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{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-4862:features","from_node_id":"-4857:data"},{"to_node_id":"-6998:features","from_node_id":"-4857:data"},{"to_node_id":"-4862:instruments","from_node_id":"-4849:data"},{"to_node_id":"-281:instruments","from_node_id":"-4849:data"},{"to_node_id":"-768:instruments","from_node_id":"-4849:data"},{"to_node_id":"-40:instruments","from_node_id":"-4849:data"},{"to_node_id":"-6998:input_data","from_node_id":"-4862:data"},{"to_node_id":"-125:input_data","from_node_id":"-2687:data"},{"to_node_id":"-2687:input_data","from_node_id":"-6998:data"},{"to_node_id":"-118:data2","from_node_id":"-281:data"},{"to_node_id":"-768:options_data","from_node_id":"-110:sorted_data"},{"to_node_id":"-40:options_data","from_node_id":"-110:sorted_data"},{"to_node_id":"-110:input_ds","from_node_id":"-118:data"},{"to_node_id":"-118:data1","from_node_id":"-125:data"}],"nodes":[{"node_id":"-768","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":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.test_data = context.options['data'].read_df()\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 # 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 context.stock_count = 3\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 = 1/context.stock_count\n #持仓周期\n context.hold_days = 7\n","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":"# 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 #------------------------------------------止赢模块START--------------------------------------------\n date = data.current_dt.strftime('%Y-%m-%d')\n positions = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}\n # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stopwin_stock = [] \n if len(positions) > 0:\n for i in positions.keys():\n stock_cost = positions[i] \n stock_market_price = data.current(context.symbol(i), 'price') \n # 赚30%就止赢\n if (stock_market_price - stock_cost ) / stock_cost>= 0.25: \n context.order_target_percent(context.symbol(i),0) \n current_stopwin_stock.append(i)\n print('日期：',date,'股票：',i,'出现止盈状况')\n #-------------------------------------------止赢模块END---------------------------------------------\n if context.trading_day_index % context.hold_days != 0:\n return \n \n today = data.current_dt.strftime('%Y-%m-%d')\n \n # 获取当前持仓\n positions = {e.symbol: p.amount for e, p in context.portfolio.positions.items()}\n \n # 按日期过滤得到今日数据\n today_data = context.test_data[context.test_data.date == today]\n #今日需要买入的股票\n stocks_buy = today_data.instrument.iloc[0:context.stock_count].to_list()\n stocks_buy_name = today_data.name.iloc[0:context.stock_count].to_list() #打印显示用\n #print(today,\"买入股票池：\",stocks_buy,'',stocks_buy_name)\n #卖出\n for instrument in positions.keys():\n if instrument not in stocks_buy:\n context.order_target(context.symbol(instrument), 0)\n #print(today,\"卖出\",instrument)\n #买入\n cash_per_instrument = context.portfolio.portfolio_value * context.stock_weights\n print(cash_per_instrument)\n for instrument in stocks_buy:\n if instrument not in positions.keys():\n context.order_value(context.symbol(instrument), cash_per_instrument)\n #print(today,\"买入\",instrument) \n ","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":"股票","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":"twap_1","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"twap_8","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"False","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":"-768"},{"name":"options_data","node_id":"-768"},{"name":"history_ds","node_id":"-768"},{"name":"benchmark_ds","node_id":"-768"}],"output_ports":[{"name":"raw_perf","node_id":"-768"}],"cacheable":false,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-40","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 # 加载预测数据\n context.test_data = context.options['data'].read_df()\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 # 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 context.stock_count = 3\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 = 1/context.stock_count\n #持仓周期\n context.hold_days = 7\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 #------------------------------------------止赢模块START--------------------------------------------\n date = data.current_dt.strftime('%Y-%m-%d')\n positions = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}\n # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stopwin_stock = [] \n if len(positions) > 0:\n for i in positions.keys():\n stock_cost = positions[i] \n stock_market_price = data.current(context.symbol(i), 'price') \n # 赚30%就止赢\n if (stock_market_price - stock_cost ) / stock_cost>= 0.25: \n context.order_target_percent(context.symbol(i),0) \n current_stopwin_stock.append(i)\n print('日期：',date,'股票：',i,'出现止盈状况')\n #-------------------------------------------止赢模块END---------------------------------------------\n if context.trading_day_index % context.hold_days != 0:\n return \n \n today = data.current_dt.strftime('%Y-%m-%d')\n \n # 获取当前持仓\n positions = {e.symbol: p.amount for e, p in context.portfolio.positions.items()}\n \n # 按日期过滤得到今日数据\n today_data = context.test_data[context.test_data.date == today]\n #今日需要买入的股票\n stocks_buy = today_data.instrument.iloc[0:context.stock_count].to_list()\n stocks_buy_name = today_data.name.iloc[0:context.stock_count].to_list() #打印显示用\n #print(today,\"买入股票池：\",stocks_buy,'',stocks_buy_name)\n #卖出\n for instrument in positions.keys():\n if instrument not in stocks_buy:\n context.order_target(context.symbol(instrument), 0)\n #print(today,\"卖出\",instrument)\n #买入\n cash_per_instrument = context.portfolio.portfolio_value * context.stock_weights\n print(cash_per_instrument)\n for instrument in stocks_buy:\n if instrument not in positions.keys():\n context.order_value(context.symbol(instrument), cash_per_instrument)\n #print(today,\"买入\",instrument) \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 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","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"twap_1","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"twap_8","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":"346937","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":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-40"},{"name":"options_data","node_id":"-40"},{"name":"history_ds","node_id":"-40"},{"name":"benchmark_ds","node_id":"-40"},{"name":"trading_calendar","node_id":"-40"}],"output_ports":[{"name":"raw_perf","node_id":"-40"}],"cacheable":false,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-4857","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释，注释需单独一行\n\nhs=sum(turn_0,5)\nhs1=shift(hs,5)\nmy=where(hs>hs1,1,0)\nbuy_condition=where(my>0,1,0)\nsell_condition=where(close_0>mean(close_0,60),1,0)\n\n\n\n\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-4857"}],"output_ports":[{"name":"data","node_id":"-4857"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-4849","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2022-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-09-06","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":"-4849"}],"output_ports":[{"name":"data","node_id":"-4849"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-4862","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":"120","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-4862"},{"name":"features","node_id":"-4862"}],"output_ports":[{"name":"data","node_id":"-4862"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-2687","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%3Atrue%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%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%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    In [4]:
    # 本代码由可视化策略环境自动生成 2022年9月20日 10:51
# 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 m1_initialize_bigquant_run(context):
    # 加载预测数据
    context.test_data = context.options['data'].read_df()

    # 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))
    # 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
    context.stock_count = 3
    # 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 = 1/context.stock_count
    #持仓周期
    context.hold_days = 7

# 交易引擎：每个单位时间开盘前调用一次。
def m1_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    pass

# 交易引擎：tick数据处理函数，每个tick执行一次
def m1_handle_tick_bigquant_run(context, tick):
    pass

# 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):
    
     #------------------------------------------止赢模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stopwin_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 赚30%就止赢
            if (stock_market_price - stock_cost ) / stock_cost>= 0.25:   
                context.order_target_percent(context.symbol(i),0)     
                current_stopwin_stock.append(i)
                print('日期：',date,'股票：',i,'出现止盈状况')
    #-------------------------------------------止赢模块END---------------------------------------------
    if context.trading_day_index % context.hold_days != 0:
        return 
    
    today = data.current_dt.strftime('%Y-%m-%d')
    
    # 获取当前持仓
    positions = {e.symbol: p.amount for e, p in context.portfolio.positions.items()}
        
    # 按日期过滤得到今日数据
    today_data = context.test_data[context.test_data.date == today]
    #今日需要买入的股票
    stocks_buy = today_data.instrument.iloc[0:context.stock_count].to_list()
    stocks_buy_name = today_data.name.iloc[0:context.stock_count].to_list() #打印显示用
    #print(today,"买入股票池：",stocks_buy,'',stocks_buy_name)
    #卖出
    for instrument in positions.keys():
        if instrument not in stocks_buy:
            context.order_target(context.symbol(instrument), 0)
            #print(today,"卖出",instrument)
    #买入
    cash_per_instrument = context.portfolio.portfolio_value * context.stock_weights
    print(cash_per_instrument)
    for instrument in stocks_buy:
        if instrument not in positions.keys():
            context.order_value(context.symbol(instrument), cash_per_instrument)
            #print(today,"买入",instrument)          
    
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m1_handle_trade_bigquant_run(context, trade):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m1_handle_order_bigquant_run(context, order):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m1_after_trading_bigquant_run(context, data):
    pass

# 回测引擎：初始化函数，只执行一次
def m2_initialize_bigquant_run(context):
    # 加载预测数据
    context.test_data = context.options['data'].read_df()

    # 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))
    # 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
    context.stock_count = 3
    # 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 = 1/context.stock_count
    #持仓周期
    context.hold_days = 7

# 回测引擎：每日数据处理函数，每天执行一次
def m2_handle_data_bigquant_run(context, data):
    
     #------------------------------------------止赢模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stopwin_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 赚30%就止赢
            if (stock_market_price - stock_cost ) / stock_cost>= 0.25:   
                context.order_target_percent(context.symbol(i),0)     
                current_stopwin_stock.append(i)
                print('日期：',date,'股票：',i,'出现止盈状况')
    #-------------------------------------------止赢模块END---------------------------------------------
    if context.trading_day_index % context.hold_days != 0:
        return 
    
    today = data.current_dt.strftime('%Y-%m-%d')
    
    # 获取当前持仓
    positions = {e.symbol: p.amount for e, p in context.portfolio.positions.items()}
        
    # 按日期过滤得到今日数据
    today_data = context.test_data[context.test_data.date == today]
    #今日需要买入的股票
    stocks_buy = today_data.instrument.iloc[0:context.stock_count].to_list()
    stocks_buy_name = today_data.name.iloc[0:context.stock_count].to_list() #打印显示用
    #print(today,"买入股票池：",stocks_buy,'',stocks_buy_name)
    #卖出
    for instrument in positions.keys():
        if instrument not in stocks_buy:
            context.order_target(context.symbol(instrument), 0)
            #print(today,"卖出",instrument)
    #买入
    cash_per_instrument = context.portfolio.portfolio_value * context.stock_weights
    print(cash_per_instrument)
    for instrument in stocks_buy:
        if instrument not in positions.keys():
            context.order_value(context.symbol(instrument), cash_per_instrument)
            #print(today,"买入",instrument)          
    
# 回测引擎：准备数据，只执行一次
def m2_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 m2_before_trading_start_bigquant_run(context, data):
    pass


m3 = M.input_features.v1(
    features="""# #号开始的表示注释，注释需单独一行

hs=sum(turn_0,5)
hs1=shift(hs,5)
my=where(hs>hs1,1,0)
buy_condition=where(my>0,1,0)
sell_condition=where(close_0>mean(close_0,60),1,0)




"""
)

m4 = M.instruments.v2(
    start_date='2022-01-01',
    end_date='2022-09-06',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m5 = M.general_feature_extractor.v7(
    instruments=m4.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=120
)

m7 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    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', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m11 = M.filter.v3(
    input_data=m6.data,
    expr='my>0',
    output_left_data=False
)

m8 = M.use_datasource.v1(
    instruments=m4.data,
    datasource_id='instruments_CN_STOCK_A',
    start_date='',
    end_date=''
)

m10 = M.join.v3(
    data1=m11.data,
    data2=m8.data,
    on='date,instrument',
    how='left',
    sort=False
)

m9 = M.sort.v5(
    input_ds=m10.data,
    sort_by='hs',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m1 = M.hftrade.v2(
    instruments=m4.data,
    options_data=m9.sorted_data,
    start_date='',
    end_date='',
    initialize=m1_initialize_bigquant_run,
    before_trading_start=m1_before_trading_start_bigquant_run,
    handle_tick=m1_handle_tick_bigquant_run,
    handle_data=m1_handle_data_bigquant_run,
    handle_trade=m1_handle_trade_bigquant_run,
    handle_order=m1_handle_order_bigquant_run,
    after_trading=m1_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='daily',
    price_type='真实价格',
    product_type='股票',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='twap_1',
    order_price_field_sell='twap_8',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

m2 = M.trade.v4(
    instruments=m4.data,
    options_data=m9.sorted_data,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    handle_data=m2_handle_data_bigquant_run,
    prepare=m2_prepare_bigquant_run,
    before_trading_start=m2_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='twap_1',
    order_price_field_sell='twap_8',
    capital_base=346937,
    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=''
)

    333333.3333333333
2022-09-20 10:51:03.550750 strategy strategy exception:Traceback (most recent call last):
  File "bigtrader/strategy/engine.py", line 713, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
  File "bigtrader/strategy/strategy_base.py", line 2244, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_data
  File "<ipython-input-4-5715c2018ab3>", line 34, in m1_handle_data_bigquant_run
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
  File "<ipython-input-4-5715c2018ab3>", line 34, in <dictcomp>
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
AttributeError: 'str' object has no attribute 'symbol'
 

    ---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-4-5715c2018ab3> in <module>
    233 )
    234 
--> 235 m1 = M.hftrade.v2(
    236     instruments=m4.data,
    237     options_data=m9.sorted_data,

<ipython-input-4-5715c2018ab3> in m1_handle_data_bigquant_run(context, data)
     32      #------------------------------------------止赢模块START--------------------------------------------
     33     date = data.current_dt.strftime('%Y-%m-%d')
---> 34     positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
     35     # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
     36     current_stopwin_stock = []

<ipython-input-4-5715c2018ab3> in <dictcomp>(.0)
     32      #------------------------------------------止赢模块START--------------------------------------------
     33     date = data.current_dt.strftime('%Y-%m-%d')
---> 34     positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
     35     # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
     36     current_stopwin_stock = []

AttributeError: 'str' object has no attribute 'symbol'