def trades():
trades = []
index = [
"2018-06-06 00:00:00",
"2018-06-06 00:01:00",
"2018-06-06 00:02:00",
"2018-06-06 00:03:00",
"2018-06-06 00:03:00",
"2018-06-06 00:04:00 ",
"2018-06-06 00:05:00",
"2018-06-06 00:05:00",
"2018-06-06 00:06:00 ",
"2018-06-06 00:06:00 ",
"2018-06-06 00:07:00 ",
"2018-06-06 00:08:00 ",
"2018-06-06 00:09:00 ",
"2018-06-06 00:09:00 ",
]
trade = pd.Series(
index=pd.to_datetime(index),
data=[1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, -1],
name="amount",
)
ids = [0, 1, 0, 1, 2, 3, 2, 4, 3, 5, 4, 5, 6, 6]
currency_unit = Currency.JPY
trades.append(make_trades("USDJPY", [trade], currency_unit, [ids]))
trades.append(make_trades("EURJPY", [trade], currency_unit, [ids]))
trades.append(make_trades("USDJPY", [trade], currency_unit, [ids]))
return trades
def trades(symbol, currency_unit):
data = [1.0, -2.0, 1.0, 2.0, -4.0, 2.0, 1.0, 0.0, 1.0, 0.0]
index = pd.date_range(start="2018-06-06", freq="1D", periods=len(data))
trades = []
for i in range(0, len(data), 2):
trade = pd.Series(index=index[i : i + 2], data=data[i : i + 2], name="amount")
trades.append(trade)
trades = tr.make_trades(symbol, trades, currency_unit)
return trades
def test_calculate_trade_performance_for_sametime_trade_case():
symbol = "USDJPY"
data = [[1.0], [2.0], [3.0]]
df = pd.DataFrame(
index=pd.date_range(start="2019-06-06", freq="3S", periods=len(data)),
data=data,
columns=["mid"],
)
mid = backlight.datasource.from_dataframe(df, symbol, Currency.USD)
data = [-1.0, 1.0]
sr = pd.Series(
index=[
pd.Timestamp("2019-06-06 00:00:03"),
pd.Timestamp("2019-06-06 00:00:03"),
],
data=data,
name="amount",
)
trade = sr
trades = make_trades(symbol, [trade], Currency.USD)
metrics = module.calculate_trade_performance(trades, mid)
expected_cnt_trade = 1
expected_cnt_win = 0
expected_cnt_lose = 0
expected_total_pl = 0
expected_win_pl = 0
expected_lose_pl = 0
expected_trade_amount = 0
# expected_sharpe = nan
expected_avg_pl = 0 # expected_total_pl / expected_trade_amount
expected_max_drawdown = 0
expected_avg_win_pl = 0 # expected_win_pl / expected_cnt_win
expected_avg_lose_pl = 0 # expected_lose_pl / expected_cnt_lose
expected_avg_pl_per_trade = expected_total_pl / expected_cnt_trade
expected_win_ratio = expected_cnt_win / expected_cnt_trade
expected_lose_ratio = expected_cnt_lose / expected_cnt_trade
assert metrics.loc["metrics", "cnt_trade"] == expected_cnt_trade
assert metrics.loc["metrics", "cnt_win"] == expected_cnt_win
assert metrics.loc["metrics", "cnt_lose"] == expected_cnt_lose
assert metrics.loc["metrics", "total_win_pl"] == expected_win_pl
assert metrics.loc["metrics", "total_win_pl"] == expected_win_pl
assert metrics.loc["metrics", "total_win_pl"] == expected_win_pl
assert metrics.loc["metrics", "total_lose_pl"] == expected_lose_pl
assert metrics.loc["metrics", "cnt_amount"] == expected_trade_amount
assert metrics.loc["metrics", "avg_pl_per_amount"] == expected_avg_pl
assert math.isnan(metrics.loc["metrics", "sharpe"])
assert metrics.loc["metrics", "max_drawdown"] == expected_max_drawdown
assert metrics.loc["metrics", "avg_win_pl"] == expected_avg_win_pl
assert metrics.loc["metrics", "avg_lose_pl"] == expected_avg_lose_pl
assert metrics.loc["metrics", "avg_pl_per_trade"] == expected_avg_pl_per_trade
assert metrics.loc["metrics", "win_ratio"] == expected_win_ratio
assert metrics.loc["metrics", "lose_ratio"] == expected_lose_ratio
def trades(symbol, currency_unit):
data = [1.0, -2.0, 1.0, 2.0, -4.0, 2.0, 1.0, 0.0, 1.0, 0.0]
sr = pd.Series(
index=pd.date_range(start="2018-06-06", freq="1min", periods=len(data)),
data=data,
name="amount",
)
trade = sr
trades = make_trades(symbol, [trade], currency_unit)
return trades
def trades():
trades = []
periods = 10
for symbol in ["USDJPY", "EURJPY", "EURUSD"]:
currency_unit = Currency[symbol[-3:]]
trade = pd.Series(
index=pd.date_range(start="2018-06-06", freq="1min", periods=periods),
data=[1, 1, -1, -1, 1, 1, -1, 1, -1, -1],
name="amount",
)
ids = [0, 1, 0, 1, 2, 3, 2, 4, 3, 4]
trades.append(make_trades(symbol, [trade], currency_unit, [ids]))
return trades
def positions():
symbol = "USDJPY"
currency_unit = Currency.JPY
data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0], [9.0], [9.0]]
df = pd.DataFrame(
index=pd.date_range(start="2018-06-06", freq="1min", periods=len(data)),
data=data,
columns=["mid"],
)
market = backlight.datasource.from_dataframe(df, symbol, currency_unit)
data = [1.0, -2.0, 1.0, 2.0, -4.0, 2.0, 1.0, 0.0, 1.0, 0.0]
index = pd.date_range(start="2018-06-06", freq="1min", periods=len(data))
trades = []
for i in range(0, len(data), 2):
trade = pd.Series(index=index[i : i + 2], data=data[i : i + 2], name="amount")
trades.append(trade)
trades = make_trades(symbol, trades, currency_unit)
return backlight.positions.calculate_positions(trades, market)
def _calc_pl(trade: pd.Series, mkt: MarketData) -> float:
mkt = mkt.loc[trade.index, :]
trades = make_trades(mkt.symbol, [trade])
positions = backlight.positions.calc_positions(trades, mkt)
pl = calc_pl(positions)
return _sum(pl)
def test_exit_by_trailing_stop(market, signal, entries):
symbol = "usdjpy"
data = [
[1.0], # 00:00:00
[2.0], # 00:01:00
[3.0], # 00:02:00
[4.0], # 00:03:00
[5.0], # 00:04:00
[4.0], # 00:05:00
[3.0], # 00:06:00
[2.0], # 00:07:00
[1.0], # 00:08:00
[0.0], # 00:09:00
[1.0], # 00:10:00
]
periods = len(data)
market = backlight.datasource.from_dataframe(
pd.DataFrame(
index=pd.date_range(start="2018-06-06",
freq="1min",
periods=periods),
data=data,
columns=["mid"],
),
symbol,
)
entries = make_trades(
symbol,
(
make_trade([Transaction(pd.Timestamp("2018-06-06 00:00:00"), 1.0)
]),
make_trade(
[Transaction(pd.Timestamp("2018-06-06 00:00:00"), -1.0)]),
make_trade([Transaction(pd.Timestamp("2018-06-06 00:00:00"), 0.0)
]),
make_trade([Transaction(pd.Timestamp("2018-06-06 00:03:00"), 1.0)
]),
make_trade([Transaction(pd.Timestamp("2018-06-06 00:03:00"), 0.5)
]),
make_trade(
[Transaction(pd.Timestamp("2018-06-06 00:03:00"), -1.0)]),
),
)
initial_stop = 2.0
trailing_stop = 1.0
trades = module.exit_by_trailing_stop(market, entries, initial_stop,
trailing_stop)
expected = make_trades(
symbol,
(
make_trade([
Transaction(pd.Timestamp("2018-06-06 00:00:00"), 1.0),
Transaction(pd.Timestamp("2018-06-06 00:05:00"),
-1.0), # trail stop
]),
make_trade([
Transaction(pd.Timestamp("2018-06-06 00:00:00"), -1.0),
Transaction(pd.Timestamp("2018-06-06 00:02:00"),
1.0), # loss cut
]),
make_trade([Transaction(pd.Timestamp("2018-06-06 00:00:00"), 0.0)
]),
make_trade([
Transaction(pd.Timestamp("2018-06-06 00:03:00"), 1.0),
Transaction(pd.Timestamp("2018-06-06 00:05:00"),
-1.0), # trail stop
]),
make_trade([
Transaction(pd.Timestamp("2018-06-06 00:03:00"), 0.5),
Transaction(pd.Timestamp("2018-06-06 00:05:00"),
-0.5), # loss cut
]),
make_trade([
Transaction(pd.Timestamp("2018-06-06 00:03:00"), -1.0),
Transaction(pd.Timestamp("2018-06-06 00:10:00"),
1.0), # trail stop
]),
),
)
pd.testing.assert_frame_equal(trades, expected)
def _calculate_pl(trade: pd.Series, mkt: MarketData) -> float:
mkt = mkt.loc[trade.index.unique(), :]
trades = make_trades(mkt.symbol, [trade], mkt.currency_unit)
positions = backlight.positions.calculate_positions(trades, mkt)
pl = calculate_pl(positions)
return _sum(pl)