feat: instrument model

backend/routers/instrument_models.py

@@ -229,6 +229,95 @@ def timeline_whatif(
         conn.close()
 
 
+# ── Magnitude estimation ──────────────────────────────────────────────────────
+
+def _parse_num(s: Optional[str]) -> Optional[float]:
+    if not s:
+        return None
+    t = str(s).strip().upper()
+    try:
+        if t.endswith('K'):   return float(t[:-1]) * 1_000
+        if t.endswith('M'):   return float(t[:-1]) * 1_000_000
+        if t.endswith('B'):   return float(t[:-1]) * 1_000_000_000
+        return float(s.replace('%', '').replace(',', ''))
+    except Exception:
+        return None
+
+
+def _event_scale(event_name: str) -> tuple[float, float]:
+    """(pips_per_unit, surprise_threshold) for rule-based estimation."""
+    n = event_name.lower()
+    if any(k in n for k in ['non-farm employment change', 'nfp']):
+        return 0.0007, 20_000      # per job
+    if 'adp non-farm' in n:
+        return 0.0005, 15_000
+    if 'unemployment claims' in n:
+        return -0.0003, 8_000      # more claims = worse = negative
+    if any(k in n for k in ['cpi', 'pce', 'ppi', 'inflation', 'hicp']):
+        return 80.0, 0.1           # per %
+    if any(k in n for k in ['interest rate', 'rate decision', 'fed fund']):
+        return 300.0, 0.1
+    if any(k in n for k in ['pmi', 'ism']):
+        return 6.0, 0.5
+    if 'gdp' in n:
+        return 70.0, 0.1
+    if any(k in n for k in ['retail sales', 'consumer spending']):
+        return 55.0, 0.2
+    if 'unemployment rate' in n:
+        return -100.0, 0.1         # higher rate = worse
+    if 'employment change' in n:
+        return 0.0005, 15_000
+    return 25.0, 0.5               # default
+
+
+_INST_DIR: Dict[tuple, int] = {
+    ("EURUSD", "USD"): -1,  ("EURUSD", "EUR"): +1,
+    ("USDJPY", "USD"): +1,  ("USDJPY", "JPY"): -1,
+    ("GBPUSD", "USD"): -1,  ("GBPUSD", "GBP"): +1,
+    ("XAUUSD", "USD"): -1,
+    ("SP500",  "USD"): +1,
+    ("TLT",    "USD"): -1,
+    ("EEM",    "USD"): -1,
+    ("QQQ",    "USD"): +1,
+}
+_INST_DIR_INFL: Dict[tuple, int] = {
+    ("SP500", "USD"): -1, ("QQQ", "USD"): -1, ("TLT", "USD"): -1,
+}
+
+
+def _estimate_pip_magnitude(
+    event_name: str, currency: str, instrument: str,
+    actual: Optional[str], forecast: Optional[str], previous: Optional[str],
+) -> tuple[float, bool, Optional[float]]:
+    """Returns (estimated_pips, has_surprise, surprise_delta). No AI."""
+    a = _parse_num(actual)
+    f = _parse_num(forecast)
+    p = _parse_num(previous)
+
+    if a is None:
+        return 0.0, False, None          # future event
+
+    ref = f if f is not None else p
+    if ref is None:
+        return 0.0, False, None
+
+    delta              = a - ref
+    scale, threshold   = _event_scale(event_name)
+    has_surprise       = abs(delta) >= threshold
+    raw                = max(-100.0, min(100.0, delta * scale))
+
+    n            = event_name.lower()
+    is_inflation = any(k in n for k in ['cpi', 'pce', 'ppi', 'inflation', 'hicp'])
+    inst         = instrument.upper()
+    cur          = currency.upper()
+    direction    = _INST_DIR_INFL.get((inst, cur), _INST_DIR.get((inst, cur), 0)) if is_inflation \
+                   else _INST_DIR.get((inst, cur), 0)
+
+    if direction == 0:
+        return round(abs(raw), 1), has_surprise, round(delta, 4)
+    return round(raw * direction, 1), has_surprise, round(delta, 4)
+
+
 _INSTRUMENT_CURRENCIES: Dict[str, List[str]] = {
     "EURUSD": ["EUR", "USD"],
     "USDJPY": ["USD", "JPY"],
@@ -315,22 +404,29 @@ def get_calendar_events(
 
         result = []
         for row in rows:
-            r        = dict(row)
-            ev_date  = r["event_date"]
+            r         = dict(row)
+            ev_date   = r["event_date"]
             is_future = ev_date > str(ref)
             category  = _ff_event_to_category(r["event_name"])
+            est_pips, has_surprise, surp_delta = _estimate_pip_magnitude(
+                r["event_name"], r["currency"], inst_upper,
+                r.get("actual_value"), r.get("forecast_value"), r.get("previous_value"),
+            )
             result.append({
-                "date":           ev_date,
-                "event_time":     r.get("event_time", ""),
-                "currency":       r["currency"],
-                "impact":         r["impact"],
-                "event_name":     r["event_name"],
-                "label":          f"[{r['currency']}] {r['event_name']}",
-                "actual_value":   r.get("actual_value"),
-                "forecast_value": r.get("forecast_value"),
-                "previous_value": r.get("previous_value"),
-                "category":       category,
-                "is_future":      is_future,
+                "date":            ev_date,
+                "event_time":      r.get("event_time", ""),
+                "currency":        r["currency"],
+                "impact":          r["impact"],
+                "event_name":      r["event_name"],
+                "label":           f"[{r['currency']}] {r['event_name']}",
+                "actual_value":    r.get("actual_value"),
+                "forecast_value":  r.get("forecast_value"),
+                "previous_value":  r.get("previous_value"),
+                "category":        category,
+                "is_future":       is_future,
+                "estimated_pips":  est_pips,
+                "has_surprise":    has_surprise,
+                "surprise_delta":  surp_delta,
             })
         return result
     finally: