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预判st并过滤避雷代码 740（50只）
策略
作者: 水滴
```python #使用“避雷”查找新添加的避雷部分代码 from jqdata import * import math import pandas as pd def initialize(context): # 设定基准 set_benchmark('000905.XSHG') # 用真实价格交易 set_option('use_real_price', True) # 打开防未来函数 set_option("avoid_future_data", True) # 设置滑点为理想情况，不同滑点影响可以在归因分析中查看 set_slippage(PriceRelatedSlippage(0.00)) # 设置交易成本 set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, close_today_commission=0, min_commission=5),type='stock') # 除非需要精简信息，否则不要过滤日志，方便debug #log.set_level('system', 'error') #初始化全局变量 g.stock_num = 50 g.high_limit_list = [] g.hold_list = [] g.weights = [1.0, 1.0, 1.6, 0.8, 2.0] g.black_list = [] #避雷 # 设置交易时间，每天运行 run_daily(prepare_stock_list, '9:05') run_daily(get_black_list, '9:05') #避雷 run_weekly(adjust_position, 1, '09:30') run_daily(check_limit_up, '14:00') #run_daily(print_position_info, '15:10') #1-1 准备股票池 def prepare_stock_list(context): #获取已持有列表 g.hold_list= [] for position in list(context.portfolio.positions.values()): stock = position.security g.hold_list.append(stock) #获取昨日涨停列表 if g.hold_list != []: df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close','high_limit'], count=1, panel=False, fill_paused=False) #原 df = df[df['close'] == df['high_limit']] g.high_limit_list = list(df.code) else: g.high_limit_list = [] #1-2 获取黑名单 #避雷 def get_black_list(context): #查看当前日期是否处于某段时间内 def today_is_between(context, start_date, end_date): today = context.current_dt.strftime('%m-%d') if (start_date <= today) and (today <= end_date): return True else: return False #计算季度 def get_fiscal_quarters(start_date): md_lst = ['-03-31','-06-30','-09-30','-12-31'] y3 = str(start_date[:4]) y2 = str(int(y3) - 1) y1 = str(int(y2) - 1) y1_lst, y2_lst, y3_lst = [], [], [] for i in range(4): y1_lst.append(y1 + md_lst[i]) y2_lst.append(y2 + md_lst[i]) y3_lst.append(y3 + md_lst[i]) fq_date_lst = [y1_lst, y2_lst, y3_lst] return fq_date_lst #初次预测风险列表 def predict_st_stocks(stock_list, stat_date, fqd): tmp = [] k1 = 'net_profit' #净利润 k2 = 'adjusted_profit' #扣非净利润 for stock in stock_list: try: df = get_history_fundamentals(stock, fields=[income.net_profit, indicator.adjusted_profit], watch_date=stat_date, count=11, interval='1q') #作 df = df.set_index('statDate') #距离观察日(ed)最近一个还未披露的季度用前一年同期替代 #由于get_history_fundamentals返回数据可能缺失，所以不要用iloc定位，会“串行”。 y1 = df.loc[fqd[0][0]][k1] + df.loc[fqd[0][1]][k1] + df.loc[fqd[0][2]][k1] + df.loc[fqd[0][3]][k1] y1a = df.loc[fqd[0][0]][k2] + df.loc[fqd[0][1]][k2] + df.loc[fqd[0][2]][k2] + df.loc[fqd[0][3]][k2] y2 = df.loc[fqd[1][0]][k1] + df.loc[fqd[1][1]][k1] + df.loc[fqd[1][2]][k1] + df.loc[fqd[1][3]][k1] y2a = df.loc[fqd[1][0]][k2] + df.loc[fqd[1][1]][k2] + df.loc[fqd[1][2]][k2] + df.loc[fqd[1][3]][k2] y3 = df.loc[fqd[2][0]][k1] + df.loc[fqd[2][1]][k1] + df.loc[fqd[2][2]][k1] + df.loc[fqd[1][3]][k1] y3a = df.loc[fqd[2][0]][k2] + df.loc[fqd[2][1]][k2] + df.loc[fqd[2][2]][k2] + df.loc[fqd[1][3]][k2] if (min(y1,y1a)<0) and (min(y2,y2a)<0) and (min(y3, y3a)<0): tmp.append(stock) except: #如不符合上述数据结构，说明上市公司可能未按时披露信息，或上市不足3年 pass return tmp #确定最近一个sd if today_is_between(context, '11-01', '12-31'): sd = context.current_dt.strftime('%Y-%m-%d')[:4] + '-11-01' elif today_is_between(context, '01-01', '05-01'): sd = str(int(context.current_dt.strftime('%Y-%m-%d')[:4])-1) + '-11-01' else: sd = 0 #5至11月为报告真空期，不需要过滤，重置黑名单到初始状态 g.black_list = [] #计算首次预测黑名单(只计算一次) if (len(g.black_list) == 0) and (sd != 0): df = get_all_securities(types=['stock'], date=sd) stock_list = list(df.index) #由于风险警示制度与主板不同，这里过滤掉了科创板跟北交所股票(聚宽目前也没有北交所数据) stock_list = filter_kcbj_stock(stock_list) #此项过滤主要是预防被st，所以只保留在循环起始日之前正常的股票 stock_list = filter_st_stock(stock_list) #上市3年以内一般不会因为连续亏损退市，所以过滤掉上市不足500个交易日的股票 stock_list = filter_new_stock(context, stock_list, 500) #获取需要查询的日期列表 fiscal_quarter_date_list = get_fiscal_quarters(sd) #预测当前非st但是有可能变st的股票，此列表为初次预测，之后需要随着时间推进更新 predict_list0 = predict_st_stocks(stock_list, sd, fiscal_quarter_date_list) g.black_list = predict_list0 #日常循环检查是否发布至少扭亏为盈的业绩预告，如果有，说明最新年度扣非前后最小净利润已经大于零，一般不会被st，可以在年报发布前提前排除出风险名单。 #这段代码收益提升不明显，可以注释掉以提升策略运行效率 if (len(g.black_list) != 0) and (sd != 0): ed = str(context.previous_date) predict_list1 = g.black_list.copy() for stock in predict_list1[:]: df = finance.run_query(query(finance.STK_FIN_FORCAST).filter(finance.STK_FIN_FORCAST.code==stock)) df = df[(df['report_type'] == '四季度预告') & (df['type_id'] <= 305004) & (df['pub_date'] < datetime.date(*map(int,ed.split('-'))))] #者 if len(df) > 0: if str(df.iloc[-1,:]['end_date'])[2:4] == str(sd)[2:4]: print('预增预盈或扭亏为盈', stock) #在一月会产生一批预盈的股票 predict_list1.remove(stock) #每天查询更新最近一期四季报 df = get_history_fundamentals(stock, fields=[income.net_profit, indicator.adjusted_profit], watch_date=ed, count=4, interval='1q') df = df.set_index('statDate') k1 = 'net_profit' #净利润 k2 = 'adjusted_profit' #扣非净利润 fqd = get_fiscal_quarters(sd) try: #这里与11月1日预判不同的是第四项，这里是每天查看如果有公司发布了年报，第四项就不要用估算值了 y3 = df.loc[fqd[2][0]][k1] + df.loc[fqd[2][1]][k1] + df.loc[fqd[2][2]][k1] + df.loc[fqd[2][3]][k1] y3a = df.loc[fqd[2][0]][k2] + df.loc[fqd[2][1]][k2] + df.loc[fqd[2][2]][k2] + df.loc[fqd[2][3]][k2] if min(y3, y3a) > 0: print('年报已出最近一年盈利', stock) predict_list1.remove(stock) except: pass #最后输出的是，去除预盈和已经公布财报盈利后，仍然有被st风险的股票 g.black_list = predict_list1 #1-3 选股模块 def get_stock_list(context): # 获取前N个单位时间当时的收盘价 def get_close(stock, n, unit): return attribute_history(stock, n, unit, 'close')['close'][0] # 获取现价相对N个单位前价格的涨幅 def get_return(stock, n, unit): price_before = attribute_history(stock, n, unit, 'close')['close'][0] price_now = get_close(stock, 1, '1m') if not isnan(price_now) and not isnan(price_before) and price_before != 0: return price_now / price_before else: return 100 # 获得初始列表 yesterday = context.previous_date initial_list = get_all_securities('stock', yesterday).index.tolist() initial_list = filter_kcbj_stock(initial_list) initial_list = filter_new_stock(context, initial_list, 375) initial_list = filter_st_stock(initial_list) q = query( valuation.code, valuation.market_cap, valuation.circulating_market_cap ).filter( valuation.code.in_(initial_list), indicator.inc_total_revenue_year_on_year > 0, #营业总收入同比增长率 indicator.inc_net_profit_year_on_year > 0 #净利润同比增长率 ).order_by( valuation.market_cap.asc()).limit(100) df = get_fundamentals(q, date=yesterday) df.index = df.code initial_list = list(df.index) #获取原始值 MC, CMC, PN, TV, RE = [], [], [], [], [] for stock in initial_list: #总市值 mc = df.loc[stock]['market_cap'] MC.append(mc) #流通市值 cmc = df.loc[stock]['circulating_market_cap'] CMC.append(cmc) #当前价格 pricenow = get_close(stock, 1, '1m') PN.append(pricenow) #5日累计成交量 total_volume_n = attribute_history(stock, 1200, '1m', 'volume')['volume'].sum() TV.append(total_volume_n) #60日涨幅 m_days_return = get_return(stock, 60, '1d') RE.append(m_days_return) #合并数据 df = pd.DataFrame(index=initial_list, columns=['market_cap','circulating_market_cap','price_now','total_volume_n','m_days_return']) df['market_cap'] = MC df['circulating_market_cap'] = CMC df['price_now'] = PN df['total_volume_n'] = TV df['m_days_return'] = RE df = df.dropna() min0, min1, min2, min3, min4 = min(MC), min(CMC), min(PN), min(TV), min(RE) #计算合成因子 temp_list = [] for i in range(len(list(df.index))): score = g.weights[0] * math.log(min0 / df.iloc[i,0]) + g.weights[1] * math.log(min1 / df.iloc[i,1]) + g.weights[2] * math.log(min2 / df.iloc[i,2]) + g.weights[3] * math.log(min3 / df.iloc[i,3]) + g.weights[4] * math.log(min4 / df.iloc[i,4]) #wywy1995 temp_list.append(score) df['score'] = temp_list #排序并返回最终选股列表 df = df.sort_values(by='score', ascending=False) final_list = list(df.index) return final_list #1-4 整体调整持仓 def adjust_position(context): #获取应买入列表 target_list = get_stock_list(context) target_list = filter_paused_stock(target_list) target_list = filter_limitup_stock(context, target_list) target_list = filter_limitdown_stock(context, target_list) #截取不超过最大持仓数的股票量 target_list = target_list[:min(g.stock_num, len(target_list))] #排除可能被st的股票 #避雷 tmp = target_list target_list = [stock for stock in target_list if stock not in g.black_list] if len(target_list) < len(tmp): print('存在财务风险的股票', list(set(tmp)-set(target_list))) #调仓卖出 for stock in g.hold_list: if (stock not in target_list) and (stock not in g.high_limit_list): log.info("卖出[%s]" % (stock)) position = context.portfolio.positions[stock] close_position(position) else: log.info("已持有[%s]" % (stock)) #调仓买入 position_count = len(context.portfolio.positions) target_num = len(target_list) if target_num > position_count: value = context.portfolio.cash / (target_num - position_count) for stock in target_list: if context.portfolio.positions[stock].total_amount == 0: if open_position(stock, value): if len(context.portfolio.positions) == target_num: break #1-5 调整昨日涨停股票 def check_limit_up(context): now_time = context.current_dt if g.high_limit_list != []: #对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有 for stock in g.high_limit_list: current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close','high_limit'], skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True) if current_data.iloc[0,0] < current_data.iloc[0,1]: log.info("[%s]涨停打开，卖出" % (stock)) position = context.portfolio.positions[stock] close_position(position) else: log.info("[%s]涨停，继续持有" % (stock)) #2-1 过滤停牌股票 def filter_paused_stock(stock_list): current_data = get_current_data() return [stock for stock in stock_list if not current_data[stock].paused] #2-2 过滤ST及其他具有退市标签的股票 def filter_st_stock(stock_list): current_data = get_current_data() return [stock for stock in stock_list if not current_data[stock].is_st and 'ST' not in current_data[stock].name and '*' not in current_data[stock].name and '退' not in current_data[stock].name] #2-3 过滤涨停的股票 def filter_limitup_stock(context, stock_list): last_prices = history(1, unit='1m', field='close', security_list=stock_list) current_data = get_current_data() return [stock for stock in stock_list if stock in context.portfolio.positions.keys() or last_prices[stock][-1] < current_data[stock].high_limit] #2-4 过滤跌停的股票 def filter_limitdown_stock(context, stock_list): last_prices = history(1, unit='1m', field='close', security_list=stock_list) current_data = get_current_data() return [stock for stock in stock_list if stock in context.portfolio.positions.keys() or last_prices[stock][-1] > current_data[stock].low_limit] #2-5 过滤科创北交股票 def filter_kcbj_stock(stock_list): for stock in stock_list[:]: if stock[0] == '4' or stock[0] == '8' or stock[:2] == '68': stock_list.remove(stock) return stock_list #2-6 过滤次新股 def filter_new_stock(context, stock_list, d): yesterday = context.previous_date return [stock for stock in stock_list if not yesterday - get_security_info(stock).start_date < datetime.timedelta(days=d)] #3-1 交易模块-自定义下单 def order_target_value_(security, value): if value == 0: log.debug("Selling out %s" % (security)) else: log.debug("Order %s to value %f" % (security, value)) return order_target_value(security, value) #3-2 交易模块-开仓 def open_position(security, value): order = order_target_value_(security, value) if order != None and order.filled > 0: return True return False #3-3 交易模块-平仓 def close_position(position): security = position.security order = order_target_value_(security, 0) # 可能会因停牌失败 if order != None: if order.status == OrderStatus.held and order.filled == order.amount: return True return False #4-1 打印每日持仓信息 def print_position_info(context): #打印当天成交记录 trades = get_trades() for _trade in trades.values(): print('成交记录：'+str(_trade)) #打印账户信息 for position in list(context.portfolio.positions.values()): securities=position.security cost=position.avg_cost price=position.price ret=100*(price/cost-1) value=position.value amount=position.total_amount print('代码:{}'.format(securities)) print('成本价:{}'.format(format(cost,'.2f'))) print('现价:{}'.format(price)) print('收益率:{}%'.format(format(ret,'.2f'))) print('持仓(股):{}'.format(amount)) print('市值:{}'.format(format(value,'.2f'))) print('———————————————————————————————————') print('———————————————————————————————————————分割线————————————————————————————————————————') ```
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