克隆策略

    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#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\nst_status_0\nlist_board_0\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-326"}],"output_ports":[{"name":"data","node_id":"-326"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-331","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"st_status_0==0","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":"-331"}],"output_ports":[{"name":"data","node_id":"-331"},{"name":"left_data","node_id":"-331"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-337","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"st_status_0==0","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":"-337"}],"output_ports":[{"name":"data","node_id":"-337"},{"name":"left_data","node_id":"-337"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-459","module_id":"BigQuantSpace.trade.trade-v3","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","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 #大盘风控模块，以上证指数5日涨幅为例，如果大盘下跌较多，全部卖出并结束当日操作\n today_date = data.current_dt.strftime('%Y-%m-%d')\n benckmarch_prices = data.history(context.symbols('000001.HIX'), ['close'], 5, '1d')['close']\n benckmarch_control = benckmarch_prices[context.symbol('000001.HIX')][-1] / benckmarch_prices[context.symbol('000001.SHA')][0]\n if benckmarch_control < 0.998:\n position_all = context.portfolio.positions.keys()\n for i in position_all:\n context.order_target(i, 0)\n print('日期',today_date,'大盘风控止损触发')\n return \n #周期控制模块\n if context.trading_day_index%3!=0:#以3天换一次仓为例\n return\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.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['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 #---------------------------START:止赢止损模块(含建仓期)--------------------\n today_date = data.current_dt.strftime('%Y-%m-%d')\n positions_stop={e.symbol:p.cost_basis \n for e,p in context.portfolio.positions.items()}\n # 新建当日止赢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_stopwin_stock=[]\n current_stoploss_stock = [] \n if len(positions_stop)>0:\n for i in positions_stop.keys():\n stock_cost=positions_stop[i] \n stock_market_price=data.current(context.symbol(i),'price') \n # 赚3元且可以交易and not context.has_unfinished_sell_order(equities[i])\n if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):\n context.order_target_percent(context.symbol(i),0) \n current_stopwin_stock.append(i)\n #print('日期：',today_date,'股票：',i,'出现止盈状况')\n print(today_date,'止盈股票列表',current_stopwin_stock)\n # 亏1元就止损and not context.has_unfinished_sell_order(equities[i])\n if stock_market_price - stock_cost <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i): \n context.order_target_percent(context.symbol(i),0) \n current_stoploss_stock.append(i)\n #print('日期：',today_date,'股票：',i,'出现止损状况')\n print(today_date,'止损股票列表',current_stoploss_stock)\n #--------------------------END: 止赢止损模块-----------------------------\n \n #--------------------------START：持有固定天数卖出(不含建仓期)---------------\n current_stopdays_stock = [] \n today = data.current_dt\n today_date = data.current_dt.strftime('%Y-%m-%d')\n # 不是建仓期（在前hold_days属于建仓期）\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n if len(equities)>0:\n for i in equities:\n sid = equities[i].sid # 交易标的\n # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)\n if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n #print('日期：',today_date,'持有固定天数卖出股票',str(i))\n print(today_date,'固定天数卖出列表',current_stopdays_stock)\n #-------------------------------END:持有固定天数卖出--------------------------\n \n \n\n # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法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 if instrument in current_stopwin_stock:\n continue\n if instrument in current_stoploss_stock:\n continue\n if instrument in current_stopdays_stock:\n continue\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：按机器学习算法预测的排序，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)","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 start_date = context.start_date\n end_date = context.end_date\n # 获取目前的行业列表\n industry = list(set(D.history_data(D.instruments(), end_date, end_date, ['industry_sw_level3']).industry_sw_level3))\n # 获取行业指数的行情数据\n industry = ['SW'+str(j)+'.SHA' for j in industry]\n data = D.history_data(industry, start_date, end_date, ['close','name'])\n\n # 获取股票名称 用于过滤st和退市股\n context.name_df = DataSource('instruments_CN_STOCK_A').read()\n # 获取涨跌停状态\n context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])\n\n \n # 计算此处每日动量较高的行业\n ret_data = data.groupby('instrument').apply(calcu_ret)\n ret_data.reset_index(inplace=True, drop=True)\n ret_data['date'] = ret_data['date'].map(lambda x:x.strftime('%Y-%m-%d'))\n context.daily_buy_industry = pd.Series({dt:seek_head_industry(ret_data.set_index('date').loc[dt]) for dt in list(set(ret_data.date))})\n\n # 每个交易日 每个行业的优质股 \n # 优质股的确定依据是：净资产收益率 (TTM)、营业收入同比增长率、归属母公司股东的净利润同比增长率\n features_data = D.features(D.instruments(start_date, end_date), start_date, end_date, ['fs_roe_ttm_0', 'fs_operating_revenue_yoy_0', 'fs_net_profit_yoy_0', 'industry_sw_level3_0'])\n # 整理出每个行业的优质股票\n context.daily_buy_stock = features_data.groupby(['date', 'industry_sw_level3_0']).apply(seek_head_stock)\n\n# 计算不同周期的动量\ndef calcu_ret(df):\n df = df.sort_values('date')\n for i in [3, 9, 21, 42, 84, 126]: # 分别代表2月、4月、半年的动量\n df['ret_%s'%i] = df['close']/df['close'].shift(i)-1 \n return df\n\n# 计算出得分\ndef seek_head_industry(df):\n for j in ['ret_3','ret_9','ret_21','ret_42','ret_84','ret_126']:\n df['%s'%j] = df['%s'%j].rank(ascending=True) \n df['score'] =0.3*df['ret_3']+ 0.2*df['ret_9']+0.15*df['ret_21']+0.15*df['ret_42']+0.1*df['ret_84']+0.1*df['ret_126'] # 得分的权重分别为0.4、0.3、0.3\n result = df.sort_values('score', ascending=False)\n return list(result.instrument)[:5] # 前3个行业\n\n# 选出特定行业优质股票\ndef seek_head_stock(df):\n result = df.sort_values(['fs_roe_ttm_0', 'fs_net_profit_yoy_0', 'fs_operating_revenue_yoy_0'], ascending=False)\n return list(result.instrument[:10]) # 每个行业选10只股票","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.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 = 0.1\n context.options['hold_days'] = 1\n\n from zipline.finance.slippage import SlippageModel\n class FixedPriceSlippage(SlippageModel):\n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price = data.current(order.asset, price_field)\n else:\n price = data.current(order.asset, self._price_field_buy)\n # 返回希望成交的价格和数量\n return (price, order.amount)\n # 设置price_field,默认是开盘买入，收盘卖出\n context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')\n context.set_slippage(us_equities=context.fix_slippage)","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"def bigquant_run(context, data):\n df_price_limit_status=context.price_limit_status.set_index('date')\n today=data.current_dt.strftime('%Y-%m-%d')\n # 得到当前未完成订单\n for orders in get_open_orders().values():\n # 循环，撤销订单\n for _order in orders:\n ins=str(_order.sid.symbol)\n if data.can_trade(_order.sid):\n #判断一下如果当日涨停，则取消卖单\n if df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.loc[today]>2 and _order.amount<0:\n #cancel_order(_order)取消卖单\n print(today,'尾盘涨停',ins) ","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","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":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-459"},{"name":"options_data","node_id":"-459"}],"output_ports":[{"name":"raw_perf","node_id":"-459"}],"cacheable":false,"seq_num":17,"comment":"","comment_collapsed":true},{"node_id":"-613","module_id":"BigQuantSpace.hyper_rolling_train.hyper_rolling_train-v1","parameters":[{"name":"run","value":"def bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m8', # 预测 模块id\n trade_mid='m17', # 回测 模块id\n start_date='2021-08-30', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2021-10-16'), # 数据结束日期\n train_update_days=6, # 更新周期，按交易日计算，每多少天更新一次\n train_update_days_for_live=6, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数\n train_data_min_days=21, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=21, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=0, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = start_date\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行，请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions,result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': trade}\n","type":"Literal","bound_global_parameter":null},{"name":"run_now","value":"True","type":"Literal","bound_global_parameter":null},{"name":"bq_graph","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"bq_graph_port","node_id":"-613"},{"name":"input_1","node_id":"-613"},{"name":"input_2","node_id":"-613"},{"name":"input_3","node_id":"-613"}],"output_ports":[{"name":"result","node_id":"-613"}],"cacheable":false,"seq_num":18,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='122,-62,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='810,-270,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-29' Position='364,96,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-35' Position='372,211,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='638,561,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='906,647,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1090,-73,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-70' Position='923,158,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-76' Position='873,291,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='376,467,200,200'/><node_position Node='-86' Position='1089,536,200,200'/><node_position Node='-326' Position='641,-68,200,200'/><node_position Node='-331' Position='450,289,200,200'/><node_position Node='-337' Position='1199,404,200,200'/><node_position Node='-459' Position='766,765,200,200'/><node_position Node='-613' Position='276,774,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
    In [7]:
    # 本代码由可视化策略环境自动生成 2021年10月19日 11:29
# 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：每日数据处理函数，每天执行一次
def m17_handle_data_bigquant_run(context, data):
    
    #大盘风控模块，以上证指数5日涨幅为例，如果大盘下跌较多，全部卖出并结束当日操作
    today_date = data.current_dt.strftime('%Y-%m-%d')
    benckmarch_prices = data.history(context.symbols('000001.HIX'), ['close'], 5, '1d')['close']
    benckmarch_control = benckmarch_prices[context.symbol('000001.HIX')][-1] / benckmarch_prices[context.symbol('000001.SHA')][0]
    if benckmarch_control < 0.998:
        position_all = context.portfolio.positions.keys()
        for i in position_all:
            context.order_target(i, 0)
        print('日期',today_date,'大盘风控止损触发')
        return    
    #周期控制模块
    if context.trading_day_index%3!=0:#以3天换一次仓为例
        return
    # 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.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['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()} 

    #---------------------------START:止赢止损模块(含建仓期)--------------------
    today_date = data.current_dt.strftime('%Y-%m-%d')
    positions_stop={e.symbol:p.cost_basis 
    for e,p in context.portfolio.positions.items()}
    # 新建当日止赢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_stopwin_stock=[]
    current_stoploss_stock = []   
    if len(positions_stop)>0:
        for i in positions_stop.keys():
            stock_cost=positions_stop[i]  
            stock_market_price=data.current(context.symbol(i),'price')  
            # 赚3元且可以交易and not context.has_unfinished_sell_order(equities[i])
            if stock_market_price-stock_cost>=0.5 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):
                context.order_target_percent(context.symbol(i),0)      
                current_stopwin_stock.append(i)
                #print('日期：',today_date,'股票：',i,'出现止盈状况')
            print(today_date,'止盈股票列表',current_stopwin_stock)
                # 亏1元就止损and not context.has_unfinished_sell_order(equities[i])
            if stock_market_price - stock_cost  <= -1 and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(i):   
                context.order_target_percent(context.symbol(i),0)     
                current_stoploss_stock.append(i)
                #print('日期：',today_date,'股票：',i,'出现止损状况')
            print(today_date,'止损股票列表',current_stoploss_stock)
    #--------------------------END: 止赢止损模块-----------------------------
    
    #--------------------------START：持有固定天数卖出(不含建仓期)---------------
    current_stopdays_stock = [] 
    today = data.current_dt
    today_date = data.current_dt.strftime('%Y-%m-%d')
    # 不是建仓期（在前hold_days属于建仓期）
    if not is_staging:
        equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        if len(equities)>0:
            for i in equities:
                sid = equities[i].sid  # 交易标的
                # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)
                if today-equities[i].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(i)) and not context.has_unfinished_sell_order(equities[i]):
                    context.order_target_percent(sid, 0)
                    current_stopdays_stock.append(i)
                    #print('日期：',today_date,'持有固定天数卖出股票',str(i))
            print(today_date,'固定天数卖出列表',current_stopdays_stock)
    #-------------------------------END:持有固定天数卖出--------------------------
       
