From d87733b80eca37404d30982c4b70c661030331c3 Mon Sep 17 00:00:00 2001
From: Giulio De Pasquale <git@depasquale.giugl.io>
Date: Sat, 18 Oct 2025 11:33:24 +0100
Subject: [PATCH] feat(indicators): add validation and force_update to
 indicator calculations

---
 paperone/indicators.py | 78 +++++++++++++++++++++---------------------
 1 file changed, 39 insertions(+), 39 deletions(-)

diff --git a/paperone/indicators.py b/paperone/indicators.py
index ab094fc..4bc91ec 100644
--- a/paperone/indicators.py
+++ b/paperone/indicators.py
@@ -52,6 +52,17 @@ class IndicatorService:
             IndicatorsData instance ready to save to database, or None if insufficient data
             or if any calculated values are NaN
         """
+        # Add indicator-specific validation
+        required_periods = {
+            "MACD": 26 + 9,  # slowperiod + signalperiod
+            "ADX": 14 + 14,  # Additional warmup needed
+            "RSI": 14 + 1,  # Plus warmup
+        }
+        min_required = max(required_periods.values()) + 10  # Safety buffer
+
+        if lookback_days < min_required:
+            return None
+
         # Fetch historical OHLCV data from database
         start_date: datetime = target_date - timedelta(
             days=lookback_days * 2
@@ -60,8 +71,7 @@ class IndicatorService:
             ticker=ticker, start_date=start_date, end_date=target_date
         )
 
-        # Verify we have enough data (minimum 40 trading days for MACD + ADX)
-        if len(ohlcv_records) < 40:
+        if len(ohlcv_records) < min_required:
             return None
 
         # Convert to numpy arrays for TA-Lib
@@ -89,7 +99,6 @@ class IndicatorService:
         ):
             return None
 
-        # Build and return strongly-typed IndicatorsData
         return IndicatorsData(
             ticker=ticker,
             date=target_date,
@@ -130,26 +139,21 @@ class IndicatorService:
         )
 
     def calculate_and_save_indicators(
-        self, ticker: str, target_date: datetime
+        self, ticker: str, target_date: datetime, force_update: bool = False
     ) -> IndicatorsData | None:
-        """
-        Calculate indicators and immediately save them to the database.
+        if not force_update:
+            existing = self._crud.get_indicators(ticker=ticker, date=target_date)
 
-        Args:
-            ticker: Stock ticker symbol
-            target_date: Date to calculate indicators for
+            if existing is not None:
+                return existing
 
-        Returns:
-            Saved IndicatorsData instance or None if calculation failed
-        """
-        indicators: IndicatorsData | None = self.calculate_indicators_for_date(
+        indicators = self.calculate_indicators_for_date(
             ticker=ticker, target_date=target_date
         )
 
         if indicators is None:
             return None
 
-        # Upsert (insert or update if exists)
         return self._crud.upsert_indicators(indicators)
 
     def bulk_calculate_indicators(
@@ -177,34 +181,30 @@ class IndicatorService:
         return results
 
     def bulk_calculate_and_save_indicators(
-        self, ticker: str, dates: List[datetime]
-    ) -> int:
-        """
-        Calculate and save indicators for multiple dates efficiently.
-        Uses upsert logic to handle existing records.
-
-        Args:
-            ticker: Stock ticker symbol
-            dates: List of dates to calculate indicators for
-
-        Returns:
-            Number of indicator records successfully saved/updated
-        """
-        indicators_list: List[IndicatorsData] = self.bulk_calculate_indicators(
-            ticker=ticker, dates=dates
-        )
-
-        if not indicators_list:
+    self, ticker: str, dates: List[datetime], force_update: bool = False
+) -> int:
+        # Batch check existing records
+        if not force_update:
+            existing_dates = self._crud.get_existing_indicator_dates(ticker, dates)
+            dates = [d for d in dates if d not in existing_dates]
+    
+        if not dates:
             return 0
+    
+        start_date = min(dates) - timedelta(days=150)
+        end_date = max(dates)
+        all_ohlcv = self._crud.get_ohlcv_range(ticker, start_date, end_date)
+    
+        results = []
+        for date in dates:
+            indicators = self._calculate_for_date_from_data(all_ohlcv, date)
 
-        # Use upsert for each indicator to avoid UNIQUE constraint violations
-        saved_count = 0
-        for indicators in indicators_list:
-            result = self._crud.upsert_indicators(indicators)
-            if result:
-                saved_count += 1
+            if indicators:
+                results.append(indicators)
+    
+        # Batch insert
+        return self._crud.bulk_upsert_indicators(results)
 
-        return saved_count
 
     # ========================================================================
     # PRIVATE: Data Conversion