{"description":"实验创建于2023/5/1","graph":{"edges":[{"to_node_id":"-464:input_data","from_node_id":"-456:data"},{"to_node_id":"-209:options_data","from_node_id":"-464:data"},{"to_node_id":"-209:instruments","from_node_id":"-161:data"}],"nodes":[{"node_id":"-209","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=0.0001, sell_cost=0.0011, 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 = 5\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.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 \n # 获取数据\n print(\"here\")\n buy_sell_data_df = context.options['data'].read_df()\n print(buy_sell_data_df.head())\n\n # 按日期过滤得到今日的预测数据\n today = data.current_dt.strftime('%Y%m%d')\n buy_sell_data = buy_sell_data[buy_sell_data_df.deal_date == today]\n \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StockRanker预测的排序末位淘汰\n sell_stocks = buy_sell_data[buy_sell_data['deal_action']=='sell']\n if len(sell_stocks) > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n for stock_code in sell_stocks['stock_code']:\n context.order_target(context.symbol(stock_code), 0) \n print(f\"日期：{today} - 卖出股票： {stock_code}\")\n\n # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票\n buy_stocks = buy_sell_data[buy_sell_data['deal_action']=='buy']\n if len(buy_stocks) > 0:\n for volumn, stock_code in zip(buy_stocks['deal_volume'], buy_stocks['stock_code']):\n context.order(context.symbol(stock_code), volumn)\n print(f\"日期：{today} - 买入股票： {stock_code} - 数量：{volumn}\")","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":"# 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。\ndef bigquant_run(context, data):\n pass\n","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":"-209"},{"name":"options_data","node_id":"-209"},{"name":"history_ds","node_id":"-209"},{"name":"benchmark_ds","node_id":"-209"},{"name":"trading_calendar","node_id":"-209"}],"output_ports":[{"name":"raw_perf","node_id":"-209"}],"cacheable":false,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-456","module_id":"BigQuantSpace.datahub_load_file.datahub_load_file-v2","parameters":[{"name":"file_path","value":"# Python 动态生成文件路径\ndef bigquant_run():\n # 示例代码如下。在这里编写您的代码\n # 如果不需要动态生成路径,直接返回path即可\n # import datetime\n # import os\n # base_path = \"/var/app/data/datahub\"\n # data_path = os.path.join(base_path, \"bigcrawler\")\n # date = datetime.datetime.now().strftime(\"%Y%m%d\")\n # file_path = os.path.join(data_path, \"{}.csv\".format(date))\n # return file_path\n\n return \"stock_transaction.csv\"\n","type":"Literal","bound_global_parameter":null},{"name":"file_type","value":"csv","type":"Literal","bound_global_parameter":null},{"name":"csv_delimiter","value":",","type":"Literal","bound_global_parameter":null},{"name":"other_parameters","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"h5_data_key","value":"data","type":"Literal","bound_global_parameter":null}],"input_ports":[],"output_ports":[{"name":"data","node_id":"-456"}],"cacheable":false,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-464","module_id":"BigQuantSpace.datahub_handler_column.datahub_handler_column-v1","parameters":[{"name":"handler","value":"# Python 代码处理数据\ndef bigquant_run(data):\n # 示例代码如下。在这里编写您的代码\n # data[\"test\"] = \"t1\"\n data['stock_code'] = data['stock_code'].str.replace(\"SH\",\"SHA\").str.replace(\"SZ\",\"SZA\")\n return data\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-464"}],"output_ports":[{"name":"data","node_id":"-464"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-161","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2002-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2002-12-31","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":"-161"}],"output_ports":[{"name":"data","node_id":"-161"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-209' Position='356,302,200,200'/><node_position Node='-456' Position='195,16,200,200'/><node_position Node='-464' Position='344,122,200,200'/><node_position Node='-161' Position='36,124,200,200'/></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5月1日 15:26
# 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。


# Python 动态生成文件路径
def m2_file_path_bigquant_run():
    # 示例代码如下。在这里编写您的代码
    # 如果不需要动态生成路径,直接返回path即可
    # import datetime
    # import os
    # base_path = "/var/app/data/datahub"
    # data_path = os.path.join(base_path, "bigcrawler")
    # date = datetime.datetime.now().strftime("%Y%m%d")
    # file_path = os.path.join(data_path, "{}.csv".format(date))
    # return file_path

    return "stock_transaction.csv"

# Python 代码处理数据
def m3_handler_bigquant_run(data):
    # 示例代码如下。在这里编写您的代码
    # data["test"] = "t1"
    data['stock_code'] = data['stock_code'].str.replace("SH","SHA").str.replace("SZ","SZA")
    return data

# 回测引擎：初始化函数，只执行一次
def m1_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=0.0001, sell_cost=0.0011, 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 = 5
    # 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.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m1_handle_data_bigquant_run(context, data):
    
    # 获取数据
    print("here")
    buy_sell_data_df = context.options['data'].read_df()
    print(buy_sell_data_df.head())

    # 按日期过滤得到今日的预测数据
    today = data.current_dt.strftime('%Y%m%d')
    buy_sell_data = buy_sell_data[buy_sell_data_df.deal_date == today]
    

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按StockRanker预测的排序末位淘汰
    sell_stocks = buy_sell_data[buy_sell_data['deal_action']=='sell']
    if len(sell_stocks) > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        for stock_code in sell_stocks['stock_code']:
            context.order_target(context.symbol(stock_code), 0) 
            print(f"日期：{today} - 卖出股票： {stock_code}")

    # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
    buy_stocks = buy_sell_data[buy_sell_data['deal_action']=='buy']
    if len(buy_stocks) > 0:
        for volumn, stock_code in zip(buy_stocks['deal_volume'], buy_stocks['stock_code']):
            context.order(context.symbol(stock_code), volumn)
            print(f"日期：{today} - 买入股票： {stock_code} - 数量：{volumn}")
# 回测引擎：准备数据，只执行一次
def m1_prepare_bigquant_run(context):
    pass

# 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 m1_before_trading_start_bigquant_run(context, data):
    pass


m2 = M.datahub_load_file.v2(
    file_path=m2_file_path_bigquant_run,
    file_type='csv',
    csv_delimiter=',',
    other_parameters='{}',
    h5_data_key='data'
)

m3 = M.datahub_handler_column.v1(
    input_data=m2.data,
    handler=m3_handler_bigquant_run
)

m4 = M.instruments.v2(
    start_date='2002-01-01',
    end_date='2002-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m1 = M.trade.v4(
    instruments=m4.data,
    options_data=m3.data,
    start_date='',
    end_date='',
    initialize=m1_initialize_bigquant_run,
    handle_data=m1_handle_data_bigquant_run,
    prepare=m1_prepare_bigquant_run,
    before_trading_start=m1_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

[2023-05-01 15:15:14.478980] INFO: 读取数据(文件): 读取数据文件: stock_transaction.csv

[2023-05-01 15:15:14.494966] INFO: moduleinvoker: datahub_load_file.v2 运行完成[0.053476s].

[2023-05-01 15:15:14.537087] INFO: 数据处理(自定义): 数据处理(自定义) 完成

[2023-05-01 15:15:14.568156] INFO: 数据处理(自定义): ds: DataSource(872bde17561e4d539e0bc57c2ef0407dT)

[2023-05-01 15:15:14.569120] INFO: moduleinvoker: datahub_handler_column.v1 运行完成[0.068548s].

[2023-05-01 15:15:14.574383] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-05-01 15:15:14.579825] INFO: moduleinvoker: 命中缓存

[2023-05-01 15:15:14.580867] INFO: moduleinvoker: instruments.v2 运行完成[0.006484s].

[2023-05-01 15:15:14.611964] INFO: moduleinvoker: backtest.v8 开始运行..

[2023-05-01 15:15:14.615991] INFO: backtest: biglearning backtest:V8.6.3

[2023-05-01 15:15:14.616942] INFO: backtest: product_type:stock by specified

[2023-05-01 15:15:14.984573] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: TypeError: object of type 'NoneType' has no len()

[2023-05-01 15:15:14.987319] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: TypeError: object of type 'NoneType' has no len()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-8fc58c782096> in <module>
     95 )
     96 
---> 97 m1 = M.trade.v4(
     98     instruments=m4.data,
     99     options_data=m3.data,

TypeError: object of type 'NoneType' has no len()