Pythonで株・為替・先物解析その１

Pythonで株・為替・先物解析の解析をしてみる。
そのままmatplotlibで描画すると土日祝日が空欄になるため、軸を別設定した。

import jsm
import datetime
import pandas as pd
import matplotlib.dates as mdate
import matplotlib.finance
import pylab as plt
import numpy as np
import sqlite3
import pandas.io.sql as psql

##################
#set stock number
##################
stock_name = 8411

#SQLlite
con = sqlite3.connect("data.db")
c = con.cursor()
try:
    c.execute("""create table all_sort(name integer ,date integer ,open integer ,high integer ,low integer ,close integer ,volume integer);""")
except:
    sql = 'select name,date from all_sort'
    a = c.execute(sql)
    for i, j in a:
        print i,j

#JSMによる日足習得
d = datetime.datetime.today()
q = jsm.Quotes()

start_date = datetime.date(2015, 1, 1)

yesterday = datetime.date.today() - datetime.timedelta(1)
end_date = yesterday

this_year = q.get_historical_prices(stock_name, jsm.DAILY, start_date, end_date)

#SQLに格納後Pandasへ移す
quotes = []
days=[]

colums_sql= u"insert into all_sort values(?,?,?,?,?,?,?)"
for i,each_day in enumerate(this_year):
    day =mdate.date2num(each_day.date)
    days.append(tuple([day]))
    quotes.append([i,each_day.open,each_day.close,each_day.high,each_day.low])
    for_sql = [stock_name,each_day.date,each_day.open,each_day.close,each_day.high,each_day.low,each_day.volume]
    c.execute(colums_sql,for_sql)

sql = 'select * from all_sort'
df = psql.read_sql(sql,con)

df2 = df[df['name'] == stock_name]
df2['date'] = df2['date'].apply(pd.to_datetime)

ascend_df = df2.sort_index(by='date',ascending=True)

#con.commit()


############################
#plot
############################

# SMA EMA
sma5 = pd.rolling_mean(df['close'], window=5)
sma5 = sma5.dropna()

ewma = pd.stats.moments.ewma
ewma5 = ewma(df['close'], span=5)

ewma25 = ewma(df['close'], span=25)
#ewma25 = ewma25.dropna()

#ロウソク足で描画
fig = plt.figure()
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
ax.set_xticks(range(0,len(quotes),5))
ax.set_xticklabels([mdate.num2date(days[index][0]).strftime('%y %b %d') for index in ax.get_xticks()]) 

matplotlib.finance.candlestick(ax, quotes,colorup='red',colordown='blue')
ax.xaxis_date()
ax.autoscale_view()
plt.plot(sma5,label="SMA5")
plt.plot(ewma5,label="EMA5")
plt.plot(ewma25,label="EMA25")
plt.setp( plt.gca().get_xticklabels(),rotation=90, horizontalalignment='left')
plt.legend(loc="best")
ax.invert_xaxis()
plt.show()