复制链接
克隆策略

    {"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":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60: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"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2017-12-31","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":"# #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, label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"close_0\nopen_0\nhigh_0\nlow_0 \namount_0\nturn_0 \nreturn_0\n \nclose_1\nopen_1\nhigh_1\nlow_1\nreturn_1\namount_1\nturn_1\n \nclose_2\nopen_2\nhigh_2\nlow_2\namount_2\nturn_2\nreturn_2\n \nclose_3\nopen_3\nhigh_3\nlow_3\namount_3\nturn_3\nreturn_3\n \nclose_4\nopen_4\nhigh_4\nlow_4\namount_4\nturn_4\nreturn_4\n \nmean(close_0, 5)\nmean(low_0, 5)\nmean(open_0, 5)\nmean(high_0, 5)\nmean(turn_0, 5)\nmean(amount_0, 5)\nmean(return_0, 5)\n \nts_max(close_0, 5)\nts_max(low_0, 5)\nts_max(open_0, 5)\nts_max(high_0, 5)\nts_max(turn_0, 5)\nts_max(amount_0, 5)\nts_max(return_0, 5)\n \nts_min(close_0, 5)\nts_min(low_0, 5)\nts_min(open_0, 5)\nts_min(high_0, 5)\nts_min(turn_0, 5)\nts_min(amount_0, 5)\nts_min(return_0, 5) \n \nstd(close_0, 5)\nstd(low_0, 5)\nstd(open_0, 5)\nstd(high_0, 5)\nstd(turn_0, 5)\nstd(amount_0, 5)\nstd(return_0, 5)\n \nts_rank(close_0, 5)\nts_rank(low_0, 5)\nts_rank(open_0, 5)\nts_rank(high_0, 5)\nts_rank(turn_0, 5)\nts_rank(amount_0, 5)\nts_rank(return_0, 5)\n \ndecay_linear(close_0, 5)\ndecay_linear(low_0, 5)\ndecay_linear(open_0, 5)\ndecay_linear(high_0, 5)\ndecay_linear(turn_0, 5)\ndecay_linear(amount_0, 5)\ndecay_linear(return_0, 5)\n \ncorrelation(volume_0, return_0, 5)\ncorrelation(volume_0, high_0, 5)\ncorrelation(volume_0, low_0, 5)\ncorrelation(volume_0, close_0, 5)\ncorrelation(volume_0, open_0, 5)\ncorrelation(volume_0, turn_0, 5)\n \ncorrelation(return_0, high_0, 5)\ncorrelation(return_0, low_0, 5)\ncorrelation(return_0, close_0, 5)\ncorrelation(return_0, open_0, 5)\ncorrelation(return_0, turn_0, 5)\n \ncorrelation(high_0, low_0, 5)\ncorrelation(high_0, close_0, 5)\ncorrelation(high_0, open_0, 5)\ncorrelation(high_0, turn_0, 5)\n \ncorrelation(low_0, close_0, 5)\ncorrelation(low_0, open_0, 5)\ncorrelation(low_0, turn_0, 5)\n \ncorrelation(close_0, open_0, 5)\ncorrelation(close_0, turn_0, 5)\n\ncorrelation(open_0, turn_0, 5)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","module_id":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v6","parameters":[{"name":"learning_algorithm","value":"排序","type":"Literal","bound_global_parameter":null},{"name":"number_of_leaves","value":30,"type":"Literal","bound_global_parameter":null},{"name":"minimum_docs_per_leaf","value":1000,"type":"Literal","bound_global_parameter":null},{"name":"number_of_trees","value":"30","type":"Literal","bound_global_parameter":null},{"name":"learning_rate","value":0.1,"type":"Literal","bound_global_parameter":null},{"name":"max_bins","value":1023,"type":"Literal","bound_global_parameter":null},{"name":"feature_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"data_row_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"ndcg_discount_base","value":1,"type":"Literal","bound_global_parameter":null},{"name":"m_lazy_run","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"training_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"features","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"test_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"base_model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"output_ports":[{"name":"model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"feature_gains","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"m_lazy_run","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"name":"data2","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","module_id":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","parameters":[{"name":"m_lazy_run","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"output_ports":[{"name":"predictions","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"name":"m_lazy_run","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2022-04-20","type":"Literal","bound_global_parameter":"交易日期"},{"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-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":9,"comment":"预测数据,用于回测和模拟","comment_collapsed":false},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","module_id":"BigQuantSpace.dropnan.dropnan-v1","parameters":[],"input_ports":[{"name":"input_data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-86","module_id":"BigQuantSpace.dropnan.dropnan-v1","parameters":[],"input_ports":[{"name":"input_data","node_id":"-86"}],"output_ports":[{"name":"data","node_id":"-86"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-215","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-215"},{"name":"features","node_id":"-215"}],"output_ports":[{"name":"data","node_id":"-215"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-222","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-222"},{"name":"features","node_id":"-222"}],"output_ports":[{"name":"data","node_id":"-222"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-231","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-231"},{"name":"features","node_id":"-231"}],"output_ports":[{"name":"data","node_id":"-231"}],"cacheable":true,"seq_num":17,"comment":"","comment_collapsed":true},{"node_id":"-238","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-238"},{"name":"features","node_id":"-238"}],"output_ports":[{"name":"data","node_id":"-238"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-250","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 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 = 50\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","type":"Literal","bound_global_parameter":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. 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 pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"真实价格","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-250"},{"name":"options_data","node_id":"-250"},{"name":"history_ds","node_id":"-250"},{"name":"benchmark_ds","node_id":"-250"},{"name":"trading_calendar","node_id":"-250"}],"output_ports":[{"name":"raw_perf","node_id":"-250"}],"cacheable":false,"seq_num":19,"comment":"","comment_collapsed":true},{"node_id":"-278","module_id":"BigQuantSpace.hyper_run.hyper_run-v1","parameters":[{"name":"run","value":"def bigquant_run(bq_graph, inputs):\n features =[0.05, 0.15, 0.2,0.25]\n\n parameters_list = []\n \n for feature in features:\n parameters = {'m6.learning_rate':feature}\n parameters_list.append({'parameters': parameters})\n\n def run(parameters):\n try:\n print(parameters)\n return g.run(parameters)\n except Exception as e:\n print('ERROR --------', e)\n return None\n \n results = T.parallel_map(run, parameters_list, max_workers=len(features), remote_run=True, silent=True)\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":"-278"},{"name":"input_1","node_id":"-278"},{"name":"input_2","node_id":"-278"},{"name":"input_3","node_id":"-278"}],"output_ports":[{"name":"result","node_id":"-278"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='224,27,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='69,184,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='657,-41,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='582,556,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='247,358,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='805,641,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1081,64,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='359,463,200,200'/><node_position Node='-86' Position='1091,355,200,200'/><node_position Node='-215' Position='381,188,200,200'/><node_position Node='-222' Position='381,258,200,200'/><node_position Node='-231' Position='1085,159,200,200'/><node_position Node='-238' Position='1090,265,200,200'/><node_position Node='-250' Position='756,755,200,200'/><node_position Node='-278' Position='185,-312.55262756347656,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [3]:
    # 本代码由可视化策略环境自动生成 2022年5月10日 09:35
    # 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 = 50
        # 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': '2017-12-31',
        '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': False,
    
