def get_benchmark(self, benchmark_ticker="^GSPC"):
if benchmark_ticker == "sp500" or benchmark_ticker == "^GSPC":
_ticker = "^GSPC"
else:
_ticker = "^GSPC" # S&P 500 as default
# create security class object for benchmark ticker
_benchmark = Security(_ticker, start=self.min_date, end=self.max_date)
# calculate returns for the benchmark
_benchmark.data, _ = returns_column(df=_benchmark.data, column="Close")
self.benchmark = _benchmark
def add_security_archive(self, ticker, min_date="2000-01-01"):
if ticker in self.securities:
self.securities_archive[ticker] = self.securities[ticker]
self.tickers_archive.append(ticker)
else:
_security = Security(ticker, start=min_date, end=self.date)
self.securities_archive[_security.ticker] = _security
self.tickers_archive.append(ticker)
self.prices[ticker] = deque()
self.prices_lifo[ticker] = deque()
self.prices_fifo[ticker] = deque()
def positions(self):
# you have to have one security in the portfolio for meaningful output
if len(self.securities) > 0:
# sum up by ticker
_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)
_df.loc[_df.Transaction == "sell",
"TradeValue"] = _df.loc[_df.Transaction == "sell",
"TradeValue"].apply(lambda x: -x)
self.positions_df = _df.groupby(by=["Ticker"])["Quantity",
"TradeValue"].sum()
# sum up purchases
self.positions_df["Bought"] = (
_df.loc[_df.Transaction == "buy", :].groupby(
by=["Ticker"])["Quantity"].sum())
self.positions_df["Invested"] = (
_df.loc[_df.Transaction == "buy", :].groupby(
by=["Ticker"])["TradeValue"].sum())
# sum up sales
self.positions_df["Sold"] = (
_df.loc[_df.Transaction == "sell", :].groupby(
by=["Ticker"])["Quantity"].sum())
self.positions_df["Devested"] = (
_df.loc[_df.Transaction == "sell", :].groupby(
by=["Ticker"])["TradeValue"].sum())
# check if sum over volume of a security is < 0
for index, row in self.positions_df.iterrows():
if row["Quantity"] < 0.0:
print("Negative volume encountered: {0:5}\t{1}".format(
index, row["Quantity"]))
# join in last price and names of securities
for ticker in set(self.positions_df.index):
if ticker in self.tickers:
self.positions_df.loc[self.positions_df.index == ticker,
"LastPrice"] = self.securities[
ticker].get_last_price()
self.positions_df.loc[
self.positions_df.index == ticker,
"Description"] = self.securities[ticker].name
elif ticker in self.tickers_archive:
self.positions_df.loc[
self.positions_df.index == ticker,
"LastPrice"] = self.securities_archive[
ticker].get_last_price()
self.positions_df.loc[
self.positions_df.index == ticker,
"Description"] = self.securities_archive[ticker].name
else:
_security = Security(ticker)
self.positions_df.loc[
self.positions_df.index == ticker,
"LastPrice"] = _security.get_last_price()
self.positions_df.loc[self.positions_df.index == ticker,
"Description"] = _security.name
self.positions_df["CurrentValue"] = (
self.positions_df["LastPrice"] * self.positions_df["Quantity"])
# sum up dividends by ticker
self.positions_df["Dividends"] = self.dividends.groupby(
by="Ticker")["Amount"].sum()
# self.positions_df['AvgPrice'] = self.positions_df['TradeValue'] / (1.0*self.positions_df['Quantity'])
self.positions_df.fillna(0.0, inplace=True)
self.positions_df["Return"] = (self.positions_df["CurrentValue"] -
self.positions_df["TradeValue"] +
self.positions_df["Dividends"])
self.positions_df["PercentGrowth"] = (
100.0 * self.positions_df["Return"] /
self.positions_df["Invested"])
self.positions_df.PercentGrowth.replace([np.inf, -np.inf],
np.nan,
inplace=True)
print(self.positions_df[[
"Quantity",
"Bought",
"Sold",
"CurrentValue",
"Invested",
"Devested",
"Dividends",
"Return",
"PercentGrowth",
"Description",
]].sort_values(by=["CurrentValue", "Invested"], ascending=False))
else:
print("No positions in portfolio.")