test: add tests for sharpe based on balance
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@@ -1,7 +1,8 @@
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from datetime import UTC, datetime, timedelta
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import numpy as np
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import pytest
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from pandas import DataFrame, DateOffset, Timestamp
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from pandas import DataFrame, DateOffset, Timestamp, to_datetime
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from freqtrade.configuration import TimeRange
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from freqtrade.data.btanalysis import (
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@@ -16,6 +17,7 @@ from freqtrade.data.metrics import (
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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_sharpe_from_balance,
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calculate_sortino,
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calculate_sqn,
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calculate_underwater,
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@@ -217,6 +219,45 @@ def test_calculate_sharpe(testdatadir):
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assert pytest.approx(sharpe) == 44.5078669
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def test_calculate_sharpe_from_balance():
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balance_history = DataFrame(
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{
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"date": to_datetime(
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[
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"2025-01-01 00:00:00+00:00",
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"2025-01-02 00:00:00+00:00",
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"2025-01-03 00:00:00+00:00",
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"2025-01-04 00:00:00+00:00",
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],
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utc=True,
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),
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"total_quote": [100.0, 110.0, 104.5, 125.4],
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}
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)
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sharpe = calculate_sharpe_from_balance(balance_history)
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expected_returns = np.array([0.1, -0.05, 0.2])
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expected_sharpe = expected_returns.mean() / expected_returns.std() * np.sqrt(365)
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assert isinstance(sharpe, float)
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assert pytest.approx(sharpe) == expected_sharpe
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def test_calculate_sharpe_from_balance_empty_or_flat():
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assert calculate_sharpe_from_balance(DataFrame()) == 0.0
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flat_balance_history = DataFrame(
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{
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"date": to_datetime(
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["2025-01-01 00:00:00+00:00", "2025-01-02 00:00:00+00:00"],
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utc=True,
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),
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"total_quote": [100.0, 100.0],
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}
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)
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assert calculate_sharpe_from_balance(flat_balance_history) == -100
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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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