Fix remaining QA issues: technicals, caches, engine, screener

src/financial_agent/broker/alpaca_client.py

@@ -213,12 +213,8 @@ def submit_order(self, order: TradeOrder, dry_run: bool = True) -> dict[str, Any
             log.info("dry_run_order", order=order.model_dump())
             return {"status": "dry_run", "order": order.model_dump()}
 
-        # Detect crypto: check asset_class or symbol pattern (e.g. SOLUSD, BTC/USD)
-        is_crypto = (
-            order.asset_class == AssetClass.CRYPTO
-            or "/" in order.symbol
-            or order.symbol.endswith("USD")
-        )
+        # Detect crypto: check asset_class or "/" in symbol (e.g. BTC/USD)
+        is_crypto = order.asset_class == AssetClass.CRYPTO or "/" in order.symbol
         tif = TimeInForce.GTC if is_crypto else TimeInForce.DAY
         side = OrderSide.BUY if order.side == "buy" else OrderSide.SELL
 

src/financial_agent/data/crypto_market.py

@@ -167,6 +167,8 @@ def _load_cache(self) -> CryptoMarketContext | None:
 
             raw = json.loads(cache_path.read_text())
             cached_time = datetime.fromisoformat(raw["timestamp"])
+            if cached_time.tzinfo is None:
+                cached_time = cached_time.replace(tzinfo=UTC)
             age_hours = (datetime.now(tz=UTC) - cached_time).total_seconds() / 3600
 
             if age_hours > _CACHE_MAX_AGE_HOURS:

src/financial_agent/data/earnings.py

@@ -79,6 +79,8 @@ def _load_cache(self, symbols: list[str]) -> list[EarningsEvent]:
 
             raw = json.loads(cache_path.read_text())
             cached_time = datetime.fromisoformat(raw["timestamp"])
+            if cached_time.tzinfo is None:
+                cached_time = cached_time.replace(tzinfo=UTC)
             age_hours = (datetime.now(tz=UTC) - cached_time).total_seconds() / 3600
 
             if age_hours > _CACHE_MAX_AGE_HOURS:
@@ -122,8 +124,13 @@ def _fetch_calendar(self, symbols: list[str]) -> list[EarningsEvent]:
         req = urllib.request.Request(url)  # noqa: S310
         req.add_header("User-Agent", "FinancialAgent/1.0")
 
-        with urllib.request.urlopen(req, timeout=10) as resp:  # noqa: S310
-            body = json.loads(resp.read().decode())
+        try:
+            with urllib.request.urlopen(req, timeout=10) as resp:  # noqa: S310
+                body = json.loads(resp.read().decode())
+        except urllib.error.HTTPError as e:
+            error_body = e.read().decode() if e.fp else ""
+            log.warning("earnings_http_error", status=e.code, error=error_body[:200])
+            raise
 
         # Handle dict response (FMP stable API may return a single object)
         if isinstance(body, dict):
@@ -151,6 +158,8 @@ def _fetch_calendar(self, symbols: list[str]) -> list[EarningsEvent]:
                 continue
 
             days_until = (earnings_date - today).days
+            if days_until < 0:
+                continue
 
             events.append(
                 EarningsEvent(

src/financial_agent/data/fundamentals.py

@@ -107,6 +107,8 @@ def _load_cache(self, symbols: list[str]) -> dict[str, FundamentalData]:
 
             raw = json.loads(cache_path.read_text())
             cached_time = datetime.fromisoformat(raw["timestamp"])
+            if cached_time.tzinfo is None:
+                cached_time = cached_time.replace(tzinfo=UTC)
             age_hours = (datetime.now(tz=UTC) - cached_time).total_seconds() / 3600
 
             if age_hours > _CACHE_MAX_AGE_HOURS:

src/financial_agent/screener_main.py

@@ -78,6 +78,26 @@ def main() -> None:
     # Add relative strength
     technicals = technical.compute_relative_strength(technicals, "SPY")
 
