vix reversal trigger in python

Published on January 27, 2010 in Uncategorized. 0 Comments

I wrote a python script to send an email whenever a particular type of VIX reversal indicator triggers. Basically if the following conditions are met, it will send an email:
* If the VIX index has a NEW 15-day high today and closed below the open, then it suggests a buy indicator.
* If the VIX index has a NEW 15-day low today and closed above the open, then it suggests a sell indicator.

I decided to write this in python since I’ve never used it before. It was a fun learning experience. I was a bit taken back by the lack of statement terminators and the indenting rules, but in all I like it better than perl.

This script runs every weekday at 4:15pm (since the yahoo stock quotes are delayed 15 minutes).

Future enhancement ideas are to include the 15-day chart from stockcharts.com as an inline attachment (I have a bash script which already does this so it shouldn’t be that hard to port), as well as proper cleanup & organization of the code once/if I ever learn more python.

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#!/usr/bin/env python
 
import sys
import datetime
import smtplib
import ystockquote
 
from email.mime.text import MIMEText
 
# ticker to look up
ticker = '^VIX'
# number of days of historical data to fetch (not counting today)
days_back = 14
 
email_from = 'redacted'
email_to = 'redacted'
 
# yesterday's date
end = datetime.date.today() + datetime.timedelta(days= -1)
# 30 days ago from today
start = datetime.date.today() + datetime.timedelta(days= -30)
 
# fetch the historical data from yahoo
data = ystockquote.get_historical_prices(ticker, start.strftime("%Y%m%d"), end.strftime("%Y%m%d"))
 
# validate that we have enough historical data to continue
if len(data) < (days_back + 1):
    print 'Not enough historical data available to continue (need at least 15 days of market data)'
    sys.exit(1)
 
current = ystockquote.get_price(ticker)
open = ystockquote.get_open(ticker)
high = ystockquote.get_high(ticker)
low = ystockquote.get_low(ticker)
 
'''
print 'start date is: ' + str(start)
print 'end date is: ' + str(end)
print 'number of days of actual market data retrieved is: ' + str((len(data) - 1))
print 'Current price: ' + str(current)
print 'Open price: ' + str(open)
print 'High price: ' + str(high)
print 'Low price: ' + str(low)
 
# Loop through the previous 14-days of data (the first item in the data array
# is omitted because it is just header information
print 'the previous 14-day of historical data:'
for i in range(1, (days_back + 1)):
    print data[i][0] + ': High:' + data[i][2] + ' Low:' + data[i][3]
'''
 
def isNewHigh(high, data):
    """
    Returns True if the 'high' is higher than any of the highs in the data
    array passed in. Otherwise returns False
    """
    for i in range(1, (days_back + 1)):
        if float(data[i][2]) >= float(high):
            return False
    return True
 
def isNewLow(low, data):
    """
    Returns True if the 'low' is lower than any of the lows in the data
    array passed in. Otherwise returns False
    """
    for i in range(1, (days_back + 1)):
        if float(data[i][3]) < = float(low):
            return False
        return True
 
def isCurrentHigherThanOpen(current, open):
    """
    Simple check to see if the current price is greater than the open price
    """
    return float(current) > float(open)
 
def isCurrentLowerThanOpen(current, open):
    """
    Simple check to see if the current price is lower than the open price
    """
    return float(current) < float(open)
 
buy = False
sell = False
 
# if the current price is higher than the open and today is a new 15-day low, 
# then this is a SELL indicator
if (isCurrentHigherThanOpen(current, open) & isNewLow(low, data)):
    #print 'today is a 15-day low (' + str(low) + ') & the current price (' + str(current) + ') is higher than the open (' + str(open) + ')'
    sell = True
 
# if the current price is lower than the open and today is a new 15-day high, 
# then this is a BUY indicator
if (isCurrentLowerThanOpen(current, open) & isNewHigh(high, data)):
    #print 'today is a 15-day high (' + str(high) + ') & the current price (' + str(current) + ') is lower than the open (' + str(open) + ')'
    buy = True
 
if (buy | sell):
    if buy:
        body = 'Today is a new 15-day high (' + str(high) + ') & the current price (' + str(current) + ') is lower than the open (' + str(open) + ')\n\n====>This is a VIX BUY indicator!'
        subject = 'VIX BUY indicator triggered'
    else:
        body = 'Today is a new 15-day low (' + str(low) + ') & the current price (' + str(current) + ') is higher than the open (' + str(open) + ')\n\n====>This is a VIX SELL indicator!'
        subject = 'VIX SELL indicator triggered'
    msg = MIMEText(body)
    msg['Subject'] = subject
    msg['From'] = email_from
    msg['To'] = email_to
    s = smtplib.SMTP('localhost')
    s.sendmail(email_from, email_to, msg.as_string())
    s.quit()

I took the ystockquote.py from Corey Goldberg and had to extend it slightly to include some additional information:
* day’s high price
* day’s low price
* last trade time
* last ‘real time’ trade + time (this doesn’t appear to be real time after all)

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#!/usr/bin/env python
#
#  Copyright (c) 2007-2008, Corey Goldberg (corey@goldb.org)
#
#  license: GNU LGPL
#
#  This library is free software; you can redistribute it and/or
#  modify it under the terms of the GNU Lesser General Public
#  License as published by the Free Software Foundation; either
#  version 2.1 of the License, or (at your option) any later version.
 
 
import urllib
 
 
"""
This is the "ystockquote" module.
 
This module provides a Python API for retrieving stock data from Yahoo Finance.
 
sample usage:
>>> import ystockquote
>>> print ystockquote.get_price('GOOG')
529.46
"""
 
 
def __request(symbol, stat):
    url = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (symbol, stat)
    return urllib.urlopen(url).read().strip().strip('"')
 
 
def get_all(symbol):
    """
    Get all available quote data for the given ticker symbol.
 
    Returns a dictionary.
    """
    values = __request(symbol, 'l1c1va2xj1b4j4dyekjm3m4rr5p5p6s7oahagat1k1').split(',')
    data = {}
    data['price'] = values[0]
    data['change'] = values[1]
    data['volume'] = values[2]
    data['avg_daily_volume'] = values[3]
    data['stock_exchange'] = values[4]
    data['market_cap'] = values[5]
    data['book_value'] = values[6]
    data['ebitda'] = values[7]
    data['dividend_per_share'] = values[8]
    data['dividend_yield'] = values[9]
    data['earnings_per_share'] = values[10]
    data['52_week_high'] = values[11]
    data['52_week_low'] = values[12]
    data['50day_moving_avg'] = values[13]
    data['200day_moving_avg'] = values[14]
    data['price_earnings_ratio'] = values[15]
    data['price_earnings_growth_ratio'] = values[16]
    data['price_sales_ratio'] = values[17]
    data['price_book_ratio'] = values[18]
    data['short_ratio'] = values[19]
    data['open'] = values[20]
    data['days_high'] = values[21]
    data['days_low'] = values[22]
    data['last_trade_time'] = values[23]
    data['last_trade_real_time'] = values[24]
    return data
 
 
def get_price(symbol): 
    return __request(symbol, 'l1')
 
 
def get_change(symbol):
    return __request(symbol, 'c1')
 
 
def get_volume(symbol): 
    return __request(symbol, 'v')
 
 
def get_avg_daily_volume(symbol): 
    return __request(symbol, 'a2')
 
 
def get_stock_exchange(symbol): 
    return __request(symbol, 'x')
 
 
def get_market_cap(symbol):
    return __request(symbol, 'j1')
 
 
def get_book_value(symbol):
    return __request(symbol, 'b4')
 
 
def get_ebitda(symbol): 
    return __request(symbol, 'j4')
 
 
def get_dividend_per_share(symbol):
    return __request(symbol, 'd')
 
 
def get_dividend_yield(symbol): 
    return __request(symbol, 'y')
 
 
def get_earnings_per_share(symbol): 
    return __request(symbol, 'e')
 
 
def get_52_week_high(symbol): 
    return __request(symbol, 'k')
 
 
def get_52_week_low(symbol): 
    return __request(symbol, 'j')
 
 
def get_50day_moving_avg(symbol): 
    return __request(symbol, 'm3')
 
 
def get_200day_moving_avg(symbol): 
    return __request(symbol, 'm4')
 
 
def get_price_earnings_ratio(symbol): 
    return __request(symbol, 'r')
 
 
def get_price_earnings_growth_ratio(symbol): 
    return __request(symbol, 'r5')
 
 
def get_price_sales_ratio(symbol): 
    return __request(symbol, 'p5')
 
 
def get_price_book_ratio(symbol): 
    return __request(symbol, 'p6')
 
 
def get_short_ratio(symbol): 
    return __request(symbol, 's7')
 
 
def get_open(symbol): 
    return __request(symbol, 'o')
 
 
def get_high(symbol): 
    return __request(symbol, 'h')
 
 
def get_low(symbol): 
    return __request(symbol, 'g')
 
 
def get_last_trade_time(symbol): 
    return __request(symbol, 't1')
 
 
def get_last_real_time_trade(symbol): 
    return __request(symbol, 'k1')
 
 
def get_historical_prices(symbol, start_date, end_date):
    """
    Get historical prices for the given ticker symbol.
    Date format is 'YYYYMMDD'
 
    Returns a nested list.
    """
    url = 'http://ichart.yahoo.com/table.csv?s=%s&' % symbol + \
          'd=%s&' % str(int(end_date[4:6]) - 1) + \
          'e=%s&' % str(int(end_date[6:8])) + \
          'f=%s&' % str(int(end_date[0:4])) + \
          'g=d&' + \
          'a=%s&' % str(int(start_date[4:6]) - 1) + \
          'b=%s&' % str(int(start_date[6:8])) + \
          'c=%s&' % str(int(start_date[0:4])) + \
          'ignore=.csv'
    days = urllib.urlopen(url).readlines()
    data = [day[:-2].split(',') for day in days]
    return data

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