fix(llm): RateLimitError retryable + retry tenacity 3→5 + backoff fino a 30s

Run phase2-5-qwen25-prompt-mut-003 fallito a gen 5 (76 evals done, $0.061
spesi) per HTTP 429 RateLimit upstream da DeepInfra su qwen-2.5-72b.
RateLimitError NON era in _RETRYABLE_EXCEPTIONS quindi tenacity falliva
subito, propagando il 429 a propose() e all'orchestrator (run failed).

Tre fix:
1) Aggiunto openai.RateLimitError a _RETRYABLE_EXCEPTIONS.
2) Bumpato stop_after_attempt(3) → 5 e wait_exponential max 10s → 30s.
   Più tempo per il rate limit upstream di sbloccare prima di rinunciare.
3) hypothesis.py: try/except RateLimitError in propose() come per
   EmptyCompletionError — anche se tenacity esaurisce i 5 retry, il genome
   viene marcato fitness=0 e il loop esterno continua senza crash totale.

Test: aggiornato test_completion_retries_on_connection_error per
assert call_count == 5 (era 3). Tutti 182 verdi.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.env.example

@@ -11,11 +11,11 @@ OPENROUTER_API_KEY=
 OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
 
 # Models per tier (override Phase 1 defaults if needed)
-LLM_MODEL_TIER_S=anthropic/claude-opus-4-7
-LLM_MODEL_TIER_A=anthropic/claude-sonnet-4-6
-LLM_MODEL_TIER_B=anthropic/claude-sonnet-4-6
+LLM_MODEL_TIER_S=google/gemini-3-flash-preview
+LLM_MODEL_TIER_A=deepseek/deepseek-v4-flash
+LLM_MODEL_TIER_B=deepseek/deepseek-v4-flash
 LLM_MODEL_TIER_C=qwen/qwen-2.5-72b-instruct
-LLM_MODEL_TIER_D=meta-llama/llama-3.3-70b-instruct
+LLM_MODEL_TIER_D=openai/gpt-oss-20b
 
 # Run config
 RUN_NAME=phase1-spike-001

docs/superpowers/plans/2026-05-11-mutate-prompt-llm-phase-2-5.md

@@ -0,0 +1,318 @@
+# `mutate_prompt_llm` — Phase 2.5 Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [x]`) syntax for tracking.
+
+**Status:** **IMPLEMENTATO 2026-05-11.** Task 1-5 completati e mergiati su main. Task 6 (cost attribution per call_kind) **deferito** — i cost mutator finiscono già in `cost_records` con l'`agent_id` del parent, quindi il totale è contabilizzato anche senza breakdown per call kind.
+
+**Trigger Phase 2.5 verificati con esito Phase 2 + run controllo:**
+- ✅ Plateau max fitness ≥ 4 gen consecutive (Phase 2 qwen3-235b stuck 8 gen a 0.0238; run controllo qwen-2.5-72b stuck 9 gen a 0.0311).
+- ✅ Diversità prompt collapsed: top genomi del run controllo hanno fitness/Sharpe/DD identici (mutazioni scalari non producono varianti significative).
+- ✗ Top quasi-fit ≥ 0.10 non raggiunto, ma 2/3 trigger sufficienti.
+
+**Decisione collaterale:** rollback tier C a `qwen/qwen-2.5-72b-instruct` (run controllo l'ha dimostrato superiore a qwen3-235b: +30% fitness, 4× entropy, metà costo e tempo).
+
+**Goal:** Introdurre un quinto operatore di mutazione che usa un LLM tier B come "mutator" per riscrivere il `system_prompt` di un genoma, generando diversità reale dove oggi `random_mutate` tocca solo quattro scalari. La pipeline GA esistente resta intatta: `mutate_prompt_llm` è solo un nuovo membro di `MUTATION_OPS` con peso configurabile.
+
+**Architecture:** Operatore puro come gli altri quattro (`mutate_temperature`, `mutate_lookback`, `mutate_feature_access`, `mutate_cognitive_style`). Riceve `parent_genome`, `llm_client`, `rng` e restituisce un child genome con `system_prompt` modificato. Il mutator LLM (tier B = `deepseek/deepseek-v4-flash`) riceve una mutation-instruction casuale tra sei tipi predefiniti (`tighten_threshold`, `swap_comparator`, `add_condition`, `remove_condition`, `change_timeframe`, `add_temporal_gate`) e produce un nuovo prompt vincolato a una mutazione "atomica". Il child viene validato (parser + adversarial dry-run); su fallimento si effettua fallback a `random_mutate`. Selezione probabilistica nel `random_mutate` dispatcher con peso configurabile (default 0.30) — i quattro operator scalari mantengono il 70% complessivo.
+
+**Tech Stack:** Python 3.13, `LLMClient` esistente (OpenAI SDK via OpenRouter), pytest + `pytest-mock`. Niente nuove dipendenze.
+
+**Spec di riferimento:** sezione "Meccanismo di mutazione" della conversazione `2026-05-11`, valutazione `mutate_prompt_llm` (questa pagina contiene la sintesi).
+
+---
+
+## Trigger condition (quando attivare)
+
+Implementare e mergiare **solo se** uno dei seguenti è vero al termine di Phase 2:
+
+1. **Plateau evolutivo**: max fitness stagnante (Δ < 0.01) per ≥ 4 generazioni consecutive su `phase2-qwen3-001` o successori.
+2. **Diversità prompt collassa**: media Levenshtein normalizzata fra i prompt della popolazione finale ≤ 0.15 (= popolazione clonata).
+3. **Top genome problematico ma quasi-fit**: max fitness ≥ 0.10 ma adversarial finding HIGH ≥ 2 per il top, suggerendo che una mutazione mirata del prompt potrebbe "ripararlo".
+
+Se Phase 2 raggiunge max fitness ≥ 0.30 senza plateau, **non attivare** (la diversità random basta).
+
+---
+
+## File map
+
+| File | Tipo | Responsabilità |
+|------|------|----------------|
+| `src/multi_swarm/genome/mutation_prompt_llm.py` | New | Operatore `mutate_prompt_llm` + helper `MUTATION_INSTRUCTIONS` + retry/fallback wrapper |
+| `src/multi_swarm/genome/mutation.py` | Modify | Estendere `MUTATION_OPS` + introdurre dispatcher pesato `weighted_random_mutate` |
+| `src/multi_swarm/ga/loop.py` | Modify | Sostituire `random_mutate(parent, rng)` con `weighted_random_mutate(parent, rng, llm_client, weights)` |
+| `src/multi_swarm/orchestrator/run.py` | Modify | Aggiungere `mutator_tier: ModelTier = ModelTier.B` e `prompt_mutation_weight: float = 0.30` a `RunConfig`, passare `LLMClient` al loop GA |
+| `src/multi_swarm/llm/cost_tracker.py` | Modify (minimo) | Loggare `mutation_call` separatamente da `hypothesis_call` per attribuzione costo |
+| `src/multi_swarm/metrics/diversity.py` | New | Funzione `population_prompt_diversity` (Levenshtein normalizzata) — usata in trigger check + telemetry |
+| `tests/unit/test_mutation_prompt_llm.py` | New | Test operator con mock `LLMClient` (success + validation fail + retry/fallback) |
+| `tests/unit/test_mutation_dispatcher.py` | New | Test `weighted_random_mutate` rispetta i pesi |
+| `tests/unit/test_diversity.py` | New | Test `population_prompt_diversity` su prompt identici/diversi |
+| `tests/integration/test_ga_loop_with_prompt_mutator.py` | New | Loop end-to-end di 2 gen × 5 genomi con mock LLM, verifica diversità prompt cresce |
+
+---
+
+## Task 1: Mutator instructions + operator stub
+
+**Files:**
+- New: `src/multi_swarm/genome/mutation_prompt_llm.py`
+- New: `tests/unit/test_mutation_prompt_llm.py`
+
+- [x] **Step 1.1: Write failing test — operator returns child con system_prompt diverso**
+
+Append a `tests/unit/test_mutation_prompt_llm.py`:
+
+```python
+def test_mutate_prompt_llm_produces_different_prompt(mock_llm: LLMClient) -> None:
+    parent = make_genome(system_prompt="Strategia: compra quando RSI < 30")
+    mock_llm.respond_with("Strategia: compra quando RSI < 25 e ora >= 14")
+    child = mutate_prompt_llm(parent, mock_llm, rng=random.Random(0))
+    assert child.system_prompt != parent.system_prompt
+    assert child.parent_ids == [*parent.parent_ids, parent.id]
+    assert child.generation == parent.generation + 1
+```
+
+- [x] **Step 1.2: Implement `MUTATION_INSTRUCTIONS` constant**
+
+`mutation_prompt_llm.py`:
+
+```python
+MUTATION_INSTRUCTIONS: dict[str, str] = {
+    "tighten_threshold": "Rendi una soglia numerica più restrittiva del 10–20%...",
+    "swap_comparator": "Inverti un comparator (gt ↔ lt, gte ↔ lte) mantenendo intent...",
+    "add_condition": "Aggiungi una condizione AND/OR alla rule più specifica...",
+    "remove_condition": "Rimuovi una condizione ridondante o debole...",
+    "change_timeframe": "Modifica una finestra rolling (lookback) di ±30%...",
+    "add_temporal_gate": "Aggiungi un gate temporale (hour, dow, is_weekend)...",
+}
+```
+
+- [x] **Step 1.3: Implement `mutate_prompt_llm`**
+
+Firma:
+```python
+def mutate_prompt_llm(
+    g: HypothesisAgentGenome,
+    llm: LLMClient,
+    rng: random.Random,
+    mutator_tier: ModelTier = ModelTier.B,
+) -> HypothesisAgentGenome:
+```
+
+Logica:
+1. Scegli `instruction_key = rng.choice(list(MUTATION_INSTRUCTIONS))`.
+2. Costruisci messaggio system + user con `MUTATION_INSTRUCTIONS[instruction_key]` + `g.system_prompt`.
+3. Crea genoma temporaneo `mutator_genome` con `model_tier=mutator_tier`.
+4. Chiama `llm.complete(mutator_genome, system, user, max_tokens=2000)`.
+5. Estrai nuovo prompt da risposta (cerca blocco `<prompt>...</prompt>` o intero output).
+6. Ritorna `_clone_with(g, system_prompt=new_prompt)` (riusa helper di `mutation.py`).
+
+- [x] **Step 1.4: Run test → green**
+
+```bash
+uv run pytest tests/unit/test_mutation_prompt_llm.py::test_mutate_prompt_llm_produces_different_prompt -xvs
+```
+
+---
+
+## Task 2: Validation + fallback
+
+**Files:**
+- Modify: `src/multi_swarm/genome/mutation_prompt_llm.py`
+- Append: `tests/unit/test_mutation_prompt_llm.py`
+
+- [x] **Step 2.1: Write failing test — fallback a random_mutate su prompt invalid**
+
+```python
+def test_mutate_prompt_llm_falls_back_on_invalid_prompt(mock_llm: LLMClient) -> None:
+    parent = make_genome()
+    mock_llm.respond_with("garbage that does not parse")
+    child = mutate_prompt_llm(parent, mock_llm, rng=random.Random(0))
+    # Garbage prompt deve fallback: child è prodotto da random_mutate, quindi
+    # system_prompt == parent.system_prompt (random_mutate tocca solo scalari)
+    assert child.system_prompt == parent.system_prompt
+    assert child.parent_ids == [*parent.parent_ids, parent.id]
+```
+
+- [x] **Step 2.2: Implement validation step**
+
+Dopo aver estratto `new_prompt`, esegui `validate_prompt(new_prompt)`:
+- Lunghezza minima 50 caratteri.
+- Contiene almeno una keyword fra `{rsi, sma, ema, atr, momentum, breakout, mean reversion, gt, lt, ...}`.
+- Non identico a `parent.system_prompt` (Levenshtein > 0.05 normalizzata).
+
+Su fail → log warning + ritorna `random_mutate(g, rng)`.
+
+- [x] **Step 2.3: Write failing test — diversity guard**
+
+Mock LLM ritorna prompt identico al parent → `validate_prompt` rifiuta → fallback.
+
+- [x] **Step 2.4: Run test suite parziale**
+
+```bash
+uv run pytest tests/unit/test_mutation_prompt_llm.py -xvs
+```
+
+---
+
+## Task 3: Weighted dispatcher
+
+**Files:**
+- Modify: `src/multi_swarm/genome/mutation.py`
+- New: `tests/unit/test_mutation_dispatcher.py`
+
+- [x] **Step 3.1: Write failing test — weighted_random_mutate rispetta pesi**
+
+```python
+def test_weighted_random_mutate_picks_prompt_op_at_configured_rate() -> None:
+    rng = random.Random(0)
+    weights = {"prompt": 1.0, "scalar": 0.0}  # 100% prompt
+    counter = Counter()
+    for _ in range(100):
+        op_name = _pick_op_name(weights, rng)
+        counter[op_name] += 1
+    assert counter["prompt"] == 100
+```
+
+- [x] **Step 3.2: Implement `weighted_random_mutate`**
+
+```python
+def weighted_random_mutate(
+    g: HypothesisAgentGenome,
+    rng: random.Random,
+    llm: LLMClient | None = None,
+    prompt_mutation_weight: float = 0.30,
+) -> HypothesisAgentGenome:
+    if llm is not None and rng.random() < prompt_mutation_weight:
+        return mutate_prompt_llm(g, llm, rng)
+    return random_mutate(g, rng)
+```
+
+- [x] **Step 3.3: Test edge cases**
+
+- `llm=None` → sempre scalar mutation (backward compat).
+- `prompt_mutation_weight=0.0` → sempre scalar.
+- `prompt_mutation_weight=1.0` → sempre prompt (se llm presente).
+
+---
+
+## Task 4: Integrazione GA loop
+
+**Files:**
+- Modify: `src/multi_swarm/ga/loop.py`
+- Modify: `src/multi_swarm/orchestrator/run.py`
+- New: `tests/integration/test_ga_loop_with_prompt_mutator.py`
+
+- [x] **Step 4.1: Estendere `GAConfig`**
+
+```python
+@dataclass(frozen=True)
+class GAConfig:
+    population_size: int
+    elite_k: int
+    tournament_k: int
+    p_crossover: float
+    prompt_mutation_weight: float = 0.0  # default off → opt-in
+```
+
+- [x] **Step 4.2: Pass `LLMClient` in `next_generation`**
+
+```python
+def next_generation(
+    population: list[HypothesisAgentGenome],
+    fitnesses: dict[str, float],
+    cfg: GAConfig,
+    rng: random.Random,
+    llm: LLMClient | None = None,
+) -> list[HypothesisAgentGenome]:
+    ...
+    child = weighted_random_mutate(parent, rng, llm, cfg.prompt_mutation_weight)
+```
+
+- [x] **Step 4.3: Wire in orchestrator**
+
+`RunConfig.prompt_mutation_weight: float = 0.0` (default off). Quando attivo via CLI `--prompt-mutation-weight 0.30`, passare a `next_generation`.
+
+- [x] **Step 4.4: Integration test**
+
+Loop 2 gen × 5 genomi, mock LLM che ritorna prompt sempre diversi. Verifica che la popolazione finale abbia più diversità prompt della iniziale.
+
+---
+
+## Task 5: Diversity metric
+
+**Files:**
+- New: `src/multi_swarm/metrics/diversity.py`
+- New: `tests/unit/test_diversity.py`
+
+- [x] **Step 5.1: Implement `population_prompt_diversity`**
+
+```python
+def population_prompt_diversity(prompts: list[str]) -> float:
+    """Levenshtein normalizzata media su tutte le coppie. 0.0 = identici, 1.0 = totalmente diversi."""
+```
+
+- [x] **Step 5.2: Test**
+
+Tre prompt identici → 0.0. Tre prompt totalmente diversi → ~1.0.
+
+- [x] **Step 5.3: Logging**
+
+Aggiungere `diversity_prompt` come campo per-generazione in `repository.save_generation` (richiede migration leggera).
+
+---
+
+## Task 6: Cost attribution
+
+**Files:**
+- Modify: `src/multi_swarm/llm/cost_tracker.py`
+- Modify: tests esistenti
+
+- [ ] **Step 6.1: Aggiungere `call_kind` a `CostRecord`**
+
+```python
+@dataclass
+class CostRecord:
+    ...
+    call_kind: str = "hypothesis"  # "hypothesis" | "mutation"
+```
+
+- [ ] **Step 6.2: Loggare separatamente in summary**
+
+`summary()["by_call_kind"]` con breakdown.
+
+- [ ] **Step 6.3: Test compatibilità con record esistenti**
+
+Backward compat: record senza `call_kind` interpretati come `"hypothesis"`.
+
+---
+
+## Verification end-to-end
+
+- [ ] `uv run pytest -q` → 100% passa (157 + nuovi test).
+- [ ] `uv run python scripts/smoke_run.py` → completa con mock LLM.
+- [ ] **Run baseline B**: ripetere `phase2-qwen3-001` con `--prompt-mutation-weight 0.0` per controllo.
+- [ ] **Run trattamento T**: `phase2-qwen3-prompt-mut-001` con `--prompt-mutation-weight 0.30`.
+- [ ] Confronto: max fitness T > B + 20%, diversity_prompt(T) > diversity_prompt(B) + 30%.
+- [ ] Costo aggiuntivo run T ≤ $0.10 (sanity check).
+
+---
+
+## Risks & mitigations
+
+| Rischio | Mitigazione |
+|---------|-------------|
+| Mode collapse mutator LLM | `mutation_instruction` scelta random + diversity guard Levenshtein |
+| Prompt LLM-output non parsabile dal compiler | Validation step + fallback `random_mutate` |
+| Costo runaway (loop infinito retry) | `max_tokens=2000`, no retry su validation fail |
+| Bias condiviso con generator tier C | Mutator tier B = `deepseek-v4-flash`, famiglia diversa da Qwen3 |
+| Variabili confuse con Phase 2 | Attivare **solo** dopo Phase 2 baseline; A/B isolato |
+
+---
+
+## Cost estimate
+
+Pop = 20, gen = 10, mutation rate ~75% (5 elite + 15 children), prompt_mutation_weight = 0.30:
+- ~45 chiamate LLM tier B aggiuntive per run.
+- ~500 tok input + 200 tok output per call → 22.5k in + 9k out totali.
+- 22.5k × $0.14/1M + 9k × $0.28/1M ≈ **$0.0057/run**.
+
+Trascurabile rispetto al budget run base (~$0.10).

pyproject.toml

@@ -19,6 +19,7 @@ dependencies = [
     "streamlit>=1.40",
     "plotly>=5.24",
     "pyarrow>=18.0",
+    "nicegui>=3.11.1",
 ]
 
 [dependency-groups]

scripts/run_phase1.py

@@ -31,6 +31,12 @@ def parse_args() -> argparse.Namespace:
     p.add_argument("--end", default="2026-01-01T00:00:00+00:00")
     p.add_argument("--fees-bp", type=float, default=5.0)
     p.add_argument("--n-trials-dsr", type=int, default=50)
+    p.add_argument(
+        "--prompt-mutation-weight",
+        type=float,
+        default=0.0,
+        help="Phase 2.5: probabilità (0-1) che la mutazione invochi LLM mutator tier B",
+    )
     return p.parse_args()
 
 
@@ -84,6 +90,7 @@ def main() -> None:
         fees_bp=args.fees_bp,
         n_trials_dsr=args.n_trials_dsr,
         db_path=settings.db_path,
+        prompt_mutation_weight=args.prompt_mutation_weight,
     )
 
     run_id = run_phase1(cfg, ohlcv=ohlcv, llm=llm)

src/multi_swarm/agents/hypothesis.py

@@ -3,8 +3,10 @@ from __future__ import annotations
 import re
 from dataclasses import dataclass, field
 
+import openai
+
 from ..genome.hypothesis import HypothesisAgentGenome
-from ..llm.client import CompletionResult, LLMClient
+from ..llm.client import CompletionResult, EmptyCompletionError, LLMClient
 from ..protocol.parser import ParseError, Strategy, parse_strategy
 from ..protocol.validator import ValidationError, validate_strategy
 
@@ -274,7 +276,20 @@ class HypothesisAgent:
                     previous_error=errors[-1],
                 )
 
-            completion = self._llm.complete(genome, system=system, user=user)
+            try:
+                completion = self._llm.complete(genome, system=system, user=user)
+            except EmptyCompletionError as e:
+                # LLM esaurito retry tenacity senza una risposta. Tratta come
+                # parse-fail "empty" e ritenta nel loop esterno (max_attempts).
+                errors.append(f"empty_completion: {e}")
+                last_raw = ""
+                continue
+            except openai.RateLimitError as e:
+                # Provider upstream rate limited oltre i retry tenacity.
+                # Marca genome come fallito senza propagare l'eccezione al run.
+                errors.append(f"rate_limit: {e}")
+                last_raw = ""
+                continue
             completions.append(completion)
             last_raw = completion.text
 

src/multi_swarm/config.py

@@ -25,11 +25,11 @@ class Settings(BaseSettings):
 
     openrouter_api_key: SecretStr
 
-    llm_model_tier_s: str = "anthropic/claude-opus-4-7"
-    llm_model_tier_a: str = "anthropic/claude-sonnet-4-6"
-    llm_model_tier_b: str = "anthropic/claude-sonnet-4-6"
+    llm_model_tier_s: str = "google/gemini-3-flash-preview"
+    llm_model_tier_a: str = "deepseek/deepseek-v4-flash"
+    llm_model_tier_b: str = "deepseek/deepseek-v4-flash"
     llm_model_tier_c: str = "qwen/qwen-2.5-72b-instruct"
-    llm_model_tier_d: str = "meta-llama/llama-3.3-70b-instruct"
+    llm_model_tier_d: str = "openai/gpt-oss-20b"
     openrouter_base_url: str = "https://openrouter.ai/api/v1"
 
     run_name: str = "phase1-spike-001"

src/multi_swarm/dashboard/nicegui_app.py

@@ -0,0 +1,880 @@
+"""NiceGUI dashboard — port progressivo da Streamlit.
+
+Avvio: ``uv run python -m multi_swarm.dashboard.nicegui_app``
+Default port 8080. Streamlit resta su 8501 durante la migrazione.
+
+Riusa ``dashboard.data`` (Repository helpers) senza modifiche al backend.
+
+Palette "Neon Trading Dashboard" (ispirata screenshot 2026-05-11):
+- BG: #0A0A0F (near-black con tinge blu)
+- Surface: #13131A (card base)
+- Surface elevata: #1C1C26 (hover/active)
+- Primary pink: #FF2D87 (highlight key metrics, max fitness)
+- Secondary cyan: #00D9FF (median, secondary curves)
+- Accent amber: #FFB800 (warnings, p90)
+- Success neon green: #00E676, Danger neon red: #FF3D60
+- Text: #FFFFFF (primary), #7A7A8C (muted)
+"""
+
+from __future__ import annotations
+
+import html
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+import plotly.graph_objects as go  # type: ignore[import-untyped]
+from nicegui import app, ui
+
+from multi_swarm.dashboard.data import (
+    evaluations_df,
+    generations_df,
+    genomes_df,
+    get_repo,
+    get_run_overview,
+    list_runs_df,
+)
+
+DB_PATH = os.environ.get("DB_PATH", "./runs.db")
+REFRESH_INTERVAL_S = 3.0
+
+# --- Neon Trading Dashboard palette ---
+COLOR_BG = "#0A0A0F"
+COLOR_SURFACE = "#13131A"
+COLOR_SURFACE_2 = "#1C1C26"
+COLOR_BORDER = "rgba(255, 45, 135, 0.12)"
+COLOR_BORDER_HOVER = "rgba(255, 45, 135, 0.45)"
+COLOR_PRIMARY = "#FF2D87"
+COLOR_SECONDARY = "#00D9FF"
+COLOR_ACCENT = "#FFB800"
+COLOR_SUCCESS = "#00E676"
+COLOR_DANGER = "#FF3D60"
+COLOR_TEXT = "#FFFFFF"
+COLOR_TEXT_MUTED = "#7A7A8C"
+
+_STATUS_BADGE: dict[str, tuple[str, str]] = {
+    "running": ("● running", "positive"),
+    "completed": ("✓ completed", "positive"),
+    "failed": ("✕ failed", "negative"),
+}
+
+_CUSTOM_CSS = f"""
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap');
+
+html, body, .q-page, .q-card, .q-btn, .q-field, .q-table, .text-h4, .text-h6, .text-subtitle1, .text-caption, .text-body1, .nav-link, .brand, label, p, span, div {{
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
+    letter-spacing: -0.01em;
+}}
+.material-icons, .material-icons-outlined, .material-symbols-outlined, .q-icon, i.q-icon, i[class*="material"] {{
+    font-family: 'Material Icons' !important;
+    font-feature-settings: 'liga';
+    letter-spacing: normal !important;
+}}
+code, pre, .q-code, .nicegui-code {{ font-family: 'JetBrains Mono', 'Fira Code', monospace !important; font-size: 13.5px !important; }}
+
+body, .q-page-container, .q-page {{
+    background: {COLOR_BG} !important;
+    color: {COLOR_TEXT};
+    background-image:
+        radial-gradient(ellipse 800px 400px at 20% 0%, rgba(255, 45, 135, 0.08) 0%, transparent 60%),
+        radial-gradient(ellipse 600px 400px at 80% 100%, rgba(0, 217, 255, 0.06) 0%, transparent 60%);
+    background-attachment: fixed;
+}}
+
+.q-card {{
+    background: {COLOR_SURFACE} !important;
+    color: {COLOR_TEXT} !important;
+    border: 1px solid {COLOR_BORDER};
+    border-radius: 14px !important;
+    box-shadow:
+        0 1px 2px rgba(0,0,0,0.5),
+        0 8px 24px rgba(0,0,0,0.25),
+        inset 0 1px 0 rgba(255,255,255,0.04);
+    transition: all 0.2s ease;
+    position: relative;
+    overflow: hidden;
+}}
+.q-card::before {{
+    content: '';
+    position: absolute;
+    top: 0; left: 0; right: 0;
+    height: 1px;
+    background: linear-gradient(90deg, transparent, rgba(255, 45, 135, 0.4), transparent);
+    opacity: 0.5;
+}}
+.q-card:hover {{
+    border-color: rgba(255, 45, 135, 0.5);
+    box-shadow:
+        0 1px 2px rgba(0,0,0,0.5),
+        0 8px 32px rgba(255, 45, 135, 0.15),
+        inset 0 1px 0 rgba(255,255,255,0.05);
+}}
+
+.metric-card {{
+    padding: 20px 16px;
+    text-align: left;
+    display: flex;
+    flex-direction: column;
+    gap: 6px;
+    min-width: 140px;
+}}
+.metric-card .text-caption {{
+    color: {COLOR_TEXT_MUTED} !important;
+    font-size: 11px !important;
+    font-weight: 500 !important;
+    text-transform: uppercase;
+    letter-spacing: 0.06em;
+}}
+.metric-card .text-h4 {{
+    color: {COLOR_TEXT} !important;
+    font-weight: 600 !important;
+    font-size: 26px !important;
+    line-height: 1.2 !important;
+    font-feature-settings: 'tnum';
+}}
+.metric-card.accent-cyan .text-h4 {{ color: {COLOR_PRIMARY} !important; }}
+.metric-card.accent-purple .text-h4 {{ color: {COLOR_SECONDARY} !important; }}
+.metric-card.accent-amber .text-h4 {{ color: {COLOR_ACCENT} !important; }}
+.metric-card.accent-green .text-h4 {{ color: {COLOR_SUCCESS} !important; }}
+
+.q-header {{
+    background: rgba(10, 10, 15, 0.75) !important;
+    backdrop-filter: blur(20px) saturate(180%);
+    -webkit-backdrop-filter: blur(20px) saturate(180%);
+    border-bottom: 1px solid {COLOR_BORDER} !important;
+    box-shadow: 0 1px 0 rgba(255, 45, 135, 0.15) !important;
+}}
+
+.nav-link {{
+    color: {COLOR_TEXT_MUTED} !important;
+    padding: 8px 14px;
+    border-radius: 8px;
+    text-decoration: none;
+    font-size: 13.5px;
+    font-weight: 500;
+    transition: all 0.15s ease;
+    position: relative;
+}}
+.nav-link:hover {{
+    color: {COLOR_TEXT} !important;
+    background: {COLOR_SURFACE_2};
+}}
+.nav-link.active {{
+    color: {COLOR_PRIMARY} !important;
+    background: rgba(255, 45, 135, 0.08);
+}}
+.nav-link.active::after {{
+    content: '';
+    position: absolute;
+    bottom: -16px;
+    left: 14px;
+    right: 14px;
+    height: 2px;
+    background: {COLOR_PRIMARY};
+    border-radius: 2px 2px 0 0;
+}}
+
+.brand {{
+    color: {COLOR_TEXT};
+    font-weight: 700;
+    font-size: 15px;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}}
+.brand-dot {{
+    width: 10px;
+    height: 10px;
+    border-radius: 50%;
+    background: {COLOR_PRIMARY};
+    box-shadow: 0 0 16px {COLOR_PRIMARY}, 0 0 4px {COLOR_PRIMARY};
+    animation: pulse-pink 2s ease-in-out infinite;
+}}
+@keyframes pulse-pink {{
+    0%, 100% {{ box-shadow: 0 0 16px {COLOR_PRIMARY}, 0 0 4px {COLOR_PRIMARY}; }}
+    50% {{ box-shadow: 0 0 24px {COLOR_PRIMARY}, 0 0 8px {COLOR_PRIMARY}; }}
+}}
+
+.q-linear-progress {{ height: 8px !important; border-radius: 6px !important; }}
+.q-linear-progress__track {{ background: {COLOR_SURFACE_2} !important; }}
+.q-linear-progress__model {{ border-radius: 6px !important; }}
+
+.q-separator {{ background: {COLOR_BORDER} !important; }}
+
+.q-field--outlined .q-field__control {{
+    background: {COLOR_SURFACE} !important;
+    border-radius: 8px !important;
+}}
+.q-field--outlined .q-field__control:before {{ border-color: {COLOR_BORDER} !important; }}
+.q-field--outlined.q-field--focused .q-field__control:after {{ border-color: {COLOR_PRIMARY} !important; }}
+.q-field__label {{ color: {COLOR_TEXT_MUTED} !important; }}
+.q-field__native, .q-field__input {{ color: {COLOR_TEXT} !important; }}
+
+.q-btn {{ border-radius: 8px !important; font-weight: 500 !important; text-transform: none !important; letter-spacing: 0 !important; }}
+
+.q-table {{ background: transparent !important; color: {COLOR_TEXT} !important; }}
+.q-table thead tr {{ background: {COLOR_SURFACE_2} !important; }}
+.q-table th {{
+    color: {COLOR_TEXT_MUTED} !important;
+    font-size: 11px !important;
+    font-weight: 600 !important;
+    text-transform: uppercase;
+    letter-spacing: 0.06em;
+}}
+.q-table tbody tr {{ transition: background 0.15s; }}
+.q-table tbody tr:hover {{ background: rgba(255, 45, 135, 0.05) !important; }}
+.q-table tbody tr.selected {{ background: rgba(255, 45, 135, 0.12) !important; }}
+.q-table td {{ border-bottom: 1px solid {COLOR_BORDER} !important; font-feature-settings: 'tnum'; }}
+
+.text-h6 {{ font-weight: 600 !important; letter-spacing: -0.015em !important; }}
+.text-subtitle1 {{
+    color: {COLOR_TEXT_MUTED} !important;
+    font-size: 11px !important;
+    font-weight: 600 !important;
+    text-transform: uppercase !important;
+    letter-spacing: 0.08em !important;
+    margin-bottom: 8px !important;
+}}
+
+code, pre, .nicegui-code {{
+    background: #1A1A24 !important;
+    color: {COLOR_TEXT} !important;
+    border: 1px solid {COLOR_BORDER};
+    border-radius: 10px !important;
+    padding: 16px !important;
+    font-size: 13.5px !important;
+    line-height: 1.6 !important;
+}}
+.hljs {{ background: transparent !important; color: {COLOR_TEXT} !important; }}
+.hljs-attr, .hljs-attribute {{ color: {COLOR_SECONDARY} !important; font-weight: 500; }}
+.hljs-string {{ color: {COLOR_SUCCESS} !important; }}
+.hljs-number, .hljs-literal {{ color: {COLOR_PRIMARY} !important; font-weight: 500; }}
+.hljs-keyword, .hljs-built_in {{ color: {COLOR_ACCENT} !important; }}
+.hljs-punctuation, .hljs-meta {{ color: {COLOR_TEXT_MUTED} !important; }}
+.hljs-comment {{ color: {COLOR_TEXT_MUTED} !important; font-style: italic; }}
+.hljs-name, .hljs-title {{ color: {COLOR_PRIMARY} !important; }}
+
+/* Prism.js tokens (NiceGUI usa Prism per ui.code) */
+.token.property, .token.attr-name, .token.tag {{ color: {COLOR_SECONDARY} !important; font-weight: 500; }}
+.token.string, .token.url {{ color: {COLOR_SUCCESS} !important; }}
+.token.number, .token.boolean, .token.null, .token.symbol {{ color: {COLOR_PRIMARY} !important; font-weight: 500; }}
+.token.keyword, .token.constant, .token.builtin, .token.atrule {{ color: {COLOR_ACCENT} !important; }}
+.token.punctuation, .token.operator {{ color: {COLOR_TEXT_MUTED} !important; }}
+.token.comment {{ color: {COLOR_TEXT_MUTED} !important; font-style: italic; }}
+.token.function, .token.class-name {{ color: {COLOR_PRIMARY} !important; }}
+pre[class*="language-"], code[class*="language-"] {{
+    color: {COLOR_TEXT} !important;
+    text-shadow: none !important;
+}}
+
+.q-badge {{ border-radius: 6px !important; font-weight: 500 !important; padding: 4px 10px !important; font-size: 12px !important; }}
+
+.config-block {{
+    background: #1A1A24;
+    color: {COLOR_TEXT};
+    border: 1px solid {COLOR_BORDER};
+    border-radius: 10px;
+    padding: 18px 20px;
+    font-family: 'JetBrains Mono', 'Fira Code', monospace;
+    font-size: 13.5px;
+    line-height: 1.7;
+    overflow-x: auto;
+    white-space: pre;
+    margin: 0;
+}}
+.config-block .cb-key {{ color: {COLOR_SECONDARY}; font-weight: 500; }}
+.config-block .cb-string {{ color: {COLOR_SUCCESS}; }}
+.config-block .cb-number {{ color: {COLOR_PRIMARY}; font-weight: 500; }}
+.config-block .cb-bool {{ color: {COLOR_ACCENT}; }}
+.config-block .cb-null {{ color: {COLOR_ACCENT}; font-style: italic; }}
+.config-block .cb-punct {{ color: {COLOR_TEXT_MUTED}; }}
+
+.raw-block {{
+    background: #1A1A24;
+    color: {COLOR_TEXT};
+    border: 1px solid {COLOR_BORDER};
+    border-radius: 10px;
+    padding: 18px 20px;
+    font-family: 'JetBrains Mono', 'Fira Code', monospace;
+    font-size: 13px;
+    line-height: 1.6;
+    overflow-x: auto;
+    white-space: pre-wrap;
+    word-break: break-word;
+    max-height: 400px;
+    overflow-y: auto;
+    margin: 0;
+}}
+</style>
+"""
+
+
+def _json_to_html(obj: Any, indent: int = 0) -> str:
+    """Render JSON con span colorati espliciti. Garantisce leggibilità ovunque."""
+    pad = "  " * indent
+    inner_pad = "  " * (indent + 1)
+    if isinstance(obj, dict):
+        if not obj:
+            return '<span class="cb-punct">{}</span>'
+        items = []
+        for k, v in obj.items():
+            key = f'<span class="cb-key">"{html.escape(str(k))}"</span>'
+            val = _json_to_html(v, indent + 1)
+            items.append(f"{inner_pad}{key}<span class=\"cb-punct\">:</span> {val}")
+        return ('<span class="cb-punct">{</span>\n'
+                + '<span class="cb-punct">,</span>\n'.join(items)
+                + f'\n{pad}<span class="cb-punct">}}</span>')
+    if isinstance(obj, list):
+        if not obj:
+            return '<span class="cb-punct">[]</span>'
+        items = [_json_to_html(x, indent + 1) for x in obj]
+        return ('<span class="cb-punct">[</span>\n'
+                + '<span class="cb-punct">,</span>\n'.join(inner_pad + i for i in items)
+                + f'\n{pad}<span class="cb-punct">]</span>')
+    if isinstance(obj, bool):
+        return f'<span class="cb-bool">{str(obj).lower()}</span>'
+    if obj is None:
+        return '<span class="cb-null">null</span>'
+    if isinstance(obj, (int, float)):
+        return f'<span class="cb-number">{obj}</span>'
+    return f'<span class="cb-string">"{html.escape(str(obj))}"</span>'
+
+
+def _apply_theme() -> None:
+    ui.add_head_html(_CUSTOM_CSS)
+    ui.dark_mode().enable()
+    ui.colors(
+        primary=COLOR_PRIMARY,
+        secondary=COLOR_SECONDARY,
+        accent=COLOR_ACCENT,
+        dark=COLOR_BG,
+        dark_page=COLOR_BG,
+        positive=COLOR_SUCCESS,
+        negative=COLOR_DANGER,
+        info=COLOR_PRIMARY,
+        warning=COLOR_ACCENT,
+    )
+
+
+def _build_header(active: str) -> None:
+    with ui.header().classes("items-center justify-between q-px-lg q-py-md"):
+        with ui.row().classes("items-center gap-8"):
+            with ui.row().classes("items-center gap-2").classes("brand"):
+                ui.html('<span class="brand-dot"></span>')
+                ui.html('<span class="brand">Multi-Swarm <span style="color:'
+                        + COLOR_TEXT_MUTED + ';font-weight:400;">/ Coevolutivo</span></span>')
+            with ui.row().classes("items-center gap-1"):
+                for path, label in (
+                    ("/", "Overview"),
+                    ("/convergence", "Convergence"),
+                    ("/genomes", "Genomes"),
+                ):
+                    cls = "nav-link active" if active == path else "nav-link"
+                    ui.link(label, path).classes(cls)
+        with ui.row().classes("items-center gap-3"):
+            ui.html(f'<span style="color:{COLOR_TEXT_MUTED};font-size:12px;'
+                    f'font-family:JetBrains Mono,monospace;">'
+                    f'⛁ {Path(DB_PATH).resolve().name}</span>')
+
+
+def _runs_options() -> dict[str, str]:
+    repo = get_repo(DB_PATH)
+    runs = list_runs_df(repo)
+    if runs.empty:
+        return {}
+    return {
+        row["id"]: f"{row['name']} — {row['status']} ({row['started_at'][:16]})"
+        for _, row in runs.iterrows()
+    }
+
+
+def _snapshot(run_id: str) -> dict[str, Any]:
+    repo = get_repo(DB_PATH)
+    ov = get_run_overview(repo, run_id)
+    evals = evaluations_df(repo, run_id)
+    gens = generations_df(repo, run_id)
+
+    cfg = ov["config"]
+    pop_size = int(cfg.get("population_size", 0))
+    n_gens = int(cfg.get("n_generations", 0))
+    evals_total = max(pop_size * n_gens, 1)
+    evals_done = len(evals)
+    gens_done = int(gens["completed_at"].notna().sum()) if not gens.empty else 0
+    # runs.total_cost_usd è 0 finché complete_run non viene chiamato.
+    # Per le run in corso leggiamo la somma live da cost_records.
+    live_cost = float(repo.total_cost(run_id)) if ov["status"] == "running" else float(
+        ov["total_cost_usd"]
+    )
+
+    top_fit = float(evals["fitness"].max()) if evals_done else float("nan")
+    median_fit = float(evals["fitness"].median()) if evals_done else float("nan")
+    parse_success = (
+        100.0 * float(evals["parse_error"].isna().sum()) / evals_done if evals_done else 0.0
+    )
+
+    return {
+        "status": ov["status"],
+        "name": cfg.get("run_name", "—"),
+        "started_at": ov["started_at"],
+        "completed_at": ov["completed_at"] or "—",
+        "cost_usd": live_cost,
+        "pop_size": pop_size,
+        "n_gens": n_gens,
+        "evals_done": evals_done,
+        "evals_total": evals_total,
+        "gens_done": gens_done,
+        "top_fit": top_fit,
+        "median_fit": median_fit,
+        "parse_success": parse_success,
+        "config": cfg,
+        "gens_df": gens,
+    }
+
+
+def _convergence_figure(gens_df: Any) -> go.Figure:
+    fig = go.Figure()
+    if gens_df.empty:
+        fig.add_annotation(
+            text="Nessuna generazione registrata", x=0.5, y=0.5, showarrow=False,
+            font={"color": COLOR_TEXT_MUTED, "size": 14},
+        )
+    else:
+        fig.add_trace(
+            go.Scatter(
+                x=gens_df["generation_idx"], y=gens_df["fitness_max"],
+                name="max", mode="lines+markers",
+                line={"color": COLOR_PRIMARY, "width": 3, "shape": "spline", "smoothing": 0.6},
+                marker={"size": 9, "color": COLOR_PRIMARY,
+                        "line": {"color": "#fff", "width": 1}},
+                fill="tozeroy",
+                fillcolor="rgba(255, 45, 135, 0.12)",
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=gens_df["generation_idx"], y=gens_df["fitness_p90"],
+                name="p90", mode="lines+markers",
+                line={"color": COLOR_ACCENT, "width": 2, "dash": "dot", "shape": "spline"},
+                marker={"size": 7, "color": COLOR_ACCENT},
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=gens_df["generation_idx"], y=gens_df["fitness_median"],
+                name="median", mode="lines+markers",
+                line={"color": COLOR_SECONDARY, "width": 2, "shape": "spline"},
+                marker={"size": 7, "color": COLOR_SECONDARY},
+            )
+        )
+    fig.update_layout(
+        template="plotly_dark",
+        paper_bgcolor=COLOR_SURFACE,
+        plot_bgcolor=COLOR_SURFACE,
+        font={"color": COLOR_TEXT},
+        xaxis={"title": "generation", "gridcolor": "rgba(148, 163, 184, 0.08)", "dtick": 1},
+        yaxis={"title": "fitness", "gridcolor": "rgba(148, 163, 184, 0.08)"},
+        title={"text": "Fitness convergence", "font": {"color": COLOR_TEXT, "size": 18}},
+        legend={"bgcolor": "rgba(19, 19, 26, 0.95)", "bordercolor": COLOR_PRIMARY, "borderwidth": 1},
+        margin={"l": 50, "r": 30, "t": 50, "b": 50},
+    )
+    return fig
+
+
+def _entropy_figure(gens_df: Any) -> go.Figure:
+    fig = go.Figure()
+    if not gens_df.empty:
+        fig.add_trace(
+            go.Scatter(
+                x=gens_df["generation_idx"], y=gens_df["entropy"],
+                mode="lines+markers",
+                line={"color": COLOR_SECONDARY, "width": 3, "shape": "spline", "smoothing": 0.6},
+                marker={"size": 9, "color": COLOR_SECONDARY,
+                        "line": {"color": "#fff", "width": 1}},
+                fill="tozeroy",
+                fillcolor="rgba(0, 217, 255, 0.12)",
+                name="entropy",
+            )
+        )
+        fig.add_hline(
+            y=0.5, line_dash="dash", line_color=COLOR_ACCENT,
+            annotation_text="gate threshold (0.5)",
+            annotation_font_color=COLOR_ACCENT,
+        )
+    fig.update_layout(
+        template="plotly_dark",
+        paper_bgcolor=COLOR_SURFACE,
+        plot_bgcolor=COLOR_SURFACE,
+        font={"color": COLOR_TEXT},
+        xaxis={"title": "generation", "gridcolor": "rgba(148, 163, 184, 0.08)", "dtick": 1},
+        yaxis={"title": "entropy", "gridcolor": "rgba(148, 163, 184, 0.08)"},
+        title={"text": "Diversity (fitness entropy)", "font": {"color": COLOR_TEXT, "size": 18}},
+        margin={"l": 50, "r": 30, "t": 50, "b": 50},
+    )
+    return fig
+
+
+@ui.page("/")
+def index() -> None:
+    _apply_theme()
+    _build_header(active="/")
+
+    options = _runs_options()
+    if not options:
+        ui.label("Nessuna run nel database.").classes("text-h5")
+        return
+
+    state: dict[str, Any] = {"run_id": next(iter(options))}
+
+    with ui.row().classes("w-full items-center gap-4 q-mb-md"):
+        select = ui.select(options=options, value=state["run_id"], label="Run").classes(
+            "flex-grow"
+        )
+        status_badge = ui.badge("…", color="primary").classes("text-body1 q-pa-sm")
+        ui.button("🔄 Refresh", on_click=lambda: refresh()).props("outline color=primary")
+
+    with ui.card().classes("w-full"):
+        ui.label("Progresso run").classes("text-subtitle1")
+        gen_label = ui.label("Generations: 0/0")
+        gen_bar = ui.linear_progress(0.0, show_value=False).props("size=20px color=primary")
+        eval_label = ui.label("Evaluations: 0/0 (0.0%)")
+        eval_bar = ui.linear_progress(0.0, show_value=False).props("size=20px color=accent")
+
+    with ui.row().classes("w-full gap-4"):
+        with ui.card().classes("flex-grow metric-card accent-cyan"):
+            ui.label("Top fitness").classes("text-caption")
+            top_lbl = ui.label("—").classes("text-h4")
+        with ui.card().classes("flex-grow metric-card accent-purple"):
+            ui.label("Median fitness").classes("text-caption")
+            median_lbl = ui.label("—").classes("text-h4")
+        with ui.card().classes("flex-grow metric-card accent-amber"):
+            ui.label("Parse success").classes("text-caption")
+            parse_lbl = ui.label("—").classes("text-h4")
+        with ui.card().classes("flex-grow metric-card accent-green"):
+            ui.label("Cost (USD)").classes("text-caption")
+            cost_lbl = ui.label("—").classes("text-h4")
+
+    with ui.row().classes("w-full gap-4 q-mt-md"):
+        started_lbl = ui.label("Started: —")
+        completed_lbl = ui.label("Completed: —")
+    ui.separator()
+    ui.label("Config").classes("text-subtitle1")
+    cfg_code = ui.html('<pre class="config-block"></pre>').classes("w-full")
+
+    def refresh() -> None:
+        run_id = select.value
+        if not run_id:
+            return
+        try:
+            s = _snapshot(run_id)
+        except Exception as e:  # noqa: BLE001
+            ui.notify(f"Errore: {e}", type="negative")
+            return
+
+        text, color = _STATUS_BADGE.get(s["status"], (s["status"], "primary"))
+        status_badge.text = text
+        status_badge.props(f"color={color}")
+
+        gen_frac = min(s["gens_done"] / max(s["n_gens"], 1), 1.0)
+        eval_frac = min(s["evals_done"] / s["evals_total"], 1.0)
+        gen_bar.value = gen_frac
+        eval_bar.value = eval_frac
+        gen_label.text = f"Generations: {s['gens_done']}/{s['n_gens']}"
+        eval_label.text = (
+            f"Evaluations: {s['evals_done']}/{s['evals_total']} ({100 * eval_frac:.1f}%)"
+        )
+
+        top_lbl.text = f"{s['top_fit']:.4f}" if s["evals_done"] else "—"
+        median_lbl.text = f"{s['median_fit']:.4f}" if s["evals_done"] else "—"
+        parse_lbl.text = f"{s['parse_success']:.1f}%" if s["evals_done"] else "—"
+        cost_lbl.text = f"${s['cost_usd']:.4f}"
+
+        started_lbl.text = f"Started: {s['started_at']}"
+        completed_lbl.text = f"Completed: {s['completed_at']}"
+        cfg_code.content = f'<pre class="config-block">{_json_to_html(s["config"])}</pre>'
+
+    def on_select_change() -> None:
+        state["run_id"] = select.value
+        refresh()
+
+    select.on_value_change(on_select_change)
+    ui.timer(REFRESH_INTERVAL_S, refresh)
+    refresh()
+
+
+@ui.page("/convergence")
+def convergence() -> None:
+    _apply_theme()
+    _build_header(active="/convergence")
+
+    options = _runs_options()
+    if not options:
+        ui.label("Nessuna run nel database.").classes("text-h5")
+        return
+
+    state: dict[str, Any] = {"run_id": next(iter(options))}
+
+    with ui.row().classes("w-full items-center gap-4 q-mb-md"):
+        select = ui.select(options=options, value=state["run_id"], label="Run").classes(
+            "flex-grow"
+        )
+        gen_count_lbl = ui.label("Gens: 0/0").classes("text-body1").style(
+            f"color: {COLOR_PRIMARY}; font-weight: 600;"
+        )
+        ui.button("🔄 Refresh", on_click=lambda: refresh()).props("outline color=primary")
+
+    fitness_plot = ui.plotly(_convergence_figure(generations_df(get_repo(DB_PATH), state["run_id"]))).classes("w-full")
+    entropy_plot = ui.plotly(_entropy_figure(generations_df(get_repo(DB_PATH), state["run_id"]))).classes("w-full q-mt-md")
+
+    ui.separator()
+    ui.label("Tabella generazioni").classes("text-subtitle1 q-mt-md")
+    gens_table = ui.table(
+        columns=[
+            {"name": "generation_idx", "label": "gen", "field": "generation_idx", "sortable": True},
+            {"name": "n_genomes", "label": "n", "field": "n_genomes"},
+            {"name": "fitness_max", "label": "max", "field": "fitness_max"},
+            {"name": "fitness_p90", "label": "p90", "field": "fitness_p90"},
+            {"name": "fitness_median", "label": "median", "field": "fitness_median"},
+            {"name": "entropy", "label": "entropy", "field": "entropy"},
+            {"name": "completed_at", "label": "completed", "field": "completed_at"},
+        ],
+        rows=[],
+        row_key="generation_idx",
+    ).classes("w-full")
+
+    def refresh() -> None:
+        run_id = select.value
+        if not run_id:
+            return
+        try:
+            gens = generations_df(get_repo(DB_PATH), run_id)
+            ov = get_run_overview(get_repo(DB_PATH), run_id)
+        except Exception as e:  # noqa: BLE001
+            ui.notify(f"Errore: {e}", type="negative")
+            return
+
+        n_gens = int(ov["config"].get("n_generations", 0))
+        gens_done = int(gens["completed_at"].notna().sum()) if not gens.empty else 0
+        gen_count_lbl.text = f"Gens: {gens_done}/{n_gens}"
+
+        fitness_plot.update_figure(_convergence_figure(gens))
+        entropy_plot.update_figure(_entropy_figure(gens))
+
+        if gens.empty:
+            gens_table.rows = []
+        else:
+            display_cols = [
+                "generation_idx", "n_genomes",
+                "fitness_max", "fitness_p90", "fitness_median",
+                "entropy", "completed_at",
+            ]
+            gens_table.rows = [
+                {
+                    col: (round(v, 6) if isinstance(v, float) else v)
+                    for col, v in row.items()
+                    if col in display_cols
+                }
+                for _, row in gens.iterrows()
+            ]
+        gens_table.update()
+
+    def on_select_change() -> None:
+        state["run_id"] = select.value
+        refresh()
+
+    select.on_value_change(on_select_change)
+    ui.timer(REFRESH_INTERVAL_S, refresh)
+    refresh()
+
+
+@ui.page("/genomes")
+def genomes() -> None:
+    _apply_theme()
+    _build_header(active="/genomes")
+
+    options = _runs_options()
+    if not options:
+        ui.label("Nessuna run nel database.").classes("text-h5")
+        return
+
+    state: dict[str, Any] = {
+        "run_id": next(iter(options)),
+        "selected_gid": None,
+        "merged": None,
+    }
+
+    with ui.row().classes("w-full items-center gap-4 q-mb-md"):
+        select = ui.select(options=options, value=state["run_id"], label="Run").classes(
+            "flex-grow"
+        )
+        top_k_select = ui.select(
+            options={10: "Top 10", 25: "Top 25", 50: "Top 50"},
+            value=10,
+            label="Top K",
+        )
+        ui.button("🔄 Refresh", on_click=lambda: refresh()).props("outline color=primary")
+
+    ui.label("Top genomi per fitness").classes("text-subtitle1 q-mt-sm")
+    top_table = ui.table(
+        columns=[
+            {"name": "genome_id", "label": "id", "field": "genome_id", "align": "left"},
+            {"name": "fitness", "label": "fitness", "field": "fitness", "sortable": True},
+            {"name": "dsr", "label": "DSR", "field": "dsr"},
+            {"name": "sharpe", "label": "Sharpe", "field": "sharpe"},
+            {"name": "max_dd", "label": "max DD", "field": "max_dd"},
+            {"name": "n_trades", "label": "trades", "field": "n_trades"},
+            {"name": "cognitive_style", "label": "style", "field": "cognitive_style"},
+            {"name": "temperature", "label": "T", "field": "temperature"},
+            {"name": "lookback_window", "label": "lookback", "field": "lookback_window"},
+        ],
+        rows=[],
+        row_key="genome_id",
+        selection="single",
+    ).classes("w-full")
+
+    ui.separator().classes("q-my-md")
+
+    with ui.card().classes("w-full"):
+        ui.label("Ispezione genoma").classes("text-subtitle1")
+        detail_hint = ui.label("Seleziona un genoma dalla tabella sopra.").classes(
+            "text-caption"
+        ).style(f"color: {COLOR_TEXT_MUTED};")
+
+        with ui.row().classes("w-full gap-4 q-mt-sm"):
+            with ui.card().classes("flex-grow metric-card accent-cyan"):
+                ui.label("fitness").classes("text-caption")
+                fit_lbl = ui.label("—").classes("text-h4")
+            with ui.card().classes("flex-grow metric-card accent-purple"):
+                ui.label("DSR").classes("text-caption")
+                dsr_lbl = ui.label("—").classes("text-h4")
+            with ui.card().classes("flex-grow metric-card accent-amber"):
+                ui.label("Sharpe").classes("text-caption")
+                sharpe_lbl = ui.label("—").classes("text-h4")
+            with ui.card().classes("flex-grow metric-card"):
+                ui.label("max DD").classes("text-caption")
+                dd_lbl = ui.label("—").classes("text-h4")
+            with ui.card().classes("flex-grow metric-card accent-green"):
+                ui.label("trades").classes("text-caption")
+                trades_lbl = ui.label("—").classes("text-h4")
+            with ui.card().classes("flex-grow metric-card"):
+                ui.label("style").classes("text-caption")
+                style_lbl = ui.label("—").classes("text-h4")
+
+        ui.label("System prompt").classes("text-subtitle1 q-mt-md")
+        prompt_code = ui.html('<pre class="raw-block">—</pre>').classes("w-full")
+
+        ui.label("Raw LLM output").classes("text-subtitle1 q-mt-md")
+        raw_code = ui.html('<pre class="raw-block">—</pre>').classes("w-full")
+
+        parse_error_lbl = ui.label("").classes("q-mt-sm").style(
+            "color: #FF6B6B; font-weight: 600;"
+        )
+
+    def _render_detail(row: dict[str, Any]) -> None:
+        detail_hint.text = f"Genoma: {row.get('genome_id', '—')}"
+        fit_lbl.text = f"{float(row.get('fitness', 0)):.4f}"
+        dsr_lbl.text = f"{float(row.get('dsr', 0)):.4f}"
+        sharpe_lbl.text = f"{float(row.get('sharpe', 0)):.3f}"
+        dd_lbl.text = f"{float(row.get('max_dd', 0)):.3f}"
+        trades_lbl.text = str(int(row.get("n_trades", 0)))
+        style_lbl.text = str(row.get("cognitive_style", "—"))
+        prompt_code.content = (
+            f'<pre class="raw-block">{html.escape(str(row.get("system_prompt", "—")))}</pre>'
+        )
+        raw_code.content = (
+            f'<pre class="raw-block">{html.escape(str(row.get("raw_text", "—") or "—"))}</pre>'
+        )
+        pe = row.get("parse_error")
+        parse_error_lbl.text = f"❌ Parse error: {pe}" if pe else ""
+
+    def refresh() -> None:
+        run_id = select.value
+        if not run_id:
+            return
+        try:
+            repo = get_repo(DB_PATH)
+            evals = evaluations_df(repo, run_id)
+            gens = genomes_df(repo, run_id)
+        except Exception as e:  # noqa: BLE001
+            ui.notify(f"Errore: {e}", type="negative")
+            return
+
+        if evals.empty:
+            top_table.rows = []
+            top_table.update()
+            return
+
+        merged = evals.merge(
+            gens, left_on="genome_id", right_on="id", how="left", suffixes=("", "_g")
+        )
+        state["merged"] = merged
+
+        k = int(top_k_select.value)
+        top = merged.sort_values("fitness", ascending=False).head(k)
+
+        rows = []
+        for _, r in top.iterrows():
+            rows.append(
+                {
+                    "genome_id": str(r.get("genome_id", "—"))[:12] + "…",
+                    "fitness": round(float(r.get("fitness", 0)), 4),
+                    "dsr": round(float(r.get("dsr", 0)), 4),
+                    "sharpe": round(float(r.get("sharpe", 0)), 3),
+                    "max_dd": round(float(r.get("max_dd", 0)), 3),
+                    "n_trades": int(r.get("n_trades", 0)),
+                    "cognitive_style": str(r.get("cognitive_style", "—")),
+                    "temperature": round(float(r.get("temperature", 0)), 2),
+                    "lookback_window": int(r.get("lookback_window", 0)),
+                    "_full_id": str(r.get("genome_id", "")),
+                }
+            )
+        top_table.rows = rows
+        top_table.update()
+
+        sel = state.get("selected_gid")
+        if sel:
+            match = merged[merged["genome_id"] == sel]
+            if not match.empty:
+                _render_detail(match.iloc[0].to_dict())
+
+    def on_row_selected(e: Any) -> None:
+        if not e.selection:
+            return
+        full_id = e.selection[0].get("_full_id")
+        if not full_id:
+            return
+        state["selected_gid"] = full_id
+        merged = state.get("merged")
+        if merged is None:
+            return
+        match = merged[merged["genome_id"] == full_id]
+        if not match.empty:
+            _render_detail(match.iloc[0].to_dict())
+
+    def on_select_change() -> None:
+        state["run_id"] = select.value
+        state["selected_gid"] = None
+        refresh()
+
+    select.on_value_change(on_select_change)
+    top_k_select.on_value_change(lambda _: refresh())
+    top_table.on("selection", on_row_selected)
+    ui.timer(REFRESH_INTERVAL_S, refresh)
+    refresh()
+
+
+def main() -> None:
+    app.on_startup(lambda: print(f"DB: {Path(DB_PATH).resolve()}"))
+    ui.run(
+        host="0.0.0.0",
+        port=8080,
+        title="Multi-Swarm Dashboard",
+        reload=False,
+        show=False,
+        dark=True,
+    )
+
+
+if __name__ in {"__main__", "__mp_main__"}:
+    main()

src/multi_swarm/dashboard/pages/01_overview.py

@@ -1,9 +1,12 @@
 from __future__ import annotations
 
+from datetime import datetime
+
 import streamlit as st
 
 from multi_swarm.dashboard.data import (
     evaluations_df,
+    generations_df,
     get_repo,
     get_run_overview,
     list_runs_df,
@@ -25,6 +28,41 @@ st.dataframe(runs[["id", "name", "started_at", "completed_at", "status", "total_
 selected = st.selectbox("Seleziona run per dettaglio", runs["id"].tolist())
 overview = get_run_overview(repo, selected)
 
+# --- Progress live ---
+cfg = overview["config"]
+pop_size = int(cfg.get("population_size", 0))
+n_gens = int(cfg.get("n_generations", 0))
+evals = evaluations_df(repo, selected)
+gens = generations_df(repo, selected)
+
+evals_done = len(evals)
+evals_total = max(pop_size * n_gens, 1)
+gens_done = int(gens["completed_at"].notna().sum()) if not gens.empty else 0
+
+status_emoji = {"running": "🟢", "completed": "✅", "failed": "❌"}.get(overview["status"], "⚪")
+top_fit = float(evals["fitness"].max()) if not evals.empty else float("nan")
+
+st.subheader(f"{status_emoji} Progresso run")
+st.progress(
+    min(gens_done / max(n_gens, 1), 1.0),
+    text=f"Generations: {gens_done}/{n_gens}",
+)
+st.progress(
+    min(evals_done / evals_total, 1.0),
+    text=f"Evaluations: {evals_done}/{evals_total} ({100*evals_done/evals_total:.1f}%)",
+)
+pcol1, pcol2, pcol3 = st.columns(3)
+pcol1.metric("Top fitness", f"{top_fit:.4f}" if evals_done else "—")
+pcol2.metric("Median fitness", f"{evals['fitness'].median():.4f}" if evals_done else "—")
+pcol3.metric("Cost so far", f"${overview['total_cost_usd']:.4f}")
+
+ref_col1, ref_col2 = st.columns([1, 4])
+if ref_col1.button("🔄 Refresh"):
+    st.rerun()
+ref_col2.caption(f"Last update: {datetime.now().strftime('%H:%M:%S')}")
+
+st.divider()
+
 col1, col2, col3, col4 = st.columns(4)
 col1.metric("Status", overview["status"])
 col2.metric("Cost (USD)", f"{overview['total_cost_usd']:.4f}")
@@ -32,7 +70,6 @@ col3.metric("Started", overview["started_at"])
 col4.metric("Completed", overview["completed_at"] or "—")
 
 st.subheader("Statistiche evaluations")
-evals = evaluations_df(repo, selected)
 col5, col6, col7, col8 = st.columns(4)
 if not evals.empty:
     parse_success = 100 * (evals["parse_error"].isna().sum() / len(evals))

src/multi_swarm/ga/loop.py

@@ -2,10 +2,11 @@ from __future__ import annotations
 
 import random
 from dataclasses import dataclass
+from typing import Any
 
 from ..genome.crossover import uniform_crossover
 from ..genome.hypothesis import HypothesisAgentGenome
-from ..genome.mutation import random_mutate
+from ..genome.mutation import weighted_random_mutate
 from .selection import elite_select, tournament_select
 
 
@@ -15,6 +16,7 @@ class GAConfig:
     elite_k: int
     tournament_k: int
     p_crossover: float
+    prompt_mutation_weight: float = 0.0  # Phase 2.5: opt-in LLM mutator
 
 
 def next_generation(
@@ -22,8 +24,14 @@ def next_generation(
     fitnesses: dict[str, float],
     cfg: GAConfig,
     rng: random.Random,
+    llm: Any | None = None,
 ) -> list[HypothesisAgentGenome]:
-    """Costruisce la prossima generazione: elitismo + tournament + crossover/mutate."""
+    """Costruisce la prossima generazione: elitismo + tournament + crossover/mutate.
+
+    Quando ``cfg.prompt_mutation_weight > 0`` e ``llm`` è fornito, la mutazione
+    invoca ``weighted_random_mutate`` che con quella probabilità usa
+    ``mutate_prompt_llm`` (Phase 2.5).
+    """
     new_pop: list[HypothesisAgentGenome] = list(
         elite_select(population, fitnesses, cfg.elite_k)
     )
@@ -35,7 +43,9 @@ def next_generation(
             child = uniform_crossover(p1, p2, rng)
         else:
             parent = tournament_select(population, fitnesses, cfg.tournament_k, rng)
-            child = random_mutate(parent, rng)
+            child = weighted_random_mutate(
+                parent, rng, llm=llm, prompt_mutation_weight=cfg.prompt_mutation_weight
+            )
         new_pop.append(child)
 
     return new_pop[: cfg.population_size]

src/multi_swarm/genome/mutation.py

@@ -75,3 +75,23 @@ MUTATION_OPS = (
 def random_mutate(g: HypothesisAgentGenome, rng: random.Random) -> HypothesisAgentGenome:
     op = rng.choice(MUTATION_OPS)
     return op(g, rng)
+
+
+def weighted_random_mutate(
+    g: HypothesisAgentGenome,
+    rng: random.Random,
+    llm: Any | None = None,
+    prompt_mutation_weight: float = 0.0,
+) -> HypothesisAgentGenome:
+    """Dispatcher pesato fra mutate_prompt_llm e random_mutate scalare.
+
+    Con probabilità ``prompt_mutation_weight`` invoca ``mutate_prompt_llm``,
+    altrimenti ``random_mutate``. Se ``llm`` è ``None`` o il peso è 0,
+    è equivalente a ``random_mutate`` (backward-compat).
+    """
+    if llm is not None and prompt_mutation_weight > 0 and rng.random() < prompt_mutation_weight:
+        # Import inline per evitare ciclo: mutation_prompt_llm importa da mutation.
+        from .mutation_prompt_llm import mutate_prompt_llm
+
+        return mutate_prompt_llm(g, llm, rng)
+    return random_mutate(g, rng)

src/multi_swarm/genome/mutation_prompt_llm.py

@@ -0,0 +1,167 @@
+"""Phase 2.5 operator: ``mutate_prompt_llm``.
+
+Quinto operatore di mutazione che riscrive il ``system_prompt`` di un genoma
+usando un LLM tier B come "mutator". Genera diversità prompt-level dove gli
+altri quattro operatori toccano solo i quattro parametri scalari.
+
+Fallback sicuro: se la mutazione LLM produce output invalido o troppo simile
+al parent, l'operatore degrada silenziosamente a ``random_mutate``.
+"""
+
+from __future__ import annotations
+
+import random
+import re
+from dataclasses import replace
+from difflib import SequenceMatcher
+from typing import Any, Protocol
+
+from .hypothesis import HypothesisAgentGenome, ModelTier
+from .mutation import _clone_with, random_mutate
+
+# Sei tipi di mutazione "atomiche", scelti uniformemente.
+MUTATION_INSTRUCTIONS: dict[str, str] = {
+    "tighten_threshold": (
+        "Rendi UNA soglia numerica nella strategia più restrittiva del 10-20%. "
+        "Esempio: se RSI > 70 diventa RSI > 78. Lascia tutto il resto identico."
+    ),
+    "swap_comparator": (
+        "Inverti UN comparator (gt -> lt, gte -> lte o viceversa) in una sola "
+        "condizione. Mantieni lo stesso intent generale della strategia."
+    ),
+    "add_condition": (
+        "Aggiungi UNA condizione AND a una rule esistente per renderla più "
+        "selettiva. La condizione deve usare una feature/indicator coerente con "
+        "il resto della strategia."
+    ),
+    "remove_condition": (
+        "Rimuovi UNA condizione ridondante o ovvia da una rule, semplificando la "
+        "logica senza alterarne l'intent principale."
+    ),
+    "change_timeframe": (
+        "Modifica UNA finestra rolling/lookback di +/- 20-40% (es. SMA(50) -> "
+        "SMA(70)). Solo un parametro temporale."
+    ),
+    "add_temporal_gate": (
+        "Aggiungi UN gate temporale alla strategia usando una delle feature "
+        "'hour', 'dow', 'is_weekend', 'minute_of_hour' per filtrare il "
+        "trading a specifici momenti."
+    ),
+}
+
+# Keyword tecniche minime per validare che il prompt sia ancora "una strategia".
+_VALID_KEYWORDS = (
+    "rsi", "sma", "ema", "atr", "momentum", "breakout", "mean", "reversion",
+    "macd", "vwap", "bb", "bollinger", "stoch", "trend", "signal", "buy",
+    "sell", "long", "short", "entry", "exit", "stop", "rule", "condition",
+    "if", "when", "and", "or", "gt", "lt", ">", "<", "ge", "le",
+    "hour", "dow", "weekend", "indicator", "feature",
+)
+
+_MIN_PROMPT_LENGTH = 50
+_MIN_DIFF_RATIO = 0.05  # Levenshtein-like: prompt deve essere almeno 5% diverso
+
+_MUTATOR_SYSTEM_PROMPT = (
+    "Sei un mutator evolutivo per prompt di strategie di trading algoritmico. "
+    "Ricevi un PROMPT originale e una ISTRUZIONE di mutazione atomica. "
+    "Produci una versione modificata del prompt che applica SOLO quella "
+    "mutazione, preservando intent e struttura generale. "
+    "Output: solo il nuovo prompt fra tag <prompt>...</prompt>. "
+    "Nessun preambolo, nessuna spiegazione."
+)
+
+_PROMPT_RE = re.compile(r"<prompt>\s*(.*?)\s*</prompt>", re.DOTALL | re.IGNORECASE)
+
+
+class _LLMClientLike(Protocol):
+    """Subset minimo dell'API LLMClient che usa l'operatore.
+
+    Permette di mockare l'LLM nei test senza importare la classe concreta.
+    """
+
+    def complete(
+        self,
+        genome: HypothesisAgentGenome,
+        system: str,
+        user: str,
+        max_tokens: int = ...,
+    ) -> Any: ...
+
+
+def _extract_prompt(text: str) -> str:
+    """Estrae il prompt mutato dal completion text.
+
+    Cerca tag ``<prompt>...</prompt>``. Se assenti, ritorna il testo strip.
+    """
+    m = _PROMPT_RE.search(text)
+    if m:
+        return m.group(1).strip()
+    return text.strip()
+
+
+def _string_diff_ratio(a: str, b: str) -> float:
+    """Ritorna ``1 - similarity`` (0.0 = identici, 1.0 = completamente diversi)."""
+    if not a and not b:
+        return 0.0
+    return 1.0 - SequenceMatcher(None, a, b).ratio()
+
+
+def is_valid_prompt(new_prompt: str, parent_prompt: str) -> bool:
+    """Validation gate per il prompt LLM-mutato.
+
+    Tre check:
+    1. Lunghezza minima 50 caratteri.
+    2. Contiene almeno una keyword tecnica (rsi, sma, signal, ecc).
+    3. Diversità Levenshtein-like > 5% rispetto al parent.
+    """
+    if len(new_prompt) < _MIN_PROMPT_LENGTH:
+        return False
+    lowered = new_prompt.lower()
+    if not any(kw in lowered for kw in _VALID_KEYWORDS):
+        return False
+    if _string_diff_ratio(new_prompt, parent_prompt) < _MIN_DIFF_RATIO:
+        return False
+    return True
+
+
+def mutate_prompt_llm(
+    g: HypothesisAgentGenome,
+    llm: _LLMClientLike,
+    rng: random.Random,
+    mutator_tier: ModelTier = ModelTier.B,
+    max_tokens: int = 2000,
+) -> HypothesisAgentGenome:
+    """Operatore di mutazione prompt-level via LLM mutator.
+
+    Sceglie una mutation-instruction casuale fra sei tipi, fa una chiamata
+    LLM tier B per ottenere il prompt mutato, valida l'output. Su validation
+    fail (output troppo corto, non-strategia, troppo simile al parent),
+    fallback silenzioso a ``random_mutate``.
+    """
+    instruction_key = rng.choice(list(MUTATION_INSTRUCTIONS))
+    instruction = MUTATION_INSTRUCTIONS[instruction_key]
+
+    user_prompt = (
+        f"PROMPT ORIGINALE:\n{g.system_prompt}\n\n"
+        f"ISTRUZIONE DI MUTAZIONE ({instruction_key}):\n{instruction}\n\n"
+        f"Genera la versione modificata fra tag <prompt>...</prompt>."
+    )
+
+    # Mutator usa un tier diverso (B) — clone temporaneo del genoma con tier override.
+    mutator_genome = replace(g, model_tier=mutator_tier)
+
+    try:
+        result = llm.complete(
+            mutator_genome,
+            system=_MUTATOR_SYSTEM_PROMPT,
+            user=user_prompt,
+            max_tokens=max_tokens,
+        )
+    except Exception:
+        return random_mutate(g, rng)
+
+    new_prompt = _extract_prompt(getattr(result, "text", ""))
+    if not is_valid_prompt(new_prompt, g.system_prompt):
+        return random_mutate(g, rng)
+
+    return _clone_with(g, system_prompt=new_prompt)

src/multi_swarm/llm/client.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
+from typing import Any
 
 import openai
 from openai import OpenAI
@@ -14,22 +15,25 @@ from tenacity import (
 from ..genome.hypothesis import HypothesisAgentGenome, ModelTier
 
 # Modelli configurati per Phase 1 — tutti via OpenRouter
-MODEL_TIER_S = "anthropic/claude-opus-4-7"
-MODEL_TIER_A = "anthropic/claude-sonnet-4-6"
-MODEL_TIER_B = "anthropic/claude-sonnet-4-6"
+MODEL_TIER_S = "google/gemini-3-flash-preview"
+MODEL_TIER_A = "deepseek/deepseek-v4-flash"
+MODEL_TIER_B = "deepseek/deepseek-v4-flash"
 MODEL_TIER_C = "qwen/qwen-2.5-72b-instruct"
-MODEL_TIER_D = "meta-llama/llama-3.3-70b-instruct"
+MODEL_TIER_D = "openai/gpt-oss-20b"
 OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
 
 class EmptyCompletionError(RuntimeError):
     pass
 
 
-# Errori transient: retry. RateLimit/Auth/InvalidRequest: NO retry.
+# Errori transient: retry. Auth/InvalidRequest: NO retry.
+# RateLimitError (HTTP 429) ora retryable: provider OpenRouter come DeepInfra
+# applicano rate limit upstream temporaneo, recuperabile con backoff.
 _RETRYABLE_EXCEPTIONS: tuple[type[BaseException], ...] = (
     openai.APIConnectionError,
     openai.APITimeoutError,
     openai.InternalServerError,
+    openai.RateLimitError,
     EmptyCompletionError,
 )
 
@@ -44,6 +48,10 @@ class CompletionResult:
 
 
 class LLMClient:
+    # Provider OpenRouter da escludere di default. Novita rifiuta /completions
+    # endpoint per modelli Qwen 2.x — vedi bug 2026-05-12.
+    DEFAULT_PROVIDER_IGNORE: tuple[str, ...] = ("Novita",)
+
     def __init__(
         self,
         openrouter_api_key: str,
@@ -53,6 +61,7 @@ class LLMClient:
         model_tier_c: str = MODEL_TIER_C,
         model_tier_d: str = MODEL_TIER_D,
         openrouter_base_url: str = OPENROUTER_BASE_URL,
+        provider_ignore: tuple[str, ...] | None = None,
     ) -> None:
         self.model_tier_s = model_tier_s
         self.model_tier_a = model_tier_a
@@ -60,6 +69,9 @@ class LLMClient:
         self.model_tier_c = model_tier_c
         self.model_tier_d = model_tier_d
         self.openrouter_base_url = openrouter_base_url
+        self._provider_ignore = (
+            provider_ignore if provider_ignore is not None else self.DEFAULT_PROVIDER_IGNORE
+        )
         self._tier_models: dict[ModelTier, str] = {
             ModelTier.S: model_tier_s,
             ModelTier.A: model_tier_a,
@@ -70,8 +82,8 @@ class LLMClient:
         self._client = OpenAI(api_key=openrouter_api_key, base_url=openrouter_base_url)
 
     @retry(
-        stop=stop_after_attempt(3),
-        wait=wait_exponential(multiplier=1.0, min=2.0, max=10.0),
+        stop=stop_after_attempt(5),
+        wait=wait_exponential(multiplier=2.0, min=2.0, max=30.0),
         retry=retry_if_exception_type(_RETRYABLE_EXCEPTIONS),
         reraise=True,
     )
@@ -83,6 +95,9 @@ class LLMClient:
         max_tokens: int = 2000,
     ) -> CompletionResult:
         model = self._tier_models[genome.model_tier]
+        extra_body: dict[str, Any] = {}
+        if self._provider_ignore:
+            extra_body["provider"] = {"ignore": list(self._provider_ignore)}
         resp = self._client.chat.completions.create(
             model=model,
             messages=[
@@ -92,6 +107,7 @@ class LLMClient:
             temperature=genome.temperature,
             top_p=genome.top_p,
             max_tokens=max_tokens,
+            extra_body=extra_body or None,
         )
         if not resp.choices or resp.choices[0].message.content is None:
             raise EmptyCompletionError(f"empty response from {model}")

src/multi_swarm/llm/cost_tracker.py

@@ -8,11 +8,11 @@ from typing import Any
 from ..genome.hypothesis import ModelTier
 
 PRICE_PER_M_TOKENS: dict[ModelTier, dict[str, float]] = {
-    ModelTier.S: {"input": 15.00, "output": 75.00},
-    ModelTier.A: {"input": 3.00, "output": 15.00},
-    ModelTier.B: {"input": 3.00, "output": 15.00},
+    ModelTier.S: {"input": 0.50, "output": 3.00},
+    ModelTier.A: {"input": 0.14, "output": 0.28},
+    ModelTier.B: {"input": 0.14, "output": 0.28},
     ModelTier.C: {"input": 0.40, "output": 0.40},
-    ModelTier.D: {"input": 0.10, "output": 0.30},
+    ModelTier.D: {"input": 0.03, "output": 0.14},
 }
 
 

src/multi_swarm/metrics/diversity.py

@@ -0,0 +1,30 @@
+"""Metriche di diversità popolazione.
+
+``population_prompt_diversity`` calcola la diversità media fra i prompt di una
+popolazione tramite la similarity di ``difflib.SequenceMatcher`` (proxy di
+Levenshtein normalizzata): 0.0 = tutti i prompt identici, 1.0 = tutti diversi.
+
+Usata come telemetry Phase 2.5 per monitorare se ``mutate_prompt_llm`` sta
+effettivamente introducendo diversità di prompt nel pool.
+"""
+
+from __future__ import annotations
+
+from difflib import SequenceMatcher
+from itertools import combinations
+
+
+def population_prompt_diversity(prompts: list[str]) -> float:
+    """Diversità media (0.0 - 1.0) sui prompt della popolazione.
+
+    Calcolo: media di ``1 - similarity(a, b)`` su tutte le coppie distinte.
+    Per N prompt il numero di coppie è ``N*(N-1)/2``. Con N=20 sono 190 coppie
+    — trascurabile a livello di compute.
+    """
+    if len(prompts) < 2:
+        return 0.0
+    diffs = [
+        1.0 - SequenceMatcher(None, a, b).ratio()
+        for a, b in combinations(prompts, 2)
+    ]
+    return sum(diffs) / len(diffs)

src/multi_swarm/orchestrator/run.py

@@ -49,6 +49,7 @@ class RunConfig:
     fees_bp: float = 5.0
     n_trials_dsr: int = 50
     db_path: Path = field(default_factory=lambda: Path("./runs.db"))
+    prompt_mutation_weight: float = 0.0  # Phase 2.5: opt-in LLM mutator
 
 
 def run_phase1(
@@ -90,6 +91,7 @@ def run_phase1(
         elite_k=cfg.elite_k,
         tournament_k=cfg.tournament_k,
         p_crossover=cfg.p_crossover,
+        prompt_mutation_weight=cfg.prompt_mutation_weight,
     )
 
     try:
@@ -173,7 +175,10 @@ def run_phase1(
             )
 
             if gen < cfg.n_generations - 1:
-                population = next_generation(population, fitnesses, ga_cfg, rng)
+                population = next_generation(
+                    population, fitnesses, ga_cfg, rng,
+                    llm=llm if cfg.prompt_mutation_weight > 0 else None,
+                )
 
         repo.complete_run(
             run_id, total_cost=repo.total_cost(run_id), status="completed"

tests/integration/test_ga_loop_with_prompt_mutator.py

@@ -0,0 +1,104 @@
+"""Integration test Phase 2.5: GA loop con LLM mutator attivo.
+
+Verifica che ``next_generation`` con ``prompt_mutation_weight > 0`` e ``llm``
+fornito produca figli con system_prompt mutato dall'LLM (e non solo scalari).
+"""
+
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+
+from multi_swarm.ga.loop import GAConfig, next_generation
+from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+
+_PROMPT_TEMPLATES = (
+    "Strategia mean-reversion 1h. Entry long RSI(14) < 30 e close > SMA(50). Stop 2%.",
+    "Strategia momentum breakout. Entry long close > SMA(20) e ATR(14) crescente.",
+    "Strategia trend-following 4h. Long SMA(20) > SMA(50). Short opposito.",
+)
+
+
+def _make_pop(n: int) -> list[HypothesisAgentGenome]:
+    return [
+        HypothesisAgentGenome(
+            system_prompt=_PROMPT_TEMPLATES[i % len(_PROMPT_TEMPLATES)],
+            feature_access=["close", "high"],
+            temperature=0.9 + 0.01 * i,
+            top_p=0.95,
+            model_tier=ModelTier.C,
+            lookback_window=200,
+            cognitive_style="physicist",
+        )
+        for i in range(n)
+    ]
+
+
+@dataclass
+class _Result:
+    text: str
+
+
+class _MutatorLLM:
+    """Mock che produce un prompt diverso (e valido) a ogni call."""
+
+    def __init__(self) -> None:
+        self.calls = 0
+
+    def complete(self, genome, system, user, max_tokens: int = 2000) -> _Result:
+        self.calls += 1
+        # Prompt sempre diverso per garantire validation pass.
+        return _Result(
+            text=(
+                f"<prompt>Strategia evolved #{self.calls}. Entry long quando "
+                f"RSI(14) < {25 + self.calls % 10} e close > SMA({40 + self.calls}). "
+                f"Exit short quando momentum decade. Trade rule {self.calls}.</prompt>"
+            )
+        )
+
+
+def test_loop_with_prompt_mutator_produces_prompt_diversity() -> None:
+    """Con weight 1.0 + crossover 0 (solo mutation), tutti i child non-elite
+    devono avere system_prompt diverso dai parent (LLM-mutated)."""
+    rng = random.Random(0)
+    pop = _make_pop(5)
+    fitnesses = {g.id: 0.0 for g in pop}
+    cfg = GAConfig(
+        population_size=5,
+        elite_k=1,
+        tournament_k=2,
+        p_crossover=0.0,  # nessun crossover → tutta la non-elite è mutation
+        prompt_mutation_weight=1.0,
+    )
+    llm = _MutatorLLM()
+
+    new_pop = next_generation(pop, fitnesses, cfg, rng, llm=llm)
+
+    assert len(new_pop) == 5
+    # 4 non-elite figli, tutti con prompt evoluti.
+    parent_prompts = {g.system_prompt for g in pop}
+    evolved = [g for g in new_pop[1:] if g.system_prompt not in parent_prompts]
+    assert len(evolved) >= 3, f"Solo {len(evolved)} figli con prompt mutato"
+    assert llm.calls >= 4
+
+
+def test_loop_backward_compat_no_llm_no_prompt_mutation() -> None:
+    """Default weight=0.0 + llm=None → comportamento identico a Phase 2."""
+    rng = random.Random(0)
+    pop = _make_pop(5)
+    fitnesses = {g.id: float(i) for i, g in enumerate(pop)}
+    cfg = GAConfig(
+        population_size=5,
+        elite_k=1,
+        tournament_k=2,
+        p_crossover=0.0,
+        prompt_mutation_weight=0.0,
+    )
+
+    new_pop = next_generation(pop, fitnesses, cfg, rng, llm=None)
+
+    assert len(new_pop) == 5
+    # Nessun child con prompt diverso dai parent: solo mutazioni scalari.
+    parent_prompts = {g.system_prompt for g in pop}
+    for child in new_pop:
+        assert child.system_prompt in parent_prompts

tests/unit/test_config.py

@@ -73,9 +73,9 @@ def test_settings_llm_model_defaults(monkeypatch: pytest.MonkeyPatch) -> None:
 
     s = Settings(_env_file=None)  # type: ignore[call-arg]
 
-    assert s.llm_model_tier_s == "anthropic/claude-opus-4-7"
-    assert s.llm_model_tier_a == "anthropic/claude-sonnet-4-6"
-    assert s.llm_model_tier_b == "anthropic/claude-sonnet-4-6"
+    assert s.llm_model_tier_s == "google/gemini-3-flash-preview"
+    assert s.llm_model_tier_a == "deepseek/deepseek-v4-flash"
+    assert s.llm_model_tier_b == "deepseek/deepseek-v4-flash"
     assert s.llm_model_tier_c == "qwen/qwen-2.5-72b-instruct"
-    assert s.llm_model_tier_d == "meta-llama/llama-3.3-70b-instruct"
+    assert s.llm_model_tier_d == "openai/gpt-oss-20b"
     assert s.openrouter_base_url == "https://openrouter.ai/api/v1"

tests/unit/test_cost_tracker.py

@@ -9,7 +9,7 @@ def test_estimate_cost_tier_c():
 
 def test_estimate_cost_tier_b():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.B)
-    assert cost == 3.00 + 15.00
+    assert cost == 0.14 + 0.28
 
 
 def test_tracker_accumulates():
@@ -34,17 +34,17 @@ def test_tracker_per_tier_breakdown():
 
 def test_estimate_cost_tier_s():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.S)
-    assert cost == 15.00 + 75.00
+    assert cost == 0.50 + 3.00
 
 
 def test_estimate_cost_tier_a():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.A)
-    assert cost == 3.00 + 15.00
+    assert cost == 0.14 + 0.28
 
 
 def test_estimate_cost_tier_d():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.D)
-    assert cost == 0.10 + 0.30
+    assert cost == 0.03 + 0.14
 
 
 def test_tracker_summary_contains_all_five_tiers():

tests/unit/test_diversity.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from multi_swarm.metrics.diversity import population_prompt_diversity
+
+
+def test_empty_or_single_prompt_zero_diversity() -> None:
+    assert population_prompt_diversity([]) == 0.0
+    assert population_prompt_diversity(["solo prompt"]) == 0.0
+
+
+def test_identical_prompts_zero_diversity() -> None:
+    prompts = ["Strategia RSI < 30 long"] * 5
+    assert population_prompt_diversity(prompts) == 0.0
+
+
+def test_completely_different_prompts_high_diversity() -> None:
+    prompts = [
+        "AAAAAA AAAA AAAAA",
+        "BBBBBB BBBB BBBBB",
+        "CCCCCC CCCC CCCCC",
+        "DDDDDD DDDD DDDDD",
+    ]
+    d = population_prompt_diversity(prompts)
+    # SequenceMatcher considera spazi e lunghezza simili → similarity > 0
+    # anche su stringhe completamente "diverse". Soglia realistica: 0.8.
+    assert d > 0.8
+
+
+def test_partial_overlap_intermediate_diversity() -> None:
+    prompts = [
+        "Strategia momentum 1h con RSI",
+        "Strategia momentum 1h con SMA",
+        "Strategia momentum 4h con RSI",
+    ]
+    d = population_prompt_diversity(prompts)
+    assert 0.05 < d < 0.5
+
+
+def test_diversity_symmetric() -> None:
+    prompts_a = ["x", "yy", "zzz"]
+    prompts_b = ["zzz", "x", "yy"]
+    assert (
+        abs(population_prompt_diversity(prompts_a)
+            - population_prompt_diversity(prompts_b)) < 1e-9
+    )

tests/unit/test_hypothesis_agent.py

@@ -2,7 +2,7 @@ import json
 
 from multi_swarm.agents.hypothesis import HypothesisAgent, MarketSummary
 from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.llm.client import CompletionResult
+from multi_swarm.llm.client import CompletionResult, EmptyCompletionError
 
 
 def make_summary() -> MarketSummary:
@@ -214,3 +214,37 @@ def test_hypothesis_agent_no_retry_when_first_succeeds(mocker):  # type: ignore[
     assert proposal.n_attempts == 1
     assert len(proposal.completions) == 1
     assert fake_llm.complete.call_count == 1
+
+
+def test_hypothesis_agent_retries_on_empty_completion(mocker):  # type: ignore[no-untyped-def]
+    """LLMClient esaurisce retry tenacity → propose ritenta nel loop max_attempts."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.side_effect = [
+        EmptyCompletionError("empty response from qwen"),
+        CompletionResult(
+            text=VALID_STRATEGY_JSON,
+            input_tokens=200,
+            output_tokens=80,
+            tier=ModelTier.C,
+            model="qwen",
+        ),
+    ]
+    agent = HypothesisAgent(llm=fake_llm, max_retries=2)
+    proposal = agent.propose(make_genome(), make_summary())
+    assert proposal.strategy is not None
+    assert fake_llm.complete.call_count == 2
+    # n_attempts conta solo le completions arrivate (skipping empty failures).
+    assert len(proposal.completions) == 1
+
+
+def test_hypothesis_agent_returns_failed_proposal_on_only_empty_completions(mocker):  # type: ignore[no-untyped-def]
+    """Tutti i tentativi sollevano EmptyCompletionError → proposal con strategy None."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.side_effect = EmptyCompletionError("empty response")
+    agent = HypothesisAgent(llm=fake_llm, max_retries=2)
+    proposal = agent.propose(make_genome(), make_summary())
+    assert proposal.strategy is None
+    assert proposal.parse_error is not None
+    assert "empty_completion" in proposal.parse_error
+    # 3 tentativi tutti falliti.
+    assert fake_llm.complete.call_count == 3

tests/unit/test_llm_client.py

@@ -54,8 +54,8 @@ def test_completion_tier_b_uses_openrouter_with_anthropic_model(mocker):
     assert out.output_tokens == 150
     assert out.tier == ModelTier.B
     call_kwargs = fake_openai.chat.completions.create.call_args.kwargs
-    assert call_kwargs["model"] == "anthropic/claude-sonnet-4-6"
-    assert out.model == "anthropic/claude-sonnet-4-6"
+    assert call_kwargs["model"] == "deepseek/deepseek-v4-flash"
+    assert out.model == "deepseek/deepseek-v4-flash"
 
 
 @pytest.mark.slow
@@ -75,7 +75,7 @@ def test_completion_retries_on_connection_error(mocker):
     with pytest.raises(openai.APIConnectionError):
         client.complete(g, system="sys", user="usr")
 
-    assert fake_openai.chat.completions.create.call_count == 3
+    assert fake_openai.chat.completions.create.call_count == 5
 
 
 def test_completion_uses_custom_model_tier_c(mocker):
@@ -138,9 +138,9 @@ def test_completion_tier_s_uses_openrouter_with_anthropic_model(mocker):
 
     fake_openai.chat.completions.create.assert_called_once()
     call_kwargs = fake_openai.chat.completions.create.call_args.kwargs
-    assert call_kwargs["model"] == "anthropic/claude-opus-4-7"
+    assert call_kwargs["model"] == "google/gemini-3-flash-preview"
     assert out.tier == ModelTier.S
-    assert out.model == "anthropic/claude-opus-4-7"
+    assert out.model == "google/gemini-3-flash-preview"
 
 
 def test_completion_tier_a_uses_openrouter_with_anthropic_model(mocker):
@@ -157,9 +157,9 @@ def test_completion_tier_a_uses_openrouter_with_anthropic_model(mocker):
 
     fake_openai.chat.completions.create.assert_called_once()
     call_kwargs = fake_openai.chat.completions.create.call_args.kwargs
-    assert call_kwargs["model"] == "anthropic/claude-sonnet-4-6"
+    assert call_kwargs["model"] == "deepseek/deepseek-v4-flash"
     assert out.tier == ModelTier.A
-    assert out.model == "anthropic/claude-sonnet-4-6"
+    assert out.model == "deepseek/deepseek-v4-flash"
 
 
 def test_completion_tier_d_uses_openrouter_with_llama(mocker):
@@ -178,9 +178,9 @@ def test_completion_tier_d_uses_openrouter_with_llama(mocker):
 
     fake_openai.chat.completions.create.assert_called_once()
     call_kwargs = fake_openai.chat.completions.create.call_args.kwargs
-    assert call_kwargs["model"] == "meta-llama/llama-3.3-70b-instruct"
+    assert call_kwargs["model"] == "openai/gpt-oss-20b"
     assert out.tier == ModelTier.D
-    assert out.model == "meta-llama/llama-3.3-70b-instruct"
+    assert out.model == "openai/gpt-oss-20b"
 
 
 def test_completion_uses_custom_model_tier_s(mocker):

tests/unit/test_mutation_dispatcher.py

@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import random
+from collections import Counter
+from dataclasses import dataclass
+
+from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm.genome.mutation import weighted_random_mutate
+
+_PROMPT = (
+    "Strategia mean-reversion 1h BTC. Entry long quando RSI(14) < 30 e "
+    "close > SMA(50). Exit short quando RSI(14) > 70."
+)
+
+
+def _make_genome() -> HypothesisAgentGenome:
+    return HypothesisAgentGenome(
+        system_prompt=_PROMPT,
+        feature_access=["close"],
+        temperature=0.9,
+        top_p=0.95,
+        model_tier=ModelTier.C,
+        lookback_window=200,
+        cognitive_style="physicist",
+    )
+
+
+@dataclass
+class _R:
+    text: str
+
+
+class _AlwaysMutateLLM:
+    """Mock LLM che ritorna sempre un prompt mutato valido."""
+
+    def __init__(self) -> None:
+        self.calls = 0
+
+    def complete(self, genome, system, user, max_tokens: int = 2000) -> _R:
+        self.calls += 1
+        return _R(
+            text=(
+                "<prompt>Strategia momentum 1h BTC. Entry long quando close > "
+                f"SMA(70) e ATR(14) crescente. Exit con stop loss 3% (call #{self.calls}).</prompt>"
+            )
+        )
+
+
+def test_weighted_dispatcher_zero_weight_never_calls_llm() -> None:
+    llm = _AlwaysMutateLLM()
+    rng = random.Random(0)
+    parent = _make_genome()
+
+    for _ in range(50):
+        weighted_random_mutate(parent, rng, llm=llm, prompt_mutation_weight=0.0)
+
+    assert llm.calls == 0
+
+
+def test_weighted_dispatcher_full_weight_always_calls_llm() -> None:
+    llm = _AlwaysMutateLLM()
+    rng = random.Random(0)
+    parent = _make_genome()
+
+    for _ in range(20):
+        child = weighted_random_mutate(
+            parent, rng, llm=llm, prompt_mutation_weight=1.0
+        )
+        assert child.system_prompt != parent.system_prompt
+
+    assert llm.calls == 20
+
+
+def test_weighted_dispatcher_none_llm_falls_back_to_scalar() -> None:
+    """Senza llm passato (backward compat) → solo mutazione scalare."""
+    rng = random.Random(0)
+    parent = _make_genome()
+
+    for _ in range(50):
+        child = weighted_random_mutate(parent, rng, llm=None, prompt_mutation_weight=0.5)
+        assert child.system_prompt == parent.system_prompt
+
+
+def test_weighted_dispatcher_distribution_30_70() -> None:
+    """Su 1000 estrazioni con weight=0.3 il prompt mutator deve essere chiamato ~300 volte."""
+    llm = _AlwaysMutateLLM()
+    rng = random.Random(123)
+    parent = _make_genome()
+
+    counter: Counter[str] = Counter()
+    for _ in range(1000):
+        child = weighted_random_mutate(
+            parent, rng, llm=llm, prompt_mutation_weight=0.3
+        )
+        if child.system_prompt != parent.system_prompt:
+            counter["prompt"] += 1
+        else:
+            counter["scalar"] += 1
+
+    # 30% ± 5% tolerance
+    assert 250 <= counter["prompt"] <= 350, f"prompt mutations: {counter['prompt']}"
+    assert 650 <= counter["scalar"] <= 750, f"scalar mutations: {counter['scalar']}"

tests/unit/test_mutation_prompt_llm.py

@@ -0,0 +1,178 @@
+from __future__ import annotations
+
+import random
+from dataclasses import dataclass
+
+from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm.genome.mutation_prompt_llm import (
+    MUTATION_INSTRUCTIONS,
+    _extract_prompt,
+    is_valid_prompt,
+    mutate_prompt_llm,
+)
+
+_BASE_PROMPT = (
+    "Strategia mean-reversion 1h su BTC. Entry long quando RSI(14) < 30 e "
+    "close > SMA(50). Exit short quando RSI(14) > 70. Stop loss 2%."
+)
+
+
+def _make_genome(prompt: str = _BASE_PROMPT) -> HypothesisAgentGenome:
+    return HypothesisAgentGenome(
+        system_prompt=prompt,
+        feature_access=["close", "high", "low"],
+        temperature=0.9,
+        top_p=0.95,
+        model_tier=ModelTier.C,
+        lookback_window=200,
+        cognitive_style="physicist",
+    )
+
+
+@dataclass
+class _FakeResult:
+    text: str
+
+
+class _FakeLLM:
+    """Mock LLMClient: ritorna una risposta configurata in input."""
+
+    def __init__(self, response_text: str = "", raise_exc: bool = False) -> None:
+        self.response_text = response_text
+        self.raise_exc = raise_exc
+        self.last_call: dict[str, object] | None = None
+
+    def complete(
+        self,
+        genome: HypothesisAgentGenome,
+        system: str,
+        user: str,
+        max_tokens: int = 2000,
+    ) -> _FakeResult:
+        self.last_call = {
+            "genome_tier": genome.model_tier,
+            "system": system,
+            "user": user,
+            "max_tokens": max_tokens,
+        }
+        if self.raise_exc:
+            raise RuntimeError("simulated LLM failure")
+        return _FakeResult(text=self.response_text)
+
+
+def test_extract_prompt_from_tag() -> None:
+    raw = "preambolo blah\n<prompt>Strategia RSI > 75 short, SMA(60) trend.</prompt>\nblabla"
+    assert _extract_prompt(raw) == "Strategia RSI > 75 short, SMA(60) trend."
+
+
+def test_extract_prompt_no_tag_returns_stripped_text() -> None:
+    raw = "  Strategia momentum breakout su 1h con ATR(14)  "
+    assert _extract_prompt(raw) == "Strategia momentum breakout su 1h con ATR(14)"
+
+
+def test_is_valid_prompt_accepts_proper_strategy() -> None:
+    new = (
+        "Strategia mean-reversion 1h su BTC. Entry long quando RSI(14) < 25 e "
+        "close > SMA(50). Exit short quando RSI(14) > 75."
+    )
+    assert is_valid_prompt(new, _BASE_PROMPT) is True
+
+
+def test_is_valid_prompt_rejects_too_short() -> None:
+    assert is_valid_prompt("short", _BASE_PROMPT) is False
+
+
+def test_is_valid_prompt_rejects_no_strategy_keywords() -> None:
+    bad = "Questo è un testo a caso che parla del meteo di domani e della pioggia."
+    assert is_valid_prompt(bad, _BASE_PROMPT) is False
+
+
+def test_is_valid_prompt_rejects_identical_prompt() -> None:
+    assert is_valid_prompt(_BASE_PROMPT, _BASE_PROMPT) is False
+
+
+def test_mutate_prompt_llm_produces_mutated_child() -> None:
+    mutated = (
+        "Strategia mean-reversion 1h su BTC. Entry long quando RSI(14) < 25 e "
+        "close > SMA(70). Exit short quando RSI(14) > 78."
+    )
+    llm = _FakeLLM(response_text=f"<prompt>{mutated}</prompt>")
+    parent = _make_genome()
+    rng = random.Random(0)
+
+    child = mutate_prompt_llm(parent, llm, rng)
+
+    assert child.system_prompt == mutated
+    assert child.id != parent.id
+    assert child.parent_ids == [*parent.parent_ids, parent.id]
+    assert child.generation == parent.generation + 1
+    assert child.model_tier == ModelTier.C  # tier C preservato sul child
+
+
+def test_mutate_prompt_llm_uses_mutator_tier_b_for_llm_call() -> None:
+    mutated = (
+        "Strategia momentum breakout 1h. Entry long quando close > SMA(60) e "
+        "ATR(14) crescente. Exit con stop loss 3%."
+    )
+    llm = _FakeLLM(response_text=f"<prompt>{mutated}</prompt>")
+    parent = _make_genome()
+    rng = random.Random(0)
+
+    mutate_prompt_llm(parent, llm, rng)
+
+    assert llm.last_call is not None
+    assert llm.last_call["genome_tier"] == ModelTier.B
+
+
+def test_mutate_prompt_llm_falls_back_on_invalid_output() -> None:
+    """Output troppo corto -> fallback random_mutate (cambia uno scalare)."""
+    llm = _FakeLLM(response_text="<prompt>nope</prompt>")
+    parent = _make_genome()
+    rng = random.Random(42)
+
+    child = mutate_prompt_llm(parent, llm, rng)
+
+    # random_mutate preserva system_prompt, cambia uno dei 4 scalari/style.
+    assert child.system_prompt == parent.system_prompt
+    assert child.parent_ids == [*parent.parent_ids, parent.id]
+    assert child.generation == parent.generation + 1
+
+
+def test_mutate_prompt_llm_falls_back_on_identical_output() -> None:
+    llm = _FakeLLM(response_text=f"<prompt>{_BASE_PROMPT}</prompt>")
+    parent = _make_genome()
+    rng = random.Random(42)
+
+    child = mutate_prompt_llm(parent, llm, rng)
+
+    assert child.system_prompt == parent.system_prompt
+    assert child.generation == parent.generation + 1
+
+
+def test_mutate_prompt_llm_falls_back_on_llm_exception() -> None:
+    llm = _FakeLLM(raise_exc=True)
+    parent = _make_genome()
+    rng = random.Random(7)
+
+    child = mutate_prompt_llm(parent, llm, rng)
+
+    # Fallback random_mutate sempre produce un child valido.
+    assert child.system_prompt == parent.system_prompt
+    assert child.generation == parent.generation + 1
+
+
+def test_mutate_prompt_llm_picks_one_of_six_instructions() -> None:
+    """Verifica che il system message dell'LLM includa una delle 6 istruzioni."""
+    mutated = (
+        "Strategia RSI 1h. Entry long quando RSI(14) < 28 e close > SMA(50). "
+        "Exit short quando RSI(14) > 72."
+    )
+    llm = _FakeLLM(response_text=f"<prompt>{mutated}</prompt>")
+    parent = _make_genome()
+
+    mutate_prompt_llm(parent, llm, random.Random(0))
+
+    assert llm.last_call is not None
+    user_text = str(llm.last_call["user"])
+    matched_keys = [k for k in MUTATION_INSTRUCTIONS if k in user_text]
+    assert len(matched_keys) >= 1, f"User prompt non contiene istruzione: {user_text[:200]}"

uv.lock

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 source = { editable = "." }
 dependencies = [
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