/
capit_vita.py
executable file
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/
capit_vita.py
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###
# Takeezi
###
import urllib2
import json
import re
import os
import glob
import time
import pylab
import datetime as dt
import numpy as np
import pandas as pd
from operator import itemgetter
from math import sqrt
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
from Robinhood import Robinhood
import smtplib
from os.path import basename
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.utils import COMMASPACE, formatdate
'''
TO DO
update stocklist
look-back 3 years to see accuracy of RSI?
https://www.investopedia.com/articles/active-trading/011815/top-technical-indicators-rookie-traders.asp
auto sell architecture
'''
class CapitVita(object):
def __init__(self, title = '', num_stocks = 15, mailing_list = [], debug = False):
self.title = title
self.num_stocks = num_stocks
self.home_path = os.path.abspath(os.getcwd())
if 'ec2-user' in self.home_path:
self.home_path = '/home/ec2-user/capit-vita'
self.file_path = self.home_path+'/data/'
self.par_path = os.path.dirname(self.home_path) + '/'
self.home_path = self.home_path + '/'
if os.path.exists(self.par_path + '/takaomattcom/static/img/stocks/'):
self.alt_file_path = self.par_path + '/takaomattcom/static/img/stocks/'
else:
self.alt_file_path = None
#print('capit', self.home_path, self.file_path, self.par_path, self.alt_file_path)
self.mailing_list = mailing_list
self.debug = debug
self.batchSize = 50
with open(self.par_path + '/auth/alphavantage.txt') as f:
self.av_API = f.read().split('\n')[0]
with open(self.par_path + '/auth/robinhood.txt') as f:
data = f.read().split('\n')
robinhood_username = data[0]
robinhood_password = data[1]
self.trader = Robinhood()
self.trader.login(username=robinhood_username, password=robinhood_password)
###########################################################################################################################################################
### Find
###########################################################################################################################################################
def find_stocks(self, graph = False):
start_time = time.time()
print('Initiating log...')
ff = open(self.file_path+'readme.txt','w')
ff.write(str(dt.datetime.now()))
ff.write('\n')
print('Deleting old files...')
os.chdir(self.file_path)
filelist = glob.glob('*.png')
for f in filelist:
os.remove(f)
if self.alt_file_path != None:
os.chdir(self.alt_file_path)
filelist = glob.glob('*.png')
for f in filelist:
os.remove(f)
os.chdir(self.home_path)
print('Fetching stock list...')
if os.path.isfile(self.home_path + 'options_stocklist.txt'):
print(' Using weekly :)')
with open(self.home_path+'options_stocklist.txt', 'r') as f:
stockset = list(f.read().split(','))[:-2]
else:
print(' Using all :(')
with open(self.home_path+'stocklist.txt', 'r') as f:
stockset = list(f.read().split(','))
if self.debug:
stockset = stockset[:10]
stockset_len = len(stockset)
# for now, ignore the last 20 trade-attempted stocks
ignore_these = [self.trader.get_url(x['instrument'])['symbol'] for x in self.trader.positions()['results'][-20:]]
stockset = [x for x in stockset if x not in ignore_these]
print('Grabbing data for {} stocks...'.format(stockset_len))
stockPoints = {}
for stock in stockset:
try:
points = self.get_points(stock)
stockPoints[stock] = [sum([points[x] for x in points if x != 'trend']), points]
#except BufferError:
except Exception as e:
print('failed {} because {}'.format(stock, e))
lenOriginalStocks = len(stockPoints)
print('Sorting stocks...')
sortedStocks = sorted(stockPoints.items(), key=itemgetter(1), reverse = True)[:self.num_stocks]
if graph:
print('Graphing stocks...')
for stock in [x[0] for x in sortedStocks]:
try:
self.graph_data(stock, saveLocation = self.file_path)
#except BufferError:
except Exception as e:
print('failed {} because {}'.format(stock, e))
print('Logging results...')
ff.write('Cheap stocks to invest in for 2 days ~ 1 week: \n\n')
ff.write('#\n')
for i in sortedStocks:
ff.write(i[0]+': '+str(round(i[1][0],1))+' '+str(i[1][1])+'\n')
ff.write('#\n')
ff.write('\n\n '+str(lenOriginalStocks)+' stocks filtered by point system to '+str(len(sortedStocks))+' stocks')
ff.write("\n\n--- %s seconds ---" % (time.time() - start_time))
ff.write('\n\n\n Capit-Vita Version 4.4 (2018-06-10)\n\n')
ff.close()
print('Sending emails...')
if len(self.mailing_list) > 0 and False:
self.send_email(self.file_path, self.title+' Top '+str(self.num_stocks)+' Prospects', self.mailing_list)
print('Done!')
return sortedStocks
###########################################################################################################################################################
### Grab
###########################################################################################################################################################
def grab_data(self, signal_name, rng=140):
try:
tries = 0
while True:
url = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol={}&outputsize=full&apikey={}'.format(signal_name, self.av_API)
request = urllib2.Request(url, headers={'User-Agent' : "Magic Browser"})
temp = eval(urllib2.urlopen(request).read())
if 'Time Series (Daily)' in temp:
break # exit if successful
else:
time.sleep(1)
tries += 1
if tries > 50: # in case we're trying too many calls?
break
self.df = pd.DataFrame.from_dict(temp['Time Series (Daily)']).transpose()
self.df = self.df.iloc[-rng:]
self.df.columns = ['open', 'high', 'low', 'close', 'adjusted close', 'volume', 'dividend amount', 'split coefficient']
self.df[['open','high','low','close','volume']] = self.df[['open','high','low','close','volume']].apply(pd.to_numeric)
self.df['rsi'] = self.RSI(self.df['close'], 14)
self.df['26 ema'] = self.df['close'].ewm(ignore_na=False,min_periods=0,adjust=True,com=26).mean()
self.df['12 ema'] = self.df['close'].ewm(ignore_na=False,min_periods=0,adjust=True,com=12).mean()
self.df['MACD'] = self.df['12 ema'] - self.df['26 ema']
self.df['MACD trigger'] = self.df['MACD'].ewm(ignore_na=False,min_periods=0,adjust=True,com=9).mean()
self.df['MACD signal'] = self.df['MACD'] - self.df['MACD'].ewm(ignore_na=False,min_periods=0,adjust=True,com=9).mean()
self.df['MACD_norm'] = self.normalize(self.df['MACD signal'])
self.df['MACD_der'] = self.derivative(self.df['MACD_norm'])
self.sanitize_data()
except BufferError:
#except Exception as e:
print('failed to get data because {}'.format(e))
def sanitize_data(self):
# this accounts for data sets where unaccounted splits are suspected
self.df['split'] = self.df['close'] < (self.df.shift()['open'] * 0.65)
m = self.df.pop('split').cumsum()
self.df.loc[m.eq(1)] *= 2
def generate_wiki_stocks(self):
date = '20160912'
#with open('rawStockList.txt', 'r') as f:
# data = json.load(f)
url = 'https://www.quandl.com/api/v3/datatables/WIKI/PRICES.json?date='+date+'&api_key='+myAPI
response = urllib2.urlopen(url)
data = json.load(response)
print(data)
stocks = [str(x[0]) for x in data['datatable']['data']]
with open('stockListAll.txt', 'wb') as f:
for s in stocks:
f.write(s)
f.write(',')
###########################################################################################################################################################
### Points
###########################################################################################################################################################
def get_points(self, signal_name, criteria = {}):
try:
points = {}
