def load_data_1(stock, y, window):
raw_data = stock.as_matrix()
length = raw_data.shape[0]
indicators = raw_data.shape[1]
prices = y.as_matrix()
data = []
for index in range(len(raw_data) - (window) + 1):
data.append(raw_data[index:index + window])
data = np.array(data)
valid_size = int(np.round(2 / 100 * data.shape[0]))
test_size = int(1)
training_size = data.shape[0] - (valid_size + test_size)
X_train = data[:training_size + valid_size, :-1]
y_train = prices[window - 1:training_size + window + valid_size - 1]
#validation for time series
X_valid = data[training_size:training_size + valid_size, :-1]
y_valid = prices[training_size + window - 1:training_size + valid_size +
window - 1]
raw_data = raw_data.reshape(1, length, indicators)
X_test = np.zeros((1, window, 8))
for i in range(0, window - 1):
X_test[0, window - 1 - i, :] = raw_data[0, length - i - 1, :]
X_test = X_test[:, 1:, :]
y_test = 0
return [X_train, y_train, X_valid, y_valid, X_test, y_test]
def __plot_backtest(self, performance, performanceBenchmark, composition, names):
# PERFORMANCE
performance.index = performance.index.date
df_to_plot = pd.concat([performance, performanceBenchmark], axis=1)
fig = px.line(df_to_plot, x=df_to_plot.index, y=df_to_plot.columns, title='Comparison of different strategies')
fig.show()
# COMPOSITION
composition.columns = list(names)
composition = composition.loc[:, (composition != 0).any(axis=0)]
data = []
for isin in composition.columns:
trace = go.Bar(
x=composition.index,
y=composition[isin],
name=str(isin)
)
data.append(trace)
layout = go.Layout(barmode='stack')
fig = go.Figure(data=data, layout=layout)
fig.update_layout(
title="Portfolio Composition",
xaxis_title="Number of the Investment Period",
yaxis_title="Composition",
legend_title="Name of the Fund")
fig.layout.yaxis.tickformat = ',.1%'
fig.show()
def get_unrolls(self):
data, label = [], []
for i in range(self.config.num_unrollings):
d, l = self.next_batch()
data.append(d)
label.append(l)
return data, label
def get_sp100():
data = []
r = requests.get('https://en.wikipedia.org/wiki/S%26P_100')
soup = BeautifulSoup(r.content,'html.parser')
table = soup.find('table', attrs={'id':'constituents'})
table_body = table.find('tbody')
rows = table_body.find_all('tr')
for row in rows:
cols = row.find_all('td')
cols = [ele.text.strip() for ele in cols]
if cols!=[]:
cols = cols[0]
data.append(cols)
else:
pass
return data
def compound_data(request):
cost = float(request.GET['cost'])
change_ratio = int(request.GET['change_ratio'])
ratio = change_ratio / float(100)
count = int(request.GET['count'])
data = list()
for i in range(count):
c = cost * ratio
if random.choice((True, False)):
cost += c
cc = '+' + dots(c)
else:
cost -= c
cc = '-' + dots(c)
data.append([cost,dots(cost), cc, ])
return HttpResponse(json.dumps(data), content_type='text/json')
def get_historical_data(name, number_of_days):
data = []
url = "https://finance.yahoo.com/quote/" + name + "/history/"
rows = bs(requests.get(url, verify = True).text, features = "lxml").findAll('table')[0].tbody.findAll('tr')
for each_row in rows:
divs = each_row.findAll('td')
if divs[1].span.text != 'Divident':
data.append({
'Date': divs[0].span.text,
'Open': float(divs[1].span.text.replace(',','')),
'Close': float(divs[4].span.text.replace(',','')),
'High': float(divs[2].span.text.replace(',','')),
'Low': float(divs[3].span.text.replace(',','')),
'Volume': float(divs[6].span.text.replace(',','')),
'Adj_Closing': float(divs[5].span.text.replace(',',''))})
return data[:number_of_days]
def _get_table_data_list(self):
'''
A function for fetching NSE stock information
Input: Url of the index page
Outout: Header and Data list
'''
url = self.url
with urllib.request.urlopen(url) as response:
html = BeautifulSoup(response.read(), 'html.parser')
table = html.find('table', attrs={'class': 'tbldata14 bdrtpg'})
header = []
data = []
for row in table.findAll('tr'):
# find header row
if len(header) < 1:
for col in row.findAll('th'):
col_b = col.find('b')
col_br = col.find('br')
if col_b:
header.append(col_b.string)
elif (col_br):
header.append('{0} {1}'.format(
col_br.previous_sibling.string, col_br.string
) if col_br.string else col_br.previous_sibling)
else:
header.append(col.string)
row_list = []
for col in row.findAll('td'):
col_b = col.find('b')
if col_b:
row_list.append(col_b.string)
href = col.find(href=re.compile('stockpricequote'))
if href:
row_list.append(href.get('href'))
else:
row_list.append(col.string)
if len(row_list) > 1:
data.append(row_list)
header.insert(1, 'Link')
return header, data
def get(self, request, pk, format=None):
try:
watch = Watch.objects.get(watchId=pk)
wSerializer = WatchSerializer(watch)
watch = wSerializer.data
data = []
user = User.objects.get(userId=watch['userId'])
userSerializer = UserSerializer(user)
if watch['watchId'] > 0:
watchassets = WatchAsset.objects.all().filter(
watchId=watch['watchId'])
wASerializer = WatchAssetSerializer(watchassets, many=True)
for wAsset in wASerializer.data:
if wAsset['assetId'] > 0:
assets = Asset.objects.all().filter(
assetId=wAsset['assetId'])
aSer = AssetSerializer(assets, many=True)
for key in aSer.data:
item = {
'watchId': watch['watchId'],
'watchName': watch['watchName'],
'assetId': wAsset['assetId'],
"assetName": key['assetName'],
"volume": key['volume'],
"open": key['open'],
"close": key['close'],
"low": key['low'],
"high": key['high'],
"adjClose": key['adjClose'],
"date": key['date'],
"userId": userSerializer.data['userId'],
"name": userSerializer.data['name'],
"email": userSerializer.data['email']
}
data.append(item)
return Response(data, status=status.HTTP_200_OK)
except (RuntimeError, TypeError, NameError):
data = {"description": "Object Not Found"}
return Response(data, status=status.HTTP_404_NOT_FOUND)
def update_in_db(df, table_name, creds):
matrix = np.array(df.to_records().view(type=np.matrix))[0]
data = []
for i in range(len(matrix)):
conv_date = pd.to_datetime(matrix[i][0])
date = "('" + str(conv_date.year) + "-" + str(
conv_date.month) + "-" + str(conv_date.day) + "')::date"
High = str(matrix[i][1])
Low = str(matrix[i][2])
Open = str(matrix[i][3])
Close = str(matrix[i][4])
Volume = str(matrix[i][5])
Adj_Clos = str(matrix[i][6])
prices = "(" + date + ", " + High + ", " + Low + ", " + Open + ", " + Close + ", " + Volume + "," + Adj_Clos + ")"
data.append(prices)
print(data)
data = str(data).replace("[", "(").replace("]", ")").replace(
'(', '', 1)[:-1].replace('"', '')
table_name = table_name.replace('-', '_')
query = """INSERT INTO {} (date, high, low, open, close, volume, adj_close) VALUES {} ON CONFLICT ON CONSTRAINT {}_date_key DO NOTHING;""".format(
table_name.upper(), data, table_name.lower())
if i % 10000 == 0:
try:
conn = db_connection(creds)
download_data(conn, query)
data = []
logging.info("Se guardó: {}".format(table_name))
except Exception as error:
logging.error("Error al tratar de insertar %s: %s" %
(table_name, error))
elif i == len(matrix) - 1:
try:
conn = db_connection(creds)
download_data(conn, query)
logging.info("Se guardó: {}".format(table_name))
except Exception as error:
logging.error("Error al tratar de insertar %s: %s" %
(table_name, error))
def update_src_image(selector):
# print('two')
data = []
data.append({
'x': pltdt.Date,
'y': pltdt.Close,
'type': 'line',
'name': 'Close'
})
if 'Bollinger Bands' in selector:
data = get_mdt_bb(df, data, stats_mdt)
else:
res = stats_mdt.pop('Bollinger Bands', None)
if 'Donchian Channels' in selector:
data = get_mdt_dc(df, data, stats_mdt)
else:
res = stats_mdt.pop('Donchian Channels', None)
if 'Double Exponential Moving Avearge' in selector:
data = get_mdt_dema(df, data, stats_mdt)
else:
res = stats_mdt.pop('Double Exponential Moving Avearge', None)
if 'Hull MA' in selector:
data = get_mdt_hma(df, data, stats_mdt)
else:
res = stats_mdt.pop('Hull MA', None)
if 'Ichimoku Cloud' in selector:
data = get_mdt_ic(df, data, stats_mdt)
else:
res = stats_mdt.pop('Ichimoku Cloud', None)
if 'Keltner Channels' in selector:
data = get_mdt_kc(df, data, stats_mdt)
else:
res = stats_mdt.pop('Keltner Channels', None)
if 'MA Cross' in selector:
data = get_mdt_macross(df, data, stats_mdt)
else:
res = stats_mdt.pop('MA Cross', None)
if 'Moving Average Exponential' in selector:
data = get_mdt_ema(df, data, stats_mdt)
else:
res = stats_mdt.pop('Moving Average Exponential', None)
if 'Moving Average Simple' in selector:
data = get_mdt_sma(df, data, stats_mdt)
else:
res = stats_mdt.pop('Moving Average Simple', None)
if 'Smoothed Moving Average' in selector:
data = get_mdt_smma(df, data, stats_mdt)
else:
res = stats_mdt.pop('Smoothed Moving Average', None)
if 'Triple EMA' in selector:
data = get_mdt_tema(df, data, stats_mdt)
else:
res = stats_mdt.pop('Triple EMA', None)
if 'Moving Average Weighted' in selector:
data = get_mdt_wma(df, data, stats_mdt)
else:
res = stats_mdt.pop('Moving Average Weighted', None)
# print(stats_mdt)
figure = {
'data': data,
'layout': {
#'height': 300
}
}
return figure
pltdt = pd.DataFrame()
pltdt['Date'] = df['Date']
pltdt['Close'] = df['Close']
# buySellData = []
initial = []
globalArray = []
stats_mdt = {}
globalStats = {}
globalbuysellSlectedValue = 0
data = []
data.append({
'x': pltdt.Date,
'y': pltdt.Close,
'type': 'line',
'name': 'Close'
})
def get_results(*args):
print('Arguments for get_result:')
print(args)
print(stats_mdt)
spv = ''
if args[0] == 'All': # Run for indicator combination.
sargslist = []
master = args[3]
sargslist.append(stats_mdt[master]) # Add the master key first.
for key in stats_mdt.keys():
def getWordCloud(q1):
text = "Insert Your text here"
try:
newsapi = NewsApiClient(api_key='Your Key')
except:
newsapi = NewsApiClient(api_key='Your another Key incase of exception')
end = date.today()
start = date.today() - relativedelta(days=+10)
s1 = str(start).split(" ")[0]
e1 = str(end).split(" ")[0]
try:
news = newsapi.get_everything(q=q1,
from_param=s1,
to=e1,
language='en',
sort_by='popularity',
page_size=100,
page=1)
#arranging the news articles in a numpy array
news_data = [
] #np.array(['publishedAt','title','description','content','url'])
index = 0
for i in news['articles']:
k = 0
data = []
while k != 1:
data.append(i["publishedAt"])
data.append(i["title"])
data.append(i["description"])
data.append(i["content"])
data.append(i["url"])
data.append(i["urlToImage"])
k = 1
news_data.append(data)
nd = np.array(news_data)
data = pd.DataFrame(nd,
columns=[
'Date', 'Title', 'Description', 'Content',
'URL', 'Image'
])
#Spliting the date and time of each data field.
new = data["Date"].str.split("T", n=1, expand=True)
new1 = new[1].str.split("Z", n=1, expand=True)
data = data.drop("Date", axis=1)
data.insert(loc=0, column='Date', value=new[0])
data.insert(loc=1, column='Time', value=new1[0])
data['Date'] = pd.to_datetime(data.Date)
dff = data[['Date', 'Description']].groupby('Date').sum()
text = " ".join(list(dff['Description'])) + q1 * 15
wave_mask = np.array(Image.open("assets/cloud.jpg"))
wordcloud = WordCloud(mask=wave_mask,
width=512,
height=512,
colormap="Greens").generate(text)
in1 = str(random.randint(0, 100000000000000))
in2 = str(random.randint(0, 100000000000000))
in3 = str(random.randint(0, 100000000000000))
in4 = in1 + in2 + in3
shutil.rmtree('assets/images')
os.mkdir("assets/images")
# Try
wordcloud.to_file("assets/images/" + in4 + ".jpg")
print("saved...")
## img1 = base64.b64encode(open("assets/images/"+in4+".jpg", 'rb').read())
with open("assets/images/" + in4 + ".jpg", "rb") as imageFile:
img1 = base64.b64encode(imageFile.read()).decode("utf-8")
print("Yes\n\n")
return [img1]
except Exception as e:
print(e)
try:
newsapi = NewsApiClient(api_key='Your another key')
news = newsapi.get_everything(q=q1,
from_param=s1,
to=e1,
language='en',
sort_by='popularity',
page_size=100,
page=1)
print(news, "1")
#arranging the news articles in a numpy array
news_data = [
] #np.array(['publishedAt','title','description','content','url'])
index = 0
for i in news['articles']:
k = 0
data = []
while k != 1:
data.append(i["publishedAt"])
data.append(i["title"])
data.append(i["description"])
data.append(i["content"])
data.append(i["url"])
data.append(i["urlToImage"])
k = 1
news_data.append(data)
nd = np.array(news_data)
data = pd.DataFrame(nd,
columns=[
'Date', 'Title', 'Description', 'Content',
'URL', 'Image'
])
#Spliting the date and time of each data field.
new = data["Date"].str.split("T", n=1, expand=True)
new1 = new[1].str.split("Z", n=1, expand=True)
data = data.drop("Date", axis=1)
data.insert(loc=0, column='Date', value=new[0])
data.insert(loc=1, column='Time', value=new1[0])
data['Date'] = pd.to_datetime(data.Date)
## data = data.sort_values(by='Date')
data = data.sort_values(by=['Date'], ascending=False)
return data
except:
return []
return []
#df["Status"] = [tesla.inc_dec(c, o) for c, o in zip(df.Close, df.Open)]
#df["Middle"] = (df.Open + df.Close) / 2
df["Daily Change"] = abs(df.Close - df.Open)
date = df.index.values
openprice = df['Open']
closeprice = df['Close']
high = df['High']
low = df['Low']
## Get data for candlestick_ochl() and append to a list ##
x = 0
y = len(date)
data = []
while x < y:
append_me = date[x], openprice[x], closeprice[x], high[x], low[x]
data.append(append_me)
x += 1
print(type(date[1]))
## Plot Candlesticks ##
fig, ax1 = pyplot.subplots()
candlestick_ochl(ax1, data, width=np.timedelta64(12, 'h'), colorup='green', colordown='red', alpha=0.75)
## Plot 20-Day SMA overlapping Candlestick plot ##
ax1.plot(df['20-day SMA'], color='#26baee', linestyle='--')
## Plot Upper Bands ##
ax1.plot(df['Upper Band'], color='#783eff', linestyle='-')
## Plot Lower Bands ##
ax1.plot(df['Lower Band'], color='#783eff', linestyle='-')
## Snap to Cursor ##
print('Variability is %2.2f.' % (variability))
# Dynamic variables
# #####################################################
outputFilename = 'Scenario' + datetime.datetime.now().strftime('%y%V.%d%H%M')
outputFilepath = plotsPath + outputFilename
graphsCreated = []
stocks = []
time = range(1, args.days + 1)
# Create stock data for all stocks
for i in range(args.stocks):
data = []
price = random.choice(range(1, args.maxPrice + 1))
for index in time:
data.append(price)
price = GetRandomPrice(price, 1, args.maxPrice, variability)
stocks.append(numpy.around(data))
print(gameStocks[i])
print(data)
# PLOTS
# #####################################################
fig = plt.figure(figsize=(16.0, 9.0))
plot1 = plt.subplot()
# Plot all stocks
for index in range(args.stocks):
plt.plot(time, stocks[index])
plt.plot(time,
stocks[index],
gameStocksTickers[index],
def getNews(df,s1,e1,q1):
try:
newsapi = NewsApiClient(api_key='Your Key')
except:
newsapi = NewsApiClient(api_key='Your another key in case of exception')
try:
news = newsapi.get_everything(q=q1,from_param=s1,
to=e1,language='en',sort_by='popularity',page_size=100,page=1)
#arranging the news articles in a numpy array
news_data = [] #np.array(['publishedAt','title','description','content','url'])
index = 0
for i in news['articles']:
k=0
data = []
while k!=1:
data.append(i["publishedAt"])
data.append(i["title"])
data.append(i["description"])
data.append(i["content"])
data.append(i["url"])
k=1
news_data.append(data)
nd = np.array(news_data)
data = pd.DataFrame(nd,columns=['Date','Title','Description','Content','URL'])
#Spliting the date and time of each data field.