    

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法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:
            if instrument  in current_stopwin_stock:
                continue
            if instrument  in current_stoploss_stock:
                continue
            if instrument  in current_stopdays_stock:
                continue
            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：按机器学习算法预测的排序，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 m17_prepare_bigquant_run(context):
    start_date = context.start_date
    end_date = context.end_date
    # 获取目前的行业列表
    industry = list(set(D.history_data(D.instruments(), end_date, end_date, ['industry_sw_level3']).industry_sw_level3))
    # 获取行业指数的行情数据
    industry = ['SW'+str(j)+'.SHA' for j in industry]
    data = D.history_data(industry, start_date, end_date, ['close','name'])

    # 获取股票名称 用于过滤st和退市股
    context.name_df = DataSource('instruments_CN_STOCK_A').read()
    # 获取涨跌停状态
    context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])

    
    # 计算此处每日动量较高的行业
    ret_data = data.groupby('instrument').apply(calcu_ret)
    ret_data.reset_index(inplace=True, drop=True)
    ret_data['date'] = ret_data['date'].map(lambda x:x.strftime('%Y-%m-%d'))
    context.daily_buy_industry = pd.Series({dt:seek_head_industry(ret_data.set_index('date').loc[dt]) for dt in list(set(ret_data.date))})

    # 每个交易日 每个行业的优质股 
    # 优质股的确定依据是：净资产收益率 (TTM)、营业收入同比增长率、归属母公司股东的净利润同比增长率
    features_data = D.features(D.instruments(start_date, end_date), start_date, end_date, ['fs_roe_ttm_0', 'fs_operating_revenue_yoy_0', 'fs_net_profit_yoy_0', 'industry_sw_level3_0'])
    # 整理出每个行业的优质股票
    context.daily_buy_stock = features_data.groupby(['date', 'industry_sw_level3_0']).apply(seek_head_stock)

# 计算不同周期的动量
def calcu_ret(df):
    df = df.sort_values('date')
    for i in [3, 9, 21, 42, 84, 126]: # 分别代表2月、4月、半年的动量
        df['ret_%s'%i] = df['close']/df['close'].shift(i)-1 
    return df

# 计算出得分
def seek_head_industry(df):
    for j in ['ret_3','ret_9','ret_21','ret_42','ret_84','ret_126']:
        df['%s'%j] = df['%s'%j].rank(ascending=True) 
    df['score'] =0.3*df['ret_3']+ 0.2*df['ret_9']+0.15*df['ret_21']+0.15*df['ret_42']+0.1*df['ret_84']+0.1*df['ret_126']  # 得分的权重分别为0.4、0.3、0.3
    result = df.sort_values('score', ascending=False)
    return list(result.instrument)[:5]  # 前3个行业

# 选出特定行业优质股票
def seek_head_stock(df):
    result = df.sort_values(['fs_roe_ttm_0', 'fs_net_profit_yoy_0', 'fs_operating_revenue_yoy_0'], ascending=False)
    return list(result.instrument[:10]) # 每个行业选10只股票
# 回测引擎：初始化函数，只执行一次
def m17_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 = 0.1
    context.options['hold_days'] = 1

    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = data.current(order.asset, self._price_field_buy)
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 设置price_field,默认是开盘买入，收盘卖出
    context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
    context.set_slippage(us_equities=context.fix_slippage)
def m17_before_trading_start_bigquant_run(context, data):
    df_price_limit_status=context.price_limit_status.set_index('date')
    today=data.current_dt.strftime('%Y-%m-%d')
    # 得到当前未完成订单
    for orders in get_open_orders().values():
        # 循环，撤销订单
        for _order in orders:
            ins=str(_order.sid.symbol)
            if data.can_trade(_order.sid):
                #判断一下如果当日涨停，则取消卖单
                if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.loc[today]>2 and _order.amount<0:
                    #cancel_order(_order)取消卖单
                    print(today,'尾盘涨停',ins) 

g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2010-01-01',
    'm1.end_date': '2015-01-01',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.label_expr': """# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_

# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -1) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 20)

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.features': """avg_amount_4/amount_0
avg_amount_9/amount_0
""",

    'm14': 'M.input_features.v1',
    'm14.features_ds': T.Graph.OutputPort('m3.data'),
    'm14.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
st_status_0
list_board_0
""",

    'm4': 'M.general_feature_extractor.v6',
    'm4.instruments': T.Graph.OutputPort('m1.data'),
    'm4.features': T.Graph.OutputPort('m14.data'),
    'm4.start_date': '',
    'm4.end_date': '',
    'm4.before_start_days': 0,

    'm5': 'M.derived_feature_extractor.v2',
    'm5.input_data': T.Graph.OutputPort('m4.data'),
    'm5.features': T.Graph.OutputPort('m14.data'),
    'm5.date_col': 'date',
    'm5.instrument_col': 'instrument',

    'm15': 'M.filter.v3',
    'm15.input_data': T.Graph.OutputPort('m5.data'),
    'm15.expr': 'st_status_0==0',
    'm15.output_left_data': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m2.data'),
    'm7.data2': T.Graph.OutputPort('m15.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm12': 'M.dropnan.v1',
    'm12.input_data': T.Graph.OutputPort('m7.data'),

    'm6': 'M.stock_ranker_train.v5',
    'm6.training_ds': T.Graph.OutputPort('m12.data'),
    'm6.features': T.Graph.OutputPort('m3.data'),
    'm6.learning_algorithm': '排序',
    'm6.number_of_leaves': 30,
    'm6.minimum_docs_per_leaf': 1000,
    'm6.number_of_trees': 20,
    'm6.learning_rate': 0.1,
    'm6.max_bins': 1023,
    'm6.feature_fraction': 1,
    'm6.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2017-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2017-3-31'),
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm10': 'M.general_feature_extractor.v6',
    'm10.instruments': T.Graph.OutputPort('m9.data'),
    'm10.features': T.Graph.OutputPort('m14.data'),
    'm10.start_date': '',
    'm10.end_date': '',
    'm10.before_start_days': 0,

    'm11': 'M.derived_feature_extractor.v2',
    'm11.input_data': T.Graph.OutputPort('m10.data'),
    'm11.features': T.Graph.OutputPort('m14.data'),
    'm11.date_col': 'date',
    'm11.instrument_col': 'instrument',

    'm16': 'M.filter.v3',
    'm16.input_data': T.Graph.OutputPort('m11.data'),
    'm16.expr': 'st_status_0==0',
    'm16.output_left_data': False,

    'm13': 'M.dropnan.v1',
    'm13.input_data': T.Graph.OutputPort('m16.data'),

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m6.model'),
    'm8.data': T.Graph.OutputPort('m13.data'),
    'm8.m_lazy_run': False,

    'm17': 'M.trade.v3',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.options_data': T.Graph.OutputPort('m8.predictions'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.handle_data': m17_handle_data_bigquant_run,
    'm17.prepare': m17_prepare_bigquant_run,
    'm17.initialize': m17_initialize_bigquant_run,
    'm17.before_trading_start': m17_before_trading_start_bigquant_run,
    'm17.volume_limit': 0.025,
    'm17.order_price_field_buy': 'open',
    'm17.order_price_field_sell': 'close',
    'm17.capital_base': 1000000,
    'm17.benchmark': '000300.SHA',
    'm17.auto_cancel_non_tradable_orders': True,
    'm17.data_frequency': 'daily',
    'm17.price_type': '后复权',
    'm17.plot_charts': True,
    'm17.backtest_only': False,
})

