test: split test_btanalysis and test_metrics
This commit is contained in:
Matthias
@@ -1,10 +1,10 @@
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from datetime import UTC, datetime, timedelta
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from datetime import UTC, datetime
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from pathlib import Path
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from unittest.mock import MagicMock
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from zipfile import ZipFile
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import pytest
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from pandas import DataFrame, DateOffset, Timestamp, to_datetime
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from pandas import DataFrame, to_datetime
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from freqtrade.configuration import TimeRange
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from freqtrade.constants import LAST_BT_RESULT_FN
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@@ -21,22 +21,7 @@ from freqtrade.data.btanalysis import (
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load_trades,
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load_trades_from_db,
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)
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from freqtrade.data.history import load_data, load_pair_history
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from freqtrade.data.metrics import (
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calculate_cagr,
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calculate_calmar,
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calculate_csum,
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calculate_expectancy,
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calculate_market_change,
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calculate_max_drawdown,
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calculate_sharpe,
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calculate_sortino,
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calculate_sqn,
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calculate_underwater,
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combine_dataframes_with_mean,
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combined_dataframes_with_rel_mean,
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create_cum_profit,
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)
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from freqtrade.data.history import load_pair_history
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from freqtrade.exceptions import OperationalException
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from freqtrade.util import dt_utc
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from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
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@@ -253,375 +238,6 @@ def test_load_trades(default_conf, mocker):
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assert bt_mock.call_count == 0
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def test_calculate_market_change(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
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result = calculate_market_change(data)
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assert isinstance(result, float)
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assert pytest.approx(result) == 0.01100002
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result = calculate_market_change(data, min_date=dt_utc(2018, 1, 20))
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assert isinstance(result, float)
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assert pytest.approx(result) == 0.0375149
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# Move min-date after the last date
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result = calculate_market_change(data, min_date=dt_utc(2018, 2, 20))
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assert pytest.approx(result) == 0.0
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def test_combine_dataframes_with_mean(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
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df = combine_dataframes_with_mean(data)
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assert isinstance(df, DataFrame)
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assert "ETH/BTC" in df.columns
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assert "ADA/BTC" in df.columns
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assert "mean" in df.columns
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def test_combined_dataframes_with_rel_mean(testdatadir):
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pairs = ["BTC/USDT", "XRP/USDT"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
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df = combined_dataframes_with_rel_mean(
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data,
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fromdt=data["BTC/USDT"].at[0, "date"],
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todt=data["BTC/USDT"].at[data["BTC/USDT"].index[-1], "date"],
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)
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assert isinstance(df, DataFrame)
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assert "BTC/USDT" not in df.columns
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assert "XRP/USDT" not in df.columns
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assert "mean" in df.columns
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assert "rel_mean" in df.columns
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assert "count" in df.columns
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assert df.iloc[0]["count"] == 2
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assert df.iloc[-1]["count"] == 2
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assert len(df) < len(data["BTC/USDT"])
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assert df["rel_mean"].between(-0.5, 0.5).all()
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def test_combine_dataframes_with_mean_no_data(testdatadir):
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pairs = ["ETH/BTC", "ADA/BTC"]
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data = load_data(datadir=testdatadir, pairs=pairs, timeframe="6m")
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with pytest.raises(ValueError, match=r"No data provided\."):
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combine_dataframes_with_mean(data)
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def test_create_cum_profit(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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timerange = TimeRange.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange)
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cum_profits = create_cum_profit(
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df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
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)
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]["cum_profits"] == 0
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assert pytest.approx(cum_profits.iloc[-1]["cum_profits"]) == 9.0225563e-05
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def test_create_cum_profit1(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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# Move close-time to "off" the candle, to make sure the logic still works
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bt_data["close_date"] = bt_data.loc[:, "close_date"] + DateOffset(seconds=20)
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timerange = TimeRange.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="TRX/BTC", timeframe="5m", datadir=testdatadir, timerange=timerange)
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cum_profits = create_cum_profit(
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df.set_index("date"), bt_data[bt_data["pair"] == "TRX/BTC"], "cum_profits", timeframe="5m"
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)
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]["cum_profits"] == 0
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assert pytest.approx(cum_profits.iloc[-1]["cum_profits"]) == 9.0225563e-05
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with pytest.raises(ValueError, match=r"Trade dataframe empty\."):
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create_cum_profit(
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df.set_index("date"),
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bt_data[bt_data["pair"] == "NOTAPAIR"],
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"cum_profits",
