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克隆策略

超参搜索：如何搜索Trade模块中的参数

背景

在策略研究阶段，想要批量测试交易的设置，比如手续费、滑点等参数，可以通过超参搜索模块，批量测试。

思路

通过"自定义python模块"可以将Trade回测模块中的参数暴露出来，并用超参搜索模块进行搜索。

  1. m4模块暴露的需要测试的参数
  2. m5模块将stockranker的预测结果和m4的参数合并，传入Trade模块中
  3. Trade模块获取到设定的参数，根据参数进行回测

实例

搜索了buy_cost\sell_cost两个参数。

    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    In [7]:
    # 本代码由可视化策略环境自动生成 2022年5月20日 11:44
# 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。


# 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 m4_run_bigquant_run(input_1, input_2, input_3, buy_cost, sell_cost):
    # 示例代码如下。在这里编写您的代码
    param = {
        "buy_cost": buy_cost,
        "sell_cost": sell_cost
    }
    data_1 = DataSource.write_pickle(param)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 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 m4_post_run_bigquant_run(outputs):
    return outputs

# 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 m5_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()
    param = input_2.read()
    
    data = {
        "param": param,
        "data": df
    }
    data_1 = DataSource.write_pickle(data)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 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 m5_post_run_bigquant_run(outputs):
    return outputs

# 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()['data']
    context.param = context.options['data'].read()["param"]

    # 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=context.param["buy_cost"], sell_cost=context.param["sell_cost"], 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': '2018-01-01',
    'm1.end_date': '2020-12-31',
    '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.HIX',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.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',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m3.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 90,

    '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,

    'm13': 'M.dropnan.v1',
    'm13.input_data': T.Graph.OutputPort('m7.data'),

    'm6': 'M.stock_ranker_train.v6',
    'm6.training_ds': T.Graph.OutputPort('m13.data'),
    'm6.features': T.Graph.OutputPort('m3.data'),
    'm6.learning_algorithm': '排序',
    'm6.number_of_leaves': 30,
    'm6.minimum_docs_per_leaf': 1000,
    'm6.number_of_trees': 20,
    'm6.learning_rate': 0.1,
    'm6.max_bins': 1023,
    'm6.feature_fraction': 1,
    'm6.data_row_fraction': 1,
    'm6.plot_charts': True,
    'm6.ndcg_discount_base': 1,
    'm6.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2021-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2021-12-31'),
    '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': 90,

    '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,

    'm14': 'M.dropnan.v1',
    'm14.input_data': T.Graph.OutputPort('m18.data'),

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m6.model'),
    'm8.data': T.Graph.OutputPort('m14.data'),
    'm8.m_lazy_run': False,

    'm4': 'M.cached.v3',
    'm4.run': m4_run_bigquant_run,
    'm4.post_run': m4_post_run_bigquant_run,
    'm4.input_ports': '',
    'm4.params': """{
    "buy_cost": 0.0003,
    "sell_cost": 0.0013 
}""",
    'm4.output_ports': '',

    'm5': 'M.cached.v3',
    'm5.input_1': T.Graph.OutputPort('m8.predictions'),
    'm5.input_2': T.Graph.OutputPort('m4.data_1'),
    'm5.run': m5_run_bigquant_run,
    'm5.post_run': m5_post_run_bigquant_run,
    'm5.input_ports': '',
    'm5.params': '{}',
    'm5.output_ports': '',

    'm19': 'M.trade.v4',
    'm19.instruments': T.Graph.OutputPort('m9.data'),
    'm19.options_data': T.Graph.OutputPort('m5.data_1'),
    '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.HIX',
})

# g.run({})


def m10_param_grid_builder_bigquant_run():
    param_grid = {}

    # 在这里设置需要调优的参数备选
    param_grid["m4.params"] = [
        """{"buy_cost": 0.0003, "sell_cost": 0.0013}""",
        """{"buy_cost": 0.001, "sell_cost": 0.001}""",
        """{"buy_cost": 0.002, "sell_cost": 0.002}""",
        """{"buy_cost": 0.003, "sell_cost": 0.003}"""
    ]
    return param_grid 

def m10_scoring_bigquant_run(result):
    # 评分：收益/最大回撤
    score = result.get('m7').read_raw_perf()['sharpe'].tail(1)[0]
    return {'score': score}


m10 = M.hyper_parameter_search.v1(
    param_grid_builder=m10_param_grid_builder_bigquant_run,
    scoring=m10_scoring_bigquant_run,
    search_algorithm='网格搜索',
    search_iterations=10,
    workers=1,
    worker_distributed_run=False,
    worker_silent=False,
    run_now=True,
    bq_graph=g
)