new = data["Date"].str.split("T", n = 1, expand = True)
new1 = new[1].str.split("Z", n=1,expand=True)
data = data.drop("Date",axis=1)
data.insert(loc=0, column='Date', value=new[0])
data.insert(loc=1, column='Time', value=new1[0])
data['Date'] =pd.to_datetime(data.Date)
data = data.sort_values(by='Date')
dff=data[['Date','Title']].groupby('Date').sum()
lTemp={'Date':[],'Title':[]}
for i in range(len(df)):
lTemp['Date'].append(df.index[i])
lTemp['Title'].append(" ")
for i in range(len(lTemp['Date'])):
if lTemp['Date'][i] in list(dff.index):
lTemp['Title'][i]=dff['Title'][list(dff.index).index(lTemp['Date'][i])]
else:
pass
lTemp=pd.DataFrame(lTemp)
print(lTemp.head())
freq_Vec=createVector(lTemp)
if freq_Vec.shape[1]>657:
freq_Vec=freq_Vec.tocsr()[:,0:658]
else:
q=np.array(freq_Vec.todense())
b=np.zeros((freq_Vec.shape[0],657-freq_Vec.shape[1]))
p = np.concatenate((q,b),axis=1)
freq_Vec = csr_matrix(p)
X_test = freq_Vec.toarray()
mean = np.mean(X_test)
X_test -= mean
prediction=predict_news(X_test)
ypred=prediction
pol_score = []
maxScore=max(list(df['Volume']))
minScore=min(list(df['Volume']))
finScore=(maxScore+minScore)//2
#prediction=predict_news(X_test)
#ypred=prediction
pol_score = []
for i in range(len(ypred)):
pol_score.append((ypred[i,1]-ypred[i,0])*finScore)
df['Polarity']=pol_score
return df
except Exception as e:
print(e)
df['Polarity']=[0 for i in range(len(df))]
try:
newsapi = NewsApiClient(api_key='Your another Key')
news = newsapi.get_everything(q=q1,from_param=s1,
to=e1,language='en',sort_by='popularity',page_size=100,page=1)
print(news,"1")
#arranging the news articles in a numpy array
news_data = [] #np.array(['publishedAt','title','description','content','url'])
index = 0
for i in news['articles']:
k=0
data = []
while k!=1:
data.append(i["publishedAt"])
data.append(i["title"])
data.append(i["description"])
data.append(i["content"])
data.append(i["url"])
data.append(i["urlToImage"])
k=1
news_data.append(data)
nd = np.array(news_data)
data = pd.DataFrame(nd,columns=['Date','Title','Description','Content','URL','Image'])
#Spliting the date and time of each data field.
new = data["Date"].str.split("T", n = 1, expand = True)
new1 = new[1].str.split("Z", n=1,expand=True)
data = data.drop("Date",axis=1)
data.insert(loc=0, column='Date', value=new[0])
data.insert(loc=1, column='Time', value=new1[0])
data['Date'] =pd.to_datetime(data.Date)
## data = data.sort_values(by='Date')
data=data.sort_values(by=['Date'])
dff=data[['Date','Title']].groupby('Date').sum()
lTemp={'Date':[],'Title':[]}
for i in range(len(df)):
lTemp['Date'].append(df.index[i])
lTemp['Title'].append(" ")
for i in range(len(lTemp['Date'])):
if lTemp['Date'][i] in list(dff.index):
lTemp['Title'][i]=dff['Title'][list(dff.index).index(lTemp['Date'][i])]
else:
pass
lTemp=pd.DataFrame(lTemp)
print(lTemp.head())
freq_Vec=createVector(lTemp)
if freq_Vec.shape[1]>657:
freq_Vec=freq_Vec.tocsr()[:,0:658]
else:
q=np.array(freq_Vec.todense())
b=np.zeros((freq_Vec.shape[0],657-freq_Vec.shape[1]))
p = np.concatenate((q,b),axis=1)
freq_Vec = csr_matrix(p)
X_test = freq_Vec.toarray()
mean = np.mean(X_test)
X_test -= mean
maxScore=max(list(df['Volume']))
minScore=min(list(df['Volume']))
finScore=(maxScore+minScore)//2
prediction=predict_news(X_test)
ypred=prediction
pol_score = []
for i in range(len(ypred)):
pol_score.append((ypred[i,1]-ypred[i,0])*finScore)
df['Polarity']=pol_score
return df
except:
df['Polarity']=[0 for i in range(len(df))]
return df
return []
def getTrendingNews(q1):
try:
newsapi = NewsApiClient(api_key='Your Key')
except:
newsapi = NewsApiClient(api_key='Your another key incase of exception')
end = date.today()
start = date.today() - relativedelta(days=+10)
s1 = str(start).split(" ")[0]
e1 = str(end).split(" ")[0]
try:
news = newsapi.get_everything(q=q1,
from_param=s1,
to=e1,
language='en',
sort_by='popularity',
page_size=100,
page=1)
print(news, "1")
#arranging the news articles in a numpy array
news_data = [
] #np.array(['publishedAt','title','description','content','url'])
index = 0
for i in news['articles']:
k = 0
data = []
while k != 1:
data.append(i["publishedAt"])
data.append(i["title"])
data.append(i["description"])
data.append(i["content"])
data.append(i["url"])
data.append(i["urlToImage"])
k = 1
news_data.append(data)
nd = np.array(news_data)
data = pd.DataFrame(nd,
columns=[
'Date', 'Title', 'Description', 'Content',
'URL', 'Image'
])
#Spliting the date and time of each data field.
