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+# Filter OHLCV Cache Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Replace three per-ticker yfinance HTTP calls in the filter stage with OHLCV cache lookups, eliminating rate limit errors that drop candidates before they reach the ranker.
+
+**Architecture:** Load `download_ohlcv_cached()` once at the start of `CandidateFilter.filter()` for the ~60 candidate tickers, pass the resulting dict into `_filter_and_enrich_candidates()`, and replace all three yfinance call sites (current price, intraday check, recent-move check) with cache lookups. The existing `get_stock_price()` call remains as a fallback for tickers missing from the cache.
+
+**Tech Stack:** Python, pandas, `tradingagents/dataflows/data_cache/ohlcv_cache.py` (`download_ohlcv_cached`), `tradingagents/dataflows/discovery/filter.py`
+
+---
+
+## File Map
+
+| File | Change |
+|---|---|
+| `tradingagents/dataflows/discovery/filter.py` | Main changes: load cache in `filter()`, remove `_fetch_batch_prices()`, update `_filter_and_enrich_candidates()` signature and three call sites |
+| `tests/test_filter_ohlcv_cache.py` | New test file |
+
+---
+
+### Task 1: Write failing tests
+
+**Files:**
+- Create: `tests/test_filter_ohlcv_cache.py`
+
+- [ ] **Step 1: Create the test file**
+
+```python
+# tests/test_filter_ohlcv_cache.py
+"""Tests for OHLCV-cache-backed filter enrichment."""
+import pandas as pd
+import pytest
+from unittest.mock import MagicMock, patch
+
+
+def _make_ohlcv(closes: list[float]) -> pd.DataFrame:
+    """Build a minimal OHLCV DataFrame from a list of closing prices."""
+    dates = pd.date_range("2026-01-01", periods=len(closes), freq="B")
+    return pd.DataFrame(
+        {
+            "Open": closes,
+            "High": closes,
+            "Low": closes,
+            "Close": closes,
+            "Volume": [1_000_000] * len(closes),
+        },
+        index=dates,
+    )
+
+
+def _make_filter(config_overrides=None):
+    """Instantiate a CandidateFilter with minimal config."""
+    from tradingagents.dataflows.discovery.filter import CandidateFilter
+
+    config = {
+        "discovery": {
+            "ohlcv_cache_dir": "data/ohlcv_cache",
+            "filters": {
+                "min_average_volume": 0,
+                "volume_lookback_days": 10,
+                "filter_same_day_movers": True,
+                "intraday_movement_threshold": 10.0,
+                "filter_recent_movers": True,
+                "recent_movement_lookback_days": 7,
+                "recent_movement_threshold": 10.0,
+                "recent_mover_action": "filter",
+                "volume_cache_key": "default",
+                "min_market_cap": 0,
+                "compression_atr_pct_max": 2.0,
+                "compression_bb_width_max": 6.0,
+                "compression_min_volume_ratio": 1.3,
+                "filter_fundamental_risk": False,
+                "min_z_score": None,
+                "min_f_score": None,
+            },
+            "enrichment": {
+                "batch_news_vendor": "google",
+                "batch_news_batch_size": 150,
+                "news_lookback_days": 0.5,
+                "context_max_snippets": 2,
+                "context_snippet_max_chars": 140,
+            },
+            "max_candidates_to_analyze": 200,
+            "analyze_all_candidates": False,
+            "final_recommendations": 15,
+            "truncate_ranking_context": False,
+            "max_news_chars": 500,
+            "max_insider_chars": 300,
+            "max_recommendations_chars": 300,
+            "log_tool_calls": False,
+            "log_tool_calls_console": False,
+            "log_prompts_console": False,
+            "tool_log_max_chars": 10_000,
+            "tool_log_exclude": [],
+        }
+    }
+    if config_overrides:
+        config["discovery"]["filters"].update(config_overrides)
+    return CandidateFilter(config)
+
+
+def test_current_price_comes_from_ohlcv_cache():
+    """current_price on the candidate should be the last close from the OHLCV cache."""
+    closes = [100.0] * 210 + [123.45]  # last close = 123.45
+    ohlcv_data = {"AAPL": _make_ohlcv(closes)}
+
+    f = _make_filter()
+    candidate = {"ticker": "AAPL", "source": "test", "context": "test", "priority": "medium"}
+
+    price = f._price_from_cache("AAPL", ohlcv_data)
+    assert price == pytest.approx(123.45)
+
+
+def test_intraday_check_from_cache_not_moved():
+    """intraday check: <10% day-over-day change → already_moved=False."""
+    closes = [100.0] * 210 + [105.0]  # +5% last day — under threshold
+    ohlcv_data = {"AAPL": _make_ohlcv(closes)}
+
+    f = _make_filter()
+    result = f._intraday_from_cache("AAPL", ohlcv_data, threshold=10.0)
+    assert result["already_moved"] is False
+    assert result["intraday_change_pct"] == pytest.approx(5.0)
+
+
+def test_intraday_check_from_cache_moved():
+    """intraday check: >10% day-over-day change → already_moved=True."""
+    closes = [100.0] * 210 + [115.0]  # +15% last day — over threshold
+    ohlcv_data = {"AAPL": _make_ohlcv(closes)}
+
+    f = _make_filter()
+    result = f._intraday_from_cache("AAPL", ohlcv_data, threshold=10.0)
+    assert result["already_moved"] is True
+    assert result["intraday_change_pct"] == pytest.approx(15.0)
+
+
+def test_recent_move_check_from_cache_leading():
+    """recent-move check: <10% change over 7 days → status=leading."""
+    closes = [100.0] * 205 + [103.0] * 7  # flat last 7 days
+    ohlcv_data = {"AAPL": _make_ohlcv(closes)}
+
+    f = _make_filter()
+    result = f._recent_move_from_cache("AAPL", ohlcv_data, lookback_days=7, threshold=10.0)
+    assert result["status"] == "leading"
+    assert abs(result["price_change_pct"]) < 10.0
+
+
+def test_recent_move_check_from_cache_lagging():
+    """recent-move check: >10% change over 7 days → status=lagging."""
+    closes = [100.0] * 205 + [100.0] * 6 + [115.0]  # +15% in last day within window
+    ohlcv_data = {"AAPL": _make_ohlcv(closes)}
+
+    f = _make_filter()
+    result = f._recent_move_from_cache("AAPL", ohlcv_data, lookback_days=7, threshold=10.0)
+    assert result["status"] == "lagging"
+
+
+def test_cache_miss_returns_none():
+    """If ticker is not in ohlcv_data, helper returns None."""
+    f = _make_filter()
+    assert f._price_from_cache("MISSING", {}) is None
+    assert f._intraday_from_cache("MISSING", {}, threshold=10.0) is None
+    assert f._recent_move_from_cache("MISSING", {}, lookback_days=7, threshold=10.0) is None
+```
+
+- [ ] **Step 2: Run tests to verify they fail**
+
+```bash
+cd /Users/youssefaitousarrah/Documents/TradingAgents
+python -m pytest tests/test_filter_ohlcv_cache.py -v 2>&1 | tail -20
+```
+
+Expected: All 5 tests FAIL with `AttributeError: 'CandidateFilter' object has no attribute '_price_from_cache'`
+
+---
+
+### Task 2: Add the three cache helper methods
+
+**Files:**
+- Modify: `tradingagents/dataflows/discovery/filter.py`
+
+- [ ] **Step 1: Add import at top of filter.py**
+
+In `tradingagents/dataflows/discovery/filter.py`, add to the existing imports block (after line 6, `import pandas as pd`):
+
+```python
+from tradingagents.dataflows.data_cache.ohlcv_cache import download_ohlcv_cached
+```
+
+- [ ] **Step 2: Add the three helper methods to `CandidateFilter`**
+
+Add these three methods to `CandidateFilter` class, just before the `_fetch_batch_volume` method (around line 301):
+
+```python
+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),
+    }
+```
+
+- [ ] **Step 3: Run tests — should pass now**
+
+```bash
+python -m pytest tests/test_filter_ohlcv_cache.py -v 2>&1 | tail -20
+```
+
+Expected: All 5 tests PASS
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add tradingagents/dataflows/discovery/filter.py tests/test_filter_ohlcv_cache.py
+git commit -m "feat(filter): add OHLCV cache helper methods for price/intraday/recent-move"
+```
+
+---
+
+### Task 3: Load OHLCV cache in `filter()` and wire into `_filter_and_enrich_candidates()`
+
+**Files:**
+- Modify: `tradingagents/dataflows/discovery/filter.py:151-188` (the `filter()` method)
+- Modify: `tradingagents/dataflows/discovery/filter.py:409` (`_filter_and_enrich_candidates()` signature)
+
+- [ ] **Step 1: Update `filter()` to load OHLCV cache and remove `_fetch_batch_prices` call**
+
+Replace lines 172-188 in `filter()`:
+
+```python
+        # Was:
+        volume_by_ticker = self._fetch_batch_volume(state, candidates)
+        news_by_ticker = self._fetch_batch_news(start_date, end_date, candidates)
+        price_by_ticker = self._fetch_batch_prices(candidates)
+
+        (
+            filtered_candidates,
+            filtered_reasons,
+            failed_tickers,
+            delisted_cache,
+        ) = self._filter_and_enrich_candidates(
+            state=state,
+            candidates=candidates,
+            volume_by_ticker=volume_by_ticker,
+            news_by_ticker=news_by_ticker,
+            price_by_ticker=price_by_ticker,
+            end_date=end_date,
+        )
+```
+
+With:
+
+```python
+        volume_by_ticker = self._fetch_batch_volume(state, candidates)
+        news_by_ticker = self._fetch_batch_news(start_date, end_date, candidates)
+
+        # Load OHLCV cache for candidate tickers — replaces per-ticker yfinance calls
+        cache_dir = self.config.get("discovery", {}).get("ohlcv_cache_dir", "data/ohlcv_cache")
+        candidate_tickers = [c["ticker"].upper() for c in candidates if c.get("ticker")]
+        logger.info(f"Loading OHLCV cache for {len(candidate_tickers)} candidate tickers...")
+        ohlcv_data = download_ohlcv_cached(candidate_tickers, period="1y", cache_dir=cache_dir)
+        logger.info(f"OHLCV cache loaded for {len(ohlcv_data)}/{len(candidate_tickers)} tickers")
+
+        (
+            filtered_candidates,
+            filtered_reasons,
+            failed_tickers,
+            delisted_cache,
+        ) = self._filter_and_enrich_candidates(
+            state=state,
+            candidates=candidates,
+            volume_by_ticker=volume_by_ticker,
+            news_by_ticker=news_by_ticker,
+            ohlcv_data=ohlcv_data,
+            end_date=end_date,
+        )
+```
+
+- [ ] **Step 2: Update `_filter_and_enrich_candidates()` signature**
+
+Change the method signature at line ~409 from:
+
+```python
+    def _filter_and_enrich_candidates(
+        self,
+        state: Dict[str, Any],
+        candidates: List[Dict[str, Any]],
+        volume_by_ticker: Dict[str, Any],
+        news_by_ticker: Dict[str, Any],
+        price_by_ticker: Dict[str, float],
+        end_date: str,
+    ):
+```
+
+To:
+
+```python
+    def _filter_and_enrich_candidates(
+        self,
+        state: Dict[str, Any],
+        candidates: List[Dict[str, Any]],
+        volume_by_ticker: Dict[str, Any],
+        news_by_ticker: Dict[str, Any],
+        ohlcv_data: Dict[str, Any],
+        end_date: str,
+    ):
+```
+
+- [ ] **Step 3: Run existing tests to make sure nothing is broken**
+
+```bash
+python -m pytest tests/test_filter_ohlcv_cache.py -v 2>&1 | tail -10
+```
+
+Expected: All 5 PASS (the helper methods don't depend on the signature change)
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add tradingagents/dataflows/discovery/filter.py
+git commit -m "feat(filter): load OHLCV cache in filter() and update _filter_and_enrich_candidates signature"
+```
+
+---
+
+### Task 4: Replace the three yfinance call sites in `_filter_and_enrich_candidates`
+
+**Files:**
+- Modify: `tradingagents/dataflows/discovery/filter.py:435-534`
+
+- [ ] **Step 1: Replace `check_intraday_movement` call site**
+
+Find this block (around line 440):
+
+```python
+                if self.filter_same_day_movers:
+                    from tradingagents.dataflows.y_finance import check_intraday_movement
+
+                    try:
+                        intraday_check = check_intraday_movement(
+                            ticker=ticker, movement_threshold=self.intraday_movement_threshold
+                        )
+
+                        # Skip if already moved significantly today
+                        if intraday_check.get("already_moved"):
+                            filtered_reasons["intraday_moved"] += 1
+                            intraday_pct = intraday_check.get("intraday_change_pct", 0)
