# DJIA = Dow Jones Industrial Average
# SAVINGS = Total Savings Deposits at all Depository Institutions
# SP500 = S&P 500
# CLVMNACSCAB1GQUK = Real Gross Domestic Product for United Kingdom
# CLVMNACSCAB1GQDE = Real Gross Domestic Product for Germany
series = ['DEXUSEU', 'UNRATE']
# ______________________________________________________ CONNECT TO FRED
econ_data = DataReader(series, 'fred', start)
# ______________________________________________________ CLEAN DATA
econ_data.columns = [
'U.S. / Euro Foreign Exchange Rate', 'USA Unemployment Rate'
]
econ_data = econ_data.reset_index()
econ_data['decade'] = econ_data['DATE'].astype(str).str[2:3]
econ_data['decade'] = econ_data['decade'].astype(str)
econ_data['decade'] = econ_data['decade'] + "0s"
#
fig, ax = plt.subplots(figsize=(15, 5))
sns.lineplot(x='DATE',
y="U.S. / Euro Foreign Exchange Rate",
data=econ_data,
hue=econ_data.decade.tolist(),
ax=ax)
#plt.xlabel('Date')
#plt.ylabel('U.S. / Euro Foreign Exchange Rate')
#plt.margins(0.2)
plt.show()
import cv2 import pytesseract import re import yfinance as yf import yahooquery as yq from pandas_datareader.data import DataReader from datetime import date import matplotlib.pyplot as plt start_time = time.time() start = date(2020,1,1) end = date.today() ticker = 'PENN' data_source = 'yahoo' penn = DataReader(ticker,data_source,start,end) penn = penn.reset_index() penn['Symbol'] = 'PENN' ticker = 'MGM' data_source = 'yahoo' mgm = DataReader(ticker,data_source,start,end) mgm = mgm.reset_index() mgm['Symbol'] = 'MGM' ticker = 'WYNN' data_source = 'yahoo' wynn = DataReader(ticker,data_source,start,end) wynn = wynn.reset_index() wynn['Symbol'] = 'WYNN' combined = penn.append(mgm).append(wynn)
def view_ticker():
stock = request.form['ticker']
#print stock
start = request.form['start']
start = datetime.strptime(start, '%Y-%m-%d')
start = start.date()
#print request.form['start']
end = request.form['end']
end = datetime.strptime(end, '%Y-%m-%d')
end = end.date()
#print "end"
value = '.4'
status = 'Close'
# if request.form.get('box1'):
# value = '.4'
# status = 'Close'
# if request.form.get('box2'):
# value = '.11'
# status = 'Adj Close'
# if request.form.get('box3'):
# value = '.1'
# status = 'Open'
df = DataReader(stock, 'yahoo', start, end)
#mydata = qd.get("WIKI/" + stock + value, rows = 20, api_key='oSvidbxNa84mVv7Kzqh2')
df.reset_index(inplace=True, drop=False)
df['changepercent'] = df.Close.pct_change() * 100
seqs = np.arange(df.shape[0])
df["seq"] = pd.Series(seqs)
df["Date"] = pd.to_datetime(df["Date"])
df['Date'] = df['Date'].apply(lambda x: x.strftime('%Y/%m/%d'))
df['changepercent'] = df['changepercent'].apply(
lambda x: str(round(x, 2)) + "%")
df['mid'] = df.apply(lambda x: (x['Open'] + x['Close']) / 2, axis=1)
df['height'] = df.apply(
lambda x: abs(x['Close'] - x['Open']
if x['Close'] != x['Open'] else 0.001),
axis=1)
inc = df.Close > df.Open
dec = df.Open > df.Close
w = 0.5
#use ColumnDataSource to pass in data for tooltips
sourceInc = ColumnDataSource(ColumnDataSource.from_df(df.loc[inc]))
sourceDec = ColumnDataSource(ColumnDataSource.from_df(df.loc[dec]))
#the values for the tooltip come from ColumnDataSource
hover = HoverTool(tooltips=[("Date", "@Date"), (
"Open", "@Open"), ("Close",
"@Close"), ("Percent",
"@changepercent"), ("Volume",
"@Volume")])
TOOLS = [CrosshairTool(), hover]
# map dataframe indices to date strings and use as label overrides
p = figure(plot_width=1000,
plot_height=700,
tools=TOOLS,
title=stock + " Candlestick with Custom Date")
p.xaxis.major_label_overrides = {
i: date.strftime('%Y-%m-%d')
for i, date in enumerate(pd.to_datetime(df["Date"], format='%Y-%m-%d'))
}
p.yaxis.axis_label = "Price"
p.xaxis.axis_label = "Date"
p.grid.grid_line_alpha = 0.5
#this is the up tail
p.segment(df.seq[inc],
df.High[inc],
df.seq[inc],
df.Low[inc],
color="green")
#this is the bottom tail
p.segment(df.seq[dec], df.High[dec], df.seq[dec], df.Low[dec], color="red")
#this is the candle body for the red dates
p.rect(x='seq',
y='mid',
width=w,
height='height',
fill_color="green",
line_color="green",
source=sourceInc)
#this is the candle body for the green dates
p.rect(x='seq',
y='mid',
width=w,
height='height',
fill_color="red",
line_color="red",
source=sourceDec)
html = file_html(p, CDN, "my plot")
return html
def view_ticker():
stock = request.form['ticker']
#print stock
start = request.form['start']
start = datetime.strptime(start, '%Y-%m-%d')
start = start.date()
#print request.form['start']
end = request.form['end']
end = datetime.strptime(end, '%Y-%m-%d')
end = end.date()
#print "end"
value = '.4'
status = 'Close'
# if request.form.get('box1'):
# value = '.4'
# status = 'Close'
# if request.form.get('box2'):
# value = '.11'
# status = 'Adj Close'
# if request.form.get('box3'):
# value = '.1'
# status = 'Open'
df = DataReader(stock, 'yahoo', start, end)
#mydata = qd.get("WIKI/" + stock + value, rows = 20, api_key='oSvidbxNa84mVv7Kzqh2')
df.reset_index(inplace=True, drop=False)
#This is where ARIMA starts
df['Natural Log'] = df['Close'].apply(lambda x: np.log(x))
price_matrix = df['Close'].as_matrix()
model = sm.tsa.ARIMA(price_matrix, order=(1, 0, 3))
results = model.fit(
disp=-1) #disp=1 (disp < 0 means no output in this case. 1 = output)
#df['Forecast'] = results.fittedvalues
#Add one more day
df['Date'] = pd.to_datetime(df['Date'])
end_new = end + pd.offsets.BDay(1)
df = df.append({'Date': end_new}, ignore_index=True)
df['changepercent'] = df.Close.pct_change() * 100
seqs = np.arange(df.shape[0])
df["seq"] = pd.Series(seqs)
df["Date"] = pd.to_datetime(df["Date"])
df['Date'] = df['Date'].apply(lambda x: x.strftime('%Y/%m/%d'))
df['changepercent'] = df['changepercent'].apply(
lambda x: str(round(x, 2)) + "%")
df['mid'] = df.apply(lambda x: (x['Open'] + x['Close']) / 2, axis=1)
df['height'] = df.apply(
lambda x: abs(x['Close'] - x['Open']
if x['Close'] != x['Open'] else 0.001),
axis=1)
inc = df.Close > df.Open
dec = df.Open > df.Close
w = 0.5
#This is for volume graph
df['volinc'] = df.Volume[inc]
df['voldec'] = df.Volume[dec]
#Add additional Forecast Day
forecast_start = df.index[0]
forecast_end = df.index[-1]
#print forecast_start
#print forecast_end