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timeframe="5m",
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)
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def test_calculate_max_drawdown(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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drawdown = calculate_max_drawdown(bt_data, value_col="profit_abs")
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assert isinstance(drawdown.relative_account_drawdown, float)
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assert pytest.approx(drawdown.relative_account_drawdown) == 0.29753914
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assert isinstance(drawdown.high_date, Timestamp)
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assert isinstance(drawdown.low_date, Timestamp)
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assert isinstance(drawdown.high_value, float)
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assert isinstance(drawdown.low_value, float)
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assert drawdown.high_date == Timestamp("2018-01-16 19:30:00", tz="UTC")
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assert drawdown.low_date == Timestamp("2018-01-16 22:25:00", tz="UTC")
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underwater = calculate_underwater(bt_data)
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assert isinstance(underwater, DataFrame)
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with pytest.raises(ValueError, match=r"Trade dataframe empty\."):
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calculate_max_drawdown(DataFrame())
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with pytest.raises(ValueError, match=r"Trade dataframe empty\."):
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calculate_underwater(DataFrame())
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def test_calculate_csum(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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csum_min, csum_max = calculate_csum(bt_data)
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assert isinstance(csum_min, float)
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assert isinstance(csum_max, float)
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assert csum_min < csum_max
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assert csum_min < 0.0001
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assert csum_max > 0.0002
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csum_min1, csum_max1 = calculate_csum(bt_data, 5)
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assert csum_min1 == csum_min + 5
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assert csum_max1 == csum_max + 5
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with pytest.raises(ValueError, match=r"Trade dataframe empty\."):
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csum_min, csum_max = calculate_csum(DataFrame())
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def test_calculate_expectancy(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
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assert expectancy == 0.0
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assert expectancy_ratio == 100
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expectancy, expectancy_ratio = calculate_expectancy(bt_data)
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assert isinstance(expectancy, float)
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assert isinstance(expectancy_ratio, float)
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assert pytest.approx(expectancy) == 5.820687070932315e-06
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assert pytest.approx(expectancy_ratio) == 0.07151374226574791
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data = {"profit_abs": [100, 200, 50, -150, 300, -100, 80, -30]}
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df = DataFrame(data)
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expectancy, expectancy_ratio = calculate_expectancy(df)
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assert pytest.approx(expectancy) == 56.25
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assert pytest.approx(expectancy_ratio) == 0.60267857
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def test_calculate_sortino(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sortino = calculate_sortino(DataFrame(), None, None, 0)
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assert sortino == 0.0
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sortino = calculate_sortino(
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bt_data,
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bt_data["open_date"].min(),
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bt_data["close_date"].max(),
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0.01,
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)
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assert isinstance(sortino, float)
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assert pytest.approx(sortino) == 35.17722
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def test_calculate_sharpe(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sharpe = calculate_sharpe(DataFrame(), None, None, 0)
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assert sharpe == 0.0
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sharpe = calculate_sharpe(
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bt_data,
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bt_data["open_date"].min(),
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bt_data["close_date"].max(),
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0.01,
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)
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assert isinstance(sharpe, float)
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assert pytest.approx(sharpe) == 44.5078669
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def test_calculate_calmar(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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calmar = calculate_calmar(DataFrame(), None, None, 0)
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assert calmar == 0.0
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calmar = calculate_calmar(
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bt_data,
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bt_data["open_date"].min(),
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bt_data["close_date"].max(),
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0.01,
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)
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assert isinstance(calmar, float)
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assert pytest.approx(calmar) == 559.040508
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def test_calculate_sqn(testdatadir):
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filename = testdatadir / "backtest_results/backtest-result.json"
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bt_data = load_backtest_data(filename)
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sqn = calculate_sqn(DataFrame(), 0)
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assert sqn == 0.0
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sqn = calculate_sqn(
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bt_data,
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0.01,
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)
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assert isinstance(sqn, float)
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assert pytest.approx(sqn) == 3.2991
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@pytest.mark.parametrize(
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"profits,starting_balance,expected_sqn,description",
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[
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([1.0, -0.5, 2.0, -1.0, 0.5, 1.5, -0.5, 1.0], 100, 1.3229, "Mixed profits/losses"),
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([], 100, 0.0, "Empty dataframe"),
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([1.0, 0.5, 2.0, 1.5, 0.8], 100, 4.3657, "All winning trades"),
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([-1.0, -0.5, -2.0, -1.5, -0.8], 100, -4.3657, "All losing trades"),
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([1.0], 100, -100, "Single trade"),
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],
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)
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def test_calculate_sqn_cases(profits, starting_balance, expected_sqn, description):
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"""
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Test SQN calculation with various scenarios:
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"""
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trades = DataFrame({"profit_abs": profits})
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sqn = calculate_sqn(trades, starting_balance=starting_balance)
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assert isinstance(sqn, float)
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assert pytest.approx(sqn, rel=1e-4) == expected_sqn
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@pytest.mark.parametrize(
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"start,end,days, expected",
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[
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(64900, 176000, 3 * 365, 0.3945),
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(64900, 176000, 365, 1.7119),
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(1000, 1000, 365, 0.0),
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(1000, 1500, 365, 0.5),
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(1000, 1500, 100, 3.3927), # sub year
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(0.01000000, 0.01762792, 120, 4.6087), # sub year BTC values
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(1000, 1010, 0, 0.0), # zero days
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(-100, 100, 365, 0.0), # negative starting balance
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],
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)
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def test_calculate_cagr(start, end, days, expected):
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assert round(calculate_cagr(days, start, end), 4) == expected
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def test_calculate_max_drawdown2():
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values = [
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0.011580,
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0.010048,
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0.011340,
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0.012161,
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0.010416,
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0.010009,
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0.020024,
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-0.024662,
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-0.022350,
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0.020496,
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-0.029859,
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-0.030511,
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0.010041,
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0.010872,
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-0.025782,
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0.010400,
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0.012374,
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0.012467,
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0.114741,
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0.010303,
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0.010088,
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-0.033961,
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0.010680,
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0.010886,
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-0.029274,
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0.011178,
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0.010693,
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0.010711,
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]
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dates = [dt_utc(2020, 1, 1) + timedelta(days=i) for i in range(len(values))]
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df = DataFrame(zip(values, dates, strict=False), columns=["profit", "open_date"])
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# sort by profit and reset index
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df = df.sort_values("profit").reset_index(drop=True)
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df1 = df.copy()
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drawdown = calculate_max_drawdown(
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df, date_col="open_date", starting_balance=0.2, value_col="profit"
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)
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# Ensure df has not been altered.
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assert df.equals(df1)
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assert isinstance(drawdown.drawdown_abs, float)
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assert isinstance(drawdown.relative_account_drawdown, float)
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# High must be before low
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assert drawdown.high_date < drawdown.low_date
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# High value must be higher than low value
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assert drawdown.high_value > drawdown.low_value
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assert drawdown.drawdown_abs == 0.091755
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assert pytest.approx(drawdown.relative_account_drawdown) == 0.32129575
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df = DataFrame(zip(values[:5], dates[:5], strict=False), columns=["profit", "open_date"])
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# No losing trade ...
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drawdown = calculate_max_drawdown(df, date_col="open_date", value_col="profit")
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assert drawdown.drawdown_abs == 0.0
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assert drawdown.low_value == 0.0
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assert drawdown.current_high_value >= 0.0
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assert drawdown.current_drawdown_abs == 0.0
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df1 = DataFrame(zip(values[:5], dates[:5], strict=False), columns=["profit", "open_date"])
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df1.loc[:, "profit"] = df1["profit"] * -1
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# No winning trade ...
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drawdown = calculate_max_drawdown(df1, date_col="open_date", value_col="profit")
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assert drawdown.drawdown_abs == 0.055545
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assert drawdown.high_value == 0.0
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assert drawdown.current_high_value == 0.0
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assert drawdown.current_drawdown_abs == 0.055545
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@pytest.mark.parametrize(
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"profits,relative,highd,lowdays,result,result_rel",
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[
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([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909),
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([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5),
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],
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)
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def test_calculate_max_drawdown_abs(profits, relative, highd, lowdays, result, result_rel):
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"""
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Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
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[1000, 500, 1000, 11000, 10000] # absolute results
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[1000, 50%, 0%, 0%, ~9%] # Relative drawdowns
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"""
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init_date = datetime(2020, 1, 1, tzinfo=UTC)
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dates = [init_date + timedelta(days=i) for i in range(len(profits))]
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df = DataFrame(zip(profits, dates, strict=False), columns=["profit_abs", "open_date"])
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# sort by profit and reset index
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df = df.sort_values("profit_abs").reset_index(drop=True)
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df1 = df.copy()
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drawdown = calculate_max_drawdown(
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df, date_col="open_date", starting_balance=1000, relative=relative
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)
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# Ensure df has not been altered.
