def check_buy(self):
#one-time buy
if self.owned_shares != 0:
return False
share = Share(self.code)
avg50 = self.get_short_moving_avg()
avg200 = self.get_long_moving_avg()
if avg50 == -1.0 or avg200 == -1.0:
self.log('Short or Long moving average cannot be obtained due to yahoo API error')
return 0
current_price_u = share.get_price()
if current_price_u is None:
self.log('Current Price is None for Stock')
return 0
current_price = float(current_price_u)
if avg50 > avg200:
#trend change, buy
buy_count = self.how_many_shares_to_buy(current_price)
if buy_count != 0:
if buy(self, buy_count, current_price) == True:
self.owned_shares = buy_count
self.bought_value = float(current_price * self.owned_shares)
return self.owned_shares
return 0
def fetch_data(dt_from, dt_to, sym):
min_dt_from = dt.datetime(2014, 1, 1)
assert(dt_from >= min_dt_from)
r = TStockHistory.find_one(
{'Symbol': sym}, projection={'Date': 1}, sort=[('Date', pymongo.DESCENDING)])
if not r:
fetch_dt_from = min_dt_from
elif r['Date'] < dt_to:
fetch_dt_from = r['Date'] + dt.timedelta(days=1)
if fetch_dt_from > dt_to:
fetch_dt_from = None
else:
fetch_dt_from = None
if fetch_dt_from:
f = '%d-%d-%d' % (
fetch_dt_from.year, fetch_dt_from.month, fetch_dt_from.day)
t = '%d-%d-%d' % (dt_to.year, dt_to.month, dt_to.day)
print('fetch %s from network...' % (sym))
share = Share(sym)
docs = share.get_historical(f, t)
if docs:
for r in docs:
for k in ['Adj_Close', 'High', 'Low', 'Close', 'Volume']:
r[k] = np.float(r[k])
r['Date'] = dateutil.parser.parse(r['Date'])
TStockHistory.insert_many(docs)
data = TStockHistory.find(
{'Symbol': sym, 'Date': {'$gte': dt_from, '$lte': dt_to}})
rs = filter(lambda x: 'placeholder' not in x, [u for u in data])
return rs
def main():
start_date = '2007-01-01'
end_date = '2015-08-31'
plotly_name = 'username'
plotly_api_key = 'api-key'
# get_historical is part of the yahoo-finance module
nflx = Share('nflx')
nflx_prices = nflx.get_historical(start_date, end_date)
# how you can just extract the dates only
nflx_dates = [x['Date'] for x in nflx_prices]
# nflx_prices is currently sorted by dates because thats how the module gave them
sorted_by_price = sorted(nflx_prices, key=lambda x: x['Adj_Close'], reverse=True)
# say you wanted multiple stock prices
ticker_symbols = ['hrl', 'tsn', 'gis', 'k']
foods = {}
if do_plot:
plot_stock(plotly_name, plotly_api_key, nflx_prices, 'nflx')
for symbol in ticker_symbols:
foods[symbol] = Share(symbol).get_historical(start_date, end_date)
foods[symbol].sort(key=lambda x: x['Date'])
dates = [x['Date'] for x in foods[symbol]]
prices = [x['Adj_Close'] for x in foods[symbol]]
if do_plot:
add_trace(dates, prices, symbol)
def getStockData(theTicker):
global startDate
print("Getting Data for ... " + theTicker)
stock = Share(theTicker)
print(startDate)
data = stock.get_historical(startDate, DateNow)
for d in data:
tmp = []
volume = int(d['Volume'])
adjclose = float(d['Adj_Close'])
high = float(d['High'])
low = float(d['Low'])
close = float(d['Close'])
date = d['Date']
open = float(d['Open'])
# newDate = datetime.strptime(date, "%Y-%m-%d")
tmp.append(date)
tmp.append(open)
tmp.append(high)
tmp.append(low)
tmp.append(close)
tmp.append(adjclose)
tmp.append(volume)
givenStock.append(tmp)
return givenStock
def get_stock_info(symbols, moreInfo=False): """ Scrape stock info from Yahoo finance @Param 'moreInfo': False for getting price, True for getting more information @Return 'message': "symbol: Price-number, Open Price-number, Pre Close Price-number, High Price-number, Low price-number" """ message = '' for symbol in symbols: try: stock = Share(symbol) price = stock.get_price() except AttributeError: price = None if price == None: # Stock symbol not exists, replace with an alert message message += '#' + symbol + ': ' + 'The stock symbol does not exit' + '\n' elif moreInfo: message += '#%s: Price-%s, Open Price-%s, Pre Close Price-%s, High Price-%s, Low price-%s\n' \ % (symbol, price, stock.get_open(), stock.get_prev_close(), stock.get_days_high(), stock.get_days_low()) else: message += '#' + symbol + ': ' + price + '\n' alert_message = 'Please type #1 followed by stock symbols to get more information' if moreInfo == True else \ 'Please type #0 followed by stock symbols to get stock price' return message if message != '' else alert_message
def lookup_by_symbol(symbol='', service='y'):
"""
TODO : Not calling this method anywhere now; think about
this method's purpose again
"""
query_symbol = Symbol.symbol_dict.get(symbol)
if query_symbol is None:
try:
if service == 'g':
g_get_quotes(symbol)
query_symbol = Symbol(g_symbol=symbol)
elif service == 'y':
print 'creating Share instance to check whether valid symbol'
y_obj = Share(symbol)
if 'start' not in y_obj.get_info():
raise Exception('Given symbol doesn\'t exist in Yahoo finance')
query_symbol = Symbol(y_symbol=symbol)
query_symbol.yahoo_handle = y_obj
except urllib2.HTTPError:
print('{symbol} is invalid'.format(symbol=symbol))
except Exception as e:
print(e.args)
return query_symbol
def stock_Prices():
# exit if the output already existed
# if os.path.isfile('./input/stockPrices.json'):
# sys.exit("Prices data already existed!")
priceSet = {}
fin = open('./input/tickerList.csv')
for num, line in enumerate(fin):
line = line.strip().split(',')
ticker, name, exchange, MarketCap = line
if 1:
print(num, ticker)
yahoo = Share(ticker)
time.sleep(np.random.poisson(3))
prices = yahoo.get_historical('2005-01-01', '2020-01-01')
priceDt = {}
for i in range(len(prices)):
date = ''.join(prices[i]['Date'].split('-'))
priceDt[date] = round(log(float(prices[i]['Close']) / float(prices[i]['Open'])), 6)
priceSet[ticker] = priceDt
#except:
#continue
with open('./input/stockPrices.json', 'w') as outfile:
json.dump(priceSet, outfile, indent=4)
def SelectStock(self):
wb = Workbook()
ws = wb.active
with open('export.csv',newline='') as f:
reader = csv.reader(f)
index = 2
title = ["代號","名稱","股價","本益比","EPS"]
for x in range(0,5,1):
ws.cell(row=1,column=x+1,value=title[x])
for row in reader:
if row!=[]:
try:
if int(row[0]) < 10000:
# print(row[2])
if "-" not in (row[2]) and float(row[2]) < 20:
# print(row)
Query = Share(row[0]+'.TW')
#print(float(Query.get_earnings_share()))
#print(float(Query.get_open()))
#print("******************")
if float(Query.get_open()) >= 20 and float(Query.get_open()) <= 90 and float(Query.get_earnings_share())>1.2:
print(row)
ws.cell(row=index , column=1 , value = row[0])
ws.cell(row=index , column=2 , value = row[1])
ws.cell(row=index , column=3 , value = Query.get_open())
ws.cell(row=index , column=4 , value = row[2])
ws.cell(row=index , column=5 , value = Query.get_earnings_share())
index = index + 1
except:
pass
wb.save("Final.xlsx")
def check_sell(self):
#UNCOMMENT WHEN USING ACTUAL DATA
if self.owned_shares == 0:
return False
share = Share(self.code)
current_price_u = share.get_price()
if current_price_u is None:
self.log('Current Price is None for Stock')
return 0
current_price = float(current_price_u)
#UNCOMMENT WHEN USING ACTUAL DATA
if self.bought_value < current_price:
return False
avg50 = self.get_short_moving_avg()
avg200 = self.get_long_moving_avg()
if avg50 < avg200:
#trend change, buy
sell(self, self.owned_shares,current_price)
return True
return False
def make_csv(self,name,now_time):
if os.path.exists(self.DIR_NAME+name+".csv"):
print "Already have a %s.csv"%name
return
print "making %s.csv....." %name
from yahoo_finance import Share
share=Share(name)
info=share.get_info()
if "start" not in info:
print "Cant make a %s.csv"%name
return -1
start=info["start"]
#wrong date
if re.match("\d{4}-\d{2}-\d{2}",start)== None:
print "invalid date cant make a file"
return -1
obj=share.get_historical(start,now_time)
filename=name+".csv"
fieldnames=("Date","High","Low","Volume" )
headers = dict( (n,n) for n in fieldnames )
f=open(self.DIR_NAME+filename,"w")
writer=csv.DictWriter(f,fieldnames)
writer.writerow(headers)
for o in obj:
row=""
writer.writerow({"Date":o["Date"],"High":o["High"],"Low":o["Low"],"Volume":o["Volume"]})
f.close()
def combine(onedayago, twodaysago, threedaysago, fivedaysago, oneweekago, companyname):
print "Analyzing data from the past few days..."
dates = [onedayago, twodaysago, threedaysago, fivedaysago, oneweekago]
for i in dates:
i == i.format('YYYY-MM-DD')
# Just gets the info and puts it into programmer friendly names
def getclosing(date, company):
# Thanks to stackoverflow user 'TessellatingHeckler' for helping me out with this next function! At the time dictionaries were a foreign concept to me.
readings = company.get_historical(date, date)
for reading in readings:
close = reading['Close']
return close
company = Share(companyname)
closingonedayago = getclosing(str(dates[0]), company)
closingtwodaysago = getclosing(str(dates[1]), company)
closingthreedaysago = getclosing(str(dates[2]), company)
closingfivedaysago = getclosing(str(dates[3]), company)
closingoneweekago = getclosing(str(dates[4]), company)
twohundredavg = company.get_200day_moving_avg()
fiftyavg = company.get_50day_moving_avg()
today = company.get_price()
decision(today, closingonedayago, closingtwodaysago, closingthreedaysago, closingfivedaysago, closingoneweekago, twohundredavg, fiftyavg)
def get_data(symbol='^GSPC'):
symbol1 = symbol
if symbol1[0] == '^':
symbol1 = symbol[1:]
fileName = 'c:/data/%s.csv' % symbol1
keys = ['Date', 'Close'] # only save these fields
gspc = Share(symbol)
if os.path.isfile(fileName):
result = read_data(fileName)
oneday = timedelta(1)
yesterday = datetime.today().date() - oneday
latest = datetime.strptime(result[0]['Date'], '%Y-%m-%d').date()+oneday
if latest < yesterday:
print('Extending from %s to %s ...' % (str(latest), str(yesterday)))
result = gspc.get_historical(str(latest), str(yesterday)) + result
write_data(fileName, result, keys)
else:
print('No need to update')
else:
result = gspc.get_historical('1976-02-01', '2016-02-21')
write_data(fileName, result, keys)
return result
def download_prices_to_today(ticker, prices):
"""Download prices till today.
:param str ticker:
:param [dict] prices: prices sorte by date
"""
try:
share = Share(ticker)
# get from_date
if not prices:
from_date = datetime.strptime('2015-08-03', '%Y-%m-%d')
else:
last_date = datetime.strptime(prices[-1]['Date'], '%Y-%m-%d')
from_date = (last_date + timedelta(days=1))
# get to_date
to_date = datetime.today()
day_of_week = to_date.weekday()
if day_of_week == 6:
to_date = to_date + timedelta(days=-2) # if sunday
elif day_of_week == 5:
to_date = to_date + timedelta(days=-1) # if saturday
# skip if already update-to-date
if from_date >= to_date:
return
str_from_date = from_date.strftime('%Y-%m-%d')
str_to_date = to_date.strftime('%Y-%m-%d')
eprint('downloading prices', ticker, str_from_date, str_to_date)
data = share.get_historical(str_from_date, str_to_date)
return data
except Exception as ex:
eprint(ex)
def start_stock():
# Busca as informações do pregão
data_corrente = start_data()
data_exec = data_corrente[0:4] + '-' + data_corrente[4:6] + '-' + data_corrente[6:8]
lista_acoes = cria_lista()
#lista_acoes = ['PETR3.SA', 'VALE3.SA', 'BBAS3.SA']
nome_arq = 'pregao' + str(data_exec) + '.txt'
arquivo = open(nome_arq, 'w')
texto = ''
informacao = None
for acao in lista_acoes:
try:
yahoo = Share(str(acao))
informacao = yahoo.get_historical(data_exec,data_exec)
except:
pass
if informacao :
simbolo = informacao[0]['Symbol'].split('.SA', 1)[0]
try:
fechamento = informacao[0]['Close']
except KeyError:
fechamento = ''
try:
volume = informacao[0]['Volume']
except KeyError:
volume = ''
texto = simbolo + ';' + fechamento + ';' + volume + "\n"
arquivo.writelines(texto)
arquivo.close()
return "Registros salvos no arquivo: " + nome_arq
def getPrice(bodyList, from_number):
retStr = ""
#interate over bodyList
for ticker in bodyList:
quote = Share(str(ticker))
price = quote.get_price()
#construct return string with price ticker if found
#appended the comma and space in the front, will remove preceeding comma after loop
if price:
retStr += ", " + ticker + " Price: " + price
else:
retStr += ", ticker not found"
#update last_lookup field so the "more info" feature works
conn = mysql.connect()
cur = conn.cursor()
q = '''INSERT INTO last_lookup (phone, ticker)
VALUES (%s, %s)
ON DUPLICATE KEY UPDATE
ticker = VALUES(ticker)
'''
try:
cur.execute(q,(from_number,ticker))
conn.commit()
except:
conn.rollback()
#strip preceeding comma and space
retStr = retStr[2:]
return retStr
def update_stock(dial, stock):
stk = Share(stock)
prev_close_price = float(stk.get_prev_close())
stk_data = json.loads(json.dumps(getQuotes(stock), indent=2))[0]
stk_price = float(stk_data['LastTradePrice'])
NIMBUS.set_dial_value(dial, percentage(stk_price, prev_close_price),
"%s:%.2f" % (stock, stk_price))
def data_frame_companies_amount(list_companies,porcentaje_volumnen_diario,max_var_pvd):
# Es una funcion que toma una lista de empresas (codificadas por yahoo.finance), el porcentaje (tanto por uno)
# sobre el volumen diario a considerar y la maxima variacion en porcentaje (tanto por uno) sobre este porcentaje.
list_index=["ask5","ask4","ask3","ask2","ask1","bid1","bid2","bid3","bid4","bid5"]
companies_amount=pd.DataFrame(index=list_index)
for i in list_companies:
company = Share(i)
volume=company.get_volume()
cantidad_per_ba=float(company.get_avg_daily_volume())*porcentaje_volumnen_diario
num=cantidad_per_ba*max_var_pvd # variacion maxima respecto al 1% del volumen diario
vec_list_amount=[]
for h in range(0,len(list_index)):
vec_list_amount.append(round(cantidad_per_ba+np.random.randint(-num,num,1)))
vec_amount=np.array(vec_list_amount)
companies_amount[i]=vec_amount
return companies_amount
def create_files():
for i in range(0,TIME_IN_MIN*60/TIME_BETWEEN_ITERATIONS):
reader=csv.DictReader(open('Yahoo_symbols.csv','rb'))
for sym in reader:
company=sym["COMPANY"]
symbol=sym["SYMBOL"]
while(1):
try:
share_name=Share(symbol)
if share_name:
break
except:
time.sleep(1)
filename = "./Q4_files/"+company+".csv"
try:
file=open(filename,"a")
except:
file=open(filename,"w")
file.write("Time,Price")
timestamp = share_name.get_trade_datetime()
price = share_name.get_price()
writer = csv.DictWriter(file, fieldnames=["Time","Price"], delimiter=",", lineterminator="\n")
writer.writerow({"Time":timestamp ,"Price":price})
time.sleep(TIME_BETWEEN_ITERATIONS)
file.close()
def get_stock_price_and_volume(symbol, start_date, end_date, dates): share = Share(symbol) hist_data = share.get_historical(start_date, end_date) hist_data.reverse() volume = [] price = [] i = 0 for d in dates: if i < len(hist_data): if (hist_data[i]['Date'] == d): # Weekday price.append(hist_data[i]['Close']) volume.append(hist_data[i]['Volume']) i += 1 else: # Weekend price.append(0) volume.append(0) else: # Get the current price and volume instead from historical data price.append(share.get_price()) volume.append(share.get_volume()) if len(dates) != len(volume) and len(dates) != len(price): print 'Dates and volume and/or price lists are not of same lenght!' return [price, volume]
def data_frame_companies_prices(list_companies,a,b):
# Es una funcion que toma una lista de empresas (codificadas por yahoo.finance) y valor minimo y maximo
# de la distribucion uniforme que sigue la diferencia entre el bid y el ask para las empresas.
list_index=["ask5","ask4","ask3","ask2","ask1","bid1","bid2","bid3","bid4","bid5"]
companies_prices=pd.DataFrame(index=list_index)
for i in list_companies:
company = Share(i)
open_price=company.get_price()
open_price=float(open_price)
a=float(a)
b=float(b)
random1=(np.random.rand(1)+(a/b-a))/(1.0/b-a)
first_ask_price=open_price+(round(random1,2))/2.0
ask_array=np.array([first_ask_price+0.04,first_ask_price+0.03,first_ask_price+0.02,first_ask_price+0.01,first_ask_price])
first_bid_price=open_price-(round(random1,2))/2.0
bid_array=np.array([first_bid_price+0.04,first_bid_price+0.03,first_bid_price+0.02,first_bid_price+0.01,first_bid_price])
ask_bid_array=np.concatenate((ask_array,bid_array))
companies_prices[i]=ask_bid_array
return companies_prices
def retrieveQuoteFromYahoo(symbol,start,end):
share = Share(symbol)
quoteList = share.get_historical(start,end)
quoteDict = {}
for quote in quoteList:
quoteDict[quote['Date']] = float(quote['Adj_Close'])
return quoteDict
def get_stock_df(ticker,start_date,end_date):
"""Get stock dataframe"""
share = Share(ticker)
share_hist = share.get_historical(start_date,end_date)
len_share_hist = len(share_hist)
dates = ['']*len_share_hist
open = [0.]*len_share_hist
close = [0.]*len_share_hist
high = [0.]*len_share_hist
low = [0.]*len_share_hist
volume = [0.]*len_share_hist
adj_close = [0.]*len_share_hist
for i in range(len_share_hist):
dates[i] = share_hist[i][DATE_STR]
open[i] = float(share_hist[i][OPEN_STR])
close[i] = float(share_hist[i][CLOSE_STR])
adj_close[i] = float(share_hist[i][ADJ_CLOSE_STR])
high[i] = float(share_hist[i][HIGH_STR])
low[i] = float(share_hist[i][LOW_STR])
volume[i] = float(share_hist[i][VOLUME_STR])
df = pd.DataFrame(open, index = pd.to_datetime(dates), columns=[OPEN_STR])
df[CLOSE_STR] = close
df[ADJ_CLOSE_STR] = adj_close
df[HIGH_STR] = high
df[LOW_STR] = low
df[VOLUME_STR] = volume
df.index.name = DATE_STR
return df.sort_index()
def get_historical(self, num_of_days):
if self._id.startswith('6'):
stk_id = self._id + '.ss'
else:
stk_id = self._id + '.sz'
stk = Share(stk_id)
start = datetime.strftime(datetime.today() - timedelta(days=num_of_days+1),
'%Y-%m-%d')
end = datetime.strftime(datetime.today() - timedelta(days=1),
'%Y-%m-%d')
hist = stk.get_historical(start, end)
high = []
low = []
opening = []
close = []
vol = []
date = []
for d in hist:
if d.has_key('High'):
high.append(float(d['High']))
low.append(float(d['Low']))
opening.append(float(d['Open']))
close.append(float(d['Close']))
vol.append(float(d['Volume']))
date.append(d['Date'])
return high, low, opening, close, vol, date
def get_data(stock,start,end):
data = Share(stock)
try:
data = pd.DataFrame(data.get_historical(start_date=start,end_date=end))
except Exception as e:
f = open('log.txt',mode='a')
f.write(stock+'\n')
f.write(str(e)+'\n')
return pd.DataFrame()
try:
data.index = data.Date
except Exception as e:
f = open('log.txt', mode='a')
f.write(stock+'\n')
f.write(str(e)+'\n')
return pd.DataFrame()
data = data.drop(['Date','Symbol'],axis=1)
data = data.sort_index()
for i in data.columns:
data[i] = data[i].astype(np.float)
#data['Adj_Open'] = 0
#data['Adj_High'] = 0
#data['Adj_Low'] = 0
#for i in range(len(data)):
# k = data['Adj_Close'][i] / data['Close'][i]
# data.loc[i:i+1,'Adj_Open'] = k*data['Open'][i]
# data.loc[i:i + 1, 'Adj_High'] = k * data['High'][i]
# data.loc[i:i + 1, 'Adj_Low'] = k * data['Low'][i]
data['Symbol'] = stock
return data
def sell():
if 'username' in session:
# Get symbol name
s = request.args.get('symbol')
# recreate users stock information
if not models.new_stock:
models.Users.reload_user()
# symbol name is valid
if s is not None and s != '':
symbol = Share(s)
if symbol.get_price() is None:
return render_template('apology.html', message=['Something went wrong. Please try again.'])
