def retrieve_yahoo_data(ticker=None, startdate="20000101", enddate=None):
"""
Downloads quote, dividend, and split data from Yahoo! Finance for a security.
"""
if enddate == None:
enddate = todays_date()
# convert input to yahoo standard
startdate = yahoo_date_format(startdate)
enddate = yahoo_date_format(enddate)
df = retrieve_yahoo_quote(ticker=ticker,
startdate=startdate,
enddate=enddate)
df_dividend = retrieve_yahoo_dividends(ticker=ticker,
startdate=startdate,
enddate=enddate)
df_splits = retrieve_yahoo_splits(ticker=ticker,
startdate=startdate,
enddate=enddate)
df = df.join(df_dividend, how="left").fillna(0.0)
df = df.join(df_splits["Modifier"],
how="left").fillna(method="bfill").fillna(1.0)
return df
def buy_security(self,
date,
ticker,
currency="USD",
price=None,
quantity=0):
# potentially add ticker to list
if ticker not in self.tickers:
self.add_security(ticker)
# print('adding', ticker)
# and to archive
if ticker not in self.tickers_archive:
self.add_security_archive(ticker)
# get closing price of security for transaction date if price not provided
try:
if np.isnan(price):
price = self.securities[ticker].get_price_at(date)
except:
if price is None:
price = self.securities[ticker].get_price_at(date)
# modify quantity for subsequent stock splits
quantity = self.securities[ticker].modify_quantity(date, quantity)
for i in range(np.int(quantity)):
self.prices[ticker].append(price)
self.prices_lifo[ticker].append(price)
self.prices_fifo[ticker].append(price)
# store point in time value in wallet
self.wallet = self.wallet.append(
{
"Date": date,
"Change": -1.0 * price * quantity
},
ignore_index=True).sort_values("Date")
# store transaction in df
self.transactions = self.transactions.append(
{
"Date": date,
"Transaction": "buy",
"Ticker": ticker,
"Currency": currency,
"Price": 1.0 * price,
"Quantity": 1.0 * quantity,
"TradeValue": 1.0 * price * quantity,
},
ignore_index=True,
sort=False,
).sort_values("Date")
self.cash = self.get_cash(date=todays_date())
print("buying {0:.2f} {1} (new balance: {2:.2f} {3})".format(
quantity, ticker, self.cash, currency))
def read_treasury_csv(path=None, startdate="2000-01-01", enddate=None):
"""
Read locally stored csv with data from US Deparment of the Treasury.
"""
if enddate == None:
enddate = todays_date()
# convert dates to pandas format
startdate = standard_date_format(startdate)
enddate = standard_date_format(enddate)
_df = pd.read_csv(path, index_col="Date", parse_dates=True)
return _df.loc[(_df.index >= startdate) & (_df.index <= enddate)]
def read_yahoo_csv(path=None, startdate="2000-01-01", enddate=None):
"""
Read locally stored csv with data from Yahoo! Finance for a security.
"""
if enddate == None:
enddate = todays_date()
# convert dates to pandas format
startdate = standard_date_format(startdate)
enddate = standard_date_format(enddate)
df = pd.read_csv(path, index_col="Date", parse_dates=True)
return df.loc[(df.index >= startdate) & (df.index <= enddate)]
def dividend(self, date, ticker="", currency="USD", price=1.0, quantity=0):
"""
"""
self.wallet = self.wallet.append(
{
"Date": date,
"Change": 1.0 * price * quantity
},
ignore_index=True).sort_values("Date")
# store transaction in df
self.transactions = self.transactions.append(
{
"Date": date,
"Transaction": "dividend",
"Ticker": ticker,
"Currency": currency,
"Price": 1.0 * price,
"Quantity": 1.0 * quantity,
"TradeValue": 1.0 * price * quantity,
},
ignore_index=True,
sort=False,
).sort_values("Date")
# store dividends in df
self.dividends = self.dividends.append(
{
"Date": date,
"Ticker": ticker,
"Amount": 1.0 * price * quantity
},
ignore_index=True,
).sort_values("Date")
self.cash = self.get_cash(date=todays_date())
if ticker == "" or ticker != ticker:
print("interest {0:.2f} {2} (new balance: {1:.2f} {2})".format(
quantity * price, self.cash, currency))
else:
print("dividend {0} {1:.2f} {3} (new balance: {2:.2f} {3})".format(
ticker, quantity * price, self.cash, currency))
def withdraw_cash(self, date, currency="USD", price=1.0, quantity=0):
"""
Takes amount of quantity*price out of wallet
"""
self.wallet = self.wallet.append(
{
"Date": date,
"Change": -1.0 * price * quantity
},
ignore_index=True).sort_values("Date")
# store transaction in df
self.transactions = self.transactions.append(
{
"Date": date,
"Transaction": "withdraw",
"Ticker": np.nan,
"Currency": currency,
"Price": 1.0 * price,
"Quantity": 1.0 * quantity,
"TradeValue": 1.0 * price * quantity,
},
ignore_index=True,
sort=False,
).sort_values("Date")
# store payments in df
self.payments = self.payments.append(
{
"Date": date,
"In": 0.0,
"Out": 1.0 * price * quantity
},
ignore_index=True).sort_values("Date")
self.cash = self.get_cash(date=todays_date())
if self.cash < 0.0:
print("Warning, cash balance negative: {0:.2f} {1}".format(
self.cash, currency))
else:
print("withdrawing {0:.2f} {2} (new balance: {1:.2f} {2})".format(
quantity * price, self.cash, currency)) #
def deposit_cash(self, date, currency="USD", price=1.0, quantity=0):
"""
Adds an amount of quantity*price to the wallet
Price acts as exchange rate if currency is not USD
"""
self.wallet = self.wallet.append(
{
"Date": date,
"Change": 1.0 * price * quantity
},
ignore_index=True).sort_values("Date")
# store transaction in df
self.transactions = self.transactions.append(
{
"Date": date,
"Transaction": "deposit",
"Ticker": np.nan,
"Currency": currency,
"Price": 1.0 * price,
"Quantity": 1.0 * quantity,
"TradeValue": 1.0 * price * quantity,
},
ignore_index=True,
sort=False,
).sort_values("Date")
# store payment in df
self.payments = self.payments.append(
{
"Date": date,
"In": 1.0 * price * quantity,
"Out": 0.0
},
ignore_index=True).sort_values("Date")
self.cash = self.get_cash(date=todays_date())
print("depositing {0:.2f} {2} (new balance: {1:.2f} {2})".format(
quantity * price, self.cash, currency))
def __init__(self, name, start=None, end=None):
super().__init__(name)
if start is None:
self.start = standard_date_format(last_trading_day("2000-01-01"))
else:
self.start = standard_date_format(last_trading_day(start))
if end is None:
self.end = standard_date_format(last_trading_day(todays_date()))
else:
self.end = standard_date_format(last_trading_day(end))
self.ticker = name
self.set_name(name)
self.load(start=self.start, end=self.end)
self.get_last_price()
self.get_max_price()
self.get_min_price()
self.get_median_price()
self.get_mean_price()
self.get_std_price()
self.dividends = 0.0
self.benchmark_ticker = "sp500"
self.benchmark = None
def __init__(self, name):
super().__init__(name)
self.securities = {}
self.securities_archive = {}
self.tickers = []
self.tickers_archive = []
self.dividends = pd.DataFrame(columns=["Date", "Ticker", "Amount"])
self.payments = pd.DataFrame(columns=["Date", "In", "Out"])
self.wallet = pd.DataFrame(columns=["Date", "Change"])
self.total_portfolio_value = 0.0
self.total_security_value = 0.0
self.cash = 0.0
self.return_value = 0.0
self.return_rate = 0.0
self.prices = {}
self.prices_fifo = {}
self.prices_lifo = {}
self.index = 0
self.benchmark_ticker = "sp500"
self.benchmark = None
self.transactions = pd.DataFrame(columns=[
"Date", "Transaction", "Ticker", "Currency", "Price", "Quantity"
])
self.date = standard_date_format(todays_date())
def retrieve_treasury_yield_curve_rates(
url="https://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yieldAll",
startdate="20000101",
enddate=None,
):
"""
Download yield curve rates data from US Deparment of the Treasury.
"""
if enddate == None:
enddate = todays_date()
# convert dates to pandas format
startdate = standard_date_format(startdate)
enddate = standard_date_format(enddate)
_headers = {
"User-Agent":
"Mozilla/5.0 (X11; U; Linux i686) Gecko/20071127 Firefox/2.0.0.11"
}
# use request to retrieve data
req = urllib.request.Request(url, headers=_headers)
f = urllib.request.urlopen(req)
html_data = f.read().decode("utf-8")
soup = BeautifulSoup(html_data, "lxml")
table = soup.find("table", {"class": "t-chart"})
_df = pd.read_html(str(table),
header=0,
index_col="Date",
parse_dates=True)[0]
return _df.loc[(_df.index >= startdate) & (_df.index <= enddate)]
def sell_security(self,
date,
ticker,
currency="USD",
price=None,
quantity=0):
# get closing price of security for transaction date if price not provided
try:
if np.isnan(price):
price = self.securities[ticker].get_price_at(date)
except:
if price is None:
price = self.securities[ticker].get_price_at(date)
# modify quantity for subsequent stock splits
quantity = self.securities[ticker].modify_quantity(date, quantity)
# remove number of securities from prices deque
for i in range(np.int(quantity)):
_ = self.prices_fifo[ticker].popleft()
_ = self.prices_lifo[ticker].pop()
# make sure security is fully removed, correct for rounding errors
_df = self.transactions.loc[self.transactions.Transaction.isin(
("buy", "sell")), :].copy()
_df.loc[_df.Transaction == "sell",
"Quantity"] = _df.loc[_df.Transaction == "sell",
"Quantity"].apply(lambda x: -x)
# set quantity to exactly match remaining quantity in portfolio
if _df.groupby(
by=["Ticker"])["Quantity"].sum()[ticker] <= quantity * 1.0001:
quantity = _df.groupby(by=["Ticker"])["Quantity"].sum()[ticker]
# store point in time value in wallet
self.wallet = self.wallet.append(
{
"Date": date,
"Change": 1.0 * price * quantity
},
ignore_index=True).sort_values("Date")
# store transaction in df
self.transactions = self.transactions.append(
{
"Date": date,
"Transaction": "sell",
"Ticker": ticker,
"Currency": currency,
"Price": 1.0 * price,
"Quantity": 1.0 * quantity,
"TradeValue": 1.0 * price * quantity,
},
ignore_index=True,
sort=False,
).sort_values("Date")
self.cash = self.get_cash(date=todays_date())
print("selling {0:.2f} {1} (new balance: {2:.2f} {3})".format(
quantity, ticker, self.cash, currency))
# potentially remove ticker from list
_df = self.transactions.loc[self.transactions.Transaction.isin(
("buy", "sell")), :].copy()
_df.loc[_df.Transaction == "sell",
"Quantity"] = _df.loc[_df.Transaction == "sell",
"Quantity"].apply(lambda x: -x)
if _df.groupby(by=["Ticker"])["Quantity"].sum()[ticker] <= 0.0001:
self.remove_security(ticker)
