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>

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+      "parent_mode": "none",
+      "status": "completed",
+      "completed_at": "2026-05-15T17:12:22.595Z",
+      "duration_ms": 202552
+    }
+  ],
+  "total_spawned": 3,
+  "total_completed": 3,
+  "total_failed": 0,
+  "last_updated": "2026-05-15T17:12:22.696Z"
+}

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(
     {

src/multi_swarm_core/multi_swarm_core/dashboard/nicegui_app.py

@@ -1,6 +1,6 @@
 """NiceGUI dashboard — port progressivo da Streamlit.
 
-Avvio: ``uv run python -m multi_swarm.dashboard.nicegui_app``
+Avvio: ``uv run python -m multi_swarm_core.dashboard.nicegui_app``
 Default port 8080. Streamlit resta su 8501 durante la migrazione.
 
 Riusa ``dashboard.data`` (Repository helpers) senza modifiche al backend.
@@ -28,7 +28,7 @@ import pandas as pd  # type: ignore[import-untyped]
 import plotly.graph_objects as go  # type: ignore[import-untyped]
 from nicegui import app, ui
 
-from multi_swarm.dashboard.data import (
+from multi_swarm_core.dashboard.data import (
     evaluations_df,
     generations_df,
     genomes_df,

src/multi_swarm_core/multi_swarm_core/paper_trading/persistence.py

@@ -1,5 +1,5 @@
 """Persistenza paper-trading: usa lo stesso ``runs.db`` con tabelle dedicate
-``paper_trading_*`` (vedi :mod:`multi_swarm.persistence.schema`).
+``paper_trading_*`` (vedi :mod:`multi_swarm_core.persistence.schema`).
 """
 
 from __future__ import annotations

tests/integration/test_e2e_minimal_run.py

@@ -5,10 +5,10 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm.genome.hypothesis import ModelTier
-from multi_swarm.llm.client import CompletionResult
-from multi_swarm.orchestrator.run import RunConfig, run_phase1
-from multi_swarm.persistence.repository import Repository
+from multi_swarm_core.genome.hypothesis import ModelTier
+from multi_swarm_core.llm.client import CompletionResult
+from multi_swarm_core.orchestrator.run import RunConfig, run_phase1
+from multi_swarm_core.persistence.repository import Repository
 
 
 @pytest.fixture

tests/integration/test_ga_loop_with_prompt_mutator.py

@@ -9,8 +9,8 @@ from __future__ import annotations
 import random
 from dataclasses import dataclass
 
-from multi_swarm.ga.loop import GAConfig, next_generation
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.ga.loop import GAConfig, next_generation
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
 
 _PROMPT_TEMPLATES = (
     "Strategia mean-reversion 1h. Entry long RSI(14) < 30 e close > SMA(50). Stop 2%.",

tests/unit/test_adversarial.py

@@ -4,14 +4,14 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm.agents.adversarial import (
+from multi_swarm_core.agents.adversarial import (
     AdversarialAgent,
     AdversarialReport,
     Severity,
 )
-from multi_swarm.backtest.engine import BacktestResult
-from multi_swarm.backtest.orders import Side, Trade
-from multi_swarm.protocol.parser import parse_strategy
+from multi_swarm_core.backtest.engine import BacktestResult
+from multi_swarm_core.backtest.orders import Side, Trade
+from multi_swarm_core.protocol.parser import parse_strategy
 
 
 @pytest.fixture
@@ -178,10 +178,10 @@ def test_undertrading_under_10_is_high(monkeypatch: pytest.MonkeyPatch,
         return lambda df: fake_signals
 
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.BacktestEngine.run", fake_run
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
     )
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
     )
 
     src = _MINIMAL_STRATEGY_SRC
@@ -220,8 +220,8 @@ def test_undertrading_threshold_parametric(monkeypatch: pytest.MonkeyPatch,
     def fake_compile(strategy):  # type: ignore[no-untyped-def]
         return lambda df: fake_signals
 
-    monkeypatch.setattr("multi_swarm.agents.adversarial.BacktestEngine.run", fake_run)
-    monkeypatch.setattr("multi_swarm.agents.adversarial.compile_strategy", fake_compile)
+    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)
     # Default threshold 10: 15 trade NON killato
@@ -269,10 +269,10 @@ def test_overtrading_with_tighter_threshold(monkeypatch: pytest.MonkeyPatch,
         return lambda df: fake_signals
 
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.BacktestEngine.run", fake_run
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
     )
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
     )
 
     src = _MINIMAL_STRATEGY_SRC
@@ -315,10 +315,10 @@ def test_flat_too_long_flagged(monkeypatch: pytest.MonkeyPatch,
         return lambda df: fake_signals
 