+                            logger.info(
+                                f"Filtered {ticker}: Already moved {intraday_pct:+.1f}% today (stale)"
+                            )
+                            continue
+
+                        # Add intraday data to candidate metadata for ranking
+                        cand["intraday_change_pct"] = intraday_check.get("intraday_change_pct", 0)
+
+                    except Exception as e:
+                        # Don't filter out if check fails, just log
+                        logger.warning(f"Could not check intraday movement for {ticker}: {e}")
+```
+
+Replace with:
+
+```python
+                if self.filter_same_day_movers:
+                    intraday_check = self._intraday_from_cache(
+                        ticker, ohlcv_data, self.intraday_movement_threshold
+                    )
+                    if intraday_check is not None:
+                        if intraday_check.get("already_moved"):
+                            filtered_reasons["intraday_moved"] += 1
+                            intraday_pct = intraday_check.get("intraday_change_pct", 0)
+                            logger.info(
+                                f"Filtered {ticker}: Already moved {intraday_pct:+.1f}% today (stale)"
+                            )
+                            continue
+                        cand["intraday_change_pct"] = intraday_check.get("intraday_change_pct", 0)
+```
+
+- [ ] **Step 2: Replace `check_if_price_reacted` call site**
+
+Find this block (around line 465):
+
+```python
+                if self.filter_recent_movers:
+                    from tradingagents.dataflows.y_finance import check_if_price_reacted
+
+                    try:
+                        reaction = check_if_price_reacted(
+                            ticker=ticker,
+                            lookback_days=self.recent_movement_lookback_days,
+                            reaction_threshold=self.recent_movement_threshold,
+                        )
+                        cand["recent_change_pct"] = reaction.get("price_change_pct")
+                        cand["recent_move_status"] = reaction.get("status")
+
+                        if reaction.get("status") == "lagging":
+                            if self.recent_mover_action == "filter":
+                                filtered_reasons["recent_moved"] += 1
+                                change_pct = reaction.get("price_change_pct", 0)
+                                logger.info(
+                                    f"Filtered {ticker}: Already moved {change_pct:+.1f}% in last "
+                                    f"{self.recent_movement_lookback_days} days"
+                                )
+                                continue
+                            if self.recent_mover_action == "deprioritize":
+                                cand["priority"] = "low"
+                                existing_context = cand.get("context", "")
+                                change_pct = reaction.get("price_change_pct", 0)
+                                cand["context"] = (
+                                    f"{existing_context} | ⚠️ Recent move: {change_pct:+.1f}% "
+                                    f"over {self.recent_movement_lookback_days}d"
+                                )
+                    except Exception as e:
+                        logger.warning(f"Could not check recent movement for {ticker}: {e}")
+```
+
+Replace with:
+
+```python
+                if self.filter_recent_movers:
+                    reaction = self._recent_move_from_cache(
+                        ticker,
+                        ohlcv_data,
+                        self.recent_movement_lookback_days,
+                        self.recent_movement_threshold,
+                    )
+                    if reaction is not None:
+                        cand["recent_change_pct"] = reaction.get("price_change_pct")
+                        cand["recent_move_status"] = reaction.get("status")
+
+                        if reaction.get("status") == "lagging":
+                            if self.recent_mover_action == "filter":
+                                filtered_reasons["recent_moved"] += 1
+                                change_pct = reaction.get("price_change_pct", 0)
+                                logger.info(
+                                    f"Filtered {ticker}: Already moved {change_pct:+.1f}% in last "
+                                    f"{self.recent_movement_lookback_days} days"
+                                )
+                                continue
+                            if self.recent_mover_action == "deprioritize":
+                                cand["priority"] = "low"
+                                existing_context = cand.get("context", "")
+                                change_pct = reaction.get("price_change_pct", 0)
+                                cand["context"] = (
+                                    f"{existing_context} | ⚠️ Recent move: {change_pct:+.1f}% "
+                                    f"over {self.recent_movement_lookback_days}d"
+                                )
+```
+
+- [ ] **Step 3: Replace `get_stock_price` / `price_by_ticker` call site**
+
+Find this block (around line 520):
+
+```python
+                try:
+                    from tradingagents.dataflows.y_finance import get_fundamentals, get_stock_price
+
+                    # Get current price — prefer batch result, fall back to per-ticker
+                    current_price = price_by_ticker.get(ticker.upper())
+                    if current_price is None:
+                        current_price = get_stock_price(ticker)
+                    cand["current_price"] = current_price
+```
+
+Replace with:
+
+```python
+                try:
+                    from tradingagents.dataflows.y_finance import get_fundamentals, get_stock_price
+
+                    # Get current price — prefer OHLCV cache, fall back to per-ticker yfinance
+                    current_price = self._price_from_cache(ticker, ohlcv_data)
+                    if current_price is None:
+                        current_price = get_stock_price(ticker)
+                    cand["current_price"] = current_price
+```
+
+- [ ] **Step 4: Run all filter tests**
+
+```bash
+python -m pytest tests/test_filter_ohlcv_cache.py -v 2>&1 | tail -10
+```
+
+Expected: All 5 PASS
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add tradingagents/dataflows/discovery/filter.py
+git commit -m "feat(filter): replace per-ticker yfinance calls with OHLCV cache lookups"
+```
+
+---
+
+### Task 5: Remove `_fetch_batch_prices` method
+
+**Files:**
+- Modify: `tradingagents/dataflows/discovery/filter.py:357-407`
+
+- [ ] **Step 1: Delete `_fetch_batch_prices`**
+
+Remove the entire method from line 357 to 407:
+
+```python
+    def _fetch_batch_prices(self, candidates: List[Dict[str, Any]]) -> Dict[str, float]:
+        """Batch-fetch current prices for all candidates in one request.
+        ...
+        """
+        # ... entire method body
+```
+
+The method is no longer called from anywhere after Task 3.
+
+- [ ] **Step 2: Verify tests still pass**
+
+```bash
+python -m pytest tests/test_filter_ohlcv_cache.py -v 2>&1 | tail -10
+```
+
+Expected: All 5 PASS
+
+- [ ] **Step 3: Commit**
+
+```bash
+git add tradingagents/dataflows/discovery/filter.py
+git commit -m "refactor(filter): remove _fetch_batch_prices (replaced by OHLCV cache)"
+```
+
+---
+
+### Task 6: Push and trigger a discovery run to validate
+
+- [ ] **Step 1: Push all commits**
+
+```bash
+git push origin main
+```
+
+- [ ] **Step 2: Trigger a discovery run**
+
+```bash
+gh workflow run "Daily Discovery" --repo Aitous/TradingAgents --ref main
+```
+
+- [ ] **Step 3: Monitor and verify**
+
+```bash
+gh run list --repo Aitous/TradingAgents --workflow "Daily Discovery" --limit 1
+```
+
+Watch for:
+- Zero `"Too Many Requests"` errors in the filter stage
+- `"OHLCV cache loaded for X/Y tickers"` log line showing high hit rate (expect >95%)
+- `"Starting candidates: N"` in filter summary — should be close to the full scan count (~60), not 11
+- Ranker receiving more than 11 candidates
+- Final recommendations > 2 picks