v1.2 strategy upgrade: small-capital fee-aware signals, two-model pipeline, auto deep-research escalation

services.py

@@ -4,7 +4,20 @@ import requests
 from config import settings
 
 
-def searx_news(symbol: str, limit: int = 10):
+def _ollama_generate(model: str, payload_obj: dict, timeout: int = 45):
+    payload = {
+        "model": model,
+        "stream": False,
+        "prompt": json.dumps(payload_obj),
+        "format": "json",
+    }
+    r = requests.post(f"{settings.ollama_url}/api/generate", json=payload, timeout=timeout)
+    r.raise_for_status()
+    resp = r.json().get("response", "{}")
+    return json.loads(resp)
+
+
+def searx_news(symbol: str, limit: int = 12):
     q = f"{symbol} stock news earnings guidance analyst macro risk"
     params = {"q": q, "format": "json", "language": "en"}
     try:
@@ -13,75 +26,129 @@ def searx_news(symbol: str, limit: int = 10):
         data = r.json()
         out = []
         for it in data.get("results", [])[:limit]:
-            out.append({"title": it.get("title", ""), "url": it.get("url", ""), "content": it.get("content", "")[:500]})
+            out.append({
+                "title": it.get("title", ""),
+                "url": it.get("url", ""),
+                "content": (it.get("content", "") or "")[:700],
+            })
+        return out
+    except Exception:
+        return []
+
+
+def extra_research(symbol: str, weak_points: list, limit: int = 6):
+    """Second-pass targeted research when confidence/coverage is weak."""
+    q = f"{symbol} {' '.join(weak_points[:3])} SEC filing guidance risks competition"
+    params = {"q": q, "format": "json", "language": "en"}
+    try:
+        r = requests.get(settings.searx_url, params=params, timeout=20)
+        r.raise_for_status()
+        data = r.json()
+        out = []
+        for it in data.get("results", [])[:limit]:
+            out.append({
+                "title": it.get("title", ""),
+                "url": it.get("url", ""),
+                "content": (it.get("content", "") or "")[:700],
+            })
         return out
     except Exception:
         return []
 
 
 def summarize_news_with_ollama(symbol: str, context_items: list):
-    payload = {
-        "model": settings.ollama_model,
-        "stream": False,
-        "prompt": json.dumps({
-            "task": "Summarize market-moving info into a concise, neutral brief.",
-            "symbol": symbol,
-            "news": context_items,
-            "format": {"summary": "<=140 words", "bullish_points": ["..."], "bearish_points": ["..."]}
-        }),
-        "format": "json",
+    prompt = {
+        "task": "Summarize market-moving info into a concise, neutral brief.",
+        "symbol": symbol,
+        "news": context_items,
+        "format": {
+            "summary": "<=140 words",
+            "bullish_points": ["..."],
+            "bearish_points": ["..."],
+            "uncertainties": ["..."]
+        }
     }
     try:
-        r = requests.post(f"{settings.ollama_url}/api/generate", json=payload, timeout=45)
-        r.raise_for_status()
-        resp = r.json().get("response", "{}")
-        parsed = json.loads(resp)
+        parsed = _ollama_generate(settings.ollama_curator_model, prompt)
         return parsed.get("summary", "no-summary")
     except Exception:
         return "summary-unavailable"
 
 
-def ollama_decide(symbol: str, context_items: list):
+def strategy_signals(symbol: str, context_items: list):
+    """Proven-ish small-capital rules encoded as interpretable signals."""
+    text_blob = " ".join((x.get("title", "") + " " + x.get("content", "")) for x in context_items).lower()
+    bullish = sum(k in text_blob for k in ["beat", "raise guidance", "upgrade", "buyback", "record revenue"])
+    bearish = sum(k in text_blob for k in ["miss", "downgrade", "lawsuit", "probe", "cut guidance", "recall"])
+
+    # simple event momentum score
+    score = bullish - bearish
+    # conservative policy for tiny capital: only trade stronger score edges
+    if score >= 2:
+        action = "buy"
+    elif score <= -2:
+        action = "sell"
+    else:
+        action = "hold"
+
+    conf = min(0.85, 0.50 + abs(score) * 0.08)
+    return {
+        "strategy": "event-momentum-v1",
+        "score": score,
+        "action": action,
+        "confidence": conf,
+        "signals": {"bullish": bullish, "bearish": bearish},
+    }
+
+
+def llm_final_decision(symbol: str, context_items: list, strategy: dict):
     prompt = {
-        "task": "You are a conservative autonomous trading policy engine. Return strict JSON only.",
+        "task": "Final trading decision using all context and a conservative small-capital profile. Return strict JSON.",
+        "symbol": symbol,
         "constraints": {
             "actions": ["buy", "sell", "hold"],
             "max_order_usd": settings.max_order_usd,
-            "risk": "do not overtrade; prefer hold on weak signal",
+            "min_expected_edge_after_fees": "positive",
+            "fee_per_trade_usd": settings.fee_per_trade_usd,
+            "slippage_bps": settings.slippage_bps,
+            "avoid_overtrading": True,
         },
-        "symbol": symbol,
+        "strategy_prior": strategy,
         "news": context_items,
         "output_schema": {
             "action": "buy|sell|hold",
             "confidence": "0-1",
-            "reason": "short rationale",
             "order_usd": f"<= {settings.max_order_usd}",
+            "reason": "short rationale",
+            "needs_more_research": True,
+            "research_topics": ["..."]
         },
     }
-    payload = {
-        "model": settings.ollama_model,
-        "prompt": json.dumps(prompt),
-        "stream": False,
-        "format": "json",
-    }
+
     try:
-        r = requests.post(f"{settings.ollama_url}/api/generate", json=payload, timeout=45)
-        r.raise_for_status()
-        resp = r.json().get("response", "{}")
-        d = json.loads(resp)
+        d = _ollama_generate(settings.ollama_decision_model, prompt, timeout=60)
         action = str(d.get("action", "hold")).lower()
         if action not in {"buy", "sell", "hold"}:
             action = "hold"
         confidence = max(0.0, min(1.0, float(d.get("confidence", 0.5))))
         order_usd = min(float(d.get("order_usd", settings.max_order_usd)), settings.max_order_usd)
         reason = d.get("reason", "fallback")
-        return {"action": action, "confidence": confidence, "order_usd": order_usd, "reason": reason}
+        return {
+            "action": action,
+            "confidence": confidence,
+            "order_usd": order_usd,
+            "reason": reason,
+            "needs_more_research": bool(d.get("needs_more_research", False)),
+            "research_topics": d.get("research_topics", []) or [],
+        }
     except Exception:
         return {
-            "action": random.choice(["hold", "hold", "buy"]),
-            "confidence": 0.3,
+            "action": strategy.get("action", "hold"),
+            "confidence": min(strategy.get("confidence", 0.5), 0.55),
             "order_usd": min(1.0, settings.max_order_usd),
-            "reason": "fallback-mode",
+            "reason": "decision-fallback-strategy",
+            "needs_more_research": False,
+            "research_topics": [],
         }