Atomic writes, improved sentiment, dynamic calendar, 10Y yield

src/financial_agent/data/macro.py

@@ -14,21 +14,15 @@
 
 _YAHOO_CHART = "https://query1.finance.yahoo.com/v8/finance/chart"
 
-# Hardcoded economic calendar events by month for 2026.
-_UPCOMING_EVENTS_2026: dict[int, list[str]] = {
-    1: ["FOMC Meeting Jan 27-28", "CPI Release Jan 14", "Jobs Report Jan 9"],
-    2: ["CPI Release Feb 12", "Jobs Report Feb 6", "Retail Sales Feb 14"],
-    3: ["FOMC Meeting Mar 17-18", "CPI Release Mar 11", "Jobs Report Mar 6"],
-    4: ["CPI Release Apr 10", "Jobs Report Apr 3", "Retail Sales Apr 15"],
-    5: ["FOMC Meeting May 5-6", "CPI Release May 13", "Jobs Report May 8"],
-    6: ["FOMC Meeting Jun 16-17", "CPI Release Jun 10", "Jobs Report Jun 5"],
-    7: ["CPI Release Jul 14", "Jobs Report Jul 2", "Retail Sales Jul 16"],
-    8: ["FOMC Meeting Aug 4-5", "CPI Release Aug 12", "Jobs Report Aug 7"],
-    9: ["FOMC Meeting Sep 15-16", "CPI Release Sep 10", "Jobs Report Sep 4"],
-    10: ["CPI Release Oct 13", "Jobs Report Oct 2", "Retail Sales Oct 16"],
-    11: ["FOMC Meeting Nov 3-4", "CPI Release Nov 12", "Jobs Report Nov 6"],
-    12: ["FOMC Meeting Dec 15-16", "CPI Release Dec 10", "Jobs Report Dec 4"],
-}
+# FOMC meetings are held ~8 times/year on a fixed schedule.
+# CPI is released around the 10th-14th of each month.
+# Jobs Report (NFP) is the first Friday of each month.
+# These approximate patterns hold year over year.
+_RECURRING_EVENTS: list[str] = [
+    "FOMC Meeting (check federalreserve.gov for exact dates)",
+    "CPI Release (~10th-14th of month)",
+    "Jobs Report (1st Friday of month)",
+]
 
 
 class MacroProvider:
@@ -52,14 +46,15 @@ def _build_context(self) -> MacroContext:
         """Build the full macro context from multiple data sources."""
         vix_level, vix_trend = self._fetch_vix()
         spy_trend = self._fetch_spy_trend()
+        ten_year_yield = self._fetch_ten_year_yield()
         regime = _determine_regime(vix_level)
         events = _get_upcoming_events()
 
         return MacroContext(
             vix_level=vix_level,
             vix_trend=vix_trend,
             spy_trend=spy_trend,
-            ten_year_yield=None,
+            ten_year_yield=ten_year_yield,
             market_regime=regime,
             upcoming_events=events,
         )
@@ -88,6 +83,17 @@ def _fetch_vix(self) -> tuple[float | None, str]:
             log.warning("vix_fetch_error", exc_info=True)
             return None, "stable"
 
+    def _fetch_ten_year_yield(self) -> float | None:
+        """Fetch the 10-year Treasury yield from Yahoo Finance."""
+        try:
+            data = _yahoo_chart("%5ETNX", "5d", "1d")
+            closes = _extract_closes(data)
+            if closes:
+                return round(closes[-1], 2)
+        except Exception:
+            log.debug("ten_year_yield_fetch_failed", exc_info=True)
+        return None
+
     def _fetch_spy_trend(self) -> str:
         """Determine SPY trend relative to its recent moving average."""
         try:
@@ -169,6 +175,28 @@ def _determine_regime(vix_level: float | None) -> str:
 
 
 def _get_upcoming_events() -> list[str]:
-    """Return hardcoded upcoming economic events for the current month."""
-    current_month = date.today().month
-    return _UPCOMING_EVENTS_2026.get(current_month, [])
+    """Return approximate upcoming economic events.
+
+    Uses recurring patterns rather than hardcoded dates, so it works
+    across years without manual updates.
+    """
+    today = date.today()
+    events: list[str] = []
+
+    # Jobs report: first Friday of the month
+    first_day = today.replace(day=1)
+    # Monday=0 ... Friday=4; days until first Friday
+    days_to_friday = (4 - first_day.weekday()) % 7
+    first_friday = first_day.replace(day=1 + days_to_friday)
+    if first_friday >= today:
+        events.append(f"Jobs Report {first_friday.strftime('%b %d')}")
+
+    # CPI: typically around the 10th-14th
+    cpi_approx = today.replace(day=12)
+    if cpi_approx >= today:
+        events.append(f"CPI Release ~{cpi_approx.strftime('%b %d')}")
+
+    # Generic FOMC reminder (meets ~8x/year, roughly every 6 weeks)
+    events.append("FOMC (check schedule)")
+
+    return events

src/financial_agent/data/news.py

@@ -18,10 +18,69 @@
 _FINNHUB_BASE = "https://finnhub.io/api/v1"
 
 _POSITIVE_WORDS: frozenset[str] = frozenset(
-    {"surge", "beat", "upgrade", "growth", "profit", "record", "breakout"}
+    {
+        "surge",
+        "surges",
+        "surging",
+        "beat",
+        "beats",
+        "upgrade",
+        "upgrades",
+        "growth",
+        "profit",
+        "profits",
+        "record",
+        "breakout",
+        "rally",
+        "rallies",
+        "soar",
+        "soars",
+        "gains",
+        "bullish",
+        "outperform",
+        "outperforms",
+        "raises",
+        "boost",
+        "boosted",
+        "jumps",
+        "strong",
+        "exceeds",
+        "tops",
+    }
 )
 _NEGATIVE_WORDS: frozenset[str] = frozenset(
-    {"miss", "downgrade", "decline", "loss", "cut", "warning", "crash"}
+    {
+        "miss",
+        "misses",
+        "downgrade",
+        "downgrades",
+        "decline",
+        "declines",
+        "loss",
+        "losses",
+        "cut",
+        "cuts",
+        "warning",
+        "warns",
+        "crash",
+        "crashes",
+        "plunge",
+        "plunges",
+        "falls",
+        "bearish",
+        "underperform",
+        "underperforms",
+        "lowers",
+        "slump",
+        "slumps",
+        "drops",
+        "weak",
+        "layoffs",
+        "recall",
+    }
+)
+_NEGATION_WORDS: frozenset[str] = frozenset(
+    {"not", "no", "never", "neither", "nor", "doesn't", "didn't", "won't", "isn't", "aren't"}
 )
 
 _MAX_SYMBOLS = 5
@@ -111,19 +170,27 @@ def _fetch_symbol_news(self, symbol: str) -> NewsSentiment | None:
 
 
 def _compute_headline_sentiment(headline: str) -> float:
-    """Compute a simple keyword-based sentiment score for a headline.
+    """Compute keyword-based sentiment with negation handling.
 
-    Returns a float in [-1.0, 1.0].
+    Returns a float in [-1.0, 1.0]. Negation words ("not", "no", etc.)
+    flip the polarity of the next sentiment word.
     """
     words = headline.lower().split()
     score = 0.0
+    negate = False
 
     for word in words:
-        # Strip punctuation for matching
         cleaned = word.strip(".,!?;:'\"()-")
+        if cleaned in _NEGATION_WORDS:
+            negate = True
+            continue
         if cleaned in _POSITIVE_WORDS:
-            score += 0.3
+            score += -0.3 if negate else 0.3
+            negate = False
         elif cleaned in _NEGATIVE_WORDS:
-            score -= 0.3
+            score += 0.3 if negate else -0.3
+            negate = False
+        else:
+            negate = False
 
     return max(-1.0, min(1.0, score))

src/financial_agent/performance/benchmarking.py

@@ -9,6 +9,8 @@
 import structlog
 from pydantic import BaseModel, Field
 
+from financial_agent.utils.io import atomic_write
+
 log = structlog.get_logger()
 
 
@@ -51,10 +53,10 @@ def _load(self) -> None:
             self._trades = []
 
     def _save(self) -> None:
-        """Persist the trade journal to disk, keeping only the last 1000 trades."""
+        """Persist the trade journal to disk atomically, keeping only the last 1000 trades."""
         self._trades = self._trades[-1000:]
         data = [record.model_dump() for record in self._trades]
-        self._path.write_text(json.dumps(data, indent=2), encoding="utf-8")
+        atomic_write(self._path, json.dumps(data, indent=2))
         log.debug("trade_journal_saved", count=len(self._trades))
 
     def record_trade(self, record: TradeRecord) -> None:

src/financial_agent/persistence/equity_tracker.py

@@ -9,6 +9,8 @@
 import structlog
 from pydantic import BaseModel
 
+from financial_agent.utils.io import atomic_write
+
 log = structlog.get_logger()
 
 _MAX_HISTORY = 365
@@ -85,14 +87,8 @@ def _save(self) -> None:
             trimmed = self._history[-_MAX_HISTORY:]
             self._history = trimmed
             data = [record.model_dump() for record in self._history]
-            self._path.write_text(
-                json.dumps(data, indent=2),
-                encoding="utf-8",
-            )
-            self._peak_path.write_text(
-                json.dumps({"peak": self._peak_equity}),
-                encoding="utf-8",
-            )
+            atomic_write(self._path, json.dumps(data, indent=2))
+            atomic_write(self._peak_path, json.dumps({"peak": self._peak_equity}))
         except Exception:
             log.error(
                 "equity_save_failed",

src/financial_agent/persistence/thesis_store.py

@@ -9,6 +9,8 @@
 import structlog
 from pydantic import BaseModel, Field
 
+from financial_agent.utils.io import atomic_write
+
 log = structlog.get_logger()
 
 
@@ -68,13 +70,10 @@ def _load(self) -> None:
             self._theses = {}
 
     def _save(self) -> None:
-        """Write all theses to disk."""
+        """Write all theses to disk atomically."""
         try:
             data = {symbol: thesis.model_dump() for symbol, thesis in self._theses.items()}
-            self._path.write_text(
-                json.dumps(data, indent=2),
-                encoding="utf-8",
-            )
+            atomic_write(self._path, json.dumps(data, indent=2))
         except Exception:
             log.error(
                 "theses_save_failed",
@@ -187,9 +186,9 @@ def _prune_expired_cooldowns(self, max_age_hours: int = 72) -> None:
             self._save_cooldowns()
 
     def _save_cooldowns(self) -> None:
-        """Write sell cooldown timestamps to disk."""
+        """Write sell cooldown timestamps to disk atomically."""
         try:
-            self._cooldown_path.write_text(json.dumps(self._cooldowns, indent=2), encoding="utf-8")
+            atomic_write(self._cooldown_path, json.dumps(self._cooldowns, indent=2))
         except Exception:
             log.debug("cooldowns_save_failed", exc_info=True)
 

src/financial_agent/utils/io.py

@@ -0,0 +1,18 @@
+"""File I/O utilities for safe persistence."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path  # noqa: TC003 — used at runtime
+
+
+def atomic_write(path: Path, content: str) -> None:
+    """Write content to a file atomically using temp file + rename.
+
+    Prevents data corruption if the process crashes or two agents write
+    concurrently. On both POSIX and Windows, ``os.replace`` is atomic
+    at the filesystem level.
+    """
+    tmp_path = path.with_suffix(".tmp")
+    tmp_path.write_text(content, encoding="utf-8")
+    os.replace(tmp_path, path)