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assert df.equals(df1)
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assert isinstance(drawdown.drawdown_abs, float)
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assert isinstance(drawdown.relative_account_drawdown, float)
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assert drawdown.high_date == init_date + timedelta(days=highd)
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assert drawdown.low_date == init_date + timedelta(days=lowdays)
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# High must be before low
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assert drawdown.high_date < drawdown.low_date
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# High value must be higher than low value
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assert drawdown.high_value > drawdown.low_value
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assert drawdown.drawdown_abs == result
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assert pytest.approx(drawdown.relative_account_drawdown) == result_rel
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def test_load_file_from_zip(tmp_path):
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with pytest.raises(ValueError, match=r"Zip file .* not found\."):
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load_file_from_zip(tmp_path / "test.zip", "testfile.txt")
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@@ -0,0 +1,395 @@
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from datetime import UTC, datetime, timedelta
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import pytest
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from pandas import DataFrame, DateOffset, Timestamp
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from freqtrade.configuration import TimeRange
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from freqtrade.data.btanalysis import (
|
||||
load_backtest_data,
|
||||
)
|
||||
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_sqn,
|
||||
calculate_underwater,
|
||||
combine_dataframes_with_mean,
|
||||
combined_dataframes_with_rel_mean,
|
||||
create_cum_profit,
|
||||
)
|
||||
from freqtrade.util import dt_utc
|
||||
|
||||
|
||||
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
|
||||
|
||||
result = calculate_market_change(data, min_date=dt_utc(2018, 1, 20))
|
||||
assert isinstance(result, float)
|
||||
assert pytest.approx(result) == 0.0375149
|
||||
|
||||
# Move min-date after the last date
|
||||
result = calculate_market_change(data, min_date=dt_utc(2018, 2, 20))
|
||||
assert pytest.approx(result) == 0.0
|
||||
|
||||
|
||||
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 = ["BTC/USDT", "XRP/USDT"]
|
||||
data = load_data(datadir=testdatadir, pairs=pairs, timeframe="5m")
|
||||
df = combined_dataframes_with_rel_mean(
|
||||
data,
|
||||
fromdt=data["BTC/USDT"].at[0, "date"],
|
||||
todt=data["BTC/USDT"].at[data["BTC/USDT"].index[-1], "date"],
|
||||
)
|
||||
assert isinstance(df, DataFrame)
|
||||
assert "BTC/USDT" not in df.columns
|
||||
assert "XRP/USDT" 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["BTC/USDT"])
|
||||
assert df["rel_mean"].between(-0.5, 0.5).all()
|
||||
|
||||
|
||||
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=r"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)
|
||||
drawdown = calculate_max_drawdown(bt_data, value_col="profit_abs")
|
||||
assert isinstance(drawdown.relative_account_drawdown, float)
|
||||
assert pytest.approx(drawdown.relative_account_drawdown) == 0.29753914
|
||||
assert isinstance(drawdown.high_date, Timestamp)
|
||||
assert isinstance(drawdown.low_date, Timestamp)
|
||||
assert isinstance(drawdown.high_value, float)
|
||||
assert isinstance(drawdown.low_value, float)
|
||||
assert drawdown.high_date == Timestamp("2018-01-16 19:30:00", tz="UTC")
|
||||
assert drawdown.low_date == Timestamp("2018-01-16 22:25:00", tz="UTC")
|
||||
|
||||
underwater = calculate_underwater(bt_data)
|
||||
assert isinstance(underwater, DataFrame)
|
||||
|
||||
with pytest.raises(ValueError, match=r"Trade dataframe empty\."):
|
||||
calculate_max_drawdown(DataFrame())
|
||||
|
||||
with pytest.raises(ValueError, match=r"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=r"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
|
||||
|
||||
|
||||
def test_calculate_sqn(testdatadir):
|
||||
filename = testdatadir / "backtest_results/backtest-result.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
|
||||
sqn = calculate_sqn(DataFrame(), 0)
|
||||
assert sqn == 0.0
|
||||
|
||||
sqn = calculate_sqn(
|
||||
bt_data,
|
||||
0.01,
|
||||
)
|
||||
assert isinstance(sqn, float)
|
||||
assert pytest.approx(sqn) == 3.2991
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"profits,starting_balance,expected_sqn,description",
|
||||
[
|
||||
([1.0, -0.5, 2.0, -1.0, 0.5, 1.5, -0.5, 1.0], 100, 1.3229, "Mixed profits/losses"),
|
||||
([], 100, 0.0, "Empty dataframe"),
|
||||
([1.0, 0.5, 2.0, 1.5, 0.8], 100, 4.3657, "All winning trades"),
|
||||
([-1.0, -0.5, -2.0, -1.5, -0.8], 100, -4.3657, "All losing trades"),
|
||||
([1.0], 100, -100, "Single trade"),
|
||||
],
|
||||
)
|
||||
def test_calculate_sqn_cases(profits, starting_balance, expected_sqn, description):
|
||||
"""
|
||||
Test SQN calculation with various scenarios:
|
||||
"""
|
||||
trades = DataFrame({"profit_abs": profits})
|
||||
sqn = calculate_sqn(trades, starting_balance=starting_balance)
|
||||
|
||||
assert isinstance(sqn, float)
|
||||
assert pytest.approx(sqn, rel=1e-4) == expected_sqn
|
||||
|
||||
|
||||
@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
|
||||
(1000, 1010, 0, 0.0), # zero days
|
||||
(-100, 100, 365, 0.0), # negative starting balance
|
||||
],
|
||||
)
|
||||
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, strict=False), columns=["profit", "open_date"])
|
||||
# sort by profit and reset index
|
||||
df = df.sort_values("profit").reset_index(drop=True)
|
||||
df1 = df.copy()
|
||||
drawdown = calculate_max_drawdown(
|
||||
df, date_col="open_date", starting_balance=0.2, value_col="profit"
|
||||
)
|
||||
# Ensure df has not been altered.
|
||||
assert df.equals(df1)
|
||||
|
||||
assert isinstance(drawdown.drawdown_abs, float)
|
||||
assert isinstance(drawdown.relative_account_drawdown, float)
|
||||
# High must be before low
|
||||
assert drawdown.high_date < drawdown.low_date
|
||||
# High value must be higher than low value
|
||||
assert drawdown.high_value > drawdown.low_value
|
||||
assert drawdown.drawdown_abs == 0.091755
|
||||
assert pytest.approx(drawdown.relative_account_drawdown) == 0.32129575
|
||||
|
||||
df = DataFrame(zip(values[:5], dates[:5], strict=False), columns=["profit", "open_date"])
|
||||
# No losing trade ...
|
||||
drawdown = calculate_max_drawdown(df, date_col="open_date", value_col="profit")
|
||||
assert drawdown.drawdown_abs == 0.0
|
||||
assert drawdown.low_value == 0.0
|
||||
assert drawdown.current_high_value >= 0.0
|
||||
assert drawdown.current_drawdown_abs == 0.0
|
||||
|
||||
df1 = DataFrame(zip(values[:5], dates[:5], strict=False), columns=["profit", "open_date"])
|
||||
df1.loc[:, "profit"] = df1["profit"] * -1
|
||||
# No winning trade ...
|
||||
drawdown = calculate_max_drawdown(df1, date_col="open_date", value_col="profit")
|
||||
assert drawdown.drawdown_abs == 0.055545
|
||||
assert drawdown.high_value == 0.0
|
||||
assert drawdown.current_high_value == 0.0
|
||||
assert drawdown.current_drawdown_abs == 0.055545
|
||||
|
||||
|
||||
@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=UTC)
|
||||
dates = [init_date + timedelta(days=i) for i in range(len(profits))]
|
||||
df = DataFrame(zip(profits, dates, strict=False), 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 = 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.drawdown_abs, float)
|
||||
assert isinstance(drawdown.relative_account_drawdown, float)
|
||||
assert drawdown.high_date == init_date + timedelta(days=highd)
|
||||
assert drawdown.low_date == init_date + timedelta(days=lowdays)
|
||||
|
||||
# High must be before low
|
||||
assert drawdown.high_date < drawdown.low_date
|
||||
# High value must be higher than low value
|
||||
assert drawdown.high_value > drawdown.low_value
|
||||
assert drawdown.drawdown_abs == result
|
||||
assert pytest.approx(drawdown.relative_account_drawdown) == result_rel
|
||||
Reference in New Issue
Block a user