test: add direct unit tests for _apply_riskmgmt_mask, safe_resolve_path, import_class, and _add_column_if_not_exists

test/backtesting/test_ftmo_oos.py

@@ -16,7 +16,9 @@ import pytest
 
 from rdagent.components.backtesting.vbt_backtest import (
     OOS_START_DEFAULT,
+    _apply_ftmo_mask,
     backtest_signal_ftmo,
+    FTMO_INITIAL_CAPITAL,
     FTMO_MAX_DAILY_LOSS,
     FTMO_MAX_TOTAL_LOSS,
     monte_carlo_trade_pvalue,
@@ -238,3 +240,141 @@ def test_wf_consistency_range(close_6yr):
     c = r.get("wf_oos_consistency")
     if c is not None:
         assert 0.0 <= c <= 1.0
+
+
+# ---------------------------------------------------------------------------
+# Direct _apply_ftmo_mask unit tests
+# ---------------------------------------------------------------------------
+
+class TestApplyFtmoMask:
+    """Direct unit tests for _apply_ftmo_mask — the core FTMO daily/total loss engine."""
+
+    @pytest.fixture
+    def flat_close(self) -> pd.Series:
+        n = 3000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        return pd.Series(1.10, index=idx)
+
+    def test_returns_compliance_dict(self, flat_close):
+        signal = _random_signal(flat_close.index)
+        masked, info = _apply_ftmo_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14)
+        assert "ftmo_daily_breaches" in info
+        assert "ftmo_total_breached" in info
+        assert "ftmo_total_breach_ts" in info
+        assert "ftmo_compliant" in info
+
+    def test_flat_market_zero_signal_fully_compliant(self, flat_close):
+        """No trades → always compliant."""
+        signal = pd.Series(0.0, index=flat_close.index)
+        masked, info = _apply_ftmo_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14)
+        assert info["ftmo_daily_breaches"] == 0
+        assert info["ftmo_total_breached"] is False
+        assert info["ftmo_compliant"] is True
+        # All signals should remain zero
+        assert (masked == 0).all()
+
+    def test_daily_loss_breach_zeroes_rest_of_day(self):
+        """When daily loss exceeds 5%, rest of that day's signals are zeroed."""
+        n = 3000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        # Price drops sharply in first few bars to trigger daily loss
+        price = pd.Series(1.10, index=idx, dtype=float)
+        price.iloc[3:20] = 0.00  # crash from 1.10 to 0.00 → massive loss
+        signal = pd.Series(1.0, index=idx)  # always long at 30x leverage
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0)
+        assert info["ftmo_daily_breaches"] > 0
+        # After breach, signals on same day must be zeroed
+        breach_day = idx[0].date()
+        same_day_late = (idx[-1] if idx[-1].date() == breach_day else idx[20])
+        if same_day_late.date() == breach_day:
+            assert masked.loc[same_day_late] == 0
+
+    def test_total_loss_breach_zeroes_all_remaining(self):
+        """When total loss exceeds 10%, ALL subsequent signals are zeroed."""
+        n = 5000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        # Price crashes → max position → total loss limit breached
+        price = pd.Series(1.10, index=idx, dtype=float)
+        price.iloc[5:50] = 0.50  # >10% drop with 30x leverage
+        signal = pd.Series(1.0, index=idx)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0)
+        assert info["ftmo_total_breached"] is True
+        assert info["ftmo_total_breach_ts"] is not None
+        # After breach, ALL later signals must be zero
+        assert (masked.iloc[100:] == 0).all()
+
+    def test_total_breach_respected_across_days(self):
+        """Total breach persists across day boundaries — no new trades after breach."""
+        n = 5000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        price = pd.Series(1.10, index=idx, dtype=float)
+        price.iloc[5:50] = 0.50
+        signal = pd.Series(1.0, index=idx)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0)
+        # All signals after breach index must be zero
+        breach_ts = pd.Timestamp(info["ftmo_total_breach_ts"])
+        assert (masked.loc[masked.index > breach_ts] == 0).all()
+
+    def test_daily_loss_resets_on_new_day(self):
+        """Daily loss limit resets at day boundary — new day starts fresh (unless total breached)."""
+        n = 5000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        price = pd.Series(1.10, index=idx, dtype=float)
+        # Trigger daily breach on day 1 by dropping 1%
+        price.iloc[5:20] = 1.09  # ~1% drop with 30x → ~30% loss
+        signal = pd.Series(1.0, index=idx)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0)
+        assert info["ftmo_daily_breaches"] >= 1
+        # Day 2 signals should be active again if not total-breached
+        day2_mask = idx.date > idx[0].date()
+        if day2_mask.any() and not info["ftmo_total_breached"]:
+            day2 = idx[day2_mask][0]
+            assert masked.loc[day2] != 0
+
+    def test_compliant_flag_false_after_daily_breach(self):
+        """Even one daily breach makes ftmo_compliant=False."""
+        n = 3000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        price = pd.Series(1.10, index=idx, dtype=float)
+        price.iloc[3:20] = 0.00
+        signal = pd.Series(1.0, index=idx)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0)
+        assert info["ftmo_compliant"] is False
+
+    def test_compliant_flag_false_after_total_breach(self):
+        """Total breach makes ftmo_compliant=False."""
+        n = 5000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        price = pd.Series(1.10, index=idx, dtype=float)
+        price.iloc[5:50] = 0.50
+        signal = pd.Series(1.0, index=idx)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0)
+        assert info["ftmo_compliant"] is False
+
+    def test_transaction_costs_reduce_equity(self):
+        """Transaction costs should reduce equity — compliant scenario with fees."""
+        n = 1000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        price = pd.Series(1.10, index=idx, dtype=float)
+        # Alternating signal → lots of position changes → high costs
+        signal = pd.Series([1.0 if i % 2 == 0 else -1.0 for i in range(n)], index=idx)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=1.0, txn_cost_bps=10.0)
+        # With high costs and flat market, equity should drop
+        assert "ftmo_daily_breaches" in info
+
+    def test_output_mask_has_same_index(self):
+        n = 2000
+        idx = pd.date_range("2024-01-01", periods=n, freq="1min")
+        price = pd.Series(1.10, index=idx)
+        signal = _random_signal(idx, seed=1)
+
+        masked, info = _apply_ftmo_mask(signal, price, leverage=1.0, txn_cost_bps=2.14)
+        assert len(masked) == len(signal)
+        assert masked.index.equals(signal.index)