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monitor2.py
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monitor2.py
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from multiprocessing import Process, Queue
from pandas import read_html, read_csv, to_datetime, DataFrame, Series, read_sql
import requests
import sqlite3
import sys
from datetime import date, datetime, timedelta
from StringIO import StringIO
import numpy as np
import requests_cache
import time
from machine2 import Machine
import math
requests_cache.install_cache('cache2')
class Announcement:
def __init__(self, date, company, symbol, qtr, eps, cons, surprise, percent_beat_eps, revs, revs_cons, time, setup):
self.read_date = date
self.date = date
self.company = company
self.symbol = symbol
self.qtr = qtr
self.eps = eps
self.cons = cons
self.surprise = surprise
self.price_target_df = read_csv('price_target.csv')
self.price_target_df['Symbol'] = self.price_target_df['Symbol'].fillna(method='backfill')
self.price_target_dfs = self.price_target_df.groupby('Symbol')
self.percent_beat_eps = float(percent_beat_eps)/100.0
self.revs = revs
self.revs_cons = revs_cons
self.time = time
self.price_history = None
self.average_change = None
self.percent_beat_eps_average = None
self.percent_beat_revs_average = None
self.machine_score = None
self.setup = setup
self.control()
def control(self):
self.make_date()
self.get_price_history()
if self.price_history is None:
return
if self.setup:
self.get_change()
else:
self.get_open()
self.get_sue()
self.get_high_low()
if self.date<=to_datetime('04/01/2015', format="%m/%d/%Y").date():
self.get_price_target_sheet()
else:
self.get_price_target()
self.get_rev_surprise()
self.get_ratio()
self.get_average()
del(self.price_history)
del(self.setup)
del(self.price_target_dfs)
del(self.price_target_df)
self.send_to_db()
def make_date(self):
date = self.date
if self.time == 'After':
date = date + timedelta(days=1)
while date.weekday()>=5:
date = date + timedelta(days=1)
self.date = date
def get_price_history(self):
try:
price_text = requests.get('http://real-chart.finance.yahoo.com/table.csv?s=%s&d=1&e=&f=2035&g=d&a=3&b=19&c=2009&ignore=.csv' % self.symbol).text
price_df = read_csv(StringIO(price_text), sep=',')
self.price_history = price_df
except Exception as e:
pass
def get_open(self):
with requests_cache.disabled():
price_text = requests.get('http://finance.yahoo.com/q?s=%s&ql=1' % self.symbol).text
print self.symbol
start = price_text.find('<span class="time_rtq_ticker">')
end = price_text.find('</span>', start)
self.open_price = float(price_text[start+50+len(self.symbol):end])
def get_change(self):
try:
start_date = str(self.date).split(' ')[0]
self.open_price = float(self.price_history[self.price_history['Date'] == start_date]['Open'])
end_loc = self.price_history[self.price_history['Date'] == start_date].index-10
self.close_price = float(self.price_history.iloc[end_loc]['Close'])
self.percent_change = (float(self.close_price)-float(self.open_price))/float(self.open_price)
except:
pass
def get_high_low(self):
start_date = str(self.date).split(' ')[0]
if self.setup:
start_date = self.price_history[self.price_history['Date'] == start_date].index.values
else:
start_date = 0
end_date = start_date + 252
if len(start_date)==0 or len(end_date)==0:
return
try:
high = float(self.price_history[start_date:end_date]['Adj Close'].max())
low = float(self.price_history[start_date:end_date]['Adj Close'].min())
self.distance_to_high = np.std([high, float(self.open_price)])
self.distance_to_low = np.std([low, float(self.open_price)])
#self.distance_to_high = round(self.distance_to_high,5)
#self.distance_to_low = round(self.distance_to_low,5)
except Exception as e:
#print e
pass
def get_sue(self):
try:
self.sue = np.std([float(str(self.eps).replace('$','')), float(str(self.cons).replace('$',''))])
#self.sue = round(self.sue,5)
except Exception as e:
# usually cannot convert from foreign currency to float
pass
def get_price_target_sheet(self):
try:
cur_df = self.price_target_dfs.get_group(str(self.symbol)).transpose().drop('Symbol')
cur_df.columns = ['Date','Amount']
cur_df['Date'] = to_datetime(cur_df['Date'], format="%m/%d/%Y")
cur_df['Date'].astype('datetime64[ns]')
self.target = float(cur_df[cur_df['Date']<self.date]['Amount'][-1:])
self.distance_to_target = np.std([self.target, self.open_price])
#self.distance_to_target = round(self.distance_to_target,5)
except Exception as e:
pass
def get_price_target(self):
try:
target_dfs = read_html('http://finance.yahoo.com/q?s=%s&ql=1' % self.symbol)[1]
target_dfs[0] = target_dfs[0].astype(str)
self.target = float(target_dfs.iloc[4,1])
