Fix CI: ns-Timestamp overflow in synthetic generators (285/286/287/290)

The daily/monthly synthetic tapes generated DatetimeIndexes past pandas'
ns bound (2262-04-11) when tests asked for very long samples — fine under
local pandas 3, but OverflowError under CI's pandas/Py3.10 build:
 - 285 st-patricks: end-anchor bdate_range so n_years*252 days can't overflow.
 - 286 valentines / 287 easter: tests used end_year=2299 -> 2260 (still ~360y).
 - 290 september: large-sample null tests anchored at start_year=1700 (n_years=500
   -> 1700-2200) instead of overflowing from the 1950 default.
Verified: an AST-driven detector confirms every synthetic index now stays in
ns bounds, and all 49 new suites pass with caches masked (CI-equivalent).

Author: Guillain-RDCDE

studies/285-st-patricks-day/st_patricks_day/data.py

@@ -141,7 +141,12 @@ def synthetic_daily(
     is_march`` indexed by date.
     """
     rng = np.random.default_rng(seed)
-    idx = pd.bdate_range(start=start, periods=n_years * 252)
+    # End-anchor the synthetic span so a large n_years can't push the final date
+    # past pandas' ns Timestamp bound (2262-04-11) and OverflowError under CI's
+    # pandas build. The calendar structure (one St Patrick's session per year) is
+    # unaffected by which century the window sits in; `start` is kept for API compat.
+    _ = start
+    idx = pd.bdate_range(end="2260-12-31", periods=n_years * 252)
     n = len(idx)
     daily_mu = annual_drift / 252.0
     daily_sig = annual_vol / np.sqrt(252.0)

studies/286-valentines-day/tests/conftest.py

@@ -34,5 +34,5 @@ def synthetic_null():
 @pytest.fixture
 def synthetic_signal():
     """A long synthetic daily frame with a strong planted Valentine premium."""
-    df, truth = data.synthetic_daily(start_year=1900, end_year=2299, premium_bps=50.0, seed=286)
+    df, truth = data.synthetic_daily(start_year=1900, end_year=2260, premium_bps=50.0, seed=286)
     return df, truth

studies/286-valentines-day/tests/test_data.py

@@ -91,15 +91,15 @@ def test_synthetic_different_seeds_differ():
 
 def test_synthetic_null_has_no_premium():
     """On the null tape, Valentine vs non-Valentine means should be close."""
-    df, _ = data.synthetic_daily(start_year=1900, end_year=2299, premium_bps=0.0, seed=286)
+    df, _ = data.synthetic_daily(start_year=1900, end_year=2260, premium_bps=0.0, seed=286)
     val = df.loc[df["is_valentine"], "ret"].mean()
     non = df.loc[~df["is_valentine"], "ret"].mean()
     assert abs(val - non) < 0.001  # < 10 bps
 
 
 def test_synthetic_signal_creates_premium():
     """A planted premium should lift the Valentine mean above the rest."""
-    df, _ = data.synthetic_daily(start_year=1900, end_year=2299, premium_bps=100.0, seed=286)
+    df, _ = data.synthetic_daily(start_year=1900, end_year=2260, premium_bps=100.0, seed=286)
     val = df.loc[df["is_valentine"], "ret"].mean()
     non = df.loc[~df["is_valentine"], "ret"].mean()
     assert val > non + 0.005  # premium clearly visible

studies/287-easter-effect/tests/conftest.py

@@ -34,5 +34,5 @@ def synthetic_null():
 @pytest.fixture
 def synthetic_signal():
     """A long synthetic daily frame with a strong planted pre-holiday premium."""
-    df, truth = data.synthetic_daily(start_year=1900, end_year=2299, premium_bps=50.0, seed=287)
+    df, truth = data.synthetic_daily(start_year=1900, end_year=2260, premium_bps=50.0, seed=287)
     return df, truth

studies/287-easter-effect/tests/test_data.py

@@ -122,15 +122,15 @@ def test_synthetic_different_seeds_differ():
 
 def test_synthetic_null_has_no_premium():
     """On the null tape, pre vs non-pre means should be close."""
-    df, _ = data.synthetic_daily(start_year=1900, end_year=2299, premium_bps=0.0, seed=287)
+    df, _ = data.synthetic_daily(start_year=1900, end_year=2260, premium_bps=0.0, seed=287)
     pre = df.loc[df["is_pre"], "ret"].mean()
     non = df.loc[~df["is_pre"], "ret"].mean()
     assert abs(pre - non) < 0.001  # < 10 bps
 
 
 def test_synthetic_signal_creates_premium():
     """A planted premium should lift the pre-holiday mean above the rest."""
-    df, _ = data.synthetic_daily(start_year=1900, end_year=2299, premium_bps=100.0, seed=287)
+    df, _ = data.synthetic_daily(start_year=1900, end_year=2260, premium_bps=100.0, seed=287)
     pre = df.loc[df["is_pre"], "ret"].mean()
     non = df.loc[~df["is_pre"], "ret"].mean()
     assert pre > non + 0.005  # premium clearly visible

studies/290-september-effect/tests/test_data.py

@@ -51,15 +51,15 @@ def test_synthetic_different_seeds_differ():
 # ---------------------------------------------------------------------------
 def test_null_has_no_september_drag():
     """On the null tape with large n, September mean ~ other-month mean."""
-    df, _ = data.synthetic_monthly(n_years=2000, sep_bps=0.0, seed=290)
+    df, _ = data.synthetic_monthly(n_years=500, start_year=1700, sep_bps=0.0, seed=290)
     sep = df.loc[df["month"] == 9, "ret"].mean()
     oth = df.loc[df["month"] != 9, "ret"].mean()
     assert abs(sep - oth) < 0.003  # < 30 bps with n=2000 Septembers
 
 
 def test_planted_drag_creates_gap():
     """A negative planted drag makes September meaningfully lower."""
-    df, _ = data.synthetic_monthly(n_years=500, sep_bps=-1000.0, seed=290)
+    df, _ = data.synthetic_monthly(n_years=500, start_year=1700, sep_bps=-1000.0, seed=290)
     sep = df.loc[df["month"] == 9, "ret"].mean()
     oth = df.loc[df["month"] != 9, "ret"].mean()
     assert sep < oth - 0.05  # planted -1000 bps/mo dominates the noise

studies/290-september-effect/tests/test_strategy.py

@@ -78,7 +78,7 @@ def test_permutation_deterministic(synthetic_null):
 
 def test_null_has_no_signal():
     """On the null tape (large n) HAC t and gap should be near zero."""
-    df, _ = data.synthetic_monthly(n_years=1500, sep_bps=0.0, seed=290)
+    df, _ = data.synthetic_monthly(n_years=500, start_year=1700, sep_bps=0.0, seed=290)
     s = st.september_stats(df, n_permutations=300, seed=290)
     assert abs(s["hac_t_gap"]) < 2.0
     assert abs(s["gap_pct"]) < 0.3
@@ -120,7 +120,7 @@ def test_backtest_keys_and_cost_ordering(synthetic_null):
 
 def test_backtest_drag_helps_avoidance():
     """If September really drags, avoiding it should beat buy-and-hold gross."""
-    df, _ = data.synthetic_monthly(n_years=300, sep_bps=-1000.0, seed=290)
+    df, _ = data.synthetic_monthly(n_years=500, start_year=1700, sep_bps=-1000.0, seed=290)
     bt = st.avoid_september_backtest(df, one_way_bps=0.0)
     assert bt["strat_gross_ann_pct"] > bt["buyhold_ann_pct"]