diff --git a/.env.example b/.env.example
index d136a30..5fb0f59 100644
--- a/.env.example
+++ b/.env.example
@@ -26,8 +26,8 @@ DB_PATH=./runs.db
 # Docker / Traefik (usati SOLO da docker-compose.yml)
 # Dominio base: traefik espone la dashboard su swarm.${DOMAIN_NAME}
 DOMAIN_NAME=tielogic.xyz
-# Porta interna della Streamlit dashboard (Traefik fa il TLS davanti)
-SWARM_DASHBOARD_PORT=8501
+# Porta interna della NiceGUI dashboard (Traefik fa il TLS davanti)
+SWARM_DASHBOARD_PORT=8080
 
 # Paper-trading runner — override del command nel compose (opzionali)
 PAPER_RUN_NAME=phase3-papertrade-prod
diff --git a/README.md b/README.md
index bf7b037..208a58b 100644
--- a/README.md
+++ b/README.md
@@ -75,17 +75,11 @@ src/multi_swarm/
 ├── orchestrator/
 │   └── run.py               End-to-end pipeline + persistence
 └── dashboard/
-    ├── streamlit_app.py     Hub multipage
-    ├── data.py              Lettura runs.db per le pagine
-    ├── aquarium.py          Helper canvas HTML5 (fish data + JS template)
-    └── pages/
-        ├── 01_overview.py       Run + metriche aggregate
-        ├── 02_ga_convergence.py Fitness convergence + entropy plot
-        ├── 03_genomes.py        Top-10 + ispezione system_prompt
-        └── 04_aquarium.py       Acquario 2D con click → info + lineage
+    ├── nicegui_app.py       NiceGUI dashboard (overview / convergence / genomes)
+    └── data.py              Lettura runs.db per le pagine
 ```
 
-Stack: Python 3.13, uv, pytest+pytest-mock+responses, openai SDK (verso OpenRouter), requests+tenacity, pandas+numpy+scipy, sqlmodel+sqlite, streamlit+plotly.
+Stack: Python 3.13, uv, pytest+pytest-mock+responses, openai SDK (verso OpenRouter), requests+tenacity, pandas+numpy+scipy, sqlmodel+sqlite, nicegui+plotly.
 
 ## Setup
 
@@ -150,17 +144,18 @@ uv run python scripts/run_phase1.py \
   --population-size 20 --n-generations 10
 
 # Dashboard
-DB_PATH=./runs.db uv run streamlit run src/multi_swarm/dashboard/streamlit_app.py
+DB_PATH=./runs.db uv run python -m multi_swarm.dashboard.nicegui_app
 ```
 
 ## Dashboard
 
-Streamlit multipage su `http://localhost:8501` (override con `--server.port`):
+NiceGUI dashboard (dark/neon palette) su `http://localhost:8080` (override con env `SWARM_DASHBOARD_PORT`):
 
-- **Overview**: lista runs, status, costo, metriche aggregate evaluations (parse success %, top fitness, median).
-- **GA Convergence**: fitness median/max/p90 per generazione, entropy con hline a soglia gate (0.5).
-- **Genomes**: top-10 ordinati per fitness, click su row per ispezione system_prompt + raw_text JSON strategy.
-- **Aquarium**: visualizzazione 2D canvas HTML5 con un pesce per agente; dimensione ∝ fitness, colore per cognitive_style, halo sui top-3, click su pesce → panel info completo + lineage BFS (parents → grandparents → ...).
+- **Overview** (`/`): lista runs, status, costo, metriche aggregate evaluations (parse success %, top fitness, median).
+- **GA Convergence** (`/convergence`): fitness median/max/p90 per generazione, entropy con hline a soglia gate (0.5).
+- **Genomes** (`/genomes`): top-K ordinati per fitness, click su riga per ispezione system_prompt + raw_text JSON strategy.
+
+In produzione gira dentro Docker dietro Traefik su `https://swarm.${DOMAIN_NAME}` — vedi `docker-compose.yml`.
 
 ## Costi tipici Phase 1
 
diff --git a/docker-compose.yml b/docker-compose.yml
index 70cac31..7273350 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -81,20 +81,16 @@ services:
       - ./data:/app/data:ro
       - ./series:/app/series:ro
     entrypoint:
-      - streamlit
-      - run
-      - /app/src/multi_swarm/dashboard/streamlit_app.py
-      - --server.address=0.0.0.0
-      - --server.port=${SWARM_DASHBOARD_PORT:-8501}
-      - --server.headless=true
-      - --browser.gatherUsageStats=false
+      - python
+      - -m
+      - multi_swarm.dashboard.nicegui_app
     command: []
     healthcheck:
       test:
         - "CMD"
         - "python"
         - "-c"
-        - "import os, urllib.request; urllib.request.urlopen(f'http://localhost:{os.environ.get(\"SWARM_DASHBOARD_PORT\",\"8501\")}/_stcore/health', timeout=3).close()"
+        - "import os, urllib.request; urllib.request.urlopen(f'http://localhost:{os.environ.get(\"SWARM_DASHBOARD_PORT\",\"8080\")}/', timeout=3).close()"
       interval: 30s
       timeout: 5s
       retries: 3
@@ -106,5 +102,5 @@ services:
       - traefik.http.routers.multi-swarm-dashboard.tls=true
       - traefik.http.routers.multi-swarm-dashboard.entrypoints=websecure
       - traefik.http.routers.multi-swarm-dashboard.tls.certresolver=mytlschallenge
-      - "traefik.http.services.multi-swarm-dashboard.loadbalancer.server.port=${SWARM_DASHBOARD_PORT:-8501}"
+      - "traefik.http.services.multi-swarm-dashboard.loadbalancer.server.port=${SWARM_DASHBOARD_PORT:-8080}"
       - com.centurylinklabs.watchtower.enable=true
diff --git a/pyproject.toml b/pyproject.toml
index c220f79..1c4e07e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,7 +16,6 @@ dependencies = [
     "requests>=2.32",
     "tenacity>=9.0",
     "pyyaml>=6.0",
-    "streamlit>=1.40",
     "plotly>=5.24",
     "pyarrow>=18.0",
     "nicegui>=3.11.1",
diff --git a/src/multi_swarm/dashboard/aquarium.py b/src/multi_swarm/dashboard/aquarium.py
deleted file mode 100644
index 817f13e..0000000
--- a/src/multi_swarm/dashboard/aquarium.py
+++ /dev/null
@@ -1,590 +0,0 @@
-"""Aquarium 2D visualization helpers.
-
-Builds fish records (with full genome attributes + ancestor lineage) and
-renders a self-contained HTML/JS canvas animation, embeddable in Streamlit
-via ``streamlit.components.v1.html``. Includes a click handler that opens
-an info panel showing genome details and BFS ancestor levels.
-"""
-
-from __future__ import annotations
-
-import json
-import math
-from typing import Any
-
-import pandas as pd  # type: ignore[import-untyped]
-
-# Color palette per cognitive style. Default fallback for unknown styles is grey.
-STYLE_COLORS: dict[str, str] = {
-    "physicist": "#4cc9f0",
-    "biologist": "#52b788",
-    "historian": "#e76f51",
-    "meteorologist": "#ffd166",
-    "ecologist": "#a78bfa",
-    "engineer": "#fb6f92",
-}
-DEFAULT_COLOR: str = "#94a3b8"
-
-
-def _is_nan(v: Any) -> bool:
-    try:
-        return bool(pd.isna(v))
-    except (TypeError, ValueError):
-        return False
-
-
-def _safe_float(v: Any, default: float = 0.0) -> float:
-    if v is None or _is_nan(v):
-        return default
-    try:
-        return float(v)
-    except (TypeError, ValueError):
-        return default
-
-
-def _safe_int(v: Any, default: int = 0) -> int:
-    if v is None or _is_nan(v):
-        return default
-    try:
-        return int(v)
-    except (TypeError, ValueError):
-        return default
-
-
-def _safe_str(v: Any, default: str = "") -> str:
-    if v is None or _is_nan(v):
-        return default
-    return str(v)
-
-
-def _safe_list(v: Any) -> list[Any]:
-    if v is None:
-        return []
-    if isinstance(v, list):
-        return list(v)
-    # pandas may store python lists in object cells; if it's e.g. a numpy array,
-    # falling back to list() is fine. NaN scalar is excluded by _is_nan.
-    if _is_nan(v):
-        return []
-    try:
-        return list(v)
-    except TypeError:
-        return []
-
-
-def build_lineage_index(
-    genomes_df: pd.DataFrame, evals_df: pd.DataFrame
-) -> dict[str, dict[str, Any]]:
-    """Build ``{genome_id: attrs}`` for every genome in the run.
-
-    ``genomes_df`` must come from ``genomes_df(repo, run_id)`` (no gen filter):
-    columns include ``id``, ``generation_idx``, ``system_prompt``,
-    ``feature_access``, ``temperature``, ``top_p``, ``model_tier``,
-    ``lookback_window``, ``cognitive_style``, ``parent_ids``, ``generation``.
-
-    ``evals_df`` must come from ``evaluations_df(repo, run_id)``: columns
-    include ``genome_id``, ``fitness``, ``dsr``, ``sharpe``, ``max_dd``,
-    ``n_trades``.
-    """
-    if genomes_df.empty:
-        return {}
-
-    if evals_df is None or evals_df.empty:
-        merged = genomes_df.copy()
-        for col in ("fitness", "dsr", "sharpe", "max_dd", "n_trades"):
-            if col not in merged.columns:
-                merged[col] = 0.0 if col != "n_trades" else 0
-    else:
-        merged = genomes_df.merge(
-            evals_df,
-            left_on="id",
-            right_on="genome_id",
-            how="left",
-            suffixes=("", "_eval"),
-        )
-
-    index: dict[str, dict[str, Any]] = {}
-    for _, row in merged.iterrows():
-        gid = _safe_str(row.get("id"))
-        if not gid:
-            continue
-        # ``generation`` is the genome's evolutionary generation (from payload).
-        # If absent, fall back to ``generation_idx`` (column added by the
-        # repository). Defensive: both may be missing in edge cases.
-        gen_val: Any = row.get("generation")
-        if gen_val is None or _is_nan(gen_val):
-            gen_val = row.get("generation_idx", 0)
-        index[gid] = {
-            "id": gid,
-            "generation": _safe_int(gen_val, 0),
-            "fitness": _safe_float(row.get("fitness"), 0.0),
-            "dsr": _safe_float(row.get("dsr"), 0.0),
-            "sharpe": _safe_float(row.get("sharpe"), 0.0),
-            "max_dd": _safe_float(row.get("max_dd"), 0.0),
-            "n_trades": _safe_int(row.get("n_trades"), 0),
-            "cognitive_style": _safe_str(row.get("cognitive_style"), ""),
-            "system_prompt": _safe_str(row.get("system_prompt"), ""),
-            "temperature": _safe_float(row.get("temperature"), 0.0),
-            "lookback_window": _safe_int(row.get("lookback_window"), 0),
-            "feature_access": _safe_list(row.get("feature_access")),
-            "model_tier": _safe_str(row.get("model_tier"), ""),
-            "parent_ids": _safe_list(row.get("parent_ids")),
-        }
-    return index
-
-
-def trace_ancestors(
-    genome_id: str,
-    lineage_index: dict[str, dict[str, Any]],
-    max_levels: int = 5,
-) -> list[list[dict[str, Any]]]:
-    """BFS over ``parent_ids`` returning levels of ancestors.
-
-    ``levels[0]`` = direct parents, ``levels[1]`` = grandparents, etc. Each
-    entry is a small dict (no ``system_prompt``, to keep JSON payload light):
-    ``{id, generation, fitness, cognitive_style}``. Cycles are guarded via a
-    ``seen`` set; missing parents (not in this run) are stubbed with sentinel
-    values so the lineage display still renders the relationship.
-    """
-    levels: list[list[dict[str, Any]]] = []
-    root = lineage_index.get(genome_id, {})
-    current_ids: list[str] = list(root.get("parent_ids", []))
-    seen: set[str] = {genome_id}
-    for _ in range(max_levels):
-        if not current_ids:
-            break
-        level_entries: list[dict[str, Any]] = []
-        next_ids: list[str] = []
-        for pid in current_ids:
-            if pid in seen:
-                continue
-            seen.add(pid)
-            entry = lineage_index.get(pid)
-            if entry is None:
-                level_entries.append(
-                    {
-                        "id": pid,
-                        "generation": -1,
-                        "fitness": 0.0,
-                        "cognitive_style": "",
-                    }
-                )
-                continue
-            level_entries.append(
-                {
-                    "id": entry["id"],
-                    "generation": entry["generation"],
-                    "fitness": entry["fitness"],
-                    "cognitive_style": entry["cognitive_style"],
-                }
-            )
-            next_ids.extend(entry.get("parent_ids", []))
-        if not level_entries:
-            break
-        levels.append(level_entries)
-        current_ids = next_ids
-    return levels
-
-
-def build_fish_dataset(
-    active_df: pd.DataFrame,
-    lineage_index: dict[str, dict[str, Any]] | None = None,
-    max_lineage_levels: int = 5,
-) -> list[dict[str, Any]]:
-    """Build full fish records for each active genome.
-
-    For every row in ``active_df`` the matching entry in ``lineage_index`` is
-    looked up by ``genome_id`` (or ``id``) and attached together with the BFS
-    ancestor levels. Rows whose id is not in the index are skipped.
-
-    Backward-compat: if ``lineage_index`` is ``None`` (legacy call site, e.g.
-    test fixtures with simple merged DataFrames) we synthesize a minimal
-    lineage from ``active_df`` itself so the function still returns useful
-    fish records.
-    """
-    if active_df.empty:
-        return []
-
-    if lineage_index is None:
-        # Legacy path: build a tiny index from the active df only.
-        synth: dict[str, dict[str, Any]] = {}
-        for _, row in active_df.iterrows():
-            gid = _safe_str(row.get("genome_id") or row.get("id"))
-            if not gid:
-                continue
-            fitness_val = _safe_float(row.get("fitness"), float("nan"))
-            if math.isnan(fitness_val):
-                continue
-            synth[gid] = {
-                "id": gid,
-                "generation": _safe_int(row.get("generation"), 0),
-                "fitness": fitness_val,
-                "dsr": _safe_float(row.get("dsr"), 0.0),
-                "sharpe": _safe_float(row.get("sharpe"), 0.0),
-                "max_dd": _safe_float(row.get("max_dd"), 0.0),
-                "n_trades": _safe_int(row.get("n_trades"), 0),
-                "cognitive_style": _safe_str(row.get("cognitive_style"), "unknown"),
-                "system_prompt": _safe_str(row.get("system_prompt"), ""),
-                "temperature": _safe_float(row.get("temperature"), 0.0),
-                "lookback_window": _safe_int(row.get("lookback_window"), 0),
-                "feature_access": _safe_list(row.get("feature_access")),
-                "model_tier": _safe_str(row.get("model_tier"), ""),
-                "parent_ids": _safe_list(row.get("parent_ids")),
-            }
-        lineage_index = synth
-
-    fish: list[dict[str, Any]] = []
-    for _, row in active_df.iterrows():
-        gid = _safe_str(row.get("genome_id") or row.get("id"))
-        if not gid:
-            continue
-        attrs = lineage_index.get(gid)
-        if attrs is None:
-            continue
-        if math.isnan(attrs.get("fitness", 0.0)):
-            continue
-        ancestors = trace_ancestors(gid, lineage_index, max_lineage_levels)
-        record = {**attrs, "ancestors": ancestors}
-        fish.append(record)
-    return fish
-
-
-def build_aquarium_html(
-    fish: list[dict[str, Any]],
-    canvas_w: int = 1000,
-    canvas_h: int = 600,
-) -> str:
-    """Build the self-contained HTML/JS string for the aquarium canvas.
-
-    The output embeds a click handler: tapping a fish opens an info panel
-    (top-right of the canvas) showing its genome attributes and BFS ancestor
-    levels. Labels are no longer rendered on the canvas itself.
-    """
-    fish_json = json.dumps(fish)
-    palette_json = json.dumps(STYLE_COLORS)
-    default_color = DEFAULT_COLOR
-
-    # All braces inside <style>/<script> are escaped to literals using {{ }}
-    # so we can use Python f-string substitution for the few JSON payloads.
-    return f"""
-<div style="position:relative;width:100%;height:{canvas_h}px;">
-  <canvas id="aquarium" width="{canvas_w}" height="{canvas_h}"
-          style="width:100%;height:{canvas_h}px;border-radius:12px;
-                 background:linear-gradient(180deg,#0a2540 0%,#1a4d80 100%);
-                 display:block;cursor:pointer;"></canvas>
-  <div id="fish-info-panel" style="
-    position:absolute;
-    top:12px;
-    right:12px;
-    width:340px;
-    max-height:580px;
-    overflow-y:auto;
-    background:rgba(8,16,32,0.92);
-    color:#e2e8f0;
-    border-radius:10px;
-    padding:14px 16px;
-    font-family:system-ui,-apple-system,sans-serif;
-    font-size:12px;
-    line-height:1.5;
-    border:1px solid rgba(255,255,255,0.1);
-    backdrop-filter:blur(6px);
-    -webkit-backdrop-filter:blur(6px);
-    display:none;
-    z-index:10;
-  ">
-    <div id="fish-info-content"></div>
-    <button id="fish-info-close" style="
-      position:absolute;top:8px;right:10px;
-      background:transparent;color:#94a3b8;border:none;
-      cursor:pointer;font-size:16px;
-    ">&times;</button>
-  </div>
-</div>
-<script>
-(function() {{
-  const FISH_DATA = {fish_json};
-  const STYLE_COLORS = {palette_json};
-  const DEFAULT_COLOR = {json.dumps(default_color)};
-
-  const canvas = document.getElementById('aquarium');
-  if (!canvas) return;
-  const ctx = canvas.getContext('2d');
-  const W = canvas.width;
-  const H = canvas.height;
-
-  const panel = document.getElementById('fish-info-panel');
-  const panelContent = document.getElementById('fish-info-content');
-  const closeBtn = document.getElementById('fish-info-close');
-  if (closeBtn) {{
-    closeBtn.addEventListener('click', function() {{
-      panel.style.display = 'none';
-    }});
-  }}
-
-  // Normalize fitness for sizing.
-  let maxFit = 0.0;
-  for (const f of FISH_DATA) {{
-    if (typeof f.fitness === 'number' && f.fitness > maxFit) maxFit = f.fitness;
-  }}
-
-  function lerp(a, b, t) {{ return a + (b - a) * t; }}
-
-  function radiusFor(fitness) {{
-    if (maxFit <= 0) return 8;
-    const t = Math.max(0.05, Math.min(1.0, fitness / maxFit));
-    return lerp(8, 40, t);
-  }}
-
-  function colorFor(style) {{
-    return STYLE_COLORS[style] || DEFAULT_COLOR;
-  }}
-
-  // Init fish state. Each entry keeps a reference to the original data dict
-  // so the click handler can show full attributes + ancestors.
-  const fishState = FISH_DATA.map(function(f, idx) {{
-    const r = radiusFor(f.fitness);
-    return {{
-      data: f,
-      color: colorFor(f.cognitive_style),
-      radius: r,
-      x: Math.random() * (W - 2 * r) + r,
-      y: Math.random() * (H - 2 * r) + r,
-      vx: (Math.random() - 0.5) * 1.5,
-      vy: (Math.random() - 0.5) * 1.0,
-      rank: idx,
-    }};
-  }});
-
-  // Bubbles for ambience.
-  const N_BUBBLES = 25;
-  const bubbles = Array.from({{length: N_BUBBLES}}, function() {{ return {{
-    x: Math.random() * W,
-    y: Math.random() * H,
-    r: 1 + Math.random() * 3,
-    vy: 0.3 + Math.random() * 0.7,
-  }}; }});
-
-  function drawBubble(b) {{
-    ctx.beginPath();
-    ctx.arc(b.x, b.y, b.r, 0, Math.PI * 2);
-    ctx.fillStyle = 'rgba(255,255,255,0.18)';
-    ctx.fill();
-  }}
-
-  function updateBubble(b) {{
-    b.y -= b.vy;
-    if (b.y < -10) {{
-      b.y = H + 5;
-      b.x = Math.random() * W;
-    }}
-  }}
-
-  function drawFish(f) {{
-    const facingLeft = f.vx < 0;
-    ctx.save();
-    ctx.translate(f.x, f.y);
-    if (facingLeft) ctx.scale(-1, 1);
-
-    // Halo for top-3 fish.
-    if (f.rank < 3) {{
-      const grad = ctx.createRadialGradient(0, 0, f.radius * 0.5, 0, 0, f.radius * 2.0);
-      grad.addColorStop(0, f.color + 'aa');
-      grad.addColorStop(1, f.color + '00');
-      ctx.fillStyle = grad;
-      ctx.beginPath();
-      ctx.arc(0, 0, f.radius * 2.0, 0, Math.PI * 2);
-      ctx.fill();
-    }}
-
-    // Body (ellipse).
-    ctx.fillStyle = f.color;
-    ctx.beginPath();
-    ctx.ellipse(0, 0, f.radius, f.radius * 0.6, 0, 0, Math.PI * 2);
-    ctx.fill();
-
-    // Tail.
-    ctx.beginPath();
-    ctx.moveTo(-f.radius, 0);
-    ctx.lineTo(-f.radius * 1.6, -f.radius * 0.5);
-    ctx.lineTo(-f.radius * 1.6, f.radius * 0.5);
-    ctx.closePath();
-    ctx.fill();
-
-    // Eye.
-    ctx.fillStyle = '#ffffff';
-    ctx.beginPath();
-    ctx.arc(f.radius * 0.45, -f.radius * 0.15, Math.max(1.5, f.radius * 0.12), 0, Math.PI * 2);
-    ctx.fill();
-    ctx.fillStyle = '#1a1a1a';
-    ctx.beginPath();
-    ctx.arc(f.radius * 0.50, -f.radius * 0.15, Math.max(0.8, f.radius * 0.06), 0, Math.PI * 2);
-    ctx.fill();
-
-    ctx.restore();
-  }}
-
-  function updateFish(f) {{
-    f.vx += (Math.random() - 0.5) * 0.05;
-    f.vy += (Math.random() - 0.5) * 0.05;
-
-    const speed = Math.hypot(f.vx, f.vy);
-    const maxSpeed = 1.5;
-    if (speed > maxSpeed) {{
-      f.vx = (f.vx / speed) * maxSpeed;
-      f.vy = (f.vy / speed) * maxSpeed;
-    }}
-
-    f.x += f.vx;
-    f.y += f.vy;
-
-    if (f.x < f.radius) {{ f.x = f.radius; f.vx = -f.vx; }}
-    if (f.x > W - f.radius) {{ f.x = W - f.radius; f.vx = -f.vx; }}
-    if (f.y < f.radius) {{ f.y = f.radius; f.vy = -f.vy; }}
-    if (f.y > H - f.radius) {{ f.y = H - f.radius; f.vy = -f.vy; }}
-  }}
-
-  function frame() {{
-    ctx.clearRect(0, 0, W, H);
-
-    ctx.strokeStyle = 'rgba(255,255,255,0.04)';
-    ctx.lineWidth = 1;
-    for (let i = 0; i < 6; i++) {{
-      const y = (H / 6) * i + (Date.now() / 50 % (H / 6));
-      ctx.beginPath();
-      ctx.moveTo(0, y);
-      ctx.lineTo(W, y);
-      ctx.stroke();
-    }}
-
-    for (const b of bubbles) {{
-      updateBubble(b);
-      drawBubble(b);
-    }}
-    for (const f of fishState) {{
-      updateFish(f);
-      drawFish(f);
-    }}
-    requestAnimationFrame(frame);
-  }}
-
-  // CLICK HANDLER: hit-test in canvas pixel space (account for CSS scaling).
-  canvas.addEventListener('click', function(e) {{
-    const rect = canvas.getBoundingClientRect();
-    const scaleX = canvas.width / rect.width;
-    const scaleY = canvas.height / rect.height;
-    const cx = (e.clientX - rect.left) * scaleX;
-    const cy = (e.clientY - rect.top) * scaleY;
-    let best = null;
-    let bestDist = Infinity;
-    for (const f of fishState) {{
-      const dx = cx - f.x;
-      const dy = cy - f.y;
-      const d = Math.sqrt(dx * dx + dy * dy);
-      const hit = Math.max(f.radius + 6, 14);
-      if (d < hit && d < bestDist) {{
-        best = f;
-        bestDist = d;
-      }}
-    }}
-    if (best) showFishInfo(best);
-  }});
-
-  const ROW_STYLE = 'display:flex;justify-content:space-between;'
-                  + 'padding:2px 0;border-bottom:1px solid rgba(255,255,255,0.05);';
-  const PROMPT_STYLE = 'margin-top:10px;padding:8px;'
-                     + 'background:rgba(255,255,255,0.04);border-radius:6px;'
-                     + 'font-size:11px;font-style:italic;color:#cbd5e1;';
-  const ANC_HEAD_STYLE = 'margin:14px 0 6px 0;color:#94a3b8;'
-                       + 'text-transform:uppercase;font-size:10px;letter-spacing:1px;';
-  const ANC_ROW_STYLE = 'display:flex;align-items:center;padding:4px 6px;'
-                      + 'margin-bottom:2px;background:rgba(255,255,255,0.03);'
-                      + 'border-radius:4px;border-left:3px solid ';
-  const NO_ANC_STYLE = 'margin-top:10px;font-size:10px;color:#64748b;';
-  const DASH = '\\u2014';
-
-  function metricRow(label, value) {{
-    return '<div style="' + ROW_STYLE + '">'
-         + '<span style="color:#94a3b8;">' + label + '</span>'
-         + '<span style="color:#e2e8f0;">' + value + '</span></div>';
-  }}
-
-  function escapeHtml(s) {{
-    const div = document.createElement('div');
-    div.appendChild(document.createTextNode(String(s)));
-    return div.innerHTML;
-  }}
-
-  function fmt(v, dp) {{
-    if (v === null || v === undefined || typeof v !== 'number' || isNaN(v)) {{
-      return DASH;
-    }}
-    return v.toFixed(dp);
-  }}
-
-  function showFishInfo(fish) {{
-    const data = fish.data;
-    const styleColor = STYLE_COLORS[data.cognitive_style] || DEFAULT_COLOR;
-    let html = '';
-    const idShort = String(data.id || '').slice(0, 8);
-    html += '<h4 style="margin:0 0 10px 0;color:' + styleColor + ';">';
-    html += escapeHtml(idShort)
-         + ' <span style="color:#94a3b8;font-weight:normal;font-size:11px;">'
-         + 'gen ' + escapeHtml(data.generation) + '</span>';
-    html += '</h4>';
-    html += metricRow('fitness', fmt(data.fitness, 3));
-    html += metricRow('DSR', fmt(data.dsr, 3));
-    html += metricRow('Sharpe', fmt(data.sharpe, 3));
-    html += metricRow('max DD', fmt(data.max_dd, 3));
-    const trades = data.n_trades == null ? 0 : data.n_trades;
-    html += metricRow('trades', escapeHtml(trades));
-    html += metricRow('style', escapeHtml(data.cognitive_style || DASH));
-    html += metricRow('tier', escapeHtml(data.model_tier || DASH));
-    html += metricRow('temp', fmt(data.temperature, 2));
-    const lookback = data.lookback_window == null ? DASH : data.lookback_window;
-    html += metricRow('lookback', escapeHtml(lookback));
-    const feats = (data.feature_access || []).join(', ');
-    html += metricRow('features', escapeHtml(feats || DASH));
-    if (data.system_prompt) {{
-      html += '<div style="' + PROMPT_STYLE + '">'
-           + escapeHtml(data.system_prompt) + '</div>';
-    }}
-    if (data.ancestors && data.ancestors.length > 0) {{
-      html += '<h5 style="' + ANC_HEAD_STYLE + '">Discendenza</h5>';
-      data.ancestors.forEach(function(level, idx) {{
-        html += '<div style="margin-bottom:8px;">';
-        html += '<div style="font-size:10px;color:#64748b;margin-bottom:4px;">'
-             + 'Gen N\\u2212' + (idx + 1) + '</div>';
-        level.forEach(function(ancestor) {{
-          const c = STYLE_COLORS[ancestor.cognitive_style] || DEFAULT_COLOR;
-          const aShort = String(ancestor.id || '').slice(0, 8);
-          html += '<div style="' + ANC_ROW_STYLE + c + ';">';
-          html += '<code style="color:' + c + ';font-size:10px;">'
-               + escapeHtml(aShort) + '</code>';
-          const af = ancestor.fitness;
-          const fitTxt = (typeof af === 'number' && !isNaN(af))
-                       ? af.toFixed(2) : DASH;
-          html += '<span style="margin-left:auto;color:#94a3b8;font-size:10px;">'
-               + 'fit ' + fitTxt + '</span>';
-          html += '</div>';
-        }});
-        html += '</div>';
-      }});
-    }} else {{
-      html += '<div style="' + NO_ANC_STYLE + '">'
-           + 'Genoma di prima generazione (no ancestors)</div>';
-    }}
-    panelContent.innerHTML = html;
-    panel.style.display = 'block';
-  }}
-
-  if (fishState.length === 0) {{
-    ctx.fillStyle = 'rgba(255,255,255,0.7)';
-    ctx.font = '16px sans-serif';
-    ctx.textAlign = 'center';
-    ctx.fillText('Acquario vuoto: nessun genoma da mostrare.', W / 2, H / 2);
-  }} else {{
-    requestAnimationFrame(frame);
-  }}
-}})();
-</script>
-"""
diff --git a/src/multi_swarm/dashboard/nicegui_app.py b/src/multi_swarm/dashboard/nicegui_app.py
index 56bcf2d..afe3fa9 100644
--- a/src/multi_swarm/dashboard/nicegui_app.py
+++ b/src/multi_swarm/dashboard/nicegui_app.py
@@ -868,7 +868,7 @@ def main() -> None:
     app.on_startup(lambda: print(f"DB: {Path(DB_PATH).resolve()}"))
     ui.run(
         host="0.0.0.0",
-        port=8080,
+        port=int(os.environ.get("SWARM_DASHBOARD_PORT", "8080")),
         title="Multi-Swarm Dashboard",
         reload=False,
         show=False,
diff --git a/src/multi_swarm/dashboard/pages/01_overview.py b/src/multi_swarm/dashboard/pages/01_overview.py
deleted file mode 100644
index 558506d..0000000
--- a/src/multi_swarm/dashboard/pages/01_overview.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from __future__ import annotations
-
-from datetime import datetime
-
-import streamlit as st
-
-from multi_swarm.dashboard.data import (
-    evaluations_df,
-    generations_df,
-    get_repo,
-    get_run_overview,
-    list_runs_df,
-)
-
-st.title("Overview")
-
-db_path = st.session_state.get("db_path", "./runs.db")
-repo = get_repo(db_path)
-
-runs = list_runs_df(repo)
-if runs.empty:
-    st.info("Nessuna run nel database. Esegui `scripts/run_phase1.py` per generarne una.")
-    st.stop()
-
-st.subheader("Tutte le run")
-st.dataframe(runs[["id", "name", "started_at", "completed_at", "status", "total_cost_usd"]])
-
-selected = st.selectbox("Seleziona run per dettaglio", runs["id"].tolist())
-overview = get_run_overview(repo, selected)
-
-# --- Progress live ---
-cfg = overview["config"]
-pop_size = int(cfg.get("population_size", 0))
-n_gens = int(cfg.get("n_generations", 0))
-evals = evaluations_df(repo, selected)
-gens = generations_df(repo, selected)
-
-evals_done = len(evals)
-evals_total = max(pop_size * n_gens, 1)
-gens_done = int(gens["completed_at"].notna().sum()) if not gens.empty else 0
-
-status_emoji = {"running": "🟢", "completed": "✅", "failed": "❌"}.get(overview["status"], "⚪")
-top_fit = float(evals["fitness"].max()) if not evals.empty else float("nan")
-
-st.subheader(f"{status_emoji} Progresso run")
-st.progress(
-    min(gens_done / max(n_gens, 1), 1.0),
-    text=f"Generations: {gens_done}/{n_gens}",
-)
-st.progress(
-    min(evals_done / evals_total, 1.0),
-    text=f"Evaluations: {evals_done}/{evals_total} ({100*evals_done/evals_total:.1f}%)",
-)
-pcol1, pcol2, pcol3 = st.columns(3)
-pcol1.metric("Top fitness", f"{top_fit:.4f}" if evals_done else "—")
-pcol2.metric("Median fitness", f"{evals['fitness'].median():.4f}" if evals_done else "—")
-pcol3.metric("Cost so far", f"${overview['total_cost_usd']:.4f}")
-
-ref_col1, ref_col2 = st.columns([1, 4])
-if ref_col1.button("🔄 Refresh"):
-    st.rerun()
-ref_col2.caption(f"Last update: {datetime.now().strftime('%H:%M:%S')}")
-
-st.divider()
-
-col1, col2, col3, col4 = st.columns(4)
-col1.metric("Status", overview["status"])
-col2.metric("Cost (USD)", f"{overview['total_cost_usd']:.4f}")
-col3.metric("Started", overview["started_at"])
-col4.metric("Completed", overview["completed_at"] or "—")
-
-st.subheader("Statistiche evaluations")
-col5, col6, col7, col8 = st.columns(4)
-if not evals.empty:
-    parse_success = 100 * (evals["parse_error"].isna().sum() / len(evals))
-    col5.metric("Evaluations totali", len(evals))
-    col6.metric("Parse success %", f"{parse_success:.1f}%")
-    col7.metric("Top fitness", f"{evals['fitness'].max():.3f}")
-    col8.metric("Median fitness", f"{evals['fitness'].median():.3f}")
-else:
-    col5.metric("Evaluations totali", 0)
-
-st.subheader("Config")
-st.json(overview["config"])
diff --git a/src/multi_swarm/dashboard/pages/02_ga_convergence.py b/src/multi_swarm/dashboard/pages/02_ga_convergence.py
deleted file mode 100644
index f01cc80..0000000
--- a/src/multi_swarm/dashboard/pages/02_ga_convergence.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from __future__ import annotations
-
-import plotly.graph_objects as go  # type: ignore[import-untyped]
-import streamlit as st
-
-from multi_swarm.dashboard.data import generations_df, get_repo, list_runs_df
-
-st.title("GA Convergence")
-
-db_path = st.session_state.get("db_path", "./runs.db")
-repo = get_repo(db_path)
-
-runs = list_runs_df(repo)
-if runs.empty:
-    st.info("Nessuna run.")
-    st.stop()
-
-selected = st.selectbox("Run", runs["id"].tolist())
-gens = generations_df(repo, selected)
-if gens.empty:
-    st.warning("Nessuna generazione registrata per questa run.")
-    st.stop()
-
-fig = go.Figure()
-fig.add_trace(
-    go.Scatter(
-        x=gens["generation_idx"],
-        y=gens["fitness_median"],
-        name="median",
-        mode="lines+markers",
-    )
-)
-fig.add_trace(
-    go.Scatter(
-        x=gens["generation_idx"],
-        y=gens["fitness_max"],
-        name="max",
-        mode="lines+markers",
-    )
-)
-fig.add_trace(
-    go.Scatter(
-        x=gens["generation_idx"],
-        y=gens["fitness_p90"],
-        name="p90",
-        mode="lines+markers",
-    )
-)
-fig.update_layout(
-    xaxis_title="generation",
-    yaxis_title="fitness",
-    title="Fitness convergence",
-)
-st.plotly_chart(fig, use_container_width=True)
-
-st.subheader("Entropy")
-fig2 = go.Figure()
-fig2.add_trace(go.Scatter(x=gens["generation_idx"], y=gens["entropy"], mode="lines+markers"))
-fig2.add_hline(y=0.5, line_dash="dash", annotation_text="gate threshold (0.5)")
-fig2.update_layout(
-    xaxis_title="generation",
-    yaxis_title="entropy",
-    title="Diversity (fitness entropy)",
-)
-st.plotly_chart(fig2, use_container_width=True)
-
-st.subheader("Tabella generazioni")
-st.dataframe(gens)
diff --git a/src/multi_swarm/dashboard/pages/03_genomes.py b/src/multi_swarm/dashboard/pages/03_genomes.py
deleted file mode 100644
index 4a4538a..0000000
--- a/src/multi_swarm/dashboard/pages/03_genomes.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from __future__ import annotations
-
-import streamlit as st
-
-from multi_swarm.dashboard.data import (
-    evaluations_df,
-    genomes_df,
-    get_repo,
-    list_runs_df,
-)
-
-st.title("Genomes")
-
-db_path = st.session_state.get("db_path", "./runs.db")
-repo = get_repo(db_path)
-
-runs = list_runs_df(repo)
-if runs.empty:
-    st.info("Nessuna run.")
-    st.stop()
-
-selected = st.selectbox("Run", runs["id"].tolist())
-evals = evaluations_df(repo, selected)
-genomes = genomes_df(repo, selected)
-
-if evals.empty:
-    st.warning("Nessuna evaluation.")
-    st.stop()
-
-merged = evals.merge(
-    genomes, left_on="genome_id", right_on="id", how="left", suffixes=("", "_g")
-)
-top = merged.sort_values("fitness", ascending=False).head(10)
-
-st.subheader("Top-10 genomi (per fitness)")
-display_cols = [
-    "genome_id",
-    "fitness",
-    "dsr",
-    "sharpe",
-    "max_dd",
-    "n_trades",
-    "cognitive_style",
-    "temperature",
-    "lookback_window",
-    "feature_access",
-]
-existing = [c for c in display_cols if c in top.columns]
-st.dataframe(top[existing])
-
-st.subheader("Ispezione genoma")
-gid = st.selectbox("Seleziona genome_id", top["genome_id"].tolist())
-row = merged[merged["genome_id"] == gid].iloc[0]
-
-col1, col2 = st.columns(2)
-with col1:
-    st.metric("fitness", f"{row['fitness']:.3f}")
-    st.metric("DSR", f"{row['dsr']:.3f}")
-    st.metric("Sharpe", f"{row['sharpe']:.3f}")
-with col2:
-    st.metric("max DD", f"{row['max_dd']:.3f}")
-    st.metric("trades", int(row["n_trades"]))
-    st.metric("style", str(row.get("cognitive_style", "—")))
-
-st.subheader("System prompt")
-st.code(row.get("system_prompt", "—"))
-
-st.subheader("Raw LLM output")
-st.code(row.get("raw_text", "—"))
-
-if row.get("parse_error"):
-    st.error(f"Parse error: {row['parse_error']}")
diff --git a/src/multi_swarm/dashboard/pages/04_aquarium.py b/src/multi_swarm/dashboard/pages/04_aquarium.py
deleted file mode 100644
index d7efeda..0000000
--- a/src/multi_swarm/dashboard/pages/04_aquarium.py
+++ /dev/null
@@ -1,87 +0,0 @@
-from __future__ import annotations
-
-import streamlit as st
-import streamlit.components.v1 as components
-
-from multi_swarm.dashboard.aquarium import (
-    STYLE_COLORS,
-    build_aquarium_html,
-    build_fish_dataset,
-    build_lineage_index,
-)
-from multi_swarm.dashboard.data import (
-    evaluations_df,
-    genomes_df,
-    get_repo,
-    list_runs_df,
-)
-
-st.title("Aquarium 2D")
-st.caption(
-    "Pesci colorati per stile cognitivo, dimensione proporzionale a fitness. "
-    "Click su un pesce per dettaglio + discendenza."
-)
-
-db_path = st.session_state.get("db_path", "./runs.db")
-repo = get_repo(db_path)
-
-runs = list_runs_df(repo)
-if runs.empty:
-    st.info("Nessuna run nel database.")
-    st.stop()
-
-selected_run = st.selectbox("Run", runs["id"].tolist())
-
-# Fetch ALL genomes of the run (no gen filter): needed to build the lineage
-# index across generations. The active set is filtered afterwards.
-all_genomes = genomes_df(repo, selected_run)
-all_evals = evaluations_df(repo, selected_run)
-
-if all_genomes.empty:
-    st.warning("Nessun genoma per questa run.")
-    st.stop()
-
-available_gens = sorted(all_genomes["generation_idx"].unique().tolist())
-selected_gen = st.selectbox(
-    "Generazione",
-    available_gens,
-    index=len(available_gens) - 1,  # default ultima
-)
-
-active_genomes = all_genomes[all_genomes["generation_idx"] == selected_gen]
-active_evals = (
-    all_evals[all_evals["genome_id"].isin(active_genomes["id"])]
-    if not all_evals.empty
-    else all_evals
-)
-if not active_evals.empty:
-    active_merged = active_genomes.merge(
-        active_evals,
-        left_on="id",
-        right_on="genome_id",
-        how="left",
-        suffixes=("", "_eval"),
-    )
-else:
-    active_merged = active_genomes.copy()
-    active_merged["genome_id"] = active_merged["id"]
-
-lineage = build_lineage_index(all_genomes, all_evals)
-fish = build_fish_dataset(active_merged, lineage, max_lineage_levels=5)
-
-if not fish:
-    st.warning("Nessun agente attivo in questa generazione.")
-    st.stop()
-
-st.caption(f"{len(fish)} agenti in generazione {selected_gen}")
-
-html_str = build_aquarium_html(fish, canvas_w=1000, canvas_h=600)
-components.html(html_str, height=620, scrolling=False)
-
-with st.expander("Legenda colori"):
-    legend_md = "\n".join(
-        f"- <span style='color:{color};font-weight:bold;'>&#9679;</span> "
-        f"`{style}`"
-        for style, color in STYLE_COLORS.items()
-    )
-    st.markdown(legend_md, unsafe_allow_html=True)
diff --git a/src/multi_swarm/dashboard/streamlit_app.py b/src/multi_swarm/dashboard/streamlit_app.py
deleted file mode 100644
index 2538167..0000000
--- a/src/multi_swarm/dashboard/streamlit_app.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import streamlit as st
-
-st.set_page_config(page_title="Multi-Swarm Phase 1", layout="wide")
-st.title("Multi-Swarm Coevolutivo — Phase 1 dashboard")
-st.markdown(
-    """
-Naviga le pagine nel menu a sinistra:
-- **Overview**: ultima run e stato globale.
-- **GA Convergence**: fitness per generazione.
-- **Genomes**: top-K genomi e ispezione qualitativa.
-- **Aquarium**: visualizzazione 2D dei genomi come pesci animati.
-"""
-)
-
-db_path = os.environ.get("DB_PATH", "./runs.db")
-st.session_state["db_path"] = db_path
-st.caption(f"DB path: `{Path(db_path).resolve()}`")
diff --git a/tests/integration/test_streamlit_smoke.py b/tests/integration/test_streamlit_smoke.py
deleted file mode 100644
index 483408f..0000000
--- a/tests/integration/test_streamlit_smoke.py
+++ /dev/null
@@ -1,310 +0,0 @@
-import importlib
-
-import pandas as pd
-
-
-def test_streamlit_app_imports():
-    importlib.import_module("multi_swarm.dashboard.data")
-
-
-def test_dashboard_data_helpers_signatures():
-    from multi_swarm.dashboard import data
-
-    assert hasattr(data, "list_runs_df")
-    assert hasattr(data, "generations_df")
-    assert hasattr(data, "evaluations_df")
-    assert hasattr(data, "genomes_df")
-
-
-def test_aquarium_helper_builds_html_with_click_handler():
-    from multi_swarm.dashboard.aquarium import build_aquarium_html
-
-    fish = [
-        {
-            "id": "abc123",
-            "fitness": 0.8,
-            "cognitive_style": "physicist",
-            "n_trades": 30,
-            "dsr": 0.7,
-            "sharpe": 1.2,
-            "max_dd": 0.1,
-            "system_prompt": "test",
-            "temperature": 0.9,
-            "lookback_window": 200,
-            "feature_access": ["close"],
-            "model_tier": "C",
-            "generation": 1,
-            "parent_ids": [],
-            "ancestors": [],
-        }
-    ]
-    html = build_aquarium_html(fish, canvas_w=800, canvas_h=400)
-    assert "canvas" in html
-    assert "abc123" in html  # fish id present in JSON payload
-    assert "addEventListener('click'" in html
-    assert "fish-info-panel" in html
-    assert "showFishInfo" in html
-    assert "Discendenza" in html
-    assert "requestAnimationFrame" in html
-
-
-def test_aquarium_build_fish_dataset_legacy_path():
-    from multi_swarm.dashboard.aquarium import build_fish_dataset
-
-    df = pd.DataFrame(
-        [
-            {
-                "genome_id": "low",
-                "fitness": 0.1,
-                "cognitive_style": "physicist",
-                "n_trades": 1,
-                "dsr": 0.0,
-            },
-            {
-                "genome_id": "high",
-                "fitness": 0.9,
-                "cognitive_style": "biologist",
-                "n_trades": 10,
-                "dsr": 0.5,
-            },
-        ]
-    )
-    out = build_fish_dataset(df)
-    ids = {f["id"] for f in out}
-    assert ids == {"low", "high"}
-    high = next(f for f in out if f["id"] == "high")
-    assert high["cognitive_style"] == "biologist"
-    assert high["ancestors"] == []
-
-
-def test_aquarium_build_fish_dataset_drops_nan_fitness():
-    from multi_swarm.dashboard.aquarium import build_fish_dataset
-
-    df = pd.DataFrame(
-        [
-            {
-                "genome_id": "ok",
-                "fitness": 0.4,
-                "cognitive_style": "historian",
-                "n_trades": 2,
-                "dsr": 0.1,
-            },
-            {
-                "genome_id": "bad",
-                "fitness": float("nan"),
-                "cognitive_style": "ecologist",
-                "n_trades": 0,
-                "dsr": 0.0,
-            },
-        ]
-    )
-    out = build_fish_dataset(df)
-    assert len(out) == 1
-    assert out[0]["id"] == "ok"
-
-
-def test_aquarium_empty_input_returns_empty():
-    from multi_swarm.dashboard.aquarium import build_aquarium_html, build_fish_dataset
-
-    assert build_fish_dataset(pd.DataFrame()) == []
-    html = build_aquarium_html([], canvas_w=400, canvas_h=200)
-    assert "canvas" in html
-    assert "Acquario vuoto" in html
-
-
-def test_build_lineage_index_returns_dict_keyed_by_id():
-    from multi_swarm.dashboard.aquarium import build_lineage_index
-
-    genomes = pd.DataFrame(
-        [
-            {
-                "id": "g1",
-                "generation_idx": 0,
-                "generation": 0,
-                "system_prompt": "x",
-                "feature_access": ["close"],
-                "temperature": 0.9,
-                "top_p": 0.95,
-                "model_tier": "C",
-                "lookback_window": 100,
-                "cognitive_style": "physicist",
-                "parent_ids": [],
-            },
-            {
-                "id": "g2",
-                "generation_idx": 1,
-                "generation": 1,
-                "system_prompt": "y",
-                "feature_access": ["close", "volume"],
-                "temperature": 1.0,
-                "top_p": 0.95,
-                "model_tier": "C",
-                "lookback_window": 200,
-                "cognitive_style": "biologist",
-                "parent_ids": ["g1"],
-            },
-        ]
-    )
-    evals = pd.DataFrame(
-        [
-            {
-                "genome_id": "g1",
-                "fitness": 0.5,
-                "dsr": 0.6,
-                "sharpe": 1.2,
-                "max_dd": 0.1,
-                "n_trades": 30,
-                "parse_error": None,
-                "raw_text": "",
-            },
-            {
-                "genome_id": "g2",
-                "fitness": 0.7,
-                "dsr": 0.8,
-                "sharpe": 1.5,
-                "max_dd": 0.05,
-                "n_trades": 40,
-                "parse_error": None,
-                "raw_text": "",
-            },
-        ]
-    )
-    idx = build_lineage_index(genomes, evals)
-    assert "g1" in idx and "g2" in idx
-    assert idx["g2"]["parent_ids"] == ["g1"]
-    assert idx["g2"]["fitness"] == 0.7
-    assert idx["g1"]["cognitive_style"] == "physicist"
-    assert idx["g2"]["feature_access"] == ["close", "volume"]
-
-
-def test_trace_ancestors_walks_levels():
-    from multi_swarm.dashboard.aquarium import trace_ancestors
-
-    idx = {
-        "child": {
-            "id": "child",
-            "parent_ids": ["p1", "p2"],
-            "fitness": 0.8,
-            "generation": 2,
-            "cognitive_style": "physicist",
-        },
-        "p1": {
-            "id": "p1",
-            "parent_ids": ["gp1"],
-            "fitness": 0.5,
-            "generation": 1,
-            "cognitive_style": "biologist",
-        },
-        "p2": {
-            "id": "p2",
-            "parent_ids": [],
-            "fitness": 0.3,
-            "generation": 1,
-            "cognitive_style": "engineer",
-        },
-        "gp1": {
-            "id": "gp1",
-            "parent_ids": [],
-            "fitness": 0.2,
-            "generation": 0,
-            "cognitive_style": "historian",
-        },
-    }
-    levels = trace_ancestors("child", idx, max_levels=5)
-    assert len(levels) == 2
-    assert {a["id"] for a in levels[0]} == {"p1", "p2"}
-    assert {a["id"] for a in levels[1]} == {"gp1"}
-
-
-def test_trace_ancestors_handles_cycles():
-    from multi_swarm.dashboard.aquarium import trace_ancestors
-
-    # Pathological cycle: a <-> b. Should terminate cleanly.
-    idx = {
-        "a": {
-            "id": "a",
-            "parent_ids": ["b"],
-            "fitness": 0.1,
-            "generation": 1,
-            "cognitive_style": "physicist",
-        },
-        "b": {
-            "id": "b",
-            "parent_ids": ["a"],
-            "fitness": 0.2,
-            "generation": 0,
-            "cognitive_style": "biologist",
-        },
-    }
-    levels = trace_ancestors("a", idx, max_levels=5)
-    # a -> b at level 0; b's only parent is a, already seen -> stop.
-    assert len(levels) == 1
-    assert levels[0][0]["id"] == "b"
-
-
-def test_trace_ancestors_no_parents_returns_empty():
-    from multi_swarm.dashboard.aquarium import trace_ancestors
-
-    idx = {
-        "solo": {
-            "id": "solo",
-            "parent_ids": [],
-            "fitness": 0.4,
-            "generation": 0,
-            "cognitive_style": "engineer",
-        },
-    }
-    assert trace_ancestors("solo", idx) == []
-
-
-def test_build_fish_dataset_attaches_ancestors():
-    from multi_swarm.dashboard.aquarium import build_fish_dataset, build_lineage_index
-
-    genomes = pd.DataFrame(
-        [
-            {
-                "id": "p",
-                "generation_idx": 0,
-                "generation": 0,
-                "system_prompt": "p",
-                "feature_access": ["close"],
-                "temperature": 0.8,
-                "top_p": 0.9,
-                "model_tier": "C",
-                "lookback_window": 100,
-                "cognitive_style": "physicist",
-                "parent_ids": [],
-            },
-            {
-                "id": "c",
-                "generation_idx": 1,
-                "generation": 1,
-                "system_prompt": "c",
-                "feature_access": ["close"],
-                "temperature": 0.8,
-                "top_p": 0.9,
-                "model_tier": "C",
-                "lookback_window": 120,
-                "cognitive_style": "biologist",
-                "parent_ids": ["p"],
-            },
-        ]
-    )
-    evals = pd.DataFrame(
-        [
-            {"genome_id": "p", "fitness": 0.3, "dsr": 0.0, "sharpe": 0.0,
-             "max_dd": 0.0, "n_trades": 0},
-            {"genome_id": "c", "fitness": 0.6, "dsr": 0.0, "sharpe": 0.0,
-             "max_dd": 0.0, "n_trades": 0},
-        ]
-    )
-    lineage = build_lineage_index(genomes, evals)
-
-    active = genomes[genomes["generation_idx"] == 1].merge(
-        evals, left_on="id", right_on="genome_id", how="left"
-    )
-    fish = build_fish_dataset(active, lineage)
-    assert len(fish) == 1
-    assert fish[0]["id"] == "c"
-    assert len(fish[0]["ancestors"]) == 1
-    assert fish[0]["ancestors"][0][0]["id"] == "p"
diff --git a/uv.lock b/uv.lock
index c6dcaea..f773f5a 100644
--- a/uv.lock
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