庄股底部特征：股票长期缩量，在缩量过程中也伴随着成交额脉冲式向上后回落。
判断缩量：利用成交额20,60,160日移动平均线判断
脉冲判断1.0：利用成交额5,160日移动平均线交叉次数来判断(修改成5,160交叉了)
值越大，庄越强
优化思路：主力在不同时期的操盘手法是不同的。
a、主力在底部建仓以后可以用上面这个方法找强势股。（本阶段应该比较适合这种方法）
b、主力在股灾后就不能用这个方法了，这个时候应该研究哪些主力被套最深，套的越深，弹得越高。还有哪些主力操盘能力强，超盘能力越强（龙虎榜），越会在大反弹中出手。
c、牛市中找板块热点，找龙头，无脑买买买。
以上是我关于大盘的三个阶段的思考。

#庄股值计算    def cow_stock_value(stock,stock_time) :q = query(valuation).filter(valuation.code == stock)pb = get_fundamentals(q, stock_time)['pb_ratio'][0]cap = get_fundamentals(q, stock_time)['circulating_market_cap'][0]if cap>100: return 0num_fall=fall_money_day_3line(stock,120,20,60,160,stock_time)num_cross=money_5_cross_60(stock,120,5,160,stock_time)return (num_fall*num_cross)/(pb*(cap**0.5))#计算脉冲（1.0版本）                 def  money_5_cross_60(stock , n,n1=5,n2=60,stock_time=datetime.datetime.now()):if  not (n2 >n1 ) : log.info("fall_money_day 参数错误")return 0 #stock_m=attribute_history(stock, n+n2+1, '1d', ['money'], True)stock_m=get_price(stock,count=n+n2+1,end_date=stock_time,frequency='daily', \                      fields=['money'], skip_paused=True)#print(len(stock_m)) i=0count=0while i<n:money_MA60=stock_m['money'][i+1:n2+i].mean()money_MA60_before=stock_m['money'][i:n2-1+i].mean()money_MA5=stock_m['money'][i+1+n2-n1:n2+i].mean()money_MA5_before=stock_m['money'][i+n2-n1:n2-1+i].mean()if (money_MA60_before-money_MA5_before)*(money_MA60-money_MA5)<0: count=count+1i=i+1    return count#3条移动平均线计算缩量 def fall_money_day_3line(stock,n,n1=20,n2=60,n3=120,stock_time=datetime.datetime.now()):if  not ( n3>n2 and n2 >n1 ) : log.info("fall_money_day 参数错误")return 0 #stock_m=attribute_history(stock, n+n3, '1d', ['money'], True)stock_m=get_price(stock,count=n+n3,end_date=stock_time,frequency='daily', \                      fields=['money'], skip_paused=True)#print(len(stock_m)) i=0count=0while i<n:money_MA200=stock_m['money'][i:n3-1+i].mean()money_MA60=stock_m['money'][i+n3-n2:n3-1+i].mean()money_MA20=stock_m['money'][i+n3-n1:n3-1+i].mean()if money_MA20<=money_MA60 and money_MA60<=money_MA200:count=count+1i=i+1return countcow_value_dict=dict()'''stock_list=['000935.XSHE']#stock_list=['600228.XSHG']for stock in stock_list:cow_value_dict[stock]=0 stock_time=datetime.datetime(2016,6,30,0,0,0)for stock in stock_list:        cow_value=cow_stock_value(stock,stock_time)    cow_value_dict[stock]=cow_value    #s='%s:%s'%(stock,cow_stock_value(stock,stock_time))tmp= sorted(cow_value_dict.items(), key=lambda item:item[1],reverse=True)print (stock_time)for u in tmp:print(u)'''stock_list = get_index_stocks('000107.XSHG') \+get_index_stocks('000108.XSHG') \+get_index_stocks('000109.XSHG') \+get_index_stocks('000111.XSHG')for stock in stock_list:cow_value_dict[stock]=0     stock_time=datetime.datetime(2016,12,9,0,0,0)for stock in stock_list:    cow_value=cow_stock_value(stock,stock_time)cow_value_dict[stock]=cow_value#s='%s:%s'%(stock,cow_stock_value(stock,stock_time))tmp= sorted(cow_value_dict.items(), key=lambda item:item[1],reverse=True)print (stock_time)for u in tmp:print(u)

2016-12-09 00:00:00
('600327.XSHG', 32.907348124501119)
('600422.XSHG', 28.733081724755511)
('600828.XSHG', 28.569388969228601)
('600195.XSHG', 27.43367617597842)
('601058.XSHG', 25.010010815244197)
('600987.XSHG', 24.313623374769843)
('600697.XSHG', 23.086544920384632)
('600251.XSHG', 20.932463647804333)
('600859.XSHG', 20.537843841240125)
('600479.XSHG', 19.506927713475207)
('600825.XSHG', 19.367889151938883)
('600386.XSHG', 19.210033509503575)
('600351.XSHG', 18.826383984531137)
('600587.XSHG', 18.441735366692377)
('600624.XSHG', 17.811558544166438)
('600814.XSHG', 15.625672618501662)
('600135.XSHG', 15.399567291047131)
('600613.XSHG', 15.350688517527868)
('603818.XSHG', 14.706823622546823)
('603567.XSHG', 14.601139903313786)
('600511.XSHG', 14.577565046952156)
('603883.XSHG', 14.562767708048556)
('603008.XSHG', 14.538967055875096)
('600729.XSHG', 14.372182464736017)
('603508.XSHG', 14.160542116895341)
('601799.XSHG', 13.766304464665215)
('600831.XSHG', 13.604646954087855)
('600337.XSHG', 13.375955638692803)
('600363.XSHG', 13.286994597643693)
('600742.XSHG', 12.982585191250546)
('600439.XSHG', 12.945037166013019)
('603328.XSHG', 12.942222515802348)
('603997.XSHG', 12.837532791481465)
('600054.XSHG', 12.700917946483107)
('600557.XSHG', 12.699609597645557)
('600257.XSHG', 11.395641129084805)
('600628.XSHG', 11.192795718789213)
('600480.XSHG', 10.579108121183481)
('600088.XSHG', 10.469803373634401)
('603306.XSHG', 10.380242509592277)
('603806.XSHG', 10.298865718554508)
('600676.XSHG', 10.24014270774285)
('600750.XSHG', 10.189349885980098)
('600563.XSHG', 9.9681666626241103)
('600289.XSHG', 9.7443909625885361)
('600305.XSHG', 9.3918001748175985)
('600081.XSHG', 9.2906001387554156)
('603939.XSHG', 8.8650582183112583)
('600756.XSHG', 8.4644714766979554)
('601689.XSHG', 8.4324929798807666)
('600661.XSHG', 7.842410229357835)
('603898.XSHG', 7.5032247348412895)
('600571.XSHG', 7.1902709223094545)
('600285.XSHG', 7.1371960203365026)
('603609.XSHG', 6.7014677749597125)
('600381.XSHG', 6.4289451692517696)
('603600.XSHG', 6.1665439855235906)
('600566.XSHG', 5.7885096178678825)
('600993.XSHG', 5.7816235474293842)
('603288.XSHG', 5.7375251550504034)
('600651.XSHG', 5.4322688671853001)
('600420.XSHG', 5.3007837177384616)
('603118.XSHG', 5.0246146056422738)
('603369.XSHG', 4.964216878182544)
('600280.XSHG', 4.9314855073214048)
('603669.XSHG', 4.7590838087629095)
('600824.XSHG', 4.4703607360721183)
('600260.XSHG', 4.4623627640545811)
('603555.XSHG', 4.3989183729661203)
('600197.XSHG', 4.3060909486657808)
('600460.XSHG', 4.2535000958613196)
('603116.XSHG', 3.8603421217673288)
('603866.XSHG', 3.8288541288393452)
('603168.XSHG', 3.7187038860476647)
('600845.XSHG', 3.5381330814688479)
('600965.XSHG', 3.2721782351115762)
('603368.XSHG', 3.1826093395614459)
('603025.XSHG', 3.0603658341267437)
('600537.XSHG', 3.0044354677221077)
('600818.XSHG', 2.7458698866481441)
('600559.XSHG', 2.1920795510378919)
('603589.XSHG', 2.0025526482377223)
('603355.XSHG', 0.72470395798593279)
('600694.XSHG', 0)
('600594.XSHG', 0)
('600056.XSHG', 0)
('600297.XSHG', 0)
('603019.XSHG', 0)
('600664.XSHG', 0)
('600073.XSHG', 0)
('600171.XSHG', 0)
('601238.XSHG', 0)
('600682.XSHG', 0)
('600418.XSHG', 0)
('600380.XSHG', 0)
('600436.XSHG', 0)
('600763.XSHG', 0)
('600600.XSHG', 0)
('601311.XSHG', 0)
('600612.XSHG', 0)
('601098.XSHG', 0)
('600718.XSHG', 0)
('600654.XSHG', 0)
('600267.XSHG', 0)
('600398.XSHG', 0)
('600584.XSHG', 0)
('603766.XSHG', 0)
('600086.XSHG', 0)
('600572.XSHG', 0)
('600687.XSHG', 0)
('600536.XSHG', 0)
('600438.XSHG', 0)
('600666.XSHG', 0)
('601801.XSHG', 0)
('600640.XSHG', 0)
('600183.XSHG', 0)
('600298.XSHG', 0)
('600300.XSHG', 0)
('600062.XSHG', 0)
('600667.XSHG', 0)
('600811.XSHG', 0)
('600261.XSHG', 0)
('600601.XSHG', 0)
('600166.XSHG', 0)
('600410.XSHG', 0)
('600335.XSHG', 0)
('600122.XSHG', 0)
('600551.XSHG', 0)
('600562.XSHG', 0)
('601012.XSHG', 0)
('600850.XSHG', 0)
('600138.XSHG', 0)
('600754.XSHG', 0)
('601010.XSHG', 0)
('600884.XSHG', 0)
('600521.XSHG', 0)
('600482.XSHG', 0)
('600872.XSHG', 0)
('601231.XSHG', 0)
('600161.XSHG', 0)
('600366.XSHG', 0)
('600373.XSHG', 0)
('600597.XSHG', 0)
('600699.XSHG', 0)
('600329.XSHG', 0)
('600216.XSHG', 0)
('600201.XSHG', 0)
('600867.XSHG', 0)
('601777.XSHG', 0)
('603000.XSHG', 0)

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