# g.run({})


def m18_run_bigquant_run(
    bq_graph,
    inputs,
    trading_days_market='CN', # 使用那个市场的交易日历
    train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
    test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
    predict_mid='m8', # 预测 模块id
    trade_mid='m17', # 回测 模块id
    start_date='2021-08-30', # 数据开始日期
    end_date=T.live_run_param('trading_date', '2021-10-16'), # 数据结束日期
    train_update_days=6, # 更新周期，按交易日计算，每多少天更新一次
    train_update_days_for_live=6, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数
    train_data_min_days=21, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
    train_data_max_days=21, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
    rolling_count_for_live=0, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制
):
    def merge_datasources(input_1):
        df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]
        df = pd.concat(df_list)
        instrument_data = {
            'start_date': df['date'].min().strftime('%Y-%m-%d'),
            'end_date': df['date'].max().strftime('%Y-%m-%d'),
            'instruments': list(set(df['instrument'])),
        }
        return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))

    def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
        # 是否实盘模式
        tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
        is_live_run = T.live_run_param('trading_date', None) is not None

        if is_live_run and train_update_days_for_live:
            train_update_days = train_update_days_for_live

        rollings = []
        train_end_date = train_data_min_days
        while train_end_date < len(tdays):
            if train_data_max_days is not None:
                train_start_date = max(train_end_date - train_data_max_days, 0)
            else:
                train_start_date = start_date
            rollings.append({
                'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
            })
            train_end_date += train_update_days

        if not rollings:
            raise Exception('没有滚动需要执行，请检查配置')

        if is_live_run and rolling_count_for_live:
            rollings = rollings[-rolling_count_for_live:]

        return rollings

    g = bq_graph

    rolling_dates = gen_rolling_dates(
        trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)

    # 训练和预测
    results = []
    for rolling in rolling_dates:
        parameters = {}
        # 先禁用回测
        parameters[trade_mid + '.__enabled__'] = False
        parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
        parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
        parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
        parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
        # print('------ rolling_train:', parameters)
        results.append(g.run(parameters))

    # 合并预测结果并回测
    mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions,result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
    parameters = {}
    parameters['*.__enabled__'] = False
    parameters[trade_mid + '.__enabled__'] = True
    parameters[trade_mid + '.instruments'] = mx.instrument_data
    parameters[trade_mid + '.options_data'] = mx.data

    trade = g.run(parameters)

    return {'rollings': results, 'trade': trade}


m18 = M.hyper_rolling_train.v1(
    run=m18_run_bigquant_run,
    run_now=True,
    bq_graph=g
)
    设置测试数据集，查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-dda50b18a84e484eb535b070c0cd2fd7"}/bigcharts-data-end
    设置测试数据集，查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9f7911352af54b229ed8bfe9a760b629"}/bigcharts-data-end
    ---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
KeyError: 'date'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-7-11063bf63a4f> in <module>
    430 
    431 
--> 432 m18 = M.hyper_rolling_train.v1(
    433     run=m18_run_bigquant_run,
    434     run_now=True,

<ipython-input-7-11063bf63a4f> in m18_run_bigquant_run(bq_graph, inputs, trading_days_market, train_instruments_mid, test_instruments_mid, predict_mid, trade_mid, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
    425     parameters[trade_mid + '.options_data'] = mx.data
    426 
--> 427     trade = g.run(parameters)
    428 
    429     return {'rollings': results, 'trade': trade}

<ipython-input-7-11063bf63a4f> in m17_prepare_bigquant_run(context)
    126     ret_data = data.groupby('instrument').apply(calcu_ret)
    127     ret_data.reset_index(inplace=True, drop=True)
--> 128     ret_data['date'] = ret_data['date'].map(lambda x:x.strftime('%Y-%m-%d'))
    129     context.daily_buy_industry = pd.Series({dt:seek_head_industry(ret_data.set_index('date').loc[dt]) for dt in list(set(ret_data.date))})
    130 

KeyError: 'date'