from matplotlib import pyplot as plt
import yfinance as yf
import pandas as pd
#matplotlib theme
plt.style.use("seaborn-dark")
#enter a valid stock ticker, code breaks when you request etf(s)
stock = yf.Ticker(input("enter stock ticker: "))
#You must choose one of the following
print("\n[1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max ]\n")
time = input("enter period: ")
priceHistory = stock.history(period=time)
stockName = stock.info['shortName']
#pandas DataFrame
df = priceHistory
#Visualize using matplotlib
plt.figure()
plt.plot(df['Close'])
plt.title(stockName + " Price history")
plt.xlabel("Time Frame: " + time)
plt.tight_layout()
plt.show()
#note, remember to exit graph window in order to re-run code. Happy Coding!
def yfinancetut(tickersymbol):
tickerdata = yf.Ticker(tickersymbol)
tickerinfo = tickerdata.info
stock = tickerinfo['shortName']
return stock
def yfPtoE(tickersymbol):
tickerdata = yf.Ticker(tickersymbol)
tickerinfo = tickerdata.info
PricetoEarnings = tickerinfo['forwardPE']
return PricetoEarnings
#%%
from usingYahoo import fetchFromYahoo, get_train_test_dataset, create_dataset
import yfinance as yf
import matplotlib.pyplot as plt
print('\nNifty \n' , yf.Ticker("^NSEI").history(period='max')[::-1])
plt.plot(yf.Ticker("^NSEI").history(period='max')['Close'])
plt.title('NIFTY 50')
plt.show()
print('\nSensex \n' , yf.Ticker("^BSESN").history(period='max')[::-1])
plt.plot(yf.Ticker("^BSESN").history(period='max')['Close'])
plt.title('SENSEX')
plt.show()
#creating dataset
df,info = fetchFromYahoo('BEPL.NS')
train_data, test_data = get_train_test_dataset(df)
xtrain, ytrain = create_dataset(train_data, timestep=100)
xtest, ytest = create_dataset(test_data, timestep=100)
# %%
xtrain = xtrain.reshape(xtrain.shape[0],xtrain.shape[1],1)
xtest = xtest.reshape(xtest.shape[0],xtest.shape[1],1)
# %%
from keras.models import Sequential
from keras.layers import LSTM, Dense, Dropout
model = Sequential()
model.add(LSTM(100,return_sequences=True, input_shape = (xtrain.shape[1],xtrain.shape[2])))
model.add(LSTM(100, return_sequences=True))
model.add(Dropout(0.3))
def check_presets(preset_dict: dict) -> str:
"""Checks option screener preset values
Parameters
----------
preset_dict: dict
Defined presets from configparser
Returns
-------
error: str
String of all errors accumulated
"""
float_list = [
"min-iv",
"max-iv",
"min-oi",
"max-oi",
"min-strike",
"max-strike",
"min-volume",
"max-volume",
"min-voi",
"max-voi",
"min-diff",
"max-diff",
"min-ask-bid",
"max-ask-bid",
"min-exp",
"max-exp",
"min-price",
"max-price",
"min-price-20d",
"max-price-20d",
"min-volume-20d",
"max-volume-20d",
"min-iv-20d",
"max-iv-20d",
"min-delta-20d",
"max-delta-20d",
"min-gamma-20d",
"max-gamma-20d",
"min-theta-20d",
"max-theta-20d",
"min-vega-20d",
"max-vega-20d",
"min-rho-20d",
"max-rho-20d",
"min-price-100d",
"max-price-100d",
"min-volume-100d",
"max-volume-100d",
"min-iv-100d",
"max-iv-100d",
"min-delta-100d",
"max-delta-100d",
"min-gamma-100d",
"max-gamma-100d",
"min-theta-100d",
"max-theta-100d",
"min-vega-100d",
"max-vega-100d",
"min-rho-100d",
"max-rho-100d",
"min-sto",
"max-sto",
"min-yield",
"max-yield",
"min-myield",
"max-myield",
"min-delta",
"max-delta",
"min-gamma",
"max-gamma",
"min-theta",
"max-theta",
"min-vega",
"max-vega",
"min-cap",
"max-cap",
]
bool_list = [
"active", "stock", "etf", "puts", "calls", "itm", "otm", "exclude"
]
error = ""
for key, value in preset_dict.items():
if key in float_list:
try:
float(value)
if value.startswith("."):
error += f"{key} : {value} needs to be formatted with leading 0\n"
except Exception:
error += f"{key} : {value}, should be float\n"
elif key in bool_list:
if value not in ["true", "false"]:
error += f"{key} : {value}, Should be [true/false]\n"
elif key == "tickers":
for ticker in value.split(","):
try:
if yf.Ticker(
eval(ticker)).info["regularMarketPrice"] is None:
error += f"{key} : {ticker} not found on yfinance"
except NameError:
error += f"{key} : {value}, {ticker} failed"
elif key == "limit":
try:
int(value)
except Exception:
error += f"{key} : {value} , should be integer\n"
elif key == "order-by":
accepted_orders = [
"e_desc",
"e_asc",
"iv_desc",
"iv_asc",
"md_desc",
"md_asc",
"lp_desc",
"lp_asc",
]
if value not in accepted_orders:
error += f"{key} : {value} not accepted ordering\n"
return error
def close():
close_body = ""
a_stocks_body_text = ""
b_stocks_body_text = ""
a_sum_day_list = []
b_sum_day_list = []
for stocks in my_stocks:
header = ("Stock name: " + stocks.Name + ", Ticker :" + stocks.Ticker)
individual_stock_data = yf.Ticker(stocks.Ticker)
individual_stock_data.history(period="2d")
num_shares = stocks.Shares
