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{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-231:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-238:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-250:options_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-250:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-1237:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"-1597:input_data","from_node_id":"-86:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-86:input_data","from_node_id":"-238:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"-1237:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-1597:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# 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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# print(today,\"cash_avg=\",cash_avg,\"context.portfolio.cash=\",context.portfolio.cash,\"cash_for_buy=\",cash_for_buy,\"cash_for_sell=\",cash_for_sell)\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. 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n 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silent=True,backend='threading')\n \n return results\n","type":"Literal","bound_global_parameter":null},{"name":"run_now","value":"True","type":"Literal","bound_global_parameter":null},{"name":"bq_graph","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"bq_graph_port","node_id":"-1389"},{"name":"input_1","node_id":"-1389"},{"name":"input_2","node_id":"-1389"},{"name":"input_3","node_id":"-1389"}],"output_ports":[{"name":"result","node_id":"-1389"}],"cacheable":false,"seq_num":11,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='327,104,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='231,206,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='721,22,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='688,505,200,200'/><node_position 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2022年12月29日 17:43
    # 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 = 1
        # 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 = [1 / stock_count] * stock_count  #T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])#[1 / stock_count] * stock_count 
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.7
        context.options['hold_days'] = 2
    
    # 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')]
        today = 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()}
    #     print(today,"cash_avg=",cash_avg,"context.portfolio.cash=",context.portfolio.cash,"cash_for_buy=",cash_for_buy,"cash_for_sell=",cash_for_sell)
        # 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': '2019-01-01',
        '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': """std(close_0, 5)
    std(low_0, 5)
    std(turn_0, 5)
    std(return_0, 5)
    return_5
    avg_turn_5
    avg_amount_0/avg_amount_5
    rank_avg_amount_0/rank_avg_amount_5
    rank_return_0
    rank_return_5
    rank_return_0/rank_return_5
    pe_ttm_0
    rank_fs_roe_0
    mf_net_amount_5""",
    
        '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'),
    
        'm4': 'M.chinaa_stock_filter.v1',
        'm4.input_data': T.Graph.OutputPort('m13.data'),
        'm4.index_constituent_cond': ['全部'],
        'm4.board_cond': ['上证主板', '深证主板', '创业板'],
        'm4.industry_cond': ['全部'],
        'm4.st_cond': ['全部'],
        'm4.delist_cond': ['非退市'],
        'm4.output_left_data': False,
    
        'm6': 'M.stock_ranker_train.v6',
        'm6.training_ds': T.Graph.OutputPort('m4.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', '2021-06-01'),
        'm9.end_date': T.live_run_param('trading_date', '2022-06-31'),
        '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'),
    
        'm5': 'M.chinaa_stock_filter.v1',
        'm5.input_data': T.Graph.OutputPort('m14.data'),
        'm5.index_constituent_cond': ['全部'],
        'm5.board_cond': ['上证主板', '深证主板', '创业板'],
        'm5.industry_cond': ['全部'],
        'm5.st_cond': ['全部'],
        'm5.delist_cond': ['非退市'],
        'm5.output_left_data': False,
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m6.model'),
        'm8.data': T.Graph.OutputPort('m5.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.HIX',
    })
    
    # g.run({})
    
    
    def m11_run_bigquant_run(bq_graph, inputs):
        g = bq_graph
        #原始因子
        features = [
            'std(close_0, 5)',
            'std(low_0, 5)',
            'std(turn_0, 5)',
            'std(return_0, 5)',
            'return_5',
            'avg_turn_5',
            'avg_amount_0/avg_amount_5',
            'rank_avg_amount_0/rank_avg_amount_5',
            'rank_return_0',
            'rank_return_5',
            'rank_return_0/rank_return_5',
            'pe_ttm_0',
            'rank_fs_roe_0',
            'mf_net_amount_5'
     ]
        
    
        parameters_list = []
        
        i = 0
        #循环减因子
        for feature in features: 
            print(i," ------------ ",feature)
            parameters = {}
            temp = []
            temp = list(set(features) - set([feature]))
            parameters['m3.features'] = '\n'.join(temp)
            parameters_list.append({'parameters': parameters})
            i+=1
    
      
        print( parameters_list)
        print('==='*20, len(parameters_list))
        
        def run(parameters):
            try:
                print(parameters)
                result = g.run(parameters)
                return result
            except Exception as e:
                print('ERROR --------', e)
                return None
    
        results = T.parallel_map(run, parameters_list, max_workers=4,remote_run=True, silent=True,backend='threading')
           
        return results
    
    
    m11 = M.hyper_run.v1(
        run=m11_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    0  ------------  std(close_0, 5)
    1  ------------  std(low_0, 5)
    2  ------------  std(turn_0, 5)
    3  ------------  std(return_0, 5)
    4  ------------  return_5
    5  ------------  avg_turn_5
    6  ------------  avg_amount_0/avg_amount_5
    7  ------------  rank_avg_amount_0/rank_avg_amount_5
    8  ------------  rank_return_0
    9  ------------  rank_return_5
    10  ------------  rank_return_0/rank_return_5
    11  ------------  pe_ttm_0
    12  ------------  rank_fs_roe_0
    13  ------------  mf_net_amount_5
    [{'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'rank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\nstd(close_0, 5)\nrank_fs_roe_0\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nmf_net_amount_5\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}, {'parameters': {'m3.features': 'avg_amount_0/avg_amount_5\nrank_return_5\nrank_avg_amount_0/rank_avg_amount_5\nstd(return_0, 5)\nrank_return_0\nrank_return_0/rank_return_5\npe_ttm_0\nstd(close_0, 5)\nrank_fs_roe_0\nstd(turn_0, 5)\nreturn_5\navg_turn_5\nstd(low_0, 5)'}}]
    ============================================================ 14
    
    In [ ]:
    #回测结果统计
    import pandas as pd
    pd.set_option("display.max_columns", None)
    pd.set_option("display.max_rows", 500)
    pd.set_option('display.width', 5000)
    pd.set_option('max_colwidth',1000) #某列最大宽度
    pd.set_option('expand_frame_repr', False)
    #pd.set_option('display.float_format',lambda x: '%.1f'%x)
    pd.set_option('display.unicode.ambiguous_as_wide',True) 
    pd.set_option('display.unicode.east_asian_width',True) 
    
    
    
    import empyrical
    # 统计策略指标
    def get_stats(results):
        return_ratio  = empyrical.cum_returns_final(results.returns)
        annual_return_ratio  = empyrical.annual_return(results.returns)
        sharp_ratio = results.iloc[-1].sharpe
        return_volatility = empyrical.annual_volatility(results.returns)
        max_drawdown  = empyrical.max_drawdown(results.returns)
        benchmark_returns = (results.benchmark_period_return+1)/(results.benchmark_period_return+1).shift(1)-1
        alpha, beta =empyrical.alpha_beta_aligned(results.returns, benchmark_returns)
    
        return {
          'return_ratio': round(return_ratio,2),
          'annual_return_ratio': round(annual_return_ratio,2),
          'beta': beta,
          'alpha': alpha,
          'sharp_ratio': sharp_ratio,
          'return_volatility': round(return_volatility,2),
          'max_drawdown': round(max_drawdown,2),
        }
    result_df = pd.DataFrame()
    for i in range(len(m11.result)):
        try:
            perf = m11.result[i]['m19'].raw_perf.read()
        except :
            print("no data",i)
            continue
        result = get_stats(perf) # 这里m是Trade模块的模块号,自行替换即可
        para = i
        result_df = result_df.append({"参数":para,'收益率':result['return_ratio'],'年化收益率':result['annual_return_ratio'],'最大回撤':abs(result['max_drawdown']),'夏普率':round(result['sharp_ratio'],2)},ignore_index=True)
        
    result_df
    result_df['收益回撤比'] = (result_df['年化收益率'] / result_df['最大回撤']).round(2)
    result_df = result_df.sort_values('夏普率',ascending=False)
    result_df
    
    In [ ]: