{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-57:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-102:options_data","from_node_id":"-86:data"},{"to_node_id":"-86:input_data","from_node_id":"-57:data"},{"to_node_id":"-57:input_data","from_node_id":"-54:data"},{"to_node_id":"-102:instruments","from_node_id":"-62: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(close>110,1,0)\nsell_condition = where(close<=110,1,0)\nfactor1 = close/total_size\n","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":false,"seq_num":1,"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":6,"comment":"","comment_collapsed":true},{"node_id":"-57","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":"True","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-57"},{"name":"features","node_id":"-57"}],"output_ports":[{"name":"data","node_id":"-57"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-102","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","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 #------------------------------------------止损模块START--------------------------------------------\n date = data.current_dt.strftime('%Y-%m-%d') \n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stoploss_stock = [] \n if len(equities) > 0:\n for i in equities.keys():\n stock_market_price = data.current(context.symbol(i), 'price') # 最新市场价格\n last_sale_date = equities[i].last_sale_date # 上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days # 持仓天数\n # 建仓以来的最高价\n highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()\n # 确定止损位置\n stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.1\n record('止损位置', stoploss_line)\n # 如果价格下穿止损位置\n if stock_market_price < stoploss_line:\n context.order_target_percent(context.symbol(i), 0) \n current_stoploss_stock.append(i)\n print('日期：', date , '股票：', i, '出现止损状况')\n #-------------------------------------------止损模块END--------------------------------------------------\n\n \n # 获取今日的日期\n today = data.current_dt.strftime('%Y-%m-%d') \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\n stock_hold_now = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\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 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=[] # 如果没有符合条件的股票，就设置为空\n \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=[] # 如果没有符合条件的股票，就设置为空\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 # 需要调仓的股票：已有持仓且不符合卖出条件的股票\n stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]\n \n # 如果有卖出信号\n if len(stock_to_sell)>0:\n for instrument in stock_to_sell:\n if instrument in current_stoploss_stock:\n continue\n sid = context.symbol(instrument) # 将标的转化为equity格式\n cur_position = context.portfolio.positions[sid].amount # 持仓\n if cur_position > 0 and data.can_trade(sid):\n context.order_target_percent(sid, 0) # 全部卖出 \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 \n # 如果有买入信号/有持仓\n if len(stock_to_buy)>0:\n weight = 1/len(set(stock_to_buy+stock_to_adjust)) # 每只股票的比重为等资金比例持有\n for instrument in set(stock_to_buy+stock_to_adjust):\n sid = context.symbol(instrument) # 将标的转化为equity格式\n if data.can_trade(sid):\n context.order_target_value(sid, weight*cash_for_buy) # 买入","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 = m6.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":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-102"},{"name":"options_data","node_id":"-102"},{"name":"history_ds","node_id":"-102"},{"name":"benchmark_ds","node_id":"-102"},{"name":"trading_calendar","node_id":"-102"}],"output_ports":[{"name":"raw_perf","node_id":"-102"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-54","module_id":"BigQuantSpace.datahub_load_datasource.datahub_load_datasource-v1","parameters":[{"name":"table","value":"market_performance_CN_CONBOND","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2015-05-29","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-04-16","type":"Literal","bound_global_parameter":null},{"name":"instruments","value":"# #号开始的表示注释，注释需单独一行\n# 每行一条\n","type":"Literal","bound_global_parameter":null},{"name":"fields","value":"# #号开始的表示注释，注释需单独一行\n# 每行一条\nconversion_chg_pct_week\nbond_prem_ratio\npure_bond_ratio\nclose_equ\nremain_size\nclose\ntotal_size","type":"Literal","bound_global_parameter":null}],"input_ports":[],"output_ports":[{"name":"data","node_id":"-54"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2015-05-29","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-04-16","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_CONBOND","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":"-62"}],"output_ports":[{"name":"data","node_id":"-62"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='1141.1865844726562,-73.16772174835205,200,200'/><node_position Node='-86' Position='1078,418,200,200'/><node_position Node='-57' Position='1076,327,200,200'/><node_position Node='-102' Position='1048,529,200,200'/><node_position Node='-54' Position='928.1172485351562,34.537200927734375,200,200'/><node_position Node='-62' Position='793,190,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2022年4月19日 20:10
# 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))

# 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):

    #------------------------------------------止损模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')  
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    
    # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stoploss_stock = [] 
    if len(equities) > 0:
        for i in equities.keys():
            stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
            last_sale_date = equities[i].last_sale_date   # 上次交易日期
            delta_days = data.current_dt - last_sale_date  
            hold_days = delta_days.days # 持仓天数
            # 建仓以来的最高价
            highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()
            # 确定止损位置
            stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.1
            record('止损位置', stoploss_line)
            # 如果价格下穿止损位置
            if stock_market_price < stoploss_line:
                context.order_target_percent(context.symbol(i), 0)     
                current_stoploss_stock.append(i)
                print('日期：', date , '股票：', i, '出现止损状况')
    #-------------------------------------------止损模块END--------------------------------------------------

       
    # 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')  
    # 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
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}

    # 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    
    
    try:
        buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]  # 如果没有符合条件的股票，就设置为空
    
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock=[] # 如果没有符合条件的股票，就设置为空
    
    # 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 ]  
    # 需要调仓的股票：已有持仓且不符合卖出条件的股票
    stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]
    
    # 如果有卖出信号
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            if instrument in current_stoploss_stock:
                continue
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) # 全部卖出 
                # 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]
    
    # 如果有买入信号/有持仓
    if len(stock_to_buy)>0:
        weight = 1/len(set(stock_to_buy+stock_to_adjust)) # 每只股票的比重为等资金比例持有
        for instrument in set(stock_to_buy+stock_to_adjust):
            sid = context.symbol(instrument) # 将标的转化为equity格式
            if  data.can_trade(sid):
                context.order_target_value(sid, weight*cash_for_buy) # 买入
# 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 = m6.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(close>110,1,0)
sell_condition = where(close<=110,1,0)
factor1 = close/total_size
""",
    m_cached=False
)

m4 = M.datahub_load_datasource.v1(
    table='market_performance_CN_CONBOND',
    start_date='2015-05-29',
    end_date='2022-04-16',
    instruments="""# #号开始的表示注释，注释需单独一行
# 每行一条
""",
    fields="""# #号开始的表示注释，注释需单独一行
# 每行一条
conversion_chg_pct_week
bond_prem_ratio
pure_bond_ratio
close_equ
remain_size
close
total_size"""
)

m8 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True
)

m6 = M.dropnan.v1(
    input_data=m8.data
)

m2 = M.instruments.v2(
    start_date='2015-05-29',
    end_date='2022-04-16',
    market='CN_CONBOND',
    instrument_list='',
    max_count=0
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.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=''
)

[2022-04-19 20:04:02.914520] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-19 20:04:02.945499] INFO: moduleinvoker: input_features.v1 运行完成[0.030982s].

[2022-04-19 20:04:05.053125] INFO: 读取数据(DataSource): 读取数据 market_performance_CN_CONBOND, count: 268583

[2022-04-19 20:04:05.282304] INFO: 读取数据(DataSource): ds: DataSource(5ef4886952d04354b208cbe620f128f0T)

[2022-04-19 20:04:05.284469] INFO: moduleinvoker: datahub_load_datasource.v1 运行完成[2.330652s].

[2022-04-19 20:04:05.293026] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-04-19 20:04:05.770746] INFO: derived_feature_extractor: 提取完成 buy_condition = where(close>110,1,0), 0.003s

[2022-04-19 20:04:05.774261] INFO: derived_feature_extractor: 提取完成 sell_condition = where(close<=110,1,0), 0.002s

[2022-04-19 20:04:05.777781] INFO: derived_feature_extractor: 提取完成 factor1 = close/total_size, 0.002s

[2022-04-19 20:04:06.399847] INFO: derived_feature_extractor: /data, 268583

[2022-04-19 20:04:06.565167] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[1.272127s].

[2022-04-19 20:04:06.575125] INFO: moduleinvoker: dropnan.v1 开始运行..

[2022-04-19 20:04:06.832299] INFO: dropnan: /data, 268583/268583

[2022-04-19 20:04:06.894051] INFO: dropnan: 行数: 268583/268583

[2022-04-19 20:04:06.900244] INFO: moduleinvoker: dropnan.v1 运行完成[0.325118s].

[2022-04-19 20:04:06.907749] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-19 20:04:06.918768] INFO: moduleinvoker: 命中缓存

[2022-04-19 20:04:06.920311] INFO: moduleinvoker: instruments.v2 运行完成[0.012562s].

[2022-04-19 20:04:06.996961] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-04-19 20:04:07.003505] INFO: backtest: biglearning backtest:V8.6.2

[2022-04-19 20:04:10.301677] INFO: backtest: product_type:stock by specified

[2022-04-19 20:04:10.304303] INFO: backtest: 其它市场:{'HCB'}

[2022-04-19 20:04:10.536431] INFO: moduleinvoker: cached.v2 开始运行..

[2022-04-19 20:04:13.209585] ERROR: moduleinvoker: module name: cached, module version: v2, trackeback: TypeError: Can only merge Series or DataFrame objects, a  was passed

[2022-04-19 20:04:15.020941] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: TypeError: Can only merge Series or DataFrame objects, a  was passed

[2022-04-19 20:04:15.027397] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: TypeError: Can only merge Series or DataFrame objects, a  was passed

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-22-37ba9a07e46a> in <module>
    150 )
    151 
--> 152 m3 = M.trade.v4(
    153     instruments=m2.data,
    154     options_data=m6.data,

TypeError: Can only merge Series or DataFrame objects, a <class 'NoneType'> was passed