feat: portfolio context injection + AI call log viewer
Portfolio context (portfolio_context.py): - get_open_trades_with_moves(): fetches open trades + 1d/5d yfinance price moves - get_portfolio_concentration(): counts by asset_class - build_portfolio_context_block(): formatted prompt block with strict AI instructions (no double positions, flag contradictions, avoid overweight classes) AI call logging: - ai_call_logs table in DB (run_id, call_type, system/user prompt, response, tokens, ms) - _chat() now accepts log_meta dict → saves call to DB non-blocking after each call - suggest and score_batch calls pass run_id + call_type for full traceability auto_cycle.py: - Builds portfolio context before snapshot and both AI calls - Context snapshot now includes portfolio_open_positions key SystemLogs.tsx: - "Contexte IA" tab gains sub-tabs: Contexte / Appels IA - AiCallRow: expandable with 3 panes (user prompt / system prompt / response) shows model, tokens breakdown, duration, call type badge Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
@@ -22,7 +22,14 @@ def get_client() -> Optional[OpenAI]:
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return _client
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def _chat(system: str, user: str, model: str = "gpt-4o-mini", json_mode: bool = True, max_tokens: int = 1500) -> Optional[Dict]:
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def _chat(
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system: str,
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user: str,
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model: str = "gpt-4o-mini",
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json_mode: bool = True,
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max_tokens: int = 1500,
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log_meta: Optional[Dict] = None,
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) -> Optional[Dict]:
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import time as _time
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client = get_client()
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if not client:
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@@ -35,18 +42,22 @@ def _chat(system: str, user: str, model: str = "gpt-4o-mini", json_mode: bool =
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}
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if json_mode:
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kwargs["response_format"] = {"type": "json_object"}
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last_exc = None
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for attempt in range(4): # up to 3 retries
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result: Optional[Dict] = None
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usage = None
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t0 = _time.time()
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for attempt in range(4):
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try:
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resp = client.chat.completions.create(**kwargs)
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content = resp.choices[0].message.content
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if json_mode:
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return json.loads(content)
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return {"text": content}
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usage = resp.usage
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result = json.loads(content) if json_mode else {"text": content}
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break # success
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except Exception as e:
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last_exc = e
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err_str = str(e)
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# 429 rate-limit: respect the retry-after hint, then back off
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if "429" in err_str or "rate_limit" in err_str:
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import re as _re
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m = _re.search(r"try again in ([\d.]+)s", err_str)
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@@ -54,7 +65,32 @@ def _chat(system: str, user: str, model: str = "gpt-4o-mini", json_mode: bool =
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_time.sleep(min(wait, 60.0))
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continue
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raise # non-429 errors propagate immediately
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raise last_exc
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if result is None:
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raise last_exc
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# Persist AI call log when requested (non-blocking)
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if log_meta and log_meta.get("run_id"):
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try:
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from services.database import save_ai_call_log
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duration_ms = int((_time.time() - t0) * 1000)
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save_ai_call_log(
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run_id=log_meta["run_id"],
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call_type=log_meta.get("call_type", "unknown"),
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system_prompt=system,
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user_prompt=user,
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response_json=json.dumps(result, ensure_ascii=False),
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model=model,
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tokens_prompt=usage.prompt_tokens if usage else 0,
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tokens_completion=usage.completion_tokens if usage else 0,
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duration_ms=duration_ms,
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pattern_id=log_meta.get("pattern_id"),
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pattern_name=log_meta.get("pattern_name"),
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)
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except Exception:
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pass
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return result
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# ── News / Article Analysis ───────────────────────────────────────────────────
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@@ -356,6 +392,8 @@ def score_patterns_with_context(
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tech_indicators_block: str = "",
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fred_block: str = "",
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price_discovery_block: str = "",
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portfolio_context_block: str = "",
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run_id: str = "",
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) -> List[Dict[str, Any]]:
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"""Score all patterns with rich context (news, prices, IV, risk clusters) using GPT-4o."""
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if not get_client():
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@@ -577,6 +615,7 @@ Instructions de notation:
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_tech_sc_section = f"\n{tech_indicators_block}\n" if tech_indicators_block else ""
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_fred_sc_section = f"\n{fred_block}\n" if fred_block else ""
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_pd_sc_section = f"\n{price_discovery_block}\n" if price_discovery_block else ""
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_portfolio_sc_section = f"\n{portfolio_context_block}\n" if portfolio_context_block else ""
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user = f"""CONTEXTE GLOBAL:
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- Score risque géopolitique: {geo_score.get('score', 50)}/100 ({geo_score.get('level', 'medium')})
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@@ -586,6 +625,7 @@ Instructions de notation:
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{_fred_sc_section}
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{_pd_sc_section}
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{_tech_sc_section}
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{_portfolio_sc_section}
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TEMPLATE DE NOTATION:
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{scoring_template}
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@@ -765,7 +805,10 @@ TEMPLATE DE NOTATION:
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+ _return_schema
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)
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try:
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res = _chat(SYSTEM_SCORER, batch_user, model="gpt-4o", json_mode=True, max_tokens=12000)
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res = _chat(
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SYSTEM_SCORER, batch_user, model="gpt-4o", json_mode=True, max_tokens=12000,
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log_meta={"run_id": run_id, "call_type": "score_batch", "pattern_name": f"batch:{','.join(ids[:3])}"} if run_id else None,
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)
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except Exception as e:
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_scorer_log.error(f"[Scorer] GPT-4o call failed for batch {ids}: {e}")
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res = None
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@@ -977,6 +1020,8 @@ def suggest_patterns_from_market_context(
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tech_indicators_block: str = "",
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fred_block: str = "",
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price_discovery_block: str = "",
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portfolio_context_block: str = "",
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run_id: str = "",
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) -> List[Dict]:
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"""Ask GPT-4o to propose new patterns based on current geo/market + macro regime context."""
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_cycle_meta = cycle_meta or {}
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@@ -1125,6 +1170,7 @@ Règles supplémentaires:
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tech_block_section = f"\n{tech_indicators_block}\n" if tech_indicators_block else ""
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fred_section = f"\n{fred_block}\n" if fred_block else ""
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pd_section = f"\n{price_discovery_block}\n" if price_discovery_block else ""
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portfolio_section = f"\n{portfolio_context_block}\n" if portfolio_context_block else ""
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user = f"""Tu es un stratège géopolitique et financier senior, expert en options.
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{macro_block}{geo_block}{lessons_block}{reliability_block}{iv_block}
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@@ -1134,6 +1180,7 @@ Règles supplémentaires:
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{fred_section}
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{pd_section}
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{tech_block_section}
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{portfolio_section}
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## Calendrier économique à venir
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{cal_block}
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@@ -1188,7 +1235,10 @@ Retourne UNIQUEMENT ce JSON:
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]
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}}"""
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result = _chat(SYSTEM_SCORER, user, model="gpt-4o", json_mode=True, max_tokens=4000)
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result = _chat(
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SYSTEM_SCORER, user, model="gpt-4o", json_mode=True, max_tokens=4000,
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log_meta={"run_id": run_id, "call_type": "suggest"} if run_id else None,
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)
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if not result:
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return []
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return result.get("patterns", [])
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