def validate_ticker(ticker):
"""Function that validates ticker from yahoo_historical api"""
now = arrow.now()
now = get_date_array_for_fetcher(now)
try:
fetcher = Fetcher(ticker, now, now)
fetcher.getHistorical()
except KeyError:
return False
return True
def getHistory(stockCode):
stockCode = str(stockCode).zfill(4)
os.makedirs(f'C:\\Users\\Fung\\Downloads\\Financial Reports {stockCode} {compNameDict[stockCode]}', exist_ok=True)
dst = f'C:\\Users\\Fung\\Downloads\\Financial Reports {stockCode} {compNameDict[stockCode]}\\historical price {stockCode}.xlsx' #result destination
now = datetime.datetime.now()
year= int(now.year)
month = int(now.month)
date = int(now.day)
stockData = Fetcher(stockCode+'.hk', [1990,4,1], [year,month,date])
df = stockData.getHistorical()
df['Date'] = pd.to_datetime(df['Date'], format='%Y-%m-%d') #change date column from string to date
df.Date=df.Date.dt.strftime('%Y') #change date format to Year for pivot table
pt= pd.pivot_table(df, values='Close', index=['Date'], #Get Max and low stock price group by each year
aggfunc={'Close':[min, max]})
sortedPT = pt.sort_values('Date',axis=0,ascending=False)
writer = ExcelWriter(dst)
sortedPT.T.to_excel(writer,'Sheet1')
writer.save()
#reformat result to desire format
wb = openpyxl.load_workbook(dst)
ws = wb.active
lastCol = ws.max_column
for i in range(lastCol,2,-1):
ws.insert_cols(i,2)
wb.save(dst)
os.startfile(f'C:\\Users\\Fung\\Downloads\\Financial Reports {stockCode} {compNameDict[stockCode]}') #open report folder
def Y3_range(loop_holdings_range): data_3ylow = Fetcher(loop_holdings_range, three_year_ago, now_year) df_3y_range = data_3ylow.getHistorical() year3_low = 100000 year3_high = 0 for index, row in df_3y_range.iterrows(): if row['Open'] < year3_low: year3_low = row['Open'] if row['High'] < year3_low: year3_low = row['High'] if row['Low'] < year3_low: year3_low = row['Low'] if row['Close'] < year3_low: year3_low = row['Close'] for index, row in df_3y_range.iterrows(): if row['Open'] > year3_high: year3_high = row['Open'] if row['High'] > year3_high: year3_high = row['High'] if row['Low'] > year3_high: year3_high = row['Low'] if row['Close'] > year3_high: year3_high = row['Close'] return(year3_low, year3_high)
def yahoo_finance(stok, m=0, y=0):
'Gets the data from Yahoo finance'
today = date.today()
df = pd.DataFrame(data={})
dif = pd.DataFrame(data={})
'Fix for months'
if y == 0 and m > 0:
y = 0
while m > today.month:
y += 1
m2 = today.month - m
m = 12 * y + m2
else:
m = today.month
'Gets the data from yahoo finance'
try:
stock_prices = Fetcher(stok, [today.year - y, m, today.day],
[today.year, today.month, today.day])
dif = stock_prices.getHistorical()
df[stok] = dif['Adj Close'].to_list()
if len(dif) < 55:
print('We need more data for: ' + stock_ticker)
except:
print(
'Oops! that was no valid ticker. Please delete or correct it from the Stock List file: ',
stok)
'Puts the date in a column'
df['Date'] = dif['Date'].to_list()
return df
def post():
K.clear_session()
namasaham = request.form['namasaham']
model = joblib.load('model' + namasaham)
#split tanggal,bulan,tahun
date = request.form['tanggal']
date = datetime.datetime.strptime(date, '%Y-%m-%d')
tanggal = date.strftime('%d')
if tanggal[0] == str(0):
tanggal1 = int(tanggal[1])
else:
tanggal1 = int(tanggal)
print(tanggal1)
bulan = date.strftime('%m')
if bulan[0] == str(0):
bulan1 = int(bulan[1])
else:
bulan1 = int(bulan)
tahun = date.strftime('%Y')
tahun1 = int(tahun)
tanggal_predict = str(tahun + '-' + bulan + '-' + tanggal)
#import data stock price
real_saham = Fetcher(namasaham + ".JK", [2018, 1, 1],
[tahun1, bulan1, tanggal1],
interval="1d")
real_saham = real_saham.getHistorical()
real_saham = real_saham.iloc[:, 0:2]
real_saham = real_saham.dropna()
#set index and drop data in predict_date
real_saham = real_saham.set_index("Date")
if tanggal_predict in real_saham.index.values:
real_saham = real_saham.drop(tanggal_predict, axis=0)
real_saham = real_saham.tail(10)
#transform
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()
saham = sc.fit_transform(real_saham)
window = 3
predictX = dataset_predict.createDataset(saham, window)
predictX = predictX.reshape(len(saham) - window + 1, window, 1)
predictY = model.predict(predictX)
#denormalisasi
predictY = sc.inverse_transform(predictY)
predict_next_day = "Rp. {}".format(int(predictY[-1][0]))
price_previous_day = "Rp. {}".format(int(real_saham['Open'][-1]))
previous_date = real_saham.index[-1]
return render_template('predict.html',
predict=predict_next_day,
price_previous=price_previous_day,
tanggal_predict=tanggal_predict,
previous_date=previous_date,
namasaham=namasaham)
def histdata():
a = datetime.datetime.now()
a = a.strftime('%Y-%m-%d')
namasaham = request.form['namasaham']
tgl1 = request.form['tanggal1']
tgl2 = request.form['tanggal2']
def convert_date(date):
date = datetime.datetime.strptime(date, '%Y-%m-%d')
tanggal = date.strftime('%d')
if tanggal[0] == str(0):
tanggal1 = int(tanggal[1])
else:
tanggal1 = int(tanggal)
bulan = date.strftime('%m')
if bulan[0] == str(0):
bulan1 = int(bulan[1])
else:
bulan1 = int(bulan)
tahun = date.strftime('%Y')
tahun1 = int(tahun)
tanggal_lengkap = str(tahun + '-' + bulan + '-' + tanggal)
return tanggal1, bulan1, tahun1, tanggal_lengkap
tgl1_1, bulan1_1, tahun1_1, tgl_lengkap1 = convert_date(tgl1)
tgl2_1, bulan2_1, tahun2_1, tgl_lengkap2 = convert_date(tgl2)
#import data stock price
real_saham = Fetcher(namasaham + ".JK", [tahun1_1, bulan1_1, tgl1_1],
[tahun2_1, bulan2_1, tgl2_1],
interval="1d")
real_saham = real_saham.getHistorical()
real_saham = real_saham.dropna()
#plot data
plt.plot(real_saham['Date'], real_saham['Open'], 'blue')
plt.title('Plot Stock Price of {}'.format(namasaham))
plt.xlabel('Date')
plt.ylabel('Price (Rp)')
plt.xticks(real_saham['Date'], rotation=90)
plt.tight_layout()
#filename
b = datetime.datetime.now()
namafile = (str(b).split('.'))[-1]
addressplot = './storage/{}.png'.format(namafile)
urlplot = '/fileupload/{}.png'.format(namafile)
plt.savefig(addressplot)
plot = urlplot
plt.close()
return render_template('histdata.html',
tables=real_saham.to_html(),
a=a,
tgl1=tgl_lengkap1,
tgl2=tgl_lengkap2,
namasaham=namasaham,
plot=plot)
def get_asx(self, tick, start_date, end_date):
''' get prices for date range '''
tick_code = '%s.ax' % tick
ticker = Fetcher(tick_code, start_date, end_date)
time_series = ticker.getHistorical()
time_series['Tick'] = tick
time_series.rename(columns=lambda x: x.strip(), inplace=True)
return time_series
def today_open(loop_holdings_today): data_today = Fetcher(loop_holdings_today, now_year) df_today = data_today.getHistorical() x = 0 for index, row in df_today.iterrows(): if row['Open'] > x: x = row['Open'] return(x)
def __init__(self, ticker):
from yahoo_historical import Fetcher
import datetime
now = datetime.datetime.now()
raw = Fetcher(ticker, [2018,1,1], [now.year,now.month,now.day])
self.data = raw.getHistorical()
self.name = ticker
self.features = []
#The data -- raw and unpruned. Needed for passing data into functions
self.r_opens = self.data['Open']
self.r_highs = self.data['High']
self.r_lows = self.data['Low']
self.r_closes = self.data['Close']
self.r_adj_close = self.data['Adj Close']
self.r_volumes = self.data['Volume']
self.m_opens = self.data['Open'][2:-1]
self.m_highs = self.data['High'][2:-1]
self.m_lows = self.data['Low'][2:-1]
self.m_closes = self.data['Close'][2:-1]
self.m_adj_close = self.data['Adj Close'][2:-1]
self.m_volumes = self.data['Volume'][2:-1]
self.pc_open_close = self.percentChange(self.r_opens, self.r_closes)
self.pc_high_low = self.percentChange(self.r_lows, self.r_highs)
self.rv_opens = self.relVelocity(self.r_opens)
self.rv_highs = self.relVelocity(self.r_highs)
self.rv_lows = self.relVelocity(self.r_lows)
self.rv_closes = self.relVelocity(self.r_closes)
self.rv_adj_closes = self.relVelocity(self.r_adj_close)
self.rv_volumes = self.relVelocity(self.r_volumes)
self.rp_opens = self.relPos(self.r_opens)
self.rp_highs = self.relPos(self.r_highs)
self.rp_lows = self.relPos(self.r_lows)
self.rp_closes = self.relPos(self.r_closes)
self.rp_adj_closes = self.relPos(self.r_adj_close)
self.rp_volumes = self.relPos(self.r_volumes)
self.features.append(self.m_adj_close)
self.features.append(self.m_closes)
self.features.append(self.m_highs)
self.features.append(self.m_lows)
self.features.append(self.m_opens)
self.features.append(self.m_volumes)
self.y = self.percentChangeY(self.r_closes, self.r_closes)#r_opens
def get_daily_returns(symbol, from_date, to_date):
# from and to date format in list [yyyy,mm,dd]
data = Fetcher(symbol, from_date, to_date)
close = data.getHistorical()["Close"].tolist()
returns = [None]
for close2, close1 in zip(close[1:], close):
returns.append(close2 / close1 - 1)
return returns
def fill_quote_history(stock):
"""
Given a stock, it fills in the last 10 years of historical data
"""
now = arrow.now()
ten_back = now.replace(years=-10)
now = get_date_array_for_fetcher(now)
ten_back = get_date_array_for_fetcher(ten_back)
fetcher = Fetcher(stock.ticker, ten_back, now)
history = fetcher.getHistorical()
save_stock_quote_from_fetcher(history, stock.id)
def get_ticker_update(symbs, ndays):
# start = datetime(2013,11, 13)
# end = datetime(2017, 5, 24)
start = datetime.today() - timedelta(days=ndays)
start1 = [
int(start.strftime('%Y')),
int(start.strftime('%m')),
int(start.strftime('%d'))
]
end = datetime.today()
end1 = [
int(end.strftime('%Y')),
int(end.strftime('%m')),
int(end.strftime('%d'))
]
result = {}
n = 0
k = 0
failed = []
t0 = time()
CRED = '\033[91m'
CEND = '\033[0m'
for symb in symbs:
try:
data = Fetcher(symb, start1, end1)
df = data.getHistorical()
#df.reset_index(level=0,inplace=True)
# df1={}
# df['Adj_Close']=df['Adj Close']
# del df['Adj Close']
# df1['data']=df.to_dict('list')
# df1['t']=df1['data']['Date']
# df1['symbol']=symb
result.update({symb: df})
n = n + 1
print('TK' + str(n) + ' :: ' + symb + ' loaded sucessfully')
except:
failed.append(symb)
n = n + 1
k = k + 1
print(CRED + 'TK' + str(n) + ' :: ' + symb + ' download failed',
'red' + CEND)
continue
# sio.savemat('result.mat',{'tsPY':result})
print('All done Total time ' + str(int(time() - t0)) + ' Seconds')
print('Total ticker tried: ' + str(n))
print('Sucessfully loaded: ' + str(n - k))
print('Failed loaded: ' + str(k))
print(*failed, sep=',')
return (result)
#ts=get_ticker_update(['C','A'],100)
def data_fetcher(etflist, start, end):
"""
Takes in a list of etf symbols
returns pandas dataframe of price details and dividends
"""
price_df = pd.DataFrame()
errors = []
for e in etflist:
try:
data = Fetcher(e, start, end)
pricedata = data.getHistorical()
if 'Date' in data.getHistorical().columns.tolist():
pricedata['ticker'] = e
price_df = price_df.append(pricedata)
except KeyError:
errors.append(e)
else:
pass
return price_df, errors
def find_price(SYB):
now = datetime.datetime.now()
time_now = [now.year, now.month, now.day]
data = Fetcher(SYB, time_now)
data_get = data.getHistorical()
#print (data_get)
open = data_get['Open']
open_price = float(open[0])
#print (SYB, open_price)
return open_price
def fill():
"""Function that fills stock data for missing days"""
stock_id_field = 'stock_id'
stock_ticker = 'stock__ticker'
date = 'date'
now = arrow.now()
data = DailyStockQuote.objects.values(
stock_ticker, stock_id_field).annotate(date=Max(date))
for stock in data:
last_date = arrow.get(stock[date]).replace(days=+1)
last_date = get_date_array_for_fetcher(last_date)
now_fetcher = get_date_array_for_fetcher(now)
ticker = stock[stock_ticker]
stock_id = stock[stock_id_field]
fetcher = Fetcher(ticker, last_date, now_fetcher)
history = fetcher.getHistorical()
save_stock_quote_from_fetcher(history, stock_id)
def run_Fetcher(stock_name, start, end, cycle): AAPL = Fetcher(stock_name, start , end , cycle) date_close = AAPL.getHistorical() #print(date_close) close = date_close.ix[:,[1,2,3]] #print(close) ar = np.empty for i in range(len(close)): typical = (close.ix[i][0] + close.ix[i][1] + close.ix[i][2]) / 3.0 ar = np.append(ar, typical) ar = ar[1:] return ar
def loadCsv(symbols: list,
output_dir: str,
start_date=[2015, 5, 24],
end_date=[2020, 10, 9]):
import time
for symbol in symbols:
print("generate csv to symbol ", symbol, "...")
time.sleep(2)
data = Fetcher(symbol, start_date, end_date)
try:
with open(output_dir + '/' + symbol + '.csv', 'w') as csvfile:
dataf = pd.DataFrame(data.getHistorical())
if dataf.empty:
raise Exception('dataframe is empty')
dataf.to_csv(path_or_buf=csvfile, mode='w')
print("success generate csv to symbol ", symbol)
except:
print("FAILD!!!! generate csv to symbol ", symbol)
def yahoo_api(tkr, reset):
poor_data = False
if not reset:
return pd.read_csv('stocks.csv'), poor_data
data = Fetcher(tkr, [2000, 1, 1])
df = data.getHistorical()
df = df.drop(['Close'], axis=1)
df = df.rename(columns={
'High': 'Max',
'Low': 'Min',
'Adj Close': 'Close',
'Volume': 'Vol'
})
df.iloc[:] = df.iloc[::-1].values
df.iloc[:, 0] = list(map(lambda x: x.replace('-', '.'), df.iloc[:, 0]))
df['Date'] = df['Date'].map(lambda x: '.'.join(x.split('.')[::-1]))
df['last_close'] = df['Close'].shift(-1)
df['Var'] = list(
map(lambda x, y: (x / y) - 1, df['Close'], df['last_close']))
df[['Open', 'Max', 'Min', 'Close',
'Vol']] = df[['Open', 'Max', 'Min', 'Close',
'Vol']].applymap(lambda x: x if x != 0 else None)
if df.isnull().sum().max() / len(df.index) > 0.3 or len(df.index) < 50:
poor_data = True
df[['Open', 'Max', 'Min', 'Close',
'Vol']] = df[['Open', 'Max', 'Min', 'Close',
'Vol']].fillna(method='bfill')
df['last_close'] = df['Close'].shift(-1)
df['Var'] = list(
map(lambda x, y: (x / y) - 1, df['Close'], df['last_close']))
df = df[['Date', 'Open', 'Close', 'Min', 'Max', 'Vol', 'Var']]
#if df[['Vol']].sum(axis=0)[0] == 0: df = df.drop(columns=['Vol'])
df = df.dropna()
df.to_csv('stocks.csv')
return df, poor_data
def fill_stocks(request):
"""Function that fills stock data for missing days"""
if request.method == "GET":
stock_id_field = 'stock_id'
stock_ticker = 'stock__ticker'
date = 'date'
data = DailyStockQuote.objects.values(
stock_ticker, stock_id_field).annotate(date=Max(date))
for stock in data:
last_date = arrow.get(stock[date]).replace(days=+1)
last_date = get_date_array_for_fetcher(last_date)
now = get_date_array_for_fetcher(arrow.now())
ticker = stock[stock_ticker]
stock_id = stock[stock_id_field]
fetcher = Fetcher(ticker, last_date, now)
history = fetcher.getHistorical()
save_stock_quote_from_fetcher(history, stock_id)
msg = "Filling data for {0} stocks".format(len(data))
return HttpResponse(msg, status=200)
return HttpResponse("405 Method Not Allowed", status=405)
def getDataSet(stockTicker):
startYear = 1980
currentDate = [
int(strNum)
for strNum in datetime.now().strftime("%Y-%m-%d").split('-')
]
while (startYear < int(datetime.now().year)):
try:
data = Fetcher(stockTicker, [startYear, 1, 1], currentDate)
break
except ValueError:
startYear += 5
parsedData = list()
