克隆策略

    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    In [13]:
    # 本代码由可视化策略环境自动生成 2021年8月18日 20:40
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m10_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read() 
    df['score'] = df['score'] * -1 
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m10_post_run_bigquant_run(outputs):
    return outputs


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/develop/datasource/deprecated/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/develop/bigexpr/usage.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=90
)

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', '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=90
)

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
)

m10 = M.cached.v3(
    input_1=m8.predictions,
    run=m10_run_bigquant_run,
    post_run=m10_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m5 = M.input_features.v1(
    features='score'
)

m4 = M.factorlens.v1(
    features=m5.data,
    user_factor_data=m10.data_1,
    title='因子分析: {factor_name}',
    start_date='2015-01-01',
    end_date='2017-01-01',
    rebalance_period=5,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0.0016,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=60,
    drop_price_limit_stocks=False,
    drop_st_stocks=False,
    drop_suspended_stocks=False,
    normalization=False,
    neutralization=[],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='left'
)
    设置测试数据集，查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-7ec92696d783457989f76809a6d8c7cc"}/bigcharts-data-end

    因子分析: score

    { "type": "factor-track", "data": { "exprs": ["score"], "options": {"BacktestInterval": ["2015-01-01", "2017-01-01"], "Benchmark": "none", "StockPool": "all", "UserDataMerge": "left", "DropSTStocks": 0, "DropPriceLimitStocks": 0, "DropNewStocks": 60, "DropSuspendedStocks": 0, "QuantileCount": 5, "CommissionRates": 0.0016, "Normalization": 0, "Neutralization": "", "DelayRebalanceDays": 0, "RebalancePeriod": 5, "RebalancePeriodsReturns": 0, "RebalancePrice": "close_0", "ReturnsCalculationMethod": "cumprod", "FactorCoverage": 0.5, "_HASH": "3270205a6c3dafb775002de79fdd30a9"} } }

    因子表现概览

      累计收益 近1年收益 近3月收益 近1月收益 近1周收益 昨日收益 最大回撤 盈亏比 胜率 夏普比率 收益波动率
    最小分位 110.69% 8.90% 4.02% -4.65% -1.57% -0.55% 56.56% 0.77 0.61 0.95 50.04%
    最大分位 -19.38% -40.43% -7.62% -7.82% -1.55% -0.38% 67.10% 0.86 0.54 -0.09 45.90%
    多空组合 63.76% 35.35% 6.05% 1.67% -0.01% -0.08% 4.65% 0.92 0.67 2.72 8.23%

    基本特征分析

    IC分析

    -0.09

    0.12

    -0.76

    91.75%

    买入信号重合分析

    因子估值分析

    因子拥挤度分析

    因子值最小的20只股票 (2016-12-29)

    股票名称 股票代码 因子值
    微光股份 002801.SZA -2.0411
    今天国际 300532.SZA -1.9389
    世名科技 300522.SZA -1.9127
    科大国创 300520.SZA -1.8045
    西藏城投 600773.SHA -1.7015
    海虹控股 000503.SZA -1.6322
    电光科技 002730.SZA -1.5762
    和科达 002816.SZA -1.4915
    巨龙管业 002619.SZA -1.3887
    熊猫金控 600599.SHA -1.3643
    昊志机电 300503.SZA -1.3521
    川金诺 300505.SZA -1.3521
    华源包装 002787.SZA -1.3351
    华媒控股 000607.SZA -1.3291
    鸿利智汇 300219.SZA -1.2995
    新晨科技 300542.SZA -1.2842
    天润曲轴 002283.SZA -1.2822
    暴风集团 300431.SZA -1.2719
    南山控股 002314.SZA -1.1047
    ST慧球 600556.SHA -1.0780

    因子值最大的20只股票 (2016-12-29)

    股票名称 股票代码 因子值
    蓝色光标 300058.SZA 0.2567
    建设机械 600984.SHA 0.2607
    广田集团 002482.SZA 0.2670
    小商品城 600415.SHA 0.2738
    中信重工 601608.SHA 0.2839
    元力股份 300174.SZA 0.2852
    四创电子 600990.SHA 0.2935
    冀凯股份 002691.SZA 0.2947
    巨力索具 002342.SZA 0.2971
    国投中鲁 600962.SHA 0.3050
    中钢天源 002057.SZA 0.3050
    中润资源 000506.SZA 0.3510
    英力特 000635.SZA 0.3681
    三钢闽光 002110.SZA 0.3681
    云南旅游 002059.SZA 0.3784
    *ST中发 600520.SHA 0.3948
    上峰水泥 000672.SZA 0.4009
    *ST舜船 002608.SZA 0.4077
    *ST黑豹 600760.SHA 0.4231
    柘中股份 002346.SZA 0.4259