克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-215:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-215:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-222:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-231:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-238:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-118:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-132:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-250:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-231:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-250:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-222:input_data","SourceOutputPortId":"-215:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-222:data"},{"DestinationInputPortId":"-238:input_data","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-136:input_data","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"-118:model"},{"DestinationInputPortId":"-118:training_ds","SourceOutputPortId":"-132:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-136:data"},{"DestinationInputPortId":"-1963:input_1","SourceOutputPortId":"-128:result"},{"DestinationInputPortId":"-128:bq_graph_port","SourceOutputPortId":"-137:result"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2011-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #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/develop/datasource/deprecated/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/develop/bigexpr/usage.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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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 = 5\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'] = 5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\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\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.portfolio.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.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\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. 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    In [15]:
    # 本代码由可视化策略环境自动生成 2020年8月27日 16:43
# 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 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'] = 5

# 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.portfolio.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.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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


g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2010-01-01',
    'm1.end_date': '2011-01-01',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.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)
""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.features': """pb_lf_0
""",

    'm15': 'M.general_feature_extractor.v7',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m3.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 20,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m3.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m2.data'),
    'm7.data2': T.Graph.OutputPort('m16.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm5': 'M.dropnan.v2',
    'm5.input_data': T.Graph.OutputPort('m7.data'),

    'm4': 'M.stock_ranker_train.v6',
    'm4.training_ds': T.Graph.OutputPort('m5.data'),
    'm4.features': T.Graph.OutputPort('m3.data'),
    'm4.learning_algorithm': '排序',
    'm4.number_of_leaves': 30,
    'm4.minimum_docs_per_leaf': 1000,
    'm4.number_of_trees': 10,
    'm4.learning_rate': 0.1,
    'm4.max_bins': 1023,
    'm4.feature_fraction': 1,
    'm4.data_row_fraction': 1,
    'm4.ndcg_discount_base': 1,
    'm4.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': '2012-01-01',
    'm9.end_date': '2012-10-01',
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm17': 'M.general_feature_extractor.v7',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.features': T.Graph.OutputPort('m3.data'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.before_start_days': 20,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m17.data'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm10': 'M.dropnan.v2',
    'm10.input_data': T.Graph.OutputPort('m18.data'),

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m4.model'),
    'm8.data': T.Graph.OutputPort('m10.data'),
    'm8.m_lazy_run': False,

    'm19': 'M.trade.v4',
    'm19.instruments': T.Graph.OutputPort('m9.data'),
    'm19.options_data': T.Graph.OutputPort('m8.predictions'),
    'm19.start_date': '',
    'm19.end_date': '',
    'm19.initialize': m19_initialize_bigquant_run,
    'm19.handle_data': m19_handle_data_bigquant_run,
    'm19.prepare': m19_prepare_bigquant_run,
    'm19.volume_limit': 0.025,
    'm19.order_price_field_buy': 'open',
    'm19.order_price_field_sell': 'close',
    'm19.capital_base': 1000000,
    'm19.auto_cancel_non_tradable_orders': True,
    'm19.data_frequency': 'daily',
    'm19.price_type': '真实价格',
    'm19.product_type': '股票',
    'm19.plot_charts': True,
    'm19.backtest_only': False,
    'm19.benchmark': '000300.SHA',
})

# g.run({})


def m6_param_grid_builder_bigquant_run():
    param_grid = {}
    param_grid['m4.number_of_trees'] = [4, 8]
    return param_grid

def m6_scoring_bigquant_run(result):
    score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]

    return score

def m11_run_bigquant_run(bq_graph, inputs):
    
    g = bq_graph
    parameters_list = []
    features =['pe_ttm_0', 'shift(close_0,5)/close_0','pb_lf_0']
    
    for feature in features:
        parameters = {'m3.features':feature}
        parameters_list.append({'parameters': parameters})

    results = T.parallel_map(g.run, parameters_list, max_workers=1, backend='multiprocessing',remote_run=False, silent=False)
    return results

# 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 m12_run_bigquant_run(input_1, input_2, input_3):
    a1=input_1[0].cv_results_['mean_train_score']
    a2=input_1[1].cv_results_['mean_train_score']
    a3=input_1[2].cv_results_['mean_train_score']
    data=numpy.array([a1,a2,a3])
    index=['pe_ttm_0','shift(close_0,5)/close_0','pb_lf_0']
    columns=['number_of_trees=4','number_of_trees=8']
    df=pd.DataFrame(data=data,columns=columns,index=index)
    df.to_csv('result.csv')
    return Outputs(data_1=input_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 m12_post_run_bigquant_run(outputs):
    return outputs


m6 = M.hyper_parameter_search.v1(
    param_grid_builder=m6_param_grid_builder_bigquant_run,
    scoring=m6_scoring_bigquant_run,
    search_algorithm='网格搜索',
    search_iterations=10,
    workers=5,
    worker_distributed_run=False,
    worker_silent=True,
    run_now=False,
    bq_graph=g
)

m11 = M.hyper_run.v1(
    bq_graph_port=m6.result,
    run=m11_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

m12 = M.cached.v3(
    input_1=m11.result,
    run=m12_run_bigquant_run,
    post_run=m12_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports='',
    m_cached=False
)

    [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV] m4.number_of_trees=4 ............................................
[CV] m4.number_of_trees=8 ............................................

