{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-202:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-209:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-202:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-53:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-209:input_data","from_node_id":"-202:data"},{"to_node_id":"-1575:input_data","from_node_id":"-209:data"},{"to_node_id":"-53:options_data","from_node_id":"-1575:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\nbuy_condition=where((mean(close_0,5)>mean(close_0,10))&(mean(close_0,10)>mean(close_0,20))&(mean(close_0,20)>mean(close_0,40))&(mean(close_0,40)>mean(close_0,120))&(low_0<mean(close_0,10)),1,0)\nsell_condition=where(mean(close_0,5)<mean(close_0,40),1,0)","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":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-12-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2022-2-01","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":2,"comment":"预测数据，用于回测和模拟","comment_collapsed":false},{"node_id":"-202","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":"300","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-202"},{"name":"features","node_id":"-202"}],"output_ports":[{"name":"data","node_id":"-202"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-209","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":"-209"},{"name":"features","node_id":"-209"}],"output_ports":[{"name":"data","node_id":"-209"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-53","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\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 context.stock_max_num = 20 # 最多同时持有20只股票\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 # 回测引擎：每日数据处理函数，每天执行一次\n today = data.current_dt.strftime('%Y-%m-%d') # 日期\n # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表和对应的最新市值\n stock_hold_now = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n hold_num=len(stock_hold_now)\n \n # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；\n # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用\n cash_for_buy = context.portfolio.cash\n \n # 获取当日符合买入/卖出条件的股票列表\n try:\n buy_stock = context.daily_stock_buy[today] # 当日符合买入条件的股票\n except:\n buy_stock=[]\n try:\n sell_stock = context.daily_stock_sell[today] # 当日符合卖出条件的股票\n except:\n sell_stock = []\n\n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [i for i in stock_hold_now if i in sell_stock]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n stock_to_buy = [i for i in buy_stock if i not in stock_hold_now]\n # 卖出\n for instrument in stock_to_sell:\n # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态\n # 如果返回真值，则可以正常下单，否则会出错\n # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式\n if data.can_trade(context.symbol(instrument)):\n # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，即卖出全部股票，可参考回测文档\n context.order_target_percent(context.symbol(instrument), 0)\n # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)\n cash_for_buy += stock_hold_now[instrument]\n hold_num-=1\n\n # 当日还允许买入建仓的股票数目\n stock_can_buy_num = context.stock_max_num - hold_num\n stock_to_buy_num = min(stock_can_buy_num,len(stock_to_buy))\n \n # 如果当天没有买入的股票，就返回\n if stock_to_buy_num == 0:\n return\n \n # 记录已经买入的股票数量\n buy_num = 0\n for instrument in stock_to_buy:\n # 使用当日可用现金等资金比例下单买入\n cash = cash_for_buy / stock_to_buy_num\n if data.can_trade(context.symbol(instrument)) and buy_num<stock_to_buy_num:\n # 整百下单\n current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\n buy_num += 1\n\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 # 加载预测数据\n df = context.options['data'].read_df()\n\n # 函数：求满足开仓条件的股票列表\n def open_pos_con(df):\n return list(df[df['buy_condition']>0].instrument)\n\n # 函数：求满足平仓条件的股票列表\n def close_pos_con(df):\n return list(df[df['sell_condition']>0].instrument)\n\n # 每日买入股票的数据框\n context.daily_stock_buy= df.groupby('date').apply(open_pos_con)\n # 每日卖出股票的数据框\n context.daily_stock_sell= df.groupby('date').apply(close_pos_con)","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":"open","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":"后复权","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":"-53"},{"name":"options_data","node_id":"-53"},{"name":"history_ds","node_id":"-53"},{"name":"benchmark_ds","node_id":"-53"},{"name":"trading_calendar","node_id":"-53"}],"output_ports":[{"name":"raw_perf","node_id":"-53"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-1575","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-1575"},{"name":"features","node_id":"-1575"}],"output_ports":[{"name":"data","node_id":"-1575"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='1285,99,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='800,101,200,200'/><node_position Node='-202' Position='1078,232,200,200'/><node_position Node='-209' Position='1076,328,200,200'/><node_position Node='-53' Position='976,539,200,200'/><node_position Node='-1575' Position='1077,425,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2023年1月11日 18:41
# 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 m3_initialize_bigquant_run(context):

    # 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))
    context.stock_max_num = 20 # 最多同时持有20只股票

# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    # 回测引擎：每日数据处理函数，每天执行一次
    today = data.current_dt.strftime('%Y-%m-%d') # 日期
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表和对应的最新市值
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}
    hold_num=len(stock_hold_now)
    
    # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；
    # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用
    cash_for_buy = context.portfolio.cash
    
    # 获取当日符合买入/卖出条件的股票列表
    try:
        buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock = []

    # 需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [i for i in stock_hold_now if i in sell_stock]
    # 需要买入的股票:没有持仓且符合买入条件的股票
    stock_to_buy = [i for i in buy_stock if i not in stock_hold_now]
    # 卖出
    for instrument in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式
        if data.can_trade(context.symbol(instrument)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(instrument), 0)
            # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)
            cash_for_buy += stock_hold_now[instrument]
            hold_num-=1

    # 当日还允许买入建仓的股票数目
    stock_can_buy_num = context.stock_max_num - hold_num
    stock_to_buy_num = min(stock_can_buy_num,len(stock_to_buy))
    
    # 如果当天没有买入的股票，就返回
    if stock_to_buy_num == 0:
        return
    
    # 记录已经买入的股票数量
    buy_num = 0
    for instrument in stock_to_buy:
        # 使用当日可用现金等资金比例下单买入
        cash = cash_for_buy / stock_to_buy_num
        if data.can_trade(context.symbol(instrument)) and buy_num<stock_to_buy_num:
            # 整百下单
            current_price = data.current(context.symbol(instrument), 'price')
            amount = math.floor(cash / current_price / 100) * 100
            context.order(context.symbol(instrument), amount)
            buy_num += 1


# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df()

    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)

    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)

    # 每日买入股票的数据框
    context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_stock_sell= df.groupby('date').apply(close_pos_con)

m1 = M.input_features.v1(
    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
buy_condition=where((mean(close_0,5)>mean(close_0,10))&(mean(close_0,10)>mean(close_0,20))&(mean(close_0,20)>mean(close_0,40))&(mean(close_0,40)>mean(close_0,120))&(low_0<mean(close_0,10)),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,40),1,0)"""
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2020-12-01'),
    end_date=T.live_run_param('trading_date', '2022-2-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=300
)

m8 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m4 = M.dropnan.v2(
    input_data=m8.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m4.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

[2023-01-11 18:38:25.594681] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-01-11 18:38:25.612247] INFO: moduleinvoker: 命中缓存

[2023-01-11 18:38:25.615431] INFO: moduleinvoker: input_features.v1 运行完成[0.020824s].

[2023-01-11 18:38:27.980419] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-01-11 18:38:28.100852] INFO: moduleinvoker: instruments.v2 运行完成[0.120424s].

[2023-01-11 18:38:28.138347] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-01-11 18:38:32.413100] INFO: 基础特征抽取: 年份 2020, 特征行数=877512

[2023-01-11 18:38:36.083936] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2023-01-11 18:38:37.212498] INFO: 基础特征抽取: 年份 2022, 特征行数=89016

[2023-01-11 18:38:37.512639] INFO: 基础特征抽取: 总行数: 2028055

[2023-01-11 18:38:37.532992] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[9.394678s].

[2023-01-11 18:38:37.584916] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-01-11 18:39:01.854736] INFO: derived_feature_extractor: 提取完成 buy_condition=where((mean(close_0,5)>mean(close_0,10))&(mean(close_0,10)>mean(close_0,20))&(mean(close_0,20)>mean(close_0,40))&(mean(close_0,40)>mean(close_0,120))&(low_0
[2023-01-11 18:39:05.374380] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close_0,5)
[2023-01-11 18:39:07.168743] INFO: derived_feature_extractor: /y_2020, 877512
[2023-01-11 18:39:09.539603] INFO: derived_feature_extractor: /y_2021, 1061527
[2023-01-11 18:39:09.977722] INFO: derived_feature_extractor: /y_2022, 89016
[2023-01-11 18:39:10.177651] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[32.592713s].
[2023-01-11 18:39:10.199820] INFO: moduleinvoker: dropnan.v2 开始运行..
[2023-01-11 18:39:12.184752] INFO: dropnan: /y_2020, 877512/877512
[2023-01-11 18:39:14.387282] INFO: dropnan: /y_2021, 1061527/1061527
[2023-01-11 18:39:14.613241] INFO: dropnan: /y_2022, 89016/89016
[2023-01-11 18:39:14.725136] INFO: dropnan: 行数: 2028055/2028055
[2023-01-11 18:39:14.733109] INFO: moduleinvoker: dropnan.v2 运行完成[4.533286s].
[2023-01-11 18:39:20.013195] INFO: moduleinvoker: backtest.v8 开始运行..
[2023-01-11 18:39:20.026560] INFO: backtest: biglearning backtest:V8.6.3
[2023-01-11 18:39:26.501268] INFO: backtest: product_type:stock by specified
[2023-01-11 18:39:26.678152] INFO: moduleinvoker: cached.v2 开始运行..
[2023-01-11 18:39:38.635508] INFO: backtest: 读取股票行情完成:2444925
[2023-01-11 18:39:42.206580] INFO: moduleinvoker: cached.v2 运行完成[15.528405s].
[2023-01-11 18:39:56.381403] INFO: backtest: algo history_data=DataSource(2b0b5437718f4b78956f52b582801ec8T)
[2023-01-11 18:39:56.383369] INFO: algo: TradingAlgorithm V1.8.9
[2023-01-11 18:39:57.110237] INFO: algo: trading transform...
[2023-01-11 18:40:10.418936] INFO: Performance: Simulated 285 trading days out of 285.
[2023-01-11 18:40:10.421182] INFO: Performance: first open: 2020-12-01 09:30:00+00:00
[2023-01-11 18:40:10.424560] INFO: Performance: last close: 2022-01-28 15:00:00+00:00
[2023-01-11 18:40:14.266917] INFO: moduleinvoker: backtest.v8 运行完成[54.253684s].
[2023-01-11 18:40:14.269210] INFO: moduleinvoker: trade.v4 运行完成[59.517227s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3eab5a0877724c6584dcdff9c0af96b2"}/bigcharts-data-end