股票涨跌预测方法之二：股票技术指标计算

        前一阵子在同学的鼓动下，花了一个多月研究了股票行情的预测方法，熟悉了常见的炒股术语及技术指标，现总结如下，纯属兴趣，如果想依照本文的方法来短线操作获利，请绕道。

         研究的第二步就是了解常用的股票技术指标，如macd、rsi、ar等，由于前面已经下载了所有的股票数据。那么使用下面代码就可以获得指定股票的所有历史行情：

import pandas as pd
import sqlite3 as db

cxn = db.connect('all_tushare_data.db')
m= pd.read_sql("select * from gp_record where code ='%s' % id,  cxn) #id是股票代码

然后就可以计算各个指标，指标的含义及计算公式可自行百度，下面实现过程仅作参考：

if 1:
#OBV线，称为成交量多空比率净额法
obv = m.volume*((m.close-m.low)-(m.high-m.close))/(m.high-m.low)
obv = obv.fillna(0.0)
obv.apply(np.cumsum)
print 'obv', len(obv)

if 1:
#MACD指标
#ema12 = m.adjust_price.copy()               #以后复权价为基础
ema12 = m.close.copy()
ema26 = ema12.copy()
for k in range(1,len(m)): ema12[k] = (ema12[k-1]*11 + ema12[k]*2)/13.0
for k in range(1,len(m)): ema26[k] = (ema26[k-1]*25 + ema26[k]*2)/27.0
dif = ema12-ema26
dea = dif.copy()
for k in range(1,len(dea)): dea[k] = (dea[k-1]*8 + dea[k]*2)/10.0
macd = dif-dea
print 'macd', len(ema12), len(ema26), len(macd)

if 1:#rsi指标，统计近段时间收盘涨数和跌数来判断买卖意向
n=6
rsi = m.change.copy()
rsi.apply(np.sign)
rsi = rsi.rolling(center=False,window=n).mean()
print 'rsi', len(rsi)

if 1:#AR BR CR 指标          #第一天的计算数据没有，少一个
refVa = m.open[1:].values           #Ar
refVb = m.close[:-1].values          #BR
refVc = (2*m.close[:-1] + m.high[:-1] +m.low[:-1]).values /4     #CR
ref = np.median([refVa, refVb, refVc])
a = m.high[1:] - ref
b = ref - m.low[1:]

c = pd.rolling_sum(a, 26)
d = pd.rolling_sum(b, 26)
cr = c/d
print 'ar br cr', len(cr)

if 1:
#kdj 指标
n = 5
Ln =  pd.rolling_min(m.low, n)
Hn =  pd.rolling_max(m.high, n)
rsv = (m.close-Ln)/(Hn-Ln)                      #跟wr指标类似
para1 = para2 = 1.0/3

K = rsv.copy()               #以后复权价为基础
K = K.fillna(0.0)
for k in range(1,len(K)): K[k] = K[k-1]*(1-para1) + K[k]*para1
D = K.copy()
for k in range(1,len(D)): D[k] = D[k-1]*(1-para2) + D[k]*para2
print 'KDJ', len(rsv), len(K), len(D)

if 1:
#cci 顺势指标
n= 10
ma = pd.rolling_mean(m.close, n)
md = pd.rolling_mean((ma-m.close).abs(), n)     #绝对偏差的平均值
cci = ((m.close+m.high+m.low)/3 -ma)/md/0.015
print 'cci',  len(cci)
if 1:
#DMI指标                               #第一天的计算数据没有，少一个
n=12
dm1 = pd.rolling_apply(m.high, 2, lambda d:max(d[1]-d[0],0.0))[1:]
dm2 = pd.rolling_apply(m.low , 2, lambda d:max(d[0]-d[1],0.0))[1:]

dm1[dm1<dm2] = 0
dm2[dm2<dm1] = 0

tr = (m.high-m.low).abs().values[1:], m.high[1:]-m.close[:-1].abs().values, m.low[1:]-m.close[:-1].abs().values
tr = np.minimum(np.minimum(tr[0], tr[1]) ,tr[2])

dm1 = pd.rolling_mean(dm1, n)
dm2 = pd.rolling_mean(dm2, n)
tr  = pd.rolling_mean(tr , n)

di1 = dm1/tr
di2 = dm2/tr

dx = (di1-di2).abs()/(di1+di2)
adx = pd.rolling_mean(dx , n)

print 'dmi', len(adx)
if 1:
#boll指标
n=10
ma = pd.rolling_mean(m.close , n)
md = np.sqrt( pd.rolling_mean((m.close-ma)**2 , n))
up = ma + 2*md
dn = ma - 2*md
print 'boll', len(ma), len(up), len(dn)