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freqtrade_origin/tests/data/test_btanalysis.py

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from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from pandas import DataFrame, DateOffset, Timestamp, to_datetime
from freqtrade.configuration import TimeRange
from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (
BT_DATA_COLUMNS,
analyze_trade_parallelism,
extract_trades_of_period,
get_latest_backtest_filename,
get_latest_hyperopt_file,
load_backtest_data,
load_backtest_metadata,
load_trades,
load_trades_from_db,
)
from freqtrade.data.history import load_data, load_pair_history
from freqtrade.data.metrics import (
calculate_cagr,
calculate_calmar,
calculate_csum,
calculate_expectancy,
calculate_market_change,
calculate_max_drawdown,
calculate_sharpe,
calculate_sortino,
calculate_underwater,
combine_dataframes_with_mean,
combined_dataframes_with_rel_mean,
create_cum_profit,
)
from freqtrade.exceptions import OperationalException
from freqtrade.util import dt_utc
from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT
def test_get_latest_backtest_filename(testdatadir, mocker):
with pytest.raises(ValueError, match=r"Directory .* does not exist\."):
get_latest_backtest_filename(testdatadir / "does_not_exist")
with pytest.raises(ValueError, match=r"Directory .* does not seem to contain .*"):
get_latest_backtest_filename(testdatadir)
testdir_bt = testdatadir / "backtest_results"
res = get_latest_backtest_filename(testdir_bt)
assert res == "backtest-result.json"
res = get_latest_backtest_filename(str(testdir_bt))
assert res == "backtest-result.json"
mocker.patch("freqtrade.data.btanalysis.json_load", return_value={})
with pytest.raises(ValueError, match=r"Invalid '.last_result.json' format."):
get_latest_backtest_filename(testdir_bt)
def test_get_latest_hyperopt_file(testdatadir):
res = get_latest_hyperopt_file(testdatadir / "does_not_exist", "testfile.pickle")
assert res == testdatadir / "does_not_exist/testfile.pickle"
res = get_latest_hyperopt_file(testdatadir.parent)
assert res == testdatadir.parent / "hyperopt_results.pickle"
res = get_latest_hyperopt_file(str(testdatadir.parent))
assert res == testdatadir.parent / "hyperopt_results.pickle"
# Test with absolute path
with pytest.raises(
OperationalException,
match="--hyperopt-filename expects only the filename, not an absolute path.",
):
get_latest_hyperopt_file(str(testdatadir.parent), str(testdatadir.parent))
def test_load_backtest_metadata(mocker, testdatadir):
res = load_backtest_metadata(testdatadir / "nonexistent.file.json")
assert res == {}
mocker.patch("freqtrade.data.btanalysis.get_backtest_metadata_filename")
mocker.patch("freqtrade.data.btanalysis.json_load", side_effect=Exception())
with pytest.raises(
OperationalException, match=r"Unexpected error.*loading backtest metadata\."
):
load_backtest_metadata(testdatadir / "nonexistent.file.json")
def test_load_backtest_data_old_format(testdatadir, mocker):
filename = testdatadir / "backtest-result_test222.json"
mocker.patch("freqtrade.data.btanalysis.load_backtest_stats", return_value=[])
with pytest.raises(
OperationalException,
match=r"Backtest-results with only trades data are no longer supported.",
):
load_backtest_data(filename)
def test_load_backtest_data_new_format(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
assert isinstance(bt_data, DataFrame)
assert set(bt_data.columns) == set(BT_DATA_COLUMNS)
assert len(bt_data) == 179
# Test loading from string (must yield same result)
bt_data2 = load_backtest_data(str(filename))
assert bt_data.equals(bt_data2)
# Test loading from folder (must yield same result)
bt_data3 = load_backtest_data(testdatadir / "backtest_results")
assert bt_data.equals(bt_data3)
with pytest.raises(ValueError, match=r"File .* does not exist\."):
load_backtest_data("filename" + "nofile")
with pytest.raises(ValueError, match=r"Unknown dataformat."):
load_backtest_data(testdatadir / "backtest_results" / LAST_BT_RESULT_FN)
def test_load_backtest_data_multi(testdatadir):
filename = testdatadir / "backtest_results/backtest-result_multistrat.json"
for strategy in ("StrategyTestV2", "TestStrategy"):
bt_data = load_backtest_data(filename, strategy=strategy)
assert isinstance(bt_data, DataFrame)
assert set(bt_data.columns) == set(BT_DATA_COLUMNS)
assert len(bt_data) == 179
# Test loading from string (must yield same result)
bt_data2 = load_backtest_data(str(filename), strategy=strategy)
assert bt_data.equals(bt_data2)
with pytest.raises(ValueError, match=r"Strategy XYZ not available in the backtest result\."):
load_backtest_data(filename, strategy="XYZ")
with pytest.raises(ValueError, match=r"Detected backtest result with more than one strategy.*"):
load_backtest_data(filename)
@pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize("is_short", [False, True])
def test_load_trades_from_db(default_conf, fee, is_short, mocker):
create_mock_trades(fee, is_short)
# remove init so it does not init again
init_mock = mocker.patch("freqtrade.data.btanalysis.init_db", MagicMock())
trades = load_trades_from_db(db_url=default_conf["db_url"])
assert init_mock.call_count == 1
assert len(trades) == MOCK_TRADE_COUNT
assert isinstance(trades, DataFrame)
assert "pair" in trades.columns
assert "open_date" in trades.columns
assert "profit_ratio" in trades.columns
for col in BT_DATA_COLUMNS:
if col not in ["index", "open_at_end"]:
assert col in trades.columns
trades = load_trades_from_db(db_url=default_conf["db_url"], strategy=CURRENT_TEST_STRATEGY)
assert len(trades) == 4
trades = load_trades_from_db(db_url=default_conf["db_url"], strategy="NoneStrategy")
assert len(trades) == 0
def test_extract_trades_of_period(testdatadir):
pair = "UNITTEST/BTC"
# 2018-11-14 06:07:00
timerange = TimeRange("date", None, 1510639620, 0)
data = load_pair_history(pair=pair, timeframe="1m", datadir=testdatadir, timerange=timerange)
trades = DataFrame(
{
"pair": [pair, pair, pair, pair],
"profit_ratio": [0.0, 0.1, -0.2, -0.5],
"profit_abs": [0.0, 1, -2, -5],
"open_date": to_datetime(
[
datetime(2017, 11, 13, 15, 40, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc),
datetime(2017, 11, 15, 3, 40, 0, tzinfo=timezone.utc),
],
utc=True,
),
"close_date": to_datetime(
[
datetime(2017, 11, 13, 16, 40, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc),
datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc),
datetime(2017, 11, 15, 3, 55, 0, tzinfo=timezone.utc),
],
utc=True,
),
}
)
trades1 = extract_trades_of_period(data, trades)
# First and last trade are dropped as they are out of range
assert len(trades1) == 2
assert trades1.iloc[0].open_date == datetime(2017, 11, 14, 9, 41, 0, tzinfo=timezone.utc)
assert trades1.iloc[0].close_date == datetime(2017, 11, 14, 10, 41, 0, tzinfo=timezone.utc)
assert trades1.iloc[-1].open_date == datetime(2017, 11, 14, 14, 20, 0, tzinfo=timezone.utc)
assert trades1.iloc[-1].close_date == datetime(2017, 11, 14, 15, 25, 0, tzinfo=timezone.utc)
def test_analyze_trade_parallelism(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
res = analyze_trade_parallelism(bt_data, "5m")
assert isinstance(res, DataFrame)
assert "open_trades" in res.columns
assert res["open_trades"].max() == 3
assert res["open_trades"].min() == 0
def test_load_trades(default_conf, mocker):
db_mock = mocker.patch("freqtrade.data.btanalysis.load_trades_from_db", MagicMock())
bt_mock = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
load_trades(
"DB",
db_url=default_conf.get("db_url"),
exportfilename=default_conf.get("exportfilename"),
no_trades=False,
strategy=CURRENT_TEST_STRATEGY,
)
assert db_mock.call_count == 1
assert bt_mock.call_count == 0
db_mock.reset_mock()
bt_mock.reset_mock()
default_conf["exportfilename"] = Path("testfile.json")
load_trades(
"file",
db_url=default_conf.get("db_url"),
exportfilename=default_conf.get("exportfilename"),
)
assert db_mock.call_count == 0
assert bt_mock.call_count == 1
db_mock.reset_mock()
bt_mock.reset_mock()
default_conf["exportfilename"] = "testfile.json"
load_trades(
"file",
db_url=default_conf.get("db_url"),
exportfilename=default_conf.get("exportfilename"),
no_trades=True,
)
assert db_mock.call_count == 0
assert bt_mock.call_count == 0
def test_calculate_market_change(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
result = calculate_market_change(data)
assert isinstance(result, float)
assert pytest.approx(result) == 0.01100002
def test_combine_dataframes_with_mean(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