self.grab_data(signal_name)
mb, tb, bb, = self.bbands(self.df['close'])
if self.df['close'].iloc[-1] < (mb.iloc[-1] + bb.iloc[-1]) / 2:
points['outside BB'] = -500
if len(self.df['close']) < 100:
points['too short'] = -500
# RSI points (max 50)
points['rsi'] = 50 - round(1.2 * abs(30-self.df['rsi'].iloc[-1]))
# MACD points (max 40)
macd_max = max(self.df['MACD'])
macd_min = min(self.df['MACD'])
macd_diff = macd_max - macd_min
#print('max, min', macd_max, macd_min, macd_diff, self.df['MACD'].iloc[-1])
#print('percentage', abs(self.df['MACD'].iloc[-1] / macd_diff))
points['macd'] = round(40 * (1. - abs(self.df['MACD'].iloc[-1] / macd_diff)))
#points['macd'] = round(30 * self.df['MACD_norm'].iloc[-1] / max([abs(x) for x in self.df['MACD_norm']]))
#points['macd2'] = round(15 * self.df['MACD_der'].iloc[-1] / max([abs(x) for x in self.df['MACD_der']]))
'''
# candlestick points (max 10)
#if style == 'option':
candlestickFactor = 0
patterns = self.detectCandlestickPatterns(self.df['open'][-7:],
self.df['close'][-7:], self.df['low'][-7:],
self.df['high'][-7:], candlestickFactor)
points['candlesticks'] = self.rangeLimit(round(sum([x[2] for x in patterns])), -20, 20)
'''
# guru points (max 50)
try:
guru = self.get_guru(signal_name)
points['guru_financial'] = 2.5*int(guru[0])
points['guru_growth'] = 2.5*int(guru[1])
except IndexError:
print('failed Guru for {}'.format(signal_name))
points['guru_financial'] = 15
points['guru_growth'] = 15
print(signal_name, points)
except BufferError as e:
#except Exception as e:
print('failed getting points for {} because: {}'.format(signal_name, e))
return points
def get_guru(self, stock):
fNp = []
try:
urlToVisit = 'http://www.gurufocus.com/stock/'+stock
request = urllib2.Request(urlToVisit, headers={'User-Agent' : "Magic Browser"})
sourceCode = urllib2.urlopen(request).read()
finances = str(sourceCode).split(r'<a class="modally popup_window" href="#" id="rank_balancesheet">')
profitability = str(sourceCode).split(r'<a href="#" class="modally popup_window" href="#" id="rank_profitability">')
fNp.append(finances[1][1:].split('<')[0])
fNp.append(profitability[1][1:].split('<')[0])
#except Exception:
except BufferError:
fNp = [0,0]
return fNp
def detectCandlestickPatterns(self, openp,closep,lowp,highp,candlestickFactor=0.7):
patterns = []
# candlestickFactor: higher value for higher impact
engulfingStrength = 6
tweezerStrength = 4
dojiStrength = 3
morningStarStrength = 6
openp = list(openp)
closep = list(closep)
lowp = list(lowp)
highp = list(highp)
for i in range(len(openp)): # one candlestick patterns
if abs(closep[i] - openp[i]) < 0.05 * (highp[i] - lowp[i]):
if min([closep[i],openp[i]]) - lowp[i] > 2 * (highp[i] - max([closep[i],openp[i]])):
patterns.append(['dragonfly doji',i,dojiStrength*(sqrt(i+1)*candlestickFactor)])
if min([closep[i],openp[i]]) - lowp[i] < 2 * (highp[i] - max([closep[i],openp[i]])):
patterns.append(['gravestone doji',i,-dojiStrength*(sqrt(i+1)*candlestickFactor)])
if min([openp[i],closep[i]]) - lowp[i] <= 2 * abs(openp[i] - closep[i]) and \
highp[i] - max([openp[i],closep[i]]) <= 0.1 * abs(openp[i] - closep[i]) and \
max([openp[i],closep[i]]) - min([openp[i],closep[i]]) < min([closep[i],openp[i]]) - lowp[i]:
patterns.append(['hammer',i,0*(sqrt(i+1)*candlestickFactor)])
for i in range(1,len(openp)): # two candlestick patterns
if closep[i] > openp[i-1] and openp[i] < closep[i-1] and openp[i-1] > closep[i-1]:
patterns.append(['bullish engulfing',i,engulfingStrength*sqrt(i)*candlestickFactor])
if closep[i] < openp[i-1] and openp[i] > closep[i-1] and openp[i-1] < closep[i-1]:
patterns.append(['bearish engulfing',i,-engulfingStrength*sqrt(i)*candlestickFactor])
if abs(openp[i]-closep[i-1]) < 0.05 * (highp[i] - lowp[i] + highp[i-1] - lowp[i-1])/2 and \
abs(closep[i]-openp[i-1]) < 0.1 * (highp[i] - lowp[i] + highp[i-1] - lowp[i-1])/2 and \
openp[i] < closep[i]:
patterns.append(['tweezer bottoms',i,tweezerStrength*sqrt(i)*candlestickFactor])
if abs(openp[i]-closep[i-1]) < 0.05 * (highp[i] - lowp[i] + highp[i-1] - lowp[i-1])/2 and \
abs(closep[i]-openp[i-1]) < 0.1 * (highp[i] - lowp[i] + highp[i-1] - lowp[i-1])/2 and \
openp[i] > closep[i]:
patterns.append(['tweezer tops',i,-tweezerStrength*sqrt(i)*candlestickFactor])
for i in range(2,len(openp)): # three candlestick patterns
if openp[i-2] > closep[i-2] and openp[i] < closep[i] and \
max([closep[i-1],openp[i-1]]) < min([closep[i-2],openp[i]]) and \
abs(closep[i-1] - openp[i-1]) < 0.4 * (abs(closep[i-2] - openp[i-2])+abs(closep[i] - openp[i])) / 2:
patterns.append(['morning star',i,morningStarStrength*(sqrt(i-1)*candlestickFactor)])
return patterns
###########################################################################################################################################################
### Graphing
###########################################################################################################################################################
def graph_data(self, signal_name, rng = 100, saveLocation = ''):
try:
# create signals
#self.grab_data(signal_name, rng)
points = self.get_points(signal_name) # this grabs the data as well
# grab data
if 'time' in self.df: ## from crypto
date = self.df['date']
volume = self.df['volumeto']
else: ## regular
date = pd.Series(self.df.index)
volume = self.df['volume']
openp = self.df['open']
closep = self.df['close']
highp = self.df['high']
lowp = self.df['low']
date = date.apply(lambda x: mdates.date2num(dt.datetime.strptime(x, '%Y-%m-%d')))
mb, tb, bb, = self.bbands(closep)
datemin = date.min() + 30
datemax = date.max() + 45
# make colors
good_color = '#53C156'
bad_color = '#ff1717'
blue_color = '#3fcaff'
spine_color = '#808080'
label_color = 'k'
rowNum = 20
colNum = 4
# identify fig
plt.figure(facecolor='w',figsize=(18.,11.))
plt.suptitle(signal_name,color=label_color, size='xx-large')
plt.subplots_adjust(left=.125,bottom=.01,right=.9,top=.92,wspace=.2,hspace=0.1)
# plot rsi ---------------------------------------------------------------------------------------
ax_rsi = plt.subplot2grid((rowNum,colNum),(0,0),rowspan=3,colspan=4)
plt.ylabel('RSI',color=label_color)
ax_rsi.yaxis.tick_right()
ax_rsi.grid(True,color=spine_color)
ax_rsi.yaxis.label.set_color(spine_color)
ax_rsi.spines['bottom'].set_color(spine_color)
ax_rsi.spines['top'].set_color(spine_color)
ax_rsi.spines['left'].set_color(spine_color)
ax_rsi.spines['right'].set_color(spine_color)
ax_rsi.tick_params(axis='y',colors=spine_color)
ax_rsi.set_yticks([30,70])
ax_rsi.axhline(70,color = bad_color)
ax_rsi.axhline(30,color = good_color)
plt.setp(ax_rsi.get_xticklabels(),visible=False)
# plot RSI
ax_rsi.plot(date, self.df['rsi'], color='k', linewidth=2, alpha=0.5)
pylab.ylim([0,100])
# main plot ---------------------------------------------------------------------------------------
ax_main = plt.subplot2grid((rowNum,colNum),(3,0),rowspan=8,colspan=4,sharex=ax_rsi)
plt.ylabel('Price and Volume',color=label_color)
ax_main.grid(True,color=spine_color, which='major')
ax_main.grid(True,color=spine_color, which='minor', alpha=0.7)
ax_main.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax_main.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax_main.yaxis.label.set_color(spine_color)