new = data["Date"].str.split("T", n=1, expand=True)
new1 = new[1].str.split("Z", n=1, expand=True)
data = data.drop("Date", axis=1)
data.insert(loc=0, column='Date', value=new[0])
data.insert(loc=1, column='Time', value=new1[0])
data['Date'] = pd.to_datetime(data.Date)
## data = data.sort_values(by='Date')
data = data.sort_values(by=['Date'], ascending=False)
return data
except Exception as e:
print(e)
try:
newsapi = NewsApiClient(api_key='Your another Key')
news = newsapi.get_everything(q=q1,
from_param=s1,
to=e1,
language='en',
sort_by='popularity',
page_size=100,
page=1)
print(news, "1")
#arranging the news articles in a numpy array
news_data = [
] #np.array(['publishedAt','title','description','content','url'])
index = 0
for i in news['articles']:
k = 0
data = []
while k != 1:
data.append(i["publishedAt"])
data.append(i["title"])
data.append(i["description"])
data.append(i["content"])
data.append(i["url"])
data.append(i["urlToImage"])
k = 1
news_data.append(data)
nd = np.array(news_data)
data = pd.DataFrame(nd,
columns=[
'Date', 'Title', 'Description', 'Content',
'URL', 'Image'
])
#Spliting the date and time of each data field.
new = data["Date"].str.split("T", n=1, expand=True)
new1 = new[1].str.split("Z", n=1, expand=True)
data = data.drop("Date", axis=1)
data.insert(loc=0, column='Date', value=new[0])
data.insert(loc=1, column='Time', value=new1[0])
data['Date'] = pd.to_datetime(data.Date)
## data = data.sort_values(by='Date')
data = data.sort_values(by=['Date'], ascending=False)
return data
except:
return []
return []
def get_history(symbols, start, end, data_path, visible=False):
from pandas_datareader import data as y_data
""" to get Yahoo data from saved csv files. If the file does not exist for the symbol,
data is read from Yahoo finance and the csv saved.
symbols: symbol list
start, end : datetime start/end dates
data_path : datapath for csv files - use double \\ and terminate path with \\
"""
symbols_ls = list(symbols)
for ticker in symbols:
print(ticker, ' ', end="")
try:
#see if csv data available
data = pd.read_csv(data_path + ticker + '.csv',
index_col='Date',
parse_dates=True)
except:
#if no csv data, create an empty dataframe
data = pd.DataFrame(data=None, index=[start])
#check if there is data for the start-end data range
if start.toordinal() < data.index[0].toordinal() \
or end.toordinal() > data.index[-1].toordinal():
if visible:
print('Refresh data.. ', )
try:
new_data = y_data.get_data_yahoo(ticker, start, end)
if new_data.empty == False:
if data.empty == False:
try:
ticker_data = data.append(new_data).groupby(
level=0, by=['rownum']).last()
except:
print('Merge failed.. ')
else:
ticker_data = new_data
try:
ticker_data.to_csv(data_path + ticker + '.csv')
if visible:
print(' UPDATED.. ')
except:
print('Save failed.. ')
else:
if visible:
print('No new data.. ')
except:
print('Download failed.. ')
# remove symbol from list
symbols_ls.remove(ticker)
else:
if visible:
print('OK.. ')
pass
pdata = pd.Panel(dict((symbols_ls[i], pd.read_csv(data_path + symbols_ls[i] + '.csv',\
index_col='Date', parse_dates=True).sort_index(ascending=True)) for i in range(len(symbols_ls))) )
return pdata.ix[:, start:end, :]