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菜场大妈高质低价法策略
策略
作者: 水滴
```python # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40850 # 标题：菜场大妈！皇城大妈！年化108%！大妈吉祥！ # 作者：Clarence.罗 # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40139 # 标题：正黄旗大妈选股法，修改版，年化108% # 作者：clarence 罗 感谢下列各位大咖 # 这是Python 2 版本，回测请调到Python 2，一创可以运行 # 但请注意PE为负值，和利润大幅下降的股票 # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40811 # 标题：策略分析辅助--实时最大回撤记录 # 作者：Bingyou # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40139 # 标题：正黄旗大妈选股法，修改版，年化92% # 作者：oupian 1211修改 # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40004 # 标题：菜场大妈选股法 # 作者：美吉姆优秀毕业代表 贴在社区下面的 # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40038 # 标题：正黄旗大妈选股法 # 作者：GoodThinker # 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。 # 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。 # 原文网址：https://www.joinquant.com/post/40004 # 标题：菜场大妈选股法 # 作者：开心果 # 注意：回测请选择python 2 import pandas as pd from jqdata import * from jqlib.technical_analysis import * def initialize(context): # setting log.set_level('order', 'error') set_option('use_real_price', True) # set_option('avoid_future_data', True) set_benchmark('000905.XSHG') # 设置滑点为理想情况，纯为了跑分好看，实际使用注释掉为好 set_slippage(PriceRelatedSlippage(0.000)) # 设置交易成本 set_order_cost(OrderCost(open_tax=0, close_tax=0.0001, open_commission=0.0003, close_commission=0.0003, close_today_commission=0, min_commission=5),type='fund') # strategy g.stock_num = 10 g.buylist = [] g.new_high_value = context.portfolio.starting_cash # 资金量新高值 g.maxdown = 0 #最大回撤 g.new_high_value = 0 run_daily(get_high_limit_stocks, time='9:05', reference_security='000300.XSHG') run_monthly(select_stocks_and_buy,1,time='9:30') #每月派息好股，按小市值排序建立了g.buylist；派息不怎么变，市值在变; #每月买了一次 run_daily(sell_stocks_opened_from_up_limit, time='14:00') #每日检查头天涨停，当日打开的股，卖出 run_daily(sell_hi_vol_stocks_at_dayend_and_buy_again, time='14:30') #每日检查涨停不放量的股，卖出；多出来的钱，又买了一次， #问题是买入了刚卖出的股. run_monthly(analyze_stocks_held, 1, time='15:01') #收盘了没事儿再算算，发现一大堆PE为负值，和利润大幅下滑的股:-( #1-1 根据最近一年分红除以当前总市值计算股息率并筛选 def get_dividend_ratio_filter_list(context, stock_list, sort, p1, p2): time1 = context.previous_date time0 = time1 - datetime.timedelta(days=365) #获取分红数据，由于finance.run_query最多返回4000行，以防未来数据超限，最好把stock_list拆分后查询再组合 interval = 1000 #某只股票可能一年内多次分红，导致其所占行数大于1，所以interval不要取满4000 list_len = len(stock_list) #截取不超过interval的列表并查询 q = query(finance.STK_XR_XD.code, finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb ).filter( finance.STK_XR_XD.a_registration_date >= time0, finance.STK_XR_XD.a_registration_date <= time1, finance.STK_XR_XD.code.in_(stock_list[:min(list_len, interval)])) df = finance.run_query(q) #对interval的部分分别查询并拼接 if list_len > interval: df_num = list_len // interval for i in range(df_num): q = query(finance.STK_XR_XD.code, finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb ).filter( finance.STK_XR_XD.a_registration_date >= time0, finance.STK_XR_XD.a_registration_date <= time1, finance.STK_XR_XD.code.in_(stock_list[interval*(i+1):min(list_len,interval*(i+2))])) temp_df = finance.run_query(q) df = df.append(temp_df) dividend = df.fillna(0) #PY3 dividend = dividend.set_index('code') dividend = dividend.groupby('code').sum() temp_list = list(dividend.index) #query查询不到无分红信息的股票，所以temp_list长度会小于stock_list #获取市值相关数据 q = query(valuation.code,valuation.market_cap).filter(valuation.code.in_(temp_list)) cap = get_fundamentals(q, date=time1) cap = cap.set_index('code') #计算股息率 #PY3 DR = pd.concat([dividend, cap] ,axis=1, sort=False) DR = pd.concat([dividend, cap], axis=1) DR['dividend_ratio'] = (DR['bonus_amount_rmb']/10000) / DR['market_cap'] #排序并筛选 #PY3 DR = DR.sort_values(by=['dividend_ratio'], ascending=sort) DR = DR.sort(columns='dividend_ratio', ascending=False) final_list = list(DR.index)[int(p1*len(DR)):int(p2*len(DR))] return final_list def select_stocks_and_buy(context): select_stocks(context) choice=g.buylist # [:g.stock_num] # Sell #sell_list = [] #for s in context.portfolio.positions: # if (s not in choice) : # sell_list.append(s) #sell_stocks(context, sell_list) # buy buy_stocks(context, g.buylist) def select_stocks(context): # all stocks dt_last = context.previous_date stocks = get_all_securities('stock', dt_last).index.tolist() stocks = filter_kcbj_stock(stocks) #stocks = filter_st_stock(stocks) #stocks = filter_paused_stock(stocks) #stocks = filter_limitup_stock(context,stocks) #stocks = filter_limitdown_stock(context,stocks) #stocks = filter_highprice_stock(context,stocks) #高股息(全市场最大25%) stocks = get_dividend_ratio_filter_list(context, stocks, False, 0, 0.25) # 获取基本面数据 q = query(valuation.code, valuation.pe_ratio / indicator.inc_net_profit_year_on_year,# PEG indicator.roe / valuation.pb_ratio, # PB-ROE indicator.roe , ).filter( valuation.pe_ratio / indicator.inc_net_profit_year_on_year>-1, valuation.pe_ratio / indicator.inc_net_profit_year_on_year<3, #indicator.roe / valuation.pb_ratio > 0, valuation.code.in_(stocks)) df_fundamentals = get_fundamentals(q, date = None) stocks = list(df_fundamentals.code) # fuandamental data #df = get_fundamentals(query(valuation.code).filter(valuation.code.in_(stocks)).order_by(valuation.market_cap.asc())) #df = get_fundamentals(query(valuation.code,valuation.market_cap).filter(valuation.code.in_(stocks)).order_by(valuation.market_cap.asc())) q = query(valuation.code, valuation.market_cap ).filter( valuation.code.in_(stocks), valuation.market_cap<=100).order_by(valuation.market_cap.asc()) df = get_fundamentals(q, date = None) print(df.shape) #print(df) choice = list(df.code) choice = filter_st_stock(choice) choice = filter_paused_stock(choice) choice = filter_limitup_stock(context,choice) choice = filter_limitdown_stock(context,choice) choice = filter_highprice_stock(context,choice) choice = choice[:g.stock_num*2] g.buylist = choice ################End select_stocks_and_buy def buy_stocks(context, choice): position_count = len(context.portfolio.positions) if g.stock_num <= position_count: return buylist = choice #buylist = [] #K,D = KD(choice, check_date=context.previous_date, N = 9, M1 = 3, M2 = 3) #VOLT,MAVOL10,MAVOL20 = VOL(choice, check_date=context.current_dt, M1=10, M2=20, include_now = True) #VOLT,MAVOL5,MAVOL20 = VOL(choice, check_date=context.current_dt, M1=5, M2=20, include_now = True) #for s in choice: #K值小于25 #20日均量不能超过10日均量的1.3倍；10日均量不能超过50日均量的1.3倍；说明前期未明显放量 #if (K[s] > 25) or (MAVOL20[s] > MAVOL10[s]*1.3) or (MAVOL10[s] > MAVOL5[s]*1.8): # continue # buylist.append(s) cdata = get_current_data() #namelist = [] #for s in buylist: # namelist.append(cdata[s].name) #log.info('bulist:', namelist) psize = context.portfolio.available_cash/(g.stock_num - position_count) for s in buylist: if s not in context.portfolio.positions: log.info('buy', s, cdata[s].name) order_value(s, psize) if len(context.portfolio.positions) == g.stock_num: break ################End buy_stocks def sell_stocks(context, sell_list): current_data=get_current_data() if len(sell_list)>0: for security in sell_list: cprice = current_data[security].last_price boughtcost = context.portfolio.positions[security].avg_cost if context.portfolio.positions[security].avg_cost==0: log.error("Sell %s " % (current_data[security].name), "avg_cost is 0") profit = 0 else: profit = (cprice - boughtcost)/boughtcost *100 log.info("Sell %s " % (current_data[security].name), "profit: %.1f%%" % profit, "init time %s" % context.portfolio.positions[security].init_time) limit_price = max(cprice*0.95,current_data[security].low_limit) ordert = order_target_value(security,0, LimitOrderStyle(limit_price)) if (None == ordert): log.info("Sell failed %s" % (current_data[security].name)) #else: # log.info("no one to sell") return ################End sell_stocks # 准备股票池 def get_high_limit_stocks(context): #获取已持有列表 g.high_limit_list = [] hold_list = list(context.portfolio.positions) if hold_list: #panel = get_price(hold_list, end_date=context.previous_date, frequency='daily', # fields=['close', 'high_limit'], # count=1) # , panel=False) #PY3 g.high_limit_list = df[df['close'] == df['high_limit']]['code'].tolist() for stock in hold_list: df=get_price(stock, count = 1, end_date=context.previous_date, frequency='daily', fields=['high_limit', 'close']) if df['high_limit'][-1]==df['close'][-1]: g.high_limit_list.append(stock) # 调整昨日涨停股票 def sell_stocks_opened_from_up_limit(context): # 获取持仓的昨日涨停列表 cdata = get_current_data() sell_list = [] if len(g.high_limit_list)>0: for stock in g.high_limit_list: if cdata[stock].last_price < cdata[stock].high_limit: log.info("[%s]涨停打开，卖出" % cdata[stock].name) #order_target(stock, 0) sell_list.append(stock) #if stock in g.buylist: # g.buylist.remove(stock) else: log.info("[%s]涨停，继续持有" % cdata[stock].name) if sell_list: # select_stocks(context) # for stock in sell_list: # if stock in g.buylist: # g.buylist.remove(stock) sell_stocks(context, sell_list) # buy_stocks(context, g.buylist) # 尾盘买卖股；放量未涨停的卖出；低位的买进 def sell_hi_vol_stocks_at_dayend_and_buy_again(context): btlist = context.portfolio.positions cdata=get_current_data() #PY3 VOLT,MAVOL5,MAVOL10 = VOL(btlist, check_date=context.current_dt, M1=5, M2=10, include_now = True) sell_list = [] for stock in btlist: if (cdata[stock].last_price == cdata[stock].high_limit): continue stock_now_vol = now_vol(context, stock) stock_ma10_vol = ma_vol(context, stock, 10) if (stock_now_vol>stock_ma10_vol*3): #放量未涨停 log.info("[%s]放量未涨停，卖出" % cdata[stock].name) sell_list.append(stock) if sell_list: # select_stocks(context) # for stock in sell_list: # if stock in g.buylist: # g.buylist.remove(stock) sell_stocks(context, sell_list) buy_stocks(context, g.buylist) def ma_vol(context, stock, number_of_days): df_vol = get_price(stock, end_date=context.previous_date, frequency='daily', count=number_of_days, fields=['volume']) ft_ma_vol = df_vol['volume'].mean() #print (stock, number_of_days, 'volume mean', ft_ma_vol) return ft_ma_vol def now_vol(context, stock): dt_zero_clock_today = context.current_dt - datetime.timedelta(hours=context.current_dt.hour, minutes=context.current_dt.minute, seconds=context.current_dt.second, microseconds=context.current_dt.microsecond) #print ('dt_zero_clock_today', dt_zero_clock_today, type(dt_zero_clock_today)) dt_trading_start_today = dt_zero_clock_today + datetime.timedelta(hours=9, minutes=15, seconds=00) #print ('dt_trading_start_today', dt_trading_start_today, type(dt_trading_start_today)) df_vol = get_price(stock, start_date=dt_trading_start_today, end_date=context.current_dt, frequency='minute', fields=['volume']) ft_now_vol = df_vol['volume'].sum() #print (stock, 'ft_now_vol', ft_now_vol) return ft_now_vol def analyze_stocks_held(context): current_data = get_current_data() #获取日期 hold_stocks = context.portfolio.positions.keys() for s in hold_stocks: q = query(valuation.code,valuation.market_cap,valuation.pe_ratio, indicator.inc_net_profit_year_on_year).filter(valuation.code == s) df = get_fundamentals(q) # log.info(s,current_data[s].name,'流值',df['circulating_market_cap'][0],'亿') #log.info(s,current_data[s].name,'市值',df['market_cap'][0],'亿') #log.info(s,current_data[s].name,'股价',current_data[s].last_price,'元') log.info(s,current_data[s].name,'市盈率',df['pe_ratio'][0]) log.info(s,current_data[s].name,'净利润同比增长率',df['inc_net_profit_year_on_year'][0]) #inc_net_profit_year_on_year #净利润同比增长率(%) #（当期的净利润-上年当期的净利润）/上年当期的净利润绝对值=净利润同比增长率。 #log.info('##############################################################') #inc_net_profit_year_on_year #净利润同比增长率(%) #（当期的净利润-上年当期的净利润）/上年当期的净利润绝对值=净利润同比增长率。 log.info('一天结束') log.info('##############################################################') def after_trading_end(context): g.total_value = context.portfolio.total_value # 若总资金大于昨天纪录的资金新高值，则将资金新高值变为今日总资金，令最大回撤值归0；否则计算今日总资金的回撤值 if g.total_value > g.new_high_value: g.new_high_value = g.total_value g.maxdown = 0 else: max_down = (g.new_high_value - g.total_value)/g.new_high_value*100 # 若今日总资金回撤值大于最大回撤值，则替代最大回撤值，否则最大回撤值不变。 g.maxdown = max_down if max_down > g.maxdown else g.maxdown record(maxdown=g.maxdown) # 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 # 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] # 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] # 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() # 已存在于持仓的股票即使涨停也不过滤，避免此股票再次可买，但因被过滤而导致选择别的股票 return [stock for stock in stock_list if stock in context.portfolio.positions.keys() or last_prices[stock][-1] < current_data[stock].high_limit] # 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] #2-4 过滤股价高于9元的股票 def filter_highprice_stock(context,stock_list): last_prices = history(1, unit='1m', field='close', security_list=stock_list) return [stock for stock in stock_list if stock in context.portfolio.positions.keys() or last_prices[stock][-1] < 9] # end ```
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