def _get_all_historical_data(self, yahoo_ticker):
"""Scrapes the Yahoo Finance website for a given Yahoo Ticker and returns all the
available data entries in a list of dictionaries
Parameters
----------
yahoo_ticker : str
Yahoo ticker to be embedded in the URL to scrap
Returns
-------
data : list of dicts
option data retrieved from the Yahoo Finance site
"""
import yahoofinancials
yahoo_cursor = yahoofinancials.YahooFinancials(yahoo_ticker)
# download one day just to have access to the metadata
today = dt.datetime.today().strftime("%Y-%m-%d")
yesterday = (dt.datetime.today() - dt.timedelta(1)).strftime("%Y-%m-%d")
tomorrow = (dt.datetime.today() + dt.timedelta(1)).strftime("%Y-%m-%d")
one_day_data = yahoo_cursor.get_historical_price_data(yesterday, today, 'daily')
first_trade_date = one_day_data[yahoo_ticker]['firstTradeDate']['formatted_date']
# now we can download since the beginning
data = yahoo_cursor.get_historical_price_data(first_trade_date, tomorrow, 'daily')
return data[yahoo_ticker]
def rebalance():
contribution_amount = input('How much are you contributing? ')
allocations = db.session.query(models.allocation.Allocation).all()
assets = []
for alloc in allocations:
for asset in alloc.assets:
assets.append(asset)
fin = yahoofinancials.YahooFinancials([asset.id for asset in assets])
current_price_map = fin.get_current_price()
current_price_map['SPAXX**'] = 1
asset_map = {}
protfolio_value = 0.0
for asset in assets:
asset_map[asset.id] = {
'shares': asset.shares,
'current_price': current_price_map[asset.id],
'current_value': asset.shares * current_price_map[asset.id],
'target_allocation': 0
}
protfolio_value += asset_map[asset.id]['current_value']
table = prettytable.PrettyTable()
table.field_names = [
'TICKER', 'BUY AMOUNT', 'CURRENT_ALLOCATION', 'CURRENT_VALUE',
'TARGET_ALLOCATION', 'TARGET_VALUE', 'ALLOCATION_NAME'
]
rows = []
for alloc in allocations:
target_value = (protfolio_value +
float(contribution_amount)) * (alloc.target / 100)
active_asset = None
current_value = 0.0
for asset in alloc.assets:
current_value += asset_map[asset.id]['current_value']
if asset.is_active:
active_asset = asset
current_allocation = round((current_value / protfolio_value) * 100, 2)
buy_amount = round(target_value - current_value, 2)
rows.append([
active_asset.id, buy_amount, current_allocation,
round(current_value, 2), alloc.target,
round(target_value, 2), alloc.name
])
rows.sort(key=lambda x: x[0])
for row in rows:
table.add_row(row)
print(table)
print('\n')
def show_allocation():
# TODO replace with orm
# funds = db_cursor.execute('SELECT ticker, shares, target_allocation FROM funds').fetchall()
funds = db.session.query(models.allocation.Asset).all()
tickers = [fund.id for fund in funds]
fin = yahoofinancials.YahooFinancials(tickers)
current_price_map = fin.get_current_price()
current_price_map['SPAXX**'] = 1
fund_dict = {}
protfolio_value = 0.0
for fund in funds:
fund_dict[fund.id] = {
'shares': fund.shares,
'current_price': current_price_map[fund.id],
}
try:
fund_dict[fund.id][
'current_value'] = fund.shares * current_price_map[fund.id]
except:
fund_dict[fund.id]['current_value'] = fund.shares
protfolio_value += fund_dict[fund.id]['current_value']
allocations = db.session.query(models.allocation.Allocation).all()
table = prettytable.PrettyTable()
table.field_names = [
'NAME', 'CURRENT_ALLOCATION', 'TARGET_ALLOCATION', 'ACTIVE FUND'
]
for alloc in allocations:
current_allocation = 0.0
active_asset = None
for asset in alloc.assets:
current_allocation += round(
(fund_dict[asset.id]['current_value'] / protfolio_value) * 100,
2)
if asset.is_active:
active_asset = asset
table.add_row(
[alloc.name, current_allocation, alloc.target, active_asset.name])
print(table)
print('\n')
def get_company_financial(symbol='NaN',
frequency='annual',
statement_type=['balance', 'income', 'cash']):
if symbol == "NaN":
return 'please input symbol'
df = pd.DataFrame()
try:
stk = yahoofinancials.YahooFinancials(symbol)
except TypeError:
return 'please input symbol'
subject_list = list(
stk.get_financial_stmts(frequency, statement_type).keys())
for subject in subject_list:
globals()['df_' + subject] = pd.DataFrame()
try:
data = stk.get_financial_stmts(frequency,
statement_type)[subject][symbol]
except KeyError:
return 'please input symbol'
for num in range(len(data)):
date_col = list(data[num].keys())[0]
data[num][date_col]['date'] = date_col
globals()['df_' + subject] = globals()['df_' + subject].append(
pd.DataFrame([data[num][date_col]]), ignore_index=True)
if frequency != 'quarterly':
if len(statement_type) == 3:
df = pd.concat([
df_balanceSheetHistory, df_cashflowStatementHistory,
df_incomeStatementHistory
],
axis=1)
elif len(statement_type) == 2:
if 'balance' not in statement_type:
df = pd.concat(
[df_cashflowStatementHistory, df_incomeStatementHistory],
axis=1)
elif 'income' not in statement_type:
df = pd.concat(
[df_balanceSheetHistory, df_cashflowStatementHistory],
axis=1)
else:
df = pd.concat(
[df_balanceSheetHistory, df_incomeStatementHistory],
axis=1)
else:
if 'balance' in statement_type:
df = df_balanceSheetHistory
elif 'income' in statement_type:
df = df_incomeStatementHistory
else:
df = df_cashflowStatementHistory
else:
if len(statement_type) == 3:
df = pd.concat([
df_balanceSheetHistoryQuarterly,
df_cashflowStatementHistoryQuarterly,
df_incomeStatementHistoryQuarterly
],
axis=1)
elif len(statement_type) == 2:
if 'balance' not in statement_type:
df = pd.concat([
df_cashflowStatementHistoryQuarterly,
df_incomeStatementHistoryQuarterly
],
axis=1)
elif 'income' not in statement_type:
df = pd.concat([
df_balanceSheetHistoryQuarterly,
df_cashflowStatementHistoryQuarterly
],
axis=1)
else:
df = pd.concat([
df_balanceSheetHistoryQuarterly,
df_incomeStatementHistoryQuarterly
],
axis=1)
else:
if 'balance' in statement_type:
df = df_balanceSheetHistoryQuarterly
elif 'income' in statement_type:
df = df_incomeStatementHistoryQuarterly
else:
df = df_cashflowStatementHistoryQuarterly
return df
@author: kv83821
"""
import pandas as pd
import yfinance as yf
import yahoofinancials
from datetime import datetime, timedelta
date_last_train = (datetime.today() - timedelta(weeks=26)).strftime('%Y-%m-%d')
date_begin_train = (datetime.today() -
timedelta(weeks=520)).strftime('%Y-%m-%d')
date_last_test = datetime.today().strftime('%Y-%m-%d')
date_begin_test = (datetime.today() - timedelta(weeks=26)).strftime('%Y-%m-%d')
yahoo_financials = yahoofinancials.YahooFinancials('INFY.NS')
data_train = yahoo_financials.get_historical_price_data(
start_date=date_begin_train,
end_date=date_last_train,
time_interval='daily')
data_test = yahoo_financials.get_historical_price_data(
start_date=date_begin_test, end_date=date_last_test, time_interval='daily')
train_df = pd.DataFrame(data_train['INFY.NS']['prices'])
train_df = train_df.drop('date', axis=1).set_index('formatted_date')
train_df = train_df.rename(columns={'open': 'Open'})
test_df = pd.DataFrame(data_test['INFY.NS']['prices'])
test_df = test_df.drop('date', axis=1).set_index('formatted_date')
test_df = test_df.rename(columns={'open': 'Open'})
for i in range(1, len(tables_data)):
a1 = tables_data[i].select('td')[0].getText().replace("\n","")
a2 = tables_data[i].select('td')[1].getText().replace("\n","")
stocks = stocks.append(pd.DataFrame([[a1, a2]], columns = col_names))
stocks = stocks.reset_index(drop = True)
stocks.head(5)
# See links:
# https://pypi.org/project/yahoofinancials/
# https://matplotlib.org/3.1.1/gallery/widgets/slider_demo.html
yahoo_financials_stocks = yf.YahooFinancials(stocks['Ticker'])
# 1 Read and get prices:
weekly_stock_prices = yahoo_financials_stocks.get_historical_price_data('2015-01-01', '2019-11-01', 'weekly')
## A. Some exploration:
weekly_stock_prices['AAPL'].keys()
pd.DataFrame(weekly_stock_prices['AAPL']['prices'])
## B. Make up the panel:
my_stocks = list(weekly_stock_prices.keys())
df_prices = pd.DataFrame(columns = ['date', 'ticker', 'formatted_date', 'adjclose', 'volume'])
for tick in my_stocks:
# tick = my_stocks[0]
try:
import yahoofinancials as yf
import pickle
from src.valuation.valuation_fcff import *
from src.valuation.back_up.valuation_input_list import *
#%%
ticker = 'TSN'
yahoo_financials = yf.YahooFinancials(ticker)
read_from_yahoo = False
if read_from_yahoo:
## Get financial statements
#%%
# all_statement_data_qt = yahoo_financials.get_financial_stmts('quarterly', ['income', 'cash', 'balance'])
# all_statement_data_qt
#%%
all_statement_data_yr = yahoo_financials.get_financial_stmts(
'annual', ['income', 'cash', 'balance'])
with open('./data/20210207_TSN_all_statement_yr.pickle', 'wb') as handle:
pickle.dump(all_statement_data_yr,
handle,
protocol=pickle.HIGHEST_PROTOCOL)
else:
with open('./data/20210207_TSN_all_statement_yr.pickle', 'rb') as handle:
all_statement_data_yr = pickle.load(handle)
balance_sheet = all_statement_data_yr['balanceSheetHistory']['TSN']
current_balance_sheet = balance_sheet[0]
current_date = [*current_balance_sheet.keys()][0]
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import yfinance as yf
import yahoofinancials as yahoof
import datetime as date
#Using yahoofinancials
today = date.datetime.today()
# Get the data for the stock Tesla by specifying the stock ticker, start date, and end date
yahoo_financials = yahoof.YahooFinancials('TSLA')
data = yahoo_financials.get_historical_price_data(start_date='2015-01-01',
end_date='2020-01-01',
time_interval='daily')
# Create pandas datframe and format
tsla_df = pd.DataFrame(data['TSLA']['prices'])
tsla_df = tsla_df.drop('date', axis=1).set_index('formatted_date')
tsla_df_close = tsla_df['close']
# Print test
print(tsla_df.head())
print(tsla_df.shape)
print(tsla_df_close)
# Plot the close price
tsla_df_close.plot()
plt.show()