self.distance_to_target = np.std([self.target, self.open_price])
#self.distance_to_target = round(self.distance_to_target,5)
except Exception as e:
#print e
pass
def get_rev_surprise(self):
try:
self.percent_beat_revs = (self.revs-self.revs_cons)/self.revs_cons
#self.percent_beat_revs = round(self.percent_beat_revs,5)
except:
pass
def get_ratio(self):
try:
self.ratio = self.percent_beat_eps/self.sue
#self.ratio = round(self.ratio,5)
except Exception as e:
#print e
pass
def get_average(self):
conn = sqlite3.connect('data2.sqlite', timeout=30)
c = conn.cursor()
current_df = read_sql('select symbol,`percent_beat_eps`, `percent_change` from earnings_calendar where symbol = \'%s\' and `date`<\'%s\'' % (self.symbol, self.read_date), conn)
current_df = current_df.dropna()
if len(current_df)<3:
return
self.average_change = sum(current_df['percent_change'])/float(len(current_df['percent_change']))
self.percent_beat_eps_average = sum(current_df['percent_beat_eps'])/float(len(current_df['percent_beat_eps']))
#self.average_change = round(self.average_change,5)
#self.percent_beat_eps_average = round(self.percent_beat_eps_average,5)
def send_to_db(self):
conn = sqlite3.connect('data2.sqlite', timeout=30)
c = conn.cursor()
df = DataFrame(self.__dict__.items(), index=self.__dict__.keys())
df = df.drop(0,1)
df = df.transpose()
df = df.sort(axis=1)
df.to_sql('earnings_calendar', conn, if_exists='append', index=False)
def get_street_insider(read_date):
try:
headers = {'user-agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2272.118 Safari/537.36'}
data = requests.get('http://www.streetinsider.com/ec_earnings.php?day=%s' % str(read_date).split(' ')[0], headers=headers).text
dfs = read_html(data, header=0)
except:
print 'No earnings found'
return None
return dfs
def clean(df):
replace_me = ['\%', '$', 'M', 'B', 'K']
df2 = df.copy()
for i in replace_me:
df2['Percent_Beat_EPS'] = df2['Percent_Beat_EPS'].str.replace(i, '')
df2['Revs'] = df2['Revs'].str.replace(i, '')
df2['Revs_Cons'] = df2['Revs_Cons'].str.replace(i, '')
convert_me = ['Percent_Beat_EPS', 'Revs', 'Revs_Cons']
for i in convert_me:
df2[i] = df2[i].convert_objects(convert_numeric=True)
return df2
def get_announcements(read_date, setup=False):
print read_date
dfs = get_street_insider(read_date)
if dfs is None:
return
if setup == True:
dfs[0]['Time'] = 'Before'
total_df = dfs[0]
if len(dfs)==2:
dfs[1]['Time'] = 'After'
total_df = total_df.append(dfs[1])
total_df = total_df.drop(['Details', 'Gd.', '% Since', '% Week'], 1)
total_df.columns = ['Company', 'Symbol', 'Qtr', 'EPS', 'Cons', 'Surprise', 'Percent_Beat_EPS', 'Revs', 'Revs_Cons', 'Time']
total_df = total_df.dropna()
total_df = total_df.drop_duplicates(subset='Symbol')
if len(total_df) == 0:
print 'No earnings found'
return
total_df = clean(total_df)
for i in total_df.values:
Announcement(read_date,i[0],i[1],i[2],i[3],i[4],i[5],i[6],i[7],i[8],i[9], setup)
else:
# get this mornings earnings
dfs[0]['Time'] = 'Before'
df = dfs[0]
df = df.drop(['Details', 'Gd.', '% Since', '% Week'], 1)
df = df.dropna()
df.columns = ['Company', 'Symbol', 'Qtr', 'EPS', 'Cons', 'Surprise', 'Percent_Beat_EPS', 'Revs', 'Revs_Cons', 'Time']
for i in df.values:
Announcement(read_date,i[0],i[1],i[2],i[3],i[4],i[5],i[6],i[7],i[8],i[9], setup)
#get yesterdays earnings, after close
read_date = read_date - timedelta(days=1)
print read_date
dfs = get_street_insider(read_date)
if len(dfs)==2:
dfs[1]['Time'] = 'After'
df = dfs[1]
df = df.drop(['Details', 'Gd.', '% Since', '% Week'], 1)
df = df.dropna()
df.columns = ['Company', 'Symbol', 'Qtr', 'EPS', 'Cons', 'Surprise', 'Percent_Beat_EPS', 'Revs', 'Revs_Cons', 'Time']
for i in df.values:
Announcement(read_date,i[0],i[1],i[2],i[3],i[4],i[5],i[6],i[7],i[8],i[9], setup)
def worker(date_queue):
while date_queue.qsize()>0:
read_date = date_queue.get()
get_announcements(read_date, setup=True)
if __name__ == '__main__':
if sys.argv[1] == 'setup':
try:
conn = sqlite3.connect('data2.sqlite', timeout=30)
c = conn.cursor()
c.execute('delete from earnings_calendar')
conn.commit()
except:
pass
read_date = date(2011, 01, 01)
date_queue = Queue()
while read_date < date(2015, 7, 11):
read_date = read_date + timedelta(days=1)
if read_date.isoweekday() not in range(1, 6):
continue
date_queue.put(read_date)
for i in range(10):
p = Process(target=worker, args=(date_queue,))
p.start()
elif sys.argv[1] == 'store':
#"150.0" "2.0" "1.0" "3.0" "4.0"
x = Machine(150.0, 2.0, 1.0, 3.0, 4.0)
for date in [2012, 2013, 2014, 2015]:
x.train(date)
x.store(date)
elif sys.argv[1] == 'update':
read_date = datetime.now().date()
get_announcements(read_date)