.SZA', '000833.SZA', '603858.SHA', '002407.SZA', '600273.SHA', '603313.SHA', '000555.SZA', '601001.SHA', 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'300519.SZA', '600396.SHA', '000026.SZA', '002398.SZA', '002049.SZA', '600728.SHA', '000755.SZA', '600183.SHA', '002109.SZA', '000905.SZA', '600478.SHA', '600380.SHA', '600836.SHA', '002012.SZA', '600602.SHA', '601021.SHA', '000726.SZA', '002321.SZA', '000655.SZA', '300075.SZA', '600415.SHA', '002802.SZA', '603323.SHA', '002549.SZA', '300235.SZA', '002214.SZA', '300368.SZA', '601669.SHA', '002238.SZA', '600612.SHA', '002005.SZA', '601678.SHA', '000078.SZA', '600740.SHA', '600336.SHA', '300066.SZA', '002503.SZA', '600076.SHA', '002461.SZA', '002324.SZA', '600777.SHA', '002156.SZA', '600219.SHA', '600802.SHA', '603160.SHA', '600466.SHA', '600246.SHA', '601006.SHA', '601238.SHA', '002373.SZA', '300059.SZA', '600523.SHA', '603168.SHA', '600642.SHA', '002187.SZA', '000562.SZA', '000707.SZA', '002621.SZA', '300476.SZA', '600192.SHA', '002074.SZA', '600030.SHA', '000505.SZA', '300120.SZA', '000585.SZA', '000061.SZA', '600179.SHA', '002769.SZA', '600967.SHA', '600784.SHA', '002052.SZA', 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'300253.SZA', '600281.SHA', '000918.SZA', '002728.SZA', '300260.SZA', '603887.SHA', '001979.SZA', '600691.SHA', '600209.SHA', '600693.SHA', '300427.SZA', '603598.SHA', '300003.SZA', '600870.SHA', '601618.SHA']