复制链接
克隆策略

    {"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":"-2738:instruments","from_node_id":"-4849:data"},{"to_node_id":"-281:instruments","from_node_id":"-4849:data"},{"to_node_id":"-2938:instruments","from_node_id":"-4849:data"},{"to_node_id":"-6998:input_data","from_node_id":"-4862:data"},{"to_node_id":"-497:input_data","from_node_id":"-2687:data"},{"to_node_id":"-2687:input_data","from_node_id":"-6998:data"},{"to_node_id":"-526:input_data","from_node_id":"-497:data"},{"to_node_id":"-119:input_data","from_node_id":"-526:data"},{"to_node_id":"-2738:options_data","from_node_id":"-61:sorted_data"},{"to_node_id":"-2938:options_data","from_node_id":"-61:sorted_data"},{"to_node_id":"-115:input_data","from_node_id":"-288:data"},{"to_node_id":"-288:data2","from_node_id":"-281:data"},{"to_node_id":"-61:input_ds","from_node_id":"-115:data"},{"to_node_id":"-288:data1","from_node_id":"-119:data"}],"nodes":[{"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\nreturn_5\nmean(close_0,20)\nmean(close_0,60)\nmarket_cap_float_0\nmy1=where(close_0>mean(close_0,20),1,0)\nmy2=where(close_0<mean(close_0,60),1,0)\nmy=my1*my2\nbuy_condition=where(my>0,1,0)\nsell_condition=where(close_0>mean(close_0,60),1,0)\n\n\n\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":false,"seq_num":2,"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":"2022-06-17","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":false,"seq_num":11,"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":false,"seq_num":12,"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%22dis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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.ranker_prediction = 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 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 = 30\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>= 0.3: \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\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 # 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 * 0.15\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 # 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_stopwin_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 = 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= 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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.ranker_prediction = 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 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 # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金\n # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）\n is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.hold_days\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\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 if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\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_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\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 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    In [3]:
    # 本代码由可视化策略环境自动生成 2022年6月21日 14:49
# 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 m20_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = 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
    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 = 30

# 回测引擎：每日数据处理函数，每天执行一次
def m20_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>= 0.3:   
                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---------------------------------------------

       
         #------------------------------------------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 * 0.15
            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_stopwin_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 m20_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)
# 回测引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = 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
    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 m3_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.hold_days
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    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()}

    # 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
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 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_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m3_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 m3_before_trading_start_bigquant_run(context, data):
    pass


m2 = M.input_features.v1(
    features="""# #号开始的表示注释，注释需单独一行
return_5
mean(close_0,20)
mean(close_0,60)
market_cap_float_0
my1=where(close_0>mean(close_0,20),1,0)
my2=where(close_0<mean(close_0,60),1,0)
my=my1*my2
buy_condition=where(my>0,1,0)
sell_condition=where(close_0>mean(close_0,60),1,0)







""",
    m_cached=False
)

m11 = M.instruments.v2(
    start_date='2022-01-01',
    end_date='2022-06-17',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0,
    m_cached=False
)

m12 = M.general_feature_extractor.v7(
    instruments=m11.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=120,
    m_cached=False
)

m14 = M.derived_feature_extractor.v3(
    input_data=m12.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={},
    m_cached=False
)

m13 = M.chinaa_stock_filter.v1(
    input_data=m14.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=True,
    m_cached=False
)

m15 = M.filter.v3(
    input_data=m13.data,
    expr='market_cap_float_0<1000000000',
    output_left_data=False,
    m_cached=False
)

m16 = M.filter.v3(
    input_data=m15.data,
    expr='market_cap_float_0>200000000',
    output_left_data=False,
    m_cached=False
)

m5 = M.filter.v3(
    input_data=m16.data,
    expr='my>-1',
    output_left_data=False,
    m_cached=False
)

m1 = M.use_datasource.v1(
    instruments=m11.data,
    datasource_id='instruments_CN_STOCK_A',
    start_date='',
    end_date='',
    m_cached=False
)

m18 = M.join.v3(
    data1=m5.data,
    data2=m1.data,
    on='date,instrument',
    how='left',
    sort=False,
    m_cached=False
)

m4 = M.dropnan.v2(
    input_data=m18.data
)

m17 = M.sort.v5(
    input_ds=m4.data,
    sort_by='return_5',
    group_by='date',
    keep_columns='--',
    ascending=True,
    m_cached=False
)

m20 = M.trade.v4(
    instruments=m11.data,
    options_data=m17.sorted_data,
    start_date='',
    end_date='',
    initialize=m20_initialize_bigquant_run,
    handle_data=m20_handle_data_bigquant_run,
    prepare=m20_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='twap_6',
    order_price_field_sell='twap_4',
    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'
)

m3 = M.trade.v4(
    instruments=m11.data,
    options_data=m17.sorted_data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    before_trading_start=m3_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'
)

    日期： 2022-01-24 股票： 301065.SZA 出现止损状况
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日期： 2022-03-29 股票： 301040.SZA 出现止损状况
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日期： 2022-04-11 股票： 301182.SZA 出现止损状况
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日期： 2022-04-26 股票： 300897.SZA 出现止损状况

    日期： 2022-05-05 股票： 002789.SZA 出现止盈状况
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日期： 2022-05-11 股票： 002789.SZA 出现止盈状况

    日期： 2022-05-13 股票： 002883.SZA 出现止盈状况
日期： 2022-05-13 股票： 301072.SZA 出现止盈状况

    日期： 2022-05-16 股票： 002789.SZA 出现止损状况
日期： 2022-05-16 股票： 300980.SZA 出现止损状况

    日期： 2022-05-17 股票： 003037.SZA 出现止损状况

    日期： 2022-05-20 股票： 605155.SHA 出现止损状况

    日期： 2022-05-23 股票： 002789.SZA 出现止损状况

    日期： 2022-05-24 股票： 300948.SZA 出现止损状况
日期： 2022-05-24 股票： 301158.SZA 出现止损状况
日期： 2022-05-24 股票： 300992.SZA 出现止损状况
日期： 2022-05-24 股票： 301126.SZA 出现止损状况
日期： 2022-05-24 股票： 300865.SZA 出现止损状况
日期： 2022-05-24 股票： 003007.SZA 出现止损状况
日期： 2022-05-24 股票： 603109.SHA 出现止损状况

    日期： 2022-05-26 股票： 300795.SZA 出现止损状况
日期： 2022-05-26 股票： 300845.SZA 出现止损状况

    日期： 2022-05-27 股票： 300975.SZA 出现止损状况
日期： 2022-05-27 股票： 003023.SZA 出现止损状况

    日期： 2022-05-30 股票： 300987.SZA 出现止损状况
日期： 2022-05-30 股票： 300839.SZA 出现止损状况
日期： 2022-05-30 股票： 603390.SHA 出现止损状况
日期： 2022-05-30 股票： 003001.SZA 出现止损状况
日期： 2022-05-30 股票： 300907.SZA 出现止损状况

    日期： 2022-05-31 股票： 300991.SZA 出现止损状况

    日期： 2022-06-01 股票： 002789.SZA 出现止损状况

    日期： 2022-06-06 股票： 300948.SZA 出现止损状况
日期： 2022-06-06 股票： 301126.SZA 出现止损状况
日期： 2022-06-06 股票： 003007.SZA 出现止损状况
日期： 2022-06-06 股票： 300975.SZA 出现止损状况
日期： 2022-06-06 股票： 003023.SZA 出现止损状况
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日期： 2022-06-06 股票： 300977.SZA 出现止损状况
日期： 2022-06-06 股票： 301019.SZA 出现止损状况

    日期： 2022-06-07 股票： 301092.SZA 出现止盈状况
日期： 2022-06-07 股票： 300987.SZA 出现止损状况
日期： 2022-06-07 股票： 002995.SZA 出现止损状况

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日期： 2022-06-08 股票： 300950.SZA 出现止损状况

    日期： 2022-06-09 股票： 003041.SZA 出现止损状况
日期： 2022-06-09 股票： 301228.SZA 出现止损状况

    日期： 2022-06-10 股票： 301091.SZA 出现止损状况

    日期： 2022-06-13 股票： 300955.SZA 出现止损状况
日期： 2022-06-13 股票： 605289.SHA 出现止损状况
日期： 2022-06-13 股票： 301066.SZA 出现止损状况
日期： 2022-06-13 股票： 003037.SZA 出现止损状况
日期： 2022-06-13 股票： 301043.SZA 出现止损状况
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    日期： 2022-06-14 股票： 300564.SZA 出现止损状况
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    日期： 2022-06-15 股票： 002988.SZA 出现止盈状况
日期： 2022-06-15 股票： 300795.SZA 出现止损状况

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日期： 2022-06-16 股票： 300913.SZA 出现止损状况

    日期： 2022-06-17 股票： 300817.SZA 出现止损状况
    • 收益率-1.42%
    • 年化收益率-3.28%
    • 基准收益率-12.38%
    • 阿尔法0.03
    • 贝塔0.28
    • 夏普比率-0.45
    • 胜率0.32
    • 盈亏比0.91
    • 收益波动率12.36%
    • 信息比率0.08
    • 最大回撤11.42%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5c6279c61fb849169a5ae808b4f85266"}/bigcharts-data-end
    • 收益率-30.76%
    • 年化收益率-57.59%
    • 基准收益率-12.38%
    • 阿尔法-0.46
    • 贝塔0.7
    • 夏普比率-2.46
    • 胜率0.44
    • 盈亏比0.67
    • 收益波动率33.69%
    • 信息比率-0.11
    • 最大回撤37.86%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-bdc24fb106d4483b82c3cc98229d4fe2"}/bigcharts-data-end