def test_performance_with_anchor():
last_trading_days = (capon.stock(
"AMZN", range="1mo",
interval="1d")["timestamp"].dt.strftime("%Y-%m-%d").tolist())
my_portfolio = Portfolio([
Lot(last_trading_days[-5], "AMZN", 2, 3263.38),
Lot(last_trading_days[-4], "GOOGL", 3, 1884.15),
])
anchor1, anchor2 = last_trading_days[-2], last_trading_days[-1]
performance1 = my_portfolio.performance(anchor=anchor1)
performance2 = my_portfolio.performance(anchor=anchor2)
npt.assert_array_equal(
performance1["symbol"].value_counts()[["AMZN", "GOOGL"]], [3, 2])
npt.assert_array_equal(
performance2["symbol"].value_counts()[["AMZN", "GOOGL"]], [4, 3])
pdt.assert_frame_equal(
performance2[performance2["timestamp"] <= anchor1].reset_index(
drop=True),
performance1.reset_index(drop=True),
)
def test_status():
my_portfolio = Portfolio([
Lot("2020-03-20", "AMZN", 2, 1888.86),
Lot("2020-03-23", "GOOGL", 3, 1037.89),
Lot("2020-03-27", "ZM", 20, 150.29),
])
my_portfolio.status()
def test_multiple_lots_per_ticker_different_time():
my_portfolio = Portfolio([
Lot("2020-03-20", "AMZN", 2, 1888.86),
Lot("2020-03-23 10:00:00", "GOOGL", 3, 1037.89),
Lot("2020-04-23 10:00:01", "GOOGL", 6, 1270.00),
])
status = my_portfolio.status()
pdt.assert_series_equal(status["symbol"].value_counts(),
my_portfolio.lots["symbol"].value_counts())
def test_cost():
my_portfolio = Portfolio([
Lot("2020-03-20", "AMZN", 2, 1888.86),
Lot("2020-03-23", "GOOGL", 3, 1037.89),
Lot("2020-03-27", "ZM", 20, 150.29),
])
lots = my_portfolio.lots
assert len(lots) == 3
npt.assert_array_equal(lots["cost"], lots["quantity"] * lots["price"])
def test_multiple_lots_per_ticker():
my_portfolio = Portfolio([
Lot("2020-03-20", "AMZN", 2, 1888.86),
Lot("2020-03-23", "GOOGL", 3, 1037.89),
Lot("2020-03-27", "ZM", 20, 150.29),
Lot("2020-04-24", "GOOGL", 6, 1270.00),
])
# performance = my_portfolio.performance()
status = my_portfolio.status()
pdt.assert_series_equal(status["symbol"].value_counts(),
my_portfolio.lots["symbol"].value_counts())
def test_status_with_multiple_timezones():
tz_portfolio = Portfolio([
Lot("2020-01-01", "3690.HK", 1, 10),
Lot("2020-01-01", "RATI-L.TA", 2, 20),
Lot("2020-01-01", "LHA.DE", 3, 30),
Lot("2020-01-01", "IVR", 4, 40),
])
performance = tz_portfolio.performance()
print(performance.dtypes)
status = tz_portfolio.status()
print(status[["timestamp_buy", "symbol", "timestamp", "price"]])
def _test_averaged_price():
my_portfolio = Portfolio([
Lot("2020-03-20", "AMZN", 2, 1888.86),
Lot("2020-03-23", "GOOGL", 1, 1037.89),
Lot("2020-03-27", "ZM", 20, 150.29),
Lot("2020-04-24", "GOOGL", 2, 1270.00),
Lot("2020-04-25", "GOOGL", 3, 1280.00),
Lot("2020-04-26", "GOOGL", 4, 1280.00),
])
my_portfolio.lots.sort_values("timestamp").groupby("symbol").agg({
"timestamp":
"last",
"quantity":
"sum",
"cost":
"sum",
}).reset_index().assign(
average_price=lambda df: df["cost"] / df["quantity"])
# Sell
symbol, quantity = "GOOGL", 5
my_portfolio.lots.sort_values("timestamp")
my_portfolio.status()
(my_portfolio.lots.sort_values("timestamp").query(f'symbol == "{symbol}"')
["quantity"].cumsum() - quantity)
import pandas as pd
pd.set_option("display.max_rows", 500)
pd.set_option("display.max_columns", 500)
pd.set_option("display.width", 1000)