{"description":"实验创建于2022/7/22","graph":{"edges":[{"to_node_id":"-783:instruments","from_node_id":"-530:data"},{"to_node_id":"-783:options_data","from_node_id":"-688:data_1"}],"nodes":[{"node_id":"-530","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2016-06-02","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-06-23","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_FUND","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"513100.HOF","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-530"}],"output_ports":[{"name":"data","node_id":"-530"}],"cacheable":false,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-688","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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\n\"\"\"\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = pd.DataFrame({'data': [1, 2, 3]})\n data_1 = DataSource.write_df(df)\n data_2 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)\n\"\"\"\n\ndef bigquant_run(input_1, input_2, input_3, spacing, cdays, divided, holding):\n # 示例代码如下。在这里编写您的代码\n data_1 = DataSource.write_pickle({'spacing':spacing, 'cdays':cdays, 'divided':divided, 'holding':holding})\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 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。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{'spacing':0.16, 'cdays':165, 'divided':10, 'holding':0.1}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-688"},{"name":"input_2","node_id":"-688"},{"name":"input_3","node_id":"-688"}],"output_ports":[{"name":"data_1","node_id":"-688"},{"name":"data_2","node_id":"-688"},{"name":"data_3","node_id":"-688"}],"cacheable":false,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-783","module_id":"BigQuantSpace.hftrade.hftrade-v2","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 交易引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n context.capital_base = 1000000\n context.ins = context.instruments #从传入参数中获取需要交易的合约\n context.set_universe(context.ins) #设置需要处理的合约\n context.last_grid = {}\n context.reset_net = {}\n context.grid_change_last = {}\n context.center = {}\n context.order_num = {}\n context.first_order_flag = {}\n context.spacing = {}\n context.cdays = {}\n context.divided = {}\n context.holding = {}\n \"\"\"\n context.spacing = context.options['data'].read()['spacing']\n context.cdays = context.options['data'].read()['cdays']\n context.holding = context.options['data'].read()['holding']\n \"\"\"\n \n for sid in context.instruments:\n context.spacing[sid] = context.options['data'].read()['spacing']\n context.cdays[sid] = context.options['data'].read()['cdays']\n context.divided[sid] = context.options['data'].read()['divided']\n context.holding[sid] = context.options['data'].read()['holding']\n context.last_grid[sid] = 0\n context.reset_net[sid] = True\n context.grid_change_last[sid] = [0,0]\n context.first_order_flag[sid] = True\n \n context.set_stock_t1(0)\n context.set_commission(PerOrder(buy_cost=0, sell_cost=0, min_cost=0))","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 交易引擎：每个单位时间开盘前调用一次。\ndef bigquant_run(context, data):\n # 盘前处理，订阅行情等\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_tick","value":"# 交易引擎：tick数据处理函数，每个tick执行一次\ndef bigquant_run(context, tick):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 交易引擎：bar数据处理函数，每个时间单位执行一次\ndef bigquant_run(context, data):\n context.subscribe(context.ins) #注册合约\n\n # 记录上一次交易时网格范围的变化情况（例如从4区到5区，记为4,5）\n for sid in context.instruments:\n if context.reset_net[sid]:\n context.center[sid] = data.history(sid, [\"close\"], context.cdays[sid], \"1d\").mean()[0]\n context.reset_net[sid] = False\n print(str(data.current_dt) + \" center: \" + str(context.center))\n \n #计算当天可交易仓位\n for sid in context.instruments:\n weight = 1/len(context.instruments)\n price = data.current(sid, \"close\")\n cash = context.capital_base * weight\n order_num = int(cash/price/1000)*1000\n context.order_num[sid] = order_num\n \n cur_date = data.current_dt\n \n for sid in context.instruments:\n # 分别获取持仓\n position = context.get_position(sid)\n \n if position.current_qty == 0 and context.first_order_flag[sid] == True:\n print(\"triggered!\")\n order_num = int(context.order_num[sid] * context.holding[sid]/100)*100\n rv = context.order(sid, order_num, price, order_type=OrderType.MARKET)\n context.first_order_flag[sid] = False\n \n # 获取当前价格\n price = data.current(sid, \"close\")\n \n # 设置网格和当前价格所处的网格区域\n band = np.arange(1-context.spacing[sid], 1+context.spacing[sid], context.spacing[sid]/10)* context.center[sid]\n grid = pd.cut([price], band, labels=np.arange(1,len(band)))[0]\n weight = 1/len(context.instruments)/len(band)\n \n if np.isnan(grid):\n print(str(cur_date) + \" \"+ sid + \" 价格波动超过网格范围，可适当调节网格宽度和数量\")\n context.reset_net[sid] = True\n\n if context.last_grid[sid] < grid:\n # 记录新旧格子范围（按照大小排序）\n grid_change_new = [context.last_grid[sid],grid]\n if context.last_grid[sid] == 0:\n context.last_grid[sid] = grid\n return\n if context.last_grid[sid] != 0:\n if grid_change_new != context.grid_change_last[sid]:\n # 更新前一次的数据\n context.last_grid[sid] = grid\n context.grid_change_last[sid] = grid_change_new\n # 如果有仓位\n if position.current_qty != 0:\n order_num = int(context.order_num[sid] / context.divided[sid]/100)*100\n if position.current_qty >= order_num:\n rv = context.order(sid, -order_num, price, order_type=OrderType.MARKET)\n else:\n rv = context.order(sid, -position.current_qty, price, order_type=OrderType.MARKET)\n \n \n # 如果新网格小于前一天的网格，开仓\n if context.last_grid[sid] > grid:\n # 记录新旧格子范围（按照大小排序）\n grid_change_new = [grid,context.last_grid[sid]]\n #print(sid + \" \" + str(context.grid_change_last[sid]) + \" \"+ str([context.last_grid[sid], grid]))\n if context.last_grid[sid] == 0:\n context.last_grid[sid] = grid\n return\n if context.last_grid[sid] != 0:\n # 如果前一次开仓是4-5，这一次是5-4，算是没有突破，不成交\n if grid_change_new != context.grid_change_last[sid]:\n # 更新前一次的数据\n context.last_grid[sid] = grid\n context.grid_change_last[sid] = grid_change_new\n order_num = int(context.order_num[sid] / context.divided[sid]/100)*100\n if context.portfolio.cash > price * order_num:\n rv = context.order(sid, order_num, price, order_type=OrderType.MARKET)","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎：成交回报处理函数，每个成交发生时执行一次\ndef bigquant_run(context, trade):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎：委托回报处理函数，每个委托变化时执行一次\ndef bigquant_run(context, order):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"after_trading","value":"# 交易引擎：盘后处理函数，每日盘后执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"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":"自动","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"0","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":1,"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":"benchmark","value":"000001.HIX","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"False","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"True","type":"Literal","bound_global_parameter":null},{"name":"replay_bdb","value":"False","type":"Literal","bound_global_parameter":null},{"name":"show_debug_info","value":"False","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-783"},{"name":"options_data","node_id":"-783"},{"name":"history_ds","node_id":"-783"},{"name":"benchmark_ds","node_id":"-783"}],"output_ports":[{"name":"raw_perf","node_id":"-783"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-2365","module_id":"BigQuantSpace.hyper_parameter_search.hyper_parameter_search-v1","parameters":[{"name":"param_grid_builder","value":"\"\"\"\ndef bigquant_run():\n param_grid = {}\n\n # 在这里设置需要调优的参数备选\n # param_grid['m3.features'] = ['close_1/close_0', 'close_2/close_0\\nclose_3/close_0']\n # param_grid['m6.number_of_trees'] = [5, 10, 20]\n\n return param_grid\n\"\"\"\n\ndef bigquant_run():\n param_grid = {}\n \n spacing = np.arange(0.1,0.5,0.02)\n cdays = np.arange(30,200,5)\n divided = np.array([10])\n holding = np.array([0.1])\n \n param_grid['m2.params'] = [\"{'spacing':%s,'cdays':%s,'divided':%s,'holding':%s}\"%(i,j,k,l) for i in spacing for j in cdays for k in divided for l in holding]\n param_grid['m1.instrument_list'] = ['513100.HOF']\n return param_grid","type":"Literal","bound_global_parameter":null},{"name":"scoring","value":"def bigquant_run(result):\n score = {'score':result.get('m3').read_raw_perf()['sharpe'].tail(1)[0], 'return':result.get('m3').read_raw_perf()['algorithm_period_return'].tail(1)[0], 'max_drawdown':result.get('m3').read_raw_perf()['max_drawdown'].tail(1)[0]}\n return score\n","type":"Literal","bound_global_parameter":null},{"name":"search_algorithm","value":"网格搜索","type":"Literal","bound_global_parameter":null},{"name":"search_iterations","value":"1000","type":"Literal","bound_global_parameter":null},{"name":"random_state","value":"","type":"Literal","bound_global_parameter":null},{"name":"workers","value":"6","type":"Literal","bound_global_parameter":null},{"name":"worker_distributed_run","value":"False","type":"Literal","bound_global_parameter":null},{"name":"worker_silent","value":"True","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":"-2365"},{"name":"input_1","node_id":"-2365"},{"name":"input_2","node_id":"-2365"},{"name":"input_3","node_id":"-2365"}],"output_ports":[{"name":"result","node_id":"-2365"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-530' Position='184,134,200,200'/><node_position Node='-688' Position='609,135,200,200'/><node_position Node='-783' Position='369,292,200,200'/><node_position Node='-2365' Position='176,-4,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}