df = combine_dataframes_with_mean(data)
assert isinstance(df, DataFrame)
assert "ETH/BTC" in df.columns
assert "ADA/BTC" in df.columns
assert "mean" in df.columns
def test_combined_dataframes_with_rel_mean(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
df = combined_dataframes_with_rel_mean(
data, datetime(2018, 1, 12, tzinfo=timezone.utc), datetime(2018, 1, 28, tzinfo=timezone.utc)
)
assert isinstance(df, DataFrame)
assert "ETH/BTC" not in df.columns
assert "ADA/BTC" not in df.columns
assert "mean" in df.columns
assert "rel_mean" in df.columns
assert "count" in df.columns
assert df.iloc[0]["count"] == 2
assert df.iloc[-1]["count"] == 2
assert len(df) < len(data["ETH/BTC"])
def test_combine_dataframes_with_mean_no_data(testdatadir):
pairs = ["ETH/BTC", "ADA/BTC"]
data = load_data(datadir=testdatadir, pairs=pairs, timeframe="6m")
with pytest.raises(ValueError, match=r"No data provided\."):
combine_dataframes_with_mean(data)
def test_create_cum_profit(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
timerange = TimeRange.parse_timerange("20180110-20180112")
df = load_pair_history(pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange)
cum_profits = create_cum_profit(
df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
)
assert "cum_profits" in cum_profits.columns
assert cum_profits.iloc[0]["cum_profits"] == 0
assert pytest.approx(cum_profits.iloc[-1]["cum_profits"]) == 9.0225563e-05
def test_create_cum_profit1(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
# Move close-time to "off" the candle, to make sure the logic still works
bt_data["close_date"] = bt_data.loc[:, "close_date"] + DateOffset(seconds=20)
timerange = TimeRange.parse_timerange("20180110-20180112")
df = load_pair_history(pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange)
cum_profits = create_cum_profit(
df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
)
assert "cum_profits" in cum_profits.columns
assert cum_profits.iloc[0]["cum_profits"] == 0
assert pytest.approx(cum_profits.iloc[-1]["cum_profits"]) == 9.0225563e-05
with pytest.raises(ValueError, match="Trade dataframe empty."):
create_cum_profit(
df.set_index("date"),
bt_data[bt_data["pair"] == "NOTAPAIR"],
"cum_profits",
timeframe="5m",
)
def test_calculate_max_drawdown(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
_, hdate, lowdate, hval, lval, drawdown = calculate_max_drawdown(
bt_data, value_col="profit_abs"
)
assert isinstance(drawdown, float)
assert pytest.approx(drawdown) == 0.29753914
assert isinstance(hdate, Timestamp)
assert isinstance(lowdate, Timestamp)
assert isinstance(hval, float)
assert isinstance(lval, float)
assert hdate == Timestamp("2018-01-16 19:30:00", tz="UTC")
assert lowdate == Timestamp("2018-01-16 22:25:00", tz="UTC")
underwater = calculate_underwater(bt_data)
assert isinstance(underwater, DataFrame)
with pytest.raises(ValueError, match="Trade dataframe empty."):
calculate_max_drawdown(DataFrame())
with pytest.raises(ValueError, match="Trade dataframe empty."):
calculate_underwater(DataFrame())
def test_calculate_csum(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
csum_min, csum_max = calculate_csum(bt_data)
assert isinstance(csum_min, float)
assert isinstance(csum_max, float)
assert csum_min < csum_max
assert csum_min < 0.0001
assert csum_max > 0.0002
csum_min1, csum_max1 = calculate_csum(bt_data, 5)
assert csum_min1 == csum_min + 5
assert csum_max1 == csum_max + 5
with pytest.raises(ValueError, match="Trade dataframe empty."):
csum_min, csum_max = calculate_csum(DataFrame())
def test_calculate_expectancy(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
assert expectancy == 0.0
assert expectancy_ratio == 100
expectancy, expectancy_ratio = calculate_expectancy(bt_data)
assert isinstance(expectancy, float)
assert isinstance(expectancy_ratio, float)
assert pytest.approx(expectancy) == 5.820687070932315e-06
assert pytest.approx(expectancy_ratio) == 0.07151374226574791
data = {"profit_abs": [100, 200, 50, -150, 300, -100, 80, -30]}
df = DataFrame(data)
expectancy, expectancy_ratio = calculate_expectancy(df)
assert pytest.approx(expectancy) == 56.25
assert pytest.approx(expectancy_ratio) == 0.60267857
def test_calculate_sortino(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
sortino = calculate_sortino(DataFrame(), None, None, 0)
assert sortino == 0.0
sortino = calculate_sortino(
bt_data,
bt_data["open_date"].min(),
bt_data["close_date"].max(),
0.01,
)
assert isinstance(sortino, float)
assert pytest.approx(sortino) == 35.17722
def test_calculate_sharpe(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
sharpe = calculate_sharpe(DataFrame(), None, None, 0)
assert sharpe == 0.0
sharpe = calculate_sharpe(
bt_data,
bt_data["open_date"].min(),
bt_data["close_date"].max(),
0.01,
)
assert isinstance(sharpe, float)
assert pytest.approx(sharpe) == 44.5078669
def test_calculate_calmar(testdatadir):
filename = testdatadir / "backtest_results/backtest-result.json"
bt_data = load_backtest_data(filename)
calmar = calculate_calmar(DataFrame(), None, None, 0)
assert calmar == 0.0
calmar = calculate_calmar(
bt_data,
bt_data["open_date"].min(),
bt_data["close_date"].max(),
0.01,
)
assert isinstance(calmar, float)
assert pytest.approx(calmar) == 559.040508
@pytest.mark.parametrize(
"start,end,days, expected",
[
(64900, 176000, 3 * 365, 0.3945),
(64900, 176000, 365, 1.7119),
(1000, 1000, 365, 0.0),
(1000, 1500, 365, 0.5),
(1000, 1500, 100, 3.3927), # sub year
(0.01000000, 0.01762792, 120, 4.6087), # sub year BTC values
],
)
def test_calculate_cagr(start, end, days, expected):
assert round(calculate_cagr(days, start, end), 4) == expected
def test_calculate_max_drawdown2():
values = [
0.011580,
0.010048,
0.011340,
0.012161,
0.010416,
0.010009,
0.020024,
-0.024662,
-0.022350,
0.020496,
-0.029859,
-0.030511,
0.010041,
0.010872,
-0.025782,
0.010400,
0.012374,
0.012467,
0.114741,
0.010303,
0.010088,
-0.033961,
0.010680,
0.010886,
-0.029274,
0.011178,
0.010693,
0.010711,
]
dates = [dt_utc(2020, 1, 1) + timedelta(days=i) for i in range(len(values))]
df = DataFrame(zip(values, dates), columns=["profit", "open_date"])
# sort by profit and reset index
df = df.sort_values("profit").reset_index(drop=True)
df1 = df.copy()
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
df, date_col="open_date", value_col="profit"
)
# Ensure df has not been altered.
assert df.equals(df1)
assert isinstance(drawdown, float)
assert isinstance(drawdown_rel, float)
# High must be before low
assert hdate < ldate
# High value must be higher than low value
assert hval > lval
assert drawdown == 0.091755
df = DataFrame(zip(values[:5], dates[:5]), columns=["profit", "open_date"])
with pytest.raises(ValueError, match="No losing trade, therefore no drawdown."):
calculate_max_drawdown(df, date_col="open_date", value_col="profit")
df1 = DataFrame(zip(values[:5], dates[:5]), columns=["profit", "open_date"])
df1.loc[:, "profit"] = df1["profit"] * -1
# No winning trade ...
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
df1, date_col="open_date", value_col="profit"
)
assert drawdown == 0.043965
@pytest.mark.parametrize(
"profits,relative,highd,lowdays,result,result_rel",
[
([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909),
([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5),
],
)
def test_calculate_max_drawdown_abs(profits, relative, highd, lowdays, result, result_rel):
"""
Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
[1000, 500, 1000, 11000, 10000] # absolute results
[1000, 50%, 0%, 0%, ~9%] # Relative drawdowns
"""
init_date = datetime(2020, 1, 1, tzinfo=timezone.utc)
dates = [init_date + timedelta(days=i) for i in range(len(profits))]
df = DataFrame(zip(profits, dates), columns=["profit_abs", "open_date"])
# sort by profit and reset index
df = df.sort_values("profit_abs").reset_index(drop=True)
df1 = df.copy()
drawdown, hdate, ldate, hval, lval, drawdown_rel = calculate_max_drawdown(
df, date_col="open_date", starting_balance=1000, relative=relative
)
# Ensure df has not been altered.
assert df.equals(df1)
assert isinstance(drawdown, float)
assert isinstance(drawdown_rel, float)
assert hdate == init_date + timedelta(days=highd)
assert ldate == init_date + timedelta(days=lowdays)
# High must be before low
assert hdate < ldate
# High value must be higher than low value
assert hval > lval
assert drawdown == result
assert pytest.approx(drawdown_rel) == result_rel
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