{"description":"实验创建于2018/6/27","graph":{"edges":[{"to_node_id":"-353:instruments","from_node_id":"-51:data"},{"to_node_id":"-370:instruments","from_node_id":"-51:data"},{"to_node_id":"-353:features","from_node_id":"-59:data"},{"to_node_id":"-360:features","from_node_id":"-59:data"},{"to_node_id":"-1372:input_1","from_node_id":"-353:data"},{"to_node_id":"-865:input_data","from_node_id":"-360:data"},{"to_node_id":"-531:input_data","from_node_id":"-390:sorted_data"},{"to_node_id":"-370:options_data","from_node_id":"-531:data"},{"to_node_id":"-93:input_data","from_node_id":"-865:data"},{"to_node_id":"-390:input_ds","from_node_id":"-93:data"},{"to_node_id":"-130:instruments","from_node_id":"-226:data"},{"to_node_id":"-443:features","from_node_id":"-234:data"},{"to_node_id":"-130:features","from_node_id":"-234:data"},{"to_node_id":"-2883:input_1","from_node_id":"-443:data"},{"to_node_id":"-360:input_data","from_node_id":"-1372:data"},{"to_node_id":"-138:instruments","from_node_id":"-2870:data"},{"to_node_id":"-138:features","from_node_id":"-2878:data"},{"to_node_id":"-1372:input_2","from_node_id":"-2883:data"},{"to_node_id":"-443:input_data","from_node_id":"-130:data"},{"to_node_id":"-2883:input_2","from_node_id":"-138:data"}],"nodes":[{"node_id":"-51","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2023-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2023-12-31","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":"-51"}],"output_ports":[{"name":"data","node_id":"-51"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-59","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"#MACD空中加油\nmy=where((sum(ta_macd_macdhist_12_26_9_0>0,5)==5)&(sum(ta_macd_macd_12_26_9_0>0,5)==5)&(ta_macd(close_0,'golden_cross', 12, 26, 9))&(sum(ta_macd(close_0,'death_cross', 12, 26, 9),5)==1),1,0)\navg_turn_20\nbuy_condition=where(my==1,1,0)\nsell_condition=where(max(close_0/max(high_1,high_2)<=0.95, ta_macd(close_0,'death_cross', 12, 26, 9)),1,0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-59"}],"output_ports":[{"name":"data","node_id":"-59"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-353","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":"60","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-353"},{"name":"features","node_id":"-353"}],"output_ports":[{"name":"data","node_id":"-353"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-360","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":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-360"},{"name":"features","node_id":"-360"}],"output_ports":[{"name":"data","node_id":"-360"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-370","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.set_commission(PerOrder(buy_cost=0.00001, sell_cost=0.0001, min_cost=1))\n \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 context.stock_count = 2\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 # 每只股票的权重平均分配\n context.stock_weights = 1/context.stock_count\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.5\n context.options['hold_days'] = 1\n\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 today = 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 stock_now = len(equities); #获取当前持仓股票数量\n stock_count = context.stock_count\n \n # 按日期过滤得到今日的预测数据\n # 加载预测数据\n df = context.options['data'].read_df()\n df_today = df[df.date == data.current_dt.strftime('%Y-%m-%d')]\n df_today.set_index('instrument')\n \n \n now_stock = []\n sell_stock = []\n \n try:\n buy_list = context.daily_buy_stock[today]\n except:\n buy_list = []\n\n \n # 1. 资金分配\n #is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天） \n #stock_cash = context.portfolio.portfolio_value/stock_count\n #cash_avg = context.portfolio.portfolio_value\n #cash_for_buy = min(context.portfolio.cash, stock_cash)\n #cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n \n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n \n \n #if not is_staging :\n if 1==1 : \n if len(equities) > 0:\n for i in equities.keys():\n last_sale_date = equities[i].last_sale_date\t# 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 if hold_days >= context.options['hold_days'] and i not in buy_list :\n print('日期:',today,'卖出2:',i)\n context.order_target(context.symbol(i), 0)\n sell_stock.append(i)\n stock_now = stock_now -1\n #print('日期：', today, '股票：', i, ' 卖出')\n \n# 3. 生成买入订单\n buy_num = stock_count - stock_now\n #if is_staging :\n # buy_num = 1\n if len(buy_list)>0:\n print('日期：', today, '选出股票数量：', len(buy_list))\n if buy_num>0 and len(buy_list)>0 :\n # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓\n buy_instruments = [i for i in buy_list if i not in now_stock][:buy_num]\n cash_for_buy = context.portfolio.cash/len(buy_instruments)\n for i, instrument in enumerate(buy_instruments):\n current_price = data.current(context.symbol(instrument), 'price')\n \n if cash_for_buy>0 and data.can_trade(context.symbol(instrument)): \n amount = math.floor(cash_for_buy / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\n print('日期:',today,'买入:',instrument)\n else :\n print('日期:',today,'无资金或不能交易未买入:',instrument)","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 # 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 # 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 # 每日卖出股票的数据框\n context.daily_sell_stock= df.groupby('date').apply(close_pos_con) \n # 每日买入股票的数据框\n context.daily_buy_stock= df.groupby('date').apply(open_pos_con) \n\n\n \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":"100000","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":"-370"},{"name":"options_data","node_id":"-370"},{"name":"history_ds","node_id":"-370"},{"name":"benchmark_ds","node_id":"-370"},{"name":"trading_calendar","node_id":"-370"}],"output_ports":[{"name":"raw_perf","node_id":"-370"}],"cacheable":false,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-390","module_id":"BigQuantSpace.sort.sort-v4","parameters":[{"name":"sort_by","value":"avg_turn_20","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"date","type":"Literal","bound_global_parameter":null},{"name":"keep_columns","value":"--","type":"Literal","bound_global_parameter":null},{"name":"ascending","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-390"},{"name":"sort_by_ds","node_id":"-390"}],"output_ports":[{"name":"sorted_data","node_id":"-390"}],"cacheable":true,"seq_num":9,"comment":"","comment_collapsed":true},{"node_id":"-531","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"my==1","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-531"}],"output_ports":[{"name":"data","node_id":"-531"},{"name":"left_data","node_id":"-531"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-865","module_id":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","parameters":[{"name":"index_constituent_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"board_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"industry_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22valu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#号开始的表示注释，注释需单独一行\n# 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# 本代码由可视化策略环境自动生成 2023年2月2日 10:53
# 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 m8_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.set_commission(PerOrder(buy_cost=0.00001, sell_cost=0.0001, min_cost=1))
    
    # 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
    context.stock_count = 2
    # 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)])
    # 每只股票的权重平均分配
    context.stock_weights = 1/context.stock_count
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.5
    context.options['hold_days'] = 1


# 回测引擎：每日数据处理函数，每天执行一次
def m8_handle_data_bigquant_run(context, data):
    today = data.current_dt.strftime('%Y-%m-%d')
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    stock_now = len(equities); #获取当前持仓股票数量
    stock_count = context.stock_count
    
    # 按日期过滤得到今日的预测数据
    # 加载预测数据
    df = context.options['data'].read_df()
    df_today = df[df.date == data.current_dt.strftime('%Y-%m-%d')]
    df_today.set_index('instrument')
    
    
    now_stock = []
    sell_stock = []
       
    try:
        buy_list = context.daily_buy_stock[today]
    except:
        buy_list = []

    
    # 1. 资金分配
    #is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天） 
    #stock_cash = context.portfolio.portfolio_value/stock_count
    #cash_avg = context.portfolio.portfolio_value
    #cash_for_buy = min(context.portfolio.cash,  stock_cash)
    #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.perf_tracker.position_tracker.positions.items()}
    
            
    #if not is_staging :
    if 1==1 :    
        if len(equities) > 0:
            for i in equities.keys():
                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
                if hold_days >= context.options['hold_days'] and i not in buy_list :
                    print('日期:',today,'卖出2:',i)
                    context.order_target(context.symbol(i), 0)
                    sell_stock.append(i)
                    stock_now = stock_now -1
                    #print('日期：', today, '股票：', i, ' 卖出')
                 
# 3. 生成买入订单
    buy_num = stock_count - stock_now
    #if is_staging :
    #    buy_num = 1
    if len(buy_list)>0:
        print('日期：', today, '选出股票数量：', len(buy_list))
    if buy_num>0 and len(buy_list)>0 :
        # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓
        buy_instruments = [i for i in buy_list if i not in now_stock][:buy_num]
        cash_for_buy = context.portfolio.cash/len(buy_instruments)
        for i, instrument in enumerate(buy_instruments):
            current_price = data.current(context.symbol(instrument), 'price')
            
            if cash_for_buy>0 and data.can_trade(context.symbol(instrument)):           
                amount = math.floor(cash_for_buy / current_price / 100) * 100
                context.order(context.symbol(instrument), amount)
                print('日期:',today,'买入:',instrument)
            else :
                print('日期:',today,'无资金或不能交易未买入:',instrument)
# 回测引擎：准备数据，只执行一次
def m8_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)
    
    # 每日卖出股票的数据框
    context.daily_sell_stock= df.groupby('date').apply(close_pos_con)  
    # 每日买入股票的数据框
    context.daily_buy_stock= df.groupby('date').apply(open_pos_con)  


    

# 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 m8_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2023-01-01',
    end_date=T.live_run_param('trading_date', '2023-12-31'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""#MACD空中加油
my=where((sum(ta_macd_macdhist_12_26_9_0>0,5)==5)&(sum(ta_macd_macd_12_26_9_0>0,5)==5)&(ta_macd(close_0,'golden_cross', 12, 26, 9))&(sum(ta_macd(close_0,'death_cross', 12, 26, 9),5)==1),1,0)
avg_turn_20
buy_condition=where(my==1,1,0)
sell_condition=where(max(close_0/max(high_1,high_2)<=0.95, ta_macd(close_0,'death_cross', 12, 26, 9)),1,0)"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=60,
    m_cached=False
)

m10 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2023-12-31',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m12 = M.input_features.v1(
    features="""zs_return_0=close/shift(close,1)
zs_return_1=shift(zs_return_0,1)
zs_open=open/shift(close,1)"""
)

m15 = M.use_datasource.v2(
    instruments=m10.data,
    features=m12.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date='',
    before_start_days=90
)

m13 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m12.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m18 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2023-12-31',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m19 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
close"""
)

m16 = M.use_datasource.v2(
    instruments=m18.data,
    features=m19.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date='',
    before_start_days=90
)

m20 = M.data_join.v3(
    input_1=m13.data,
    input_2=m16.data,
    on='date',
    how='left',
    sort=False
)

m14 = M.data_join.v3(
    input_1=m5.data,
    input_2=m20.data,
    on='date',
    how='left',
    sort=False
)

m7 = M.derived_feature_extractor.v3(
    input_data=m14.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m6 = M.chinaa_stock_filter.v1(
    input_data=m7.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m11 = M.dropnan.v2(
    input_data=m6.data
)

m9 = M.sort.v4(
    input_ds=m11.data,
    sort_by='avg_turn_20',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m3 = M.filter.v3(
    input_data=m9.sorted_data,
    expr='my==1',
    output_left_data=False
)

m8 = M.trade.v4(
    instruments=m1.data,
    options_data=m3.data,
    start_date='',
    end_date='',
    initialize=m8_initialize_bigquant_run,
    handle_data=m8_handle_data_bigquant_run,
    prepare=m8_prepare_bigquant_run,
    before_trading_start=m8_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    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=''
)

[2023-02-02 10:51:44.376715] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-02 10:51:44.475716] INFO: moduleinvoker: instruments.v2 运行完成[0.098978s].

[2023-02-02 10:51:44.485543] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-02 10:51:44.521671] INFO: moduleinvoker: input_features.v1 运行完成[0.036165s].

[2023-02-02 10:51:44.539564] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-02-02 10:51:45.611848] INFO: 基础特征抽取: 年份 2022, 特征行数=215335

[2023-02-02 10:51:45.975287] INFO: 基础特征抽取: 年份 2023, 特征行数=86083

[2023-02-02 10:51:46.041765] INFO: 基础特征抽取: 总行数: 301418

[2023-02-02 10:51:46.044534] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[1.504979s].

[2023-02-02 10:51:46.050617] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-02 10:51:46.072720] INFO: moduleinvoker: instruments.v2 运行完成[0.022088s].

[2023-02-02 10:51:46.080858] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-02 10:51:46.092558] INFO: moduleinvoker: 命中缓存

[2023-02-02 10:51:46.095794] INFO: moduleinvoker: input_features.v1 运行完成[0.014934s].

[2023-02-02 10:51:46.107806] INFO: moduleinvoker: use_datasource.v2 开始运行..

[2023-02-02 10:51:46.359381] INFO: moduleinvoker: use_datasource.v2 运行完成[0.251541s].

[2023-02-02 10:51:46.378603] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-02-02 10:51:46.431165] INFO: derived_feature_extractor: 提取完成 zs_return_0=close/shift(close,1), 0.004s

[2023-02-02 10:51:46.435938] INFO: derived_feature_extractor: 提取完成 zs_return_1=shift(zs_return_0,1), 0.003s

[2023-02-02 10:51:46.440979] INFO: derived_feature_extractor: 提取完成 zs_open=open/shift(close,1), 0.003s

[2023-02-02 10:51:46.494763] INFO: derived_feature_extractor: /data, 2025

[2023-02-02 10:51:46.549827] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.17123s].

[2023-02-02 10:51:46.556154] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-02 10:51:46.580152] INFO: moduleinvoker: instruments.v2 运行完成[0.023984s].

[2023-02-02 10:51:46.593066] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-02-02 10:51:46.605783] INFO: moduleinvoker: 命中缓存

[2023-02-02 10:51:46.608439] INFO: moduleinvoker: input_features.v1 运行完成[0.015399s].

[2023-02-02 10:51:46.615332] INFO: moduleinvoker: use_datasource.v2 开始运行..

[2023-02-02 10:51:46.805580] INFO: moduleinvoker: use_datasource.v2 运行完成[0.190234s].

[2023-02-02 10:51:48.726117] INFO: moduleinvoker: data_join.v3 开始运行..

[2023-02-02 10:51:49.020620] INFO: moduleinvoker: data_join.v3 运行完成[0.294538s].

[2023-02-02 10:51:49.031000] INFO: moduleinvoker: data_join.v3 开始运行..

[2023-02-02 10:51:50.219801] INFO: moduleinvoker: data_join.v3 运行完成[1.188793s].

[2023-02-02 10:51:50.231467] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-02-02 10:52:29.332060] INFO: derived_feature_extractor: 提取完成 my=where((sum(ta_macd_macdhist_12_26_9_0>0,5)==5)&(sum(ta_macd_macd_12_26_9_0>0,5)==5)&(ta_macd(close_0,'golden_cross', 12, 26, 9))&(sum(ta_macd(close_0,'death_cross', 12, 26, 9),5)==1),1,0), 37.577s

[2023-02-02 10:52:29.338281] INFO: derived_feature_extractor: 提取完成 buy_condition=where(my==1,1,0), 0.004s

[2023-02-02 10:52:47.776684] INFO: derived_feature_extractor: 提取完成 sell_condition=where(max(close_0/max(high_1,high_2)<=0.95, ta_macd(close_0,'death_cross', 12, 26, 9)),1,0), 18.436s

[2023-02-02 10:52:49.681164] INFO: derived_feature_extractor: /data, 301418

[2023-02-02 10:52:49.857862] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[59.626381s].

[2023-02-02 10:52:49.885473] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-02-02 10:52:52.221728] INFO: A股股票过滤: 过滤 /data, 183199/0/301418

[2023-02-02 10:52:52.225843] INFO: A股股票过滤: 过滤完成, 183199 + 0

[2023-02-02 10:52:52.262149] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[2.376656s].

[2023-02-02 10:52:52.311754] INFO: moduleinvoker: dropnan.v2 开始运行..

[2023-02-02 10:52:52.839851] INFO: dropnan: /data, 182928/183199

[2023-02-02 10:52:52.905946] INFO: dropnan: 行数: 182928/183199

[2023-02-02 10:52:52.919053] INFO: moduleinvoker: dropnan.v2 运行完成[0.607291s].

[2023-02-02 10:52:52.933319] INFO: moduleinvoker: sort.v4 开始运行..

[2023-02-02 10:52:53.702902] INFO: moduleinvoker: sort.v4 运行完成[0.769575s].

[2023-02-02 10:52:53.730550] INFO: moduleinvoker: filter.v3 开始运行..

[2023-02-02 10:52:53.752301] INFO: filter: 使用表达式 my==1 过滤

[2023-02-02 10:52:54.148547] INFO: filter: 过滤 /data, 45/0/182928

[2023-02-02 10:52:54.185631] INFO: moduleinvoker: filter.v3 运行完成[0.455093s].

[2023-02-02 10:52:56.739964] INFO: moduleinvoker: backtest.v8 开始运行..

[2023-02-02 10:52:56.749621] INFO: backtest: biglearning backtest:V8.6.3

[2023-02-02 10:52:56.821709] INFO: backtest: product_type:stock by specified

[2023-02-02 10:52:58.724480] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: IndexError: index 4435 is out of bounds for axis 0 with size 4435

[2023-02-02 10:52:58.737740] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: IndexError: index 4435 is out of bounds for axis 0 with size 4435

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-1-11775cc02167> in <module>
    248 )
    249 
--> 250 m8 = M.trade.v4(
    251     instruments=m1.data,
    252     options_data=m3.data,

IndexError: index 4435 is out of bounds for axis 0 with size 4435