feat(paper): ETH a tick 5m + tooling per-year/per-tick analysis

scripts/run_paper_trading.py: AssetConfig ETH ora usa timeframe="5m" invece
del default 1h. Il winner c04dff7086 e' stato trovato dal GA su dati 5m
e a 1h la strategia perde:
- ETH @ 5m (native): +359.50% cum 7y, 77% winrate, max DD/yr 19%
- ETH @ 1h (precedente): -33.03% cum 7y, 67% winrate, max DD 74%
BTC resta a 1h (winner 238e4812 native a 1h, +104% 7y, Sharpe 2+ in 3 anni).

Nuovi script di analisi:
- scripts/yearly_strategies.py: breakdown per anno (2019-2025) di 4
  strategie su tick di discovery (trade/winrate/return/maxDD/Sharpe).
- scripts/multi_tick_strategies.py: confronto cross-tick (5m/15m/1h)
  per i 2 winner correnti. Documenta la divergenza tick-paper di ETH.

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

.env.example

@@ -29,12 +29,15 @@ GA_DB_PATH=./state/runs.db
 STRATEGY_CRYPTO_DB_PATH=./state/strategy_crypto.db
 
 # Docker / Traefik (usati SOLO da docker-compose.yml)
-# Dominio base: traefik espone la dashboard su swarm.${DOMAIN_NAME}/strategy_crypto_gui
+# Dominio base: traefik espone le dashboard su swarm.${DOMAIN_NAME}/...
 DOMAIN_NAME=tielogic.xyz
 # Porta interna della NiceGUI dashboard (Traefik fa il TLS davanti)
 SWARM_DASHBOARD_PORT=8080
-# Subpath URL del dashboard NiceGUI (usato come root_path in produzione)
-DASHBOARD_ROOT_PATH=/strategy_crypto_gui
+# Subpath URL del dashboard NiceGUI — ora PER-SERVIZIO nel docker-compose.yml:
+#   strategy-crypto-gui  -> DASHBOARD_ROOT_PATH=/strategy_crypto_gui
+#   multi-swarm-core-gui -> DASHBOARD_ROOT_PATH=/multi_swarm_core_gui
+# In sviluppo locale lascia vuoto (nessun subpath).
+DASHBOARD_ROOT_PATH=
 
 # Paper-trading runner — override del command nel compose (opzionali)
 PAPER_RUN_NAME=phase3-papertrade-prod

Dockerfile

@@ -37,8 +37,12 @@ COPY scripts ./scripts
 
 ENV PATH="/app/.venv/bin:$PATH" \
     PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONPATH=/app/src
+    PYTHONDONTWRITEBYTECODE=1
+# NO PYTHONPATH: con uv workspace + layout doppio-nest, PYTHONPATH=/app/src
+# farebbe ombra alla venv risolvendo le member-dir (multi_swarm_core/,
+# strategy_crypto/) come namespace packages senza i sub-package del codice.
+# I pacchetti sono installati come editable dal `uv sync --frozen` del builder
+# e risolvibili direttamente via /app/.venv/.
 
 RUN useradd -m -u 1000 app \
     && mkdir -p /app/data /app/series /app/state /app/strategies \

README.md

@@ -12,23 +12,23 @@ git clone ssh://git@git.tielogic.xyz:222/Adriano/Multi_Swarm_Coevolutive.git
 
 ## Layout monorepo (uv workspace)
 
-Il repo è un **workspace uv** con due member packages indipendenti:
+Il repo è un **workspace uv** con due member packages indipendenti, principio "**core = framework, strategy = contenuto**":
 
 ```
 multi_swarm_coevolutive/             repo root (workspace coordinator)
 ├── pyproject.toml                    workspace + dev deps + ruff/mypy/pytest
-├── docker-compose.yml                strategy-crypto-paper + strategy-crypto-gui
+├── docker-compose.yml                3 servizi su immagine condivisa
 ├── Dockerfile                        immagine multi-swarm-coevolutive:dev
 ├── uv.lock                           lock unico del workspace
 ├── data/, series/, state/            cache OHLCV + DB (runtime, gitignored)
 ├── scripts/                          CLI entrypoints (run_phase1, run_paper_trading, ...)
 └── src/
     ├── multi_swarm_core/             WORKSPACE MEMBER (wheel: multi-swarm-core)
-    │   ├── pyproject.toml             deps: pandas, numpy, openai, pydantic, ...
+    │   ├── pyproject.toml             core deps (pandas, numpy, openai, pydantic, nicegui, ...)
     │   ├── multi_swarm_core/          GA + genome + protocol + backtest + cerbero +
     │   │                              data + llm + agents + ga + orchestrator +
-    │   │                              metrics + persistence + config
-    │   ├── tests/                     unit + integration (182 test)
+    │   │                              metrics + persistence + config + dashboard (GA-only)
+    │   ├── tests/                     unit + integration
     │   └── docs/                      design/ + decisions/ + reports/
     │
     └── strategy_crypto/              WORKSPACE MEMBER (wheel: strategy-crypto)
@@ -36,27 +36,73 @@ multi_swarm_coevolutive/             repo root (workspace coordinator)
         ├── README.md                  overview strategia + pattern per nuove strategie
         ├── strategy_crypto/
         │   ├── backend/               paper-trading (executor, portfolio, persistence, schema)
-        │   ├── frontend/               NiceGUI dashboard dual-DB
-        │   └── strategies/             JSON freezate (btc_*.json, eth_*.json)
-        └── tests/                      smoke regression (import + json + schema)
+        │   ├── frontend/              NiceGUI paper-only dashboard
+        │   ├── strategies/            JSON freezate (btc_*.json, eth_*.json)
+        │   └── prompts.json           v3.2: agent_role/pattern_guidance/instruction/
+        │                              domain_warnings/anti_patterns/output_priorities +
+        │                              7 stili cognitive (directive + focus_metrics)
+        └── tests/                     smoke regression
 ```
 
 **DB separati per dominio:** `state/runs.db` (GA core universale) + `state/strategy_crypto.db` (paper della strategia crypto). Pattern scala a N strategie senza naming collision.
 
-**Pattern N strategie future:** aggiungere `src/strategy_<asset>/` con lo stesso scheletro (`backend/`, `frontend/`, `strategies/`, `tests/`), DB dedicato `state/strategy_<asset>.db`, servizi Docker `strategy-<asset>-paper` + `strategy-<asset>-gui`, GUI su `/strategy_<asset>_gui`.
+**Pattern N strategie future:** aggiungere `src/strategy_<asset>/` con stesso scheletro (`backend/`, `frontend/`, `strategies/`, `tests/`, `prompts.json`), DB dedicato `state/strategy_<asset>.db`, servizi Docker `strategy-<asset>-paper` + `strategy-<asset>-gui`, GUI su `/strategy_<asset>_gui`. **Zero modifiche al core** richieste.
+
+## Architettura prompt (v3.2)
+
+**Compositor**: il SYSTEM prompt al LLM viene COMPOSTO at-runtime da scaffold core + contenuto strategy:
+
+```
+[1] agent_role           ← strategy (prompts.json — chi è l'agente)
+[2] cognitive_style + directive  ← genome (evoluti dal GA)
+[3] GRAMMAR_SPEC         ← core scaffold (operatori, indicatori, units rules)
+[4] pattern_guidance     ← strategy (forme di curva astratte, no indicatori prescritti)
+[5] domain_warnings      ← strategy (es. "crypto trada 24/7, NON inferire funding rate")
+[6] CONSTRAINTS          ← core scaffold (validator semantics)
+[7] anti_patterns        ← strategy (7 voci: no >4 AND, no chattering, isteresi, ecc.)
+[8] output_priorities    ← strategy (5 voci, #1 coerenza con lente cognitiva)
+[9] EXAMPLE              ← core scaffold
+```
+
+**Input USER (calcolato da `build_market_summary`):**
+- Base (5): mean, std, skew, kurt, vol_regime
+- Regime recente rolling 500 (6): autocorr_lag1 (recent + baseline), hurst, vol_percentile, seasonality (hour + dow)
+- Geometria & frattali (7): efficiency_ratio (Kaufman), tail_index (left + right Hill), structural_uptrend (HH/HL), compression, spectral_entropy, dominant_cycle (gated)
+- Feature accessibili dal genome + lookback_window
+- **Focus per la tua lente**: blocco style-aware (4 metriche prioritarie da `focus_metrics` di prompts.json)
+- Instruction finale (da strategy)
+
+**Grammar protocol JSON (8 indicatori):**
+| Indicatore | Output | Range |
+|------------|--------|-------|
+| `sma(length)` | media mobile | unità prezzo |
+| `sma_pct(length)` | (close-sma)/sma | ±0.1 frazione |
+| `rsi(length)` | oscillator | 0-100 |
+| `atr(length)` | true range | unità prezzo |
+| `atr_pct(length)` | atr/close | 0-0.1 frazione |
+| `realized_vol(window)` | std returns | 0-0.1 frazione |
+| `macd(fast,slow,signal)` | momentum | unità prezzo |
+| `macd_pct(...)` | macd/close | ±0.02 frazione |
+
+**7 stili cognitive** (in `prompts.json`, editable): physicist, biologist, historian, meteorologist, engineer, military_strategist, psychologist. Ognuno con directive 800-950 char, ASCII-strict, archetipo dominante + lookback consigliato + 4 focus_metrics.
 
 ## Stato del progetto
 
-**Phase 3 (paper-trading forward-test) in corso** dal 13 maggio 2026 su VPS. Runner `scripts/run_paper_trading.py` long-running in Docker, dashboard NiceGUI su `https://swarm.tielogic.xyz/strategy_crypto_gui/`. Due strategie freezate:
+**Phase 3 (paper-trading forward-test) in corso** dal 13 maggio 2026 su VPS. Runner `scripts/run_paper_trading.py` long-running in Docker, dashboard NiceGUI su `https://swarm.tielogic.xyz/strategy_crypto_gui/`. Due strategie freezate in `src/strategy_crypto/strategy_crypto/strategies/`:
 
-- `strategy_crypto/strategies/btc_fb63e851.json` — BTC-PERPETUAL, RSI estremi + ATR vs SMA + filtro orario 9-17 (Sharpe OOS +0,26 su 7,33 anni).
-- `strategy_crypto/strategies/eth_facd6af85d5d.json` — ETH-PERPETUAL, regime-based (Sharpe OOS +0,19 su 6,75 anni).
+- `btc_fb63e851.json` — BTC-PERPETUAL, RSI estremi + ATR vs SMA + filtro orario 9-17 (Sharpe OOS +0,26 su 7,33 anni).
+- `eth_facd6af85d5d.json` — ETH-PERPETUAL, regime-based (con `atr_pct` post-fix bug unità).
 
-Phase 1 → 2.7 chiuse (30 run GA, $3.74 cumulato LLM).
+Phase 1 → 2.7 chiuse (30 run GA, $3.74 cumulato LLM). Sessione refactor 15 maggio 2026:
+- Split repo invertito, monorepo unificato come uv workspace
+- Family `*_pct` completa (atr_pct, sma_pct, macd_pct) per fix bug unità
+- Dashboard split: core (GA) vs strategy (paper)
+- Prompt architecture compositor + prompts.json v3.2 (vedi decision log)
 
-- [**Stato progetto e roadmap (14 maggio 2026)**](src/multi_swarm_core/docs/reports/2026-05-14-stato-progetto-e-roadmap.md) — riepilogo fasi, decisioni, caveat, roadmap.
-- Decision log: [`src/multi_swarm_core/docs/decisions/`](src/multi_swarm_core/docs/decisions/) (gate Phase 1, scelta nemotron).
-- Design docs concettuali: [`src/multi_swarm_core/docs/design/`](src/multi_swarm_core/docs/design/).
+Documenti:
+- [**Stato progetto e roadmap (14 maggio 2026)**](src/multi_swarm_core/docs/reports/2026-05-14-stato-progetto-e-roadmap.md)
+- Decision log: [`src/multi_swarm_core/docs/decisions/`](src/multi_swarm_core/docs/decisions/) (gate Phase 1, nemotron, atr_pct fix)
+- Design docs concettuali: [`src/multi_swarm_core/docs/design/`](src/multi_swarm_core/docs/design/)
 
 Stack: Python 3.13, uv workspace, hatchling, pytest+pytest-mock+responses, openai SDK (verso OpenRouter), requests+tenacity, pandas+numpy+scipy, sqlmodel+sqlite, nicegui+plotly, yfinance.
 
@@ -65,7 +111,7 @@ Stack: Python 3.13, uv workspace, hatchling, pytest+pytest-mock+responses, opena
 ```bash
 uv sync                           # installa entrambi i workspace member come editable
 cp .env.example .env              # compila CERBERO_*_TOKEN e OPENROUTER_API_KEY
-uv run pytest                     # 186 test attesi (182 core + 4 smoke strategy_crypto)
+uv run pytest                     # 252 test attesi (248 core + 4 smoke strategy_crypto)
 ```
 
 ### Variabili .env richieste
@@ -84,10 +130,9 @@ OPENROUTER_API_KEY=<sk-or-v1-...>
 GA_DB_PATH=./state/runs.db
 STRATEGY_CRYPTO_DB_PATH=./state/strategy_crypto.db
 
-# Deploy Docker
+# Deploy Docker — DASHBOARD_ROOT_PATH ora per-servizio (vedi docker-compose.yml)
 DOMAIN_NAME=tielogic.xyz
 SWARM_DASHBOARD_PORT=8080
-DASHBOARD_ROOT_PATH=/strategy_crypto_gui   # subpath traefik per la dashboard
 ```
 
 Backcompat: `DB_PATH` legacy continua a funzionare come alias di `GA_DB_PATH`.
@@ -105,14 +150,27 @@ uv run mypy src/ scripts/
 # Smoke run (MockLLM + OHLCV sintetico, no API calls)
 uv run python scripts/smoke_run.py
 
-# Run reale Phase 1/2 (Cerbero + OpenRouter, ~$0.07 per run K=20 10gen)
+# Run reale Phase 1/2 (Cerbero + OpenRouter, ~$0.15-0.25 per run K=20 10gen,
+# ~$0.40-0.55 per run esteso K=40 20gen con WFA OOS).
+# Default --start ora 2018-09-01 (7.3y, copre bear+halving+covid+ATH+winter+ETF).
 uv run python scripts/run_phase1.py \
   --name run-XXX \
   --exchange deribit --symbol BTC-PERPETUAL --timeframe 1h \
-  --start 2024-01-01T00:00:00+00:00 \
-  --end 2026-01-01T00:00:00+00:00 \
   --population-size 20 --n-generations 10 \
-  --prompt-mutation-weight 0.30 --fitness-v2
+  --prompt-mutation-weight 0.30 --fitness-v2 \
+  --llm-concurrency 8                          # 5-8x speedup wall time (default 1)
+# fitness-v2 hardened: hard-kill su {no_trades, degenerate, undertrading,
+# fees_eat_alpha, negative_net_pnl}. Override via --fitness-hard-kill CSV.
+# Default --prompt-library: importlib.resources del package strategy_crypto/prompts.json
+
+# Multi-fold validation di un run esistente (anti-overfit, 7y expanding-window)
+uv run python scripts/validate_run.py \
+  --run-id <run_id> \
+  --top-k 10 --n-folds 4 --train-ratio 0.5 \
+  --start 2018-09-01T00:00:00+00:00 --end 2026-01-01T00:00:00+00:00 \
+  --fitness-v2 \
+  --output-json state/validation-XXX.json
+# Ranking per "robust_score" = min(fitness_oos) su tutti i fold.
 
 # Backtest standalone di una strategia JSON
 uv run python scripts/backtest_strategy.py \
@@ -123,32 +181,50 @@ uv run python scripts/backtest_strategy.py \
 uv run python scripts/run_paper_trading.py \
   --name phase3-papertrade-XXX \
   --initial-capital 1000 --poll-seconds 300
-# Default --strategies-dir: importlib.resources del package strategy_crypto
 
-# Dashboard NiceGUI locale
-uv run python -m strategy_crypto.frontend.nicegui_app
-# → http://localhost:8080 (env SWARM_DASHBOARD_PORT)
+# Dashboard NiceGUI locale (2 distinte)
+uv run python -m multi_swarm_core.dashboard.nicegui_app   # GA core (/, /convergence, /genomes)
+uv run python -m strategy_crypto.frontend.nicegui_app     # Strategy crypto (/ paper)
 ```
 
-## Dashboard
+## Performance & Validation
 
-NiceGUI dashboard (dark palette) — **dual-DB reader** (GA + paper):
+**Backtest engine vettorializzato** (`backtest/engine.py`): rimosso il loop `pd.iterrows()` a favore di state machine numpy. Speedup misurati:
 
+| Dataset | Before (iterrows) | After (vectorized) | Speedup |
+|---------|-------------------|--------------------|---------|
+| 2 anni (17545 bar) | 470 ms | **28 ms** | **16.8×** |
+| 7 anni (64297 bar) | 1744 ms | **154 ms** | **11.3×** |
+
+Equivalenza numerica garantita: 5 parity test parametrici vs. reference implementation legacy (`test_backtest_engine_vectorized.py`).
+
+**Parallel propose LLM** (`orchestrator/run.py`): `--llm-concurrency N` lancia N chiamate `hypothesis_agent.propose()` concorrenti per generazione tramite `ThreadPoolExecutor`. OpenRouter qwen-2.5 regge 6-10 concorrenti senza rate-limit. Default 1 = backward-compat.
+
+**Multi-fold validation tool** (`scripts/validate_run.py`): qualunque run completato puo' essere rivalutato post-hoc su N fold expanding-window di un dataset esteso (tipicamente 7 anni). Vital per evitare il single-hold-out overfit: il GA puo' selezionare un genome con `fitness_is` alta che collassa OOS (osservato su `phase1-extended-001`: elite IS Sharpe 1.93, OOS Sharpe -1.00). Ranking finale per `robust_score = min(fitness_oos)`. Output JSON con per-fold breakdown + aggregati mean/min/std.
+
+## Dashboard (split core + strategy)
+
+Due NiceGUI dashboard distinte (dark palette, palette neon):
+
+**Core GA** — `multi_swarm_core.dashboard.nicegui_app` → `https://swarm.tielogic.xyz/multi_swarm_core_gui/`:
 - **Overview** (`/`): lista runs GA, costo cumulato, metriche aggregate evaluations.
 - **GA Convergence** (`/convergence`): fitness median/max/p90 per generazione + entropy.
 - **Genomes** (`/genomes`): top-K ordinati per fitness, ispezione system_prompt + JSON strategy.
-- **Paper** (`/paper`): forward-test live con equity curve, posizioni aperte, trade list, tick log.
 
-In produzione su `https://swarm.tielogic.xyz/strategy_crypto_gui/` (subpath gestito via `DASHBOARD_ROOT_PATH` + Traefik PathPrefix). La root del dominio resta libera per future GUI di altre strategie.
+**Strategy crypto** — `strategy_crypto.frontend.nicegui_app` → `https://swarm.tielogic.xyz/strategy_crypto_gui/`:
+- **Paper** (`/`): forward-test live con equity curve, posizioni aperte, trade list, tick log.
+
+In produzione subpath gestiti via `DASHBOARD_ROOT_PATH` (per-servizio) + Traefik `replacepathregex` (NB: NON `stripprefix`, vedi sezione Deploy). La root del dominio resta libera per future GUI di altre strategie.
 
 ## Deploy
 
-`docker-compose.yml` definisce due servizi su immagine `multi-swarm-coevolutive:dev`:
+`docker-compose.yml` definisce 3 servizi su immagine condivisa `multi-swarm-coevolutive:dev`:
 
 - **`strategy-crypto-paper`** — runner `scripts/run_paper_trading.py` long-running.
-- **`strategy-crypto-gui`** — NiceGUI dashboard dietro Traefik su `https://swarm.${DOMAIN_NAME}/strategy_crypto_gui/`.
+- **`strategy-crypto-gui`** — NiceGUI paper dashboard su `https://swarm.${DOMAIN_NAME}/strategy_crypto_gui/`.
+- **`multi-swarm-core-gui`** — NiceGUI GA dashboard su `https://swarm.${DOMAIN_NAME}/multi_swarm_core_gui/`.
 
-Persistenza via bind mount: `./data/`, `./series/`, `./state/`. Le strategie JSON sono bind-mounted in read-only dal package: `./src/strategy_crypto/strategy_crypto/strategies/`.
+Persistenza via bind mount: `./data/`, `./series/`, `./state/`. Strategie JSON bind-mounted in read-only dal package: `./src/strategy_crypto/strategy_crypto/strategies/`.
 
 ```bash
 docker compose up -d --build
@@ -158,12 +234,16 @@ docker compose ps
 
 Note operative:
 
-- Le bind-mount dir devono essere `chown 1000:1000` (uid utente `app` nel container).
+- Bind-mount dir devono essere `chown 1000:1000` (uid utente `app` nel container). **Anche `src/strategy_crypto/strategy_crypto/strategies/`** (creata da `git mv`, default `root:root`).
 - Override del command paper via env (`PAPER_RUN_NAME`, `PAPER_INITIAL_CAPITAL`, ecc.).
 - `SWARM_DASHBOARD_PORT` controlla la porta interna del container (Traefik fa il TLS).
+- **Traefik subpath**: usa `replacepathregex` middleware (NON `stripprefix`) per evitare doppio root_path (uvicorn legge `X-Forwarded-Prefix` da stripprefix + applica `root_path` di NiceGUI = doppio prefix). Vedi commit `436613b`.
+- Dopo cambio label Traefik: `docker restart traefik-traefik-1` per forzare refresh discovery.
 
 ## Sviluppo
 
 Conventional commits con prefix `feat:` `fix:` `chore:` `docs:` `refactor:` `test:`. Body italiano. Footer `Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>` su ogni commit collaborativo.
 
 Branch attuale: `main`. Workspace single-repo, monorepo unificato dal 15 maggio 2026 (split temporaneo monorepo→figlio invertito, vedi tag `v0.1.0-pre-split` come bookmark).
+
+**Modificare il prompt LLM** senza toccare codice: edita `src/strategy_crypto/strategy_crypto/prompts.json`. Schema documentato in `_design_invariants` del JSON stesso. I 3 regression guard test (`test_strategy_crypto_directives_ascii_safe`, `..._have_archetype_marker`, `..._have_lookback_hint`) bloccano regressioni accidentali sulle invarianti di design.

docker-compose.yml

@@ -1,12 +1,14 @@
 # docker-compose.yml — Multi-Swarm Coevolutive
 #
-# Due servizi della strategia crypto, condividono la stessa immagine
+# Tre servizi della strategia crypto, condividono la stessa immagine
 # `multi-swarm-coevolutive:dev` buildata dal Dockerfile root (uv workspace):
 #
-#   * strategy-crypto-paper — paper-trading runner long-running
-#                             (scripts/run_paper_trading.py)
-#   * strategy-crypto-gui   — NiceGUI dashboard esposta da Traefik su
-#                             https://swarm.${DOMAIN_NAME:-tielogic.xyz}/strategy_crypto_gui
+#   * strategy-crypto-paper   — paper-trading runner long-running
+#                               (scripts/run_paper_trading.py)
+#   * strategy-crypto-gui     — NiceGUI dashboard esposta da Traefik su
+#                               https://swarm.${DOMAIN_NAME:-tielogic.xyz}/strategy_crypto_gui
+#   * multi-swarm-core-gui    — NiceGUI dashboard GA esposta su
+#                               https://swarm.${DOMAIN_NAME:-tielogic.xyz}/multi_swarm_core_gui
 #
 # Entrambi joinano la rete external `traefik` cosi' il client Cerbero
 # risolve direttamente l'host `cerbero-mcp` (porta 9000) senza passare
@@ -36,8 +38,11 @@ x-swarm-env: &swarm-env
   # DB separati per dominio:
   GA_DB_PATH: /app/state/runs.db
   STRATEGY_CRYPTO_DB_PATH: /app/state/strategy_crypto.db
-  # Subpath sotto cui la dashboard NiceGUI e' esposta da Traefik
-  DASHBOARD_ROOT_PATH: /strategy_crypto_gui
+  # DASHBOARD_ROOT_PATH e' ora per-servizio (vedi environment blocks sotto).
+  # IMPORTANT: NON usare StripPrefix middleware con questo. NiceGUI/Starlette
+  # gestisce internamente il root_path su request path che ARRIVANO con prefix.
+  # StripPrefix causa doppio prefix negli asset URL (NiceGUI prefixa + uvicorn
+  # rilegge X-Forwarded-Prefix e prefixa di nuovo).
 
 services:
   strategy-crypto-paper:
@@ -81,11 +86,10 @@ services:
     env_file: .env
     environment:
       <<: *swarm-env
+      DASHBOARD_ROOT_PATH: /strategy_crypto_gui
     volumes:
-      # Dashboard legge entrambi i DB: state/ in read-only (WAL: vedi nota)
+      # Dashboard legge solo strategy_crypto.db: state/ in read-only (WAL: vedi nota)
       - ./state:/app/state:ro
-      - ./data:/app/data:ro
-      - ./series:/app/series:ro
     entrypoint:
       - python
       - -m
@@ -109,4 +113,46 @@ services:
       - traefik.http.routers.strategy-crypto-gui.entrypoints=websecure
       - traefik.http.routers.strategy-crypto-gui.tls.certresolver=mytlschallenge
       - "traefik.http.services.strategy-crypto-gui.loadbalancer.server.port=${SWARM_DASHBOARD_PORT:-8080}"
+      # replacepathregex (NON stripprefix): strippa il prefix dalla request senza
+      # aggiungere X-Forwarded-Prefix header. uvicorn vede solo root_path da
+      # ui.run(root_path=...), quindi gli asset URL vengono prefissati una sola
+      # volta (no doppio prefix come succederebbe con stripprefix).
+      - "traefik.http.middlewares.strategy-crypto-replace.replacepathregex.regex=^/strategy_crypto_gui(/.*|$$)"
+      - "traefik.http.middlewares.strategy-crypto-replace.replacepathregex.replacement=$$1"
+      - "traefik.http.routers.strategy-crypto-gui.middlewares=strategy-crypto-replace"
+      - com.centurylinklabs.watchtower.enable=true
+
+  multi-swarm-core-gui:
+    image: multi-swarm-coevolutive:dev
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: multi-swarm-core-gui
+    restart: unless-stopped
+    networks: [traefik]
+    env_file: .env
+    environment:
+      <<: *swarm-env
+      DASHBOARD_ROOT_PATH: /multi_swarm_core_gui
+    volumes:
+      - ./state:/app/state:ro
+    entrypoint: [python, -m, multi_swarm_core.dashboard.nicegui_app]
+    command: []
+    healthcheck:
+      test: ["CMD", "python", "-c", "import os, urllib.request; urllib.request.urlopen(f'http://localhost:{os.environ.get(\"SWARM_DASHBOARD_PORT\",\"8080\")}/', timeout=3).close()"]
+      interval: 30s
+      timeout: 5s
+      retries: 3
+      start_period: 30s
+    labels:
+      - traefik.enable=true
+      - traefik.docker.network=traefik
+      - "traefik.http.routers.multi-swarm-core-gui.rule=Host(`swarm.${DOMAIN_NAME:-tielogic.xyz}`) && PathPrefix(`/multi_swarm_core_gui`)"
+      - traefik.http.routers.multi-swarm-core-gui.tls=true
+      - traefik.http.routers.multi-swarm-core-gui.entrypoints=websecure
+      - traefik.http.routers.multi-swarm-core-gui.tls.certresolver=mytlschallenge
+      - "traefik.http.services.multi-swarm-core-gui.loadbalancer.server.port=${SWARM_DASHBOARD_PORT:-8080}"
+      - "traefik.http.middlewares.multi-swarm-core-replace.replacepathregex.regex=^/multi_swarm_core_gui(/.*|$$)"
+      - "traefik.http.middlewares.multi-swarm-core-replace.replacepathregex.replacement=$$1"
+      - "traefik.http.routers.multi-swarm-core-gui.middlewares=multi-swarm-core-replace"
       - com.centurylinklabs.watchtower.enable=true

pyproject.toml

@@ -4,6 +4,13 @@ version = "0.1.0"
 description = "Multi-Swarm Coevolutive: monorepo workspace (core + strategie)"
 authors = [{ name = "Adriano Dal Pastro", email = "adrianodalpastro@tielogic.com" }]
 requires-python = ">=3.13"
+# I due workspace member sono dipendenze runtime del deployable app root.
+# Cosi' `uv sync --frozen --no-dev` (nel builder Docker) li installa entrambi
+# come editable nella venv senza tirare pytest/ruff/mypy.
+dependencies = [
+    "multi-swarm-core",
+    "strategy-crypto",
+]
 
 [tool.uv.workspace]
 members = ["src/multi_swarm_core", "src/strategy_crypto"]
@@ -21,8 +28,6 @@ dev = [
     "ruff>=0.7",
     "mypy>=1.13",
     "types-requests>=2.32",
-    "multi-swarm-core",
-    "strategy-crypto",
 ]
 
 [tool.ruff]

scripts/analyze_btc_winners.py

@@ -0,0 +1,125 @@
+"""Analisi per-trade dei top-K candidate del run BTC.
+
+Per ciascun genome top-K, ri-esegue il backtest su ogni fold WFA e raccoglie:
+- n_trades, n_wins, n_losses, win_rate
+- max_drawdown
+- return, sharpe
+- list trade pnl summary
+
+Output stampato a stdout, non scrive su disco.
+"""
+from __future__ import annotations
+
+import argparse
+from datetime import datetime
+
+import pandas as pd  # type: ignore[import-untyped]
+
+from multi_swarm_core.agents.hypothesis import _try_parse
+from multi_swarm_core.backtest.engine import BacktestEngine
+from multi_swarm_core.cerbero.client import CerberoClient
+from multi_swarm_core.config import load_settings
+from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
+from multi_swarm_core.data.splits import expanding_walk_forward
+from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
+from multi_swarm_core.persistence.repository import Repository
+from multi_swarm_core.protocol.compiler import compile_strategy
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--run-id", required=True)
+    ap.add_argument("--top-k", type=int, default=10)
+    ap.add_argument("--n-folds", type=int, default=4)
+    ap.add_argument("--train-ratio", type=float, default=0.5)
+    ap.add_argument("--symbol", default="BTC-PERPETUAL")
+    ap.add_argument("--timeframe", default="1h")
+    ap.add_argument("--start", default="2018-09-01T00:00:00+00:00")
+    ap.add_argument("--end", default="2026-01-01T00:00:00+00:00")
+    ap.add_argument("--fees-bp", type=float, default=5.0)
+    args = ap.parse_args()
+
+    settings = load_settings()
+    repo = Repository(settings.ga_db_path)
+    repo.init_schema()
+    all_evals = repo.list_evaluations(args.run_id)
+    parseable = [
+        e for e in all_evals
+        if e.get("raw_text") and not e.get("parse_error") and e["fitness"] > 0
+    ]
+    parseable.sort(key=lambda e: e["fitness"], reverse=True)
+    seen: set[str] = set()
+    top: list[dict] = []
+    for e in parseable:
+        if e["genome_id"] in seen:
+            continue
+        seen.add(e["genome_id"])
+        top.append(e)
+        if len(top) >= args.top_k:
+            break
+
+    token = (
+        settings.cerbero_mainnet_token.get_secret_value()
+        if settings.cerbero_mainnet_token
+        else settings.cerbero_testnet_token.get_secret_value()
+    )
+    cerbero = CerberoClient(
+        base_url=settings.cerbero_base_url,
+        token=token,
+        bot_tag=settings.cerbero_bot_tag,
+    )
+    loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
+    ohlcv = loader.load(OHLCVRequest(
+        symbol=args.symbol,
+        timeframe=args.timeframe,
+        start=datetime.fromisoformat(args.start),
+        end=datetime.fromisoformat(args.end),
+    ))
+    splits = expanding_walk_forward(ohlcv.index, train_ratio=args.train_ratio, n_folds=args.n_folds)
+    engine = BacktestEngine(fees_bp=args.fees_bp)
+
+    print(f"\n{'=' * 110}")
+    print(f"PER-TRADE ANALYSIS — top-{len(top)} genomes × {len(splits)} folds")
+    print(f"{'=' * 110}")
+
+    for ev in top:
+        strat, err = _try_parse(ev["raw_text"] or "")
+        if strat is None:
+            print(f"\n>>> {ev['genome_id'][:16]} — parse error: {err}")
+            continue
+
+        print(f"\n>>> {ev['genome_id']} (fit_IS={ev['fitness']:.4f}, sharpe_IS={ev['sharpe']:.3f})")
+        print(f"{'fold':<5} {'period':<26} {'trades':>7} {'wins':>5} {'losses':>7} {'win%':>6} {'avg_w':>9} {'avg_l':>9} {'ret':>7} {'maxDD':>7} {'sharpe':>7}")
+        for s in splits:
+            test_df = ohlcv.loc[s.test_idx]
+            try:
+                signal_fn = compile_strategy(strat)
+                signals = signal_fn(test_df)
+                bt = engine.run(test_df, signals)
+            except Exception as e:
+                print(f"  fold {s.fold}: error {e}")
+                continue
+            trades = bt.trades
+            n_trades = len(trades)
+            wins = [t.net_pnl for t in trades if t.net_pnl > 0]
+            losses = [t.net_pnl for t in trades if t.net_pnl <= 0]
+            n_wins = len(wins)
+            n_losses = len(losses)
+            win_rate = (n_wins / n_trades * 100) if n_trades else 0.0
+            avg_w = (sum(wins) / n_wins) if n_wins else 0.0
+            avg_l = (sum(losses) / n_losses) if n_losses else 0.0
+            # Normalize equity for DD/return
+            if n_trades > 0:
+                notional = float(test_df["close"].iloc[0])
+                equity_pos = (bt.equity_curve / notional) + 1.0
+                ret_pct = total_return(equity_pos)
+                dd = max_drawdown(equity_pos)
+                sr = sharpe_ratio(bt.returns, periods_per_year=8760)
+            else:
+                ret_pct = dd = sr = 0.0
+            period = f"{str(s.test_idx[0])[:10]}..{str(s.test_idx[-1])[:10]}"
+            print(f"{s.fold:<5} {period:<26} {n_trades:>7} {n_wins:>5} {n_losses:>7} {win_rate:>5.1f}% {avg_w:>9.1f} {avg_l:>9.1f} {ret_pct:>6.2%} {dd:>6.2%} {sr:>7.3f}")
+
+
+if __name__ == "__main__":
+    main()

scripts/compare_winners.py

@@ -0,0 +1,139 @@
+"""Confronto per-trade dei 4 winner cross-run (BTC/ETH × 1h/5m).
+
+Per ogni winner: ri-esegue il backtest su 4 fold WFA expanding-window e raccoglie
+trade buoni/non buoni, win-rate, avg PnL, return, max DD, Sharpe.
+"""
+from __future__ import annotations
+
+import argparse
+from datetime import datetime
+
+import pandas as pd  # type: ignore[import-untyped]
+
+from multi_swarm_core.agents.hypothesis import _try_parse
+from multi_swarm_core.backtest.engine import BacktestEngine
+from multi_swarm_core.cerbero.client import CerberoClient
+from multi_swarm_core.config import load_settings
+from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
+from multi_swarm_core.data.splits import expanding_walk_forward
+from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
+from multi_swarm_core.persistence.repository import Repository
+from multi_swarm_core.protocol.compiler import compile_strategy
+
+
+# (run_name, genome_id, symbol, timeframe, label)
+WINNERS = [
+    ("phase1-btc-100-001", "238e481262c1594c", "BTC-PERPETUAL", "1h", "BTC 1h sharpshooter (Gen 7)"),
+    ("phase1-btc-100-001", "23a24989e2ed0f84", "BTC-PERPETUAL", "1h", "BTC 1h robust (Gen 0 elite)"),
+    ("phase1-eth-100-001", "4b45a72c13acf1d5", "ETH-PERPETUAL", "1h", "ETH 1h best-by-sharpe (killed)"),
+    ("phase1-btc-100-5m-001", "f8ca6642adf7e0cd", "BTC-PERPETUAL", "5m", "BTC 5m robust winner"),
+    ("phase1-eth-100-5m-001", "c04dff7086bb9588", "ETH-PERPETUAL", "5m", "ETH 5m OOS winner"),
+]
+
+
+def analyze_genome(run_id: str, genome_id: str, symbol: str, timeframe: str, label: str,
+                   settings, cerbero, loader) -> None:
+    repo = Repository(settings.ga_db_path)
+    repo.init_schema()
+    evs = [e for e in repo.list_evaluations(run_id) if e["genome_id"] == genome_id]
+    if not evs:
+        print(f"  no eval for {genome_id} in {run_id}")
+        return
+    ev = evs[0]
+    strat, err = _try_parse(ev.get("raw_text") or "")
+    if strat is None:
+        print(f"  parse error: {err}")
+        return
+
+    req = OHLCVRequest(
+        symbol=symbol, timeframe=timeframe,
+        start=datetime.fromisoformat("2018-09-01T00:00:00+00:00"),
+        end=datetime.fromisoformat("2026-01-01T00:00:00+00:00"),
+    )
+    ohlcv = loader.load(req)
+    splits = expanding_walk_forward(ohlcv.index, train_ratio=0.5, n_folds=4)
+    engine = BacktestEngine(fees_bp=5.0)
+
+    print(f"\n>>> {label}")
+    print(f"    {genome_id} | fit_IS={ev['fitness']:.4f} sharpe_IS={ev['sharpe']:.3f} trades_IS={ev['n_trades']}")
+    print(f"    {'fold':<5} {'period':<26} {'trades':>7} {'wins':>5} {'losses':>7} {'win%':>6} {'avg_w':>10} {'avg_l':>10} {'ret':>8} {'maxDD':>7} {'sharpe':>7}")
+
+    sum_ret = 0.0
+    sum_trades = 0
+    sum_wins = 0
+    for s in splits:
+        test_df = ohlcv.loc[s.test_idx]
+        try:
+            signal_fn = compile_strategy(strat)
+            signals = signal_fn(test_df)
+            bt = engine.run(test_df, signals)
+        except Exception as e:
+            print(f"  fold {s.fold}: error {e}")
+            continue
+
+        trades = bt.trades
+        n = len(trades)
+        wins = [t.net_pnl for t in trades if t.net_pnl > 0]
+        losses = [t.net_pnl for t in trades if t.net_pnl <= 0]
+        nw, nl = len(wins), len(losses)
+        wr = (nw / n * 100) if n else 0.0
+        aw = (sum(wins) / nw) if nw else 0.0
+        al = (sum(losses) / nl) if nl else 0.0
+        if n > 0:
+            notional = float(test_df["close"].iloc[0])
+            eq = (bt.equity_curve / notional) + 1.0
+            ret = total_return(eq)
+            dd = max_drawdown(eq)
+            sr = sharpe_ratio(bt.returns, periods_per_year=8760)
+        else:
+            ret = dd = sr = 0.0
+        period = f"{str(s.test_idx[0])[:10]}..{str(s.test_idx[-1])[:10]}"
+        print(f"    {s.fold:<5} {period:<26} {n:>7} {nw:>5} {nl:>7} {wr:>5.1f}% {aw:>10.1f} {al:>10.1f} {ret:>7.2%} {dd:>6.2%} {sr:>7.3f}")
+        sum_ret += ret
+        sum_trades += n
+        sum_wins += nw
+    overall_wr = (sum_wins / sum_trades * 100) if sum_trades else 0.0
+    print(f"    {'='*5} TOTALS:                {sum_trades:>7} {sum_wins:>5} {sum_trades-sum_wins:>7} {overall_wr:>5.1f}%  cum_ret={sum_ret:+.2%}")
+
+
+def main() -> None:
+    settings = load_settings()
+    repo = Repository(settings.ga_db_path)
+    repo.init_schema()
+    name_to_id: dict[str, str] = {}
+    for w in WINNERS:
+        run_name = w[0]
+        if run_name in name_to_id:
+            continue
+        runs = repo.list_runs()
+        for r in runs:
+            if r["name"] == run_name:
+                name_to_id[run_name] = r["id"]
+                break
+
+    token = (
+        settings.cerbero_mainnet_token.get_secret_value()
+        if settings.cerbero_mainnet_token
+        else settings.cerbero_testnet_token.get_secret_value()
+    )
+    cerbero = CerberoClient(
+        base_url=settings.cerbero_base_url,
+        token=token,
+        bot_tag=settings.cerbero_bot_tag,
+    )
+    loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
+
+    print(f"{'='*120}")
+    print(f"PER-TRADE COMPARISON — {len(WINNERS)} winner candidates × 4 folds WFA")
+    print(f"{'='*120}")
+
+    for run_name, genome_id, symbol, timeframe, label in WINNERS:
+        run_id = name_to_id.get(run_name)
+        if not run_id:
+            print(f"!!! run not found: {run_name}")
+            continue
+        analyze_genome(run_id, genome_id, symbol, timeframe, label, settings, cerbero, loader)
+
+
+if __name__ == "__main__":
+    main()

scripts/multi_tick_strategies.py

@@ -0,0 +1,112 @@
+"""2 winner cross-tick: BTC 238e4812 + ETH c04dff7086 su 5m / 15m / 1h.
+
+Per ogni combinazione strategy × timeframe: backtest year-by-year (2019-2025)
+con metriche per-anno e totale 7y.
+"""
+from __future__ import annotations
+
+from datetime import datetime
+from pathlib import Path
+
+from multi_swarm_core.backtest.engine import BacktestEngine
+from multi_swarm_core.cerbero.client import CerberoClient
+from multi_swarm_core.config import load_settings
+from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
+from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
+from multi_swarm_core.protocol.compiler import compile_strategy
+from multi_swarm_core.protocol.parser import parse_strategy
+
+
+WINNERS = [
+    # (label, path, symbol)
+    ("BTC NEW (238e4812, native=1h)", "btc_238e4812.json", "BTC-PERPETUAL"),
+    ("ETH NEW (c04dff7086, native=5m)", "eth_c04dff7086.json", "ETH-PERPETUAL"),
+]
+TIMEFRAMES = ["5m", "15m", "1h"]
+
+YEARS = [
+    ("2019", "2019-01-01T00:00:00+00:00", "2020-01-01T00:00:00+00:00"),
+    ("2020", "2020-01-01T00:00:00+00:00", "2021-01-01T00:00:00+00:00"),
+    ("2021", "2021-01-01T00:00:00+00:00", "2022-01-01T00:00:00+00:00"),
+    ("2022", "2022-01-01T00:00:00+00:00", "2023-01-01T00:00:00+00:00"),
+    ("2023", "2023-01-01T00:00:00+00:00", "2024-01-01T00:00:00+00:00"),
+    ("2024", "2024-01-01T00:00:00+00:00", "2025-01-01T00:00:00+00:00"),
+    ("2025", "2025-01-01T00:00:00+00:00", "2026-01-01T00:00:00+00:00"),
+]
+
+
+def evaluate(strat, ohlcv, engine, label, tf) -> None:
+    print(f"\n  >>> tick={tf} | {len(ohlcv)} bars")
+    print(f"  {'year':<6} {'trades':>7} {'wins':>5} {'losses':>7} {'win%':>6} {'ret':>8} {'maxDD':>7} {'sharpe':>7}")
+    sum_ret = 0.0
+    sum_trades = 0
+    sum_wins = 0
+    for year_label, start, end in YEARS:
+        mask = (ohlcv.index >= datetime.fromisoformat(start)) & (ohlcv.index < datetime.fromisoformat(end))
+        slice_df = ohlcv[mask]
+        if len(slice_df) == 0:
+            continue
+        try:
+            signal_fn = compile_strategy(strat)
+            signals = signal_fn(slice_df)
+            bt = engine.run(slice_df, signals)
+        except Exception as e:
+            print(f"  {year_label:<6} ERROR: {e}")
+            continue
+        trades = bt.trades
+        n = len(trades)
+        wins = [t.net_pnl for t in trades if t.net_pnl > 0]
+        losses = [t.net_pnl for t in trades if t.net_pnl <= 0]
+        nw, nl = len(wins), len(losses)
+        wr = (nw / n * 100) if n else 0.0
+        if n > 0:
+            notional = float(slice_df["close"].iloc[0])
+            eq = (bt.equity_curve / notional) + 1.0
+            ret = total_return(eq)
+            dd = max_drawdown(eq)
+            sr = sharpe_ratio(bt.returns, periods_per_year=8760)
+        else:
+            ret = dd = sr = 0.0
+        print(f"  {year_label:<6} {n:>7} {nw:>5} {nl:>7} {wr:>5.1f}% {ret:>7.2%} {dd:>6.2%} {sr:>7.3f}")
+        sum_ret += ret
+        sum_trades += n
+        sum_wins += nw
+    overall_wr = (sum_wins / sum_trades * 100) if sum_trades else 0.0
+    print(f"  ===== 7y TOT: {sum_trades:>7} {sum_wins:>5} {sum_trades-sum_wins:>7} {overall_wr:>5.1f}%  cum_ret={sum_ret:+.2%}")
+
+
+def main() -> None:
+    settings = load_settings()
+    token = (
+        settings.cerbero_mainnet_token.get_secret_value()
+        if settings.cerbero_mainnet_token
+        else settings.cerbero_testnet_token.get_secret_value()
+    )
+    cerbero = CerberoClient(
+        base_url=settings.cerbero_base_url,
+        token=token,
+        bot_tag=settings.cerbero_bot_tag,
+    )
+    loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
+    engine = BacktestEngine(fees_bp=5.0)
+    strategies_dir = Path("/app/strategies")
+
+    for label, fname, symbol in WINNERS:
+        path = strategies_dir / fname
+        strat = parse_strategy(path.read_text())
+        print(f"\n{'='*100}")
+        print(f">>> {label} — symbol={symbol}")
+        for tf in TIMEFRAMES:
+            try:
+                ohlcv = loader.load(OHLCVRequest(
+                    symbol=symbol, timeframe=tf,
+                    start=datetime.fromisoformat("2018-09-01T00:00:00+00:00"),
+                    end=datetime.fromisoformat("2026-01-01T00:00:00+00:00"),
+                ))
+                evaluate(strat, ohlcv, engine, label, tf)
+            except Exception as e:
+                print(f"\n  >>> tick={tf} FAILED TO LOAD: {e}")
+
+
+if __name__ == "__main__":
+    main()

scripts/run_paper_trading.py

@@ -70,9 +70,11 @@ def load_assets(strategies_dir: Path) -> list[AssetConfig]:
         raise FileNotFoundError(
             f"Expected btc_*.json and eth_*.json in {strategies_dir}"
         )
+    # ETH winner c04dff7086 e' tunato su 5m: a 1h la strategia perde (cum_ret -33% 7y).
+    # BTC winner 238e4812 e' tunato su 1h: tick native = paper tick.
     return [
-        AssetConfig(symbol="BTC-PERPETUAL", strategy_file=btc_files[0]),
-        AssetConfig(symbol="ETH-PERPETUAL", strategy_file=eth_files[0]),
+        AssetConfig(symbol="BTC-PERPETUAL", strategy_file=btc_files[0], timeframe="1h"),
+        AssetConfig(symbol="ETH-PERPETUAL", strategy_file=eth_files[0], timeframe="5m"),
     ]
 
 

scripts/run_phase1.py

@@ -1,16 +1,48 @@
 from __future__ import annotations
 
 import argparse
+import importlib.resources
 from datetime import datetime
+from pathlib import Path
 
 from multi_swarm_core.cerbero.client import CerberoClient
 from multi_swarm_core.config import load_settings
 from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
 from multi_swarm_core.genome.hypothesis import ModelTier
+from multi_swarm_core.genome.prompt_library import PromptLibrary
 from multi_swarm_core.llm.client import LLMClient
 from multi_swarm_core.orchestrator.run import RunConfig, run_phase1
 
 
+def _default_prompt_library_path() -> Path:
+    """Default: prompts.json shippato col package strategy_crypto."""
+    return Path(str(importlib.resources.files("strategy_crypto") / "prompts.json"))
+
+
+# Default v2 hard-kill list: oltre ai degenerate originali, fees_eat_alpha e
+# negative_net_pnl sono deal-breaker non recuperabili via soft penalty (vedi
+# 7y-overfit incident 2026-05-16: elite IS Sharpe 1.93 -> net -5% su 7y per fees).
+_DEFAULT_V2_HARD_KILL: tuple[str, ...] = (
+    "no_trades",
+    "degenerate",
+    "undertrading",
+    "fees_eat_alpha",
+    "negative_net_pnl",
+)
+
+
+def _resolve_hard_kill(args) -> tuple[str, ...] | None:
+    """Resolve la lista hard-kill da CLI args.
+
+    Priority: ``--fitness-hard-kill`` esplicito > default v2 > ``None`` (v1).
+    """
+    if args.fitness_hard_kill:
+        return tuple(s.strip() for s in args.fitness_hard_kill.split(",") if s.strip())
+    if args.fitness_v2:
+        return _DEFAULT_V2_HARD_KILL
+    return None
+
+
 def parse_args() -> argparse.Namespace:
     p = argparse.ArgumentParser(description="Multi-Swarm Phase 1 runner")
     p.add_argument("--name", default="phase1-spike-001")
@@ -27,7 +59,10 @@ def parse_args() -> argparse.Namespace:
     )
     p.add_argument("--symbol", default="BTC-PERPETUAL")
     p.add_argument("--timeframe", default="1h")
-    p.add_argument("--start", default="2024-01-01T00:00:00+00:00")
+    # Default esteso a 7.3 anni: copre bear 2018-19, halving 2020, COVID,
+    # ATH 2021, winter 2022, ETF rally 2024, regime corrente. Una finestra
+    # corta lascia il GA libero di overfit a un singolo regime.
+    p.add_argument("--start", default="2018-09-01T00:00:00+00:00")
     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)
@@ -59,8 +94,10 @@ def parse_args() -> argparse.Namespace:
         "--fitness-v2",
         action="store_true",
         help=(
-            "Attiva fitness v2: solo {no_trades, degenerate, undertrading} azzerano; "
-            "gli altri HIGH applicano soft penalty multiplicativa"
+            "Attiva fitness v2: hard-kill su {no_trades, degenerate, undertrading, "
+            "fees_eat_alpha, negative_net_pnl}; gli altri HIGH applicano soft penalty "
+            "multiplicativa. Versione hardened post 7y-overfit incident: fees + "
+            "net negativo non sono recuperabili."
         ),
     )
     p.add_argument(
@@ -69,6 +106,16 @@ def parse_args() -> argparse.Namespace:
         default=0.4,
         help="v2: fattore soft penalty per HIGH non-hard (default 0.4 → 1 HIGH → 0.71x)",
     )
+    p.add_argument(
+        "--fitness-hard-kill",
+        type=str,
+        default=None,
+        help=(
+            "Override comma-separated della lista di finding name che azzerano la "
+            "fitness in modalita' v2. Es: 'no_trades,degenerate'. Default v2: "
+            "no_trades,degenerate,undertrading,fees_eat_alpha,negative_net_pnl."
+        ),
+    )
     p.add_argument(
         "--wfa-train-split",
         type=float,
@@ -96,6 +143,26 @@ def parse_args() -> argparse.Namespace:
         default=0.5,
         help="Multi-objective: peso IS (1-alpha = OOS). 1.0=solo IS, 0.5=bilanciato, 0.0=solo OOS",
     )
+    p.add_argument(
+        "--prompt-library",
+        type=Path,
+        default=None,
+        help=(
+            "Path al file JSON con stili cognitivi + direttive system_prompt. "
+            "Default: strategy_crypto/prompts.json shippato col package. "
+            "Schema: {styles: {<name>: {directive: <testo>}}}"
+        ),
+    )
+    p.add_argument(
+        "--llm-concurrency",
+        type=int,
+        default=1,
+        help=(
+            "Numero di propose() LLM concorrenti per generazione (default 1 = "
+            "serial). 6-10 tipicamente accettati da OpenRouter qwen-2.5 senza "
+            "rate-limit; riduce wall time GA loop di 5-8x."
+        ),
+    )
     return p.parse_args()
 
 
@@ -103,6 +170,13 @@ def main() -> None:
     args = parse_args()
     settings = load_settings()
 
+    prompt_lib_path = args.prompt_library or _default_prompt_library_path()
+    prompt_library = PromptLibrary.from_json(prompt_lib_path)
+    print(
+        f"PromptLibrary loaded from {prompt_lib_path}: "
+        f"{len(prompt_library.styles)} stili ({', '.join(prompt_library.cognitive_styles)})"
+    )
+
     token = (
         settings.cerbero_mainnet_token.get_secret_value()
         if settings.cerbero_mainnet_token
@@ -153,14 +227,14 @@ def main() -> None:
         fees_eat_alpha_threshold=args.fees_eat_alpha_threshold,
         flat_too_long_threshold=args.flat_too_long_threshold,
         undertrading_threshold=args.undertrading_threshold,
-        fitness_hard_kill_findings=(
-            ("no_trades", "degenerate", "undertrading") if args.fitness_v2 else None
-        ),
+        fitness_hard_kill_findings=_resolve_hard_kill(args),
         fitness_adversarial_soft_penalty=args.fitness_soft_penalty,
         wfa_train_split=args.wfa_train_split,
         wfa_top_k=args.wfa_top_k,
         eval_oos_during_loop=args.eval_oos_during_loop,
         fitness_combined_alpha=args.fitness_combined_alpha,
+        prompt_library=prompt_library,
+        llm_concurrency=args.llm_concurrency,
     )
 
     run_id = run_phase1(cfg, ohlcv=ohlcv, llm=llm)

scripts/validate_run.py

@@ -0,0 +1,271 @@
+"""Multi-fold validation di un run esistente.
+
+Prende un ``run_id`` da ``state/runs.db``, seleziona i top-K genomi per fitness IS,
+e li rivaluta su N fold expanding-window di un dataset OHLCV (tipicamente piu'
+lungo del train del GA). Output: per-fold + aggregati (mean / min / std) della
+fitness OOS.
+
+Use case: il GA puo' selezionare un "lucky-shot" overfit a uno specifico regime.
+Validare i top-K su finestre temporali diverse rivela quali strategie sono
+robuste vs overfitter.
+
+Esempio::
+
+  python scripts/validate_run.py \\
+    --run-id e263651598894da688d95fda90a34a96 \\
+    --top-k 10 --n-folds 4 \\
+    --symbol BTC-PERPETUAL --timeframe 1h \\
+    --start 2018-09-01 --end 2026-01-01
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+from datetime import datetime
+from pathlib import Path
+
+import pandas as pd  # type: ignore[import-untyped]
+
+from multi_swarm_core.agents.adversarial import AdversarialAgent
+from multi_swarm_core.agents.falsification import FalsificationAgent
+from multi_swarm_core.agents.hypothesis import _try_parse
+from multi_swarm_core.cerbero.client import CerberoClient
+from multi_swarm_core.config import load_settings
+from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
+from multi_swarm_core.data.splits import expanding_walk_forward
+from multi_swarm_core.ga.fitness import compute_fitness
+from multi_swarm_core.persistence.repository import Repository
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Multi-fold validation di top-K genomi")
+    p.add_argument("--run-id", required=True, help="run_id da validare")
+    p.add_argument("--top-k", type=int, default=10, help="quanti genomi top valutare")
+    p.add_argument("--n-folds", type=int, default=4, help="numero fold expanding-window")
+    p.add_argument(
+        "--train-ratio",
+        type=float,
+        default=0.5,
+        help="frazione iniziale per il train iniziale (folds testano la coda)",
+    )
+    p.add_argument("--symbol", default="BTC-PERPETUAL")
+    p.add_argument("--timeframe", default="1h")
+    p.add_argument("--exchange", default="deribit", choices=["deribit", "bybit", "hyperliquid"])
+    p.add_argument("--start", default="2018-09-01T00:00:00+00:00")
+    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(
+        "--fees-eat-alpha-threshold", type=float, default=0.5,
+    )
+    p.add_argument(
+        "--flat-too-long-threshold", type=float, default=0.95,
+    )
+    p.add_argument(
+        "--undertrading-threshold", type=int, default=10,
+    )
+    p.add_argument(
+        "--fitness-v2", action="store_true",
+        help="Coerente con --fitness-v2 del run originale",
+    )
+    p.add_argument(
+        "--fitness-soft-penalty", type=float, default=0.4,
+    )
+    p.add_argument(
+        "--output-json",
+        type=Path,
+        default=None,
+        help="Path JSON dove salvare i risultati (default: stdout solo)",
+    )
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    settings = load_settings()
+
+    # Repository: top-K genomi per fitness IS, con raw_text parsable.
+    repo = Repository(settings.ga_db_path)
+    repo.init_schema()
+    run = repo.get_run(args.run_id)
+    if run is None:
+        raise SystemExit(f"run_id non trovato: {args.run_id}")
+    print(f"Validating run: {run['name']} ({args.run_id})")
+    print(f"  status: {run['status']}, cost: ${run['total_cost_usd']:.4f}")
+
+    all_evals = repo.list_evaluations(args.run_id)
+    parseable = [
+        e for e in all_evals
+        if e.get("raw_text") and not e.get("parse_error") and e["fitness"] > 0
+    ]
+    parseable.sort(key=lambda e: e["fitness"], reverse=True)
+
+    # Dedup by genome_id (gli elite vengono salvati una sola volta ma possono apparire
+    # in evaluations multiple se rivalutati con eval_oos_during_loop).
+    seen_ids: set[str] = set()
+    top_genomes: list[dict] = []
+    for e in parseable:
+        if e["genome_id"] in seen_ids:
+            continue
+        seen_ids.add(e["genome_id"])
+        top_genomes.append(e)
+        if len(top_genomes) >= args.top_k:
+            break
+    print(f"  selected top-{len(top_genomes)} genomes for validation")
+
+    # OHLCV: carica il dataset esteso.
+    token = (
+        settings.cerbero_mainnet_token.get_secret_value()
+        if settings.cerbero_mainnet_token
+        else settings.cerbero_testnet_token.get_secret_value()
+    )
+    cerbero = CerberoClient(
+        base_url=settings.cerbero_base_url,
+        token=token,
+        bot_tag=settings.cerbero_bot_tag,
+    )
+    loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
+    req = OHLCVRequest(
+        symbol=args.symbol,
+        timeframe=args.timeframe,
+        start=datetime.fromisoformat(args.start),
+        end=datetime.fromisoformat(args.end),
+        exchange=args.exchange,
+    )
+    ohlcv = loader.load(req)
+    print(f"  OHLCV: {len(ohlcv)} bars from {ohlcv.index[0]} to {ohlcv.index[-1]}")
+
+    splits = expanding_walk_forward(
+        ohlcv.index, train_ratio=args.train_ratio, n_folds=args.n_folds,
+    )
+    print(f"  generated {len(splits)} folds")
+    for s in splits:
+        print(f"    fold {s.fold}: test [{s.test_idx[0]} -> {s.test_idx[-1]}] ({len(s.test_idx)} bars)")
+
+    fals_agent = FalsificationAgent(fees_bp=args.fees_bp, n_trials_dsr=args.n_trials_dsr)
+    adv_agent = AdversarialAgent(
+        fees_bp=args.fees_bp,
+        fees_eat_alpha_threshold=args.fees_eat_alpha_threshold,
+        flat_too_long_threshold=args.flat_too_long_threshold,
+        undertrading_threshold=args.undertrading_threshold,
+    )
+    hard_kill = (
+        ("no_trades", "degenerate", "undertrading") if args.fitness_v2 else None
+    )
+
+    # Itera per ogni genome + fold.
+    results: list[dict] = []
+    for gi, ev in enumerate(top_genomes):
+        strategy, parse_err = _try_parse(ev["raw_text"] or "")
+        if strategy is None:
+            print(f"  [{gi}] {ev['genome_id'][:12]} skip (parse error: {parse_err})")
+            continue
+
+        per_fold: list[dict] = []
+        for s in splits:
+            test_df = ohlcv.loc[s.test_idx]
+            try:
+                fals = fals_agent.evaluate(strategy, test_df)
+                adv = adv_agent.review(strategy, test_df)
+                fit = compute_fitness(
+                    fals, adv,
+                    hard_kill_findings=hard_kill,
+                    adversarial_soft_penalty=args.fitness_soft_penalty,
+                )
+            except Exception as e:
+                print(f"    fold {s.fold} eval failed: {e}")
+                continue
+            per_fold.append({
+                "fold": s.fold,
+                "fitness": float(fit),
+                "sharpe": float(fals.sharpe),
+                "dsr": float(fals.dsr),
+                "dsr_pvalue": float(fals.dsr_pvalue),
+                "return": float(fals.total_return),
+                "max_dd": float(fals.max_drawdown),
+                "n_trades": int(fals.n_trades),
+                "test_start": str(s.test_idx[0]),
+                "test_end": str(s.test_idx[-1]),
+            })
+
+        if not per_fold:
+            continue
+
+        fits = [pf["fitness"] for pf in per_fold]
+        sharps = [pf["sharpe"] for pf in per_fold]
+        results.append({
+            "genome_id": ev["genome_id"],
+            "fitness_is": float(ev["fitness"]),
+            "sharpe_is": float(ev["sharpe"]),
+            "folds": per_fold,
+            "fitness_oos_mean": statistics.mean(fits),
+            "fitness_oos_min": min(fits),
+            "fitness_oos_max": max(fits),
+            "fitness_oos_std": statistics.pstdev(fits) if len(fits) > 1 else 0.0,
+            "sharpe_oos_mean": statistics.mean(sharps),
+            "sharpe_oos_min": min(sharps),
+            "robust_score": min(fits),  # min across folds = pessimismo
+        })
+
+    # Ranking finale: per robust_score (min fitness) decrescente.
+    results.sort(key=lambda r: r["robust_score"], reverse=True)
+
+    print()
+    print(f"{'='*120}")
+    print(f"VALIDATION RESULTS ({len(results)} genomes, {len(splits)} folds)")
+    print(f"{'='*120}")
+    print(
+        f"{'rank':>4} {'genome':12} {'fit_is':>8} {'sh_is':>7} "
+        f"{'fit_mean':>9} {'fit_min':>8} {'fit_max':>8} {'fit_std':>8} "
+        f"{'sh_mean':>8} {'sh_min':>8} {'robust':>7}"
+    )
+    print("-" * 120)
+    for rank, r in enumerate(results, 1):
+        print(
+            f"{rank:>4} {r['genome_id'][:12]:12} "
+            f"{r['fitness_is']:>8.4f} {r['sharpe_is']:>7.3f} "
+            f"{r['fitness_oos_mean']:>9.4f} {r['fitness_oos_min']:>8.4f} "
+            f"{r['fitness_oos_max']:>8.4f} {r['fitness_oos_std']:>8.4f} "
+            f"{r['sharpe_oos_mean']:>8.3f} {r['sharpe_oos_min']:>8.3f} "
+            f"{r['robust_score']:>7.4f}"
+        )
+
+    if results:
+        winner = results[0]
+        print()
+        print(f"ROBUST WINNER: {winner['genome_id']}")
+        print(f"  fitness_is={winner['fitness_is']:.4f}, "
+              f"fitness_oos_min={winner['fitness_oos_min']:.4f}, "
+              f"fitness_oos_mean={winner['fitness_oos_mean']:.4f}")
+        print(f"  sharpe_is={winner['sharpe_is']:.3f}, "
+              f"sharpe_oos_min={winner['sharpe_oos_min']:.3f}")
+        print(f"  per-fold breakdown:")
+        for pf in winner["folds"]:
+            print(
+                f"    fold {pf['fold']} [{pf['test_start'][:10]} .. {pf['test_end'][:10]}]: "
+                f"fit={pf['fitness']:.4f} sharpe={pf['sharpe']:.3f} "
+                f"ret={pf['return']:.3f} n_trades={pf['n_trades']}"
+            )
+
+    if args.output_json:
+        payload = {
+            "run_id": args.run_id,
+            "run_name": run["name"],
+            "n_folds": len(splits),
+            "top_k_requested": args.top_k,
+            "top_k_evaluated": len(results),
+            "symbol": args.symbol,
+            "timeframe": args.timeframe,
+            "start": args.start,
+            "end": args.end,
+            "ohlcv_bars": len(ohlcv),
+            "results": results,
+        }
+        args.output_json.write_text(json.dumps(payload, indent=2, default=str))
+        print(f"\nResults saved to: {args.output_json}")
+
+
+if __name__ == "__main__":
+    main()

scripts/yearly_strategies.py

@@ -0,0 +1,112 @@
+"""Per-year breakdown delle 4 strategie: 2 NEW (BTC 238e4812 + ETH c04dff7086)
++ 2 OLD freezate (btc_9cf506b8 hardened-001 + eth_facd6af85d5d).
+
+Backtest anno-per-anno (2019-2025) sul tick di discovery di ciascuna strategia.
+Output: trade, wins/losses, win%, return%, max DD%, Sharpe per ogni anno.
+"""
+from __future__ import annotations
+
+from datetime import datetime
+from pathlib import Path
+
+from multi_swarm_core.backtest.engine import BacktestEngine
+from multi_swarm_core.cerbero.client import CerberoClient
+from multi_swarm_core.config import load_settings
+from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
+from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
+from multi_swarm_core.protocol.compiler import compile_strategy
+from multi_swarm_core.protocol.parser import parse_strategy
+
+
+STRATEGIES = [
+    # (label, path, symbol, timeframe)
+    ("BTC NEW (238e4812, paper attuale)", "btc_238e4812.json", "BTC-PERPETUAL", "1h"),
+    ("BTC OLD (9cf506b8, hardened-001 prev paper)", "archive/btc_9cf506b8.json", "BTC-PERPETUAL", "1h"),
+    ("ETH NEW (c04dff7086, paper attuale)", "eth_c04dff7086.json", "ETH-PERPETUAL", "5m"),
+    ("ETH OLD (facd6af85d5d, prev paper)", "archive/eth_facd6af85d5d.json", "ETH-PERPETUAL", "1h"),
+]
+
+YEARS = [
+    ("2019", "2019-01-01T00:00:00+00:00", "2020-01-01T00:00:00+00:00"),
+    ("2020", "2020-01-01T00:00:00+00:00", "2021-01-01T00:00:00+00:00"),
+    ("2021", "2021-01-01T00:00:00+00:00", "2022-01-01T00:00:00+00:00"),
+    ("2022", "2022-01-01T00:00:00+00:00", "2023-01-01T00:00:00+00:00"),
+    ("2023", "2023-01-01T00:00:00+00:00", "2024-01-01T00:00:00+00:00"),
+    ("2024", "2024-01-01T00:00:00+00:00", "2025-01-01T00:00:00+00:00"),
+    ("2025", "2025-01-01T00:00:00+00:00", "2026-01-01T00:00:00+00:00"),
+]
+
+
+def main() -> None:
+    settings = load_settings()
+    token = (
+        settings.cerbero_mainnet_token.get_secret_value()
+        if settings.cerbero_mainnet_token
+        else settings.cerbero_testnet_token.get_secret_value()
+    )
+    cerbero = CerberoClient(
+        base_url=settings.cerbero_base_url,
+        token=token,
+        bot_tag=settings.cerbero_bot_tag,
+    )
+    loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
+    engine = BacktestEngine(fees_bp=5.0)
+    strategies_dir = Path("/app/strategies")
+
+    for label, fname, symbol, timeframe in STRATEGIES:
+        path = strategies_dir / fname
+        strat = parse_strategy(path.read_text())
+
+        # Carica intero range una volta sola
+        ohlcv = loader.load(OHLCVRequest(
+            symbol=symbol, timeframe=timeframe,
+            start=datetime.fromisoformat("2018-09-01T00:00:00+00:00"),
+            end=datetime.fromisoformat("2026-01-01T00:00:00+00:00"),
+        ))
+
+        print(f"\n{'=' * 110}")
+        print(f">>> {label}")
+        print(f"    symbol={symbol} timeframe={timeframe} | {len(ohlcv)} bars total")
+        print(f"    {'year':<6} {'bars':>6} {'trades':>7} {'wins':>5} {'losses':>7} {'win%':>6} {'avg_w':>10} {'avg_l':>10} {'ret':>8} {'maxDD':>7} {'sharpe':>7}")
+
+        sum_ret = 0.0
+        sum_trades = 0
+        sum_wins = 0
+        for year_label, start, end in YEARS:
+            mask = (ohlcv.index >= datetime.fromisoformat(start)) & (ohlcv.index < datetime.fromisoformat(end))
+            slice_df = ohlcv[mask]
+            if len(slice_df) == 0:
+                continue
+            try:
+                signal_fn = compile_strategy(strat)
+                signals = signal_fn(slice_df)
+                bt = engine.run(slice_df, signals)
+            except Exception as e:
+                print(f"    {year_label:<6} ERROR: {e}")
+                continue
+            trades = bt.trades
+            n = len(trades)
+            wins = [t.net_pnl for t in trades if t.net_pnl > 0]
+            losses = [t.net_pnl for t in trades if t.net_pnl <= 0]
+            nw, nl = len(wins), len(losses)
+            wr = (nw / n * 100) if n else 0.0
+            aw = (sum(wins) / nw) if nw else 0.0
+            al = (sum(losses) / nl) if nl else 0.0
+            if n > 0:
+                notional = float(slice_df["close"].iloc[0])
+                eq = (bt.equity_curve / notional) + 1.0
+                ret = total_return(eq)
+                dd = max_drawdown(eq)
+                sr = sharpe_ratio(bt.returns, periods_per_year=8760)
+            else:
+                ret = dd = sr = 0.0
+            print(f"    {year_label:<6} {len(slice_df):>6} {n:>7} {nw:>5} {nl:>7} {wr:>5.1f}% {aw:>10.1f} {al:>10.1f} {ret:>7.2%} {dd:>6.2%} {sr:>7.3f}")
+            sum_ret += ret
+            sum_trades += n
+            sum_wins += nw
+        overall_wr = (sum_wins / sum_trades * 100) if sum_trades else 0.0
+        print(f"    {'='*5} TOTALS 7y:                 {sum_trades:>7} {sum_wins:>5} {sum_trades-sum_wins:>7} {overall_wr:>5.1f}%                       cum_ret={sum_ret:+.2%}")
+
+
+if __name__ == "__main__":
+    main()

src/multi_swarm_core/docs/decisions/2026-05-15-atr-pct-fix.md

@@ -1,8 +1,8 @@
-# Decisione: indicatore `atr_pct` per fix bug protocollo unità
+# Decisione: indicatori `*_pct` per fix bug protocollo unità
 
 **Data:** 2026-05-15
-**Status:** Implementato
-**Scope:** `multi_swarm_core.protocol.{compiler,grammar,validator}` + `strategy_crypto/strategies/eth_*.json`
+**Status:** Implementato (atr_pct + sma_pct + macd_pct + prompt LLM aggiornato)
+**Scope:** `multi_swarm_core.protocol.{compiler,grammar,validator}` + `agents/hypothesis.py` + `strategy_crypto/strategies/eth_*.json`
 
 ## Contesto
 
@@ -73,12 +73,25 @@ _ind_atr_pct(df, 14).mean()  # ~0.011 (1.1% del prezzo)
 
 `atr > 0.02` → 500/500 (broken). `atr_pct > 0.02` → 0/500 (real signal).
 
+## Estensione: sma_pct + macd_pct (2026-05-15)
+
+Aggiunti anche `sma_pct` e `macd_pct` per coerenza famiglia `*_pct`:
+
+- **`sma_pct(length) = (close - sma) / sma`** — deviazione frazionale del
+  close dalla SMA. Range tipico ±0.1. NB: NON è `sma/close` (che sarebbe
+  sempre ~1.0, inutile per literal). Uso: `sma_pct(50) > 0.05` significa
+  "close 5% sopra la media a 50 barre" (mean reversion).
+- **`macd_pct(fast, slow, signal) = macd / close`** — MACD come frazione
+  del prezzo. Range tipico ±0.02. Uso: `macd_pct > 0.005` significa
+  "momentum > 0.5% del prezzo".
+
+Prompt LLM aggiornato di conseguenza (`agents/hypothesis.py`):
+- Lista indicatori include `sma_pct` e `macd_pct` con annotazioni unità
+- Pattern guidance: "Mean reversion strutturale: sma_pct(long) > 0.05",
+  "Momentum positivo conferma: macd_pct(12,26,9) > 0.005"
+- 3 nuovi test (sma_pct identity, macd_pct identity, validator integration)
+
 ## Open items
 
-- ~~Aggiornare il system prompt LLM~~ ✅ **chiuso 2026-05-15**:
-  `agents/hypothesis.py` ora elenca `atr_pct` con annotazione unità e
-  include una sezione "UNITÀ — REGOLA CRITICA" che spiega esplicitamente
-  al modello quando usare `atr_pct` (literal frazionali) vs `atr`
-  (confronti relativi). `mutation_prompt_llm._VALID_KEYWORDS` esteso.
-- Considerare l'aggiunta di `sma_pct`, `macd_pct` se emergono usi
-  analoghi in future strategie (still open).
+- ~~Aggiornare il system prompt LLM~~ ✅ chiuso 2026-05-15
+- ~~`sma_pct`, `macd_pct` se emergono usi analoghi~~ ✅ chiuso 2026-05-15

src/multi_swarm_core/multi_swarm_core/agents/adversarial.py

@@ -172,10 +172,11 @@ class AdversarialAgent:
                 )
             )
 
-        # Fees-eat-alpha: gross_pnl > 0 ma fees > 50% del lordo.
+        # Fees-eat-alpha: gross_pnl > 0 ma fees > soglia del lordo.
         # La strategia ha edge teorico ma il margine viene mangiato dai
         # costi di transazione: non sostenibile in produzione.
-        # Se gross_pnl <= 0 il check non si applica (gia' perdente).
+        # Se gross_pnl <= 0 il check non si applica (la condizione e' coperta
+        # da ``negative_net_pnl`` sotto).
         gross_pnl = sum(t.gross_pnl for t in result.trades)
         total_fees = sum(t.fees for t in result.trades)
         if gross_pnl > 0 and total_fees / gross_pnl > self._fees_eat_alpha_threshold:
@@ -190,4 +191,22 @@ class AdversarialAgent:
                 )
             )
 
+        # Negative-net-pnl: somma di ``trade.net_pnl`` < 0 sul training.
+        # Cattura sia il caso "gross negativo" (no edge direzionale) sia il
+        # caso "gross positivo ma fees superiori a gross" (edge insufficiente).
+        # Sintesi del net-after-fees su finestra continua: deal-breaker, non
+        # negoziabile via soft penalty.
+        net_pnl = gross_pnl - total_fees
+        if net_pnl < 0:
+            report.findings.append(
+                Finding(
+                    name="negative_net_pnl",
+                    severity=Severity.HIGH,
+                    detail=(
+                        f"Net PnL ${net_pnl:.2f} < 0 after fees over {n_bars} bars; "
+                        f"gross ${gross_pnl:.2f}, fees ${total_fees:.2f}"
+                    ),
+                )
+            )
+
         return report

src/multi_swarm_core/multi_swarm_core/agents/hypothesis.py

@@ -2,10 +2,12 @@ from __future__ import annotations
 
 import re
 from dataclasses import dataclass, field
+from typing import Any
 
 import openai
 
 from ..genome.hypothesis import HypothesisAgentGenome
+from ..genome.prompt_library import PromptLibrary
 from ..llm.client import CompletionResult, EmptyCompletionError, LLMClient
 from ..protocol.parser import ParseError, Strategy, parse_strategy
 from ..protocol.validator import ValidationError, validate_strategy
@@ -21,6 +23,19 @@ class MarketSummary:
     skew: float
     kurtosis: float
     volatility_regime: str
+    autocorr_lag1_recent: float = 0.0    # AR(1) ultimi 500 bar
+    autocorr_lag1_baseline: float = 0.0  # AR(1) full sample (riferimento)
+    hurst_recent: float = 0.5            # Hurst ultimi 500 bar
+    vol_percentile: float = 50.0         # 0-100 percentile della vol corrente
+    seasonality_hour: float = 0.0        # 0-1 varianza spiegata da hour
+    seasonality_dow: float = 0.0         # 0-1 varianza spiegata da dow
+    efficiency_ratio: float = 0.0        # Kaufman, 0-1
+    tail_index_left: float = 5.0         # Hill left tail
+    tail_index_right: float = 5.0        # Hill right tail
+    structural_uptrend: float = 0.5      # HH/HL score 0-1
+    compression: float = 1.0             # range recent / range ref
+    spectral_entropy: float = 1.0        # 0-1, Shannon FFT normalizzata
+    dominant_cycle: float | None = None  # periodo barre, None se spectrum piatto
 
 
 @dataclass(frozen=True)
@@ -41,12 +56,9 @@ class HypothesisProposal:
     n_attempts: int = 1
 
 
-SYSTEM_TEMPLATE = """\
-Sei un agente generatore di ipotesi di trading quantitativo per un sistema swarm.
-
-Il tuo stile cognitivo: {cognitive_style}
-Direttiva personale: {system_prompt}
+# === CORE SCAFFOLD constants (universal, legato al protocol/compiler) ===
 
+_SYSTEM_GRAMMAR_SPEC = """\
 Devi proporre una strategia di trading espressa in JSON STRETTO.
 La risposta deve essere un singolo oggetto JSON dentro fence ```json...```
 con questa shape:
@@ -77,20 +89,29 @@ Crossover (eventi su 2 serie):
 
 Leaf - indicatori (calcolati su close):
   {{"kind": "indicator", "name": "sma",          "params": [<length>]}}      // media mobile, UNITÀ PREZZO
+  {{"kind": "indicator", "name": "sma_pct",      "params": [<length>]}}      // (close-sma)/sma, FRAZIONE ±0.1
   {{"kind": "indicator", "name": "rsi",          "params": [<length>]}}      // 0-100, adimensionale
   {{"kind": "indicator", "name": "atr",          "params": [<length>]}}      // true range, UNITÀ PREZZO
   {{"kind": "indicator", "name": "atr_pct",      "params": [<length>]}}      // atr/close, FRAZIONE 0.0-0.1
   {{"kind": "indicator", "name": "realized_vol", "params": [<window>]}}      // std dei returns, FRAZIONE
-  {{"kind": "indicator", "name": "macd",         "params": [<fast>, <slow>, <signal>]}}
-    // 0-3 numeri (tutti opzionali con default 12, 26, 9)
+  {{"kind": "indicator", "name": "macd",         "params": [<fast>, <slow>, <signal>]}}  // UNITÀ PREZZO
+  {{"kind": "indicator", "name": "macd_pct",     "params": [<fast>, <slow>, <signal>]}}  // macd/close, FRAZIONE ±0.02
+    // params: 0-3 numeri (tutti opzionali, default 12, 26, 9)
 
 UNITÀ — REGOLA CRITICA per i confronti con literal numerici:
-  * Confronti con literal FRAZIONALI (0.01, 0.02, 0.05): usa `atr_pct`, `realized_vol`
-    Esempio CORRETTO: `atr_pct(14) > 0.02` significa "ATR > 2% del prezzo"
-    Esempio ERRATO:   `atr(14) > 0.02` è sempre TRUE su asset $>1 (atr in dollari)
-  * Confronti RELATIVI fra indicatori in stessa unità: usa `atr`, `sma`, `macd`
-    Esempio: `atr(14) > sma(14)` (entrambi in dollari, confronto valido)
-  * RSI usa literal 0-100 (mai frazione): `rsi(14) > 70`
+  * Confronti con literal FRAZIONALI (0.01, 0.02, 0.05): usa le varianti _pct
+    Esempi CORRETTI:
+      `atr_pct(14) > 0.02`   "ATR > 2% del prezzo" (volatilità alta)
+      `sma_pct(50) > 0.05`   "close 5% sopra SMA(50)" (deviazione media)
+      `macd_pct(12,26,9) > 0.005`  "momentum > 0.5% del prezzo"
+    Esempi ERRATI (sempre TRUE/FALSE su crypto, dead branch):
+      `atr(14) > 0.02`   atr in dollari (~30 su ETH) >> 0.02
+      `sma(50) > 0.02`   sma in dollari (~3000) >> 0.02
+      `macd > 0.02`      macd in dollari, ordine ±10
+  * Confronti RELATIVI fra indicatori in stessa unità: usa nomi senza _pct
+    Esempi: `atr(14) > sma(14)` (entrambi $), `sma(50) > sma(200)` (golden cross)
+    `close > sma(50)` (entrambi $) — preferito su `sma_pct(50) > 0` (equivalente)
+  * RSI usa literal 0-100 (mai frazione): `rsi(14) > 70`, `rsi(14) < 30`
 
 Leaf - feature OHLCV:
   {{"kind": "feature", "name": "open|high|low|close|volume"}}
@@ -112,34 +133,18 @@ Esempi di gating temporale:
   {{"op": "eq", "args": [{{"kind": "feature", "name": "is_weekend"}}, {{"kind": "literal", "value": 1}}]}}
 
 Leaf - letterale numerico:
-  {{"kind": "literal", "value": 70.0}}
-
-PATTERN GUIDANCE (oltre agli indicatori, considera forma delle curve e ripetibilità):
-
-  Forme di curva:
-  - Trend ascendente: SMA(short) > SMA(long) E close > SMA(short)
-  - Trend discendente: SMA(short) < SMA(long) E close < SMA(short)
-  - Compressione di volatilità (pre-breakout): atr_pct(N) < 0.01 (sotto 1% del prezzo)
-  - Espansione di volatilità: atr_pct(N) > 0.03 (sopra 3%) OPPURE ATR(N) > ATR(N*2) confronto relativo
-  - Mean reversion strutturale: |close - SMA(long)| eccessivo → reversal atteso
-
-  Ripetibilità dell'andamento:
-  - Eventi crossover/crossunder ricorrenti (golden/death cross, RSI cross zone)
-  - Pattern intra-day: usa 'hour' per sfruttare orari di forte volatilità ricorrente
-  - Pattern settimanali: usa 'dow' o 'is_weekend' per cicli mercato
-  - Doppio top approx: RSI > 70 + crossunder RSI 70 (1° picco), poi entro N bar
-    nuovo crossover RSI 70 a livello close simile → entry short
-  - Range breakout: close > SMA(N) con SMA(short) > SMA(long) (compressione + spinta)
-
-  Cerca pattern che si REPLICANO nei dati storici, non singoli eventi rari.
+  {{"kind": "literal", "value": 70.0}}"""
 
+_SYSTEM_CONSTRAINTS = """\
 VINCOLI
 - Gli indicator NON sono annidabili: 'params' accetta solo numeri, mai altri nodi.
 - Le regole sono valutate in ordine; la prima che matcha vince per ogni timestamp.
 - Default action se nessuna regola matcha = flat.
 - 'op' e 'kind' sono mutuamente esclusivi sullo stesso nodo.
 
-Rispondi SOLO con il fence ```json...``` contenente l'oggetto strategy.
+Rispondi SOLO con il fence ```json...``` contenente l'oggetto strategy."""
+
+_SYSTEM_EXAMPLE = """\
 Esempio:
 
 ```json
@@ -161,8 +166,49 @@ Esempio:
     }}
   ]
 }}
-```
-"""
+```"""
+
+
+def _build_system_prompt(lib: PromptLibrary, genome: HypothesisAgentGenome) -> str:
+    """Compone il SYSTEM prompt da scaffold core + contenuto strategy-specific."""
+    parts: list[str] = []
+    # 1. Header strategy-specific (da prompts.json)
+    parts.append(lib.agent_role)
+    parts.append("")
+    # 2. Cognitive style + directive (sempre, dal genome)
+    parts.append(f"Il tuo stile cognitivo: {genome.cognitive_style}")
+    parts.append(f"Direttiva personale: {genome.system_prompt}")
+    parts.append("")
+    # 3. Grammar spec (core scaffold)
+    parts.append(_SYSTEM_GRAMMAR_SPEC)
+    # 4. Pattern guidance (da prompts.json, opzionale)
+    if lib.pattern_guidance:
+        parts.append(
+            "\nPATTERN GUIDANCE (oltre agli indicatori, considera forma delle curve e ripetibilità):\n"
+        )
+        parts.append(lib.pattern_guidance)
+        parts.append(
+            "\n  Cerca pattern che si REPLICANO nei dati storici, non singoli eventi rari.\n"
+        )
+    # 5. Domain warnings (da prompts.json, opzionale)
+    if lib.domain_warnings:
+        parts.append("\nWARNING DI DOMINIO:\n")
+        parts.append(lib.domain_warnings)
+        parts.append("")
+    # 6. Vincoli (core scaffold)
+    parts.append(_SYSTEM_CONSTRAINTS)
+    # 7. NEW v3.1: anti-pattern e output priorities (da prompts.json, opzionali)
+    if lib.anti_patterns:
+        parts.append("\nANTI-PATTERN DA EVITARE:\n")
+        parts.append(lib.anti_patterns)
+        parts.append("")
+    if lib.output_priorities:
+        parts.append("\nPRIORITA' DI OUTPUT (trade-off):\n")
+        parts.append(lib.output_priorities)
+        parts.append("")
+    # 8. Esempio (core scaffold)
+    parts.append(_SYSTEM_EXAMPLE)
+    return "\n".join(parts)
 
 
 USER_TEMPLATE = """\
@@ -171,13 +217,72 @@ Statistiche return: mean={return_mean:.5f}, std={return_std:.5f}, \
 skew={skew:.3f}, kurt={kurtosis:.3f}.
 Regime volatilità: {volatility_regime}.
 
+Regime recente (ultime 500 barre):
+  autocorr_lag1: {autocorr_lag1_recent:.3f}  (baseline storica: {autocorr_lag1_baseline:.3f})
+  hurst: {hurst_recent:.3f}  (0.5 = random walk, >0.55 trending, <0.45 mean rev)
+  vol_pct: {vol_percentile:.0f}° percentile storico
+  stagionalita: hour={seasonality_hour:.2f}, dow={seasonality_dow:.2f}  (0-1, varianza spiegata)
+
+Geometria & frattali:
+  efficiency_ratio: {efficiency_ratio:.3f}  (Kaufman, 0=noise, 1=trend efficiente)
+  tail_index: left={tail_index_left:.2f}, right={tail_index_right:.2f}  (Hill; <2 fat tail, >5 light)
+  structural_uptrend: {structural_uptrend:.2f}  (HH/HL score, 0.5=range)
+  compression: {compression:.2f}  (range recent / ref; <1 compressione, >1 espansione)
+  spectral_entropy: {spectral_entropy:.2f}  (0=struttura, 1=rumore bianco)
+  dominant_cycle: {dominant_cycle_str}  (None se spettro piatto)
+
 Feature accessibili dal tuo genoma: {feature_access}.
 Lookback massimo che puoi usare nel ragionamento: {lookback_window} barre.
 
-Genera una strategia che cerchi anomalie sfruttabili in questo regime.
+{instruction}
 """
 
 
+def _render_focus_block(keys: list[str], market: MarketSummary) -> str:
+    """Renderizza 'Focus per la tua lente' come riga del USER_TEMPLATE.
+
+    Mappa nomi simbolici a valori della MarketSummary. Skippa silenziosamente
+    chiavi sconosciute (fault-tolerant per evoluzione futura).
+    """
+    field_map: dict[str, Any] = {
+        # Statistiche base
+        "mean": market.return_mean,
+        "std": market.return_std,
+        "skew": market.skew,
+        "kurt": market.kurtosis,
+        "vol_regime": market.volatility_regime,
+        # Regime recente
+        "autocorr_recent": market.autocorr_lag1_recent,
+        "autocorr_baseline": market.autocorr_lag1_baseline,
+        "hurst": market.hurst_recent,
+        "vol_pct": market.vol_percentile,
+        "seasonality_hour": market.seasonality_hour,
+        "seasonality_dow": market.seasonality_dow,
+        # Geometria/frattali
+        "efficiency_ratio": market.efficiency_ratio,
+        "tail_left": market.tail_index_left,
+        "tail_right": market.tail_index_right,
+        "structural_uptrend": market.structural_uptrend,
+        "compression": market.compression,
+        "spectral_entropy": market.spectral_entropy,
+        "dominant_cycle": market.dominant_cycle,
+    }
+    parts: list[str] = []
+    for k in keys:
+        if k not in field_map:
+            continue
+        v = field_map[k]
+        if v is None:
+            parts.append(f"{k}=N/A")
+        elif isinstance(v, float):
+            parts.append(f"{k}={v:.3f}")
+        else:
+            parts.append(f"{k}={v}")
+    if not parts:
+        return ""
+    return "\nFocus per la tua lente: " + ", ".join(parts) + "\n"
+
+
 _RETRY_TEMPLATE = """\
 {original_user}
 
@@ -260,20 +365,34 @@ def _try_parse(text: str) -> tuple[Strategy | None, str | None]:
 
 
 class HypothesisAgent:
-    def __init__(self, llm: LLMClient, max_retries: int = 1):
+    def __init__(
+        self,
+        llm: LLMClient,
+        max_retries: int = 1,
+        prompt_library: PromptLibrary | None = None,
+    ):
         if max_retries < 0:
             raise ValueError("max_retries must be >= 0")
         self._llm = llm
         self._max_retries = max_retries
+        self._prompt_library = prompt_library or PromptLibrary.default()
 
     def propose(
         self,
         genome: HypothesisAgentGenome,
         market: MarketSummary,
     ) -> HypothesisProposal:
-        system = SYSTEM_TEMPLATE.format(
-            cognitive_style=genome.cognitive_style,
-            system_prompt=genome.system_prompt,
+        system = _build_system_prompt(self._prompt_library, genome)
+        dominant_cycle_str = (
+            f"{market.dominant_cycle:.0f} barre"
+            if market.dominant_cycle is not None
+            else "N/A (spettro piatto)"
+        )
+        focus_keys = self._prompt_library.focus_metrics_for(genome.cognitive_style)
+        focus_block = _render_focus_block(focus_keys, market) if focus_keys else ""
+        instruction = (
+            self._prompt_library.instruction
+            or "Genera una strategia che cerchi anomalie sfruttabili in questo regime."
         )
         original_user = USER_TEMPLATE.format(
             symbol=market.symbol,
@@ -284,9 +403,23 @@ class HypothesisAgent:
             skew=market.skew,
             kurtosis=market.kurtosis,
             volatility_regime=market.volatility_regime,
+            autocorr_lag1_recent=market.autocorr_lag1_recent,
+            autocorr_lag1_baseline=market.autocorr_lag1_baseline,
+            hurst_recent=market.hurst_recent,
+            vol_percentile=market.vol_percentile,
+            seasonality_hour=market.seasonality_hour,
+            seasonality_dow=market.seasonality_dow,
+            efficiency_ratio=market.efficiency_ratio,
+            tail_index_left=market.tail_index_left,
+            tail_index_right=market.tail_index_right,
+            structural_uptrend=market.structural_uptrend,
+            compression=market.compression,
+            spectral_entropy=market.spectral_entropy,
+            dominant_cycle_str=dominant_cycle_str,
             feature_access=", ".join(genome.feature_access),
             lookback_window=genome.lookback_window,
-        )
+            instruction=instruction,
+        ) + focus_block
 
         completions: list[CompletionResult] = []
         errors: list[str] = []

src/multi_swarm_core/multi_swarm_core/agents/market_summary.py

@@ -3,6 +3,17 @@ from __future__ import annotations
 import pandas as pd  # type: ignore[import-untyped]
 from scipy import stats  # type: ignore[import-untyped]
 
+from ..metrics.basic import (
+    autocorr_lag1,
+    compression_ratio,
+    efficiency_ratio_kaufman,
+    hurst_exponent,
+    seasonality_strength,
+    spectral_entropy_and_cycle,
+    structural_uptrend_score,
+    tail_index_hill,
+    vol_percentile_historical,
+)
 from .hypothesis import MarketSummary
 
 
@@ -24,6 +35,23 @@ def build_market_summary(
     else:
         regime = "high"
 
+    recent_window = min(500, len(returns))
+    recent_returns = returns.iloc[-recent_window:]
+
+    autocorr_recent = autocorr_lag1(recent_returns)
+    autocorr_baseline = autocorr_lag1(returns)
+    hurst_r = hurst_exponent(recent_returns)
+    vol_pct = vol_percentile_historical(returns, current_window=24, ref_window=2000)
+    season_h = seasonality_strength(returns, by="hour")
+    season_d = seasonality_strength(returns, by="dow")
+
+    eff_ratio = efficiency_ratio_kaufman(ohlcv["close"], length=100)
+    tail_l = tail_index_hill(returns, side="left")
+    tail_r = tail_index_hill(returns, side="right")
+    hh_hl = structural_uptrend_score(ohlcv["close"], window=5)
+    compress = compression_ratio(ohlcv["close"], recent_window=50, ref_window=200)
+    spec_entropy, dom_cycle = spectral_entropy_and_cycle(returns, length=256)
+
     return MarketSummary(
         symbol=symbol,
         timeframe=timeframe,
@@ -33,4 +61,17 @@ def build_market_summary(
         skew=skew,
         kurtosis=kurt,
         volatility_regime=regime,
+        autocorr_lag1_recent=autocorr_recent,
+        autocorr_lag1_baseline=autocorr_baseline,
+        hurst_recent=hurst_r,
+        vol_percentile=vol_pct,
+        seasonality_hour=season_h,
+        seasonality_dow=season_d,
+        efficiency_ratio=eff_ratio,
+        tail_index_left=tail_l,
+        tail_index_right=tail_r,
+        structural_uptrend=hh_hl,
+        compression=compress,
+        spectral_entropy=spec_entropy,
+        dominant_cycle=dom_cycle,
     )

src/multi_swarm_core/multi_swarm_core/backtest/engine.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 from dataclasses import dataclass
 
+import numpy as np
 import pandas as pd  # type: ignore[import-untyped]
 
 from .orders import Position, Side, Trade
@@ -28,74 +29,110 @@ class BacktestEngine:
         self.fees_bp = fees_bp
 
     def run(self, ohlcv: pd.DataFrame, signals: pd.Series) -> BacktestResult:
+        n = len(ohlcv)
+        if n == 0:
+            empty = pd.Series([], dtype=float)
+            return BacktestResult(equity_curve=empty, returns=empty, trades=[])
+
         signals = signals.reindex(ohlcv.index).ffill().fillna(Side.FLAT)
-
         # Esecuzione con delay 1: segnale a t-1 esegue a open di t.
-        shifted = [Side.FLAT, *list(signals.iloc[:-1])]
-        executed_side = pd.Series(shifted, index=ohlcv.index, dtype=object)
+        executed = pd.Series(
+            [Side.FLAT, *list(signals.iloc[:-1])],
+            index=ohlcv.index,
+            dtype=object,
+        )
+
+        # Codifica side in int per vectorizzazione: 0=FLAT, +1=LONG, -1=SHORT.
+        side_code = np.where(
+            executed.values == Side.LONG, 1,
+            np.where(executed.values == Side.SHORT, -1, 0),
+        ).astype(np.int8)
+        opens = ohlcv["open"].to_numpy(dtype=np.float64)
+        closes = ohlcv["close"].to_numpy(dtype=np.float64)
+        ts_index = ohlcv.index
+
+        # Identifica transizioni: punto in cui side[i] != side[i-1] (con side[-1]=0).
+        prev = np.concatenate(([0], side_code[:-1]))
+        change = side_code != prev
+
+        # Indici di entry (cambio verso side != 0).
+        entry_idxs = np.flatnonzero(change & (side_code != 0))
+        # Indici di chiusura: per ogni entry, il prossimo indice dove side[i] != side_entry.
+        # Vectorized: per ogni entry_idx, cerca change & side != side_entry oltre l'entry.
 
-        position: Position | None = None
-        position_entry_ts: pd.Timestamp | None = None
         trades: list[Trade] = []
-        equity = 0.0
-        equity_history: list[float] = []
-        returns_history: list[float] = []
-        prev_equity = 0.0
+        # realized_pnl[t]: PnL netto cumulato dopo le chiusure avvenute a OPEN di t.
+        realized_pnl = np.zeros(n, dtype=np.float64)
+        fees_rate = self.fees_bp / 10000.0
+        size = 1.0
 
-        for ts, row in ohlcv.iterrows():
-            target_side = executed_side.loc[ts]
-            current_side = position.side if position else Side.FLAT
+        # Posizione corrente all'inizio di ogni indice t (prima di applicare il transitorio):
+        # used per MtM computation. open_side_at_t / open_entry_at_t.
+        open_side = np.zeros(n, dtype=np.int8)
+        open_entry = np.zeros(n, dtype=np.float64)
 
-            if target_side != current_side:
-                if position is not None:
-                    assert position_entry_ts is not None
-                    trade = Trade(
-                        entry_ts=position_entry_ts,
-                        exit_ts=ts,
-                        side=position.side,
-                        size=position.size,
-                        entry_price=position.entry_price,
-                        exit_price=float(row["open"]),
-                        fees_bp=self.fees_bp,
-                    )
-                    trades.append(trade)
-                    equity += trade.net_pnl
-                    position = None
-                    position_entry_ts = None
-                if target_side in (Side.LONG, Side.SHORT):
-                    position = Position(
-                        side=target_side, entry_price=float(row["open"]), size=1.0
-                    )
-                    position_entry_ts = ts
+        for entry_idx in entry_idxs:
+            entry_side = int(side_code[entry_idx])
+            entry_price = opens[entry_idx]
+            # Cerca exit: primo indice > entry_idx dove side differisce.
+            after = side_code[entry_idx + 1:]
+            rel = np.flatnonzero(after != entry_side)
+            if rel.size > 0:
+                exit_idx = entry_idx + 1 + int(rel[0])
+                exit_price = opens[exit_idx]
+                exit_ts = ts_index[exit_idx]
+                gross = entry_side * (exit_price - entry_price) * size
+                fees = fees_rate * size * (entry_price + exit_price)
+                net = gross - fees
+                # La chiusura avviene a open[exit_idx]: dal bar exit_idx in poi il
+                # PnL e' realizzato (non piu' MtM).
+                realized_pnl[exit_idx:] += net
+                # Posizione aperta in [entry_idx, exit_idx-1].
+                open_side[entry_idx:exit_idx] = entry_side
+                open_entry[entry_idx:exit_idx] = entry_price
+                trades.append(Trade(
+                    entry_ts=ts_index[entry_idx],
+                    exit_ts=exit_ts,
+                    side=Side.LONG if entry_side == 1 else Side.SHORT,
+                    size=size,
+                    entry_price=entry_price,
+                    exit_price=exit_price,
+                    fees_bp=self.fees_bp,
+                ))
+            else:
+                # Ultima posizione ancora aperta: chiusura forced a close[-1].
+                # Parita' col loop legacy: MtM su [entry_idx, n-1), realized totale
+                # SOLO al bar n-1 (legacy fa equity_history[-1] = equity).
+                last_close = closes[-1]
+                gross = entry_side * (last_close - entry_price) * size
+                fees = fees_rate * size * (entry_price + last_close)
+                net = gross - fees
+                if entry_idx < n - 1:
+                    open_side[entry_idx:n - 1] = entry_side
+                    open_entry[entry_idx:n - 1] = entry_price
+                realized_pnl[-1] += net
+                trades.append(Trade(
+                    entry_ts=ts_index[entry_idx],
+                    exit_ts=ts_index[-1],
+                    side=Side.LONG if entry_side == 1 else Side.SHORT,
+                    size=size,
+                    entry_price=entry_price,
+                    exit_price=last_close,
+                    fees_bp=self.fees_bp,
+                ))
 
-            mark = float(row["close"])
-            mtm = position.unrealized_pnl(mark) if position else 0.0
-            current_equity = equity + mtm
-            equity_history.append(current_equity)
-            returns_history.append(current_equity - prev_equity)
-            prev_equity = current_equity
-
-        if position is not None:
-            assert position_entry_ts is not None
-            last_ts = ohlcv.index[-1]
-            last_close = float(ohlcv["close"].iloc[-1])
-            trade = Trade(
-                entry_ts=position_entry_ts,
-                exit_ts=last_ts,
-                side=position.side,
-                size=position.size,
-                entry_price=position.entry_price,
-                exit_price=last_close,
-                fees_bp=self.fees_bp,
-            )
-            trades.append(trade)
-            equity += trade.net_pnl
-            equity_history[-1] = equity
-            if len(returns_history) >= 2:
-                returns_history[-1] = equity - equity_history[-2]
+        # MtM unrealized per ogni bar in cui c'e' una posizione aperta.
+        mtm = open_side.astype(np.float64) * (closes - open_entry) * size
+        equity_arr = realized_pnl + mtm
+        # Returns = first diff dell'equity (col loop legacy il primo bar e' equity[0]-0).
+        returns_arr = np.concatenate(([equity_arr[0]], np.diff(equity_arr)))
 
         return BacktestResult(
-            equity_curve=pd.Series(equity_history, index=ohlcv.index, name="equity"),
-            returns=pd.Series(returns_history, index=ohlcv.index, name="returns"),
+            equity_curve=pd.Series(equity_arr, index=ts_index, name="equity"),
+            returns=pd.Series(returns_arr, index=ts_index, name="returns"),
             trades=trades,
         )
+
+
+# Lo facade Position re-export e' tenuto per backward-compat con import legacy.
+__all__ = ["BacktestEngine", "BacktestResult", "Position", "Side", "Signal", "Trade"]

src/multi_swarm_core/multi_swarm_core/dashboard/data.py

@@ -0,0 +1,68 @@
+"""GA data access functions for the core dashboard.
+
+Reads exclusively from runs.db (GA tables).
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+import pandas as pd  # type: ignore[import-untyped]
+
+from multi_swarm_core.persistence.repository import Repository
+
+__all__ = [
+    "get_repo",
+    "list_runs_df",
+    "get_run_overview",
+    "generations_df",
+    "evaluations_df",
+    "genomes_df",
+]
+
+
+def get_repo(db_path: str | Path) -> Repository:
+    return Repository(db_path=db_path)
+
+
+def list_runs_df(repo: Repository) -> pd.DataFrame:
+    return pd.DataFrame(repo.list_runs())
+
+
+def get_run_overview(repo: Repository, run_id: str) -> dict[str, Any]:
+    run = repo.get_run(run_id)
+    return {
+        "name": run["name"],
+        "started_at": run["started_at"],
+        "completed_at": run["completed_at"],
+        "status": run["status"],
+        "total_cost_usd": run["total_cost_usd"],
+        "config": json.loads(run["config_json"]),
+    }
+
+
+def generations_df(repo: Repository, run_id: str) -> pd.DataFrame:
+    return pd.DataFrame(repo.list_generations(run_id))
+
+
+def evaluations_df(repo: Repository, run_id: str) -> pd.DataFrame:
+    return pd.DataFrame(repo.list_evaluations(run_id))
+
+
+def genomes_df(
+    repo: Repository, run_id: str, generation_idx: int | None = None
+) -> pd.DataFrame:
+    rows = repo.list_genomes(run_id, generation_idx)
+    flat: list[dict[str, Any]] = []
+    for r in rows:
+        payload = json.loads(r["payload_json"])
+        flat.append(
+            {
+                "id": r["id"],
+                "generation_idx": r["generation_idx"],
+                **payload,
+            }
+        )
+    return pd.DataFrame(flat)

src/multi_swarm_core/multi_swarm_core/dashboard/nicegui_app.py

@@ -0,0 +1,560 @@
+"""Multi-Swarm Core Dashboard — GA pages: /, /convergence, /genomes.
+
+Avvio: ``uv run python -m multi_swarm_core.dashboard.nicegui_app``
+Legge SOLO runs.db (tabelle GA).
+"""
+
+from __future__ import annotations
+
+import html
+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_core.dashboard.data import (
+    evaluations_df,
+    generations_df,
+    genomes_df,
+    get_repo,
+    get_run_overview,
+    list_runs_df,
+)
+from multi_swarm_core.dashboard.theme import (
+    COLOR_ACCENT,
+    COLOR_PRIMARY,
+    COLOR_SECONDARY,
+    COLOR_SURFACE,
+    COLOR_TEXT,
+    COLOR_TEXT_MUTED,
+    _STATUS_BADGE,
+    _apply_theme,
+    _build_header,
+    _json_to_html,
+)
+
+GA_DB_PATH = os.environ.get("GA_DB_PATH", "./state/runs.db")
+DASHBOARD_ROOT_PATH = os.environ.get("DASHBOARD_ROOT_PATH", "")
+REFRESH_INTERVAL_S = 3.0
+
+
+def _runs_options() -> dict[str, str]:
+    repo = get_repo(GA_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(GA_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
+    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="/",
+        brand_subtitle="Coevolutivo / GA",
+        nav_items=[("/", "Overview"), ("/convergence", "Convergence"), ("/genomes", "Genomes")],
+        db_label=f"⛁ {Path(GA_DB_PATH).resolve().name}",
+    )
+
+    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",
+        brand_subtitle="Coevolutivo / GA",
+        nav_items=[("/", "Overview"), ("/convergence", "Convergence"), ("/genomes", "Genomes")],
+        db_label=f"⛁ {Path(GA_DB_PATH).resolve().name}",
+    )
+
+    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(GA_DB_PATH), state["run_id"]))).classes("w-full")
+    entropy_plot = ui.plotly(_entropy_figure(generations_df(get_repo(GA_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(GA_DB_PATH), run_id)
+            ov = get_run_overview(get_repo(GA_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",
+        brand_subtitle="Coevolutivo / GA",
+        nav_items=[("/", "Overview"), ("/convergence", "Convergence"), ("/genomes", "Genomes")],
+        db_label=f"⛁ {Path(GA_DB_PATH).resolve().name}",
+    )
+
+    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(GA_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:
+        rows = (e.args or {}).get("rows") or []
+        if not rows:
+            return
+        full_id = rows[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"GA DB: {Path(GA_DB_PATH).resolve()} | root_path: {DASHBOARD_ROOT_PATH or '/'}"
+        )
+    )
+    ui.run(
+        host="0.0.0.0",
+        port=int(os.environ.get("SWARM_DASHBOARD_PORT", "8080")),
+        title="Multi-Swarm Core Dashboard",
+        reload=False,
+        show=False,
+        dark=True,
+        root_path=DASHBOARD_ROOT_PATH,
+    )
+
+
+if __name__ in {"__main__", "__mp_main__"}:
+    main()

src/multi_swarm_core/multi_swarm_core/dashboard/theme.py

@@ -0,0 +1,383 @@
+"""Shared theme module for NiceGUI dashboards.
+
+Exports palette constants, CSS, and helper functions used by both
+multi_swarm_core.dashboard and strategy_crypto.frontend dashboards.
+"""
+
+from __future__ import annotations
+
+import html
+from typing import Any
+
+from nicegui import ui
+
+__all__ = [
+    "COLOR_BG",
+    "COLOR_SURFACE",
+    "COLOR_SURFACE_2",
+    "COLOR_BORDER",
+    "COLOR_BORDER_HOVER",
+    "COLOR_PRIMARY",
+    "COLOR_SECONDARY",
+    "COLOR_ACCENT",
+    "COLOR_SUCCESS",
+    "COLOR_DANGER",
+    "COLOR_TEXT",
+    "COLOR_TEXT_MUTED",
+    "_STATUS_BADGE",
+    "_CUSTOM_CSS",
+    "_json_to_html",
+    "_apply_theme",
+    "_build_header",
+]
+
+# --- 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,
+    brand_subtitle: str,
+    nav_items: list[tuple[str, str]],
+    db_label: str,
+) -> None:
+    """Render the top navigation header.
+
+    Args:
+        active: URL path of the currently active page (e.g. "/").
+        brand_subtitle: Text shown after the brand dot, e.g. "Coevolutivo / GA".
+        nav_items: List of (path, label) tuples for nav links.
+        db_label: Short DB identifier shown in the top-right corner.
+    """
+    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(
+                    f'<span class="brand">Multi-Swarm <span style="color:{COLOR_TEXT_MUTED}'
+                    f';font-weight:400;">/ {brand_subtitle}</span></span>'
+                )
+            with ui.row().classes("items-center gap-1"):
+                for path, label in nav_items:
+                    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;">{db_label}</span>'
+            )

src/multi_swarm_core/multi_swarm_core/ga/initial.py

@@ -3,34 +3,12 @@ from __future__ import annotations
 import random
 
 from ..genome.hypothesis import HypothesisAgentGenome, ModelTier
-from ..genome.mutation import COGNITIVE_STYLES
+from ..genome.prompt_library import PromptLibrary
 
-STYLE_PROMPTS: dict[str, str] = {
-    "physicist": (
-        "Cerca leggi conservative, simmetrie, regimi di scala. "
-        "Pensa in termini di flussi e potenziali."
-    ),
-    "biologist": (
-        "Cerca pattern adattivi, nicchie ecologiche, "
-        "predator-prey dynamics tra partecipanti del mercato."
-    ),
-    "historian": (
-        "Cerca pattern ricorrenti su scale temporali multiple, "
-        "analogie con regimi storici, mean reversion strutturali."
-    ),
-    "meteorologist": (
-        "Cerca regimi di volatilità che si autoalimentano, "
-        "transizioni di stato come fronti, persistenza locale."
-    ),
-    "ecologist": (
-        "Cerca interazioni multi-asset, correlazioni cluster, "
-        "segnali di stress sistemico nelle dinamiche di flusso."
-    ),
-    "engineer": (
-        "Cerca segnali con rapporto S/N favorevole, filtri causali, "
-        "robustezza a perturbazioni di calibrazione."
-    ),
-}
+# Mantenuto come alias backcompat: equivalente a PromptLibrary.default().styles.
+# Nuovi caller dovrebbero usare PromptLibrary direttamente per supportare
+# l'override via prompts.json di una strategia.
+STYLE_PROMPTS: dict[str, str] = PromptLibrary.default().styles
 
 
 def build_initial_population(
@@ -38,15 +16,22 @@ def build_initial_population(
     model_tier: ModelTier,
     rng: random.Random,
     feature_pool: tuple[str, ...] = ("close", "high", "low", "volume"),
+    prompt_library: PromptLibrary | None = None,
 ) -> list[HypothesisAgentGenome]:
-    """Costruisce una popolazione iniziale K varia per stile cognitivo + parametri."""
+    """Costruisce una popolazione iniziale K varia per stile cognitivo + parametri.
+
+    ``prompt_library`` controlla quali stili sono disponibili e quale system_prompt
+    iniziale viene assegnato. Default = builtin 6 stili (physicist, biologist, ...).
+    Override tipico: ``PromptLibrary.from_json(strategy_crypto/prompts.json)``.
+    """
+    lib = prompt_library or PromptLibrary.default()
     population: list[HypothesisAgentGenome] = []
     for i in range(k):
-        style = COGNITIVE_STYLES[i % len(COGNITIVE_STYLES)]
+        style = lib.style_at(i)
         n_features = rng.randint(1, len(feature_pool))
         feats = sorted(rng.sample(feature_pool, k=n_features))
         g = HypothesisAgentGenome(
-            system_prompt=STYLE_PROMPTS[style],
+            system_prompt=lib.directive(style),
             feature_access=feats,
             temperature=round(rng.uniform(0.7, 1.2), 2),
             top_p=0.95,

src/multi_swarm_core/multi_swarm_core/genome/mutation.py

@@ -7,6 +7,10 @@ from .hypothesis import HypothesisAgentGenome
 
 FEATURE_POOL: tuple[str, ...] = ("open", "high", "low", "close", "volume")
 
+# Lista di default builtin (allineata con PromptLibrary.default()).
+# Il dispatcher run_phase1 sovrascrive `COGNITIVE_STYLES` con la lista letta
+# da prompts.json prima del loop GA, cosi' `mutate_cognitive_style` pesca
+# dai candidati corretti per la strategia in corso.
 COGNITIVE_STYLES: tuple[str, ...] = (
     "physicist",
     "biologist",
@@ -17,6 +21,18 @@ COGNITIVE_STYLES: tuple[str, ...] = (
 )
 
 
+def set_cognitive_styles(styles: tuple[str, ...]) -> None:
+    """Sovrascrive la lista globale di stili candidati per la mutation.
+
+    Da chiamare PRIMA del GA loop (es. in run_phase1 dopo aver caricato la
+    PromptLibrary). Non thread-safe: pensata per uno script CLI.
+    """
+    global COGNITIVE_STYLES
+    if not styles:
+        raise ValueError("set_cognitive_styles: lista vuota")
+    COGNITIVE_STYLES = tuple(styles)
+
+
 def _clone_with(g: HypothesisAgentGenome, **overrides: Any) -> HypothesisAgentGenome:
     payload: dict[str, Any] = g.to_dict()
     payload.update(overrides)

src/multi_swarm_core/multi_swarm_core/genome/mutation_prompt_llm.py

@@ -51,9 +51,9 @@ MUTATION_INSTRUCTIONS: dict[str, str] = {
 
 # Keyword tecniche minime per validare che il prompt sia ancora "una strategia".
 _VALID_KEYWORDS = (
-    "rsi", "sma", "ema", "atr", "atr_pct", "realized_vol",
+    "rsi", "sma", "sma_pct", "ema", "atr", "atr_pct", "realized_vol",
     "momentum", "breakout", "mean", "reversion",
-    "macd", "vwap", "bb", "bollinger", "stoch", "trend", "signal", "buy",
+    "macd", "macd_pct", "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",

src/multi_swarm_core/multi_swarm_core/genome/prompt_library.py

@@ -0,0 +1,234 @@
+"""Libreria di stili cognitivi + direttive system_prompt per il GA.
+
+Carica da un file JSON esterno (tipicamente shippato dal singolo strategy
+member, es. ``strategy_crypto/prompts.json``) le coppie ``style -> directive``
+usate da:
+  - :func:`multi_swarm_core.ga.initial.build_initial_population` per il
+    bootstrap della popolazione (style assegnato round-robin, directive
+    come system_prompt iniziale).
+  - :func:`multi_swarm_core.genome.mutation.mutate_cognitive_style` per
+    pescare i candidati di mutazione (range = key del JSON).
+
+Schema JSON atteso::
+
+  {
+    "styles": {
+      "<name>": {"directive": "<testo system_prompt>"},
+      ...
+    },
+    "agent_role": "<descrizione agente, opzionale>",
+    "pattern_guidance": "<sezione PATTERN GUIDANCE, opzionale>",
+    "instruction": "<frase finale USER, opzionale>",
+    "domain_warnings": "<warning di dominio, opzionale>"
+  }
+
+I 6 stili default (physicist, biologist, historian, meteorologist,
+ecologist, engineer) sono comunque disponibili via :meth:`PromptLibrary.default`
+per backcompat con test/script senza file esterno.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from pathlib import Path
+
+_DEFAULT_STYLES: dict[str, str] = {
+    "physicist": (
+        "Cerca leggi conservative, simmetrie, regimi di scala. "
+        "Pensa in termini di flussi e potenziali."
+    ),
+    "biologist": (
+        "Cerca pattern adattivi, nicchie ecologiche, "
+        "predator-prey dynamics tra partecipanti del mercato."
+    ),
+    "historian": (
+        "Cerca pattern ricorrenti su scale temporali multiple, "
+        "analogie con regimi storici, mean reversion strutturali."
+    ),
+    "meteorologist": (
+        "Cerca regimi di volatilità che si autoalimentano, "
+        "transizioni di stato come fronti, persistenza locale."
+    ),
+    "ecologist": (
+        "Cerca interazioni multi-asset, correlazioni cluster, "
+        "segnali di stress sistemico nelle dinamiche di flusso."
+    ),
+    "engineer": (
+        "Cerca segnali con rapporto S/N favorevole, filtri causali, "
+        "robustezza a perturbazioni di calibrazione."
+    ),
+}
+
+
+class PromptLibraryError(ValueError):
+    """Sollevata su JSON malformato o stili invalid."""
+
+
+_DEFAULT_PATTERN_GUIDANCE = (
+    "Forme di curva: trend (SMA cross), compressione volatilita (atr_pct basso), "
+    "espansione volatilita (atr_pct alto), mean reversion (sma_pct estremo), "
+    "momentum (macd_pct, rsi zone).\n\n"
+    "  Ripetibilita dell'andamento: crossover/crossunder ricorrenti, pattern intra-day "
+    "(hour gate), pattern settimanali (dow/is_weekend), range breakout."
+)
+
+_DEFAULT_AGENT_ROLE = (
+    "Sei un agente generatore di ipotesi di trading quantitativo per un sistema swarm."
+)
+
+_DEFAULT_INSTRUCTION = (
+    "Genera una strategia che cerchi anomalie sfruttabili in questo regime."
+)
+
+
+@dataclass(frozen=True)
+class PromptLibrary:
+    """Set immutabile di stili cognitivi + direttive system_prompt.
+
+    v3.0: aggiunge 4 campi top-level strategy-specific iniettabili nel prompt
+    dal compositor ``_build_system_prompt``. Tutti opzionali con default sensati
+    per backcompat.
+    v3.1: aggiunge anti_patterns e output_priorities iniettati dopo i VINCOLI core.
+    """
+
+    styles: dict[str, str]
+    focus: dict[str, list[str]]  # style -> lista metriche prioritarie
+    # NEW v3.0: contenuto strategy-specific iniettabile nel prompt
+    agent_role: str = field(default="")       # header SYSTEM, descrive chi e' l'agente
+    pattern_guidance: str = field(default="") # sezione PATTERN GUIDANCE del SYSTEM
+    instruction: str = field(default="")      # frase finale USER ("Genera una strategia...")
+    domain_warnings: str = field(default="")  # opzionale: warning di dominio (es. crypto 24/7)
+    anti_patterns: str = field(default="")       # NEW v3.1: lista esplicita di pattern da evitare
+    output_priorities: str = field(default="")   # NEW v3.1: trade-off espliciti di output
+
+    def __post_init__(self) -> None:
+        if not self.styles:
+            raise PromptLibraryError("PromptLibrary deve avere almeno uno stile")
+        for name, directive in self.styles.items():
+            if not isinstance(name, str) or not name.strip():
+                raise PromptLibraryError(f"nome stile invalido: {name!r}")
+            if not isinstance(directive, str) or not directive.strip():
+                raise PromptLibraryError(
+                    f"directive vuota o invalida per stile {name!r}"
+                )
+        # Validate new optional string fields: if provided must be non-whitespace
+        for field_name, value in (
+            ("agent_role", self.agent_role),
+            ("pattern_guidance", self.pattern_guidance),
+            ("instruction", self.instruction),
+            ("domain_warnings", self.domain_warnings),
+            ("anti_patterns", self.anti_patterns),
+            ("output_priorities", self.output_priorities),
+        ):
+            if not isinstance(value, str):
+                raise PromptLibraryError(
+                    f"campo '{field_name}' deve essere stringa, non {type(value)}"
+                )
+            if value and not value.strip():
+                raise PromptLibraryError(
+                    f"campo '{field_name}' fornito ma contiene solo whitespace"
+                )
+
+    @classmethod
+    def default(cls) -> PromptLibrary:
+        """Libreria builtin con i 6 stili originali (fallback senza file)."""
+        return cls(
+            styles=dict(_DEFAULT_STYLES),
+            focus={},
+            agent_role=_DEFAULT_AGENT_ROLE,
+            pattern_guidance=_DEFAULT_PATTERN_GUIDANCE,
+            instruction=_DEFAULT_INSTRUCTION,
+            domain_warnings="",
+            anti_patterns="",
+            output_priorities="",
+        )
+
+    @classmethod
+    def from_json(cls, path: Path | str) -> PromptLibrary:
+        """Carica da file JSON.
+
+        Schema::
+
+          {
+            "styles": {<name>: {"directive": "...", "focus_metrics": [...]}},
+            "agent_role": "...",          // opzionale
+            "pattern_guidance": "...",    // opzionale
+            "instruction": "...",         // opzionale
+            "domain_warnings": "..."      // opzionale
+          }
+        """
+        p = Path(path)
+        try:
+            data = json.loads(p.read_text(encoding="utf-8"))
+        except FileNotFoundError as e:
+            raise PromptLibraryError(f"file non trovato: {p}") from e
+        except json.JSONDecodeError as e:
+            raise PromptLibraryError(f"JSON malformato in {p}: {e}") from e
+
+        if not isinstance(data, dict):
+            raise PromptLibraryError(f"root JSON deve essere un object, non {type(data)}")
+        styles_raw = data.get("styles")
+        if not isinstance(styles_raw, dict):
+            raise PromptLibraryError("manca chiave 'styles' (object) nel JSON")
+
+        styles: dict[str, str] = {}
+        focus: dict[str, list[str]] = {}
+        for name, entry in styles_raw.items():
+            if not isinstance(entry, dict):
+                raise PromptLibraryError(
+                    f"entry per stile {name!r} deve essere object, non {type(entry)}"
+                )
+            directive = entry.get("directive")
+            if not isinstance(directive, str):
+                raise PromptLibraryError(
+                    f"manca 'directive' (string) per stile {name!r}"
+                )
+            styles[name] = directive
+            fm = entry.get("focus_metrics", [])
+            if not isinstance(fm, list):
+                raise PromptLibraryError(
+                    f"focus_metrics di {name!r} deve essere lista, non {type(fm)}"
+                )
+            focus[name] = [str(x) for x in fm]
+
+        # Parse new optional top-level fields (v3.0)
+        agent_role = data.get("agent_role", "")
+        pattern_guidance = data.get("pattern_guidance", "")
+        instruction = data.get("instruction", "")
+        domain_warnings = data.get("domain_warnings", "")
+        # Parse new optional top-level fields (v3.1)
+        anti_patterns_raw = data.get("anti_patterns", "")
+        output_priorities_raw = data.get("output_priorities", "")
+        if not isinstance(anti_patterns_raw, str):
+            raise PromptLibraryError(f"anti_patterns deve essere stringa, non {type(anti_patterns_raw)}")
+        if not isinstance(output_priorities_raw, str):
+            raise PromptLibraryError(f"output_priorities deve essere stringa, non {type(output_priorities_raw)}")
+
+        return cls(
+            styles=styles,
+            focus=focus,
+            agent_role=agent_role,
+            pattern_guidance=pattern_guidance,
+            instruction=instruction,
+            domain_warnings=domain_warnings,
+            anti_patterns=anti_patterns_raw,
+            output_priorities=output_priorities_raw,
+        )
+
+    @property
+    def cognitive_styles(self) -> tuple[str, ...]:
+        """Tupla immutabile dei nomi degli stili, nell'ordine di insertion del JSON."""
+        return tuple(self.styles)
+
+    def directive(self, style: str) -> str:
+        """Ritorna la directive di ``style`` o solleva KeyError."""
+        return self.styles[style]
+
+    def style_at(self, index: int) -> str:
+        """Round-robin: ``cognitive_styles[index % len()]``."""
+        return self.cognitive_styles[index % len(self.cognitive_styles)]
+
+    def focus_metrics_for(self, style: str) -> list[str]:
+        """Ritorna lista delle metriche prioritarie per ``style``. Empty list se non specificate."""
+        return self.focus.get(style, [])

src/multi_swarm_core/multi_swarm_core/metrics/basic.py

@@ -4,6 +4,122 @@ import numpy as np
 import pandas as pd  # type: ignore[import-untyped]
 
 
+def autocorr_lag1(returns: pd.Series) -> float:
+    """Autocorrelazione dei ritorni con lag 1 (correlazione di Pearson)."""
+    if len(returns) < 2:
+        return 0.0
+    val = returns.autocorr(lag=1)
+    return float(val) if pd.notna(val) else 0.0
+
+
+def hurst_exponent(returns: pd.Series) -> float:
+    """Hurst exponent via R/S analysis (rescaled range).
+
+    Range 0-1: 0.5 random walk, >0.55 trending persistente, <0.45 mean reverting.
+    Implementazione classica con scale multiple (2^k bins).
+    """
+    n = len(returns)
+    if n < 100:
+        return 0.5  # insufficiente dati, ritorna random-walk default
+
+    series = returns.dropna().values
+    n = len(series)
+    if n < 100:
+        return 0.5
+
+    # Scale: 2^k che dividono n in segmenti
+    scales = [2**k for k in range(4, int(np.log2(n // 2)) + 1)]
+    if not scales:
+        return 0.5
+
+    rs_values: list[float] = []
+    for scale in scales:
+        n_chunks = n // scale
+        if n_chunks < 1:
+            continue
+        rs_chunk: list[float] = []
+        for i in range(n_chunks):
+            chunk = series[i * scale : (i + 1) * scale]
+            mean = chunk.mean()
+            cumdev = (chunk - mean).cumsum()
+            r = cumdev.max() - cumdev.min()
+            s = chunk.std()
+            if s > 0:
+                rs_chunk.append(r / s)
+        if rs_chunk:
+            rs_values.append(float(np.mean(rs_chunk)))
+
+    if len(rs_values) < 2:
+        return 0.5
+
+    log_scales = np.log(scales[: len(rs_values)])
+    log_rs = np.log(rs_values)
+    # Hurst = slope della regressione log-log
+    h, _ = np.polyfit(log_scales, log_rs, 1)
+    return float(np.clip(h, 0.0, 1.0))
+
+
+def vol_percentile_historical(
+    returns: pd.Series,
+    current_window: int = 24,
+    ref_window: int = 2000,
+) -> float:
+    """Percentile (0-100) della vol corrente nella distribuzione storica.
+
+    Vol = std rolling su current_window barre. Confronta l'ultimo valore contro
+    la distribuzione dei valori della stessa std rolling sugli ultimi ref_window.
+    Output: 0 (vol attuale tra le piu basse), 100 (tra le piu alte).
+    """
+    if len(returns) < max(current_window, 100):
+        return 50.0
+
+    rolling_vol = returns.rolling(current_window, min_periods=current_window).std()
+    rolling_vol = rolling_vol.dropna()
+    if len(rolling_vol) < 10:
+        return 50.0
+
+    # Limita ref_window all'effettiva disponibilita
+    ref = rolling_vol.iloc[-ref_window:] if len(rolling_vol) > ref_window else rolling_vol
+    current = float(rolling_vol.iloc[-1])
+    pct = float((ref < current).sum()) / len(ref) * 100.0
+    return pct
+
+
+def seasonality_strength(
+    returns: pd.Series,
+    by: str,
+) -> float:
+    """Frazione di varianza dei ritorni spiegata dalla feature temporale `by`.
+
+    `by` in {"hour", "dow"}. Output 0-1: 0 = no seasonality, 1 = tutta la varianza
+    e dovuta al ciclo. Calcolato come 1 - (var residua / var totale) usando i
+    gruppi indotti dalla feature.
+    """
+    if not isinstance(returns.index, pd.DatetimeIndex):
+        return 0.0
+    if len(returns) < 50:
+        return 0.0
+
+    if by == "hour":
+        groups = returns.index.hour
+    elif by == "dow":
+        groups = returns.index.dayofweek
+    else:
+        raise ValueError(f"by deve essere 'hour' o 'dow', non {by!r}")
+
+    total_var = float(returns.var())
+    if total_var <= 0:
+        return 0.0
+
+    grouped = returns.groupby(groups)
+    group_means = grouped.transform("mean")
+    residuals = returns - group_means
+    residual_var = float(residuals.var())
+
+    explained = 1.0 - (residual_var / total_var)
+    return float(np.clip(explained, 0.0, 1.0))
+
+
 def sharpe_ratio(returns: pd.Series, periods_per_year: int = 8760, rf: float = 0.0) -> float:
     """Sharpe annualizzato. periods_per_year=8760 per dati orari."""
     excess = returns - rf / periods_per_year
@@ -25,3 +141,135 @@ def total_return(equity: pd.Series) -> float:
     if equity.iloc[0] == 0:
         return float(equity.iloc[-1])
     return float(equity.iloc[-1] / equity.iloc[0] - 1.0)
+
+
+def efficiency_ratio_kaufman(prices: pd.Series, length: int = 100) -> float:
+    """Kaufman Efficiency Ratio: |net move| / sum(|step|) su rolling length.
+
+    Output 0-1: 0 = puro noise (movimento dissipativo), 1 = puro trend efficiente.
+    Discriminatore trending/ranging robusto.
+    """
+    if len(prices) < length + 1:
+        return 0.0
+    recent = prices.iloc[-length:]
+    net_move = abs(recent.iloc[-1] - recent.iloc[0])
+    total_move = recent.diff().abs().sum()
+    if total_move == 0 or pd.isna(total_move):
+        return 0.0
+    return float(net_move / total_move)
+
+
+def tail_index_hill(returns: pd.Series, side: str, top_frac: float = 0.05) -> float:
+    """Hill estimator del tail index (pendenza coda) per side in {'left', 'right'}.
+
+    Output: indice di pesantezza coda. Valori piu bassi = coda piu pesante.
+    <2 = varianza infinita (Cauchy-like), 3-4 = tipico crypto, >5 = quasi-Gaussiana.
+    Robusto al singolo outlier (vs kurtosis).
+    """
+    r = returns.dropna()
+    n = len(r)
+    if n < 50:
+        return 5.0  # default quasi-Gaussiano
+    k = max(int(n * top_frac), 5)
+    if side == "left":
+        tail = (-r).nlargest(k)
+    elif side == "right":
+        tail = r.nlargest(k)
+    else:
+        raise ValueError(f"side deve essere 'left' o 'right', non {side!r}")
+    # Filtra valori non-positivi (Hill richiede tail positiva)
+    tail = tail[tail > 0]
+    if len(tail) < 5:
+        return 5.0
+    log_tail = np.log(tail.values)
+    threshold = log_tail[-1]
+    excess = log_tail[:-1] - threshold
+    if excess.sum() <= 0:
+        return 5.0
+    hill = (len(excess)) / excess.sum()
+    return float(np.clip(hill, 1.0, 10.0))
+
+
+def structural_uptrend_score(prices: pd.Series, window: int = 5) -> float:
+    """Frazione di periodi in struttura HH/HL (Dow-style uptrend).
+
+    Identifica swing high/low usando rolling max/min con finestra ``window``.
+    Conta sequenze higher-high + higher-low (uptrend) vs lower-high + lower-low (downtrend).
+    Output: 0 (puro downtrend) - 0.5 (range/mixed) - 1 (puro uptrend).
+    """
+    if len(prices) < 4 * window:
+        return 0.5
+    swing_high = prices.rolling(2 * window + 1, center=True).max() == prices
+    swing_low = prices.rolling(2 * window + 1, center=True).min() == prices
+
+    highs = prices[swing_high].dropna()
+    lows = prices[swing_low].dropna()
+
+    if len(highs) < 3 or len(lows) < 3:
+        return 0.5
+
+    hh = (highs.diff() > 0).sum()
+    lh = (highs.diff() < 0).sum()
+    hl = (lows.diff() > 0).sum()
+    ll = (lows.diff() < 0).sum()
+
+    up_signals = hh + hl
+    down_signals = lh + ll
+    total = up_signals + down_signals
+    if total == 0:
+        return 0.5
+    return float(up_signals / total)
+
+
+def compression_ratio(prices: pd.Series, recent_window: int = 50, ref_window: int = 200) -> float:
+    """Range(recent) / Range(ref). <1 = compressione vol, >1 = espansione.
+
+    Range = high - low della finestra. Cattura il "coil" pre-breakout.
+    """
+    if len(prices) < ref_window:
+        return 1.0
+    recent = prices.iloc[-recent_window:]
+    ref = prices.iloc[-ref_window:]
+    recent_range = float(recent.max() - recent.min())
+    ref_range = float(ref.max() - ref.min())
+    if ref_range <= 0:
+        return 1.0
+    return recent_range / ref_range
+
+
+def spectral_entropy_and_cycle(
+    returns: pd.Series, length: int = 256
+) -> tuple[float, float | None]:
+    """FFT su returns -> (entropy normalizzata, dominant_cycle gated).
+
+    entropy: 0-1, Shannon entropy normalizzata dello spettro di potenza.
+      0 = una sola frequenza domina, 1 = spettro piatto (rumore bianco).
+    dominant_cycle: periodo (barre) della frequenza dominante,
+      MA solo se entropy < 0.6 (struttura presente). Altrimenti None.
+    """
+    r = returns.dropna()
+    if len(r) < length:
+        return 1.0, None
+    series = r.iloc[-length:].values
+    # Detrend
+    series = series - series.mean()
+    fft = np.fft.rfft(series)
+    power = np.abs(fft) ** 2
+    if power.sum() <= 0:
+        return 1.0, None
+    p = power / power.sum()
+    # Entropy normalizzata (Shannon / log(N))
+    nonzero = p[p > 0]
+    entropy = float(-(nonzero * np.log(nonzero)).sum() / np.log(len(nonzero)))
+    entropy = max(0.0, min(1.0, entropy))
+
+    if entropy >= 0.6:
+        return entropy, None
+    # Skip DC component (index 0)
+    if len(power) <= 1:
+        return entropy, None
+    peak_idx = int(np.argmax(power[1:])) + 1
+    if peak_idx == 0:
+        return entropy, None
+    cycle = float(length / peak_idx)
+    return entropy, cycle

src/multi_swarm_core/multi_swarm_core/orchestrator/run.py

@@ -13,6 +13,7 @@ possa leggere lo stato a run terminato (o in corso).
 from __future__ import annotations
 
 import random
+from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass, field
 from pathlib import Path
 
@@ -20,13 +21,15 @@ import pandas as pd  # type: ignore[import-untyped]
 
 from ..agents.adversarial import AdversarialAgent
 from ..agents.falsification import FalsificationAgent
-from ..agents.hypothesis import HypothesisAgent
+from ..agents.hypothesis import HypothesisAgent, HypothesisProposal, MarketSummary
 from ..agents.market_summary import build_market_summary
 from ..ga.fitness import compute_fitness
 from ..ga.initial import build_initial_population
 from ..ga.loop import GAConfig, next_generation
 from ..ga.summary import generation_summary
-from ..genome.hypothesis import ModelTier
+from ..genome.hypothesis import HypothesisAgentGenome, ModelTier
+from ..genome.mutation import set_cognitive_styles
+from ..genome.prompt_library import PromptLibrary
 from ..llm.client import LLMClient
 from ..llm.cost_tracker import CostTracker
 from ..persistence.repository import Repository
@@ -67,6 +70,33 @@ class RunConfig:
     # 2x costo backtest engine.
     eval_oos_during_loop: bool = False
     fitness_combined_alpha: float = 0.5  # peso IS (1-alpha = OOS)
+    # Libreria di stili cognitivi + system_prompt iniziali. Se None usa
+    # i 6 builtin (PromptLibrary.default()). Tipicamente caricata da
+    # strategy_crypto/prompts.json via PromptLibrary.from_json().
+    prompt_library: PromptLibrary | None = None
+    # Numero di propose() LLM concorrenti per generazione. 1 = sequenziale (default,
+    # backward compat). 6-10 tipicamente accettati da OpenRouter qwen-2.5 senza
+    # rate-limit. Riduce wall time GA loop di 5-8x su tier C.
+    llm_concurrency: int = 1
+
+
+def _parallel_propose(
+    agent: HypothesisAgent,
+    genomes: list[HypothesisAgentGenome],
+    market: MarketSummary,
+    n_workers: int,
+) -> list[HypothesisProposal]:
+    """Esegue ``agent.propose()`` su una lista di genomi, opzionalmente in parallelo.
+
+    ``n_workers <= 1`` mantiene il comportamento serial originale (ordine fisso,
+    determinismo data un seed). ``n_workers > 1`` usa un thread pool: l'order
+    dei risultati e' preservato (1:1 con ``genomes``). OpenAI/openrouter client
+    e' thread-safe; ``PromptLibrary`` e ``HypothesisAgent`` non hanno stato mutabile.
+    """
+    if n_workers <= 1 or len(genomes) <= 1:
+        return [agent.propose(g, market) for g in genomes]
+    with ThreadPoolExecutor(max_workers=n_workers) as pool:
+        return list(pool.map(lambda g: agent.propose(g, market), genomes))
 
 
 def run_phase1(
@@ -82,10 +112,16 @@ def run_phase1(
 
     repo = Repository(cfg.db_path)
     repo.init_schema()
+    # Escludi prompt_library (PromptLibrary dataclass non e' JSON-serializable);
+    # salva solo i nomi degli stili per reproducibility.
     config_dict = {
-        **cfg.__dict__,
+        **{k: v for k, v in cfg.__dict__.items() if k != "prompt_library"},
         "db_path": str(cfg.db_path),
         "model_tier": cfg.model_tier.value,
+        "prompt_library_styles": (
+            list(cfg.prompt_library.cognitive_styles)
+            if cfg.prompt_library is not None else None
+        ),
     }
     run_id = repo.create_run(name=cfg.run_name, config=config_dict)
 
@@ -100,7 +136,13 @@ def run_phase1(
 
     market = build_market_summary(train_ohlcv, symbol=cfg.symbol, timeframe=cfg.timeframe)
 
-    hypothesis_agent = HypothesisAgent(llm=llm)
+    # Propaga la libreria di stili al modulo mutation (cosi' mutate_cognitive_style
+    # pesca dai candidati coerenti col JSON della strategia in corso). Va FATTO
+    # PRIMA di istanziare HypothesisAgent (che la riceve in costruttore).
+    prompt_library = cfg.prompt_library or PromptLibrary.default()
+    set_cognitive_styles(prompt_library.cognitive_styles)
+
+    hypothesis_agent = HypothesisAgent(llm=llm, prompt_library=prompt_library)
     falsification_agent = FalsificationAgent(
         fees_bp=cfg.fees_bp, n_trials_dsr=cfg.n_trials_dsr
     )
@@ -113,7 +155,10 @@ def run_phase1(
     cost_tracker = CostTracker()
 
     population = build_initial_population(
-        k=cfg.population_size, model_tier=cfg.model_tier, rng=rng
+        k=cfg.population_size,
+        model_tier=cfg.model_tier,
+        rng=rng,
+        prompt_library=prompt_library,
     )
     fitnesses: dict[str, float] = {}
 
@@ -127,11 +172,20 @@ def run_phase1(
 
     try:
         for gen in range(cfg.n_generations):
+            # Step 1: raccogli i genomi da valutare in questa generazione (esclude
+            # elite gia' presenti nella cache fitnesses) e lancia propose() in
+            # parallelo. La sezione DB-write resta serial sotto.
+            uncached = [g for g in population if g.id not in fitnesses]
+            proposals = _parallel_propose(
+                hypothesis_agent, uncached, market, cfg.llm_concurrency
+            )
+            proposal_by_id = {g.id: p for g, p in zip(uncached, proposals, strict=True)}
+
             for genome in population:
                 if genome.id in fitnesses:
                     continue  # elite gia' valutata in generazione precedente
                 repo.save_genome(run_id=run_id, generation_idx=gen, genome=genome)
-                proposal = hypothesis_agent.propose(genome, market)
+                proposal = proposal_by_id[genome.id]
                 # Registra costo per OGNI completion (incluse retry).
                 for completion in proposal.completions:
                     cost_record = cost_tracker.record(
@@ -205,7 +259,7 @@ def run_phase1(
                             cfg.fitness_combined_alpha * fit
                             + (1.0 - cfg.fitness_combined_alpha) * fit_oos_inloop
                         )
-                    except Exception:  # noqa: BLE001
+                    except Exception:
                         pass  # fallback: usa solo IS
                 repo.save_evaluation(
                     run_id=run_id,
@@ -246,7 +300,7 @@ def run_phase1(
         # WFA re-eval: i top_k genomi (by fitness in-sample > 0) vengono rivalutati
         # sul test_ohlcv. Le metriche OOS finiscono in evaluations.fitness_oos etc.
         if test_ohlcv is not None and len(test_ohlcv) >= 100:
-            from ..agents.hypothesis import _try_parse  # noqa: PLC0415
+            from ..agents.hypothesis import _try_parse
 
             all_evals = repo.list_evaluations(run_id)
             top_evals = sorted(
@@ -261,7 +315,7 @@ def run_phase1(
                 try:
                     fals_oos = falsification_agent.evaluate(strategy, test_ohlcv)
                     adv_oos = adversarial_agent.review(strategy, test_ohlcv)
-                except Exception:  # noqa: BLE001
+                except Exception:
                     continue
                 fit_oos = compute_fitness(
                     fals_oos, adv_oos,

src/multi_swarm_core/multi_swarm_core/protocol/compiler.py

@@ -88,6 +88,29 @@ def _ind_atr_pct(df: pd.DataFrame, length: float) -> pd.Series:
     return _atr(df, int(length)) / df["close"]
 
 
+def _ind_sma_pct(df: pd.DataFrame, length: float) -> pd.Series:
+    # Deviazione frazionale del close dalla SMA: (close - sma) / sma.
+    # Range tipico +/- 0.1 (close +/- 10% dalla media). Uso ideale:
+    #   sma_pct(50) > 0.05 -> "close 5% sopra la media a 50 barre"
+    # NB: non e' "sma/close" perche' quel valore (sempre ~1.0) e' inutile
+    # per confronti con literal frazionali.
+    sma = _sma(df["close"], int(length))
+    return (df["close"] - sma) / sma
+
+
+def _ind_macd_pct(
+    df: pd.DataFrame,
+    fast: float = 12,
+    slow: float = 26,
+    signal: float = 9,
+) -> pd.Series:
+    # MACD normalizzato come frazione del prezzo close: macd_value / close.
+    # Range tipico +/- 0.02. Uso: `macd_pct > 0` (momentum positivo) o
+    # `macd_pct > 0.005` (momentum positivo >= 0.5% del prezzo).
+    macd = _ind_macd(df, fast, slow, signal)
+    return macd / df["close"]
+
+
 def _ind_realized_vol(df: pd.DataFrame, window: float) -> pd.Series:
     return _realized_vol(df["close"], int(window))
 
@@ -109,11 +132,13 @@ def _ind_macd(
 # against this map.
 INDICATOR_FNS: dict[str, Any] = {
     "sma": _ind_sma,
+    "sma_pct": _ind_sma_pct,
     "rsi": _ind_rsi,
     "atr": _ind_atr,
     "atr_pct": _ind_atr_pct,
     "realized_vol": _ind_realized_vol,
     "macd": _ind_macd,
+    "macd_pct": _ind_macd_pct,
 }
 
 _TIME_FEATURE_FNS: dict[str, Callable[[pd.DatetimeIndex], pd.Series]] = {

src/multi_swarm_core/multi_swarm_core/protocol/grammar.py

@@ -17,7 +17,7 @@ ACTION_VALUES: frozenset[str] = frozenset(
 KIND_VALUES: frozenset[str] = frozenset({"indicator", "feature", "literal"})
 
 KNOWN_INDICATORS: frozenset[str] = frozenset(
-    {"sma", "rsi", "atr", "atr_pct", "macd", "realized_vol"}
+    {"sma", "sma_pct", "rsi", "atr", "atr_pct", "macd", "macd_pct", "realized_vol"}
 )
 KNOWN_FEATURES: frozenset[str] = frozenset(
     {"open", "high", "low", "close", "volume",

src/multi_swarm_core/multi_swarm_core/protocol/validator.py

@@ -31,12 +31,14 @@ from .parser import (
 # Numero di parametri numerici accettati dopo il nome dell'indicatore.
 # (min, max) sui soli numeri. Indicatori non sono annidabili in Phase 1.
 INDICATOR_ARITY: dict[str, tuple[int, int]] = {
-    "sma": (1, 1),           # length
+    "sma": (1, 1),           # length (assoluto, unita' prezzo)
+    "sma_pct": (1, 1),       # length: (close - sma)/sma, deviazione frazionale
     "rsi": (1, 1),           # length
     "atr": (1, 1),           # length (assoluto, unita' prezzo)
     "atr_pct": (1, 1),       # length (frazione del close, per confronti con literal)
     "realized_vol": (1, 1),  # window
     "macd": (0, 3),          # fast, slow, signal (tutti opzionali)
+    "macd_pct": (0, 3),      # macd/close, frazionale (per confronti con literal)
 }
 
 

src/multi_swarm_core/pyproject.toml

@@ -18,6 +18,8 @@ dependencies = [
     "pyyaml>=6.0",
     "pyarrow>=18.0",
     "yfinance>=1.3.0",
+    "nicegui>=3.11.1",
+    "plotly>=5.24",
 ]
 
 [build-system]

src/multi_swarm_core/tests/integration/test_e2e_minimal_run.py

@@ -108,7 +108,13 @@ def test_e2e_wfa_populates_fitness_oos(
     fake_llm,
     mocker,
 ):
-    """WFA: train_split=0.7 → top genomi devono avere fitness_oos popolato."""
+    """WFA: train_split=0.7 → top genomi devono avere fitness_oos popolato.
+
+    Usa fitness v2 con hard-kill minimale (solo no_trades): il fixture sintetico
+    non produce strategie profittevoli, quindi i check aggressivi
+    fees_eat_alpha/negative_net_pnl azzererebbero tutti i genomi rendendo
+    inverificabile il wiring WFA.
+    """
     cfg = RunConfig(
         run_name="e2e-wfa-test",
         population_size=5,
@@ -125,6 +131,7 @@ def test_e2e_wfa_populates_fitness_oos(
         db_path=tmp_path / "runs.db",
         wfa_train_split=0.7,
         wfa_top_k=3,
+        fitness_hard_kill_findings=("no_trades",),
     )
     run_id = run_phase1(cfg, ohlcv=synthetic_ohlcv, llm=fake_llm)
     repo = Repository(db_path=tmp_path / "runs.db")

src/multi_swarm_core/tests/unit/test_adversarial.py

@@ -3,7 +3,6 @@ import json
 import numpy as np
 import pandas as pd
 import pytest
-
 from multi_swarm_core.agents.adversarial import (
     AdversarialAgent,
     AdversarialReport,
@@ -54,7 +53,10 @@ def test_degenerate_always_long_flagged(ohlcv: pd.DataFrame) -> None:
     assert any(f.name == "degenerate" and f.severity == Severity.HIGH for f in report.findings)
 
 
-def test_no_findings_on_reasonable_strategy(ohlcv: pd.DataFrame) -> None:
+def test_rsi_mean_reversion_loses_money_on_synthetic_data(ohlcv: pd.DataFrame) -> None:
+    """RSI mean-reversion sul fixture sintetico ha net negativo: deve firare
+    negative_net_pnl (deal-breaker). Conferma che il check cattura strategie
+    che perdono sul training, indipendentemente dal motivo (no edge / fees)."""
     src = json.dumps(
         {
             "rules": [
@@ -84,8 +86,59 @@ def test_no_findings_on_reasonable_strategy(ohlcv: pd.DataFrame) -> None:
     ast = parse_strategy(src)
     agent = AdversarialAgent()
     report = agent.review(ast, ohlcv)
+    assert any(
+        f.name == "negative_net_pnl" and f.severity == Severity.HIGH
+        for f in report.findings
+    )
+
+
+def test_profitable_strategy_no_high_findings(
+    monkeypatch: pytest.MonkeyPatch, ohlcv: pd.DataFrame
+) -> None:
+    """Sanity test: una strategia con gross > 0 e fees << gross + n_trades ragionevole
+    + signal misto non deve produrre nessun finding HIGH."""
+    n = 15
+    # entry=100 exit=110 gross=10 per trade, fees a 5bp -> 0.105 per trade
+    # totali: gross=150, fees=1.575 -> net=+148.4
+    fake_trades = [
+        _make_trade(
+            ohlcv.index[i * 30],
+            ohlcv.index[i * 30 + 1],
+            entry_price=100.0,
+            exit_price=110.0,
+        )
+        for i in range(n)
+    ]
+    # 50/50 LONG/FLAT per evitare degenerate/flat_too_long/time_in_market.
+    fake_signals = pd.Series(
+        [Side.LONG if i % 2 == 0 else Side.FLAT for i in range(len(ohlcv))],
+        index=ohlcv.index,
+        dtype=object,
+    )
+
+    def fake_run(self, ohlcv: pd.DataFrame, signals: pd.Series) -> BacktestResult:  # type: ignore[no-untyped-def]
+        return BacktestResult(
+            equity_curve=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="equity"),
+            returns=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="returns"),
+            trades=fake_trades,
+        )
+
+    def fake_compile(strategy):  # type: ignore[no-untyped-def]
+        return lambda df: fake_signals
+
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
+    )
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
+    )
+
+    ast = parse_strategy(_MINIMAL_STRATEGY_SRC)
+    report = AdversarialAgent().review(ast, ohlcv)
     high_findings = [f for f in report.findings if f.severity == Severity.HIGH]
-    assert len(high_findings) == 0
+    assert high_findings == [], (
+        f"expected no HIGH findings, got: {[f.name for f in high_findings]}"
+    )
 
 
 def test_zero_trade_strategy_flagged(ohlcv: pd.DataFrame) -> None:
@@ -383,6 +436,55 @@ def test_fees_eat_alpha_flagged(monkeypatch: pytest.MonkeyPatch,
     )
 
 
+def test_negative_net_pnl_fires_on_negative_gross(
+    monkeypatch: pytest.MonkeyPatch, ohlcv: pd.DataFrame
+) -> None:
+    """gross_pnl < 0 (perdente direzionale) -> HIGH negative_net_pnl.
+    fees_eat_alpha NON deve firare perche' la sua condizione richiede gross > 0.
+    """
+    n = 15
+    # entry=100 exit=95 gross=-5 per trade (LONG perdente)
+    fake_trades = [
+        _make_trade(
+            ohlcv.index[i * 30],
+            ohlcv.index[i * 30 + 1],
+            entry_price=100.0,
+            exit_price=95.0,
+        )
+        for i in range(n)
+    ]
+    fake_signals = pd.Series(
+        [Side.LONG if i % 2 == 0 else Side.FLAT for i in range(len(ohlcv))],
+        index=ohlcv.index,
+        dtype=object,
+    )
+
+    def fake_run(self, ohlcv, signals):  # type: ignore[no-untyped-def]
+        return BacktestResult(
+            equity_curve=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="equity"),
+            returns=pd.Series([0.0] * len(ohlcv), index=ohlcv.index, name="returns"),
+            trades=fake_trades,
+        )
+
+    def fake_compile(strategy):  # type: ignore[no-untyped-def]
+        return lambda df: fake_signals
+
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
+    )
+    monkeypatch.setattr(
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
+    )
+
+    ast = parse_strategy(_MINIMAL_STRATEGY_SRC)
+    report = AdversarialAgent().review(ast, ohlcv)
+    assert any(
+        f.name == "negative_net_pnl" and f.severity == Severity.HIGH
+        for f in report.findings
+    )
+    assert not any(f.name == "fees_eat_alpha" for f in report.findings)
+
+
 def test_time_in_market_too_high_flagged(monkeypatch: pytest.MonkeyPatch,
                                           ohlcv: pd.DataFrame) -> None:
     """Signal LONG per >80% delle bar -> HIGH time_in_market_too_high."""

src/multi_swarm_core/tests/unit/test_backtest_engine_vectorized.py

@@ -0,0 +1,160 @@
+"""Parity check: engine vettorializzato vs reference iterrows implementation.
+
+Mantiene una copia inline del loop ``iterrows`` come reference per garantire
+che la vettorizzazione produca esattamente gli stessi trades, equity_curve e
+returns su input pseudocasuali, indipendentemente dal regime di prezzo.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from multi_swarm_core.backtest.engine import BacktestEngine, BacktestResult
+from multi_swarm_core.backtest.orders import Position, Side, Trade
+
+
+def _legacy_run(
+    ohlcv: pd.DataFrame, signals: pd.Series, fees_bp: float = 5.0
+) -> BacktestResult:
+    """Reference implementation: il loop iterrows originale (pre-vectorize).
+
+    Mantenuto qui esclusivamente come oracolo per i test di parità.
+    """
+    signals = signals.reindex(ohlcv.index).ffill().fillna(Side.FLAT)
+    shifted = [Side.FLAT, *list(signals.iloc[:-1])]
+    executed_side = pd.Series(shifted, index=ohlcv.index, dtype=object)
+
+    position: Position | None = None
+    position_entry_ts: pd.Timestamp | None = None
+    trades: list[Trade] = []
+    equity = 0.0
+    equity_history: list[float] = []
+    returns_history: list[float] = []
+    prev_equity = 0.0
+
+    for ts, row in ohlcv.iterrows():
+        target_side = executed_side.loc[ts]
+        current_side = position.side if position else Side.FLAT
+        if target_side != current_side:
+            if position is not None:
+                assert position_entry_ts is not None
+                trade = Trade(
+                    entry_ts=position_entry_ts,
+                    exit_ts=ts,
+                    side=position.side,
+                    size=position.size,
+                    entry_price=position.entry_price,
+                    exit_price=float(row["open"]),
+                    fees_bp=fees_bp,
+                )
+                trades.append(trade)
+                equity += trade.net_pnl
+                position = None
+                position_entry_ts = None
+            if target_side in (Side.LONG, Side.SHORT):
+                position = Position(
+                    side=target_side, entry_price=float(row["open"]), size=1.0
+                )
+                position_entry_ts = ts
+        mark = float(row["close"])
+        mtm = position.unrealized_pnl(mark) if position else 0.0
+        current_equity = equity + mtm
+        equity_history.append(current_equity)
+        returns_history.append(current_equity - prev_equity)
+        prev_equity = current_equity
+    if position is not None:
+        assert position_entry_ts is not None
+        last_ts = ohlcv.index[-1]
+        last_close = float(ohlcv["close"].iloc[-1])
+        trade = Trade(
+            entry_ts=position_entry_ts,
+            exit_ts=last_ts,
+            side=position.side,
+            size=position.size,
+            entry_price=position.entry_price,
+            exit_price=last_close,
+            fees_bp=fees_bp,
+        )
+        trades.append(trade)
+        equity += trade.net_pnl
+        equity_history[-1] = equity
+        if len(returns_history) >= 2:
+            returns_history[-1] = equity - equity_history[-2]
+
+    return BacktestResult(
+        equity_curve=pd.Series(equity_history, index=ohlcv.index, name="equity"),
+        returns=pd.Series(returns_history, index=ohlcv.index, name="returns"),
+        trades=trades,
+    )
+
+
+def _random_ohlcv(n: int, seed: int) -> pd.DataFrame:
+    rng = np.random.default_rng(seed)
+    rets = rng.normal(0.0, 0.01, size=n)
+    close = 100.0 * np.exp(np.cumsum(rets))
+    idx = pd.date_range("2024-01-01", periods=n, freq="1h", tz="UTC")
+    return pd.DataFrame(
+        {
+            "open": close * (1 + rng.normal(0, 0.001, n)),
+            "high": close * 1.005,
+            "low": close * 0.995,
+            "close": close,
+            "volume": rng.uniform(1.0, 100.0, n),
+        },
+        index=idx,
+    )
+
+
+def _random_signals(n: int, seed: int, p_change: float = 0.1) -> pd.Series:
+    """Segnali con persistenza: ad ogni bar con prob p_change cambia stato."""
+    rng = np.random.default_rng(seed + 999)
+    states = [Side.LONG, Side.SHORT, Side.FLAT]
+    out: list[Side] = [rng.choice(states)]
+    for _ in range(1, n):
+        out.append(rng.choice(states) if rng.random() < p_change else out[-1])
+    idx = pd.date_range("2024-01-01", periods=n, freq="1h", tz="UTC")
+    return pd.Series(out, index=idx, dtype=object)
+
+
+@pytest.mark.parametrize("seed", [0, 1, 42, 123, 999])
+def test_vectorized_equals_legacy(seed: int) -> None:
+    df = _random_ohlcv(500, seed)
+    signals = _random_signals(500, seed)
+    engine = BacktestEngine(fees_bp=5.0)
+    new = engine.run(df, signals)
+    ref = _legacy_run(df, signals, fees_bp=5.0)
+
+    pd.testing.assert_series_equal(
+        new.equity_curve, ref.equity_curve, rtol=1e-9, atol=1e-9
+    )
+    pd.testing.assert_series_equal(
+        new.returns, ref.returns, rtol=1e-9, atol=1e-9
+    )
+    assert len(new.trades) == len(ref.trades)
+    for nt, rt in zip(new.trades, ref.trades, strict=True):
+        assert nt.entry_ts == rt.entry_ts
+        assert nt.exit_ts == rt.exit_ts
+        assert nt.side == rt.side
+        assert nt.entry_price == pytest.approx(rt.entry_price, abs=1e-12)
+        assert nt.exit_price == pytest.approx(rt.exit_price, abs=1e-12)
+        assert nt.net_pnl == pytest.approx(rt.net_pnl, abs=1e-12)
+
+
+def test_vectorized_handles_position_still_open_at_end() -> None:
+    """Edge case: signal LONG fino all'ultimo bar (exit a close[-1] forced)."""
+    df = _random_ohlcv(100, seed=7)
+    signals = pd.Series([Side.LONG] * 100, index=df.index)
+    new = BacktestEngine(fees_bp=10.0).run(df, signals)
+    ref = _legacy_run(df, signals, fees_bp=10.0)
+    pd.testing.assert_series_equal(new.equity_curve, ref.equity_curve, atol=1e-9)
+    assert len(new.trades) == 1
+    assert new.trades[0].side == Side.LONG
+
+
+def test_vectorized_zero_signals_no_trades() -> None:
+    df = _random_ohlcv(50, seed=3)
+    signals = pd.Series([Side.FLAT] * 50, index=df.index)
+    new = BacktestEngine().run(df, signals)
+    assert len(new.trades) == 0
+    assert (new.equity_curve == 0).all()

src/multi_swarm_core/tests/unit/test_hypothesis_agent.py

@@ -2,6 +2,7 @@ import json
 
 from multi_swarm_core.agents.hypothesis import HypothesisAgent, MarketSummary
 from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.genome.prompt_library import PromptLibrary
 from multi_swarm_core.llm.client import CompletionResult, EmptyCompletionError
 
 
@@ -248,3 +249,174 @@ def test_hypothesis_agent_returns_failed_proposal_on_only_empty_completions(mock
     assert "empty_completion" in proposal.parse_error
     # 3 tentativi tutti falliti.
     assert fake_llm.complete.call_count == 3
+
+
+def test_propose_renders_focus_block_for_style(mocker):  # type: ignore[no-untyped-def]
+    """Con PromptLibrary che ha focus_metrics, il LLM mock riceve 'Focus per la tua lente' nel user message."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    # PromptLibrary con focus_metrics per physicist
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={"physicist": ["efficiency_ratio", "spectral_entropy", "hurst"]},
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    proposal = agent.propose(make_genome(), make_summary())
+    assert proposal.strategy is not None
+    # Verifica che il user message contenga il focus block
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    user_msg = call_kwargs["user"]
+    assert "Focus per la tua lente:" in user_msg
+    assert "efficiency_ratio=" in user_msg
+    assert "spectral_entropy=" in user_msg
+    assert "hurst=" in user_msg
+
+
+def test_propose_no_focus_block_when_style_not_in_library(mocker):  # type: ignore[no-untyped-def]
+    """Stile senza focus_metrics → nessun focus block nel user message."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    # PromptLibrary senza focus_metrics per physicist
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={},
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    proposal = agent.propose(make_genome(), make_summary())
+    assert proposal.strategy is not None
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    user_msg = call_kwargs["user"]
+    assert "Focus per la tua lente:" not in user_msg
+
+
+def test_build_system_prompt_includes_role_and_guidance_from_library(mocker):  # type: ignore[no-untyped-def]
+    """agent_role e pattern_guidance da PromptLibrary appaiono nel SYSTEM prompt."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={},
+        agent_role="ROLE_X",
+        pattern_guidance="GUIDE_X",
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    agent.propose(make_genome(), make_summary())
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    system_msg = call_kwargs["system"]
+    assert "ROLE_X" in system_msg
+    assert "GUIDE_X" in system_msg
+
+
+def test_build_system_prompt_skips_empty_optional_sections(mocker):  # type: ignore[no-untyped-def]
+    """domain_warnings='' e pattern_guidance='' → sezioni opzionali assenti nel SYSTEM."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={},
+        domain_warnings="",
+        pattern_guidance="",
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    agent.propose(make_genome(), make_summary())
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    system_msg = call_kwargs["system"]
+    assert "WARNING DI DOMINIO" not in system_msg
+    assert "PATTERN GUIDANCE" not in system_msg
+
+
+def test_user_template_uses_instruction_from_library(mocker):  # type: ignore[no-untyped-def]
+    """instruction da PromptLibrary appare nel USER message; default non viene usato."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={},
+        instruction="INSTR_X",
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    agent.propose(make_genome(), make_summary())
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    user_msg = call_kwargs["user"]
+    assert "INSTR_X" in user_msg
+    assert "Genera una strategia che cerchi anomalie sfruttabili in questo regime." not in user_msg
+
+
+def test_build_system_prompt_includes_anti_patterns_and_priorities(mocker):  # type: ignore[no-untyped-def]
+    """anti_patterns e output_priorities da PromptLibrary appaiono nel SYSTEM prompt."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={},
+        anti_patterns="EVITA_X",
+        output_priorities="PRIORI_X",
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    agent.propose(make_genome(), make_summary())
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    system_msg = call_kwargs["system"]
+    assert "ANTI-PATTERN DA EVITARE" in system_msg
+    assert "EVITA_X" in system_msg
+    assert "PRIORITA' DI OUTPUT" in system_msg
+    assert "PRIORI_X" in system_msg
+
+
+def test_build_system_prompt_skips_anti_patterns_and_priorities_when_empty(mocker):  # type: ignore[no-untyped-def]
+    """anti_patterns='' e output_priorities='' -> sezioni opzionali assenti nel SYSTEM."""
+    fake_llm = mocker.MagicMock()
+    fake_llm.complete.return_value = CompletionResult(
+        text=VALID_STRATEGY_JSON,
+        input_tokens=200,
+        output_tokens=80,
+        tier=ModelTier.C,
+        model="qwen",
+    )
+    lib = PromptLibrary(
+        styles={"physicist": "Pensa come un fisico."},
+        focus={},
+        anti_patterns="",
+        output_priorities="",
+    )
+    agent = HypothesisAgent(llm=fake_llm, prompt_library=lib)
+    agent.propose(make_genome(), make_summary())
+    call_kwargs = fake_llm.complete.call_args.kwargs
+    system_msg = call_kwargs["system"]
+    assert "ANTI-PATTERN" not in system_msg
+    assert "PRIORITA' DI OUTPUT" not in system_msg

src/multi_swarm_core/tests/unit/test_market_summary.py

@@ -31,3 +31,83 @@ def test_volatility_regime_high_for_volatile() -> None:
     )
     s = build_market_summary(df, symbol="BTC/USDT", timeframe="1h")
     assert s.volatility_regime in {"medium", "high"}
+
+
+def test_build_summary_new_fields_populated() -> None:
+    """I 6 nuovi campi devono essere float nei range attesi."""
+    idx = pd.date_range("2024-01-01", periods=600, freq="1h", tz="UTC")
+    np.random.seed(42)
+    close = 100 + np.cumsum(np.random.normal(0, 1, 600))
+    df = pd.DataFrame(
+        {"open": close, "high": close + 0.5, "low": close - 0.5, "close": close, "volume": 1.0},
+        index=idx,
+    )
+    s = build_market_summary(df, symbol="ETH/USDT", timeframe="1h")
+
+    # autocorr fields: float in [-1, 1]
+    assert isinstance(s.autocorr_lag1_recent, float)
+    assert isinstance(s.autocorr_lag1_baseline, float)
+    assert -1.0 <= s.autocorr_lag1_recent <= 1.0
+    assert -1.0 <= s.autocorr_lag1_baseline <= 1.0
+
+    # hurst: float in [0, 1]
+    assert isinstance(s.hurst_recent, float)
+    assert 0.0 <= s.hurst_recent <= 1.0
+
+    # vol_percentile: float in [0, 100]
+    assert isinstance(s.vol_percentile, float)
+    assert 0.0 <= s.vol_percentile <= 100.0
+
+    # seasonality fields: float in [0, 1]
+    assert isinstance(s.seasonality_hour, float)
+    assert isinstance(s.seasonality_dow, float)
+    assert 0.0 <= s.seasonality_hour <= 1.0
+    assert 0.0 <= s.seasonality_dow <= 1.0
+
+
+def test_build_summary_geometric_fields_populated() -> None:
+    """I 7 nuovi campi geometrico-frattali devono essere non-default e nei range attesi."""
+    idx = pd.date_range("2024-01-01", periods=600, freq="1h", tz="UTC")
+    np.random.seed(5)
+    close = 100 + np.cumsum(np.random.normal(0, 1, 600))
+    df = pd.DataFrame(
+        {"open": close, "high": close + 0.5, "low": close - 0.5, "close": close, "volume": 1.0},
+        index=idx,
+    )
+    s = build_market_summary(df, symbol="BTC/USDT", timeframe="1h")
+
+    # efficiency_ratio: float in [0, 1]
+    assert isinstance(s.efficiency_ratio, float)
+    assert 0.0 <= s.efficiency_ratio <= 1.0
+
+    # tail_index_left/right: float in [1, 10]
+    assert isinstance(s.tail_index_left, float)
+    assert isinstance(s.tail_index_right, float)
+    assert 1.0 <= s.tail_index_left <= 10.0
+    assert 1.0 <= s.tail_index_right <= 10.0
+
+    # structural_uptrend: float in [0, 1]
+    assert isinstance(s.structural_uptrend, float)
+    assert 0.0 <= s.structural_uptrend <= 1.0
+
+    # compression: float > 0
+    assert isinstance(s.compression, float)
+    assert s.compression > 0.0
+
+    # spectral_entropy: float in [0, 1]
+    assert isinstance(s.spectral_entropy, float)
+    assert 0.0 <= s.spectral_entropy <= 1.0
+
+    # dominant_cycle: None or positive float
+    assert s.dominant_cycle is None or (isinstance(s.dominant_cycle, float) and s.dominant_cycle > 0)
+
+    # Verifica che i campi siano stati popolati (non tutti ai valori default)
+    defaults_only = (
+        s.efficiency_ratio == 0.0
+        and s.tail_index_left == 5.0
+        and s.tail_index_right == 5.0
+        and s.structural_uptrend == 0.5
+        and s.compression == 1.0
+        and s.spectral_entropy == 1.0
+    )
+    assert not defaults_only, "Tutti i campi geometrici sono rimasti ai valori default: calcolo non avvenuto"

src/multi_swarm_core/tests/unit/test_metrics_basic.py

@@ -2,7 +2,20 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
+from multi_swarm_core.metrics.basic import (
+    autocorr_lag1,
+    compression_ratio,
+    efficiency_ratio_kaufman,
+    hurst_exponent,
+    max_drawdown,
+    seasonality_strength,
+    sharpe_ratio,
+    spectral_entropy_and_cycle,
+    structural_uptrend_score,
+    tail_index_hill,
+    total_return,
+    vol_percentile_historical,
+)
 
 
 def test_sharpe_zero_returns():
@@ -38,3 +51,240 @@ def test_max_drawdown_known_curve():
 def test_total_return():
     eq = pd.Series([100.0, 110.0, 105.0, 120.0])
     assert total_return(eq) == pytest.approx(0.20)
+
+
+# --- New metrics tests ---
+
+
+def test_autocorr_lag1_short_series():
+    """Serie corta ritorna 0.0 senza errori."""
+    r = pd.Series([0.01])
+    assert autocorr_lag1(r) == 0.0
+
+
+def test_autocorr_lag1_all_nan():
+    """Serie di NaN ritorna 0.0."""
+    r = pd.Series([float("nan")] * 10)
+    assert autocorr_lag1(r) == 0.0
+
+
+def test_autocorr_lag1_range():
+    """Autocorrelazione sempre in [-1, 1]."""
+    np.random.seed(7)
+    r = pd.Series(np.random.normal(0, 0.01, 500))
+    val = autocorr_lag1(r)
+    assert -1.0 <= val <= 1.0
+
+
+def test_autocorr_lag1_trending_positive():
+    """Serie con autocorrelazione positiva artificiale deve dare val > 0."""
+    # costruiamo una serie con forte AR(1): x_t = 0.8*x_{t-1} + noise
+    np.random.seed(1)
+    n = 300
+    x = np.zeros(n)
+    for i in range(1, n):
+        x[i] = 0.8 * x[i - 1] + np.random.normal(0, 0.1)
+    r = pd.Series(x)
+    assert autocorr_lag1(r) > 0.5
+
+
+def test_hurst_short_series():
+    """Serie < 100 elementi ritorna 0.5 (random-walk default)."""
+    r = pd.Series(np.random.normal(0, 0.01, 50))
+    assert hurst_exponent(r) == pytest.approx(0.5)
+
+
+def test_hurst_range():
+    """Hurst sempre in [0, 1]."""
+    np.random.seed(3)
+    r = pd.Series(np.random.normal(0, 0.01, 500))
+    h = hurst_exponent(r)
+    assert 0.0 <= h <= 1.0
+
+
+def test_hurst_random_walk_approx_half():
+    """Random walk iid deve avere Hurst vicino a 0.5."""
+    np.random.seed(42)
+    r = pd.Series(np.random.normal(0, 0.01, 2000))
+    h = hurst_exponent(r)
+    assert 0.3 <= h <= 0.7
+
+
+def test_vol_percentile_short_series():
+    """Serie troppo corta ritorna 50.0."""
+    r = pd.Series(np.random.normal(0, 0.01, 10))
+    assert vol_percentile_historical(r) == pytest.approx(50.0)
+
+
+def test_vol_percentile_range():
+    """Percentile sempre in [0, 100]."""
+    np.random.seed(5)
+    r = pd.Series(np.random.normal(0, 0.01, 500))
+    p = vol_percentile_historical(r, current_window=24, ref_window=200)
+    assert 0.0 <= p <= 100.0
+
+
+def test_vol_percentile_high_vol_at_end():
+    """Vol molto alta alla fine deve dare percentile elevato."""
+    np.random.seed(9)
+    low_vol = np.random.normal(0, 0.001, 400)
+    high_vol = np.random.normal(0, 0.05, 100)
+    r = pd.Series(np.concatenate([low_vol, high_vol]))
+    p = vol_percentile_historical(r, current_window=24, ref_window=400)
+    assert p > 70.0
+
+
+def test_seasonality_strength_no_datetimeindex():
+    """Senza DatetimeIndex ritorna 0.0."""
+    r = pd.Series(np.random.normal(0, 0.01, 200))
+    assert seasonality_strength(r, by="hour") == 0.0
+
+
+def test_seasonality_strength_short_series():
+    """Serie < 50 elementi ritorna 0.0."""
+    idx = pd.date_range("2024-01-01", periods=30, freq="1h", tz="UTC")
+    r = pd.Series(np.random.normal(0, 0.01, 30), index=idx)
+    assert seasonality_strength(r, by="hour") == 0.0
+
+
+def test_seasonality_strength_range():
+    """Risultato sempre in [0, 1]."""
+    np.random.seed(11)
+    idx = pd.date_range("2024-01-01", periods=500, freq="1h", tz="UTC")
+    r = pd.Series(np.random.normal(0, 0.01, 500), index=idx)
+    val = seasonality_strength(r, by="hour")
+    assert 0.0 <= val <= 1.0
+
+
+def test_seasonality_strength_dow_range():
+    """Stagionalita dow sempre in [0, 1]."""
+    np.random.seed(13)
+    idx = pd.date_range("2024-01-01", periods=500, freq="1h", tz="UTC")
+    r = pd.Series(np.random.normal(0, 0.01, 500), index=idx)
+    val = seasonality_strength(r, by="dow")
+    assert 0.0 <= val <= 1.0
+
+
+def test_seasonality_strength_invalid_by():
+    """by non valido solleva ValueError."""
+    idx = pd.date_range("2024-01-01", periods=100, freq="1h", tz="UTC")
+    r = pd.Series(np.random.normal(0, 0.01, 100), index=idx)
+    with pytest.raises(ValueError, match="'minute'"):
+        seasonality_strength(r, by="minute")
+
+
+# --- Geometric/fractal metrics tests ---
+
+
+def test_efficiency_ratio_pure_trend():
+    """Slope perfetto (prezzi linearmente crescenti) -> efficiency_ratio vicino a 1.0."""
+    prices = pd.Series(np.linspace(100.0, 200.0, 200))
+    er = efficiency_ratio_kaufman(prices, length=100)
+    assert er == pytest.approx(1.0, abs=1e-6)
+
+
+def test_efficiency_ratio_random_walk():
+    """Random walk iid -> efficiency_ratio basso (attorno a 0.1-0.4)."""
+    np.random.seed(42)
+    prices = pd.Series(100.0 + np.cumsum(np.random.normal(0, 1, 300)))
+    er = efficiency_ratio_kaufman(prices, length=100)
+    assert 0.0 <= er <= 0.6
+
+
+def test_efficiency_ratio_short_series():
+    """Serie troppo corta ritorna 0.0."""
+    prices = pd.Series([100.0, 101.0, 102.0])
+    assert efficiency_ratio_kaufman(prices, length=100) == 0.0
+
+
+def test_tail_index_hill_left_right_separate():
+    """tail_index_hill left e right ritornano float in [1, 10]."""
+    np.random.seed(7)
+    r = pd.Series(np.random.normal(0, 0.01, 500))
+    left = tail_index_hill(r, side="left")
+    right = tail_index_hill(r, side="right")
+    assert 1.0 <= left <= 10.0
+    assert 1.0 <= right <= 10.0
+
+
+def test_tail_index_hill_invalid_side():
+    """side non valido solleva ValueError."""
+    r = pd.Series(np.random.normal(0, 0.01, 200))
+    with pytest.raises(ValueError, match="'both'"):
+        tail_index_hill(r, side="both")
+
+
+def test_tail_index_hill_short_series():
+    """Serie < 50 ritorna default 5.0."""
+    r = pd.Series([0.01, -0.01, 0.02])
+    assert tail_index_hill(r, side="left") == pytest.approx(5.0)
+    assert tail_index_hill(r, side="right") == pytest.approx(5.0)
+
+
+def test_structural_uptrend_pure_uptrend():
+    """Prezzi costantemente crescenti -> structural_uptrend vicino a 1.0."""
+    prices = pd.Series(np.linspace(100.0, 200.0, 100))
+    score = structural_uptrend_score(prices, window=5)
+    assert score >= 0.5
+
+
+def test_structural_uptrend_pure_downtrend():
+    """Prezzi costantemente decrescenti -> structural_uptrend vicino a 0.0."""
+    prices = pd.Series(np.linspace(200.0, 100.0, 100))
+    score = structural_uptrend_score(prices, window=5)
+    assert score <= 0.5
+
+
+def test_structural_uptrend_short_series():
+    """Serie troppo corta ritorna 0.5 (neutro)."""
+    prices = pd.Series([100.0, 101.0, 99.0])
+    assert structural_uptrend_score(prices, window=5) == pytest.approx(0.5)
+
+
+def test_compression_ratio_compress_then_expand():
+    """Finestra recente compressa rispetto a ref -> ratio < 1."""
+    np.random.seed(1)
+    # Ref window: alta volatilita
+    ref_part = np.random.normal(0, 5.0, 200)
+    # Recent window: bassa volatilita
+    recent_part = np.random.normal(0, 0.2, 50)
+    prices = pd.Series(100.0 + np.cumsum(np.concatenate([ref_part, recent_part])))
+    ratio = compression_ratio(prices, recent_window=50, ref_window=200)
+    assert ratio < 1.0
+
+
+def test_compression_ratio_short_series():
+    """Serie troppo corta ritorna 1.0 (neutro)."""
+    prices = pd.Series([100.0, 101.0, 99.0])
+    assert compression_ratio(prices, recent_window=50, ref_window=200) == pytest.approx(1.0)
+
+
+def test_spectral_entropy_pure_sine():
+    """Seno puro -> entropy bassa e dominant_cycle ben definito."""
+    n = 256
+    t = np.arange(n)
+    cycle_period = 32  # barre
+    series = np.sin(2 * np.pi * t / cycle_period)
+    r = pd.Series(series)
+    entropy, cycle = spectral_entropy_and_cycle(r, length=256)
+    assert entropy < 0.6
+    assert cycle is not None
+    # Il ciclo dominante deve essere vicino a cycle_period
+    assert abs(cycle - cycle_period) <= 5.0
+
+
+def test_spectral_entropy_white_noise():
+    """Rumore bianco -> entropy alta e dominant_cycle = None."""
+    np.random.seed(99)
+    r = pd.Series(np.random.normal(0, 1, 512))
+    entropy, cycle = spectral_entropy_and_cycle(r, length=256)
+    assert entropy >= 0.6
+    assert cycle is None
+
+
+def test_spectral_entropy_short_series():
+    """Serie troppo corta ritorna (1.0, None)."""
+    r = pd.Series([0.01, -0.01, 0.02])
+    entropy, cycle = spectral_entropy_and_cycle(r, length=256)
+    assert entropy == pytest.approx(1.0)
+    assert cycle is None

src/multi_swarm_core/tests/unit/test_orchestrator_parallel_propose.py

@@ -0,0 +1,78 @@
+"""Test che `_parallel_propose` preservi l'ordine dei risultati e funzioni
+sia in modalita' sequenziale (workers=1) che concorrente (workers>1).
+
+Non vogliamo testare il vero `HypothesisAgent.propose()` (che fa chiamate
+LLM); usiamo un dummy con una latenza simulata per validare ordine e parallelismo.
+"""
+
+from __future__ import annotations
+
+import time
+from dataclasses import dataclass
+from typing import Any
+
+from multi_swarm_core.orchestrator.run import _parallel_propose
+
+
+@dataclass
+class _FakeGenome:
+    id: str
+
+
+@dataclass
+class _FakeProposal:
+    genome_id: str
+
+
+class _FakeAgent:
+    """Agent dummy: propose() dorme 50ms e ritorna un proposal con l'id del genome."""
+
+    def __init__(self, delay_s: float = 0.05) -> None:
+        self._delay = delay_s
+        self.call_count = 0
+
+    def propose(self, genome: _FakeGenome, market: Any) -> _FakeProposal:
+        time.sleep(self._delay)
+        self.call_count += 1
+        return _FakeProposal(genome_id=genome.id)
+
+
+def test_parallel_propose_preserves_order_serial() -> None:
+    agent = _FakeAgent(delay_s=0.01)
+    genomes = [_FakeGenome(id=f"g{i}") for i in range(5)]
+    results = _parallel_propose(agent, genomes, market=None, n_workers=1)
+    assert [r.genome_id for r in results] == ["g0", "g1", "g2", "g3", "g4"]
+    assert agent.call_count == 5
+
+
+def test_parallel_propose_preserves_order_concurrent() -> None:
+    agent = _FakeAgent(delay_s=0.05)
+    genomes = [_FakeGenome(id=f"g{i}") for i in range(8)]
+    results = _parallel_propose(agent, genomes, market=None, n_workers=4)
+    assert [r.genome_id for r in results] == [f"g{i}" for i in range(8)]
+    assert agent.call_count == 8
+
+
+def test_parallel_propose_actually_parallelizes() -> None:
+    """Wall time con 4 worker su 4 task da 100ms deve essere ~100ms, non ~400ms."""
+    agent = _FakeAgent(delay_s=0.1)
+    genomes = [_FakeGenome(id=f"g{i}") for i in range(4)]
+    t0 = time.time()
+    _parallel_propose(agent, genomes, market=None, n_workers=4)
+    elapsed = time.time() - t0
+    # serial sarebbe 0.4s; con 4 worker scendiamo a ~0.1s (max 0.2 per overhead).
+    assert elapsed < 0.2, f"expected <200ms with 4 workers, got {elapsed * 1000:.0f}ms"
+
+
+def test_parallel_propose_handles_single_genome() -> None:
+    agent = _FakeAgent()
+    results = _parallel_propose(agent, [_FakeGenome(id="solo")], market=None, n_workers=8)
+    assert len(results) == 1
+    assert results[0].genome_id == "solo"
+
+
+def test_parallel_propose_empty_input() -> None:
+    agent = _FakeAgent()
+    results = _parallel_propose(agent, [], market=None, n_workers=4)
+    assert results == []
+    assert agent.call_count == 0

src/multi_swarm_core/tests/unit/test_prompt_library.py

@@ -0,0 +1,146 @@
+"""Unit tests per PromptLibrary v3.0 — nuovi campi top-level."""
+
+from __future__ import annotations
+
+import json
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from multi_swarm_core.genome.prompt_library import PromptLibrary, PromptLibraryError
+
+
+def _write_json(data: dict, tmp_path: Path) -> Path:
+    p = tmp_path / "prompts.json"
+    p.write_text(json.dumps(data), encoding="utf-8")
+    return p
+
+
+def test_from_json_loads_new_top_level_fields(tmp_path: Path) -> None:
+    """from_json() legge agent_role, pattern_guidance, instruction, domain_warnings."""
+    data = {
+        "styles": {"physicist": {"directive": "Cerca leggi conservative."}},
+        "agent_role": "Sei un agente test.",
+        "pattern_guidance": "Forme di curva: trend, compressione.",
+        "instruction": "Genera qualcosa di utile.",
+        "domain_warnings": "Attenzione: mercato 24/7.",
+    }
+    lib = PromptLibrary.from_json(_write_json(data, tmp_path))
+    assert lib.agent_role == "Sei un agente test."
+    assert lib.pattern_guidance == "Forme di curva: trend, compressione."
+    assert lib.instruction == "Genera qualcosa di utile."
+    assert lib.domain_warnings == "Attenzione: mercato 24/7."
+
+
+def test_from_json_defaults_new_fields_when_absent(tmp_path: Path) -> None:
+    """from_json() usa stringa vuota come default per i 4 campi opzionali."""
+    data = {
+        "styles": {"physicist": {"directive": "Cerca leggi conservative."}},
+    }
+    lib = PromptLibrary.from_json(_write_json(data, tmp_path))
+    assert lib.agent_role == ""
+    assert lib.pattern_guidance == ""
+    assert lib.instruction == ""
+    assert lib.domain_warnings == ""
+
+
+def test_default_provides_universal_fallbacks() -> None:
+    """PromptLibrary.default() ha agent_role, pattern_guidance, instruction non vuoti; domain_warnings vuoto."""
+    lib = PromptLibrary.default()
+    assert lib.agent_role != ""
+    assert lib.pattern_guidance != ""
+    assert lib.instruction != ""
+    assert lib.domain_warnings == ""
+
+
+def test_prompt_library_rejects_whitespace_only_fields() -> None:
+    """Campi opzionali forniti ma solo whitespace sollevano PromptLibraryError."""
+    with pytest.raises(PromptLibraryError, match="agent_role"):
+        PromptLibrary(
+            styles={"physicist": "Cerca leggi."},
+            focus={},
+            agent_role="   ",
+        )
+
+
+def test_prompt_library_accepts_empty_string_for_optional_fields() -> None:
+    """Stringa vuota '' e' accettata per tutti i campi opzionali."""
+    lib = PromptLibrary(
+        styles={"physicist": "Cerca leggi."},
+        focus={},
+        agent_role="",
+        pattern_guidance="",
+        instruction="",
+        domain_warnings="",
+    )
+    assert lib.agent_role == ""
+    assert lib.domain_warnings == ""
+
+
+def test_from_json_loads_anti_patterns_and_output_priorities(tmp_path: Path) -> None:
+    """from_json() legge anti_patterns e output_priorities (v3.1)."""
+    data = {
+        "styles": {"physicist": {"directive": "Cerca leggi conservative."}},
+        "anti_patterns": "Evita overfitting.",
+        "output_priorities": "Robustezza > ottimalita.",
+    }
+    lib = PromptLibrary.from_json(_write_json(data, tmp_path))
+    assert lib.anti_patterns == "Evita overfitting."
+    assert lib.output_priorities == "Robustezza > ottimalita."
+
+
+def test_strategy_crypto_directives_ascii_safe() -> None:
+    """REGRESSION GUARD: nessuna directive contiene caratteri > U+007F.
+
+    v3.1 aveva regredito introducendo il carattere circa-uguale (U+2248) in 3 stili.
+    v3.2 ripristina ASCII-strict come invariante permanente.
+    """
+    import importlib.resources
+
+    path = importlib.resources.files("strategy_crypto") / "prompts.json"
+    lib = PromptLibrary.from_json(str(path))
+
+    for style, directive in lib.styles.items():
+        non_ascii = [c for c in directive if ord(c) > 127]
+        assert not non_ascii, (
+            f"directive di {style!r} contiene caratteri non-ASCII: "
+            f"{non_ascii} (codepoints: {[hex(ord(c)) for c in non_ascii]})"
+        )
+
+
+def test_strategy_crypto_directives_have_archetype_marker() -> None:
+    """REGRESSION GUARD: ogni directive chiude con 'Archetipo dominante: ...'.
+
+    L'archetipo e' l'ancora semantica identitaria della lente; deve essere
+    presente per resistere alle riscritture di mutate_prompt_llm.
+    """
+    import importlib.resources
+
+    path = importlib.resources.files("strategy_crypto") / "prompts.json"
+    lib = PromptLibrary.from_json(str(path))
+
+    for style, directive in lib.styles.items():
+        assert "Archetipo dominante:" in directive, (
+            f"directive di {style!r} manca del marker 'Archetipo dominante:'"
+        )
+
+
+def test_strategy_crypto_directives_have_lookback_hint() -> None:
+    """REGRESSION GUARD: ogni directive contiene un hint 'Lookback consigliato: X-Y barre'.
+
+    Il range numerico orienta il parametro evoluto lookback_window del genoma;
+    differenziato per stile per favorire diversita di scala temporale nella
+    popolazione iniziale.
+    """
+    import re
+    import importlib.resources
+
+    path = importlib.resources.files("strategy_crypto") / "prompts.json"
+    lib = PromptLibrary.from_json(str(path))
+
+    pattern = re.compile(r"[Ll]ookback consigliato:\s*\d+\s*-\s*\d+", re.IGNORECASE)
+    for style, directive in lib.styles.items():
+        assert pattern.search(directive), (
+            f"directive di {style!r} manca dell'hint 'Lookback consigliato: X-Y'"
+        )

src/multi_swarm_core/tests/unit/test_protocol_compiler.py

@@ -309,3 +309,84 @@ def test_atr_pct_in_strategy_eval(ohlcv: pd.DataFrame) -> None:
     # senza dead-branch (qualche match e' possibile in warmup).
     non_warmup = signal.iloc[30:]
     assert (non_warmup == Side.FLAT).all() or (non_warmup == Side.LONG).any()
+
+
+def test_sma_pct_is_close_deviation_from_sma() -> None:
+    """sma_pct = (close - sma) / sma: deviazione frazionale del close dalla SMA."""
+    from multi_swarm_core.protocol.compiler import _ind_sma, _ind_sma_pct
+
+    idx = pd.date_range("2024-01-01", periods=100, freq="1h", tz="UTC")
+    # Prezzo che cresce poi torna: sma_pct passa da +qualcosa a -qualcosa
+    close = np.concatenate([np.linspace(100, 120, 50), np.linspace(120, 95, 50)])
+    df = pd.DataFrame(
+        {"open": close, "high": close + 0.5, "low": close - 0.5, "close": close, "volume": 1.0},
+        index=idx,
+    )
+
+    sma = _ind_sma(df, 20)
+    sma_pct = _ind_sma_pct(df, 20)
+    expected = (df["close"] - sma) / sma
+    assert np.allclose(sma_pct.dropna(), expected.dropna())
+    # In salita: close > sma -> sma_pct positivo
+    assert (sma_pct.iloc[30:50] > 0).any()
+    # In discesa estesa: close < sma -> sma_pct negativo
+    assert (sma_pct.iloc[80:] < 0).any()
+
+
+def test_macd_pct_is_macd_divided_by_close() -> None:
+    """macd_pct = macd / close: momentum normalizzato al prezzo."""
+    from multi_swarm_core.protocol.compiler import _ind_macd, _ind_macd_pct
+
+    idx = pd.date_range("2024-01-01", periods=300, freq="1h", tz="UTC")
+    # Random walk realistico su crypto (~3000 USDT, vol ~30/bar): MACD ha
+    # ampiezza non trascurabile vs trend lineare puro.
+    rng = np.random.default_rng(42)
+    close = 3000.0 + np.cumsum(rng.standard_normal(300) * 30)
+    df = pd.DataFrame(
+        {"open": close, "high": close + 5, "low": close - 5, "close": close, "volume": 1.0},
+        index=idx,
+    )
+
+    macd = _ind_macd(df, 12, 26, 9)
+    macd_pct = _ind_macd_pct(df, 12, 26, 9)
+    # Identita' algebrica: macd_pct == macd / close
+    assert np.allclose(macd_pct.dropna(), (macd / df["close"]).dropna())
+    # macd_pct ha scala << 1 (frazione del prezzo, ordine 1e-3)
+    assert macd_pct.abs().mean() < 0.05
+    # macd assoluto e' >> macd_pct (rapporto = close ~3000)
+    ratio = macd.abs().mean() / max(macd_pct.abs().mean(), 1e-12)
+    assert 1000 < ratio < 5000
+
+
+def test_sma_pct_and_macd_pct_in_validator() -> None:
+    """Regression: i nuovi indicatori sono accettati dal validator."""
+    from multi_swarm_core.protocol.validator import validate_strategy
+
+    spec = {
+        "rules": [
+            {
+                "condition": {
+                    "op": "and",
+                    "args": [
+                        {
+                            "op": "gt",
+                            "args": [
+                                {"kind": "indicator", "name": "sma_pct", "params": [50]},
+                                {"kind": "literal", "value": 0.05},
+                            ],
+                        },
+                        {
+                            "op": "gt",
+                            "args": [
+                                {"kind": "indicator", "name": "macd_pct", "params": [12, 26, 9]},
+                                {"kind": "literal", "value": 0.005},
+                            ],
+                        },
+                    ],
+                },
+                "action": "entry-long",
+            }
+        ]
+    }
+    strat = parse_strategy(json.dumps(spec))
+    validate_strategy(strat)  # no exception

src/strategy_crypto/pyproject.toml

@@ -18,3 +18,4 @@ build-backend = "hatchling.build"
 
 [tool.hatch.build.targets.wheel.force-include]
 "strategy_crypto/strategies" = "strategy_crypto/strategies"
+"strategy_crypto/prompts.json" = "strategy_crypto/prompts.json"

src/strategy_crypto/strategy_crypto/frontend/data.py

@@ -1,3 +1,8 @@
+"""Paper-trading data access functions for the strategy_crypto dashboard.
+
+Reads exclusively from strategy_crypto.db (paper_trading_* tables).
+"""
+
 from __future__ import annotations
 
 import json
@@ -7,53 +12,6 @@ from typing import Any
 
 import pandas as pd  # type: ignore[import-untyped]
 
-from multi_swarm_core.persistence.repository import Repository
-
-
-def get_repo(db_path: str | Path) -> Repository:
-    return Repository(db_path=db_path)
-
-
-def list_runs_df(repo: Repository) -> pd.DataFrame:
-    return pd.DataFrame(repo.list_runs())
-
-
-def get_run_overview(repo: Repository, run_id: str) -> dict[str, Any]:
-    run = repo.get_run(run_id)
-    return {
-        "name": run["name"],
-        "started_at": run["started_at"],
-        "completed_at": run["completed_at"],
-        "status": run["status"],
-        "total_cost_usd": run["total_cost_usd"],
-        "config": json.loads(run["config_json"]),
-    }
-
-
-def generations_df(repo: Repository, run_id: str) -> pd.DataFrame:
-    return pd.DataFrame(repo.list_generations(run_id))
-
-
-def evaluations_df(repo: Repository, run_id: str) -> pd.DataFrame:
-    return pd.DataFrame(repo.list_evaluations(run_id))
-
-
-def genomes_df(
-    repo: Repository, run_id: str, generation_idx: int | None = None
-) -> pd.DataFrame:
-    rows = repo.list_genomes(run_id, generation_idx)
-    flat: list[dict[str, Any]] = []
-    for r in rows:
-        payload = json.loads(r["payload_json"])
-        flat.append(
-            {
-                "id": r["id"],
-                "generation_idx": r["generation_idx"],
-                **payload,
-            }
-        )
-    return pd.DataFrame(flat)
-
 
 def _paper_conn(db_path: str | Path) -> sqlite3.Connection:
     conn = sqlite3.connect(str(db_path))

src/strategy_crypto/strategy_crypto/frontend/nicegui_app.py

@@ -1,9 +1,7 @@
-"""NiceGUI dashboard — port progressivo da Streamlit.
+"""Strategy Crypto Dashboard — paper-trading page: /.
 
 Avvio: ``uv run python -m strategy_crypto.frontend.nicegui_app``
-Default port 8080. Streamlit resta su 8501 durante la migrazione.
-
-Riusa ``dashboard.data`` (Repository helpers) senza modifiche al backend.
+Default port 8080. Legge SOLO strategy_crypto.db (paper_trading_* tables).
 
 Palette "Neon Trading Dashboard" (ispirata screenshot 2026-05-11):
 - BG: #0A0A0F (near-black con tinge blu)
@@ -18,8 +16,6 @@ Palette "Neon Trading Dashboard" (ispirata screenshot 2026-05-11):
 
 from __future__ import annotations
 
-import html
-import json
 import os
 from pathlib import Path
 from typing import Any
@@ -29,12 +25,6 @@ import plotly.graph_objects as go  # type: ignore[import-untyped]
 from nicegui import app, ui
 
 from strategy_crypto.frontend.data import (
-    evaluations_df,
-    generations_df,
-    genomes_df,
-    get_repo,
-    get_run_overview,
-    list_runs_df,
     paper_equity_df,
     paper_positions_df,
     paper_run_summary,
@@ -42,840 +32,21 @@ from strategy_crypto.frontend.data import (
     paper_ticks_df,
     paper_trades_df,
 )
+from multi_swarm_core.dashboard.theme import (
+    COLOR_PRIMARY,
+    COLOR_SURFACE,
+    COLOR_SURFACE_2,
+    COLOR_TEXT,
+    COLOR_TEXT_MUTED,
+    _STATUS_BADGE,
+    _apply_theme,
+    _build_header,
+)
 
-# Dual-DB: GA core e paper strategy_crypto vivono in DB separati.
-GA_DB_PATH = os.environ.get("GA_DB_PATH", "./state/runs.db")
 PAPER_DB_PATH = os.environ.get("STRATEGY_CRYPTO_DB_PATH", "./state/strategy_crypto.db")
-# Subpath per Traefik: "" in dev, "/strategy_crypto_gui" in prod.
 DASHBOARD_ROOT_PATH = os.environ.get("DASHBOARD_ROOT_PATH", "")
 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 {{
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-.q-header {{
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-    color: {COLOR_TEXT_MUTED} !important;
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-
-code, pre, .nicegui-code {{
-    background: #1A1A24 !important;
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-.hljs {{ background: transparent !important; color: {COLOR_TEXT} !important; }}
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-.hljs-string {{ color: {COLOR_SUCCESS} !important; }}
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-.hljs-keyword, .hljs-built_in {{ color: {COLOR_ACCENT} !important; }}
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-.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; }}
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-.config-block .cb-bool {{ color: {COLOR_ACCENT}; }}
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-.config-block .cb-punct {{ color: {COLOR_TEXT_MUTED}; }}
-
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-    background: #1A1A24;
-    color: {COLOR_TEXT};
-    border: 1px solid {COLOR_BORDER};
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-    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"),
-                    ("/paper", "Paper"),
-                ):
-                    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(GA_DB_PATH).resolve().name} + {Path(PAPER_DB_PATH).resolve().name}</span>')
-
-
-def _runs_options() -> dict[str, str]:
-    repo = get_repo(GA_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(GA_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(GA_DB_PATH), state["run_id"]))).classes("w-full")
-    entropy_plot = ui.plotly(_entropy_figure(generations_df(get_repo(GA_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(GA_DB_PATH), run_id)
-            ov = get_run_overview(get_repo(GA_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(GA_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:
-        rows = (e.args or {}).get("rows") or []
-        if not rows:
-            return
-        full_id = rows[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 _paper_runs_options(only_running: bool = False) -> dict[str, str]:
     runs = paper_runs_df(PAPER_DB_PATH)
@@ -925,10 +96,15 @@ def _paper_equity_figure(eq_df: Any, initial_capital: float) -> go.Figure:
     return fig
 
 
-@ui.page("/paper")
+@ui.page("/")
 def paper() -> None:
     _apply_theme()
-    _build_header(active="/paper")
+    _build_header(
+        active="/",
+        brand_subtitle="Strategy Crypto",
+        nav_items=[("/", "Paper")],
+        db_label=f"⛁ {Path(PAPER_DB_PATH).resolve().name}",
+    )
 
     options = _paper_runs_options()
     if not options:
@@ -1087,7 +263,6 @@ def paper() -> None:
 def main() -> None:
     app.on_startup(
         lambda: print(
-            f"GA DB: {Path(GA_DB_PATH).resolve()} | "
             f"Paper DB: {Path(PAPER_DB_PATH).resolve()} | "
             f"root_path: {DASHBOARD_ROOT_PATH or '/'}"
         )

src/strategy_crypto/strategy_crypto/prompts.json

@@ -0,0 +1,50 @@
+{
+  "_comment": "Stili cognitivi e direttive del system_prompt per il GA di strategy_crypto. Modifica liberamente: cambia 'directive' di uno stile esistente o aggiungi nuove voci a 'styles'. Il nome dello stile (key) viene usato come 'cognitive_style' del genoma; la 'directive' diventa il system_prompt iniziale.",
+  "_schema": "3.2",
+  "_changelog": "v3.2 - Patch consolidamento: ripristinati 3 invarianti regrediti in v3.1 (ASCII-safe, archetipo dominante, hint lookback); voce attiva rinforzata; anti_patterns +2 (chattering, isteresi); output_priorities +1 (#1 coerenza con lente cognitiva); domain_warnings +1 frase (soglia seasonality 0.05); NEW _design_invariants metadata. Lunghezza directive 800-950 char (era 545-614 in v3.1, troppo snellite). v3.1 - Refactor contenuto post-diagnosi. v3.0 - Refactor compositore. v2.2 - Metriche geometrico-frattali. v2.1 - directive estese. v2.0 - Riprogettato per blind-generator GA.",
+  "_focus_metrics_design": "Le focus_metrics sono ENFASI per la lente, non filtri. Standardizzate a 4 per stile (cognitive budget). Evitano ridondanze con la sezione 'Regime recente' del USER_TEMPLATE.",
+  "_design_invariants": "Caratteristiche che future versioni DEVONO preservare: (1) ASCII-safe: nessun carattere > U+007F nelle directive (es. il carattere circa-uguale U+2248 era una regressione di v3.1, ripristinato in v3.2); (2) Archetipo dominante: ogni directive chiude con 'Archetipo dominante: <metafora>.' come ancora identitaria resistente a mutate_prompt_llm; (3) Lookback consigliato: ogni directive include un range numerico diversificato per stile per orientare il parametro evoluto lookback_window del genoma; (4) Metafora ancorante: la lente cognitiva e' descritta in forma 'Il mercato e ...' come prima frase; (5) Lunghezza directive: tra 800 e 950 char (sweet spot per robustezza a mutate_prompt_llm).",
+
+  "agent_role": "Sei un agente generatore di ipotesi di trading quantitativo per un sistema swarm coevolutivo specializzato in mercati crypto. Sei parte di una popolazione che esplora collettivamente lo spazio delle strategie: la diversita delle ipotesi e un asset critico per il sistema. Preferisci esplorare territori meno ovvi rispetto a quelli che la tua lente cognitiva renderebbe predicibili.",
+
+  "pattern_guidance": "Forme di curva da cercare:\n  - Trend strutturale (direzione persistente con basso ritracciamento)\n  - Compressione di volatilita (pre-breakout, energia accumulata)\n  - Espansione di volatilita (regime di shock: momentum o cattura difensiva)\n  - Mean reversion strutturale (deviazione eccessiva dalla media verso ritorno atteso)\n  - Esaurimento direzionale (estremi di oscillatori, divergenza prezzo-momento)\n\nRipetibilita da sfruttare:\n  - Eventi crossover ricorrenti su serie correlate\n  - Cicli intra-day se la seasonality oraria e significativa\n  - Cicli settimanali se la seasonality settimanale e significativa\n  - Pattern doppio (top/bottom) con conferma su livello simile\n  - Range breakout dopo periodo di compressione\n\nCerca pattern che si REPLICANO nei dati storici, non singoli eventi rari.",
+
+  "instruction": "Genera una strategia che cerchi anomalie sfruttabili in questo regime crypto.",
+
+  "domain_warnings": "Crypto trada 24/7 senza CME gap: non assumere chiusure settimanali. Tail pesanti e vol clustering esteso caratterizzano BTC/ETH: evita ipotesi gaussiane. NON tentare di inferire funding rate, news o eventi macro: non sono accessibili. Le statistiche fornite sono l'unica informazione su cui basarsi. Una seasonality_hour o seasonality_dow > 0 NON significa che la stagionalita sia significativa: usa la soglia 0.05 come gate minimo (sotto e' rumore statistico).",
+
+  "anti_patterns": "Evita: (1) basare strategia su singolo evento estremo (kurt outlier senza ricorrenza nelle 500 barre recenti); (2) usare piu di 4 condizioni in AND (sovra-fitting combinatorio, brittle a piccoli shift di regime); (3) confondere correlazione storica con causalita (autocorr o pattern temporali sono BIAS, non leggi); (4) assumere stazionarieta perfetta del regime (i delta 'recente vs baseline' indicano lo scostamento); (5) usare feature temporali (hour, dow, is_weekend) quando la seasonality corrispondente e' < 0.05 (rumore, no signal); (6) crossover tra indicatori dello stesso tipo con lookback vicini (chattering: oscilla tra entry/exit senza segnale netto); (7) soglie hard senza isteresi tra entry ed exit (genera chattering al confine; usa soglie diverse per entry vs exit, almeno 10-20% di gap).",
+
+  "output_priorities": "Quando emerge trade-off: (1) coerenza con la lente cognitiva: la strategia deve essere riconoscibile come emanata dal tuo stile (es. engineer = pochi gate robusti, psychologist = contrarian su estremi). E' il fondamento del design swarm: ipotesi omogeneizzate riducono la diversita della popolazione; (2) robustezza cross-regime > ottimalita su singolo regime; (3) semplicita interpretabile (2-3 condizioni con razionale chiaro) > complessita raffinata (5+ condizioni accordate); (4) selettivita (poche entry forti, alto SNR) > attivita (molte entry deboli); (5) condizioni con razionale meccanico > pattern statistici fortuiti.",
+
+  "styles": {
+    "physicist": {
+      "directive": "Il mercato e un sistema fisico con energia (std), simmetrie (skew) e memoria (autocorr). Leggi kurt come densita di eventi estremi (fat tails = fuori equilibrio), skew come forzante asimmetrica. AR(1) positivo molto sopra baseline = memoria coerente, costruisci ipotesi di momentum legittimo; Hurst > 0.55 conferma persistenza di scala su orizzonti multipli; vol_pct alto con kurt bassa = energia immagazzinata non ancora rilasciata, cattura il rilascio; efficiency_ratio elevato = movimento efficiente, sfrutta direzionalita; spectral_entropy bassa con dominant_cycle definito = modi armonici sfruttabili, combina con conferma sulla fase. Preferisci pattern coerenti su piu lookback rispetto a singoli eventi rumorosi. Diagnostica regimi simmetrici e rotture di simmetria. Lookback consigliato: 150-300 barre. Archetipo dominante: sistema fisico in equilibrio (o pre-rottura di simmetria).",
+      "focus_metrics": ["hurst", "dominant_cycle", "efficiency_ratio", "spectral_entropy"]
+    },
+    "biologist": {
+      "directive": "Il mercato e un ecosistema dove strategie competono per alpha finito. Skew negativo segnala predazione asimmetrica (vol-selling crowded subisce shock), positivo predatori che cacciano breakout. Kurt alta = eventi di estinzione o fioritura. AR(1) positivo persistente = una specie sta colonizzando la nicchia (overcrowding imminente, preferisci fade); Hurst > 0.55 con vol_pct basso = nicchia stabile (occupa con strategie direzionali); tail asimmetrico (left molto piu pesante di right) = predazione asimmetrica strutturale, costruisci contrarian sulla coda; structural_uptrend persistente = specie dominante stabile. Combina seasonality con uno o due gate di regime per evitare di sovrapporti a fasi gia mature. Cattura la coda opposta al consensus. Lookback consigliato: 80-200 barre. Archetipo dominante: ecosistema con dinamiche predator-prey e nicchie evolutive.",
+      "focus_metrics": ["tail_left", "tail_right", "structural_uptrend", "seasonality_hour"]
+    },
+    "historian": {
+      "directive": "Il mercato attraversa fasi cicliche che si ripetono in forma simile. Mean = drift strutturale, std = ampiezza ciclo, kurt alta + vol regime medium/high = fase tardiva (pre-transizione); kurt bassa + skew vicino a zero = fase di accumulazione o stabilita. AR(1) recente molto sopra baseline storica = regime accelera rispetto al normale, diagnostica se markup o distribuzione; Hurst > 0.55 con vol_pct alto = fase markup matura, costruisci ipotesi di mean reversion strutturale attesa; structural_uptrend sostenuto = fase di accumulo o markup attiva, sfrutta la direzionalita; compression < 1 = consolidamento pre-fase nuova, preferisci breakout direzionali a conferma di rottura. Identifica analogie tra il regime corrente e fasi tipiche (accumulazione, markup, distribuzione, markdown). Lookback consigliato: 200-500 barre. Archetipo dominante: ciclo storico ricorrente in fasi tipiche.",
+      "focus_metrics": ["autocorr_recent", "structural_uptrend", "compression", "hurst"]
+    },
+    "meteorologist": {
+      "directive": "La volatilita ha climi persistenti e fronti di transizione. Vol_regime + std + kurt definiscono il microclima: std bassa + kurt bassa = calma stabile (preferisci vendere vol con gate sicuri); std alta + kurt alta = tempesta (compra convexity o resta flat); std bassa + kurt alta = calma ingannevole pre-fronte (riduci esposizione). AR(1) recente sopra baseline = fronte persistente in arrivo, cattura la direzione del fronte; Hurst > 0.55 = sistema su scala lunga (ciclone), Hurst < 0.45 = turbolenza locale (no trend persistente, preferisci range-trading); vol_pct estremo = posizione nel ciclo seasonal, modula la size; compression < 1 = compressione vol pre-rilascio, posizionati per il breakout. Costruisci strategie con gate espliciti su vol che attivano logiche diverse. Lookback consigliato: 50-150 barre. Archetipo dominante: clima atmosferico con fronti e regimi persistenti.",
+      "focus_metrics": ["vol_pct", "compression", "tail_right", "dominant_cycle"]
+    },
+    "engineer": {
+      "directive": "Tratta ogni segnale come un sistema di controllo: serve SNR favorevole, causalita, robustezza. Std e il rumore di fondo. Kurt alta riduce affidabilita dei segnali medi (gli estremi dominano le statistiche). AR(1) > 0.05 con std contenuta = SNR favorevole, costruisci ipotesi di momentum filtrato; AR(1) vicino a zero = random walk, evita di costruire signal su questo; Hurst < 0.45 = filtro mean-reversion causale efficace, sfrutta con isteresi; efficiency_ratio < 0.2 = no signal, non costruire; spectral_entropy > 0.8 = white noise, regime non modellabile; tail_index < 2.5 = saturazione dei sensori, riduci leverage; seasonality < 0.05 = feature temporali sono rumore, NON usarle. Preferisci pattern semplici e tarabili: poche condizioni in AND, soglie con margine, isteresi entry/exit. Lookback consigliato: 60-120 barre. Archetipo dominante: sistema di controllo ingegneristico con SNR e robustezza.",
+      "focus_metrics": ["efficiency_ratio", "spectral_entropy", "tail_left", "autocorr_recent"]
+    },
+    "military_strategist": {
+      "directive": "Distingui campagna offensiva da campagna difensiva e adatta dottrina. Vol regime medium/low + skew positivo + kurt moderata = terreno favorevole all'attacco (costruisci entry direzionali su breakout o momentum). Vol regime high + kurt elevata = terreno ostile (difesa: posizioni limitate, exit rapide, gate restrittivi). AR(1) > 0 = vento alle spalle, carica con momentum; AR(1) negativo = imboscata possibile, preferisci contrarian; Hurst > 0.55 = posizione difendibile, hold trade; structural_uptrend > 0.7 = terreno occupato dall'avversario (decidi se attaccare o ritirarti); compression < 0.5 = preparazione attacco silenziosa, posizionati per breakout; vol_pct alta = artiglieria nemica attiva, ritirata. Concentrazione: poche condizioni forti. Sorpresa: contrarian su consensus estremo. Lookback consigliato: 100-200 barre. Archetipo dominante: stratega militare che bilancia offesa e difesa.",
+      "focus_metrics": ["structural_uptrend", "compression", "vol_pct", "tail_left"]
+    },
+    "psychologist": {
+      "directive": "Il mercato e folla con emozioni misurabili. Skew e kurt sono il termometro emotivo: skew neg + kurt alta = paura ricorrente (capitulation spikes, cattura il rimbalzo); skew pos + kurt alta = euforia (FOMO spikes, preferisci fade gli estremi al rialzo); skew vicino a zero + kurt bassa = apatia o range (gioca i bordi del range). AR(1) recente molto sopra baseline = euforia coordinata in corso, posizionati contro l'ultimo arrivato; Hurst > 0.55 = trance collettiva (trend trance, dura piu del razionale); tail_left pesante (Hill < 2.5) = paura sistemica strutturale, contrarian sulla capitulation; spectral_entropy alta = caos comportamentale, riduci dimensionalita del signal; vol_pct estremo = momentum emozionale puro, fade gli estremi. Sfrutta crossover di oscillatori in regimi razionali (kurt vicina a 3). Lookback consigliato: 50-120 barre. Archetipo dominante: psicologo del comportamento collettivo.",
+      "focus_metrics": ["tail_left", "tail_right", "autocorr_recent", "spectral_entropy"]
+    }
+  }
+}

src/strategy_crypto/strategy_crypto/strategies/archive/btc_9cf506b8.json

@@ -0,0 +1,140 @@
+{
+  "rules": [
+    {
+      "condition": {
+        "op": "and",
+        "args": [
+          {
+            "op": "gt",
+            "args": [
+              {
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+      "action": "entry-short"
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+            "op": "eq",
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+              {
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+                "name": "sma_pct",
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+              {
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+      "action": "exit"
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+  ]
+}

src/strategy_crypto/strategy_crypto/strategies/btc_238e4812.json

@@ -0,0 +1,84 @@
+{
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+          }
+        ]
+      },
+      "action": "exit"
+    }
+  ]
+}

src/strategy_crypto/strategy_crypto/strategies/eth_c04dff7086.json

@@ -0,0 +1,120 @@
+{
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+          {
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+            ]
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+        ]
+      },
+      "action": "exit"
+    }
+  ]
+}

state/validation-btc-100-001-thr3.json

@@ -0,0 +1,634 @@
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state/validation-btc-100-001.json

@@ -0,0 +1,634 @@
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state/validation-btc-100-5m-thr3.json

@@ -0,0 +1,634 @@
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state/validation-extended-001.json

@@ -0,0 +1,634 @@
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+          "return": 0.05047188141942249,
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+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
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+          "max_dd": 0.00995276327591354,
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+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
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+      "fitness_oos_mean": 0.09080117984127524,
+      "fitness_oos_min": 0.0,
+      "fitness_oos_max": 0.32995249837249846,
+      "fitness_oos_std": 0.13873981434768828,
+      "sharpe_oos_mean": 0.34155944422926565,
+      "sharpe_oos_min": -1.0706470661093082,
+      "robust_score": 0.0
+    }
+  ]
+}

uv.lock

@@ -897,32 +897,36 @@ wheels = [
 name = "multi-swarm-coevolutive"
 version = "0.1.0"
 source = { virtual = "." }
+dependencies = [
+    { name = "multi-swarm-core" },
+    { name = "strategy-crypto" },
+]
 
 [package.dev-dependencies]
 dev = [
-    { name = "multi-swarm-core" },
     { name = "mypy" },
     { name = "pytest" },
     { name = "pytest-asyncio" },
     { name = "pytest-mock" },
     { name = "responses" },
     { name = "ruff" },
-    { name = "strategy-crypto" },
     { name = "types-requests" },
 ]
 
 [package.metadata]
+requires-dist = [
+    { name = "multi-swarm-core", editable = "src/multi_swarm_core" },
+    { name = "strategy-crypto", editable = "src/strategy_crypto" },
+]
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "multi-swarm-core", editable = "src/multi_swarm_core" },
     { name = "mypy", specifier = ">=1.13" },
     { name = "pytest", specifier = ">=8.3" },
     { name = "pytest-asyncio", specifier = ">=0.24" },
     { name = "pytest-mock", specifier = ">=3.14" },
     { name = "responses", specifier = ">=0.25" },
     { name = "ruff", specifier = ">=0.7" },
-    { name = "strategy-crypto", editable = "src/strategy_crypto" },
     { name = "types-requests", specifier = ">=2.32" },
 ]
 
@@ -932,9 +936,11 @@ version = "0.1.0"
 source = { editable = "src/multi_swarm_core" }
 dependencies = [
     { name = "httpx" },
+    { name = "nicegui" },
     { name = "numpy" },
     { name = "openai" },
     { name = "pandas" },
+    { name = "plotly" },
     { name = "pyarrow" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
@@ -949,9 +955,11 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "httpx", specifier = ">=0.28" },
+    { name = "nicegui", specifier = ">=3.11.1" },
     { name = "numpy", specifier = ">=2.1" },
     { name = "openai", specifier = ">=1.55" },
     { name = "pandas", specifier = ">=2.2" },
+    { name = "plotly", specifier = ">=5.24" },
     { name = "pyarrow", specifier = ">=18.0" },
     { name = "pydantic", specifier = ">=2.9" },
     { name = "pydantic-settings", specifier = ">=2.6" },