+    # Fetch VIX for adaptive thresholds
+    vix_level: float | None = None
+    try:
+        from financial_agent.data.macro import MacroProvider
+
+        macro = MacroProvider().fetch()
+        vix_level = macro.vix_level
+    except Exception:
+        log.debug("screener_vix_fetch_failed", exc_info=True)
+
+    # Adaptive thresholds based on market volatility
+    if vix_level is not None and vix_level < 15:
+        vol_thresh, move_thresh, rs_thresh = 3.0, 5.0, 85
+    elif vix_level is not None and vix_level > 25:
+        vol_thresh, move_thresh, rs_thresh = 1.5, 2.0, 70
+    else:
+        vol_thresh, move_thresh, rs_thresh = 2.0, 3.0, 80
+
+    log.info("screener_thresholds", vix=vix_level, vol=vol_thresh, move=move_thresh, rs=rs_thresh)
+
     # Screen for actionable setups
     alerts: list[dict[str, str]] = []
 
@@ -87,14 +107,14 @@ def main() -> None:
 
         reasons: list[str] = []
 
-        # Unusual volume (2x+ average)
+        # Unusual volume
         rel_vol = ind.get("relative_volume", 0)
-        if rel_vol >= 2.0:
+        if rel_vol >= vol_thresh:
             reasons.append(f"unusual volume ({rel_vol:.1f}x avg)")
 
-        # Big daily move (3%+)
+        # Big daily move
         daily_ret = abs(ind.get("daily_return_pct", 0))
-        if daily_ret >= 3.0:
+        if daily_ret >= move_thresh:
             direction = "up" if ind.get("daily_return_pct", 0) > 0 else "down"
             reasons.append(f"big move {direction} ({daily_ret:.1f}%)")
 
@@ -103,9 +123,9 @@ def main() -> None:
         if pct_from_high > -3.0:
             reasons.append(f"near 52w high ({pct_from_high:+.1f}%)")
 
-        # Strong relative strength (top 20%)
+        # Strong relative strength
         rs_rank = ind.get("rs_rank_pct", 0)
-        if rs_rank >= 80:
+        if rs_rank >= rs_thresh:
             reasons.append(f"strong RS (top {100 - rs_rank:.0f}%)")
 
         # Price above 200-day SMA with positive MACD
@@ -172,7 +192,19 @@ def main() -> None:
     ]
     _run_gh_command(cmd)
 
-    _write_github_output({"alerts": len(top_alerts)})
+    # Auto-add top 3 screener picks to watchlist for immediate trading
+    top_picks = [a["symbol"] for a in top_alerts[:3] if a["symbol"] not in current_watchlist]
+    if top_picks:
+        expanded = ",".join(sorted(current_watchlist | set(top_picks)))
+        try:
+            cmd_var = ["gh", "variable", "set", "TRADING_WATCHLIST", "--body", expanded]
+            ok, _ = _run_gh_command(cmd_var)
+            if ok:
+                log.info("screener_watchlist_expanded", added=top_picks, new_watchlist=expanded)
+        except Exception:
+            log.warning("screener_watchlist_update_failed", exc_info=True)
+
+    _write_github_output({"alerts": len(top_alerts), "auto_added": top_picks})
     log.info("screener_complete")
 
 

src/financial_agent/strategy/engine.py

@@ -246,15 +246,15 @@ def _size_buy_order(
 
         # Get current price: existing position > technicals > skip
         current_pos = portfolio.get_position(signal.symbol)
-        if current_pos:
+        if current_pos and current_pos.current_price > 0:
             est_price = current_pos.current_price
-        elif signal.symbol in technicals and "current_price" in technicals[signal.symbol]:
+        elif signal.symbol in technicals and technicals[signal.symbol].get("current_price", 0) > 0:
             est_price = technicals[signal.symbol]["current_price"]
         else:
             log.warning("skip_buy_no_price", symbol=signal.symbol)
             return None
 
-        qty = round(target_value / est_price, 2) if est_price > 0 else 0
+        qty = round(target_value / est_price, 2)
 
         if qty <= 0:
             return None
@@ -317,7 +317,7 @@ def _size_sell_order(
         # Limit order support for sells
         order_type = OrderType.MARKET
         limit_price: float | None = None
-        if self._data_config and self._data_config.use_limit_orders:
+        if self._data_config and self._data_config.use_limit_orders and position.current_price > 0:
             slippage = self._data_config.slippage_tolerance_pct
             order_type = OrderType.LIMIT
             limit_price = round(position.current_price * (1 - slippage), 2)

src/financial_agent/strategy/technical.py

@@ -2,13 +2,17 @@
 
 from __future__ import annotations
 
+import math
 from typing import TYPE_CHECKING
 
+import structlog
 import ta  # type: ignore[import-untyped]
 
 if TYPE_CHECKING:
     import pandas as pd
 
+log = structlog.get_logger()
+
 
 class TechnicalAnalyzer:
     """Compute technical indicators for a set of symbols."""
@@ -25,7 +29,8 @@ def compute_indicators(self, bars: pd.DataFrame) -> dict[str, dict[str, float]]:
             try:
                 df = bars.loc[symbol].copy()
                 results[symbol] = self._indicators_for_symbol(df)
-            except Exception:  # noqa: S112
+            except (KeyError, ValueError, IndexError) as e:
+                log.warning("indicator_calc_failed", symbol=symbol, error=str(e))
                 continue
 
         return results
@@ -93,23 +98,39 @@ def _indicators_for_symbol(self, df: pd.DataFrame) -> dict[str, float]:
 
         # Weekly trend proxy from daily data (Issue #22)
         if len(close) >= 60:
-            weekly_close = close.iloc[::5]  # Sample every 5 days
-            indicators["weekly_sma_10"] = weekly_close.rolling(window=10).mean().iloc[-1]
-            indicators["weekly_trend"] = (
-                1.0 if close.iloc[-1] > indicators.get("weekly_sma_10", 0) else -1.0
-            )
+            weekly_close = close.iloc[-60:].iloc[::5]  # Sample last 60 bars, every 5th
+            weekly_mean = weekly_close.rolling(window=10).mean().iloc[-1]
+            if not math.isnan(weekly_mean):
+                indicators["weekly_sma_10"] = weekly_mean
+                indicators["weekly_trend"] = 1.0 if close.iloc[-1] > weekly_mean else -1.0
 
         # Momentum indicators
         indicators["rsi_14"] = ta.momentum.rsi(close, window=14).iloc[-1]
         stoch = ta.momentum.StochasticOscillator(high, low, close)
         indicators["stoch_k"] = stoch.stoch().iloc[-1]
         indicators["stoch_d"] = stoch.stoch_signal().iloc[-1]
 
+        # ADX for trend strength (critical for momentum confirmation)
+        if len(close) >= 14:
+            adx_val = ta.trend.adx(high, low, close, window=14).iloc[-1]
+            if not math.isnan(adx_val):
+                indicators["adx_14"] = adx_val
+
+        # Rate of Change for momentum velocity
+        if len(close) >= 12:
+            roc_val = ta.momentum.roc(close, window=12).iloc[-1]
+            if not math.isnan(roc_val):
+                indicators["roc_12"] = roc_val
+
         # Volatility indicators
         bb = ta.volatility.BollingerBands(close)
         indicators["bb_upper"] = bb.bollinger_hband().iloc[-1]
         indicators["bb_lower"] = bb.bollinger_lband().iloc[-1]
         indicators["bb_width"] = bb.bollinger_wband().iloc[-1]
+        # Normalized BB width for cross-symbol comparison
+        bb_mid = (indicators["bb_upper"] + indicators["bb_lower"]) / 2
+        if bb_mid > 0:
+            indicators["bb_width_pct"] = (indicators["bb_width"] / bb_mid) * 100
         indicators["atr_14"] = ta.volatility.average_true_range(high, low, close).iloc[-1]
 
         # ATR as % of price (Issue #28: volatility-aware sizing)
@@ -130,7 +151,10 @@ def _indicators_for_symbol(self, df: pd.DataFrame) -> dict[str, float]:
         # Current price context
         indicators["current_price"] = current_price
         indicators["price_vs_sma20"] = (close.iloc[-1] / indicators["sma_20"] - 1) * 100
-        indicators["daily_return_pct"] = ((close.iloc[-1] / close.iloc[-2]) - 1) * 100
+        if len(close) >= 2:
+            indicators["daily_return_pct"] = ((close.iloc[-1] / close.iloc[-2]) - 1) * 100
+        else:
+            indicators["daily_return_pct"] = 0.0
 
         # Multi-period returns for relative strength (Issue #29)
         if len(close) >= 20:
@@ -150,7 +174,8 @@ def _indicators_for_symbol(self, df: pd.DataFrame) -> dict[str, float]:
         indicators["pct_from_52w_high"] = ((current_price / high_252) - 1) * 100
         indicators["pct_from_52w_low"] = ((current_price / low_252) - 1) * 100
 
-        return indicators
+        # Filter out NaN values to prevent downstream issues
+        return {k: v for k, v in indicators.items() if not (isinstance(v, float) and math.isnan(v))}
 
     def _support_resistance(
         self,