ax_main.spines['bottom'].set_color(spine_color)
ax_main.spines['top'].set_color(spine_color)
ax_main.spines['left'].set_color(spine_color)
ax_main.spines['right'].set_color(spine_color)
ax_main.tick_params(axis='y',colors=spine_color)
ax_main.yaxis.set_major_locator(mticker.MaxNLocator(prune='both'))
plt.setp(ax_main.get_xticklabels(),visible=False)
# plot Bollinger bands
ax_main.plot(date,tb,'#adbdd6',alpha=0.9,label='TB')
ax_main.plot(date,bb,'#adbdd6',alpha=0.9,label='BB')
ax_main.plot(date,mb,'#ba970d',alpha=0.7,label='MA')
# min max stuff
min1, min2 = self.finalMinIndex(lowp)
max_main, max_macd = self.finalMaxIndex(highp)
d1 = int((min1+max_main)/2)
d2 = int((min2+max_macd)/2)
ax_main.plot([date[d1],date[d2]],[closep[d1],closep[d2]],linewidth=8,color=blue_color,alpha=0.8,linestyle=':')
# plot candlestick
candleAr = [[date[x],openp[x],closep[x],highp[x],lowp[x]] for x in range(len(date))]
self.candlestick(ax_main, candleAr, width = 0.5, colorup = good_color, colordown = bad_color)
#ax_main.yaxis.tick_right()
ax_main.yaxis.set_ticks_position('both')
''' commented out because it moves the axis ticks
# plot volume
ax_main_v = ax_main.twinx()
ax_main_v.grid(False)
ax_main_v.axes.yaxis.set_ticklabels([])
ax_main_v.spines['bottom'].set_color(spine_color)
ax_main_v.spines['top'].set_color(spine_color)
ax_main_v.spines['left'].set_color(spine_color)
ax_main_v.spines['right'].set_color(spine_color)
ax_main_v.yaxis.label.set_color(spine_color)
ax_main_v.xaxis.label.set_color(spine_color)
ax_main_v.fill_between(date,0,volume,facecolor='#8e8e87',alpha=.1)
'''
# plot MACD ---------------------------------------------------------------------------------------
ax_macd = plt.subplot2grid((rowNum,colNum),(11,0),rowspan=3,colspan=4,sharex=ax_rsi)
plt.ylabel('MACD',color=label_color)
#ax_macd.yaxis.tick_right()
ax_macd.grid(True,color=spine_color, which='both')
ax_macd.yaxis.label.set_color(spine_color)
ax_macd.spines['bottom'].set_color(spine_color)
ax_macd.spines['top'].set_color(spine_color)
ax_macd.spines['left'].set_color(spine_color)
ax_macd.spines['right'].set_color(spine_color)
ax_macd.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5,prune='upper'))
ax_macd.tick_params(axis='x',colors=spine_color)
ax_macd.tick_params(axis='y',colors=spine_color, labelright=True)
ax_macd.axes.yaxis.set_ticklabels([])
ax_macd.set_xlim(datemin, datemax)
ax_macd.xaxis.set_major_locator(mdates.MonthLocator())
ax_macd.xaxis.set_major_formatter(mdates.DateFormatter('%B'))
ax_macd.xaxis.set_minor_locator(mdates.WeekdayLocator(mdates.MONDAY))
for label in ax_macd.xaxis.get_ticklabels():
label.set_rotation(45)
ax_macd.fill_between(date, self.df['MACD signal'], 0, alpha=0.5, facecolor=blue_color, edgecolor='k')
'''
ax_macd_der = ax_macd.twinx()
ax_macd_der.plot(date, self.df['MACD_der'], color='k', linewidth=1, alpha=0.5)
ax_macd_der.spines['bottom'].set_color(spine_color)
ax_macd_der.spines['top'].set_color(spine_color)
ax_macd_der.spines['left'].set_color(spine_color)
ax_macd_der.spines['right'].set_color(spine_color)
ax_macd_der.axes.yaxis.set_ticklabels([])
#ax_macd.plot(date, self.df['MACD trigger'], color='k', linewidth=1)
#ax_macd.fill_between(date, self.df['MACD signal'], 0, alpha=0.5, facecolor='g', edgecolor='k')
ax_macd_der.set_xlim(datemin, datemax)
'''
# plot last week ---------------------------------------------------------------------------------------
c_length = 7
ax_lastweek = plt.subplot2grid((rowNum,colNum),(15,0),rowspan=4,colspan=2)
plt.ylabel(str(c_length)+' day candlesticks',color=label_color)
ax_lastweek.yaxis.label.set_color(spine_color)
ax_lastweek.grid(True, color='k', alpha=0.5)
ax_lastweek.spines['bottom'].set_color(spine_color)
ax_lastweek.spines['top'].set_color(spine_color)
ax_lastweek.spines['left'].set_color(spine_color)
ax_lastweek.spines['right'].set_color(spine_color)
ax_lastweek.tick_params(axis='y',colors=spine_color)
ax_lastweek.tick_params(axis='x',colors=spine_color)
ax_lastweek.xaxis.set_major_locator(mdates.DayLocator())
ax_lastweek.xaxis.set_major_formatter(mdates.DateFormatter('%a'))
ax_lastweek.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
ax_lastweek.set_xlim(date.max() - c_length - 1, date.max() + 1)
ax_main.xaxis.set_major_locator(mticker.MaxNLocator(prune='both'))
# plot candlesticks
candleAr = [[date[x],openp[x],closep[x],highp[x],lowp[x],volume[x]] for x in range(len(date)-c_length,len(date))]
self.candlestick(ax_lastweek, candleAr[-c_length:], width=.5, colorup=good_color, colordown=bad_color)
ax_lastweek.yaxis.tick_left()
# points ---------------------------------------------------------------------------------------
ax_description = plt.subplot2grid((rowNum,colNum),(15,2),rowspan=4,colspan=2)
ax_description.grid(False)
ax_description.get_xaxis().set_visible(False)
ax_description.get_yaxis().set_visible(False)
ax_description.spines['bottom'].set_color(spine_color)
ax_description.spines['top'].set_color(spine_color)
ax_description.spines['left'].set_color(spine_color)
ax_description.spines['right'].set_color(spine_color)
criteria_text = '\n'.join(['{}:'.format(x) for x in points])
points_text = '\n'.join(['{}'.format(points[x]) for x in points])
total_points = sum([points[x] for x in points])
ax_description.text(0.03, 0.9, 'Total points: {}'.format(total_points), horizontalalignment='left', verticalalignment='top')
ax_description.text(0.03, 0.7, criteria_text, horizontalalignment='left', verticalalignment='top')
ax_description.text(0.25, 0.7, points_text, horizontalalignment='left', verticalalignment='top')
# finished creating plot -----------------------------------------------------------------------
signal_name = re.sub('[^0-9a-zA-Z]+', '-', signal_name) # replace invalid filename chars
if saveLocation == '':
plt.show()
else:
pylab.savefig(saveLocation+signal_name + '.png', facecolor='w', edgecolor='w')
if self.alt_file_path != None:
pylab.savefig(self.alt_file_path + signal_name + '.png', facecolor='w', edgecolor='w')
plt.close()
except BufferError:
#except Exception as e:
plt.close()
print('failed to plot',signal_name,'because',e)
###########################################################################################################################################################
### Graphing tools
###########################################################################################################################################################
def normalize(self, signal):
return [float(i)/sum(signal) for i in signal]
def movingAverage(self, values,window):
weights = np.repeat(1.0, window)/window
return np.convolve(values, weights, 'valid')
def expMovingAverage(self, values,window):
weights = np.exp(np.linspace(-1.,0.,window))
weights /= weights.sum()
a = np.convolve(values,weights,mode='full')[:len(values)]
a[:window] = a[window]
return a
def standard_deviation(self, date,tf,prices):
sd = []
sddate = []
x = tf
while x <= len(prices):
a2c = prices[x-tf:x]
standev = a2c.std()
sd.append(standev)
sddate.append(date[x])
x+=1
return sddate,sd
def derivative(self, signal):