        'm3': 'M.input_features.v1',
        'm3.features': """close_0
    open_0
    high_0
    low_0 
    amount_0
    turn_0 
    return_0
     
    close_1
    open_1
    high_1
    low_1
    return_1
    amount_1
    turn_1
     
    close_2
    open_2
    high_2
    low_2
    amount_2
    turn_2
    return_2
     
    close_3
    open_3
    high_3
    low_3
    amount_3
    turn_3
    return_3
     
    close_4
    open_4
    high_4
    low_4
    amount_4
    turn_4
    return_4
     
    mean(close_0, 5)
    mean(low_0, 5)
    mean(open_0, 5)
    mean(high_0, 5)
    mean(turn_0, 5)
    mean(amount_0, 5)
    mean(return_0, 5)
     
    ts_max(close_0, 5)
    ts_max(low_0, 5)
    ts_max(open_0, 5)
    ts_max(high_0, 5)
    ts_max(turn_0, 5)
    ts_max(amount_0, 5)
    ts_max(return_0, 5)
     
    ts_min(close_0, 5)
    ts_min(low_0, 5)
    ts_min(open_0, 5)
    ts_min(high_0, 5)
    ts_min(turn_0, 5)
    ts_min(amount_0, 5)
    ts_min(return_0, 5) 
     
    std(close_0, 5)
    std(low_0, 5)
    std(open_0, 5)
    std(high_0, 5)
    std(turn_0, 5)
    std(amount_0, 5)
    std(return_0, 5)
     