    Fitting 1 folds for each of 4 candidates, totalling 4 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV 1/1; 1/4] START m4.params={"buy_cost": 0.0003, "sell_cost": 0.0013}.........
    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-eba9e11df5ac453db7df4098863b8c65"}/bigcharts-data-end
    • 收益率58.34%
    • 年化收益率61.06%
    • 基准收益率-5.2%
    • 阿尔法0.66
    • 贝塔0.39
    • 夏普比率1.87
    • 胜率0.51
    • 盈亏比1.42
    • 收益波动率25.72%
    • 信息比率0.13
    • 最大回撤15.12%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6c9b5b1c163a4700a96d627233b44876"}/bigcharts-data-end
    [CV 1/1; 1/4] END m4.params={"buy_cost": 0.0003, "sell_cost": 0.0013}; total time=   8.4s
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    8.4s remaining:    0.0s
[CV 1/1; 2/4] START m4.params={"buy_cost": 0.001, "sell_cost": 0.001}...........
    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-4de775a6eda44b81a03e6ece8c2f13f1"}/bigcharts-data-end
    • 收益率58.67%
    • 年化收益率61.4%
    • 基准收益率-5.2%
    • 阿尔法0.67
    • 贝塔0.42
    • 夏普比率1.77
    • 胜率0.52
    • 盈亏比1.41
    • 收益波动率27.44%
    • 信息比率0.12
    • 最大回撤15.23%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-456400c0f48f42a78b12b1fbc67ccec5"}/bigcharts-data-end
    [CV 1/1; 2/4] END m4.params={"buy_cost": 0.001, "sell_cost": 0.001}; total time=   5.7s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   14.2s remaining:    0.0s
[CV 1/1; 3/4] START m4.params={"buy_cost": 0.002, "sell_cost": 0.002}...........
    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-dd9d12ff66e0474bbfa6079eba2c2e0a"}/bigcharts-data-end
    • 收益率45.64%
    • 年化收益率47.68%
    • 基准收益率-5.2%
    • 阿尔法0.53
    • 贝塔0.44
    • 夏普比率1.45
    • 胜率0.53
    • 盈亏比1.38
    • 收益波动率27.49%
    • 信息比率0.1
    • 最大回撤15.77%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9941db7e01b947f893568541ae694686"}/bigcharts-data-end
    [CV 1/1; 3/4] END m4.params={"buy_cost": 0.002, "sell_cost": 0.002}; total time=   8.3s
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   22.4s remaining:    0.0s
[CV 1/1; 4/4] START m4.params={"buy_cost": 0.003, "sell_cost": 0.003}...........
    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-2f28f1b7f06f42d48fbefa8db9d06b04"}/bigcharts-data-end
    • 收益率28.8%
    • 年化收益率30.02%
    • 基准收益率-5.2%
    • 阿尔法0.35
    • 贝塔0.42
    • 夏普比率0.98
    • 胜率0.53
    • 盈亏比1.4
    • 收益波动率27.51%
    • 信息比率0.07
    • 最大回撤16.49%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6b7eda8651db4449a707bbbace29ebb2"}/bigcharts-data-end
    [CV 1/1; 4/4] END m4.params={"buy_cost": 0.003, "sell_cost": 0.003}; total time=   6.6s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   29.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   29.1s 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-c02b93430b444c50800cee9e265a431b"}/bigcharts-data-end
    • 收益率58.34%
    • 年化收益率61.06%
    • 基准收益率-5.2%
    • 阿尔法0.66
    • 贝塔0.39
    • 夏普比率1.87
    • 胜率0.51
    • 盈亏比1.42
    • 收益波动率25.72%
    • 信息比率0.13
    • 最大回撤15.12%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-12a29de57cc949b291f35e7c00db8b66"}/bigcharts-data-end