return np.gradient(signal)
def bbands(self, price, length=20, numsd=2):
ave = price.rolling(window=length, center=False).mean()
sd = price.rolling(window=length, center=False).std()
upband = ave + (sd*numsd)
dnband = ave - (sd*numsd)
return np.round(ave,3), np.round(upband,3), np.round(dnband,3)
def RSI(self, series, period = 14):
delta = series.diff().dropna()
u = delta * 0
d = u.copy()
u[delta > 0] = delta[delta > 0]
d[delta < 0] = -delta[delta < 0]
u[u.index[period-1]] = np.mean( u[:period] ) #first value is sum of avg gains
u = u.drop(u.index[:(period-1)])
d[d.index[period-1]] = np.mean( d[:period] ) #first value is sum of avg losses
d = d.drop(d.index[:(period-1)])
rs = u.ewm(ignore_na=False,min_periods=0,adjust=False,com=period-1).mean() / \
d.ewm(ignore_na=False,min_periods=0,adjust=False,com=period-1).mean()
return 100 - 100 / (1 + rs)
def finalMinIndex(self, lowp):
m1 = int(len(lowp)/2)
for i in range(int(len(lowp)/2),int(3*len(lowp)/4)):
if lowp[i] < lowp[m1]:
m1 = i
m2 = int(3*(len(lowp))/4)
for i in range(int(3*len(lowp)/4),len(lowp)):
if lowp[i] < lowp[m2]:
m2 = i
return m1, m2
def finalMaxIndex(self, highp):
m1 = int(len(highp)/2)
for i in range(int(len(highp)/2),int(3*len(highp)/4)):
if highp[i] > highp[m1]:
m1 = i
m2 = int(3*(len(highp))/4)
for i in range(int(3*len(highp)/4),len(highp)):
if highp[i] > highp[m2]:
m2 = i
return m1, m2
def rangeLimit(self, v,l,h):
if v < l:
v = l
if v > h:
v = h
return v
def average(self, l):
return sum(l)/len(l)
def increasingness(self, signal):
shortTerm = signal[-1] - self.average(signal[-3:])
longTerm = self.average(signal[-3:]) - self.average(signal[-10:-3])
return self.average([shortTerm, longTerm])
def candlestick(self, ax, quotes, width=0.4, colorup='k', colordown='r',
alpha=1.0, ochl=True):
OFFSET = width / 2.0
lines = []
patches = []
for q in quotes:
if ochl:
t, open, close, high, low = q[:5]
else:
t, open, high, low, close = q[:5]
if close >= open:
color = colorup
lower = open
height = close - open
else:
color = colordown
lower = close
height = open - close
vline = Line2D(
xdata=(t, t), ydata=(low, high),
color=color,
linewidth=0.7,
antialiased=True,
)
rect = Rectangle(
xy=(t - OFFSET, lower),
width=width,
height=height,
facecolor=color,
edgecolor=color,
)
rect.set_alpha(alpha)
lines.append(vline)
patches.append(rect)
ax.add_line(vline)
ax.add_patch(rect)
ax.autoscale_view()
ax.yaxis.tick_right()
return lines, patches
###########################################################################################################################################################
### Email
###########################################################################################################################################################
def send_email(self, directory, title, mailing_list):
with open(self.par_path + '/auth/takaomattpython.txt') as f:
password = f.read()
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login('takaomattpython@gmail.com', password)
msg = MIMEMultipart()
msg['Date'] = formatdate(localtime=True)
msg['Subject'] = title+': '+str(formatdate(localtime=True))
fList = []
for file in os.listdir(directory):
fList.append(directory + file)
for f in fList:
with open(f, "rb") as fil:
part = MIMEApplication(
fil.read(),
Name=basename(f)
)
part['Content-Disposition'] = 'attachment; filename="%s"' % basename(f)
msg.attach(part)
print('Emails away!')
for i in mailing_list:
server.sendmail('takaomattpython@gmail.com', i, msg.as_string())
server.quit()
# for debugging
'''
stock = 'CMI'
C = CapitVita()
#C.graph_data(stock)
C.find_stocks()
'''