stock_draw.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

#***********************************************************************************
"""
Sample :
python stock_draw.py 600028.ss cn D:\Dev\stockdb\ipagp.ttf
python stock_draw.py 002050.sz cn D:\Dev\stockdb\ipagp.ttf
python stock_draw.py 6734 jp D:\Dev\stockdb\ipagp.ttf
python stock_draw.py AAPL us D:\Dev\stockdb\ipagp.ttf
"""
#twitter : @ithurricanept
#***********************************************************************************

from __future__ import unicode_literals
import time, os, sys, datetime
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, WeekdayLocator, MonthLocator, DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo_ochl, candlestick2_ochl, volume_overlay
from matplotlib import gridspec
from matplotlib.dates import num2date, IndexDateFormatter
from matplotlib.ticker import  IndexLocator, FuncFormatter
from matplotlib.font_manager import FontProperties
from matplotlib.dates import date2num, AutoDateFormatter, AutoDateLocator
from operator import itemgetter
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from urllib2 import urlopen
import urllib2
import pandas.io.data as web

def get_quote_yahoojp(code, start=None, end=None, interval='d'):
    base = 'http://info.finance.yahoo.co.jp/history/?code={0}.T&{1}&{2}&tm={3}&p={4}'
    start, end = web._sanitize_dates(start, end)
    start = 'sy={0}&sm={1}&sd={2}'.format(start.year, start.month, start.day)
    end = 'ey={0}&em={1}&ed={2}'.format(end.year, end.month, end.day)
    p = 1
    results = []

    if interval not in ['d', 'w', 'm', 'v']:
        raise ValueError("Invalid interval: valid values are 'd', 'w', 'm' and 'v'")

    while True:
        url = base.format(code, start, end, interval, p)
        tables = pd.read_html(url, header=0)
        if len(tables) < 2 or len(tables[1]) == 0:
            break
        results.append(tables[1])
        p += 1
    result = pd.concat(results, ignore_index=True)
    result.columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close']
    if interval == 'm':
        result['Date'] = pd.to_datetime(result['Date'], format='%Y年%m月')
    else:
        result['Date'] = pd.to_datetime(result['Date'], format='%Y年%m月%d日')
    result = result.set_index('Date')
    result = result.sort_index()
    return result

def get_stock_name(code, contry):
    name = code
    if contry == 'jp' or contry == 'japan':
        base = 'http://info.finance.yahoo.co.jp/history/?code={0}'
        url = base.format(code)
        tables = pd.read_html(url, header=0)
        name = tables[0].columns[0]
    elif contry == 'cn' or contry == 'china':
        if code[-2:] == 'ss':
            code2 = 'sh' + code[0:-3]
        elif code[-2:] == 'sz':
            code2 = 'sz' + code[0:-3]

        url = 'http://hq.sinajs.cn/?list=%s' % code2
        req = urllib2.Request(url)
        content = urlopen(req).read()
        str = content.decode('gbk')
        data = str.split('"')[1].split(',')
        name = "%-6s" % data[0]

    return name

def movavg(s, n):
    """
    returns an n period moving average for the time series s

    s is a list ordered from oldest (index 0) to most recent (index -1)
    n is an integer

        returns a numeric array of the moving average

    See also ema in this module for the exponential moving average.
    """
    s = np.array(s)
    c = np.cumsum(s)
    return (c[n-1:] - c[:-n+1]) / float(n-1)

def get_locator():

    """

    the axes cannot share the same locator, so this is a helper

    function to generate locators that have identical functionality

    """

    return IndexLocator(10, 1)


def millions(x, pos):

    'The two args are the value and tick position'

    return '%1.1fM' % (x*1e-6)



def thousands(x, pos):

    'The two args are the value and tick position'

    return '%1.1fK' % (x*1e-3)

def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.

    type is 'simple' | 'exponential'

    """
    x = np.asarray(x)
    if type == 'simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()

    a = np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a

def moving_average_convergence(x, nslow=26, nfast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = moving_average(x, nslow, type='exponential')
    emafast = moving_average(x, nfast, type='exponential')
    return emaslow, emafast, emafast - emaslow

if __name__ == '__main__':
    print sys.argv[:]

    if len(sys.argv) < 4 :
        print "Usage : python stock_draw.py code contry fontpath."
        print "eg : python stock_draw.py AAPL US D:\Dev\stockdb\ipagp.ttf"
        raise SystemExit

    symbol = sys.argv[1]
    contry = sys.argv[2]
    fontpath = sys.argv[3]

    # (Year, month, day) tuples suffice as args for quotes_historical_yahoo
    date0 = (2016, 1, 1)
    date1 = (2016, 3, 1)
    date2 = (2016, 6, 1)
    start = '2016-01-01'

    quotes = quotes_historical_yahoo_ochl(symbol, date1, date2)
    if len(quotes) == 0:
        raise SystemExit

    ds, opens, closes, highs, lows, volumes = zip(*quotes)
    days = len(closes)

    '''
    idx = pd.Index(closes)
    vales = np.arange(len(idx)).astype(float)
    s = Series(vales, index=idx)

    # SMA EMA
    sma5 = pd.rolling_mean(s, window=5)
    sma5 = sma5.dropna()
    ewma = pd.stats.moments.ewma
    ewma5 = ewma(s, span=5)
    ewma25 = ewma(s, span=25)

    #ewma25 = ewma25.dropna()
    #ax0.plot(pd.rolling_mean(s, window=25),'b.',label='SMA(p,25)')
    #plt.plot(ewma25,label="EMA25")
    #plt.setp( plt.gca().get_xticklabels(),rotation=90, horizontalalignment='left')
    # SMA5  SMA25
    # sma5 = pd.rolling_mean(stock_tse['Adj Close'], window=5)
    # ma5 = moving_average(closes2, 5, type='simple')
    # ma25 = moving_average(closes2, 25, type='simple')
   '''

    stockname = get_stock_name(symbol, contry)
    stockname = symbol + stockname

    if contry=='jp' or contry=='japan':
        stock_tse = get_quote_yahoojp(symbol, start=start)
        sma5 = pd.rolling_mean(stock_tse['Adj Close'], window=5)
        sma5 = sma5.dropna()
        ewma = pd.stats.moments.ewma
        ewma5 = ewma(stock_tse['Adj Close'], span=5)
        ewma25 = ewma(stock_tse['Adj Close'], span=25)
        ewma25 = ewma25.dropna()

        ma5 = np.array(ewma5).astype(float)
        ma25 = np.array(ewma25).astype(float)
    else:
        quotes2 = quotes_historical_yahoo_ochl(symbol, date0, date2)
        ds2, opens2, closes2, highs2, lows2, volumes2 = zip(*quotes2)
        ma5 = moving_average(closes2, 5, type='simple')
        ma25 = moving_average(closes2, 25, type='simple')

    ma5 = ma5[-days:]
    ma25 = ma25[-days:]

    formatter = IndexDateFormatter(ds, '%Y-%m-%d')
    millionformatter = FuncFormatter(millions)
    thousandformatter = FuncFormatter(thousands)
    #fig = plt.figure(figsize=(8, 6))
    #fp = FontProperties(fname=r'D:\Dev\stockdb\ipagp.ttf')
    fp = FontProperties(fname=fontpath)

    fig = plt.figure()
    fig.subplots_adjust(bottom=0.15)
    fig.subplots_adjust(hspace=0)

    fig.suptitle(stockname, fontproperties=fp, fontsize=24, fontweight='bold')

    gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1])
    ax0 = plt.subplot(gs[0])

    candles = candlestick2_ochl(ax0, opens, closes, highs, lows, width=1, colorup='blue',colordown='r')
    textsize = 8        # size for axes text

    left, height, top = 0.025, 0.06, 0.9
    t1 = ax0.text(left, top, '%s daily'%symbol, fontsize=textsize,
                   transform=ax0.transAxes)
    t2 = ax0.text(left, top-height, 'EMA(5)', color='b', fontsize=textsize, transform=ax0.transAxes)
    t3 = ax0.text(left, top-2*height, 'EMA(25)', color='r', fontsize=textsize, transform=ax0.transAxes)

    s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' %(
        time.strftime('%d-%b-%Y'),
        opens[-1], highs[-1],
        lows[-1], closes[-1],
        volumes[-1]*1e-6,
        closes[-1]-opens[-1])
    t4 = ax0.text(0.4, top, s, fontsize=textsize,
               transform=ax0.transAxes)
    ax0.xaxis_date()
    ax0.autoscale_view()

    ax0.plot(ma5, color='b', lw=2)
    ax0.plot(ma25, color='r', lw=2)

    ax0.legend(loc='best')
    ax0.set_ylabel(u'株価', fontproperties = fp, fontsize=16)

    #ax0.xaxis_date()
    #ax0.autoscale_view()

    ax1 = plt.subplot(gs[1], sharex=ax0)

    #vc = volume_overlay3(ax1, quotes, colorup='k', colordown='r', width=4, alpha=1.0)
    #volume_overlay(ax1, opens, closes, volumes, colorup='g', alpha=0.5, width=1)
    #ax1.set_xticks(ds)

    vc = volume_overlay(ax1, opens, closes, volumes, colorup='g', alpha=0.5, width=1)
    ax1.add_collection(vc)

    ax1.xaxis.set_major_locator(get_locator())
    ax1.xaxis.set_major_formatter(formatter)
    ax1.yaxis.set_major_formatter(millionformatter)
    ax1.yaxis.tick_right()
    ax1.set_ylabel(u'出来高', fontproperties = fp, fontsize=16)

    plt.setp(ax0.get_xticklabels(), visible=False)
    plt.setp(ax1.get_xticklabels(), rotation=30, horizontalalignment='left')
    plt.legend(prop = fp);

    plt.show()