feat: instrument model

2026-07-03 00:04:37 +02:00
parent ff6f0b390d
commit a8ee808937
4 changed files with 817 additions and 104 deletions
--- a/backend/services/instrument_models.py
+++ b/backend/services/instrument_models.py
@@ -193,6 +193,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
  "name": "EUR/USD",
  "description": "Taux de change Euro/Dollar — modèle causal 3 couches avec 4 domaines d'influence",
  "output_node": "eurusd",
+  "price_intercept": 1.10,
+  "pip_to_price": 0.0001,
+  "yf_ticker": "EURUSD=X",
  "nodes": [
    # ── Layer 0a : event inputs ───────────────────────────────────────────────
    {"id":"in_cb",        "label":"Banques Centrales",       "node_type":"input_event", "category":"central_bank",  "unit":"pips","display_col":0,"description":"Décisions Fed/BCE, minutes, forward guidance. Décroissance exp ~14j.","event_category":"central_bank"},
@@ -246,6 +249,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "USDJPY": {
  "name": "USD/JPY", "output_node": "usdjpy",
  "description": "Carry & safe haven — yield diff 10Y + BoJ + risk appetite",
+  "price_intercept": 145.0,
+  "pip_to_price": 0.01,
+  "yf_ticker": "USDJPY=X",
  "nodes": [
    {"id":"in_cb",        "label":"Banques Centrales",       "node_type":"input_event","category":"central_bank", "unit":"pips","display_col":0,"event_category":"central_bank","description":"Fed/BoJ décisions."},
    {"id":"in_macro",     "label":"Surprises Macro",         "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"NFP, CPI US, Tankan, CPI Japon."},
@@ -283,6 +289,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "XAUUSD": {
  "name": "XAU/USD (Or)", "output_node": "xauusd",
  "description": "Or/Dollar — taux réels, dollar, géopolitique, banques centrales",
+  "price_intercept": 2800.0,
+  "pip_to_price": 1.0,
+  "yf_ticker": "GC=F",
  "nodes": [
    {"id":"in_cb",     "label":"Banques Centrales",     "node_type":"input_event","category":"central_bank", "unit":"pips","display_col":0,"event_category":"central_bank","description":"Fed (taux réels) → or."},
    {"id":"in_macro",  "label":"Surprises Macro",       "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"CPI, PCE → anticipations taux réels → or."},
@@ -324,6 +333,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "SP500": {
  "name": "S&P 500", "output_node": "sp500",
  "description": "Indice actions US — taux, bénéfices, risque, liquidités",
+  "price_intercept": 5000.0,
+  "pip_to_price": 1.0,
+  "yf_ticker": "^GSPC",
  "nodes": [
    {"id":"in_cb",      "label":"Banques Centrales",      "node_type":"input_event","category":"central_bank","unit":"pips","display_col":0,"event_category":"central_bank","description":"Fed pivot/hike → SP500 directement."},
    {"id":"in_macro",   "label":"Surprises Macro",        "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"NFP, CPI, PIB US."},
@@ -366,6 +378,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "TLT": {
  "name": "TLT (US Long Bonds)", "output_node": "tlt",
  "description": "ETF obligations US 20Y+ — duration, inflation, récession, supply",
+  "price_intercept": 85.0,
+  "pip_to_price": 0.01,
+  "yf_ticker": "TLT",
  "nodes": [
    {"id":"in_cb",     "label":"Banques Centrales","node_type":"input_event","category":"central_bank","unit":"pips","display_col":0,"event_category":"central_bank","description":"FOMC décisions/minutes → impact direct TLT."},
    {"id":"in_macro",  "label":"Surprises Macro",  "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"CPI, PCE, NFP → réévaluation taux."},
@@ -406,6 +421,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "GBPUSD": {
  "name": "GBP/USD", "output_node": "gbpusd",
  "description": "Livre sterling/Dollar — BoE, données UK, risque politique",
+  "price_intercept": 1.26,
+  "pip_to_price": 0.0001,
+  "yf_ticker": "GBPUSD=X",
  "nodes": [
    {"id":"in_cb",      "label":"Banques Centrales","node_type":"input_event","category":"central_bank","unit":"pips","display_col":0,"event_category":"central_bank","description":"BoE, Fed."},
    {"id":"in_macro",   "label":"Surprises Macro",  "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"CPI UK/US, NFP, GDP UK."},
@@ -441,6 +459,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "EEM": {
  "name": "EEM (Marchés Émergents)", "output_node": "eem",
  "description": "ETF EM — dollar, Chine, commodités, risk appetite",
+  "price_intercept": 42.0,
+  "pip_to_price": 0.01,
+  "yf_ticker": "EEM",
  "nodes": [
    {"id":"in_cb",      "label":"Banques Centrales","node_type":"input_event","category":"central_bank","unit":"pips","display_col":0,"event_category":"central_bank","description":"Fed pivot → EM bénéficient du dollar faible."},
    {"id":"in_macro",   "label":"Surprises Macro",  "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"Données Chine, US macro."},
@@ -478,6 +499,9 @@ INSTRUMENT_MODELS: dict[str, dict] = {
 "QQQ": {
  "name": "QQQ (NASDAQ-100 Tech)", "output_node": "qqq",
  "description": "Tech US — taux réels, bénéfices big tech, IA, réglementation",
+  "price_intercept": 480.0,
+  "pip_to_price": 0.10,
+  "yf_ticker": "QQQ",
  "nodes": [
    {"id":"in_cb",      "label":"Banques Centrales","node_type":"input_event","category":"central_bank","unit":"pips","display_col":0,"event_category":"central_bank","description":"Fed pivot → QQQ amplificateur (duration longue)."},
    {"id":"in_macro",   "label":"Surprises Macro",  "node_type":"input_event","category":"monetary_shock","unit":"pips","display_col":0,"event_category":"monetary_shock","description":"CPI, NFP → réévaluation Fed → QQQ."},
@@ -804,6 +828,20 @@ def get_model_state(conn, instrument: str, at_date: Optional[str] = None) -> Opt
    output_id = graph_def["output_node"]
    net_pips  = round(float(all_vals.get(output_id, 0.0)), 1)

+    # Compute structural pips (manual inputs only, no events, BALANCED regime)
+    inputs_struct = _build_inputs(graph_def, overrides, {}, saturation=True)
+    gj_struct     = _graph_json_for_eval(graph_def, {})
+    vals_struct   = evaluate_graph(gj_struct, inputs_struct)
+    structural_pips = round(float(vals_struct.get(output_id, 0.0)), 1)
+    event_pips      = round(net_pips - structural_pips, 1)
+
+    meta            = INSTRUMENT_MODELS.get(inst_upper, {})
+    price_intercept = meta.get("price_intercept", 0.0)
+    pip_to_price    = meta.get("pip_to_price", 0.0001)
+    yf_ticker       = meta.get("yf_ticker", "")
+    fundamental_level = round(price_intercept + structural_pips * pip_to_price, 6)
+    synthetic_price   = round(price_intercept + net_pips * pip_to_price, 6)
+
    nodes_out = []
    for node in graph_def["nodes"]:
        nid   = node["id"]
@@ -852,20 +890,28 @@ def get_model_state(conn, instrument: str, at_date: Optional[str] = None) -> Opt
    direction = "bullish" if net_pips > 5 else "bearish" if net_pips < -5 else "neutral"

    return {
-        "instrument":  inst_upper,
-        "name":        graph_def["name"],
-        "description": graph_def.get("description", ""),
-        "at_date":     str(ref_date),
-        "net_pips":    net_pips,
-        "direction":   direction,
-        "nodes":       nodes_out,
-        "output_node": output_id,
-        "regime":      regime_info,
+        "instrument":       inst_upper,
+        "name":             graph_def["name"],
+        "description":      graph_def.get("description", ""),
+        "at_date":          str(ref_date),
+        "net_pips":         net_pips,
+        "structural_pips":  structural_pips,
+        "event_pips":       event_pips,
+        "price_intercept":  price_intercept,
+        "pip_to_price":     pip_to_price,
+        "yf_ticker":        yf_ticker,
+        "fundamental_level": fundamental_level,
+        "synthetic_price":  synthetic_price,
+        "direction":        direction,
+        "nodes":            nodes_out,
+        "output_node":      output_id,
+        "regime":           regime_info,
    }


 def simulate_timeline(
-    conn, instrument: str, period: str = "1y"
+    conn, instrument: str, period: str = "1y",
+    virtual_events: Optional[list] = None,
 ) -> list[dict]:
    """
    Simulate all node values day by day over the period.
@@ -945,8 +991,31 @@ def simulate_timeline(
        events.append({
            "ev_date": ev_date, "category": r["category"], "pips": pips,
            "rise": rise, "plateau": plateau, "absorption": absorption, "dtype": dtype,
+            "virtual": False,
        })

+    # Inject virtual events
+    for ve in (virtual_events or []):
+        try:
+            ev_date = date_type.fromisoformat(str(ve["date"])[:10])
+            events.append({
+                "ev_date":    ev_date,
+                "category":   ve.get("category", "unclassified"),
+                "pips":       float(ve.get("pips", 0.0)),
+                "rise":       int(ve.get("rise_days", 1)),
+                "plateau":    int(ve.get("plateau_days", 0)),
+                "absorption": int(ve.get("absorption_days", 14)),
+                "dtype":      ve.get("decay_type", "exp"),
+                "virtual":    True,
+                "label":      ve.get("label", "Event virtuel"),
+            })
+        except (KeyError, ValueError):
+            continue
+
+    meta            = INSTRUMENT_MODELS.get(inst_upper, {})
+    price_intercept = meta.get("price_intercept", 0.0)
+    pip_to_price    = meta.get("pip_to_price", 0.0001)
+
    from services.causal_graphs import evaluate_graph

    timeline = []
@@ -970,11 +1039,21 @@ def simulate_timeline(
        vals   = evaluate_graph(gj, inputs)
        net    = round(float(vals.get(output_id, 0.0)), 1)

+        # Structural pips (manual only, no events)
+        inputs_struct   = _build_inputs(graph_def, overrides, {}, saturation=True)
+        gj_struct       = _graph_json_for_eval(graph_def, {})
+        vals_struct     = evaluate_graph(gj_struct, inputs_struct)
+        structural_pips = round(float(vals_struct.get(output_id, 0.0)), 1)
+
        timeline.append({
-            "date":     str(cur),
-            "net_pips": net,
-            "regime":   ri["regime"],
-            "nodes":    {k: round(float(v), 1) for k, v in vals.items()},
+            "date":              str(cur),
+            "net_pips":          net,
+            "structural_pips":   structural_pips,
+            "event_pips":        round(net - structural_pips, 1),
+            "fundamental_level": round(price_intercept + structural_pips * pip_to_price, 6),
+            "synthetic_price":   round(price_intercept + net * pip_to_price, 6),
+            "regime":            ri["regime"],
+            "nodes":             {k: round(float(v), 1) for k, v in vals.items()},
        })
        cur += timedelta(days=1)