else:
# Get user's stock info
holding = models.Stockholding.objects.get(username=session['username'], symbol=s)
amount_to_add = int(holding.shares) * float(symbol.get_price())
# add value of shares to user's cash
models.Users.objects(username=session['username']).update(inc__cash=amount_to_add)
# log transaction
transaction = models.Transaction(username=session['username'], date=time.strftime("%d/%m/%Y"), \
type=models.Transaction.SELL, symbol=s, shares=holding.shares)
transaction.save()
holding.delete() # Remove stock
flash('Stock sold successfully', 'text-success')
models.Users.reload_user()
print models.new_stock
return redirect('portfolio')
return render_template('sell.html', stocks=models.new_stock)
return render_template('apology.html', message=['Log In to see this page'])
def get_data():
filename = 'data/symbols.csv'
data = []
with open(filename, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for row in reader:
symbol = row[0]
print ("Getting share for %s" % symbol)
name = row[1]
sector = row[2]
share = Share(symbol)
_open = share.get_open()
if _open:
data.append({
'id' : symbol,
'symbol' : symbol,
'name' : name,
'sector' : sector,
'open' : _open
})
return data
def report_current_from_yahoo(symbol):
yahoo = Share(symbol)
price_cur = yahoo.get_price()
eps = yahoo.get_earnings_share()
pe = yahoo.get_price_earnings_ratio()
print 'yahoo: price=%s eps=%s, pe=%s'%(price_cur, eps, pe)
def generateData(self):
global StartingDate
global DataSize
i = datetime.datetime.now()
EndingDate = '%s-%s-%s' % (i.year,i.month,i.day)
stock = Share(Stock_name)
data = stock.get_historical(StartingDate,EndingDate)
file = open('stock_data', 'w')
closes = [c['Close'] for c in data]
opens = [o['Open'] for o in data]
oArray = []
cArray = []
for c in closes:
cArray.append(c)
for o in opens:
oArray.append(o)
for x in range(len(data)-2):
# %Difference, Next Day %Difference, Money Made Holding for a Day
file.write(str((float(cArray[x])-float(oArray[x+1]))/100) + ' ' + str((float(cArray[x+1]) - float(oArray[x+2]))/100) + ' ' + str((float(oArray[x]) - float(oArray[x+1]))) + '\n')
self.dayChange.append((float(cArray[x])-float(oArray[x+1]))/100)
self.nextDayChange.append((float(cArray[x+1]) - float(oArray[x+2]))/100)
self.profit.append(float(oArray[x]) - float(oArray[x+1]))
#Makes sure the population size is
DataSize = len(self.dayChange)
file.close()
def getData(startDate, endDate, period):
tickerSymbols = []
sp500 = finsymbols.get_sp500_symbols()
nasdaq = finsymbols.get_nasdaq_symbols()
amex = finsymbols.get_amex_symbols()
nyse = finsymbols.get_nyse_symbols()
for companies in amex:
tickerSymbols.append(companies.get("symbol"))
print tickerSymbols
return
# start a period before the startdate to obtain the data
print startDate, " ", endDate
startDate = startDate - timedelta(days=period)
print startDate
data = {}
for company in tickerSymbols:
print company
c = Share(company)
histData = c.get_historical(str(startDate), str(endDate))
histData = histData[::-1] # reverse data so its easier to use
data[company] = histData
addTools(data, period)
with open("Data/Amex_data.json", "w") as fp:
json.dump(data, fp, indent=2)
return data
def getDifferencePercentage(self):
closing = []
shareName = Share(self.company)
self.getTimeStamp()
startDate=''
endDate=''
hist=''
for t in self.times:
todayTimeStamp = t
yesterdayTimeStamp = t-86400
startDate = str(datetime.datetime.fromtimestamp(todayTimeStamp).strftime('%Y-%m-%d'))
yesterdayDate=str(datetime.datetime.fromtimestamp(yesterdayTimeStamp).strftime('%Y-%m-%d'))
todayHist = shareName.get_historical(startDate, startDate)
yesterdayHist = shareName.get_historical(yesterdayDate,yesterdayDate)
while(len(todayHist)==0):
todayTimeStamp = todayTimeStamp+86400
startDate = str(datetime.datetime.fromtimestamp(todayTimeStamp).strftime('%Y-%m-%d'))
todayHist = shareName.get_historical(startDate, startDate)
while(len(yesterdayHist)==0):
yesterdayTimeStamp= yesterdayTimeStamp-86400
yesterdayDate=str(datetime.datetime.fromtimestamp(yesterdayTimeStamp).strftime('%Y-%m-%d'))
yesterdayHist = shareName.get_historical(yesterdayDate,yesterdayDate)
closingPriceToday = float(todayHist[0]['Close'])
closingPriceYesterday = float(yesterdayHist[0]['Close'])
difference = (float(closingPriceYesterday) - float(closingPriceToday))*100.0/float(closingPriceYesterday)
diff2 = float(format(difference, '.3f'))
closing.append(diff2)
self.differencePercentage = closing
return closing
from yahoo_finance import Share
import csv
goog = Share('GOOG')
data = goog.get_historical('2012-01-01', '2017-03-17')
write = []
write = [[d["Date"], d["Close"]] for d in data]
write = sorted(write, key=lambda x: x[0])
write.insert(0, ["date", "price"])
with open("prices.csv", "w+") as f:
writer = csv.writer(f)
writer.writerows(write)
def getData(startDate, endDate, stock):
yahoo = Share(stock)
yahoo.get_open()
return json.dumps(yahoo.get_historical(startDate, endDate))
import string
import time
# file to add valid tickers to
stockFile = open('stockFile.txt', 'w')
# just keeps track of the start time, so the time taken to process all possibilities can be printed at the end
start = time.time()
#will serve as "root" for each next layer to add new letters onto
list = [""]
# will test alphabetical combinations up to 5 characters long
for x in range(0, 5):
# for storing values to later become list for the next "layer" of letters
temp = []
for e in list:
for letter in string.ascii_uppercase:
s = (e + letter)
temp.append(s)
price = Share(s).get_price()
if (price != None and float(price) >= 5.0):
# makes it clear that it is in fact working
print(s + ": " + price)
stockFile.write(s + "\n")
list = temp
# prints how many seconds it took to complete the task
duration = time.time() - start
print(duration)
class Symbol(object):
def __init__(self, symbol, s_date=None, e_date=None):
self.log=Logging()
self.name=symbol
self.created=datetime.datetime.utcnow()
self.log.info("created {}".format(self.name))
try:
self.share=Share(symbol)
except:
self.log.error("platform is offline or not connecting")
if s_date and e_date:
self.begin=s_date
self.end=e_date
try:
self.share=Share(symbol)
self.data=self.share.get_historical(self.begin, self.end)
self.log.refresh("{} data collected".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def refresh_data(self, s_date=None, e_date=None):
if s_date and e_date:
try:
share=Share(self.name)
self.begin=s_date
self.end=e_date
share.get_historical(s_date, e_date)
self.log.refresh("{} data collected".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def market_cap(self):
try:
self.market_cap = self.share.get_market_cap()
self.log.info("{} market cap refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def earnings_per_share(self):
try:
self.eps = self.share.get_earnings_share()
self.log.info("{} eps refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def moving_average_50(self):
try:
self.moving_average_50 = self.share.get_50day_moving_average()
self.log.info("{} 50 day moving ave refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
#implement TODO
def nday_moving_average(self, n):
try:
self.moving_average_n = None
self.log.info("{} {} day moving ave refreshed".format(self.name, n))
except:
self.log.error("platform is offline or not connecting")
def price_earnings_ratio(self):
try:
self.price_to_earnings = self.share.get_price_earnings_ratio()
self.log.info("{} price to earnings refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def book_value(self):
try:
self.book = self.share.get_price_book()
self.log.info("{} book value refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def year_high(self):
try:
self.year_high = self.share.get_change_from_year_high()
self.log.info("{} year high change refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def year_low(self):
try:
self.year_low = self.share.get_change_from_year_low()
self.log.info("{} year low change refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def target_price(self):
try:
self.year_target = self.share.get_change_from_year_high()
self.log.info("{} year target change refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def year_range(self):
try:
self.year_range = self.share.get_change_from_year_high()
self.log.info("{} year range change refreshed".format(self.name))
except:
self.log.error("platform is offline or not connecting")
def getCurrentPrice(self, stockName):
stock = Share(stockName)
return stock.get_price()
STEP = 10000 # Step limitation in an episode, this is actually a bug in my code. This parameter should be equal or larger than the training data size, so here I revised into a large number. However, you may still tune this parameter by yourself, it won't occur any error.
TEST = 10 # Test episode
GAMMA = 0.9 # discount factor
BUFFER_SIZE = 10000 # the replay buffer size
BATCH_SIZE = 32 # minibatch size
INITIAL_EPSILON = 0.8 # initial large, in order to explore more
FINAL_EPSILON = 0.1 # explore less, greedy most time
DAY_LENGTH = 10 # the total days for a training data, also the dim of features
FEATURE_NUM = 8 # Currently: close price, volume, K, D, RSI9, MA5, MA20, MA5-MA20
START = "2011-01-01" # Training data start date
TRAIN_END = "2015-12-31" # Training data end date
_ID = 2330 # By default, TSMC (2330)
# Get stock data from yahoo finance
stock = Share(str(_ID) + '.TW')
today = datetime.date.today()
stock_data = stock.get_historical(START, str(today)) # Total data downloaded
print("Historical data since", START, ": ",
len(stock_data)) # Total length of raw data
stock_data.reverse() # Reverse the data since it starts from the latest data.
#----------Remove the data with 0 volume---------#
i = 0
while (i < len(stock_data)):
if (int(stock_data[i].get('Volume')) <= 0):
stock_data.remove(stock_data[i])
i = -1
i += 1
#----------Remove the data with 0 volume---------#
print("Remove the datas with zero volume, total data ", len(stock_data))
def get_Share(symbol):
stock = Share(clean_stock_search(symbol))
return stock
sp900_chg = {}
for stock in sp900:
sys.stdout.write(stock['symbol'])
try:
if stock['symbol'] not in google_bad_list and '.' in stock['symbol']:
equity_parsed = getQuotes(stock['symbol'])[0]
sp900_all[stock['symbol']] = float(equity_parsed['ChangePercent'])
sys.stdout.write(" {}\n".format(
float(equity_parsed['ChangePercent'])))
if DEBUG:
sys.stdout.write("{}\t{}\t{}\t{}%".format(
stock['symbol'], stock['company'],
equity_parsed['LastTradePrice'],
equity_parsed['ChangePercent']))
else:
equity = Share(stock['symbol'])
sp900_all[stock['symbol']] = float(
equity.get_percent_change().strip('%')) / 100
sys.stdout.write(" {}\n".format(
float(equity.get_percent_change().strip('%')) / 100))
if DEBUG:
sys.stdout.write("{}\t{}\t{}\t{}".format(
stock['symbol'], equity.get_name(), equity.get_price(),
equity.get_percent_change()))
except:
pass
for ticker in sp900_all:
if sp900_all[ticker] > 0.03 or sp900_all[ticker] < -0.03:
sp900_chg[ticker] = sp900_all[ticker]
import csv
#apple = Share('AAPL')
#print(apple.get_open())
#print(apple.get_price())
#print(apple.get_trade_datetime())
start = datetime.datetime(2016, 1, 1)
end = datetime.datetime(2016, 8, 1)
stocks = []
with open('companyList.csv') as csvfile:
stockreader = csv.reader(csvfile)
for row in stockreader:
print(str(row)[2:][:-2].upper())
x = Share(str(row)[2:][:-2].upper())
print(x.get_price())
#stocks.append(str(row)[2:][:-2].upper())
#for stock in stocks:
# q = web.get_quote_google(stock)
# print(q)
#amazon = web.DataReader("AAPL", "yahoo", start, end)
#close = amazon["Close"]
#close.plot()
#print(type(amazon))
#print(amazon)
#pylab.rcParams['figure.figsize'] = (15,9)
#plt.show()
conn = sqlite3.connect('stocks.db')
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS stocks (
stock_date datetime, ticker text, open real, close real, high real, low real, volume long, PRIMARY KEY (stock_date, ticker))''')
# - get today data
now = datetime.datetime.now()
today = "{0}-{1}-{2}".format(now.year,str(now.month).zfill(2),str(now.day).zfill(2))
with open("../data/nasdaq107list.csv", "r") as f:
content = f.readlines()
for ticker in content:
ticker = ticker.rstrip('\n')
try:
yahoo= Share(ticker)
except AttributeError:
pass
stock = (yahoo.get_historical('2017-03-17', today))
for st in stock :
row= []
print (st)
row.append(st['Date'])
row.append(st['Symbol'])
row.append(st['Open'])
row.append(st['Close'])
row.append(st['High'])
row.append(st['Low'])
row.append(st['Volume'])
import json
import os
import flask
from flask import Flask, Response, render_template, send_from_directory, send_file
# Needed for the demo, but perhaps not in actual use
import itertools
import random
import time
from yahoo_finance import Share
yahoo = Share('AAPL')
# Server sent events
class ServerSentEvent:
FIELDS = ('event', 'data', 'id')
def __init__(self, data, event=None, event_id=None):
self.data = data
self.event = event
self.id = event_id
def encode(self):
if not self.data:
return ""
ret = []
for field in self.FIELDS:
entry = getattr(self, field)
if entry is not None:
ret.extend(
["%s: %s" % (field, line) for line in entry.split("\n")])
from yahoo_finance import Share
yahoo = Share('YHOO')
print (yahoo.get_open())
import sqlite3
from yahoo_finance import Share
#yahoo = Share('GOOG')
company_list = ['GOOG', 'YHOO', 'AMZN', 'AAPL', 'FB', 'INTC', 'IBM', 'MSFT', 'TWTR', 'HPQ']
li=[]
for i in range(len(company_list)):
comp=Share(company_list[i])
li.append(comp.get_historical('2016-04-25', '2017-04-26'))
print li[1][1]
#comp=Share(company_list[1])
#li.append(comp.get_historical('2014-04-25', '2015-04-25'))
conn = sqlite3.connect('PyFolio.sqlite3')
cur = conn.cursor()
cur.execute('''DROP TABLE historical_data_10stocks''')
cur.execute('''CREATE TABLE historical_data_10stocks
(ID INTEGER PRIMARY KEY AUTOINCREMENT,
Symbol VARCHAR,
Time DATE NOT NULL,
Open REAL NOT NULL,
Close REAL,
High REAL,
Low REAL,
Volume BIGINT
);''')
#print len(li[i])
for i in range(len(company_list)):
for j in range(len(li[i])):
cur.execute("INSERT INTO historical_data_10stocks (Symbol,Time,Open,Close,High,Low,Volume) \
def portfolio(request):
'''The main method for all the user functionality'''
user_id = request.user.id
if request.method == 'POST':
which_form = request.POST.get('which-form', '').strip()
if which_form == 'find-stock':
symbol = request.POST.get(
'stock', '').strip().split(' ')[0].strip().upper()
if symbol != '':
portfolio_stock = portfolio_stocks(user_id)
money = portfolio_stock['money']
porfolio = portfolio_stock['portfolio_info']
return render(
request, 'StockFolio/portfolio.html', {
'stock': get_current_info(['' + symbol]),
'news': get_news_feed(symbol),
'portfolio': porfolio,
'stock_rows': plot(user_id),
'symbols': json.dumps(STOCK_SYMBOLS),
'money': money
})
elif which_form == 'download-historical':
return download_historical(
request.POST.get('stock-symbol', '').strip())
elif which_form == 'buy-stock':
symbol = request.POST.get('stock-symbol', '').strip()
StockPortfolio.buy(user_id, symbol,
request.POST.get('shares', '').strip(),
request.POST.get('cost-per-share', '').strip())
portfolio_stock = portfolio_stocks(user_id)
money = portfolio_stock['money']
porfolio = portfolio_stock['portfolio_info']
return render(
request, 'StockFolio/portfolio.html', {
'stock': get_current_info(['' + symbol]),
'news': get_news_feed(symbol),
'portfolio': porfolio,
'portfolio_rows': plot(user_id),
'symbols': json.dumps(STOCK_SYMBOLS),
'money': money
})
elif which_form == 'recommend':
symbol = request.POST.get('stock-symbol', '').strip()
portfolio_stock = portfolio_stocks(user_id)
money = portfolio_stock['money']
porfolio = portfolio_stock['portfolio_info']
reco_svm = []
my_stocks = []
conn = sqlite3.connect('PyFolio.sqlite3')
cur = conn.cursor()
sql = ''' SELECT svm_prediction FROM stockapp_prediction_svm_all WHERE symbol LIKE "%''' + symbol + '''%" '''
sql2 = ''' SELECT ANN_prediction FROM stockapp_prediction_svm_all WHERE symbol LIKE "%''' + symbol + '''%" '''
sql3 = ''' SELECT bayesian_prediction FROM stockapp_prediction_svm_all WHERE symbol LIKE "%''' + symbol + '''%" '''
cur.execute(sql)
result = cur.fetchall()
cur.execute(sql2)
reco_ann = cur.fetchall()
cur.execute(sql3)
reco_bay = cur.fetchall()
for i in range(30):
reco_svm.append(result[i][0])
stock_price = float(Share(symbol).get_price())
for i in range(len(portfolio_stock['portfolio_info'])):
my_stocks.append(
portfolio_stock['portfolio_info'][i]['symbol'])
x, y = 0, 0
if float(reco_ann[0][0]) > float(stock_price) + 5 or float(
reco_svm[29]) > 1.05 * float(stock_price):
x, y = 0, 0
if float(reco_ann[0][0]) > float(stock_price) + 5 and float(
reco_ann[0][0]) <= 1.1 * float(stock_price):
x = 1
elif float(reco_svm[29]) > 1.05 * float(stock_price) and float(
reco_svm[29]) <= 1.1 * float(stock_price):
y = 1
if float(reco_ann[0][0]) > float(stock_price) + 5 and float(
reco_svm[29]) > 1.05 * float(stock_price):
x, y = 1, 1
if (x == 1 and y == 0):
message = "Buy for Short term gains"
elif (y == 1 and x == 0):
message = "Buy for Long term gains"
elif (y == 1 and x == 1):
message = "Buy for short and long term gains"
if symbol in my_stocks:
if float(reco_ann[0][0]) < float(stock_price) + 5 and float(
reco_ann[0]
[0]) > float(stock_price) - 5 and float(
reco_svm[29]) <= 1.05 * float(stock_price) and float(
reco_svm[29]) > 0.95 * float(stock_price):
x, y = 0, 0
if float(
reco_ann[0][0]) < float(stock_price) + 5 and float(
reco_ann[0][0]) > float(stock_price) - 5:
x = 1
elif float(reco_svm[29]
) <= 1.05 * float(stock_price) and float(
reco_svm[29]) > 0.95 * float(stock_price):
y = 1
if float(reco_ann[0][0]) < float(stock_price) + 5 and float(
reco_ann[0][0]) > float(stock_price) - 5 and float(
reco_svm[29]
) <= 1.05 * float(stock_price) and float(
reco_svm[29]) > 0.95 * float(stock_price):
x, y = 1, 1
if (x == 1 and y == 0):
message = "HOLD"
elif (y == 1 and x == 0):
message = "HOLD"
elif (y == 1 and x == 1):
message = "HOLD"
else:
message = "Stock not in your portfolio"
if symbol in my_stocks:
if float(reco_ann[0][0]) < float(stock_price) - 5 and float(
reco_ann[0]
[0]) >= 0.90 * float(stock_price) or float(
reco_svm[29]) < 0.95 * float(stock_price) and float(
reco_svm[29]) > 0.90 * float(stock_price):
x, y = 0, 0
if float(
reco_ann[0][0]) > float(stock_price) - 5 and float(
reco_ann[0][0]) > 0.90 * float(stock_price):
x = 1
elif float(reco_svm[29]
) > 0.95 * float(stock_price) and float(
reco_svm[29]) > 0.90 * float(stock_price):
y = 1
if float(
reco_ann[0][0]) > float(stock_price) - 5 and float(
reco_ann[0]
[0]) >= 0.90 * float(stock_price) and float(
reco_svm[29]
) > 0.95 * float(stock_price) and float(
reco_svm[29]) > 0.90 * float(stock_price):
x, y = 1, 1
if (x == 1 and y == 0):
message = "SELL"
elif (y == 1 and x == 0):
message = "SELL"
elif (y == 1 and x == 1):
message = "SELL"
else:
message = "Stock not in your portfolio"
ann_pred = "Tomorrow's predicted price: " + str(reco_ann[0][0])
return render(
request, 'StockFolio/portfolio.html', {
'stock': get_current_info(['' + symbol]),
'news': get_news_feed(symbol),
'portfolio': porfolio,
'portfolio_rows': plot(user_id),
'symbols': json.dumps(STOCK_SYMBOLS),
'money': money,
'value_reco': message,
'ann_pred': ann_pred
})
elif which_form == 'buy-sell':
symbol = request.POST.get('stock-symbol', '').strip()
if request.POST.get('buy-stock'):
StockPortfolio.buy(
user_id, symbol,
request.POST.get('shares', '').strip(),
request.POST.get('cost-per-share', '').strip())
elif request.POST.get('sell-stock'):
StockPortfolio.sell(
user_id, symbol,
request.POST.get('shares', '').strip(),
request.POST.get('cost-per-share', '').strip())
portfolio_stock = portfolio_stocks(user_id)
money = portfolio_stock['money']
porfolio = portfolio_stock['portfolio_info']
return render(
request, 'StockFolio/portfolio.html', {
'stock': get_current_info(['' + symbol]),
'news': get_news_feed(symbol),
'portfolio': porfolio,
'portfolio_rows': plot(user_id),
'symbols': json.dumps(STOCK_SYMBOLS),
'money': money
})
portfolio_stock = portfolio_stocks(user_id)
money = portfolio_stock['money']
porfolio = portfolio_stock['portfolio_info']
return render(
request, 'StockFolio/portfolio.html', {
'portfolio': porfolio,
'portfolio_rows': plot(user_id),
'symbols': json.dumps(STOCK_SYMBOLS),
'money': money
})
import time
import sys
from yahoo_finance import Share
from pprint import pprint
from datetime import datetime, timedelta
#connet into firehose with boto3
firehose_client = boto3.client('firehose', region_name='us-east-1')
date = datetime.now() #today date
print("Ex) US Dallar Index: DX-Y.NYB | Apple: aapl | Google: googl"
) #print example for symbols in market
# symbol1 = raw_input("#1 Stock/Curreny: ") #raw_input symbol1
# symbol2 = raw_input("#2 Stock/Curreny: ") #raw_input symbol2
symbol1 = sys.argv[1] #first argment (symbol1)
symbol2 = sys.argv[2] #second argment (symbol2)
symbol1 = Share(symbol1) #symbol1 with yahoo api
symbol2 = Share(symbol2) #symbol2 with yahoo api
#days_before = int(raw_input("Previous Data(Days from today): ")) #type date before today for historical data; 0 is current data
days_before = int(sys.argv[3])
def yahoo_his_years(symbols, date, days_before):
'''
INPUT: historical price for symbols in seleted period of time (raw_input or sys.argv) .
OUTPUT: symbols price in str format inserted in S3 using Kinesis.
'''
counter = 0
date = date - timedelta(days=days_before)
for day in range(days_before):
if date.strftime('%Y-%m-%d') <= datetime.now().strftime('%Y-%m-%d'):
try:
from yahoo_finance import Share
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
import subprocess
yahoo = Share('YHOO')
print(yahoo.get_open())
print(yahoo.get_price())
print(yahoo.get_trade_datetime())
yahoo.refresh()
print('refresh')
print(yahoo.get_price())
dadosHistoricos = yahoo.get_historical('2017-06-06', '2017-06-06')
pprint(dadosHistoricos.__len__())
def getDadosHistoricos():
pprint(dadosHistoricos)
print('teste')
getDadosHistoricos()
def getPercentChange(Sym): ticker = Share(Sym) return ticker.get_percent_change()
index = Codes.index(Code)
print Names[index]
break
#function for search by company
#try for max regex match
else:
while True:
Company_name = raw_input('Enter the name of the company\t')
if Company_name not in Names:
print "The Name Entered is not correct or not in our Database"
Flag = raw_input('If you want to retry press 4 or press 1 to search by name')
if int(Flag) == 4:
continue
if int(Flag) == 1:
break
share = Share(Code)
Start_Date = raw_input('Enter the Start Date in YYYY/MM/DD')
End_Date = raw_input('Enter the End Date in YYYY/MM/DD')
Stocks = share.get_historical(Start_Date,End_Date)
Opening = []
Closing = []
Low = []
High = []
import time
# pull data every day at 15:55 [Sydney], 13:55 [Perth]
from datetime import datetime
from threading import Timer
# x=datetime.today()
# y=x.replace(day=x.day+1, hour=13, minute=55, second=0, microsecond=0)
# delta_t=y-x
# secs=delta_t.seconds+1
# t = Timer(secs, stocks)
# t.start()
# companies | sort by industries
# banks
cba = Share("CBA.ax")
nab = Share("NAB.ax")
anz = Share("ANZ.ax")
wbc = Share("WBC.ax") # westpac
# retail
wow = Share("WOW.ax")
jbh = Share("JBH.ax") # jb hifi
wes = Share("WES.ax") # west farmer
# misc
asx = Share("ASX.ax")
bhp = Share("BHP.ax")
ccl = Share("CCL.ax") # coca cola
dmp = Share("DMP.ax") # domino pizza
price = [cba.get_price(), nab.get_price(), anz.get_price(), wbc.get_price(), wow.get_price(), jbh.get_price(), wes.get_price(), asx.get_price(), bhp.get_price(), ccl.get_price(), dmp.get_price()]
volume = [cba.get_volume(), nab.get_volume(), anz.get_volume(), wbc.get_volume(), wow.get_volume(), jbh.get_volume(), wes.get_volume(), asx.get_volume(), bhp.get_volume(), ccl.get_volume(), dmp.get_volume()]
change = [cba.get_change(), nab.get_change(), anz.get_change(), wbc.get_change(), wow.get_change(), jbh.get_change(), wes.get_change(), asx.get_change(), bhp.get_change(), ccl.get_change(), dmp.get_change()]
def getCurrentPrice(Sym): ticker = Share(Sym) return ticker.get_price()
url = urllib2.urlopen("http://finance.yahoo.com/q?s=" + stock)
summary = url.read()
(monthurl, date) = GetOptionsMonth(stock)
url = urllib2.urlopen(monthurl)
futureoptions = url.read()
if len(options) < 180000:
print("\nThis stock does not appear to exist.")
#buyvaluelist.append("\nThis stock does not appear to exist.")
else:
title = Title(summary)
#This one uses a different method
yahoo = Share(stock)
stockp = float(yahoo.get_price())
spricelist = GetAllSprices(futureoptions, stock)
print spricelist
#trims dupicates from list
a = len(spricelist)
spricelist.reverse()
while len(spricelist) > a / 2:
spricelist.remove(spricelist[0])
spricelist.reverse()
#finds strike price nearest stock price
while stockp > spricelist[i]:
i += 1
from yahoo_finance import Share
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
from io import BytesIO
apple = Share('AAPL')
print(apple.get_name())
end = datetime.now()
start = end - timedelta(days=20)
start = start.strftime('%Y-%m-%d')
end = end.strftime('%Y-%m-%d')
data = apple.get_historical(start, end)
data = [point['Close'] for point in data]
plt.plot(data)
plt.axis('off')
plt.savefig('stocks.svg', bbox_inches='tight', format='svg', transparent=True)
def stock(symbol):
stock = get_Share(symbol)
if stock.data_set['Open'] == None:
# flash("Couldn't find that stock. Try another symbol.")
stock = None
return redirect(url_for('stocks'))
else:
# you wrote a function for these two lines, replace here!
stock = set_stock_data(Share(symbol))
write_stock_to_db(stock)
### ^^
title = stock.name
loggedin_user = get_user()
user, allplayers, leaders = get_leaderboard(loggedin_user)
form = StockSearchForm(request.form)
tradeform = TradeForm(request.form)
stocks = Stock.query.order_by(desc(Stock.view_count)).limit(10).all()
if user != None:
portfolio = user.portfolio
portfolio.prettycash = pretty_numbers(portfolio.cash)
# This is to show many shares much of that particular stock a user has in his/her position.
positions = portfolio.positions
position = portfolio.positions.filter_by(symbol=symbol).first()
if position:
position.prettysharecount = pretty_ints(position.sharecount)
else:
portfolio = None
position = None
positions = None
if request.method == 'POST' and tradeform.validate():
share_amount = tradeform.amount.data
buy_or_sell = tradeform.buy_or_sell.data
if stock.get_price() == None:
# If it's POST and valid, but there's no such stock
flash("Couldn't find stock matching " + symbol.upper() +
". Try another symbol.")
return redirect(url_for('stocks'))
else:
# if it's POSTed, validated, and there is a real stock
trade(stock, share_amount, buy_or_sell, user, portfolio, positions)
return redirect(url_for('user'))
elif request.method == 'POST' and not tradeform.validate():
flash("Invalid share amount; please try again.")