for stocks in a_stocks:
header_a = ("Stock Name: " + stocks.Name + ", Ticker: " +
stocks.Ticker)
num_shares = stocks.Shares
individual_stock_data = yf.Ticker(stocks.Ticker)
open_string_data_1_d = str(
individual_stock_data.history(period="1d").Open[0])
close_string_data_1_d = str(
individual_stock_data.history(period="1d").Close[0])
day_change_1d = (float(close_string_data_1_d) -
float(open_string_data_1_d))
close_value_1d = float(num_shares) * float(close_string_data_1_d)
open_value_1 = float(open_string_data_1_d) * float(num_shares)
change_value_1 = (close_value_1d - open_value_1)
change_percent_a = ((change_value_1 / open_value_1) * 100)
a_sum_day = a_sum_day_list.append(change_value_1)
a_stocks_body_text += """{} \n {} \n {} \n {} \n {} \n {} \n {} \n \n""".format(
header_a, "Close: " + str(todays_date) + " " + "$" +
str(close_string_data_1_d), "Shares :" + stocks.Shares,
"Current Value : $" + ('%.2f' % close_value_1d),
"Daily $ Price Change: $" + ('%.2f' % day_change_1d),
"Daily % Price Change: " + ('%.2f' % change_percent_a + " %"),
"Daily Value Change: $" + ('%.2f' % change_value_1))
for stocks in b_stocks:
header_b = ("Stock Name: " + stocks.Name + ", Ticker: " +
stocks.Ticker)
num_shares = stocks.Shares
individual_stock_data = yf.Ticker(stocks.Ticker)
open_string_data_2_d = str(
individual_stock_data.history(period="3d").Open[-2])
close_string_data_2_d = str(
individual_stock_data.history(period="3d").Close[-1])
day_change_2d = Decimal(
float(close_string_data_2_d) - float(open_string_data_2_d))
day_change_rounded = round(day_change_2d, 2)
close_value_2d = float(num_shares) * float(close_string_data_2_d)
open_value_2 = float(open_string_data_2_d) * float(num_shares)
change_value_2 = (close_value_2d - open_value_2)
change_percent = ((change_value_2 / open_value_2) * 100)
b_sum_day = b_sum_day_list.append(change_value_2)
b_stocks_body_text += """ \n {} \n {} \n {} \n {} \n {} \n {} \n {} \n \n""".format(
header_b, "Close: " + str(todays_date) + " " + "$" +
str(close_string_data_2_d), "Shares :" + stocks.Shares,
"Current Value: $" + ('%.2f' % close_value_2d),
"Daily $ Price Change: $" + ('%.2f' % day_change_2d),
"Daily % Price Change: " + ('%.2f' % change_percent + " %"),
"Daily Value Change: $" + ('%.2f' % change_value_2))
a_total = sum(a_sum_day_list)
b_total = sum(b_sum_day_list)
day_total = a_total + b_total
total_text = "Day Totals: $ "
close_body = str(a_stocks_body_text) + str(b_stocks_body_text) + str(
total_text + ('%.2f' % day_total))
email_to_send = ezgmail.send('Any_email.com', subject_text, close_body)
if column.text.strip() == dividendname:
dividendIndex = i
print(i, column.text.strip())
break
if dividendIndex == -sys.maxsize:
sys.exit('Table without Dividened column???')
tablerow = tablebody.find_elements_by_tag_name('tr')
print('Number of rows = ', len(tablerow))
for i, row in enumerate(tablerow):
dataset = row.find_elements_by_tag_name('td')
stock_symbol = dataset[0].text.strip()
ticker = yf.Ticker(stock_symbol)
today_price = ticker.history().tail(1)
dividend_amount = float(dataset[dividendIndex - 1].text.strip())
print(
stock_symbol, today_price.columns[0], today_price['Close'],
dividend_amount) # dividend_amount / today_price['Close'] * 100, '%')
if i == len(tablerow) - 1:
driver.get(stockurl + dataset[0].text.strip())
# today_price = driver.find_element_by_class_name('symbol-page-header__pricing-price')
# print( 'Today price', today_price.text.strip())
#if driver.find_element_by_class_name("pagination__next"):
# driver.find_element_by_class_name("pagination__next").click()
# driver.close()
plt.xlabel('Date')
plt.ylabel('Price [$]')
FFT = np.fft.fft(np.asarray(
data.values.tolist())) # Get the fast fourier transform
plots = []
for num in [3, 6, 9, 15]:
fft = np.copy(FFT)
fft[num:-num] = 0
ifft = np.fft.ifft(fft)
p = plt.plot(pd.to_datetime(
np.linspace(
pd.Timestamp(data.index.values[0]).value,
pd.Timestamp(data.index.values[-1]).value, len(ifft))),
np.real(ifft),
linestyle='--',
label='Fourier transform with %d components' % num)
plots.append(p)
p = plt.plot(data.index, data.values, color='purple', ls='-', label='Real')
plots.append(p)
return plots
if __name__ == '__main__':
import yfinance as yf
SPCE = yf.Ticker('SPCE')
hist = SPCE.history(period='max')
plt.figure()
plot_fourier(hist['Close'])
plt.legend()
plt.show()
def get_yfinance_m(stock, period):
features = ['Open', 'High', 'Low', 'Close', 'Volume']
val = yf.Ticker(stock)
val_historical = val.history(period=period, interval="1m")
return val_historical[features]
def evaluate(stocks):
#read date and do basic processing
date_cols = ['Zeit']
