def get_raw_data(index_name,retry_attempts = 3):
if index_name:
while retry_attempts > 0 :
try:
df = pdr.get_data_yahoo(index_name)
new_df = df.reindex(index=pd.date_range(df.index.min(),
df.index.max(),
freq='D')).fillna(method='ffill')
retry_attempts = 0
return new_df
except:
print("Data pull failed. {} retry attempts remaining".\
format(retry_attempts))
retry_attempts = retry_attempts - 1
else:
print("Invalid usage. Parameter index_name is required")
return None
def pressPlot(x):
global stockFg, lineC, arrow
try:
#Getting stock open price, live price and %netrise
livP = si.get_live_price(x) #not very live
table = si.get_quote_table(x)
opnP = table['Open']
pclP = table['Previous Close']
netPrise = opnP - pclP
prcntrise = (netPrise / pclP) * 100
print("Open {0}, Previous Close {1}, Current {2}".format(
opnP, pclP, round(livP, 2)))
if netPrise >= 0:
stockFg = 'green'
lineC = 'g'
arrow = '↑'
else:
stockFg = 'red'
lineC = 'r'
arrow = '↓'
#destroys any existing toolbars or labels if they already existed
#prior to a new plot
if self.tool == True:
self.toolbar.destroy()
self.tickerPriceL.destroy()
self.tickerRiseL.destroy()
self.tickerprcntL.destroy
self.tickerTitleL.destroy()
#Plots ticker and ticker Price Live
self.tickerPrice = round(livP, 2)
self.tickerPriceL = tk.Label(
stockFrame,
text=str(self.tickerPrice) + ' USD',
font=("malgun gothic semilight", 15, "bold"),
bg='grey',
fg='white')
self.tickerPriceL.place(relx=0.41, rely=0.81, anchor='n')
self.tickerRiseL = tk.Label(stockFrame,
text=round(netPrise, 2),
font=("malgun gothic semilight",
10, "bold"),
bg='grey',
fg=stockFg)
self.tickerRiseL.place(relx=0.05, rely=0.875, anchor='nw')
self.tickerprcntL = tk.Label(
stockFrame,
text='(' + str(round(prcntrise, 2)) + '%) ' + arrow,
font=("malgun gothic semilight", 10, "bold"),
bg='grey',
fg=stockFg)
self.tickerprcntL.place(relx=0.13, rely=0.875, anchor='nw')
self.tickerTitleL = tk.Label(stockFrame,
text=x.upper(),
font=("malgun gothic semilight",
25, "bold"),
bg='grey',
fg='white')
self.tickerTitleL.place(relx=0.05, rely=0.8, anchor='nw')
#Plotting the graph of given stock (Ticker)
pt.style.use("ggplot")
start = dt.datetime(year1, month1,
date1) #Start date, xi on plot
end = dt.datetime(year2, month2,
date2) #End date as today,xf on plot
#Creates button which opens window for allowing investing
investB = ttk.Button(stockFrame, text="Invest", command=Invest)
investB.place(relx=0.68, rely=0.918, anchor='n')
#Acquires data of given stock (ticker) from
#Yahoo Finance API
df = web.get_data_yahoo(str(x), start, end)
#Plots given data, with identifier "Adj Close" on axes
df["Adj Close"].plot()
#Prints title for Stock graph
print(x.upper(),
"stock price from " + str(start) + " to " + str(end))
#Graph styling on size
f = Figure(figsize=(5, 4), dpi=100)
a = f.add_subplot(111)
#Makes plot visible on select frame
f.patch.set_facecolor('#121f1f') #sets AXES colour
a.patch.set_facecolor('xkcd:grey') #sets actual PLOT colour
a.set_title(str(x.upper()) + " stock price from " +
str(start) + " \nto " + str(end),
color="#%02x%02x%02x" % (255, 250, 250))
a.set_xlabel('Date') #sets x axis label to "date"
a.set_ylabel('Price($)') #sets y axis label to "price"
a.plot(df["Adj Close"], lineC)
#Allocates canvas area to display graph (embeds mtplot graph
#using FigureCanvasTkAgg function
canvas = FigureCanvasTkAgg(f, stockFrame)
canvas.draw()
canvas.get_tk_widget().place(relx=0.5, rely=0.9, anchor='n')
canvas._tkcanvas.place(relx=0.5, rely=0.1, anchor="n")
#Creates toolbar to manipulate stock graph
self.toolbar = NavigationToolbar2Tk(canvas, self)
self.toolbar.configure(bg='#2e3131')
self.toolbar.update()
self.tool = True
if self.open == True:
self.r.destroy()
if self.i == True:
self.LabelI.destroy()
#Exception handling in case that invalid ticker is entered
except web._utils.RemoteDataError:
print("Invalid ticker, try again - must be NYSE")
self.LabelI = tk.Label(
stockFrame,
text="Invalid ticker, try again - must be NYSE",
bg="green",
fg="red")
self.LabelI.place(relx=0.5, rely=0.9, anchor="n")
self.i = True #True since invalid text has appeared
except AttributeError:
print("Invalid ticker, try again - must be NYSE")
self.LabelI = tk.Label(
stockFrame,
text="Invalid ticker, try again - must be NYSE",
bg="green",
fg="red")
self.LabelI.place(relx=0.5, rely=0.9, anchor="n")
self.i = True
except ValueError:
print("Please input viewing domain")
def collect_stock_data(ticker):
get_price_data(ticker, '15min')
get_price_data(ticker, '1hour')
ddf = pdr.get_data_yahoo(ticker)
ddf.to_csv('data/' + ticker + 'daily.csv')
use_column_width=True)
st.markdown("*[Image Courtesy : {}]*".format("Yahoo Finance"))
## Create a sidebar for user input
st.sidebar.header("User input")
## Get user input
tickers = st.sidebar.text_input("Ticker","AAPL")
start_date = st.sidebar.text_input("From","2015-01-01")
end_date = st.sidebar.text_input("To",str(datetime.date.today()))
## Fetch live data
df = pdr.get_data_yahoo(tickers,
start=pd.to_datetime(start_date),
end=pd.to_datetime(end_date))
st.header("Showing results for {}".format(tickers))
st.markdown("Closing Price")
st.line_chart(df["Close"])
## Sentiment analysis
def find_missing(lst):
return [i for x, y in zip(lst, lst[1:])
for i in range(x + 1, y) if y - x > 1]
import pandas as pd
import pandas_datareader as pdr
import datetime
aapl = pdr.get_data_yahoo(
'AAPL',
start=datetime.datetime(2006, 10, 1),
end=datetime.datetime(2012, 1,
1)) # Copied from the article to import the data
# Abhi Uppal | [email protected]
# Github Username: AbhiUppal
#------------------------------
# Head, tail, and describe
#------------------------------
# Return first rows of `aapl`
aapl.head()
# Return last rows of `aapl`
aapl.tail()
# Describe `aapl`
aapl.describe()
#------------------------------
# Index, columns, and subsetting
#------------------------------
# Inspect the index
aapl.index
import pandas as pd
#import numpy as np
import pandas_datareader as web
import datetime
import matplotlib.pyplot as plt
start = datetime.datetime(2017,1,1)
end = datetime.datetime.today()
df = web.get_data_yahoo('2454.tw', start, end)
datas = len(df)+3
del df['date']
del df['volume']
del df['high']
del df['low']
del df['open']
#del df['close']
del df['adjclose']
df["ma30"] = round(df["close"].rolling(window = 30, center = False, min_periods=1).mean(), 2)
df["ma60"] = round(df["close"].rolling(window = 60, center = False, min_periods=1).mean(), 2)
df["ma90"] = round(df["close"].rolling(window = 90, center = False, min_periods=1).mean(), 2)
df.plot()
plt.title("2454.tw")
plt.xlabel("Days")
plt.ylabel("Price")
plt.gca().invert_xaxis()
plt.show()
writer = pd.ExcelWriter('stock2454 copy.xlsx')
df.to_excel(writer,'Sheet2')
def update_graph(stock, radioval):
# {"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",}
radio = {
"High": "High Prices",
"Low": "Low Prices",
"Volume": "Market Volume",
}
trace1 = []
trace2 = []
df = pdr.get_data_yahoo(str(stock),
start=datetime(2006, 10, 1),
end=datetime.now())
df.reset_index(inplace=True)
count = df['Date'].count() + 1
train_data = df[:][0:int(count * 0.8)]
test_data = df[:][int(count * 0.8):]
if (stock == None):
trace1.append(
go.Scatter(x=[0],
y=[0],
mode='markers',
opacity=0.7,
textposition='bottom center'))
traces = [trace1]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(colorway=['#FF7400', '#FFF400', '#FF0056'],
height=600,
title=f"{radio[radioval]}",
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
else:
trace1.append(
go.Scatter(x=train_data['Date'],
y=train_data[radioval],
mode='lines',
opacity=0.7,
name=f'Training Set',
textposition='bottom center'))
trace2.append(
go.Scatter(x=test_data['Date'],
y=test_data[radioval],
mode='lines',
opacity=0.6,
name=f'Test Set',
textposition='bottom center'))
traces = [trace1, trace2]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(colorway=[
"#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400',
'#FF0056'
],
height=600,
title=f"{radio[radioval]} Train-Test Sets for {stock}",
xaxis={
"title": "Date",
'rangeselector': {
'buttons':
list([{
'count': 1,
'label': '1M',
'step': 'month',
'stepmode': 'backward'
}, {
'count': 6,
'label': '6M',
'step': 'month',
'stepmode': 'backward'
}, {
'step': 'all'
}])
},
'rangeslider': {
'visible': True
},
'type': 'date'
},
yaxis={"title": "Price (USD)"},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return figure
def update_graph(selected_dropdown):
# dropdown = {"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",}
trace1 = []
trace2 = []
for stock in selected_dropdown:
df = pdr.get_data_yahoo(str(stock),
start=datetime(2006, 10, 1),
end=datetime.now())
df.reset_index(inplace=True)
trace1.append(
go.Scatter(x=df["Date"],
y=df["High"],
mode='lines',
opacity=0.7,
name=f'High {stock}',
textposition='bottom center'))
trace2.append(
go.Scatter(x=df["Date"],
y=df["Low"],
mode='lines',
opacity=0.6,
name=f'Low {stock}',
textposition='bottom center'))
traces = [trace1, trace2]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(
colorway=[
"#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400',
'#FF0056'
],
height=600,
title=
f"High and Low Prices for {', '.join(str(stock) for stock in selected_dropdown)} Over Time",
xaxis={
"title": "Date",
'rangeselector': {
'buttons':
list([{
'count': 1,
'label': '1M',
'step': 'month',
'stepmode': 'backward'
}, {
'count': 6,
'label': '6M',
'step': 'month',
'stepmode': 'backward'
}, {
'step': 'all'
}])
},
'rangeslider': {
'visible': True
},
'type': 'date'
},
yaxis={"title": "Price (USD)"},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return figure
first_flag = False
else:
json_file.write(',\n')
json_file.write('[' + str(int(time_stamp * 1000)) + ',' +
str(stock_name[indicator_type][x]) + ']')
x += 1
json_file.write(']')
json_file.close()
db = MySQLdb.connect("localhost", "root", "1119", "StockTestDatabase")
cursor = db.cursor()
sql = "SELECT Name FROM Stock;"
result = cursor.execute(sql)
result = cursor.fetchall()
for item in result:
stock_name = pdr.get_data_yahoo(symbols=str(item[0]),
start=oneyear,
end=now)
stock_df = Sdf.retype(stock_name)
#rsi_14
stock_name['rsi'] = stock_df['rsi_14']
write_file(str(item[0]), 'rsi')
#macd
stock_name['macd'] = stock_df.get('macd')
write_file(str(item[0]), 'macd')
#dma
stock_name['dma'] = stock_df['dma']
write_file(str(item[0]), 'dma')
import pandas as pd
import datetime
import pandas_datareader as pdr
import matplotlib.pyplot as plt
from matplotlib import style
style.use('ggplot')
# style.use('fivethirtyeight')
start = datetime.datetime(2018, 1, 1)
end = datetime.date.today()
baba = pdr.get_data_yahoo('BABA', start, end)
print(baba.head())
baba['High'].plot()
plt.legend()
plt.show()
b = df_iris.species == specie
fig.add_trace(go.Scatter3d(x=df_iris["sepal_length"][b], y=df_iris["sepal_width"][b], z=df_iris["petal_width"][b], name=specie, mode="markers"))
fig.show()
# In[ ]:
yf.pdr_override()
symbols = ["AAPL","MSFT"]
stocks = []
for symbol in symbols:
stocks.append(pdr.get_data_yahoo(symbol, start="2020-01-01", end="2020-05-31"))
# In[ ]:
fig = go.Figure()
for stock,symbol in zip(stocks,symbols):
fig.add_trace(go.Scatter(x=stock.index, y=stock.Close, name=symbol))
fig.show()
# In[ ]:
import pandas as pd
import pandas_datareader as reader
import datetime as dt
start = dt.datetime(2017, 9, 1)
end = dt.datetime(2017, 10, 1)
aapl = reader.get_data_yahoo('AAPL', start, end)
print(aapl)
f = reader.data.DataReader('F', 'google', start, end)
f = f.ix['2017-09-27']
file = 'Foreign_Equity_Mapping.csv'
p = pd.read_csv(filepath_or_buffer=file, sep=',')
print(p)
def compute_correlation_companies(company_1, company_2, start_year,
start_month, start_day, end_year, end_month,
end_day):
a = int(start_year)
b = int(start_month)
c = int(start_day)
d = int(end_year)
e = int(end_month)
f = int(end_day)
# Error: same start and end date
if ((a == d) and (b == e) and (c == f)):
print("The start and end dates must be different")
sys.exit()
# Error: the months exist out of range
if ((b < 1 or b > 12) or (e < 1 or e > 12)):
print("Month out of range")
sys.exit()
# Error: days are out of range for non-leap years and regular months
if ((c < 1) or (f < 1) or (c > 31) or (f > 31)
or ((b == 2 or e == 2) and (c > 28 or f > 28) and
(a % 4 != 0 or d % 4 != 0))):
print("Day out of range")
sys.exit()
# Error: day out of range for leap year
if ((b == 2 or e == 2) and (c > 29 or f > 29)
and (a % 4 == 0 or d % 4 == 0)):
print("Day out of range")
sys.exit()
# Error: handle months with 31 days
if ((b == 1 or b == 3 or b == 5 or b == 7 or b == 8 or b == 10 or b == 12
or e == 1 or e == 3 or e == 5 or e == 7 or e == 8 or e == 10
or e == 12) and (c > 31)):
print("Day out of range")
sys.exit()
# Error: handle months with 30 days
if ((b == 4 or b == 6 or b == 9 or b == 11 or e == 4 or e == 6 or e == 9
or e == 11) and (c > 30)):
print("Day out of range")
sys.exit()
if ((a > d) or (a == d and ((b > e) or ((b == e and c > f))))):
print("Start date must be earlier than end date")
sys.exit()
temp_1 = pdr.get_data_yahoo(symbols=company_1,
start=datetime(a, b, c),
end=datetime(d, e, f))
temp_2 = pdr.get_data_yahoo(symbols=company_2,
start=datetime(a, b, c),
end=datetime(d, e, f))
comp1_list = []
comp2_list = []
x = 0
while (x < len(temp_1)):
entry = temp_1['Close'][x]
entry = round(entry, 2)
comp1_list.append(entry)
x += 1
y = 0
while (y < len(temp_2)):
entry = temp_2['Close'][y]
entry = round(entry, 2)
comp2_list.append(entry)
y += 1
comp1_list = pd.Series(comp1_list)
comp2_list = pd.Series(comp2_list)
return round(comp1_list.corr(comp2_list), 2)
pd.set_option('display.max_columns',None)
# We would like all available data from 01/01/2000 until 12/31/2016.
start_date = '2007-08-01'
end_date = '2018-08-31'
stock_code = '0700.HK'
stock_code = '^HSI'
start = datetime(2016, 1, 1)
end = datetime(2018, 9, 30)
# User pandas_reader.data.DataReader to load the desired data. As simple as that.
# df = data.get_data_yahoo('2800.hk', start_date, end_date)
# the early start day is 2007-12-31
df = data.get_data_yahoo(stock_code, start=start, end=end)
print(len(df.columns))
print(df.columns)
no_of_record = len(df.index)
print(len(df.index))
print(df.head(5))
print(df.tail(5))
# print("--------------------")
# print(df.iloc[0:15]) # from 0 to 2
# print("--------------------")
# close_price_df = df[['Close']].copy()
# print(close_price_df.head(10))
import pandas_datareader as pdr
import datetime
msft = pdr.get_data_yahoo('MSFT',
start=datetime.datetime(2018, 1, 1),
end=datetime.datetime(2018, 8, 1))
print msft
import pandas_datareader as web
price = web.get_data_yahoo('AAPL', '2015-01-01')['Adj Close']
print price
# -*- coding:utf-8 -*-
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import pandas_datareader as pdr
import matplotlib.pyplot as plt
from datetime import datetime
start = datetime(2009, 12, 31)
Apple = pdr.get_data_yahoo('AAPL', start=start)
Microsoft = pdr.get_data_yahoo('MSFT', start=start)
# 股价对比
Apple['Adj Close'].plot(legend=True, label='Apple.Inc')
Microsoft['Adj Close'].plot(legend=True, label='Microsoft')
plt.show()
# 交易量对比
Apple['Volume'].plot(legend=True, label='Apple.Inc')
Microsoft['Volume'].plot(legend=True, label='Microsoft')
plt.show()
# 股票价格极差对比
Apple['high-low'] = Apple['High'] - Apple['Low']
Microsoft['high-low'] = Apple['High'] - Apple['Low']
Apple['high-low'].plot(label='Apple.Inc')
Microsoft['high-low'].plot(label='Microsoft')
plt.show()
# daily return(每天变化情况)
def update_graph(stock, stock2, radioval):
# dropdown = {"TSLA": "Tesla", "AAPL": "Apple", "FB": "Facebook", "MSFT": "Microsoft", }
radio = {
"High": "High Prices",
"Low": "Low Prices",
"Volume": "Market Volume",
}
trace1 = []
if (stock == None) or (stock2 == None):
trace1.append(
go.Scatter(x=[0],
y=[0],
mode='markers',
opacity=0.7,
textposition='bottom center'))
traces = [trace1]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(colorway=['#FF7400', '#FFF400', '#FF0056'],
height=600,
title=f"{radio[radioval]}",
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
else:
df = pdr.get_data_yahoo(str(stock),
start=datetime(2006, 10, 1),
end=datetime.now())
df.reset_index(inplace=True)
df2 = pdr.get_data_yahoo(str(stock2),
start=datetime(2006, 10, 1),
end=datetime.now())
df2.reset_index(inplace=True)
trace1.append(
go.Scatter(x=df[radioval][-1000:],
y=df2[radioval][-1000:],
mode='markers',
opacity=0.7,
textposition='bottom center'))
traces = [trace1]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(
colorway=['#FF7400', '#FFF400', '#FF0056'],
height=600,
title=
f"{radio[radioval]} of {stock} vs {stock2} Over Time (1000 iterations)",
xaxis={
"title": stock,
},
yaxis={"title": stock2},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return figure
"Clicca sul link qua sotto per attivare una prova gratuita")
st.sidebar.markdown("https://wp.me/P9LLei-3v")
titolo = ("^GSPC")
mesi_proiezione = st.number_input("Mesi per la proiezione statistica",
min_value=1,
max_value=240,
value=60)
# # Scarica la serie storica e disegnala
# In[78]:
try:
df = pdr.get_data_yahoo(titolo, start='1980-1-1')['Close']
df = df.resample('M').last()
df = pd.DataFrame(df)
df['index'] = df.index
df = df.set_index('index', 1)
except:
try:
client = Client(
'http://ws.bbfinance.net/_bnb_ws/bnb_ws.wsdl') #4G58NHO2VNH3RJD9
LOGIN = client.service.Login("*****@*****.**",
"PMuxLBzkFvRib21")
TOK = LOGIN.LOGIN_RESPONSE.token
A = client.service.GetHistoryFromDate(TOK, "SP", titolo, '1980-1-1',
def update_graph(stock, radioval):
radio = {
"High": "High Prices",
"Low": "Low Prices",
"Volume": "Market Volume",
}
#{"TSLA": "Tesla","AAPL": "Apple","FB": "Facebook","MSFT": "Microsoft",}
trace1 = []
trace2 = []
if (stock == None):
trace1.append(
go.Scatter(x=[0],
y=[0],
mode='markers',
opacity=0.7,
textposition='bottom center'))
traces = [trace1]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(colorway=['#FF7400', '#FFF400', '#FF0056'],
height=600,
title=f"{radio[radioval]}",
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
else:
df = pdr.get_data_yahoo(str(stock),
start=datetime(2006, 10, 1),
end=datetime.now())
df.reset_index(inplace=True)
count = df['Date'].count() + 1
train_data = df[:][0:int(count * 0.8)]
test_data = df[:][int(count * 0.8):]
train_ar = train_data[radioval].values
test_ar = test_data[radioval].values
history = [x for x in train_ar]
predictions = list()
for t in range(len(test_ar)):
global model
model = ARIMA(history, order=(3, 1, 0))
model_fit = model.fit(disp=0)
output = model_fit.forecast()
yhat = output[0]
predictions.append(yhat)
obs = test_ar[t]
history.append(obs)
error = mean_squared_error(test_ar, predictions)
trace1.append(
go.Scatter(x=test_data['Date'],
y=test_data['High'],
mode='lines',
opacity=0.6,
name=f'Actual Series',
textposition='bottom center'))
trace2.append(
go.Scatter(x=test_data['Date'],
y=np.concatenate(predictions).ravel(),
mode='lines',
opacity=0.7,
name=f'Predicted Series (MSE: {error})',
textposition='bottom center'))
traces = [trace1, trace2]
data = [val for sublist in traces for val in sublist]
figure = {
'data':
data,
'layout':
go.Layout(
colorway=[
"#5E0DAC", '#FF4F00', '#375CB1', '#FF7400', '#FFF400',
'#FF0056'
],
height=600,
title=
f"{radio[radioval]} ARIMA Predictions vs Actual for {stock}",
xaxis={
"title": "Date",
'rangeselector': {
'buttons':
list([{
'count': 1,
'label': '1M',
'step': 'month',
'stepmode': 'backward'
}, {
'count': 6,
'label': '6M',
'step': 'month',
'stepmode': 'backward'
}, {
'step': 'all'
}])
},
'rangeslider': {
'visible': True
},
'type': 'date'
},
yaxis={"title": "Price (USD)"},
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)')
}
return figure
'''
Created on 9 Aug 2020
@author: snake91
'''
import pandas as pd
import pandas_datareader as web
import tensorflow as tf
import numpy as np
data = web.get_data_yahoo(["GOOG"])
data.columns = data.columns.droplevel(level=1)
data["Returns"] = data["Adj Close"] / data["Adj Close"].shift()
lags = 50
arr = np.array(
[np.array(data["Returns"].shift(i))[lags:] for i in range(1, lags + 1)])
arr = np.where(np.isnan(arr), 0, arr)
x_train = arr[1:, :1100]
Y_train = arr[0, :1100]
Y_train = np.reshape(Y_train, (1, 1100))
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=[x_train.shape[0], x_train.shape[1]]),
tf.keras.layers.Dense(lags, activation="softmax", name="layer1"),
tf.keras.layers.Dense(lags, activation="softmax", name="layer2")
])
def result():
if request.method == 'POST':
print(request.form)
stocks = request.form['stocks'].split()
strt_date = datetime.strptime(request.form['start_date'], '%Y-%m-%d')
window = int(request.form['window'])
print(stocks)
# Get data
start_date = strt_date - timedelta(window+5)
end_date = datetime.today()
initial_capital = 1000000
# stocks = ['MSFT','AAPL','GOOG','AMZN','XOM','HSBC','BRK-B','JPM','BAC','WFC']
print("Loading data for following stocks: ")
print(stocks)
spy = pdr.get_data_yahoo(symbols='SPY', start=start_date, end=end_date)
stock_data = pdr.get_data_yahoo(symbols=stocks, start=start_date, end=end_date)
print("Stock data loaded ...")
# Calculate Benchmark Returns
spy['Returns'] = spy['Adj Close'].pct_change(1)
# Calculate returns
for ticker in stock_data.columns.levels[1]:
stock_data['Return', ticker] = stock_data['Adj Close', ticker].pct_change(1)
print("Calculated Returns ...")
# Calculate Inverse Variance
for ticker in stock_data.columns.levels[1]:
stock_data['InvVar', ticker] = stock_data['Return', ticker].rolling(window).apply(lambda x: 1/x.var(), raw=False)
# Calculate Portfolio weights
df = stock_data.InvVar
df = df.div(df.sum(axis=1), axis=0)
for ticker in stock_data.columns.levels[1]:
stock_data['Weights', ticker] = df[ticker].round(2)
del df
print("Calculated Portfolio Weights ...")
# Remove NaN values
stock_data.dropna(inplace=True)
# # Portfolio Returns
# 1. Get Change in weights
# 2. Get number of shares
# 3. Get Trading costs
# 4. Calculate Porfolio Value and Cumulative returns.
Portfolio = stock_data[['Adj Close', 'Weights']]
stock_val = np.array(Portfolio['Adj Close'])
stock_weight = np.array(Portfolio['Weights'])
port_val = np.empty(shape=stock_val.shape[0])
shares_holding = np.empty(shape=stock_val.shape)
traded_volume = np.zeros(shape=stock_val.shape)
traded_value = np.zeros(shape=stock_val.shape)
trading_cost = np.zeros(shape=stock_val.shape)
shares_holding[0] = np.round_(stock_weight[0]/stock_val[0]*initial_capital)
port_val[0] = np.dot(shares_holding[0], stock_val[0])
for ind in range(1, len(stock_val)):
shares_holding[ind] = shares_holding[ind-1] + np.round_((stock_weight[ind]-stock_weight[ind-1])/stock_val[ind]*port_val[ind-1])
traded_volume[ind] = shares_holding[ind] - shares_holding[ind-1]
traded_value[ind] = traded_volume[ind]*stock_val[ind]
trading_cost[ind] = 0.01*abs(traded_value[ind])
port_val[ind] = np.dot(shares_holding[ind], stock_val[ind])
port_val[ind] = port_val[ind] - traded_value[ind].sum() - trading_cost[ind].sum()
print("Calculated Portfolio returns ...")
Portfolio['Value'] = pd.DataFrame(data=port_val, index=Portfolio['Adj Close'].index)
# Portfolio['Return'] = Portfolio['Value'].pct_change(1)
Traded_Volume = pd.DataFrame(data=traded_volume, index=Portfolio['Adj Close'].index, columns=Portfolio['Adj Close'].columns)
# Traded_Value = pd.DataFrame(data=traded_value, index=Portfolio['Adj Close'].index, columns=Portfolio['Adj Close'].columns)
# Trading_Cost = pd.DataFrame(data=trading_cost)
# Shares_Holding = pd.DataFrame(data=shares_holding, index=Portfolio['Adj Close'].index, columns=Portfolio['Adj Close'].columns)
del stock_val
del stock_weight
del port_val
del shares_holding
del traded_volume
del traded_value
del trading_cost
to_trade = Traded_Volume.iloc[-1].to_dict()
#Plot traded volume and shares holding
# ax1 = plt.gca()
# (Portfolio['Value']/Portfolio['Value'].iloc[0])[start_date:].plot(figsize=(10,6), ax=ax1, title="Portfolio Performance");
# (spy['Adj Close']/spy['Adj Close'][Portfolio['Value'].index.values[0]])[start_date:].plot(ax=ax1);
# ax1.legend(['Portfolio','S&P 500'])
# portfolio_value_url = 'static/img/portfolio_value.png'
# plt.savefig(portfolio_value_url)
fig = plt.figure(figsize=(10,6))
ax = plt.subplot(111)
ax.plot((Portfolio['Value']/Portfolio['Value'].iloc[0])[start_date:])
ax.plot((spy['Adj Close']/spy['Adj Close'][Portfolio['Value'].index.values[0]])[start_date:])
plt.xlabel("Date")
plt.ylabel("Returns")
plt.title('Portfolio Value')
ax.legend(['Portfolio','SP 500'])
#plt.show()
portfolio_value_url = 'static/img/portfolio_value.png'
fig.savefig(portfolio_value_url)
fig2 = plt.figure(figsize=(10,6))
ax2 = plt.subplot(111)
ax2.pie(Portfolio['Weights'].iloc[-1], autopct='%.2f%%', labels=Portfolio['Weights'].columns)
plt.title('Portfolio Composition')
portfolio_weights_url = 'static/img/portfolio_weights.png'
fig2.savefig(portfolio_weights_url)
# ax2 = plt.gca()
# Portfolio['Weights'].iloc[-1].plot(kind='pie', autopct='%.2f%%', ax=ax2, title="Portfolio Weights", legend=False);
# portfolio_weights_url = 'static/img/portfolio_weights.png'
# plt.savefig(portfolio_weights_url)
return render_template('results.html', stocks=stocks, start_date=strt_date, to_trade=to_trade,
window=window,portfolio_weights_url=portfolio_weights_url,
portfolio_value_url=portfolio_value_url)
import datetime
from datetime import date, datetime
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, PercentFormatter, FormatStrFormatter
import matplotlib.dates as mdates
from matplotlib.dates import MonthLocator, DateFormatter
from matplotlib import rc, rcParams
#####################################################################################
## VIX racker
## Data
start_data = date(2019, 7, 1)
today = datetime.now()
vix = pdr.get_data_yahoo('^VIX', start_data, today)
#####################################################################################
## Inputs
start = start_data
end = date(2019, 8, 30)
line1_1_color = 'blue'
linewidth = 3
title1 = 'CBOE VIX (July - August 2019)'
title_fontsize = 30
y1_label_text = ''
x1_label_text = ''
y1_label_fontsize = 25
x1_label_fontsize = 25
y1_tick_min = 10
def data(ticker):
return (pdr.get_data_yahoo(ticker, start=startdate, end=enddate))
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as web
df = web.get_data_yahoo("DELTACORP.NS", start="2019-04-01", end="2020-03-31")
#adds reproducability of code and flexibility of time period
"""We can also use the following code in comments if the scraped csv from attached zip is used
df = pd.read_csv('DELTACORP.csv',header=[0]) """
df['Daily_Return'] = df['Close'].pct_change()
#Daily Returns have been stored in a separate column
df['Daily_Return'] = df['Daily_Return'] * 100
#will be relevant as we need to return values in% and not /100
print("Daily Returns %")
print("Maximum:" + str(round(df['Daily_Return'].max(axis=0), 3)))
print("Minimum:" + str(round(df['Daily_Return'].min(axis=0), 3)))
print("Mean:" + str(round(df['Daily_Return'].mean(), 4)))
print("Sample Standard Deviation:" +
str(round(df['Daily_Return'].std(axis=0), 4)))
df['Daily_Return'].plot(kind='line', title='Line Graph for Daily Returns')
plt.show()
df.drop(df.columns.difference(['Date', 'Close', 'Daily_Return']),
1,
inplace=True)
df.to_csv(
r'D:\000Finance\FOFA ASSIGNMENT 2\85_Shubh_2018A2PS0832H_DELTACORP\2 daily Returns (risk unadjusted) DELTACORP.csv',
import streamlit as st
import pandas_datareader as pdr
st.write('''
# 삼성전자 주식 데이터
마감 가격과 거래량을 차트로 보여줍니다!
''')
df = pdr.get_data_yahoo('005930.KS', '2020-01-01', '2020-09-30')
st.line_chart(df.Close)
st.line_chart(df.Volume)
# I'm using daily close prices # SPDR S$p500 ETF (SPY) SPY = "SPY" # My two other ETFs are # 1. Vanguard S$P500 ETF (VOO) # 2. iShares Core S&P500 ETF (IVV) etfs_tickers = ["IVV", "SPY", "VOO", "^GSPC"] # using 2 years of data from January 01, 2018 to December 31, 2019 starttime = datetime.datetime(2018, 1, 1) endtime = datetime.datetime(2019, 12, 31) # get only the closing prices etfs = pdr.get_data_yahoo(etfs_tickers, starttime, endtime)['Close'] etfs.columns = ["iShares", "SPDR", "Vanguard", "S&P500"] # print out dataset head print2(etfs.head()) # compute simple reurns etfs_return = etfs.pct_change().dropna() # etfs_return.fillna(0, inplace=True) returns2 = round(etfs_return * 100, 3) print2(etfs_return, returns2) # compute active returns eft_index = etfs_return["S&P500"] ppp = returns2.sub(returns2.iloc[:, -1], axis=0).drop(['S&P500'], axis=1)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas_datareader as web
import requests_cache
import datetime
session = requests_cache.CachedSession(cache_name='cache',
backend='sqlite',
expire_after=datetime.timedelta(days=3))
one_day = datetime.timedelta(days=1)
one_week = datetime.timedelta(days=7)
stock = web.get_data_yahoo("VTSAX",
start="2009-01-01",
end="2019-04-30",
session=session)
test_start = datetime.datetime(2009, 1, 5)
test_end = datetime.datetime(2019, 1, 7)
assert test_start.weekday() == 0 # Monday
assert test_end.weekday() == test_start.weekday()
# Strategy: invest on 1st day each month (or whatever next possible day is)
def strategy1(is_valid_day, *args, **kwargs):
"""1st"""
investments = []
for i in range(2009, 2019):
for j in range(1, 13):
import pandas_datareader as pdr
import datetime
import quandl
aapl = pdr.get_data_yahoo('AAPL',
start=datetime.datetime(2006, 10, 1),
end=datetime.datetime(2012, 1, 1))
aapl2 = quandl.get("WIKI/AAPL", start_date="2006-10-01", end_date="2012-01-01")
# -*- coding: utf-8 -*-
"""
Created on Sun May 10 19:13:30 2020
@author: Hossein
"""
import pandas as pd
import pandas_datareader as pdr
import matplotlib.pyplot as plt
spy = pdr.get_data_yahoo('spy', "2002-01-01")
spy['Adj Close'].plot()
daily_return = spy['Adj Close'].pct_change()
daily_return1 = (spy['Adj Close'][1:] / spy['Adj Close'][:-1].values) - 1
daily_return1.plot()
plt.figure(figsize=(60, 30))
daily_return.plot()
daily_return.hist(bins=10)
mean = daily_return.mean()
std = daily_return.std()
daily_return.hist(bins=30)
plt.axvline(mean, color='w', Linestyle='dashed', linewidth=2)
plt.axvline(std, color='r', Linestyle='dashed', linewidth=2)
plt.axvline(-std, color='r', Linestyle='dashed', linewidth=2)
def MAIN():
data = web.get_data_yahoo(USA, start, end)[["Close"]]
data = data.rename(columns={"Close": "S&P 500"})
data_2 = web.get_data_yahoo(Canada, start, end)[["Close"]]
data_2 = data_2.rename(columns={"Close": "TSX 60"})
data_3 = web.get_data_yahoo(Japan, start, end)[["Close"]]
data_3 = data_3.rename(columns={"Close": "Nikkei 225"})
frames = [data, data_2, data_3]
result = pd.concat(frames, axis=1)
result.dropna(inplace=True)
index = pd.DataFrame(index=result.index)
SP_List = []
TSX_List = []
Nikkei_List = []
seven_list = []
for i in range(len(result)):
indexed = (100 / result["S&P 500"][0]) * result["S&P 500"][i]
SP_List.append(indexed)
indexed = (100 / result["TSX 60"][0]) * result["TSX 60"][i]
TSX_List.append(indexed)
indexed = (100 / result["Nikkei 225"][0]) * result["Nikkei 225"][i]
Nikkei_List.append(indexed)
indexed = (100 * ((1 + 0.00026852275)**i))
seven_list.append(indexed)
index["S&P 500 index"] = SP_List
index["TSX index"] = TSX_List
index["Nikkei 225 index"] = Nikkei_List
index["7%"] = seven_list
index_log = pd.DataFrame(index=result.index)
SP_List_Log = []
TSX_List_Log = []
Nikkei_List_Log = []
seven_list_Log = []
for i in range(len(result)):
SP_List_Log.append(math.log(index["S&P 500 index"][i]))
TSX_List_Log.append(math.log(index["TSX index"][i]))
Nikkei_List_Log.append(math.log(index["Nikkei 225 index"][i]))
seven_list_Log.append(math.log(index["7%"][i]))
index_log["S&P 500"] = SP_List_Log
index_log["S&P/TSX 60"] = TSX_List_Log
index_log["Nikkei 225 "] = Nikkei_List_Log
index_log["Consistent 7% Return"] = seven_list_Log
my_colors = ['b', 'r', 'g', 'k']
my_linetype = ["solid", "solid", "solid", "dashed"]
fig, ax = plt.subplots()
for col, color, linetype in zip(index_log.columns, my_colors, my_linetype):
index_log[col].plot(color=color, linestyle=linetype, ax=ax)
plt.ylabel("Natural Log of Index", labelpad=20)
plt.xlabel("Years", labelpad=20)
plt.legend(loc="upper left")
plt.show()
def get_data_from_yahoo(self, ticker: str, start: str, end: str):
start = dt.datetime.strptime(start, "%Y-%m-%d")
end = dt.datetime.strptime(end, "%Y-%m-%d")
df = pdr.get_data_yahoo(ticker, start=start, end=end)
df['Ticker'] = ticker
return df
else:
price = K * exp(-r * T) * N(-d2) - S * exp(-q * T) * N(-d1)
return price
def bs_vega(cp_flag, S, K, T, r, v, q=0.0):
d1 = (log(S / K) + (r + v * v / 2.) * T) / (v * sqrt(T))
return S * sqrt(T) * n(d1)
app = dash.Dash(__name__)
server = app.server
app.config['suppress_callback_exceptions'] = True
model = ''
df = pdr.get_data_yahoo('ibm', start=datetime(2006, 10, 1), end=datetime.now())
df.reset_index(inplace=True)
# df2 = pd.read_csv("dataset_Facebook.csv",";")
#
# df_ml = df2.copy()
#
# lb_make = LabelEncoder()
# type = df_ml['Type']
# df_ml["Type"] = lb_make.fit_transform(type)
# df_ml = df_ml.fillna(0)
#
# X = df_ml.drop(['like'], axis = 1).values
# Y = df_ml['like'].values
#
# X = StandardScaler().fit_transform(X)
#