return redirect(url_for('stocks'))
if request.method == 'GET':
start = '2015-09-30'
end = '2015-10-31'
prices, dates = prepare_stock_graph(symbol, start, end)
script, div = build_stock_plot(symbol, dates, prices)
return render_template('stock.html',
form=form,
tradeform=tradeform,
stock=stock,
stocks=stocks,
leaders=leaders,
title=title,
user=user,
loggedin_user=loggedin_user,
position=position,
script=script,
div=div)
class stockdata:
def __init__(self, symb, online=1):
self._symb = symb
# <class 'yahoo_finance.Share'>
if (online):
try:
self.body = Share(symb)
except:
self.body = np.nan
print("Share error:", self._symb)
def get_price(self):
'''
get today's price
if market closed, the price is the close price
Returns
-------
price : str
'''
price = self.body.get_price()
return price
def get_days_high(self):
'''
get today's high price
Returns
-------
dayhigh : str
'''
dayhigh = self.body.get_days_high()
return dayhigh
def get_days_low(self):
'''
get today's low price
Returns
-------
daylow : str
'''
daylow = self.body.get_days_low()
return daylow
def get_historical(self, start_date, end_date):
'''
Parameters
----------
start_date : str
'year-month-day', for example '2015-10-01'
end_date : str
Returns
-------
historical : dlist, dict list
historical : <class 'list'>
historical[0] : <class 'dict'>
historical[0][High] : <class 'str'>
for example:
[{'Adj_Close': '75.620003',
'Close': '75.620003',
'Date': '2015-10-23',
'High': '75.760002',
'Low': '72.839996',
'Open': '73.980003',
'Symbol': 'BABA',
'Volume': '22342100'},
{}, ... {}]
'''
historical = self.body.get_historical(start_date, end_date)
return historical
def save_historical_file(self, start_date, end_date):
'''
save historical data to excel file in form of DataFrame
the file name is 'get_historical_SYMB_ENDATE.xlsx', for example, 'get_historical_YHOO_2015-10-24.xlsx'
Parameters
----------
start_date : str
'year-month-day', for example '2015-10-01'
end_date : str
Returns
-------
len : int
length of historical
'''
try:
historical = self.body.get_historical(start_date, end_date)
except:
print("get_historical error:", self._symb, start_date, end_date)
return 0
length = len(historical)
if (length == 0):
#print("len is 0:", self._symb)
return 0
# check data
try:
high = float(historical[0]['High'])
except:
print("get_high error:", self._symb)
return 0
df = pd.DataFrame(historical)
file_name = './data/' + self._symb + '_historical_' + end_date + '.xlsx'
df.to_excel(file_name)
return length
def read_historical_file(self, end_date):
'''
read historical data from file
the file name is 'get_historical_SYMB_ENDATE.xlsx', for example, 'get_historical_YHOO_2015-10-24.xlsx'
Parameters
----------
end_date : str
'year-month-day', for example '2015-10-01'
Returns
-------
df : DataFrame of Pandas
df : <class 'pandas.core.frame.DataFrame'>
df[:1] : <class 'pandas.core.frame.DataFrame'>
df.High(df['High']) : <class 'pandas.core.series.Series'>
df.loc[0] : <class 'pandas.core.indexing._LocIndexer'>
df.iloc[0] : <class 'pandas.core.indexing._iLocIndexer'>
for example:
Adj_Close Close Date High Low Open Symbol Volume
0 75.620003 75.620003 2015-10-23 75.760002 72.839996 73.980003 BABA 22342100
1 70.989998 70.989998 2015-10-22 71.629997 70.070000 70.160004 BABA 10691400
2 69.480003 69.480003 2015-10-21 71.790001 68.889999 71.790001 BABA 16800200
'''
file_name = './data/' + self._symb + '_historical_' + end_date + '.xlsx'
# file_name = './data/get_historical_' + self._symb + '_' + end_date + '.xlsx'
try:
df = pd.read_excel(file_name)
except FileNotFoundError as e:
df = pd.DataFrame({[]})
print(e, self._symb)
self._df = df
return df
# get date through row
def fget_date(self, row):
val = self._df.Date[row]
return val
def fget_close(self, row):
val = self._df.Close[row]
return val
def fget_high(self, row):
val = self._df.High[row]
return val
def fget_low(self, row):
val = self._df.Low[row]
return val
# get date through row && col
def fget_data(self, col, row):
# val = self._df[col][row]
# return val
return 0
def set_ETF_data():
etf_data = []
for index, etf_symbol in enumerate(settings.ETF_MASTER_LIST):
etf_dict = {
'model': 'portfolio.ETF',
'pk': index + 1,
'fields': {},
}
fund = Share(etf_symbol)
fields = {
'name': fund.get_name(),
'symbol': etf_symbol,
'last_trade': fund.get_price(),
'dividend_yield': fund.get_dividend_yield(),
'absolute_change': fund.get_change(),
'percentage_change': fund.get_percent_change(),
'year high': fund.get_year_high(),
'year low': fund.get_year_low(),
'50 day moving average': fund.get_50day_moving_avg(),
'200 day moving average': fund.get_200day_moving_avg(),
'average_daily_volume': fund.get_avg_daily_volume()
}
etf_dict['fields'] = fields
etf_data.append(etf_dict)
json_data = json.dumps(etf_data)
# print(json_data)
output_dict = [y for y in etf_data if y['fields']['dividend_yield'] > 1]
output_dict = [
x for x in output_dict if x['fields']['average_daily_volume'] > 100000
]
output_dict = [
z for z in output_dict
if z['fields']['200 day moving average'] < z['fields']['last_trade']
]
sorted_list = sorted(output_dict,
key=lambda k: k['fields']['dividend_yield'],
reverse=True)
for etf in sorted_list[:5]:
ETF.objects.create(
portfolio=Portfolio.objects.get(pk=1),
name=etf['fields']['name'],
symbol=etf['fields']['symbol'],
investment_style=1,
last_trade=etf['fields']['last_trade'],
dividend_yield=etf['fields']['dividend_yield'],
absolute_change=etf['fields']['absolute_change'],
percentage_change=etf['fields']['percentage_change'],
currency='USD',
last_updated=timezone.now())
def stock_lookup_and_write(symbol):
stock = set_stock_data(Share(symbol))
write_stock_to_db(stock)
return stock
# https://www.toptal.com/machine-learning/s-p-500-automated-trading
from __future__ import division
import graphlab as gl
from datetime import datetime
from yahoo_finance import Share
import urllib2
# download historical prices of S&P 500 index
print('before download')
today = datetime.strftime(datetime.today(), "%Y-%m-%d")
print('after download')
try:
stock = Share(
'^GSPC') # ^GSPC is the Yahoo finance symbol to refer S&P 500 index
print('Share')
# we gather historical quotes from 2001-01-01 up to today
hist_quotes = stock.get_historical('2011-01-01', today)
# here is how a row looks like
hist_quotes[0]
{
'Adj_Close': '2091.580078',
'Close': '2091.580078',
'Date': '2016-04-22',
'High': '2094.320068',
'Low': '2081.199951',
'Open': '2091.48999',
'Symbol': '%5eGSPC',
'Volume': '3790580000'
}
except urllib2.URLError, err:
print err
from yahoo_finance import Share
import json
from pprint import pprint
IBM = Share('IBM').get_historical('2017-04-15', '2017-05-14')
for item in IBM:
variation = float(item['Close']) - float(item['Open'])
date = item['Date']
print(date + ' ---> open: ' + str(item['Open']) + ' Close: ' +
str(item['Close']) + ' Variation: ' + str(variation))
if __name__ == '__main__':
#Variables to Change
symbol = "NFLX"
side = 'call'
exp_start = date(2017, 5, 5)
exp_end = date(2017, 5, 20)
strike_low = 145
strike_high = 160
####Calculate Alpha and volatility######
start_date = datetime.datetime(2016, 5, 5)
end_date = datetime.datetime(2017, 5, 13)
dcgr = contGrowthRate(symbol, start_date, end_date)
alpha = np.mean(dcgr)
volatility = np.std(dcgr)
###########################################
kde_price_option = build_kde_option_pricer(symbol, start_date, end_date)
#The three option pricing models you can use is
# kde_price_option (kernal density estimator)
# bs_price_option (black scholes)
# rolldice_price_option (roll dice)
labels, data = compute_model_error(bs_price_option, symbol, side, exp_start, exp_end, strike_low, strike_high)
options_df = pd.DataFrame.from_records(data, columns=labels)
print("stock: " + symbol)
print("price: " + str(Share(symbol).get_price()))
print(options_df)
config = {
'user': '******',
'password': '******',
'host': '192.168.XXX.XXX',
'port': '3306',
'database': 'dbname',
'raise_on_warnings': True,
}
cnx = mysql.connector.connect(**config)
cursor = cnx.cursor()
cursor.execute("SHOW TABLES")
tables = cursor.fetchall()
for YTicker in tables:
x = str(YTicker[0])
Symbol = x.replace("_", ".")
print(Symbol)
yahoo = Share(Symbol)
Date = time.strftime("%Y-%m-%d")
Adj_Close = yahoo.get_price()
High = yahoo.get_days_high()
Low = yahoo.get_days_low()
Volume = yahoo.get_volume()
add_value = (
"INSERT INTO " + YTicker[0] +
" (Symbol, Date, Adj_Close, High, Low, Volume) VALUES (%s, %s, %s, %s, %s, %s)"
)
data = (Symbol, Date, Adj_Close, High, Low, Volume)
cursor.execute(add_value, data)
cnx.commit()
print(YTicker[0] + " updated... to" + Adj_Close)