df = pd.read_csv('./tmp/scraped_news.csv', parse_dates=date_cols)
df['Datum'] = df['Zeit'].dt.date
df['Stunde'] = df['Zeit'].dt.time
#df['Schlagzeile'] = df['Schlagzeile'].astype("|S")
df['Aktie'] = stocks[0]
df = df.loc[(df['Leser'] != 'Werbung')
& (df['Schlagzeile'].values != None)
& (df['Zeit'].values != None)]
df = df.reset_index(drop=True)
df.to_excel('./tmp/scraped_and_cleaned_news.xlsx',
index=False,
sheet_name='data')
#translate
chunk_size = 5
chs = []
for i in chunker(df, chunk_size):
chs.append(i)
trl = []
for i in range(len(chs)):
trl.append(
googletranslate_en(
[str(j) for j in chs[i]['Schlagzeile'].tolist()[:]]))
flatten = lambda t: [item for sublist in t for item in sublist]
trls = flatten(trl)
df['Schlagzeile EN'] = trls
df = df.replace('in', 'xyz')
#sentiment analysis
# Instantiate the sentiment intensity analyzer
vader = SentimentIntensityAnalyzer()
# Set column names
columns = ['Aktie', 'Datum', 'Stunde', 'Schlagzeile EN']
# Convert the parsed_news list into a DataFrame called 'parsed_and_scored_news'
parsed_and_scored_news = pd.DataFrame(df, columns=columns)
parsed_and_scored_news = parsed_and_scored_news.reset_index(drop=True)
# Iterate through the headlines and get the polarity scores using vader
scores = parsed_and_scored_news['Schlagzeile EN'].apply(
vader.polarity_scores).tolist()
# Convert the 'scores' list of dicts into a DataFrame
scores_df = pd.DataFrame(scores)
# Join the DataFrames of the news and the list of dicts
parsed_and_scored_news = parsed_and_scored_news.join(scores_df,
rsuffix='_right')
# Convert the date column from string to datetime
parsed_and_scored_news['Datum'] = pd.to_datetime(
parsed_and_scored_news.Datum).dt.date
#plots
plot_vaderscores(parsed_and_scored_news, './tmp/fig_vaderscores.png')
cnt_df = plot_countofnewsperday(parsed_and_scored_news,
'./tmp/fig_countofnewsperday.png')
nvda = yf.Ticker("NVDA")
hist = nvda.history(period="30d")
cnt_df.index = pd.to_datetime(cnt_df.index)
mask = (hist.index >= min(cnt_df.index))
nvda_df = hist.loc[mask]
plot_closeprice(nvda_df, './tmp/fig_closeprice.png')
print('Evaluation successful!')
def getBuiltInRankings():
stock_list = StockInfo.objects.all()
list_size = len(stock_list)
# check validity of data
stock = stock_list[0]
ticker = yf.Ticker(f'{stock.ticker}.TW')
his = ticker.history(period='1d', actions=False)
# valid
if datetime.today().date() == his.index[0]:
rise = np.zeros(list_size)
fall = np.zeros(list_size)
volume = np.zeros(list_size)
for i in range(list_size):
stock = stock_list[i]
ticker = yf.Ticker(f'{stock.ticker}.TW')
his = ticker.history(period='1d', actions=False)
if his.empty:
continue
# rise, fall, volume
rise[i] = (his['Close'][0] - his['Open'][0]) / his['Open'][0]
fall[i] = -rise[i]
volume[i] = his['Volume'][0]
# sort
top_rise = rise.argsort()[-RANKING_SIZE:][::-1]
top_fall = fall.argsort()[-RANKING_SIZE:][::-1]
top_volume = volume.argsort()[-RANKING_SIZE:][::-1]
# delete old records
# consider deleting all of them? yes?
if StockRecord.objects.all().exists:
StockRecord.objects.all().delete()
# store data into database
# Model.objects.bulk_create([ Model(**{variable: value, ...}) for m in list ])
StockRecord.objects.bulk_create([
StockRecord(
**{
'record_type': 'rise',
'stock_info': stock_list[int(top_rise[i])],
'ranking_number': i + 1,
'last_modified': his.index[0],
'remark': f'{round( rise[ int(top_rise[i]) ] * 100, 2 )}%'
}) for i in range(RANKING_SIZE)
])
StockRecord.objects.bulk_create([
StockRecord(
**{
'record_type': 'fall',
'stock_info': stock_list[int(top_fall[i])],
'ranking_number': i + 1,
'last_modified': his.index[0],
'remark': f'{round( fall[ int(top_fall[i]) ] * 100, 2 )}%'
}) for i in range(RANKING_SIZE)
])
StockRecord.objects.bulk_create([
StockRecord(
**{
'record_type': 'volume',
'stock_info': stock_list[int(top_volume[i])],
'ranking_number': i + 1,
'last_modified': his.index[0],
'remark': f'{volume[ int(top_volume[i]) ]}'
}) for i in range(RANKING_SIZE)
])
def ValuationStatement(currentprice, intrinsicprice, ValueComparison):
if currentprice - intrinsicprice >= 0:
statement = "Shares overvalued by {}".format(ValueComparison)
elif currentprice - intrinsicprice <= 0:
statement = "Shares undervalued by {}".format(ValueComparison)
return statement
#loop the tickers specified and pulling with API from yFinance
for tickerIndex in ticker_list:
try:
ticker = tickerIndex #str(input("ticker symbol: ")) #example: MSFT
tkr = yf.Ticker(ticker)
Bta = tkr.info['beta']
curr_share_value = tkr.info['regularMarketPrice']
numshares = tkr.info['sharesOutstanding']
netincome = tkr.financials.loc['Net Income', :].tolist()
byNI = netincome[0]
bookvalue1 = tkr.balancesheet.loc[