#forcast = results.predict(forecast_start, forecast_end, dynamic=False) #, dynamic= True means in-sample
df['Forcast_New'] = results.predict(forecast_start,
forecast_end,
dynamic=False)
#print df
# print df.iloc[-3:]
#forecast= results.predict(start, end, dynamic=True)
# print forecast
#use ColumnDataSource to pass in data for tooltips
sourceInc = ColumnDataSource(ColumnDataSource.from_df(df.loc[inc]))
sourceDec = ColumnDataSource(ColumnDataSource.from_df(df.loc[dec]))
sourceAll = ColumnDataSource(ColumnDataSource.from_df(df.loc[:]))
#will not need this one because we are putting a separate hoover to the forecast line
#sourceforecast=ColumnDataSource(ColumnDataSource.from_df(df.loc[:]))
#the values for the tooltip come from ColumnDataSource
hover = HoverTool(
names=['source_Inc', 'source_Dec', 'volinc', 'voldec'],
tooltips=[
("Date", "@Date"),
("Open", "@Open"),
("Close", "@Close"),
("High", "@High"),
("Low", "@Low"),
("Volume", "@Volume"),
("Percent", "@changepercent"),
# ("Forecast", "@Forecast"),
])
TOOLS = [CrosshairTool(), hover]
# map dataframe indices to date strings and use as label overrides
p = figure(plot_width=900,
plot_height=500,
tools=TOOLS,
title=stock + " Candlestick with Custom Date")
p.xaxis.major_label_overrides = {
i: date.strftime('%Y-%m-%d')
for i, date in enumerate(pd.to_datetime(df["Date"], format='%Y-%m-%d'))
}
p.yaxis.axis_label = "Price"
p.xaxis.axis_label = "Date"
p.grid.grid_line_alpha = 0.5
#this is the up tail
r1 = p.segment(df.seq[inc],
df.High[inc],
df.seq[inc],
df.Low[inc],
color="green",
name='seg_INC')
#p.add_tools(HoverTool(renderers=[r1], tooltips=[('High', '@y0'), ("Low", "@y1"),]))
#this is the bottom tail
r2 = p.segment(df.seq[dec],
df.High[dec],
df.seq[dec],
df.Low[dec],
color="red",
name='seg_DEC')
#p.add_tools(HoverTool(renderers=[r2], tooltips=[('High', '@y0'), ("Low", "@y1"),]))
#this is the candle body for the red dates
p.rect(x='seq',
y='mid',
width=w,
height='height',
fill_color="green",
name='source_Inc',
line_color="green",
legend='Close High',
source=sourceInc)
#this is the candle body for the green dates
p.rect(x='seq',
y='mid',
width=w,
height='height',
fill_color="red",
name='source_Dec',
line_color="red",
legend='Close Low',
source=sourceDec)
#this is where the ARIMA line
#p.circle(df.seq, df['Forecast'], color='darkgrey', alpha=0.2, legend='Forecast')
r3 = p.line(x='seq',
y='Forcast_New',
line_width=2,
color='navy',
legend='Forecast_line',
source=sourceAll)
p.add_tools(
HoverTool(renderers=[r3],
tooltips=[('Date', '@Date'), ('Forecast', '@Forcast_New')]))
#r4 = p.line(df.seq, df['Forecast2'], line_width=2, color='yellow', legend='Future_Day1')
#p.add_tools(HoverTool(renderers=[r4], tooltips=[('Forecast', '@y')]))
p.legend.location = "top_left"
#This is the histogram graph
p2 = figure(width=p.plot_width,
x_range=p.x_range,
tools=TOOLS,
height=150,
title='Volume')
p2.vbar(x='seq',
top='volinc',
width=1,
bottom=0,
color="green",
source=sourceInc,
name='volinc')
p2.vbar(x='seq',
top='voldec',
width=1,
bottom=0,
color="red",
source=sourceDec,
name='voldec')
p_all = (column(p, p2))
html = file_html(p_all, CDN, "my plot")
return html
