克隆策略

    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    In [20]:
    # 本代码由可视化策略环境自动生成 2021年8月12日 22:40
    # 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。
    
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m6_handle_data_bigquant_run(context, data):
        # curr_data:用于当前handle_bar,各处理函数可以用 curr_data 传递数据
        context.curr_data = {}
        if 'handle_bar_functions' in context.options:
            for func in context.options['handle_bar_functions']:
                if not func(context, data):
                    # 如果有处理函数返回False,则表示跳过后续执行
                    return
    
    # 回测引擎:准备数据,只执行一次
    def m6_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m6_initialize_bigquant_run(context):
        if 'initialize_functions' in context.options:
            for func in context.options['initialize_functions']:
                if not func(context):
                    # 如果有处理函数返回False,则表示跳过后续执行
                    return
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m6_before_trading_start_bigquant_run(context, data):
        pass
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2015-01-01',
        'm1.end_date': '2019-01-01',
        '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': """# #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,
    
        'm4': 'M.chinaa_stock_filter.v1',
        'm4.input_data': T.Graph.OutputPort('m7.data'),
        'm4.index_constituent_cond': ['全部'],
        'm4.board_cond': ['上证主板', '深证主板'],
        'm4.industry_cond': ['全部'],
        'm4.st_cond': ['正常'],
        'm4.delist_cond': ['非退市'],
        'm4.output_left_data': False,
    
        'm13': 'M.dropnan.v1',
        'm13.input_data': T.Graph.OutputPort('m4.data'),
    
        'm21': 'M.replace_inf_dropna.v1',
        'm21.input_1': T.Graph.OutputPort('m13.data'),
    
        'm19': 'M.stock_ranker_train.v6',
        'm19.training_ds': T.Graph.OutputPort('m21.data_1'),
        'm19.features': T.Graph.OutputPort('m3.data'),
        'm19.learning_algorithm': '排序',
        'm19.number_of_leaves': 30,
        'm19.minimum_docs_per_leaf': 1000,
        'm19.number_of_trees': 20,
        'm19.learning_rate': 0.3,
        'm19.max_bins': 1023,
        'm19.feature_fraction': 1,
        'm19.data_row_fraction': 1,
        'm19.ndcg_discount_base': 1,
        'm19.m_lazy_run': False,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2018-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2021-07-1'),
        '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,
    
        'm5': 'M.chinaa_stock_filter.v1',
        'm5.input_data': T.Graph.OutputPort('m18.data'),
        'm5.index_constituent_cond': ['全部'],
        'm5.board_cond': ['上证主板', '深证主板'],
        'm5.industry_cond': ['全部'],
        'm5.st_cond': ['正常'],
        'm5.delist_cond': ['非退市'],
        'm5.output_left_data': False,
    
        'm14': 'M.dropnan.v1',
        'm14.input_data': T.Graph.OutputPort('m5.data'),
    
        'm22': 'M.replace_inf_dropna.v1',
        'm22.input_1': T.Graph.OutputPort('m14.data'),
    
        'm23': 'M.stock_ranker_predict.v5',
        'm23.model': T.Graph.OutputPort('m19.model'),
        'm23.data': T.Graph.OutputPort('m22.data_1'),
        'm23.m_lazy_run': False,
    
        'm10': 'M.input_features.v1',
        'm10.features': """
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    name""",
    
        'm11': 'M.use_datasource.v1',
        'm11.instruments': T.Graph.OutputPort('m9.data'),
        'm11.features': T.Graph.OutputPort('m10.data'),
        'm11.datasource_id': 'instruments_CN_STOCK_A',
        'm11.start_date': '',
        'm11.end_date': '',
    
        'm12': 'M.join.v3',
        'm12.data1': T.Graph.OutputPort('m23.predictions'),
        'm12.data2': T.Graph.OutputPort('m11.data'),
        'm12.on': 'date,instrument',
        'm12.how': 'inner',
        'm12.sort': False,
    
        'm20': 'M.sort.v4',
        'm20.input_ds': T.Graph.OutputPort('m12.data'),
        'm20.sort_by': 'position',
        'm20.group_by': 'date',
        'm20.keep_columns': '--',
        'm20.ascending': True,
    
        'm24': 'M.trade_func_set_commission.v1',
        'm24.mode': '订单/PerOrder',
        'm24.buy_cost': 0.0003,
        'm24.sell_cost': 0.0013,
        'm24.min_cost': 3,
    
        'm25': 'M.trade_func_rebalance_manage.v1',
        'm25.mode': '滚动',
        'm25.period': 5,
    
        'm26': 'M.trade_func_buy_by_rank.v1',
        'm26.input_functions': T.Graph.OutputPort('m25.functions'),
        'm26.mode': '对数权重',
        'm26.bias': 2,
        'm26.stock_count': 10,
        'm26.unit': 0,
        'm26.max_position_per_instrument': 1,
    
        'm27': 'M.trade_func_sell_by_rank.v1',
        'm27.input_functions': T.Graph.OutputPort('m26.functions'),
    
        'm6': 'M.tradex.v1',
        'm6.instruments': T.Graph.OutputPort('m9.data'),
        'm6.initialize_functions': T.Graph.OutputPort('m24.functions'),
        'm6.handle_bar_functions': T.Graph.OutputPort('m27.functions'),
        'm6.options_data': T.Graph.OutputPort('m20.sorted_data'),
        'm6.start_date': '',
        'm6.end_date': '',
        'm6.handle_data': m6_handle_data_bigquant_run,
        'm6.prepare': m6_prepare_bigquant_run,
        'm6.initialize': m6_initialize_bigquant_run,
        'm6.before_trading_start': m6_before_trading_start_bigquant_run,
        'm6.volume_limit': 0.025,
        'm6.order_price_field_buy': 'open',
        'm6.order_price_field_sell': 'close',
        'm6.capital_base': 500000,
        'm6.auto_cancel_non_tradable_orders': True,
        'm6.data_frequency': 'daily',
        'm6.price_type': '真实价格',
        'm6.product_type': '股票',
        'm6.plot_charts': True,
        'm6.backtest_only': False,
    })
    
    # g.run({})
    
    
    def m8_param_grid_builder_bigquant_run():
        param_grid = {}
    
        # 在这里设置需要调优的参数备选
        # param_grid['m3.features'] = ['close_1/close_0', 'close_2/close_0\nclose_3/close_0']
        # param_grid['m19.number_of_trees'] = [5, 10, 20]
    
        return param_grid
    
    def m8_scoring_bigquant_run(result):
        score = result.get('m6').read_raw_perf()['sharpe'].tail(1)[0]
    
        return {'score': score}
    
    
    m8 = M.hyper_parameter_search.v1(
        param_grid_builder=m8_param_grid_builder_bigquant_run,
        scoring=m8_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=1,
        worker_distributed_run=True,
        worker_silent=True,
        run_now=True,
        bq_graph=g
    )
    
    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-d93e46ff548e49d4addfb16f57f82be5"}/bigcharts-data-end
    • 收益率101.92%
    • 年化收益率23.19%
    • 基准收益率29.74%
    • 阿尔法0.18
    • 贝塔0.7
    • 夏普比率0.81
    • 胜率0.52
    • 盈亏比1.16
    • 收益波动率26.29%
    • 信息比率0.04
    • 最大回撤28.52%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-8ea84dc9ecfe41839981d2bacd28fec2"}/bigcharts-data-end