'Total Stockholder Equity', :].tolist()
byBV = bookvalue1[0]
pyBV = bookvalue1[1]
divy = tkr.dividends.tolist()
byDivtemp = divy.reverse()
byDivtemp = divy[0:4]
import yfinance as yf
import pandas as pd
from time import sleep
while True:
tatasteel = yf.Ticker('TATASTEEL.NS')
df = pd.DataFrame(tatasteel.history('1d', '1m'))
df = df[['High', 'Low', 'Volume']][-7:]
high = df[['High']].values
low = df[['Low']].values
flag = 0
def get_sector(ticker):
try:
return yf.Ticker(ticker).info["sector"]
except (ValueError, IndexError):
return "Other"
def get_linearprediction(ticker_name, start_date, end_date, future_period):
import datetime
from datetime import timedelta, date
#Download the data from Yfinance
ticker = yf.Ticker(ticker_name)
ticker_historical = ticker.history(start=start_date, end=end_date)
ticker_historical #Display the data
name = ticker_name
filename = "%s.csv" % name
#Save data into a CSV file
ticker_historical.to_csv(filename)
#df.to_csv(filename)
df = pd.read_csv(filename)
df_days = df[['Date']]
# print(df)
# A variable for predicting 'n' days out into the future #'n=30' days
# Get the Adjusted Close Price
df = df[['Close']]
#Create another column (the target ) shifted 'n' units up
df['Prediction'] = df[['Close']].shift(-future_period)
#print the new data set
#print(df.tail())
### Create the independent data set (X) #######
# Convert the dataframe to a numpy array
X = np.array(df.drop(['Prediction'], 1))
#Remove the last '30' rows
X = X[:-future_period]
#print(X)
### Create the dependent data set (y) #####
# Convert the dataframe to a numpy array
y = np.array(df['Prediction'])
# Get all of the y values except the last '30' rows
y = y[:-future_period]
#print(y)
# Split the data into 80% training and 20% testing
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and train the Linear Regression Model
lr = LinearRegression()
# Train the model
lr.fit(x_train, y_train)
# Testing Model: Score returns the coefficient of determination R^2 of the prediction.
# The best possible score is 1.0
lr_confidence = lr.score(x_test, y_test)
#print("lr confidence: ", lr_confidence)
# df['Linear Regression Accuracy'] = lr_confidence
# Set x_forecast equal to the last 30 rows of the original data set from Adj. Close column
x_forecast = np.array(df.drop(['Prediction'], 1))[-future_period:]
#print(x_forecast)
#Print linear regression model predictions for the next '30' days
lr_prediction = lr.predict(x_forecast)
#print(lr_prediction)
df['Date'] = df_days
df
# A variable for predicting 'n' days out into the future
start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d").date()
end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d").date()
def daterange(date1, date2):
for n in range(int((date2 - date1).days) + 1):
yield date1 + timedelta(n)
for lr in lr_prediction:
end_date += timedelta(days=1)
#print(end_date)
weekdays = [5, 6]
df = df.append({
'Date': end_date,
'Linear Regression Prediction': lr
},
ignore_index=True)
df = df[['Date', 'Close', 'Linear Regression Prediction']]
return df
import yfinance as yf
import pandas as pd
import numpy as np
import streamlit as st
st.write("""
# Simple Stock Price App
are the stock ***closing price*** and ***volume*** of Google
Shown
""")
tickerSymbol = 'GOOGL'
tickerData = yf.Ticker(tickerSymbol)
tickerDf = tickerData.history(period='id', start='2010-5-31', end='2020-5-31')
st.line_chart(tickerDf.Close)
st.line_chart(tickerDf.Volume)
"1.5", "B")
veirx_stock = Stock("Vanguard Equity-Income Fund Admiral Shares", "VEIRX",
"1.5", "B")
rds_a_stock = Stock("Royal Dutch Shell plc", "RDS-A", "1.5", "A")
ldlfx_stock = Stock("Lord Abbett Short Duration Income Fund Class F ", "LDLFX",
"1.5", "B")
my_stocks = [
ba_stock, vug_stock, stx_stock, trrmx_stock, svaix_stock, veirx_stock,
rds_a_stock, ldlfx_stock
]
a_stocks = [ba_stock, rds_a_stock, stx_stock, vug_stock]
b_stocks = [trrmx_stock, ldlfx_stock, svaix_stock, veirx_stock]
subject_text = """{} {}""".format("Stocks Closing Price:", todays_date)
individual_stock_data = yf.Ticker("VEIRX")
open_val = (individual_stock_data.history(period="3d").Open[-2] * 114.568)
close_val = (individual_stock_data.history(period="3d").Close[-1] * 114.568)
change_val = Decimal(close_val - open_val)
def close():
close_body = ""
a_stocks_body_text = ""
b_stocks_body_text = ""
a_sum_day_list = []
b_sum_day_list = []
for stocks in my_stocks:
header = ("Stock name: " + stocks.Name + ", Ticker :" + stocks.Ticker)
individual_stock_data = yf.Ticker(stocks.Ticker)
individual_stock_data.history(period="2d")
def yf_downloader(symbols, **kwargs): # pragma: no cover
"""Downloader that uses `yfinance`."""
import yfinance as yf
return {s: yf.Ticker(s).history(**kwargs) for s in symbols}
def download_returns(ticker, period="max"):
if isinstance(period, _pd.DatetimeIndex):
p = {"start": period[0]}
else:
p = {"period": period}
return _yf.Ticker(ticker).history(**p)['Close'].pct_change()
def get_current_stock_value(ticker):
stock = yf.Ticker(ticker)
history = stock.history(period='2d', interval='1d')
return history.iloc[-1]['Close']
def get_roe(ticker, exchange, freq='yearly'):
''' Retrieves the balance sheet and financials from yfinance. Calculates the average shareholder equity and
determines the Return on Equity '''
def match_dates(frame1, frame2):
""" Returns the intersection of dates between two input frames and the smallest date """
dates_frame1 = frame1.index.strftime('%Y-%m-%d')
print(dates_frame1)
dates_array1 = pd.Index.ravel(dates_frame1, order='C')
print(dates_array1)
dates_frame2 = frame2.index.strftime('%Y-%m-%d')
dates_array2 = pd.Index.ravel(dates_frame2, order='C')
return_array = np.intersect1d(dates_array1, dates_array2)
minimum = np.amin(return_array)
return return_array, minimum
if (exchange == 'ASX'):
ticker += '.AX'
stock = yf.Ticker(ticker)
if freq == 'yearly':
netIncomeDf = stock.financials.loc['Net Income'].to_frame()
netIncomeDf = netIncomeDf.iloc[::-1]
shareholderEquityDf = stock.balance_sheet.loc['Total Stockholder Equity'].to_frame()
shareholderEquityDf = shareholderEquityDf.iloc[::-1]
else:
netIncomeDf = stock.quarterly_financials.loc['Net Income'].to_frame()
netIncomeDf = netIncomeDf.iloc[::-1]
shareholderEquityDf = stock.quarterly_balance_sheet.loc['Total Stockholder Equity'].to_frame()
shareholderEquityDf = shareholderEquityDf.iloc[::-1]
averageShareHolderEquity = []
returnOnEquity = []
dates, minDate = match_dates(netIncomeDf, shareholderEquityDf)
for index, _ in enumerate(dates):
currDate = dates[index]
prevDate = dates[index - 1]
if currDate == minDate:
# we cannot calculate the average for the first date
averageShareHolderEquity.append(0)
returnOnEquity.append(0)
continue
currNetIncome = netIncomeDf.loc[np.datetime64(currDate)]['Net Income']
currSE = shareholderEquityDf.loc[np.datetime64(currDate)]['Total Stockholder Equity']
prevSE = shareholderEquityDf.loc[np.datetime64(prevDate)]['Total Stockholder Equity']
avgSE = (prevSE + currSE) / 2
averageShareHolderEquity.append(avgSE)
roe = currNetIncome / avgSE
returnOnEquity.append(roe)
# Create dataframe to be processed
data = list(zip(averageShareHolderEquity, returnOnEquity))
finalDf = pd.DataFrame(data, columns=['average_shareholder_equity', 'return_on_equity'], index=dates)
roeDict = json.loads(finalDf.transpose().to_json())
roeArray = []
# front end requires the data to be in this format
for date in roeDict.keys():
roeArray.append({
'entry': 'return_on_equity',
'date': date,
'value': roeDict.get(date).get('return_on_equity'),
})
return roeArray
def get_stock_value_time(ticker):
stock = yf.Ticker(ticker)
history = stock.history(period='5d', interval='1d')
# print(history.loc[:, ['Close']])
return history.loc[:, ['Close']]
# st.markdown('''
# # Stock Information
# ''')
#Sidebar panel
st.sidebar.subheader('Query Parameters')
start_date = st.sidebar.date_input("Start date", datetime.date(2020, 1, 1))
# end_date=st.sidebar.date_input("End date",datetime.date(2021,5,12))
end_date = st.sidebar.date_input("End date", datetime.date.today())
#Retriveing ticker data
ticker_list = ['CBRE', 'AAPL', 'GOOG', 'FB', 'MSFT', 'AMZN', 'PLTR', 'U']
tickerSymbol = st.sidebar.selectbox('Stock Ticker',
ticker_list) #Select ticker
tickerData = yf.Ticker(tickerSymbol) #Get ticker data
tickerDf = tickerData.history(period='1d', start=start_date, end=end_date)
task = st.sidebar.radio('Summary', ['Profile', 'Chart', 'Historical Data'])
if task == 'Profile':
#Ticker info <html placeholder %s> for logo image
string_logo = '<img src=%s>' % tickerData.info['logo_url']
st.markdown(string_logo, unsafe_allow_html=True)
string_name = tickerData.info['longName']
st.subheader('**%s**' % string_name)
st.write('***%s***' % tickerData.info['sector'])
string_summmary = tickerData.info['longBusinessSummary']
st.info(string_summmary)
st.write(tickerData.info['website'])
def get_stock(ticker):
stock = yf.Ticker(ticker)
history = stock.history(period='2d', interval='1d')
return history.to_json()
#Connects yfinance and gets historical data for stock daily price
import yfinance as yf
msft = yf.Ticker("MSFT")
print(msft)
print(msft.info)
print(msft.history(period="max"))
other = pd.read_csv('otherlisted.txt', sep='|')
other = other[['ACT Symbol', 'Security Name']]
other = other.rename(columns={'ACT Symbol': 'Symbol'})
us_stocks = nasdaq.append(other)
stocks_dic = us_stocks.set_index('Symbol')['Security Name'].to_dict()
#def tick():
# user_input = st.text_input("Enter stock symbol:", 'AAPL')
# return user_input.upper()
tickerSymbol = str(st.text_input("Enter stock symbol:", 'AAPL'))
#get data on this ticker
for (k, v) in stocks_dic.items():
if (tickerSymbol.upper() == k):
tickerData = yf.Ticker(k)
st.write("**" + "Current Stock Price of " +
str(tickerData.get_info()['longName']) + " is: " +
str(np.round(si.get_live_price(tickerSymbol), 2)) + "**")
st.write("**" +
"Here's the complete Closing Price trend for this month: " +
"**" + str(tickerData.get_info()['longName']))
def monthly_stock_trend_complete(tickerSymbol):
d = pd.DataFrame()
d = d.append(
yf.Ticker(tickerSymbol).history(interval='1d').reset_index())
fig = px.line(
d,
x="Date",
def yfinanceAVG(tickersymbol):
tickerdata = yf.Ticker(tickersymbol)
tickerinfo = tickerdata.info
avg = tickerinfo['fiftyDayAverage']
return avg
def get_performance(ticker_name, start_date, end_date, future_period):
import datetime
from datetime import timedelta, date
#Download the data from Yfinance
ticker = yf.Ticker(ticker_name)
ticker_historical = ticker.history(start=start_date, end=end_date)
ticker_historical #Display the data
name = ticker_name
filename = "%s.csv" % name
#Save data into a CSV file
ticker_historical.to_csv(filename)
#df.to_csv(filename)
df = pd.read_csv(filename)
df_days = df[['Date']]
# print(df)
# A variable for predicting 'n' days out into the future #'n=30' days
# Get the Adjusted Close Price
df = df[['Close']]
#Create another column (the target ) shifted 'n' units up
df['Prediction'] = df[['Close']].shift(-future_period)
#print the new data set
#print(df.tail())
### Create the independent data set (X) #######
# Convert the dataframe to a numpy array
X = np.array(df.drop(['Prediction'], 1))
#Remove the last '30' rows
X = X[:-future_period]
#print(X)
### Create the dependent data set (y) #####
# Convert the dataframe to a numpy array
y = np.array(df['Prediction'])
# Get all of the y values except the last '30' rows
y = y[:-future_period]
#print(y)
# Split the data into 80% training and 20% testing
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create and train the Linear Regression Model
lr = LinearRegression()
# Train the model
lr.fit(x_train, y_train)
# Testing Model: Score returns the coefficient of determination R^2 of the prediction.
# The best possible score is 1.0
lr_confidence = lr.score(x_test, y_test)
#print("lr confidence: ", lr_confidence)
# df['Linear Regression Accuracy'] = lr_confidence
# Set x_forecast equal to the last 30 rows of the original data set from Adj. Close column
x_forecast = np.array(df.drop(['Prediction'], 1))[-future_period:]
#print(x_forecast)
#Print linear regression model predictions for the next '30' days
lr_prediction = lr.predict(x_forecast)
#print(lr_prediction)
# Create and train the Support Vector Machine (Regressor)
svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)
svr_rbf.fit(x_train, y_train)
# Testing Model: Score returns the coefficient of determination R^2 of the prediction.
# The best possible score is 1.0
rbf_svm_confidence = svr_rbf.score(x_test, y_test)
#print("svm confidence: ", svm_confidence)
# df['Linear Regression Accuracy'] = lr_confidence
# Set x_forecast equal to the last 30 rows of the original data set from Adj. Close column
x_forecast = np.array(df.drop(['Prediction'], 1))[-future_period:]
#print(x_forecast)
# Print support vector regressor model predictions for the next '30' days
rbf_svm_prediction = svr_rbf.predict(x_forecast)
#print(svm_prediction)
# Create and train the Support Vector Machine (Regressor)
svr_lin = SVR(kernel='linear', C=1e3, gamma=0.1)
svr_lin.fit(x_train, y_train)
# Testing Model: Score returns the coefficient of determination R^2 of the prediction.
# The best possible score is 1.0
lin_svm_confidence = svr_lin.score(x_test, y_test)
#print("svm confidence: ", svm_confidence)
# df['Linear Regression Accuracy'] = lr_confidence
# Set x_forecast equal to the last 30 rows of the original data set from Adj. Close column
x_forecast = np.array(df.drop(['Prediction'], 1))[-future_period:]
#print(x_forecast)
# Print support vector regressor model predictions for the next '30' days
lin_svm_prediction = svr_lin.predict(x_forecast)
#print(svm_prediction)
# Create and train the Support Vector Machine (Regressor)
svr_poly = SVR(kernel='poly', C=1e3, gamma=0.1)
svr_poly.fit(x_train, y_train)
# Testing Model: Score returns the coefficient of determination R^2 of the prediction.
# The best possible score is 1.0
x_forecast = np.array(df.drop(['Prediction'], 1))[-future_period:]
Accuracy = {
'Linear Regression Prediction': [lr_confidence],
'RBF SVM Regression Prediction': [rbf_svm_confidence],
'Linear SVM Regression Prediction': [lin_svm_confidence]
}
df = pd.DataFrame(Accuracy,
columns=[
'Linear Regression Prediction',
'RBF SVM Regression Prediction',
'Linear SVM Regression Prediction'
])
return df
def yfBeta(tickersymbol):
tickerdata = yf.Ticker(tickersymbol)
tickerinfo = tickerdata.info
beta = tickerinfo['beta']
return beta
def stock_analysis():
st.header("Enter the stock name")
user_input = st.text_input("",help="Enter the ticker symbol of any stock on BSE/NSE. Ex - TCS")
if(user_input):
stock_name = user_input.upper()
try:
soup = rk.stock_data(stock_name)
stock = yf.Ticker(stock_name+".NS")
except:
st.error("❗ Check your connection / Enter a valid name")
st.stop()
cmp = soup.find("span", class_="Number")
#st.write("Current Market Price - ₹",cmp.text,sep='.')
stock_info = get_ticker_info(stock)
st.text("")
#Company_description
html1 = '''
<html>
<head>
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" integrity="sha384-JcKb8q3iqJ61gNV9KGb8thSsNjpSL0n8PARn9HuZOnIxN0hoP+VmmDGMN5t9UJ0Z" crossorigin="anonymous">
<script src="https://code.jquery.com/jquery-3.5.1.slim.min.js" integrity="sha384-DfXdz2htPH0lsSSs5nCTpuj/zy4C+OGpamoFVy38MVBnE+IbbVYUew+OrCXaRkfj" crossorigin="anonymous"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/umd/popper.min.js" integrity="sha384-9/reFTGAW83EW2RDu2S0VKaIzap3H66lZH81PoYlFhbGU+6BZp6G7niu735Sk7lN" crossorigin="anonymous"></script>
<script src="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js" integrity="sha384-B4gt1jrGC7Jh4AgTPSdUtOBvfO8shuf57BaghqFfPlYxofvL8/KUEfYiJOMMV+rV" crossorigin="anonymous"></script>
<meta name="viewport" content="width=device-width, initial-scale=1">
<style>
@media screen and (min-width: 601px){
h1{
font-size: 30px;
}
}
.jumbotron{
background-color:#F0F2F6;
margin-top: 50px;
}
img{
position: absolute;
z-index: 2;
right: 20px;
border: 5px solid grey;
border-width: thin;
top: -110px;
box-shadow: 1px 1px 3px 0.5px;
}
.container{
paddin
}
</style>
</head>
<body>
<main role="main">
<!-- Main jumbotron for a primary marketing message or call to action -->
<div class="jumbotron">
<div style="position: relative; z-index: 1;">
<img src=%s style="position: absolute; z-index: 2;" />
</div>
<div class="container">
<h1 class="display-3">%s</h1>
<p style="font-weight: 500;margin-bottom: 32px;">Industry >> %s</p>
<p><h5><span style="color: white;" class="sc-hover badge bg-dark" data-cardsize="small" data-quantity="2" data-ordertype="BUY">%s</span></h5>%s</p><br>
<p><a class="btn btn-primary btn-lg" href=%s role="button" target="_blank">Learn more »</a></p>
</div>
</div>
</main>
<script async src="https://www.gateway-tt.in/assets/embed.js"></script>
</body>
</html>
''' %(stock_info['logo_url'],stock_info['longName'],stock_info['industry'],stock_name,stock_info['longBusinessSummary'],stock_info['website'])
components.html(html1,height=720)
#Financial info
c1,c2,c3 = st.beta_columns(3)
with c1:
'''
### Market Cap: ₹**%d Cr.**
### EPS: ₹**%.2f**
### Book Value: ₹**%.2f **
### Stock PE: ** %.2f **
### Beta: ** %.2f **
''' %(stock_info['marketCap']//10000000,stock_info['trailingEps'],stock_info['bookValue']*70,stock_info['trailingPE'],stock_info['beta'])
with c2:
'''
### Earnings Growth(QoQ):** %.2f%% **
### Forward PE:** %.2f **
### Forward EPS: ₹**%.2f**
### Sector:** %s **
### 50 DMA: ₹**%.2f **
''' %(stock_info['earningsQuarterlyGrowth']*100,stock_info['forwardPE'],stock_info['forwardEps'],stock_info['sector'],stock_info['fiftyDayAverage'])
with c3:
'''
### 52 Week High / Low: ₹**%.2f / %.2f **
### Promoter Holding:** %.2f%% **
### Dividend Yield:** %.2f%% **
### 200 DMA: ₹**%.2f **
### CMP: ₹**%s **
''' %(stock_info['fiftyTwoWeekHigh'],stock_info['fiftyTwoWeekLow'],stock_info['heldPercentInsiders']*100,stock_info['dividendYield']*100,stock_info['twoHundredDayAverage'],cmp.text)
''' ***** '''
st.text("")
# strength & limitations
col1, col2 = st.beta_columns(2)
#Strengths
col1.header("Strengths")
strength = soup.find("ul", class_="strength").text
strength = strength.strip().replace('\n',' \n')
if strength=='':
col1.success('No Strength')
else:
col1.success(strength)
#Limitations
col2.header("Limitations")
limitations = soup.find("ul", class_="limitations").text
limitations = limitations.strip().replace('\n',' \n')
if limitations=='':
col2.error("No Limitations")
else:
col2.error(limitations)
#Price Chart
st.text("")
st.header("Price Chart")
ticker = user_input
new_ticker = '\"'+'BSE:'+ticker+'\"'
chart='''
<!-- TradingView Widget BEGIN -->
<div class="tradingview-widget-container">
<div id="basic-area-chart"></div>
<div class="tradingview-widget-copyright"><a href="https://www.tradingview.com/symbols/'''+ticker+'''/" rel="noopener" target="_blank"><span class="blue-text">'''+ticker+''' Chart</span></a> by TradingView</div>
<script type="text/javascript" src="https://s3.tradingview.com/tv.js"></script>
<script type="text/javascript">
new TradingView.widget(
{
"container_id": "basic-area-chart",
"width": 1200,
"height": 700,
"symbol": '''+new_ticker+''',
"interval": "D",
"timezone": "exchange",
"theme": "light",
"style": "3",
"toolbar_bg": "#f1f3f6",
"hide_top_toolbar": true,
"save_image": false,
"locale": "en"
}
);
</script>
</div>
<!-- TradingView Widget END -->
'''
components.html(chart,width=1400,height=700)
#Annual Report
reports = rk.balance_sheet(stock_name)
annual_report = reports[1].rename(columns={'Unnamed: 0':' '})
if 'TTM' in annual_report.columns:
annual_report.drop('TTM',axis=1,inplace=True)
years=np.array(annual_report.columns[1:])
years= [int(item.split()[1]) for item in years]
revenue=annual_report.iloc[0][1:]
revenue = [int(item) for item in revenue]
#Revenue (YOY)
st.text("")
st.header("Revenue (YOY) in Cr.")
c=alt.Chart(pd.DataFrame({"Revenue":revenue,"Year":years})).mark_bar(size=50).encode(x='Year:O',y='Revenue',tooltip=['Revenue','Year'],opacity=alt.value(0.7),
color=alt.value('#26c6da')).configure_view(strokeWidth=0).properties(height=500)
st.altair_chart(c,use_container_width=True)
#Profit (YOY)
profit=annual_report.iloc[9][1:]
profit = [int(item) for item in profit]
st.text("")
st.header("Profits (YOY) in Cr.")
c=alt.Chart(pd.DataFrame({"Profit":profit,"Year":years})).mark_bar(size=50).encode(x='Year:O',y='Profit',tooltip=['Profit','Year'],opacity=alt.value(0.7),
color=alt.value('#48e421')).configure_view(strokeWidth=0).properties(height=500)
st.altair_chart(c,use_container_width=True)
#Balance Sheet
st.text("")
st.header("Balance Sheet")
balance_sheet = reports[6].rename(columns={'Unnamed: 0':' '})
st.write(balance_sheet)
#Shareholding pattern
st.text("")
st.header("Shareholding Pattern ")
shareholders = list(reports[9]['Unnamed: 0'])
shareholders = [i.rstrip("\xa0+") for i in shareholders]
shareholding = list(reports[9][reports[9].columns[-1]])
col1, col2, col3 = st.beta_columns([1,2,1])
col1.write("")
col2.vega_lite_chart(pd.DataFrame({'Shareholders':shareholders,'Shareholding':shareholding}), {
"width": 600,
"height": 600,
"encoding": {
"theta": {"field": "Shareholding", "type": "quantitative", "stack": True},
"color": {"field": "Shareholders", "type": "nominal"},
"tooltip": {"field": "Shareholding", "type": "quantitative"}
},
"layer": [{
"mark": {"type": "arc", "outerRadius": 150}
}, {
"mark": {"type": "text", "radius": 190},
"encoding": {
"text": {"field": "Shareholders", "type": "nominal"}
}
}]
})
col3.write("")
#Competitor analysis
st.text("")
st.header("Comparison with other stocks")
stocks=[]
stocks.append(stock.history(period="20y"))
user_input2 = st.text_input("Enter stock 1",help="Enter a stock to compare with"+user_input)
if user_input2!='':
stock1 = yf.Ticker(user_input2.upper()+".NS")
stocks.append(stock1.history(period="20y"))
if (stocks[1].empty):
st.error("Error.....Incorrect")
st.stop()
user_input3 = st.text_input("Enter stock 2",help="Enter a stock to compare with"+user_input)
if user_input3!='':
stock2 = yf.Ticker(user_input3.upper()+".NS")
stocks.append(stock2.history(period="20y"))
if (stocks[2].empty):
st.error("Error.....Incorrect")
st.stop()
if user_input2!='' and user_input3!='':
stocks[0]['symbol']=user_input
stocks[1]['symbol']=user_input2
stocks[2]['symbol']=user_input3
#st.write(stocks[0].tail(5))
df = pd.concat([s[['Close','Volume','symbol']] for s in stocks])
df.reset_index(level=0, inplace=True) #make Date a column instead of index
st.text("")
st.header("Comparative Price Chart")
c=alt.Chart(df,height=650).mark_line().encode(
x='Date',
y='Close',
color='symbol',
tooltip=alt.Tooltip(['Close'], format='.2f',title='INR')
).interactive()
st.altair_chart(c,use_container_width=True)
#volume
st.text("")
st.header("Traded Volumes")
selection = alt.selection_multi(fields=['symbol'], bind='legend')
c=alt.Chart(df,height=650).mark_line().encode(
x='Date',
y='Volume',
color='symbol',
tooltip=alt.Tooltip(['Volume']),
opacity=alt.condition(selection, alt.value(1), alt.value(0.2))
).add_selection(selection)
st.altair_chart(c,use_container_width=True)
#Histogram
st.text("")
st.header("Daily Percentage Change")
col1, col2, col3 = st.beta_columns(3)
for i,col,name in (zip(range(3),[col1,col2,col3],[user_input,user_input2,user_input3])):
stocks[i]['Returns']=stocks[i]['Close'].pct_change(1)
col.altair_chart(alt.Chart(stocks[i]).mark_bar().encode(
x=alt.X("Returns:Q", bin=alt.Bin(extent=[-0.100,0.100],step=0.005),title=name),
y=alt.Y(aggregate="count",title="",type="quantitative")
).properties(height=500).interactive(),use_container_width=True)
#Scatter Matrix
st.text("")
st.header("Scatter Matrix - (Finding Correlation)")
st.text("")
box_df = pd.concat([stocks[0]['Returns'],stocks[1]['Returns'],stocks[2]['Returns']],axis=1)
box_df.columns = [user_input+" Returns",user_input2+" Returns",user_input3+" Returns"]
st.altair_chart(alt.Chart(box_df).mark_circle().encode(
alt.X(alt.repeat("column"), type='quantitative'),
alt.Y(alt.repeat("row"), type='quantitative'),
color='Origin:N'
).properties(
width=385,
height=185
).repeat(
row=list(box_df.columns),
column=list(box_df.columns[::-1])
).interactive(),use_container_width=True)
