Upgrade pandas-ta to pandas-ta-classic

- Update pyproject.toml dependency from pandas-ta to pandas-ta-classic>=0.3.59
- Update imports in signals.py and ui/tools/_indicators.py
- Update conftest.py mock module name and BB column format
- Fix BB column naming: BBU_{len}_{std} (pandas-ta-classic) vs BBU_{len}_{std}_{std} (old)
- Update test fixture bar index (53→55) to match pandas-ta-classic RSI values

Co-authored-by: michaelchu <540510+michaelchu@users.noreply.github.com>

Author: Copilot

conftest.py

@@ -1,14 +1,14 @@
-"""Root conftest: mock pandas_ta when it's not installable (Python < 3.12)."""
+"""Root conftest: mock pandas_ta_classic when it's not installable (Python < 3.12)."""
 
 import sys
 import types
 
 try:
-    import pandas_ta  # noqa: F401
+    import pandas_ta_classic  # noqa: F401
 except (ImportError, ModuleNotFoundError, Exception):
     import pandas as pd
 
-    pta = types.ModuleType("pandas_ta")
+    pta = types.ModuleType("pandas_ta_classic")
     pta.version = "0.0.0-mock"
 
     def _rsi(prices, length=14):
@@ -54,9 +54,9 @@ def _bbands(prices, length=20, std=2.0):
         lower = mid - std * std_dev
         return pd.DataFrame(
             {
-                f"BBU_{length}_{std}_{std}": upper,
-                f"BBM_{length}_{std}_{std}": mid,
-                f"BBL_{length}_{std}_{std}": lower,
+                f"BBU_{length}_{std}": upper,
+                f"BBM_{length}_{std}": mid,
+                f"BBL_{length}_{std}": lower,
             },
             index=prices.index,
         )
@@ -75,4 +75,4 @@ def _atr(high, low, close, length=14):
     pta.bbands = _bbands
     pta.atr = _atr
 
-    sys.modules["pandas_ta"] = pta
+    sys.modules["pandas_ta_classic"] = pta

optopsy/signals.py

@@ -33,7 +33,7 @@
 from typing import Callable
 
 import pandas as pd
-import pandas_ta as ta
+import pandas_ta_classic as ta
 
 from .timestamps import normalize_dates
 
@@ -69,10 +69,10 @@ def _groupby_symbol(
 
 def _compute_rsi(prices: pd.Series, period: int) -> pd.Series:
     """
-    Compute RSI for a price series using pandas_ta.
+    Compute RSI for a price series using pandas_ta_classic.
 
     Kept as a named function for test compatibility. Uses Wilder smoothing
-    (RMA) internally via pandas_ta, which is equivalent to the previous
+    (RMA) internally via pandas_ta_classic, which is equivalent to the previous
     hand-rolled EWM implementation.
 
     Args:
@@ -81,7 +81,7 @@ def _compute_rsi(prices: pd.Series, period: int) -> pd.Series:
 
     Returns:
         Series of RSI values (0-100), with NaN for the first `period` entries.
-        Returns a NaN series if pandas_ta cannot compute (insufficient data).
+        Returns a NaN series if pandas_ta_classic cannot compute (insufficient data).
     """
     result = ta.rsi(prices, length=period)
     if result is None:
@@ -310,14 +310,14 @@ def _bb_signal(length: int, std: float, above: bool) -> SignalFunc:
         above: True → price > upper band; False → price < lower band
     """
     length, std = int(length), float(std)
-    # pandas_ta names BB columns as BBU_{length}_{std}_{std} where std is a float.
-    # The float() cast above ensures we match this format (e.g. 2 -> 2.0 -> "BBU_20_2.0_2.0").
+    # pandas_ta_classic names BB columns as BBU_{length}_{std} where std is a float.
+    # The float() cast above ensures we match this format (e.g. 2 -> 2.0 -> "BBU_20_2.0").
     if above:
-        band_col = f"BBU_{length}_{std}_{std}"
+        band_col = f"BBU_{length}_{std}"
         fill_val = float("inf")
         cmp = operator.gt
     else:
-        band_col = f"BBL_{length}_{std}_{std}"
+        band_col = f"BBL_{length}_{std}"
         fill_val = float("-inf")
         cmp = operator.lt
 

optopsy/ui/tools/_indicators.py

@@ -10,7 +10,7 @@
 from typing import Any
 
 import pandas as pd
-import pandas_ta as ta
+import pandas_ta_classic as ta
 
 # ---------------------------------------------------------------------------
 # Indicator classification
@@ -120,7 +120,7 @@ def add_bbands_traces(
     bb = ta.bbands(close, length=period, std=std)  # type: ignore[arg-type]
     if bb is None or bb.empty:
         return
-    # pandas_ta column names vary (e.g. BBL_20_2.0 vs BBL_20_2.0_2.0)
+    # pandas_ta_classic column names use format: BBL_{length}_{std} (e.g. BBL_20_2.0)
     upper_col = next(c for c in bb.columns if c.startswith("BBU_"))
     lower_col = next(c for c in bb.columns if c.startswith("BBL_"))
     mid_col = next(c for c in bb.columns if c.startswith("BBM_"))

pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
     "pandas",
     "numpy",
     "tabulate>=0.9.0,<1.0.0",
-    "pandas-ta>=0.4.67b0",
+    "pandas-ta-classic>=0.3.59",
     "empyrical-reloaded>=0.5.7",
     "pytz",
     "pydantic>=2.0,<3.0",
@@ -74,7 +74,7 @@ exclude = ["optopsy/ui/"]
 
 [tool.pytest.ini_options]
 addopts = "--cov=optopsy --cov-report=term-missing"
-filterwarnings = ["ignore::DeprecationWarning:pandas_ta"]
+filterwarnings = ["ignore::DeprecationWarning:pandas_ta_classic"]
 
 [dependency-groups]
 dev = [

tests/conftest.py

@@ -457,12 +457,12 @@ def stock_data_long_history():
     - Bars 140–199: recovery from ~174 → ~227 (SMA crossover, RSI > 70)
 
     The bounce at bars 41–42 creates a gap where rsi_below(14,30) resets,
-    so sustained(rsi_below(14,30), days=3) rejects bar 53 (RSI just crossed
+    so sustained(rsi_below(14,30), days=3) rejects bar 55 (RSI just crossed
     back below 30 for <3 bars) but accepts bar 30 (deep in the streak).
 
     Key bar values:
       bar 30:  RSI=0,    sma_above(20)=False, rsi_below(14,30)=True,  sustained(3)=True
-      bar 53:  RSI≈29.8, sma_above(20)=False, rsi_below(14,30)=True,  sustained(3)=False
+      bar 55:  RSI≈27.1, sma_above(20)=False, rsi_below(14,30)=True,  sustained(3)=False
       bar 100: RSI≈14,   rsi_above(14,70)=False (exit signal rejects exp-A)
       bar 160: RSI≈99.8, sma_above(20)=True,  rsi_below(14,30)=False
       bar 170: RSI≈99.9, sma_above(20)=True,  rsi_below(14,30)=False
@@ -505,7 +505,7 @@ def option_data_with_stock(stock_data_long_history):
 
     Entry dates (4):
       - Bar 30 (decline, RSI=0, sma=False)
-      - Bar 53 (post-bounce, RSI≈29.8, sma=False — sustained(3) rejects this)
+      - Bar 55 (post-bounce, RSI≈27.1, sma=False — sustained(3) rejects this)
       - Bar 160 (recovery, RSI≈99.8, sma=True)
       - Bar 170 (recovery, RSI≈99.9, sma=True)
 
@@ -514,11 +514,11 @@ def option_data_with_stock(stock_data_long_history):
       - Exp B = bar 195 (recovery, RSI≈100 → rsi_above(14,70) keeps)
 
     Recovery entries (bars 160, 170) are AFTER exp-A so they only match
-    exp-B. Decline entries (bars 30, 53) can match both.
+    exp-B. Decline entries (bars 30, 55) can match both.
 
     Baseline row count for long_calls: 18
       - Bar 30: 3 calls × 2 exps = 6
-      - Bar 53: 3 calls × 2 exps = 6
+      - Bar 55: 3 calls × 2 exps = 6
       - Bar 160: 3 calls × 1 exp (B only) = 3
       - Bar 170: 3 calls × 1 exp (B only) = 3
     """
@@ -528,7 +528,7 @@ def option_data_with_stock(stock_data_long_history):
 
     # Entry dates
     entry_decline_1 = pd.Timestamp(dates[30])  # deep in decline
-    entry_decline_2 = pd.Timestamp(dates[53])  # post-bounce, RSI just < 30
+    entry_decline_2 = pd.Timestamp(dates[55])  # post-bounce, RSI just < 30
     entry_recovery_1 = pd.Timestamp(dates[160])  # recovery phase
     entry_recovery_2 = pd.Timestamp(dates[170])  # recovery phase
     exit_date_a = pd.Timestamp(dates[100])  # flat phase, RSI < 70

tests/test_signals.py

@@ -1560,11 +1560,11 @@ class TestTASignalE2E:
 
     Key entry-bar properties:
       bar 30:  RSI=0,    sma_above=False, rsi_below=True,  sustained(3)=True
-      bar 53:  RSI≈29.8, sma_above=False, rsi_below=True,  sustained(3)=False
+      bar 55:  RSI≈27.1, sma_above=False, rsi_below=True,  sustained(3)=False
       bar 160: RSI≈99.8, sma_above=True,  rsi_below=False, atr_ohlcv=True,  atr_close=False
       bar 170: RSI≈99.9, sma_above=True,  rsi_below=False, atr_ohlcv=True,  atr_close=True
 
-    Decline entries (30, 53) match both exps → 3 calls × 2 exps = 6 each.
+    Decline entries (30, 55) match both exps → 3 calls × 2 exps = 6 each.
     Recovery entries (160, 170) are after exp-A → 3 calls × 1 exp = 3 each.
     Baseline: 6 + 6 + 3 + 3 = 18 call rows.
 
@@ -1578,7 +1578,7 @@ def _get_entry_dates(self, stock_data_long_history):
         return {
             "decline": {
                 pd.Timestamp(dates[30]),
-                pd.Timestamp(dates[53]),
+                pd.Timestamp(dates[55]),
             },
             "recovery": {
                 pd.Timestamp(dates[160]),
@@ -1754,10 +1754,10 @@ def test_entry_and_exit_dates_both_filter(
     def test_sustained_ta_signal_with_apply_signal(
         self, option_data_with_stock, stock_data_long_history
     ):
-        """sustained(rsi_below(14, 30), days=3) must reject bar 53.
+        """sustained(rsi_below(14, 30), days=3) must reject bar 55.
 
-        The bounce at bars 41-42 resets the RSI streak.  At bar 53,
-        RSI just crossed back below 30 for <3 consecutive bars, so
+        The bounce at bars 41-42 resets the RSI streak.  At bar 55,
+        RSI has crossed back below 30 for only 2 consecutive bars, so
         sustained(days=3) rejects it.  Bar 30 is deep in the streak
         (30+ consecutive bars with RSI=0) and survives.
 
@@ -1777,7 +1777,7 @@ def test_sustained_ta_signal_with_apply_signal(
         )
         assert len(results_plain) > 0
 
-        # sustained(days=3) rejects bar 53 (streak < 3 bars)
+        # sustained(days=3) rejects bar 55 (streak < 3 bars)
         entry_dates_sust = apply_signal(
             stock_data_long_history, sustained(rsi_below(14, 30), days=3)
         )