diff --git a/freqtrade/data/metrics.py b/freqtrade/data/metrics.py
index de8ebac4a..d2ae2d64b 100644
--- a/freqtrade/data/metrics.py
+++ b/freqtrade/data/metrics.py
@@ -375,3 +375,32 @@ def calculate_calmar(
 
     # print(expected_returns_mean, max_drawdown, calmar_ratio)
     return calmar_ratio
+
+
+def calculate_sqn(trades: pd.DataFrame, starting_balance: float) -> float:
+    """
+    Calculate System Quality Number (SQN) - Van K. Tharp.
+    SQN measures systematic trading quality and takes into account both
+    the number of trades and their standard deviation.
+
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :param starting_balance: Starting balance of the trading system
+    :return: SQN value
+    """
+    if len(trades) == 0:
+        return 0.0
+
+    total_profit = trades["profit_abs"] / starting_balance
+    number_of_trades = len(trades)
+
+    # Calculate average trade and standard deviation
+    average_profits = total_profit.mean()
+    profits_std = total_profit.std()
+
+    if profits_std != 0 and not np.isnan(profits_std):
+        sqn = math.sqrt(number_of_trades) * (average_profits / profits_std)
+    else:
+        # Define negative SQN to indicate this is NOT optimal
+        sqn = -100.0
+
+    return round(sqn, 4)
diff --git a/tests/data/test_btanalysis.py b/tests/data/test_btanalysis.py
index 1c901bc16..eec3ac4e4 100644
--- a/tests/data/test_btanalysis.py
+++ b/tests/data/test_btanalysis.py
@@ -30,6 +30,7 @@ from freqtrade.data.metrics import (
     calculate_max_drawdown,
     calculate_sharpe,
     calculate_sortino,
+    calculate_sqn,
     calculate_underwater,
     combine_dataframes_with_mean,
     combined_dataframes_with_rel_mean,
@@ -457,6 +458,42 @@ def test_calculate_calmar(testdatadir):
     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",
     [