    Fitting 1 folds for each of 2 candidates, totalling 2 fits
[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.
    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-0ef03d727c8944f7a70e1ca97877a5b3"}/bigcharts-data-end
    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-d4266a80193e47ed8a5aff6e10e268a9"}/bigcharts-data-end
    • 收益率-4.01%
    • 年化收益率-5.51%
    • 基准收益率-2.24%
    • 阿尔法-0.02
    • 贝塔0.81
    • 夏普比率-0.24
    • 胜率0.5
    • 盈亏比1.0
    • 收益波动率24.04%
    • 信息比率-0.01
    • 最大回撤22.01%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-1bf98e020a714e3e8d36a2e7fb8d105a"}/bigcharts-data-end
    • 收益率6.29%
    • 年化收益率8.81%
    • 基准收益率-2.24%
    • 阿尔法0.11
    • 贝塔0.78
    • 夏普比率0.35
    • 胜率0.53
    • 盈亏比1.02
    • 收益波动率22.66%
    • 信息比率0.05
    • 最大回撤24.9%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-00b7cd5d0bfc46f9a5521b510038bab3"}/bigcharts-data-end
    [CV] . m4.number_of_trees=4, score=-0.23896662832222307, total=   1.8s

    [Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    1.9s
[CV] ... m4.number_of_trees=8, score=0.3549665229903602, total=   2.5s
[Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    2.6s remaining:    0.0s
[Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    2.6s finished
    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-c96b76de1c064fccaa227e162907b2c8"}/bigcharts-data-end
    • 收益率6.29%
    • 年化收益率8.81%
    • 基准收益率-2.24%
    • 阿尔法0.11
    • 贝塔0.78
    • 夏普比率0.35
    • 胜率0.53
    • 盈亏比1.02
    • 收益波动率22.66%
    • 信息比率0.05
    • 最大回撤24.9%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4e711865e4084f579996b146c03b7351"}/bigcharts-data-end
    [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.8s remaining:    0.0s
[CV] m4.number_of_trees=4 ............................................
[CV] m4.number_of_trees=8 ............................................

    Fitting 1 folds for each of 2 candidates, totalling 2 fits
[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.
    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-141b48e6840d4598916b1f741f6d9c32"}/bigcharts-data-end
    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-aeae890bb71347b4a696373be4717d06"}/bigcharts-data-end
    • 收益率31.15%
    • 年化收益率45.57%
    • 基准收益率-2.24%
    • 阿尔法0.42
    • 贝塔1.02
    • 夏普比率1.49
    • 胜率0.54
    • 盈亏比1.19
    • 收益波动率25.38%
    • 信息比率0.18
    • 最大回撤13.14%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e2e66ae20a084d2fb147854b9765e97f"}/bigcharts-data-end
    • 收益率3.8%
    • 年化收益率5.29%
    • 基准收益率-2.24%
    • 阿尔法0.11
    • 贝塔1.04
    • 夏普比率0.22
    • 胜率0.54
    • 盈亏比0.95
    • 收益波动率28.9%
    • 信息比率0.03
    • 最大回撤23.44%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-8a456b6c85dc40c6aa468df24237f8a8"}/bigcharts-data-end
    [CV] .. m4.number_of_trees=8, score=0.22081275874265358, total=   3.0s
[CV] ... m4.number_of_trees=4, score=1.4906014574585014, total=   3.2s
[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    3.8s
[Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.9s remaining:    0.0s
[Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.9s finished
    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-c3e5b5d634dc424d91512845b500a509"}/bigcharts-data-end
    • 收益率31.15%
    • 年化收益率45.57%
    • 基准收益率-2.24%
    • 阿尔法0.42
    • 贝塔1.02
    • 夏普比率1.49
    • 胜率0.54
    • 盈亏比1.19
    • 收益波动率25.38%
    • 信息比率0.18
    • 最大回撤13.14%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-52a59114fdf94db3971f8f5eab6e5168"}/bigcharts-data-end
    [Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   11.7s remaining:    0.0s
[CV] m4.number_of_trees=4 ............................................
[CV] m4.number_of_trees=8 ............................................

    Fitting 1 folds for each of 2 candidates, totalling 2 fits
[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.
    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-71771358151549d3b5e58027737c8a01"}/bigcharts-data-end
    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-59d27d867d2e4027a567d59a9895bb53"}/bigcharts-data-end
    • 收益率-8.31%
    • 年化收益率-11.33%
    • 基准收益率-2.24%
    • 阿尔法-0.09
    • 贝塔0.84
    • 夏普比率-0.49
    • 胜率0.49
    • 盈亏比0.99
    • 收益波动率24.35%
    • 信息比率-0.03
    • 最大回撤27.29%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-cab75440b9824027a4fceb0c3e4aa473"}/bigcharts-data-end
    [CV] . m4.number_of_trees=8, score=-0.49330727483953474, total=   1.7s
[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    1.8s
    • 收益率11.79%
    • 年化收益率16.68%
    • 基准收益率-2.24%
    • 阿尔法0.18
    • 贝塔0.77
    • 夏普比率0.66
    • 胜率0.52
    • 盈亏比1.15
    • 收益波动率22.95%
    • 信息比率0.07
    • 最大回撤20.47%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5d6faa4e7b544a02b682a3404c0f868c"}/bigcharts-data-end
    [CV] .... m4.number_of_trees=4, score=0.657894609342273, total=   3.0s
[Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.1s remaining:    0.0s
[Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.1s finished
    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-867bdf33f40a4f6a9e1e803b54e73e3d"}/bigcharts-data-end
    • 收益率11.79%
    • 年化收益率16.68%
    • 基准收益率-2.24%
    • 阿尔法0.18
    • 贝塔0.77
    • 夏普比率0.66
    • 胜率0.52
    • 盈亏比1.15
    • 收益波动率22.95%
    • 信息比率0.07
    • 最大回撤20.47%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-12ec39b9b83549f88b57cc9f650b1040"}/bigcharts-data-end
    [Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   17.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   17.0s finished