def load(self, datadir="../data/", start=None, end=None):
"""
Tries to load from csv first, then pulls from Yahoo!
"""
filepath = "{0}{1}.csv".format(datadir, self.ticker)
print("Checking {}".format(filepath))
if start is None:
start = self.start
else:
start = standard_date_format(last_trading_day(start))
if end is None:
end = self.end
else:
end = standard_date_format(last_trading_day(end))
if path.isfile(filepath):
self.data = read_yahoo_csv(path=filepath)
csv_start = standard_date_format(self.data.index.min())
csv_end = standard_date_format(self.data.index.max())
if (pd.to_datetime(csv_end) < pd.to_datetime(end)) | (
pd.to_datetime(csv_start) > pd.to_datetime(start)
):
_refresh_success = self.refresh(
datadir=datadir, start=min(start, csv_start), end=max(end, csv_end)
)
else:
_refresh_success = 1
if _refresh_success:
self.data = read_yahoo_csv(path=filepath, startdate=start, enddate=end)
else:
self.data = retrieve_yahoo_data(
ticker=self.ticker, startdate=start, enddate=end
)
self.save(filename="{}.csv".format(self.ticker), datadir=datadir)
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 refresh(self, datadir="../data/", start=None, end=None):
"""
Tries to load from csv first, then pulls from Yahoo!
"""
if start is None:
start = self.start
else:
start = standard_date_format(last_trading_day(start))
if end is None:
end = self.end
else:
end = standard_date_format(last_trading_day(end))
try:
self.data = retrieve_yahoo_data(
ticker=self.ticker, startdate=start, enddate=end
)
self.save(filename="{}.csv".format(self.ticker), datadir=datadir)
return 1
except:
print("Refresh failed")
return 0
def modify_quantity(self, date, quantity):
date = last_trading_day(date)
try:
return quantity * self.data.loc[self.data.index == date, "Modifier"].values[0]
except:
return quantity
def get_price_at(self, date, column="Close"):
date = last_trading_day(date)
return self.data.loc[self.data.index == date, column].values[0]
def parse_portfolio(df=None, p=None):
"""
Takes a dataframe with transactions and performs those
on a given portfolio
Parameters
==========
df : input dataframe
p : portfolio (Portfolio class object)
Returns
=======
p : modified portfolio (Portfolio class object)
"""
# use a lower bound on the minimum number of days pulled
minimum_date_for_data = last_trading_day() - timedelta(weeks=1)
# put input dataframe(s) in list
dfs = []
if type(df) == pd.core.frame.DataFrame:
dfs.append(df)
else:
dfs.extend(df)
# loop through list of dataframes
Tickers_all = []
for df in dfs:
Tickers_all = Tickers_all + list(df.Ticker.values)
# load data for all securities
for ticker in list(set(Tickers_all)):
if ticker:
if ticker not in p.securities_archive:
if str(ticker).isalnum() & (str(ticker) != "nan"):
print("Adding ", ticker)
min_date = minimum_date_for_data
for df in dfs:
if ticker in list(set(df.Ticker)):
first_date = min(df.loc[df.Ticker == ticker,
"Date"].values)
min_date = min(first_date, min_date)
p.add_security_archive(ticker, min_date)
for df in dfs:
# define a priority for transaction types so ordering makes sense
df.loc[df.Transaction == "deposit", "Priority"] = 1
df.loc[df.Transaction == "Contribution", "Priority"] = 1
df.loc[df.Transaction == "Funds Received", "Priority"] = 1
df.loc[df.Transaction == "Conversion (incoming)", "Priority"] = 1
df.loc[df.Transaction == "buy", "Priority"] = 2
df.loc[df.Transaction == "Buy", "Priority"] = 2
df.loc[df.Transaction == "Reinvestment", "Priority"] = 2
df.loc[df.Transaction == "dividend", "Priority"] = 3
df.loc[df.Transaction == "Dividend", "Priority"] = 3
df.loc[df.Transaction == "sell", "Priority"] = 4
df.loc[df.Transaction == "Sell", "Priority"] = 4
df.loc[df.Transaction == "withdraw", "Priority"] = 5
df.loc[df.Transaction == "Distribution", "Priority"] = 5
df.sort_values(by=["Date", "Priority"], inplace=True)
for index, row in df.iterrows():
if row.notnull()["Date"]:
# print(row['Date'], row['Transaction'], row['Ticker'], row['Currency'], row['Price'], row['Quantity'])
if str.lower(row["Transaction"]) == "buy":
p.buy_security(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=row["Price"],
quantity=row["Quantity"],
)
elif str.lower(row["Transaction"]) == "sell":
p.sell_security(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=row["Price"],
quantity=row["Quantity"],
)
# FINRA fee of $.000119 per share up to $5.95
#_FINRAfee = min(
# max(ceil(0.0119 * row["Quantity"]), 1.0) / 100.0, 5.95
#)
# SEC fee of $.000013 per trade of up to $1M
#if not np.isnan(row["Price"]):
# _SECfee = max(ceil(row["Quantity"] * row["Price"] / 800.0), 1.0) / 100.0
#else:
# _SECfee = 0.000013 # need to find a better place to put this fee where price is known
#p.wallet = p.wallet.append(
# {"Date": row["Date"], "Change": -_FINRAfee - _SECfee},
# ignore_index=True,
#)
elif str.lower(row["Transaction"]) == "deposit":
p.deposit_cash(
date=row["Date"],
currency=row["Currency"],
price=row["Price"],
quantity=row["Quantity"],
)
elif str.lower(row["Transaction"]) == "withdraw":
p.withdraw_cash(
date=row["Date"],
currency=row["Currency"],
price=row["Price"],
quantity=row["Quantity"],
)
elif row["Transaction"] == "Dividend":
p.dividend(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=1.0,
quantity=row["Dollars"],
)
elif row["Transaction"] == "dividend":
p.dividend(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=1.0,
quantity=row["Quantity"],
)
else:
pass
else:
pass
return p
def parse_portfolio_vanguard(df=None, p=None):
"""
Takes a dataframe with transactions in Vanguard format
and performs those on a given portfolio
Parameters
==========
df : input dataframe
p : portfolio (Portfolio class object)
Returns
=======
p : modified portfolio (Portfolio class object)
"""
# use a lower bound on the minimum number of days pulled
minimum_date_for_data = last_trading_day() - timedelta(weeks=1)
# put input dataframe(s) in list
dfs = []
if type(df) == pd.core.frame.DataFrame:
dfs.append(df)
else:
dfs.extend(df)
# loop through list of dataframes
Tickers_all = []
for df in dfs:
Tickers_all = Tickers_all + list(df.Ticker.values)
# load data for all securities
for ticker in list(set(Tickers_all)):
if ticker:
if ticker not in p.securities_archive:
if str(ticker).isalnum() & (str(ticker) != "nan"):
print("Adding ", ticker)
min_date = minimum_date_for_data
for df in dfs:
if ticker in list(set(df.Ticker)):
first_date = min(df.loc[df.Ticker == ticker,
"Date"].values)
min_date = min(first_date, min_date)
p.add_security_archive(ticker, min_date)
for df in dfs:
# define a priority for transaction types so ordering makes sense
df.loc[df.Transaction == "deposit", "Priority"] = 1
df.loc[df.Transaction == "Contribution", "Priority"] = 1
df.loc[df.Transaction == "Funds Received", "Priority"] = 1
df.loc[df.Transaction == "Conversion (incoming)", "Priority"] = 1
df.loc[df.Transaction == "buy", "Priority"] = 2
df.loc[df.Transaction == "Buy", "Priority"] = 2
df.loc[df.Transaction == "Reinvestment", "Priority"] = 2
df.loc[df.Transaction == "Reinvestment (LT)", "Priority"] = 2
df.loc[df.Transaction == "Reinvestment (ST)", "Priority"] = 2
df.loc[df.Transaction == "dividend", "Priority"] = 3
df.loc[df.Transaction == "Dividend", "Priority"] = 3
df.loc[df.Transaction == "Capital gain (LT)", "Priority"] = 3
df.loc[df.Transaction == "Capital gain (ST)", "Priority"] = 3
df.loc[df.Transaction == "sell", "Priority"] = 4
df.loc[df.Transaction == "Sell", "Priority"] = 4
df.loc[df.Transaction == "Withdrawal", "Priority"] = 5
df.loc[df.Transaction == "withdraw", "Priority"] = 5
df.loc[df.Transaction == "Distribution", "Priority"] = 5
df.sort_values(by=["Date", "Priority"], inplace=True)
for index, row in df.iterrows():
if row.notnull()["Date"]:
# print(row['Date'], row['Transaction'], row['Ticker'], row['Currency'], row['Price'], row['Quantity'], row['Dollars'])
if (row["Transaction"] == "Buy"
or row["Transaction"] == "buy"):
p.buy_security(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=row["Price"],
quantity=row["Quantity"],
)
elif (row["Transaction"] == "Sell"
or row["Transaction"] == "sell"):
p.sell_security(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=row["Price"],
quantity=-row["Quantity"],
) # note the minus sign
elif (row["Transaction"] == "Contribution"
or row["Transaction"] == "Funds Received"
or row["Transaction"] == "Conversion (incoming)"
or row["Transaction"] == "deposit"):
p.deposit_cash(
date=row["Date"],
currency=row["Currency"],
price=1.0,
quantity=row["Dollars"],
)
elif (row["Transaction"] == "Distribution"
or row["Transaction"] == "Withdrawal"
or row["Transaction"] == "withdraw"):
p.withdraw_cash(
date=row["Date"],
currency=row["Currency"],
price=1.0,
quantity=-1.0 * row["Dollars"],
)
elif (row["Transaction"] == "Dividend"
or row["Transaction"] == "Capital gain (LT)"
or row["Transaction"] == "Capital gain (ST)"):
p.dividend(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=1.0,
quantity=row["Dollars"],
)
elif (row["Transaction"] == "Reinvestment"
or row["Transaction"] == "Reinvestment (LT)"
or row["Transaction"]
== "Reinvestment (ST)") and row["Quantity"] != 0:
p.buy_security(
date=row["Date"],
ticker=row["Ticker"],
currency=row["Currency"],
price=row["Price"],
quantity=row["Quantity"],
)
else:
pass
else:
pass
return p