diff --git a/scripts/analyze_btc_winners.py b/scripts/analyze_btc_winners.py
new file mode 100644
index 0000000..3d8b6a4
--- /dev/null
+++ b/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()
diff --git a/scripts/compare_winners.py b/scripts/compare_winners.py
new file mode 100644
index 0000000..5b890aa
--- /dev/null
+++ b/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()
diff --git a/state/validation-btc-100-001-thr3.json b/state/validation-btc-100-001-thr3.json
new file mode 100644
index 0000000..6c35e37
--- /dev/null
+++ b/state/validation-btc-100-001-thr3.json
@@ -0,0 +1,634 @@
+{
+  "run_id": "0392aa1c2d644459afa5a23f43c38ac6",
+  "run_name": "phase1-btc-100-001",
+  "n_folds": 4,
+  "top_k_requested": 10,
+  "top_k_evaluated": 10,
+  "symbol": "BTC-PERPETUAL",
+  "timeframe": "1h",
+  "start": "2018-09-01T00:00:00+00:00",
+  "end": "2026-01-01T00:00:00+00:00",
+  "ohlcv_bars": 64297,
+  "results": [
+    {
+      "genome_id": "23a24989e2ed0f84",
+      "fitness_is": 0.25047738452013774,
+      "sharpe_is": 0.5152551943136504,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.4454407113532186,
+          "sharpe": 0.940612398713799,
+          "dsr": 0.09856838950479485,
+          "dsr_pvalue": 0.9014316104952051,
+          "return": 0.12691347502077277,
+          "max_dd": 0.08467873586477132,
+          "n_trades": 50,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.33651846595831003,
+          "sharpe": 0.6297236131089199,
+          "dsr": 0.05704792862404472,
+          "dsr_pvalue": 0.9429520713759553,
+          "return": 0.16916039262594973,
+          "max_dd": 0.2420995418754207,
+          "n_trades": 61,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.08496628060413243,
+          "sharpe": -0.291593157960215,
+          "dsr": 0.006828013272159182,
+          "dsr_pvalue": 0.9931719867278408,
+          "return": -0.06496567446731383,
+          "max_dd": 0.1933746053658072,
+          "n_trades": 72,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.10029133262703777,
+          "sharpe": -0.08634860278039096,
+          "dsr": 0.01165220864726802,
+          "dsr_pvalue": 0.988347791352732,
+          "return": -0.007636913661893563,
+          "max_dd": 0.061872083556258554,
+          "n_trades": 29,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.2418041976356747,
+      "fitness_oos_min": 0.08496628060413243,
+      "fitness_oos_max": 0.4454407113532186,
+      "fitness_oos_std": 0.15416115393045135,
+      "sharpe_oos_mean": 0.2980985627705282,
+      "sharpe_oos_min": -0.291593157960215,
+      "robust_score": 0.08496628060413243
+    },
+    {
+      "genome_id": "ddda3a5d7fcf95d8",
+      "fitness_is": 0.24345612215631274,
+      "sharpe_is": 0.4859910845049414,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.38630034957174436,
+          "sharpe": 0.6292230751631145,
+          "dsr": 0.05660411470808308,
+          "dsr_pvalue": 0.9433958852919169,
+          "return": 0.0808908197444953,
+          "max_dd": 0.08123461559976199,
+          "n_trades": 44,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.3444344428619903,
+          "sharpe": 0.670768621640302,
+          "dsr": 0.06172291934756436,
+          "dsr_pvalue": 0.9382770806524356,
+          "return": 0.1769344040247678,
+          "max_dd": 0.24038922925189188,
+          "n_trades": 46,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.08496628060413243,
+          "sharpe": -0.291593157960215,
+          "dsr": 0.006828013272159182,
+          "dsr_pvalue": 0.9931719867278408,
+          "return": -0.06496567446731383,
+          "max_dd": 0.1933746053658072,
+          "n_trades": 72,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.10134367585820397,
+          "sharpe": -0.25028965416710486,
+          "dsr": 0.0070613574740692985,
+          "dsr_pvalue": 0.9929386425259307,
+          "return": -0.01793962898000656,
+          "max_dd": 0.05380115145734951,
+          "n_trades": 18,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.22926118722401778,
+      "fitness_oos_min": 0.08496628060413243,
+      "fitness_oos_max": 0.38630034957174436,
+      "fitness_oos_std": 0.13703109785336132,
+      "sharpe_oos_mean": 0.18952722116902415,
+      "sharpe_oos_min": -0.291593157960215,
+      "robust_score": 0.08496628060413243
+    },
+    {
+      "genome_id": "75fffb926a15ff30",
+      "fitness_is": 0.2317839302261713,
+      "sharpe_is": 0.5074946608465971,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.06821040478798467,
+          "sharpe": -0.42408026342979865,
+          "dsr": 0.004964388516380099,
+          "dsr_pvalue": 0.9950356114836199,
+          "return": -0.004117833402575766,
+          "max_dd": 0.01551842276077859,
+          "n_trades": 12,
+          "test_start": "2022-05-02 12:00:00+00:00",
+          "test_end": "2023-04-02 08:00:00+00:00"
+        },
+        {
+          "fold": 1,
+          "fitness": 0.30367484453258525,
+          "sharpe": 0.7648008345130556,
+          "dsr": 0.0596988345287043,
+          "dsr_pvalue": 0.9403011654712957,
+          "return": 0.040989700605122525,
+          "max_dd": 0.036878373324561994,
+          "n_trades": 31,
+          "test_start": "2023-04-02 09:00:00+00:00",
+          "test_end": "2024-03-02 05:00:00+00:00"
+        },
+        {
+          "fold": 2,
+          "fitness": 0.014172300674502565,
+          "sharpe": -1.443859476065376,
+          "dsr": 9.400808942425867e-05,
+          "dsr_pvalue": 0.9999059919105757,
+          "return": -0.02894431062955649,
+          "max_dd": 0.036019686142963456,
+          "n_trades": 7,
+          "test_start": "2024-03-02 06:00:00+00:00",
+          "test_end": "2025-01-31 02:00:00+00:00"
+        },
+        {
+          "fold": 3,
+          "fitness": 0.15455497647301103,
+          "sharpe": 0.11384980407793575,
+          "dsr": 0.018924861238402986,
+          "dsr_pvalue": 0.981075138761597,
+          "return": 0.004017688385377749,
+          "max_dd": 0.034520559216801125,
+          "n_trades": 21,
+          "test_start": "2025-01-31 03:00:00+00:00",
+          "test_end": "2025-12-31 23:00:00+00:00"
+        }
+      ],
+      "fitness_oos_mean": 0.1351531316170209,
+      "fitness_oos_min": 0.014172300674502565,
+      "fitness_oos_max": 0.30367484453258525,
+      "fitness_oos_std": 0.1094231344810833,
+      "sharpe_oos_mean": -0.24732227522604583,
+      "sharpe_oos_min": -1.443859476065376,
+      "robust_score": 0.014172300674502565
+    },
+    {
+      "genome_id": "1cba64abfb67fd63",
+      "fitness_is": 0.24779915639787098,
+      "sharpe_is": 0.686744434641618,
+      "folds": [
+        {
+          "fold": 0,
+          "fitness": 0.23623418911693914,
+          "sharpe": 0.3904002210885913,
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+}
\ No newline at end of file
diff --git a/state/validation-btc-100-001.json b/state/validation-btc-100-001.json
new file mode 100644
index 0000000..95fe943
--- /dev/null
+++ b/state/validation-btc-100-001.json
@@ -0,0 +1,634 @@
+{
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+}
\ No newline at end of file
diff --git a/state/validation-btc-100-5m-thr3.json b/state/validation-btc-100-5m-thr3.json
new file mode 100644
index 0000000..95f13ed
--- /dev/null
+++ b/state/validation-btc-100-5m-thr3.json
@@ -0,0 +1,634 @@
+{
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+}
\ No newline at end of file