feat: trade mandate (budget + horizon) wired end-to-end

- database.py: add trade_budget_eur / preferred_horizon_min/max config
  defaults and include them in cycle config migrations
- auto_cycle.py: read trade params from config and inject into cycle_meta
- ai_analyzer.py: inject INVESTOR TRADE MANDATE block into scoring and
  suggestion prompts so GPT-4o penalises horizon mismatches and sizes
  within the capital cap
- Config.tsx: Trade Parameters card with budget + horizon sliders and live
  mandate summary
- TradeIdeas.tsx: horizon filter pills (< 1M / 1-3M / 3-6M / > 6M) and
  budget/horizon indicator pulled from saved config
- useApi.ts: extend useUpdateCycleConfig type with new config fields

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/services/ai_analyzer.py

@@ -619,10 +619,25 @@ Scoring instructions:
     _portfolio_sc_section = f"\n{portfolio_context_block}\n" if portfolio_context_block else ""
     _inst_sc_section = f"\n{institutional_block}\n" if institutional_block else ""
 
+    # Trade mandate block from cycle_meta
+    _budget = _cm.get("trade_budget_eur", 5000)
+    _h_min  = _cm.get("preferred_horizon_min", 30)
+    _h_max  = _cm.get("preferred_horizon_max", 180)
+    _max_pos = round(_budget * 0.25)
+    _trade_mandate_sc = (
+        f"\n## INVESTOR TRADE MANDATE\n"
+        f"- Available capital budget: €{_budget:,.0f}\n"
+        f"- Preferred horizon: {_h_min}–{_h_max} days\n"
+        f"- Max capital per position: €{_max_pos:,.0f} (25% of budget)\n"
+        f"⚠️ Patterns whose horizon_days is outside [{_h_min}, {_h_max}] should receive a penalty"
+        f" in the R/R pillar (poor timing fit). Size trade suggestions within the budget cap.\n"
+    )
+
     user = f"""GLOBAL CONTEXT:
 - Geopolitical risk score: {geo_score.get('score', 50)}/100 ({geo_score.get('level', 'medium')})
 - Top risks: {geo_score.get('top_risks', [])}
 {temporal_section_sc}
+{_trade_mandate_sc}
 {macro_section}
 {_fred_sc_section}
 {_pd_sc_section}
@@ -1411,7 +1426,20 @@ Additional rules:
     portfolio_section = f"\n{portfolio_context_block}\n" if portfolio_context_block else ""
     convergence_section = f"\n{convergence_block}\n" if convergence_block else ""
 
+    _sg_budget  = _cycle_meta.get("trade_budget_eur", 5000)
+    _sg_h_min   = _cycle_meta.get("preferred_horizon_min", 30)
+    _sg_h_max   = _cycle_meta.get("preferred_horizon_max", 180)
+    _sg_max_pos = round(_sg_budget * 0.25)
+    _suggest_mandate = (
+        f"\n## INVESTOR TRADE MANDATE\n"
+        f"- Available capital budget: €{_sg_budget:,.0f} | Max per position: €{_sg_max_pos:,.0f}\n"
+        f"- Target horizon: {_sg_h_min}–{_sg_h_max} days\n"
+        f"⚠️ Only propose patterns whose horizon_days is within [{_sg_h_min}, {_sg_h_max}]."
+        f" Patterns outside this window will be rejected. Size each trade so capital ≤ €{_sg_max_pos:,.0f}.\n"
+    )
+
     user = f"""You are a senior geopolitical and financial strategist, expert in options.
+{_suggest_mandate}
 {macro_block}{geo_block}{lessons_block}{reliability_block}{iv_block}
 {temporal_news_block}
 ## Market prices (D-1 change)

backend/services/auto_cycle.py

@@ -255,6 +255,10 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
             _market_session = "after_hours"
             _market_note = "After-hours US — prix indicatifs, liquidité réduite."
 
+        _budget_eur       = float(get_config("trade_budget_eur") or "5000")
+        _horizon_min      = int(get_config("preferred_horizon_min") or "30")
+        _horizon_max      = int(get_config("preferred_horizon_max") or "180")
+
         cycle_meta = {
             "current_cycle_ts": _now.isoformat(),
             "last_cycle_ts": _last_cycle_ts_str,
@@ -265,6 +269,9 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
             "is_weekend": _is_weekend,
             "market_session": _market_session,
             "market_note": _market_note,
+            "trade_budget_eur": _budget_eur,
+            "preferred_horizon_min": _horizon_min,
+            "preferred_horizon_max": _horizon_max,
         }
         logger.info(
             f"[Cycle {run_id[:16]}] Cycle meta: delta={_delta_minutes:.0f}min depuis dernier cycle"

backend/services/database.py

@@ -311,6 +311,9 @@ def init_db():
         "auto_cycle_similarity_threshold": "0.30",
         "min_ev_threshold": "0.0",
         "min_score_threshold": "0",
+        "trade_budget_eur": "5000",
+        "preferred_horizon_min": "30",
+        "preferred_horizon_max": "180",
         "exit_defaults": json.dumps({
             "target_pct": 30.0,
             "stop_loss_pct": -50.0,