dataFrame = data.getHistorical()
for _, row in dataFrame.iterrows():
try:
parsedData.append([
parseTime(row['Date']),
round(float(row['Close']) + 0.005, 2)
])
except ValueError:
continue
return parsedData
from yahoo_historical import Fetcher
data = Fetcher("AAPL", [2007, 1, 1], [2017, 1, 1])
df = data.getHistorical()
print(df)
tickers = pd.read_csv(ticker_file)["Symbol"].values
#DEBUG
tickers = ["ALR", "ECOM ", "MITT^A"]
tickers = [t.strip() for t in tickers]
# Keep track of the saved tickers and unsaved tickers
saved_tickers = []
failed_tickers = []
for ticker in tickers:
# output file name
output_file = os.path.join(sub_output_dir, ticker + ".csv")
try:
data = Fetcher(ticker, START, END)
if data.getHistorical().empty:
failed_tickers.append(ticker)
else:
data.getHistorical().to_csv(output_file, index=False)
saved_tickers.append(ticker)
except:
failed_tickers.append(ticker)
# If there are tickers failed to download,
# report the name list of tickers.
if failed_tickers:
saved_tickers_output = os.path.join(sub_output_dir,
"saved_tickers.txt")
failed_tickers_output = os.path.join(sub_output_dir,
"failed_tickers.txt")
# Write successful ticker list
# load model with joblib
import numpy as np
import pandas as pd
namasaham="bbri"
namasaham=namasaham.upper()
import joblib
model=joblib.load('model{}'.format(namasaham))
#dataset from yahoo finance
from yahoo_historical import Fetcher
real_saham= Fetcher(namasaham+".JK", [2019,6,18], [2019,7,18], interval="1d")
real_saham=real_saham.getHistorical()
date_and_open=real_saham.iloc[:,0:2]
real_saham=real_saham.iloc[:,1:2]
real_saham=real_saham.dropna()
print(real_saham)
from sklearn.preprocessing import MinMaxScaler
sc=MinMaxScaler()
saham=sc.fit_transform(real_saham)
print(saham)
def createDataset(data, window):
dataX= []
for i in range(len(data)-window):
temp = []
for j in range(i, i+window):
temp.append(data[j,0])
dataX.append(temp)
return np.array(dataX)
f = open("/Users/ahsentob/stock_predictions/own_prediction/data_collect/complete_stock_data.txt",'r')
message = [line.rstrip('\n') for line in f]
fetch_list.extend(message)
store_file_list.extend(message)
start_pos = 0
for i in range(len(fetch_list)):
store_path = '/Users/ahsentob/stock_predictions/own_prediction/data_collect/historical_data/'+store_file_list[i]+'.csv'
if os.path.exists(store_path):
start_pos = i
now = datetime.datetime.now()
#time_list = [now.year-10,now.month,now.day]
time_list = [2005,1,1]
for i in range(start_pos,len(fetch_list)):
store_path = '/Users/ahsentob/stock_predictions/own_prediction/data_collect/historical_data/'+store_file_list[i]+'.csv'
if os.path.exists(store_path):
continue
if i % 1 == 0:
print(i)
print(fetch_list[i])
try:
data = Fetcher(fetch_list[i], time_list)
hist = data.getHistorical()
hist.to_csv(store_path)
except:
print("Could not get data for "+fetch_list[i])
def __init__(self,
tick,
url="google",
folder="cache",
begin=None,
end=None,
sep=",",
intern=False,
use_dtime=False):
"""
@param tick tick name, ex ``NASDAQ:MSFT``
@param url if yahoo, downloads the data from there if it was not done before
url is possible, ``'google'``, ``'yahoo_new'``,
``'quandl'`` are predefined values
@param folder cache folder (created if it does not exists
@param begin first day (datetime), see below
@param end last day (datetime), see below
@param sep column separator
@param intern do not use unless you know what to do
(see :meth:`__getitem__ <pyensae.finance.astock.StockPrices.__getitem__>`)
@param use_dtime if True, use DateTime instead of string
"""
if isinstance(url, pandas.DataFrame):
self.datadf = url
self.tickname = tick
if "Date" not in url.columns:
raise StockPricesHTTPException(
"the dataframe does not contain any column 'Date': {0}".
format(",".join(_ for _ in url.columns)))
elif isinstance(tick,
str) and is_file_string(tick) and os.path.exists(tick):
self.tickname = os.path.split(tick)[-1]
with open(tick, "r") as f:
for line in f.readlines():
if line.startswith('<!DOCTYPE html PUBLIC'):
raise StockPricesHTTPException(
"pandas cannot parse the file, check your have access to internet: "
+ str(tick))
break
try:
self.datadf = pandas.read_csv(tick, sep=sep)
except Exception as e:
with open(tick, "r") as t:
content = t.read()
if "Firewall Authentication" in content:
raise StockPricesException(
"pandas cannot parse the file, check your have access to internet: "
+ str(tick)) from e
raise
else:
if not os.path.exists(folder):
try:
os.mkdir(folder)
except PermissionError as e:
raise StockPricesException((
"PermissionError, unable to create directory '{0}', " +
"check you execute the program in a folder you have " +
"permission to modify ({1})").format(
folder, os.getcwd())) from e
self.tickname = tick
if begin is None:
begin = datetime.datetime(2000, 1, 3)
if end is None:
now = datetime.datetime.now()
end = now - datetime.timedelta(1)
sbeg = begin.strftime("%Y-%m-%d")
send = end.strftime("%Y-%m-%d")
name = os.path.join(
folder,
tick.replace(":", "_").replace("/", "_").replace("\\\\", "_") +
".{0}.{1}.txt".format(sbeg, send))
date_format = None
if not os.path.exists(name):
if url == "google":
use_url = True
url_string = "https://finance.google.com/finance/historical?q={0}".format(
self.tickname)
url_string += "&startdate={0}&enddate={1}&output=csv".format(
begin.strftime('%b %d, %Y'), end.strftime('%b %d, %Y'))
url = url_string.replace(" ", "+").replace(",", "%2C")
date_format = "%b-%d-%Y"
elif url == "quandl":
import quandl # pylint: disable=C0415
df = quandl.get("EURONEXT/BNP",
start_date=begin.strftime('%Y-%m-%d'),
end_date=end.strftime('%Y-%m-%d'))
df.reset_index(drop=False).to_csv(name,
sep=sep,
index=False)
use_url = False
elif url == 'yahoo_new':
from yahoo_historical import Fetcher
data = Fetcher(tick, [begin.year, begin.month, begin.day],
[end.year, end.month, end.day])
df = data.getHistorical()
df.to_csv(name, sep=sep, index=False)
use_url = False
elif url in ("yahoo", "google", "fred", "famafrench"):
import pandas_datareader.data as web # pylint: disable=C0415
df = web.DataReader(self.tickname, url, begin,
end).reset_index(drop=False)
df.to_csv(name, sep=sep, index=False)
use_url = False
else:
raise StockPricesHTTPException(
"Unable to download data '{0}' from the following website '{1}'"
.format(tick, url))
if use_url:
self.url_ = url
try:
u = urllib.request.urlopen(url)
text = u.read()
u.close()
except urllib.error.HTTPError as e:
raise StockPricesHTTPException(
"HTTPError, unable to load tick '{0}'\nURL: {1}".
format(tick, url)) from e
if len(text) < 10:
raise StockPricesHTTPException(
"nothing to download for '{0}' less than 10 downloaded bytes"
.format(tick))
try:
f = open(name, "wb")
f.write(text)
f.close()
except PermissionError as e:
raise StockPricesException((
"PermissionError, unable to create directory '{0}', "
+
"check you execute the program in a folder you have "
+ "permission to modify ({1})").format(
folder, os.getcwd())) from e
else:
self.url_ = name
try:
self.datadf = pandas.read_csv(name, sep=sep)
except Exception as e:
with open(tick, "r") as t:
content = t.read()
if "Firewall Authentication" in content:
raise StockPricesException(
"pandas cannot parse the file, check your have access to internet '{0}'"
.format(tick)) from e
raise
if date_format is not None:
self.datadf["Date"] = pandas.to_datetime(self.datadf["Date"])
self.datadf["Date"] = self.datadf["Date"].apply(
lambda x: x.strftime('%Y-%m-%d'))
self.datadf.to_csv(name, sep=sep, index=False)
if use_dtime:
self.datadf["Date"] = pandas.to_datetime(self.datadf["Date"])
if not intern:
try:
self.datadf = self.datadf.sort_values("Date")
except ValueError as e:
if "'Date' is both an index level and a column label" in str(
e):
vals = self.datadf['Date']
ind = self.datadf.index
if numpy.array_equal(vals, ind):
self.datadf = self.datadf.sort_index()
else:
raise StockPricesException(
"Columns Date and index are different.") from e
else:
raise
except AttributeError:
self.datadf = self.datadf.sort("Date")
except KeyError as e:
raise StockPricesException("schema: {}".format(",".join(
self.datadf.columns))) from e
self.datadf.reset_index(drop=True, inplace=True)
self.datadf.set_index("Date", drop=False, inplace=True)
import numpy as np import pandas as pd import matplotlib.pyplot as plt ##Dataset from yahoo finance from yahoo_historical import Fetcher namasaham="bmri" namasaham=namasaham.upper() saham= Fetcher(namasaham+".JK", [2012,7,12], [2019,7,12], interval="1d") saham=saham.getHistorical() #Open Price saham=saham.iloc[:,1:2] # print(saham) ##Data Preprocessing #delete na values saham=saham.dropna() #split dataset into training and testing percent_train=0.75 trainingset=saham.iloc[:int(len(saham)*percent_train),:] testset=saham.iloc[int(len(saham)*percent_train):,:] # Scale the features from sklearn.preprocessing import MinMaxScaler sc=MinMaxScaler() trainingset=sc.fit_transform(trainingset) testset=sc.fit_transform(testset)
#List of tickers
Symbols = data1['Symbol'].append(data2['Symbol']).drop_duplicates()
#Calculate time for run
import time
t0 = time.time()
# Create Empty Dataframe
stock_final = pd.DataFrame()
#loop through each ticker and download
for i in Symbols:
print(i, sep=',', end=',', flush=True)
try:
stock = []
data = Fetcher(i, [2019, 5, 16], [2019, 7, 16])
stock = data.getHistorical()
if len(stock) == 0:
None
else:
stock['Name'] = i
stock_final = stock_final.append(stock, sort=False)
except Exception: # Replace Exception with something more specific.
None
t1 = time.time()
#total time taken
total = t1 - t0
import numpy as np
from pandas_datareader import data as wb
import fix_yahoo_finance as yf
yf.pdr_override()
#DJ = wb.get_data_yahoo("DJI", start="2015-05-01", end="2018-05-01")
from yahoo_historical import Fetcher
data = Fetcher("VZ", [2015, 5, 1], [2018, 5, 2], '1mo')
print(data.getHistorical().ix[:, [0, 5]])
data.getHistorical().ix[:, [0, 5]].to_csv(
"/Users/adityaprasann/IdeaProjects/PythonProj/downloaddata/temp.csv")
import csv
with open("/Users/adityaprasann/IdeaProjects/PythonProj/downloaddata/temp.csv",
"rt") as source:
rdr = csv.reader(source)
with open(
"/Users/adityaprasann/IdeaProjects/PythonProj/downloaddata/VZ.csv",
"wt") as result:
wtr = csv.writer(result)
for r in rdr:
wtr.writerow((r[1], r[2]))
#print(DJ.head())
import pandas as pd
from yahoo_historical import Fetcher
df = pd.read_csv('ind_nifty50list.csv', parse_dates=True, index_col=0)
-- print(df)
data = Fetcher("AAPL", [2007,1,1], [2017,1,1])
data = Fetcher()
print(data.getHistorical())