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.BacktestEngine.run", fake_run
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
     )
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
     )
 
     src = _MINIMAL_STRATEGY_SRC
@@ -367,10 +367,10 @@ def test_fees_eat_alpha_flagged(monkeypatch: pytest.MonkeyPatch,
         return lambda df: fake_signals
 
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.BacktestEngine.run", fake_run
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
     )
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
     )
 
     src = _MINIMAL_STRATEGY_SRC
@@ -413,10 +413,10 @@ def test_time_in_market_too_high_flagged(monkeypatch: pytest.MonkeyPatch,
         return lambda df: fake_signals
 
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.BacktestEngine.run", fake_run
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
     )
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
     )
 
     src = _MINIMAL_STRATEGY_SRC
@@ -461,10 +461,10 @@ def test_reasonable_balanced_strategy_not_flagged(monkeypatch: pytest.MonkeyPatc
         return lambda df: fake_signals
 
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.BacktestEngine.run", fake_run
+        "multi_swarm_core.agents.adversarial.BacktestEngine.run", fake_run
     )
     monkeypatch.setattr(
-        "multi_swarm.agents.adversarial.compile_strategy", fake_compile
+        "multi_swarm_core.agents.adversarial.compile_strategy", fake_compile
     )
 
     src = _MINIMAL_STRATEGY_SRC

tests/unit/test_backtest_engine.py

@@ -2,8 +2,8 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm.backtest.engine import BacktestEngine
-from multi_swarm.backtest.orders import Side
+from multi_swarm_core.backtest.engine import BacktestEngine
+from multi_swarm_core.backtest.orders import Side
 
 
 @pytest.fixture

tests/unit/test_backtest_orders.py

@@ -2,7 +2,7 @@ from datetime import UTC, datetime
 
 import pytest
 
-from multi_swarm.backtest.orders import Order, Position, Side, Trade
+from multi_swarm_core.backtest.orders import Order, Position, Side, Trade
 
 
 def test_order_validates_side() -> None:

tests/unit/test_cerbero_client.py

@@ -1,7 +1,7 @@
 import pytest
 import responses
 
-from multi_swarm.cerbero.client import CerberoClient
+from multi_swarm_core.cerbero.client import CerberoClient
 
 
 @responses.activate

tests/unit/test_cerbero_ohlcv.py

@@ -6,7 +6,7 @@ from pathlib import Path
 import pandas as pd
 import pytest
 
-from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
+from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
 
 
 @pytest.fixture

tests/unit/test_cerbero_tools.py

@@ -1,6 +1,6 @@
 import pytest
 
-from multi_swarm.cerbero.tools import CerberoTools
+from multi_swarm_core.cerbero.tools import CerberoTools
 
 
 def test_tools_dispatch_sma(mocker):

tests/unit/test_config.py

@@ -1,4 +1,4 @@
-"""Tests for multi_swarm.config.Settings.
+"""Tests for multi_swarm_core.config.Settings.
 
 Note on .env isolation:
 The happy-path test relies on monkeypatch.setenv to provide values.
@@ -10,7 +10,7 @@ absence of required env vars. This keeps the test deterministic both in CI
 
 import pytest
 
-from multi_swarm.config import Settings
+from multi_swarm_core.config import Settings
 
 
 def test_settings_loads_from_env(monkeypatch: pytest.MonkeyPatch) -> None:

tests/unit/test_cost_tracker.py

@@ -1,5 +1,5 @@
-from multi_swarm.genome.hypothesis import ModelTier
-from multi_swarm.llm.cost_tracker import CostTracker, estimate_cost
+from multi_swarm_core.genome.hypothesis import ModelTier
+from multi_swarm_core.llm.cost_tracker import CostTracker, estimate_cost
 
 
 def test_estimate_cost_tier_c():

tests/unit/test_diversity.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from multi_swarm.metrics.diversity import population_prompt_diversity
+from multi_swarm_core.metrics.diversity import population_prompt_diversity
 
 
 def test_empty_or_single_prompt_zero_diversity() -> None:

tests/unit/test_falsification.py

@@ -4,8 +4,8 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm.agents.falsification import FalsificationAgent, FalsificationReport
-from multi_swarm.protocol.parser import parse_strategy
+from multi_swarm_core.agents.falsification import FalsificationAgent, FalsificationReport
+from multi_swarm_core.protocol.parser import parse_strategy
 
 
 @pytest.fixture

tests/unit/test_fitness.py

@@ -1,8 +1,8 @@
 from itertools import pairwise
 
-from multi_swarm.agents.adversarial import AdversarialReport, Finding, Severity
-from multi_swarm.agents.falsification import FalsificationReport
-from multi_swarm.ga.fitness import compute_fitness
+from multi_swarm_core.agents.adversarial import AdversarialReport, Finding, Severity
+from multi_swarm_core.agents.falsification import FalsificationReport
+from multi_swarm_core.ga.fitness import compute_fitness
 
 
 def make_falsification(

tests/unit/test_ga_initial.py

@@ -1,7 +1,7 @@
 import random
 
-from multi_swarm.ga.initial import build_initial_population
-from multi_swarm.genome.hypothesis import ModelTier
+from multi_swarm_core.ga.initial import build_initial_population
+from multi_swarm_core.genome.hypothesis import ModelTier
 
 
 def test_initial_population_size():

tests/unit/test_ga_loop.py

@@ -1,7 +1,7 @@
 import random
 
-from multi_swarm.ga.loop import GAConfig, next_generation
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.ga.loop import GAConfig, next_generation
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
 
 
 def make(idx: int) -> HypothesisAgentGenome:

tests/unit/test_ga_summary.py

@@ -2,7 +2,7 @@ import math
 
 import pytest
 
-from multi_swarm.ga.summary import generation_summary
+from multi_swarm_core.ga.summary import generation_summary
 
 
 def test_summary_basic_stats():

tests/unit/test_genome_crossover.py

@@ -1,7 +1,7 @@
 import random
 
-from multi_swarm.genome.crossover import uniform_crossover
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.genome.crossover import uniform_crossover
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
 
 
 def make(name: str) -> HypothesisAgentGenome:

tests/unit/test_genome_hypothesis.py

@@ -1,4 +1,4 @@
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
 
 
 def test_genome_creation_defaults():

tests/unit/test_genome_mutation.py

@@ -2,8 +2,8 @@ import random
 
 import pytest
 
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.genome.mutation import (
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.genome.mutation import (
     COGNITIVE_STYLES,
     FEATURE_POOL,
     mutate_cognitive_style,

tests/unit/test_hypothesis_agent.py

@@ -1,8 +1,8 @@
 import json
 
-from multi_swarm.agents.hypothesis import HypothesisAgent, MarketSummary
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.llm.client import CompletionResult, EmptyCompletionError
+from multi_swarm_core.agents.hypothesis import HypothesisAgent, MarketSummary
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.llm.client import CompletionResult, EmptyCompletionError
 
 
 def make_summary() -> MarketSummary:

tests/unit/test_llm_client.py

@@ -1,7 +1,7 @@
 import pytest
 
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.llm.client import CompletionResult, LLMClient
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.llm.client import CompletionResult, LLMClient
 
 
 def make_genome(tier: ModelTier) -> HypothesisAgentGenome:
@@ -23,7 +23,7 @@ def test_completion_tier_c_uses_openrouter(mocker):
     fake_response.usage = mocker.MagicMock(prompt_tokens=100, completion_tokens=200)
     fake_openai.chat.completions.create.return_value = fake_response
 
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.C)
@@ -43,7 +43,7 @@ def test_completion_tier_b_uses_openrouter_with_anthropic_model(mocker):
     fake_response.choices = [mocker.MagicMock(message=mocker.MagicMock(content="(strategy ...)"))]
     fake_response.usage = mocker.MagicMock(prompt_tokens=80, completion_tokens=150)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.B)
@@ -67,7 +67,7 @@ def test_completion_retries_on_connection_error(mocker):
     fake_openai.chat.completions.create.side_effect = openai.APIConnectionError(
         request=mocker.MagicMock()
     )
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.C)
@@ -86,7 +86,7 @@ def test_completion_uses_custom_model_tier_c(mocker):
     ]
     fake_response.usage = mocker.MagicMock(prompt_tokens=10, completion_tokens=20)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(
         openrouter_api_key="or-x",
@@ -109,7 +109,7 @@ def test_completion_uses_custom_model_tier_b(mocker):
     ]
     fake_response.usage = mocker.MagicMock(prompt_tokens=10, completion_tokens=20)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(
         openrouter_api_key="or-x",
@@ -130,7 +130,7 @@ def test_completion_tier_s_uses_openrouter_with_anthropic_model(mocker):
     fake_response.choices = [mocker.MagicMock(message=mocker.MagicMock(content="(strategy s)"))]
     fake_response.usage = mocker.MagicMock(prompt_tokens=50, completion_tokens=100)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.S)
@@ -149,7 +149,7 @@ def test_completion_tier_a_uses_openrouter_with_anthropic_model(mocker):
     fake_response.choices = [mocker.MagicMock(message=mocker.MagicMock(content="(strategy a)"))]
     fake_response.usage = mocker.MagicMock(prompt_tokens=40, completion_tokens=80)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.A)
@@ -170,7 +170,7 @@ def test_completion_tier_d_uses_openrouter_with_llama(mocker):
     ]
     fake_response.usage = mocker.MagicMock(prompt_tokens=30, completion_tokens=70)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.D)
@@ -191,7 +191,7 @@ def test_completion_uses_custom_model_tier_s(mocker):
     ]
     fake_response.usage = mocker.MagicMock(prompt_tokens=10, completion_tokens=20)
     fake_openai.chat.completions.create.return_value = fake_response
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(
         openrouter_api_key="or-x",
@@ -221,7 +221,7 @@ def test_completion_succeeds_after_one_retry(mocker):
         openai.APITimeoutError(request=mocker.MagicMock()),
         fake_response,
     ]
-    mocker.patch("multi_swarm.llm.client.OpenAI", return_value=fake_openai)
+    mocker.patch("multi_swarm_core.llm.client.OpenAI", return_value=fake_openai)
 
     client = LLMClient(openrouter_api_key="or-x")
     g = make_genome(ModelTier.C)

tests/unit/test_market_summary.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from multi_swarm.agents.market_summary import build_market_summary
+from multi_swarm_core.agents.market_summary import build_market_summary
 
 
 def test_build_summary_basic() -> None:

tests/unit/test_metrics_basic.py

@@ -2,7 +2,7 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm.metrics.basic import max_drawdown, sharpe_ratio, total_return
+from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
 
 
 def test_sharpe_zero_returns():

tests/unit/test_metrics_dsr.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from multi_swarm.metrics.dsr import deflated_sharpe_ratio, expected_max_sharpe
+from multi_swarm_core.metrics.dsr import deflated_sharpe_ratio, expected_max_sharpe
 
 
 def test_expected_max_sharpe_grows_with_n_trials():

tests/unit/test_mutation_dispatcher.py

@@ -4,8 +4,8 @@ import random
 from collections import Counter
 from dataclasses import dataclass
 
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.genome.mutation import weighted_random_mutate
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.genome.mutation import weighted_random_mutate
 
 _PROMPT = (
     "Strategia mean-reversion 1h BTC. Entry long quando RSI(14) < 30 e "

tests/unit/test_mutation_prompt_llm.py

@@ -3,8 +3,8 @@ from __future__ import annotations
 import random
 from dataclasses import dataclass
 
-from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
-from multi_swarm.genome.mutation_prompt_llm import (
+from multi_swarm_core.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm_core.genome.mutation_prompt_llm import (
     MUTATION_INSTRUCTIONS,
     _extract_prompt,
     is_valid_prompt,

tests/unit/test_protocol_compiler.py

@@ -6,9 +6,9 @@ import numpy as np
 import pandas as pd
 import pytest
 
-from multi_swarm.backtest.orders import Side
-from multi_swarm.protocol.compiler import compile_strategy
-from multi_swarm.protocol.parser import parse_strategy
+from multi_swarm_core.backtest.orders import Side
+from multi_swarm_core.protocol.compiler import compile_strategy
+from multi_swarm_core.protocol.parser import parse_strategy
 
 
 @pytest.fixture

tests/unit/test_protocol_parser.py

@@ -2,7 +2,7 @@ import json
 
 import pytest
 
-from multi_swarm.protocol.grammar import (
+from multi_swarm_core.protocol.grammar import (
     ACTION_VALUES,
     ALL_OPS,
     COMPARATOR_OPS,
@@ -10,7 +10,7 @@ from multi_swarm.protocol.grammar import (
     KIND_VALUES,
     LOGICAL_OPS,
 )
-from multi_swarm.protocol.parser import (
+from multi_swarm_core.protocol.parser import (
     FeatureNode,
     IndicatorNode,
     LiteralNode,

tests/unit/test_protocol_validator.py

@@ -2,8 +2,8 @@ import json
 
 import pytest
 
-from multi_swarm.protocol.parser import parse_strategy
-from multi_swarm.protocol.validator import ValidationError, validate_strategy
+from multi_swarm_core.protocol.parser import parse_strategy
+from multi_swarm_core.protocol.validator import ValidationError, validate_strategy
 
 
 def _wrap(condition: dict, action: str = "entry-long") -> str: