def create_simulated_portfolio(
portfolio: pd.DataFrame, sim_symbol: str = "SWPPX"
) -> pd.DataFrame:
"""Given a portfolio, create a parallel portfolio which has
all of the same contributions and withdrawals. In the parallel
simulated portfolio, all contributions are immediately used to
purchase the security indicated by the `sim_symbol`, and all
withdrawals are financed by selling this security.
"""
sim_port = port.init_portfolio(portfolio.index)
sim_port = port.init_symbol(sim_port, sim_symbol)
prices = quotes.get_price(sim_symbol, sim_port.index)
for column in ["contributions", "withdrawals"]:
sim_port[column] = portfolio[column].copy()
deposits = port.get_deposits(portfolio)
for date, amt in deposits[deposits != 0].iteritems():
qty = np.abs(amt) / prices.loc[date]
sign = -1 if amt < 0 else 1
sim_port.loc[date:, f"{sim_symbol}"] += sign * qty
# We've recorded simulated purchases, but the simulated security
# would also have generated dividends.
sim_port = add_sim_dividend(sim_port, sim_symbol)
port.total_portfolio(sim_port)
return sim_port
def total_portfolio(portfolio: pd.DataFrame) -> pd.DataFrame:
symbols = [c for c in portfolio.columns if c == c.upper()]
portfolio["_total"] = 0.0
for symbol in symbols:
price = quotes.get_price(symbol, portfolio.index)
portfolio[f"{symbol}_value"] = portfolio[symbol] * price
portfolio["_total"] += portfolio[f"{symbol}_value"]
return portfolio
def record_inv_action(portfolio: pd.DataFrame,
action: pd.Series) -> pd.DataFrame:
"""Mark the results of an AceMoney investment transaction in a portfolio
`portfolio`: pd.DataFrame
Initialized as per `init_portfolio`
`action`: pd.Series
A row from the all-investment-transactions AceMoney export
"""
port.init_symbol(portfolio, symbol=action['Symbol'])
if not pd.isnull(action['Dividend']) and action['Dividend'] != 0:
portfolio.loc[action.Date:,
f"{action.Symbol}_dist"] += action['Dividend']
portfolio.loc[action.Date:, f"_total_dist"] += action.Dividend
if not pd.isnull(action['Quantity']) and action['Quantity'] != 0:
sign = -1 if action['Action'] in ['Sell', 'Remove Shares'] else 1
portfolio.loc[action.Date:,
f"{action.Symbol}"] += sign * action['Quantity']
if not pd.isnull(action['Total']) and action['Total'] != 0:
sign = -1 if action['Action'] == 'Buy' else 1
portfolio.loc[action['Date']:, "cash"] += sign * action['Total']
if action['Action'] == 'Add Shares' and '__contribution__' in action[
'Comment']:
price = quotes.get_price(action.Symbol, [action.Date]).values[0]
log.debug(f"Contribution of {action.Quantity} shares of "
f"{action.Symbol} @ {price} per share.")
portfolio.loc[action.Date:,
'contributions'] += price * action['Quantity']
if action['Action'] == 'Add Shares' and '__dividend__' in action['Comment']:
value = (quotes.get_price(action.Symbol, [action.Date]).values[0] *
action['Quantity'])
log.debug(f"Dividend of {action.Quantity} shares of {action.Symbol} "
f"of {action.Date} is ${value}.")
portfolio.loc[action.Date:, f"{action.Symbol}_dist"] += value
portfolio.loc[action.Date:, f"_total_dist"] += value
return portfolio
def record_inv_action(portfolio: pd.DataFrame,
action: pd.Series) -> pd.DataFrame:
"""Mark the results of an AceMoney investment transaction in a portfolio
`portfolio`: pd.DataFrame
Initialized as per `init_portfolio`
`action`: pd.Series
A row from the all-investment-transactions AceMoney export
"""
port.init_symbol(portfolio, symbol=action["Symbol"])
if not pd.isnull(action["Dividend"]) and action["Dividend"] != 0:
portfolio.loc[action.Date:,
f"{action.Symbol}_dist"] += action["Dividend"]
portfolio.loc[action.Date:, f"_total_dist"] += action.Dividend
if not pd.isnull(action["Quantity"]) and action["Quantity"] != 0:
sign = -1 if action["Action"] in ["Sell", "Remove Shares"] else 1
portfolio.loc[action.Date:,
f"{action.Symbol}"] += sign * action["Quantity"]
if not pd.isnull(action["Total"]) and action["Total"] != 0:
sign = -1 if action["Action"] == "Buy" else 1
portfolio.loc[action["Date"]:, "cash"] += sign * action["Total"]
if action["Action"] == "Add Shares" and "__contribution__" in action[
"Comment"]:
price = quotes.get_price(action.Symbol, [action.Date]).values[0]
log.debug(f"Contribution of {action.Quantity} shares of "
f"{action.Symbol} @ {price} per share.")
portfolio.loc[action.Date:,
"contributions"] += price * action["Quantity"]
if action["Action"] == "Add Shares" and "__dividend__" in action["Comment"]:
value = (quotes.get_price(action.Symbol, [action.Date]).values[0] *
action["Quantity"])
log.debug(f"Dividend of {action.Quantity} shares of {action.Symbol} "
f"of {action.Date} is ${value}.")
portfolio.loc[action.Date:, f"{action.Symbol}_dist"] += value
portfolio.loc[action.Date:, f"_total_dist"] += value
return portfolio
def add_sim_dividend(portfolio: pd.DataFrame, sim_symbol: str) -> pd.DataFrame:
"""Look at a portfolio and add the dividends that you would have gotten
from a particular security. For simulated portfolios.
"""
div = quotes.get_dividend(sim_symbol, portfolio.index)
for date, amount in div.iteritems():
if date in portfolio.index:
price = quotes.get_price(sim_symbol, [date]).values[0]
# Create a Series with 0s before the date, and the value of
# the distribution at and after the date.
value = portfolio.loc[[date], sim_symbol] * amount
value = value.reindex(portfolio.index, method="ffill").fillna(0)
qty = value / price
portfolio[f"{sim_symbol}_dist"] += value
portfolio[sim_symbol] += qty
portfolio["_total_dist"] += value
return portfolio
def _include_reinvest_dividend(
buy_transactions: pd.DataFrame, dates: pd.DatetimeIndex
) -> pd.DataFrame:
"""Include "Reinvest Dividend" transactions in a buy transactions DataFrame
This assumes that the `buy_transactions` input is a
single security in a single account.
"""
trans = buy_transactions.set_index("Date").sort_index(ascending=True)
assert len(buy_transactions["Symbol"].unique()) == 1
assert len(buy_transactions["Account"].unique()) == 1
symbol = buy_transactions["Symbol"].unique()[0]
div = quotes.get_dividend(symbol, dates).sort_index()
prices = quotes.get_price(symbol, div.index)
div_trans = pd.DataFrame(columns=TRANS_HEADER) # Empty DF for appending
for date, div_value in div.iteritems():
n_shares = trans.loc[:date, "Quantity"].sum() + div_trans["Quantity"].sum()
div_trans_row = pd.DataFrame(
{
"Date": date,
"Action": "Reinvest Dividend",
"Symbol": symbol,
"Commission": 0.0,
"Total": 0.0,
"Comment": "",
"Account": trans["Account"].unique()[0],
"Dividend": round(n_shares * div_value, 2),
"Price": prices.loc[date],
"Quantity": round((n_shares * div_value) / prices.loc[date], 4),
},
columns=TRANS_HEADER,
index=[len(div_trans)],
)
div_trans = div_trans.append(div_trans_row)
assert len(div_trans) == len(div) == len(prices)
return (
buy_transactions.append(div_trans)
.sort_values("Date", ascending=True)
.reset_index(drop=True)
)
def _create_single_buy_transactions(dates: pd.DatetimeIndex,
contribution: float, symbol: str,
account: str) -> pd.DataFrame:
"""Create buy transactions in a format matching
AceMoney's investment transactions
"""
prices = quotes.get_price(symbol, dates)
trans = pd.DataFrame(
{
"Date": dates,
"Action": "Buy",
"Symbol": symbol,
"Account": account,
"Dividend": 0.,
"Price": prices,
"Quantity": np.round(contribution / prices, 4),
"Commission": 0.,
"Total": contribution,
"Comment": ""
},
columns=TRANS_HEADER)
return trans.reset_index(drop=True)