    ts_rank(close_0, 5)
    ts_rank(low_0, 5)
    ts_rank(open_0, 5)
    ts_rank(high_0, 5)
    ts_rank(turn_0, 5)
    ts_rank(amount_0, 5)
    ts_rank(return_0, 5)
     
    decay_linear(close_0, 5)
    decay_linear(low_0, 5)
    decay_linear(open_0, 5)
    decay_linear(high_0, 5)
    decay_linear(turn_0, 5)
    decay_linear(amount_0, 5)
    decay_linear(return_0, 5)
     
    correlation(volume_0, return_0, 5)
    correlation(volume_0, high_0, 5)
    correlation(volume_0, low_0, 5)
    correlation(volume_0, close_0, 5)
    correlation(volume_0, open_0, 5)
    correlation(volume_0, turn_0, 5)
      
    correlation(return_0, high_0, 5)
    correlation(return_0, low_0, 5)
    correlation(return_0, close_0, 5)
    correlation(return_0, open_0, 5)
    correlation(return_0, turn_0, 5)
     
    correlation(high_0, low_0, 5)
    correlation(high_0, close_0, 5)
    correlation(high_0, open_0, 5)
    correlation(high_0, turn_0, 5)
     
    correlation(low_0, close_0, 5)
    correlation(low_0, open_0, 5)
    correlation(low_0, turn_0, 5)
     
    correlation(close_0, open_0, 5)
    correlation(close_0, turn_0, 5)
    
    correlation(open_0, turn_0, 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'),
    
        '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': 30,
        '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', '2018-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2022-04-20'),
        '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,
    
        '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 m4_run_bigquant_run(bq_graph, inputs):
        features =[0.05, 0.15, 0.2,0.25]
    
        parameters_list = []
         
        for feature in features:
            parameters = {'m6.learning_rate':feature}
            parameters_list.append({'parameters': parameters})
    
        def run(parameters):
            try:
                print(parameters)
                return g.run(parameters)
            except Exception as e:
                print('ERROR --------', e)
                return None
            
        results = T.parallel_map(run, parameters_list, max_workers=len(features), remote_run=True, silent=True)
    
        return results
    
    
    m4 = M.hyper_run.v1(
        run=m4_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    In [4]:
    from datetime import datetime
    print(datetime.now())
    print(len(m4.result))
    for i in range(len(m4.result)):
        try:
            print('==='*15, i)
            perf = m4.result[i]['m19'].raw_perf.read()
            T.render_perf(perf, buy_moment="buy", sell_moment="sell")
        except Exception as e:
            print(e)
            continue
    
    2022-05-10 09:11:30.906178
    4
    ============================================= 0
    
    • 收益率17.05%
    • 年化收益率3.88%
    • 基准收益率0.99%
    • 阿尔法0.04
    • 贝塔0.65
    • 夏普比率0.15
    • 胜率0.54
    • 盈亏比-0.95
    • 收益波动率22.53%
    • 信息比率0.01
    • 最大回撤26.31%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3e63efa748d74eb5bff0d78ea4b97331"}/bigcharts-data-end
    ============================================= 1
    
    • 收益率0.68%
    • 年化收益率0.16%
    • 基准收益率0.99%
    • 阿尔法-0.0
    • 贝塔0.62
    • 夏普比率-0.02
    • 胜率0.52
    • 盈亏比-1.0
    • 收益波动率21.66%
    • 信息比率0.0
    • 最大回撤32.92%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-874765532d6246489087385f129aac0b"}/bigcharts-data-end
    ============================================= 2
    
    • 收益率8.22%
    • 年化收益率1.93%
    • 基准收益率0.99%
    • 阿尔法0.01
    • 贝塔0.57
    • 夏普比率0.05
    • 胜率0.52
    • 盈亏比-1.04
    • 收益波动率20.42%
    • 信息比率0.01
    • 最大回撤25.93%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5277f6e450974638a38d10657a0bd411"}/bigcharts-data-end
    ============================================= 3
    
    • 收益率16.2%
    • 年化收益率3.69%
    • 基准收益率0.99%
    • 阿尔法0.03
    • 贝塔0.63
    • 夏普比率0.14
    • 胜率0.52
    • 盈亏比-1.0
    • 收益波动率21.99%
    • 信息比率0.01
    • 最大回撤29.15%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-df2dbae3616e434ead061b0e7a94100b"}/bigcharts-data-end
    In [ ]: