refactor(layout): rename multi_swarm → multi_swarm_core con doppia nidificazione uv workspace

- mv src/multi_swarm → src/multi_swarm_core/multi_swarm_core (member layout)
- sed-replace globale degli import: from/import multi_swarm.* → multi_swarm_core.*
- 115 occorrenze aggiornate in src/ scripts/ tests/
- multi_swarm_coevolutive (nome repo) preservato dal word boundary

Pre-condizione per il setup uv workspace della Fase 3.

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

scripts/backtest_strategy.py

@@ -17,13 +17,13 @@ import math
 from datetime import datetime
 from pathlib import Path
 
-from multi_swarm.agents.adversarial import AdversarialAgent
-from multi_swarm.agents.falsification import FalsificationAgent
-from multi_swarm.cerbero.client import CerberoClient
-from multi_swarm.config import load_settings
-from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
-from multi_swarm.protocol.parser import parse_strategy
-from multi_swarm.protocol.validator import validate_strategy
+from multi_swarm_core.agents.adversarial import AdversarialAgent
+from multi_swarm_core.agents.falsification import FalsificationAgent
+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.protocol.parser import parse_strategy
+from multi_swarm_core.protocol.validator import validate_strategy
 
 
 def main() -> None:

scripts/replay_strategies_window.py

@@ -0,0 +1,127 @@
+"""Replay diagnostico: per ciascuna strategia conta quanti bar avrebbero
+soddisfatto le condizioni di ciascuna regola sull'ultimo `--days` di storico.
+
+Ouput tabellare per branch: total_bars, fires, fire_rate, primo/ultimo fire.
+Esegue anche un backtest grezzo (entry-on-signal, exit-on-flat) per stimare
+n_trades e total_return realistici nel periodo.
+
+Esempio:
+    docker compose exec multi-swarm-paper \
+        python /app/scripts/replay_strategies_window.py --days 30
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from datetime import UTC, datetime, timedelta
+from pathlib import Path
+
+import pandas as pd
+
+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.protocol.compiler import _eval_node, compile_strategy
+from multi_swarm_core.protocol.parser import parse_strategy
+
+PROJECT_ROOT = Path(__file__).resolve().parent.parent
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser()
+    p.add_argument("--days", type=int, default=30)
+    p.add_argument("--strategies-dir", default=str(PROJECT_ROOT / "strategies"))
+    return p.parse_args()
+
+
+def fetch_window(loader: CerberoOHLCVLoader, symbol: str, days: int) -> pd.DataFrame:
+    end = datetime.now(UTC).replace(minute=0, second=0, microsecond=0)
+    start = end - timedelta(days=days)
+    req = OHLCVRequest(
+        symbol=symbol, timeframe="1h", start=start, end=end, exchange="deribit"
+    )
+    return loader._fetch(req)  # noqa: SLF001 — bypass cache
+
+
+def per_branch_fires(strategy_path: Path, ohlcv: pd.DataFrame) -> list[dict]:
+    raw = strategy_path.read_text()
+    parsed = parse_strategy(raw)
+    out = []
+    for idx, rule in enumerate(parsed.rules):
+        cond_series = _eval_node(rule.condition, ohlcv).fillna(False).astype(bool)
+        n = int(cond_series.sum())
+        first = ohlcv.index[cond_series.argmax()] if n > 0 else None
+        # last fire: argmax on reversed
+        last = ohlcv.index[len(cond_series) - 1 - cond_series[::-1].argmax()] if n > 0 else None
+        out.append({
+            "branch_idx": idx,
+            "action": rule.action,
+            "fires": n,
+            "fire_rate_pct": round(100.0 * n / len(ohlcv), 2),
+            "first_fire": first,
+            "last_fire": last,
+        })
+    return out
+
+
+def quick_pnl(strategy_path: Path, ohlcv: pd.DataFrame, fees_bp: float = 5.0) -> dict:
+    """Approx: at each bar evaluate compiled signal series (long/short/flat),
+    apply position to next-bar return, charge fees on changes. No leverage."""
+    raw = strategy_path.read_text()
+    parsed = parse_strategy(raw)
+    sig_fn = compile_strategy(parsed)
+    signals = sig_fn(ohlcv)  # series of "long"/"short"/"flat"
+    # map to position: long=+1, short=-1, flat=0
+    pos = signals.map({"long": 1, "short": -1, "flat": 0}).fillna(0).astype(int)
+    rets = ohlcv["close"].pct_change().fillna(0.0)
+    # next-bar execution: position decided at bar t applies to return t+1 -> shift
+    pnl = pos.shift(1).fillna(0) * rets
+    # fees on position changes
+    changes = pos.diff().abs().fillna(0).astype(int)
+    fee_per_change = fees_bp / 10_000.0
+    pnl_after_fees = pnl - changes * fee_per_change
+    cum = (1 + pnl_after_fees).prod() - 1
+    n_trades = int((changes > 0).sum())
+    time_in_market = float((pos != 0).mean())
+    return {
+        "n_trades": n_trades,
+        "total_return_pct": round(100.0 * float(cum), 3),
+        "time_in_market_pct": round(100.0 * time_in_market, 2),
+    }
+
+
+def main() -> None:
+    args = parse_args()
+    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)
+
+    strategies_dir = Path(args.strategies_dir)
+    pairs = [
+        ("BTC-PERPETUAL", sorted(strategies_dir.glob("btc_*.json"))[0]),
+        ("ETH-PERPETUAL", sorted(strategies_dir.glob("eth_*.json"))[0]),
+    ]
+
+    for symbol, strat_path in pairs:
+        print(f"\n=== {symbol}  strategy={strat_path.name}  window={args.days}d ===")
+        ohlcv = fetch_window(loader, symbol, args.days)
+        print(f"bars: {len(ohlcv)}  range: {ohlcv.index[0]} -> {ohlcv.index[-1]}")
+        print("\n-- per branch --")
+        for row in per_branch_fires(strat_path, ohlcv):
+            print(json.dumps(row, default=str))
+        print("\n-- quick pnl (next-bar exec, fees=5bp) --")
+        print(json.dumps(quick_pnl(strat_path, ohlcv), default=str))
+
+
+if __name__ == "__main__":
+    main()

scripts/run_paper_trading.py

@@ -25,13 +25,13 @@ from dataclasses import dataclass
 from datetime import UTC, datetime, timedelta
 from pathlib import Path
 
-from multi_swarm.cerbero.client import CerberoClient
-from multi_swarm.config import load_settings
-from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
-from multi_swarm.paper_trading.executor import PaperExecutor
-from multi_swarm.paper_trading.persistence import PaperRepository
-from multi_swarm.paper_trading.portfolio import Portfolio
-from multi_swarm.persistence.repository import Repository
+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.paper_trading.executor import PaperExecutor
+from multi_swarm_core.paper_trading.persistence import PaperRepository
+from multi_swarm_core.paper_trading.portfolio import Portfolio
+from multi_swarm_core.persistence.repository import Repository
 
 PROJECT_ROOT = Path(__file__).resolve().parent.parent
 

scripts/run_phase1.py

@@ -3,12 +3,12 @@ from __future__ import annotations
 import argparse
 from datetime import datetime
 
-from multi_swarm.cerbero.client import CerberoClient
-from multi_swarm.config import load_settings
-from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
-from multi_swarm.genome.hypothesis import ModelTier
-from multi_swarm.llm.client import LLMClient
-from multi_swarm.orchestrator.run import RunConfig, run_phase1
+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.llm.client import LLMClient
+from multi_swarm_core.orchestrator.run import RunConfig, run_phase1
 
 
 def parse_args() -> argparse.Namespace:

scripts/smoke_run.py

@@ -6,9 +6,9 @@ from pathlib import Path
 import numpy as np
 import pandas as pd  # type: ignore[import-untyped]
 
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.llm.client import CompletionResult
-from multi_swarm.orchestrator.run import RunConfig, run_phase1
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.llm.client import CompletionResult
+from multi_swarm_core.orchestrator.run import RunConfig, run_phase1
 
 _MOCK_STRATEGY = json.dumps(
     {