From d7e1b93509af515ea680a9922e3fb23682108f38 Mon Sep 17 00:00:00 2001
From: Youssef Aitousarrah <youssef.aitousarrah@gmail.com>
Date: Wed, 15 Apr 2026 12:45:04 -0700
Subject: [PATCH] feat(filter): add OHLCV cache helper methods for
 price/intraday/recent-move

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---
 tradingagents/dataflows/discovery/filter.py | 62 +++++++++++++++++++++
 1 file changed, 62 insertions(+)

diff --git a/tradingagents/dataflows/discovery/filter.py b/tradingagents/dataflows/discovery/filter.py
index 9444f32f..e2c82e45 100644
--- a/tradingagents/dataflows/discovery/filter.py
+++ b/tradingagents/dataflows/discovery/filter.py
@@ -5,6 +5,7 @@ from typing import Any, Callable, Dict, List
 
 import pandas as pd
 
+from tradingagents.dataflows.data_cache.ohlcv_cache import download_ohlcv_cached
 from tradingagents.dataflows.discovery.candidate import Candidate
 from tradingagents.dataflows.discovery.discovery_config import DiscoveryConfig
 from tradingagents.dataflows.discovery.utils import (
@@ -298,6 +299,67 @@ class CandidateFilter:
             f"Priority breakdown: {critical_priority} critical, {high_priority} high, {medium_priority} medium, {low_priority} low"
         )
 
+    def _price_from_cache(
+        self, ticker: str, ohlcv_data: Dict[str, Any]
+    ) -> Any:
+        """Return last closing price from OHLCV cache, or None if ticker missing."""
+        df = ohlcv_data.get(ticker.upper())
+        if df is None or df.empty or "Close" not in df.columns:
+            return None
+        close = df["Close"].dropna()
+        if close.empty:
+            return None
+        return float(close.iloc[-1])
+
+    def _intraday_from_cache(
+        self, ticker: str, ohlcv_data: Dict[str, Any], threshold: float
+    ) -> Any:
+        """Compute day-over-day % change from last two daily closes.
+
+        Returns dict with 'already_moved' (bool) and 'intraday_change_pct' (float),
+        or None if ticker missing from cache or insufficient data.
+        """
+        df = ohlcv_data.get(ticker.upper())
+        if df is None or df.empty or "Close" not in df.columns:
+            return None
+        close = df["Close"].dropna()
+        if len(close) < 2:
+            return None
+        prev_close = float(close.iloc[-2])
+        last_close = float(close.iloc[-1])
+        if prev_close <= 0:
+            return None
+        pct = (last_close - prev_close) / prev_close * 100
+        return {
+            "already_moved": pct > threshold,
+            "intraday_change_pct": round(pct, 2),
+        }
+
+    def _recent_move_from_cache(
+        self, ticker: str, ohlcv_data: Dict[str, Any], lookback_days: int, threshold: float
+    ) -> Any:
+        """Compute % change over last N daily closes.
+
+        Returns dict with 'status' ('leading'|'lagging') and 'price_change_pct' (float),
+        or None if ticker missing from cache or insufficient data.
+        """
+        df = ohlcv_data.get(ticker.upper())
+        if df is None or df.empty or "Close" not in df.columns:
+            return None
+        close = df["Close"].dropna()
+        if len(close) < lookback_days + 1:
+            return None
+        price_start = float(close.iloc[-(lookback_days + 1)])
+        price_end = float(close.iloc[-1])
+        if price_start <= 0:
+            return None
+        pct = (price_end - price_start) / price_start * 100
+        reacted = abs(pct) >= threshold
+        return {
+            "status": "lagging" if reacted else "leading",
+            "price_change_pct": round(pct, 2),
+        }
+
     def _fetch_batch_volume(
         self, state: Dict[str, Any], candidates: List[Dict[str, Any]]
     ) -> Dict[str, Any]: