* gui/data_layer.py — adds load_position_by_id, load_decisions_for_position,
compute_payoff_curve (pure math: bull_put / bear_call piecewise linear
P&L at expiry, with breakeven), compute_distance_metrics (OTM%,
days-to-expiry, days-held, width%).
* gui/pages/5_💼_Position.py — selector across open + 10 most-recent
closed positions (with deep-link support via ?proposal_id=…), header
metrics, distance summary, leg snapshot table (entry-time only —
the GUI never calls MCP), plotly payoff diagram with strike/breakeven/
entry-spot annotations and max profit/max loss tiles, decision
history table from the decisions table.
Live greeks/mid are deliberately not pulled: per docs/11-gui-streamlit.md
the GUI reads SQLite + audit log only and lets the engine refresh data.
Validated math against a synthetic bull_put 2475/2350 × 2 contracts:
breakeven 2452.50, max profit $45, max loss $-160 — all matching the
expected formulas (credit, width × n − credit).
353/353 tests still pass; ruff clean; mypy strict src clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds the analytics surface of the dashboard:
* gui/data_layer.py — extended with load_closed_positions (windowed
filter on closed_at) and three pure-function aggregators:
compute_equity_curve, compute_kpis, compute_monthly_stats. Drawdown
is measured against the running peak of cumulative realised P&L.
* gui/pages/3_📈_Equity.py — KPI strip, plotly cumulative-PnL line,
drawdown area below, P&L histogram by close_reason, per-month table
with win-rate.
* gui/pages/4_📜_History.py — windowed table of closed trades with
multiselect close-reason and winners/losers radio filters, six-tile
KPI strip, CSV export button.
* pyproject.toml — relax mypy on plotly + pandas (no shipped stubs).
Validated with synthetic data: 3 trades, 67% win rate, $50 total,
max drawdown $30 — all matching expected math. GUI launches, HTTP 200
on / and /_stcore/health.
353/353 tests still pass; ruff clean; mypy strict src clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Implements the foundation of the local observation dashboard described
in docs/11-gui-streamlit.md:
* gui/data_layer.py — read-only wrappers over Repository (system_state,
open positions) and audit_log (tail iteration, chain verify). The GUI
never imports runtime/ nor calls MCP services.
* gui/main.py — Streamlit entry point with sidebar (engine health
badge, kill switch banner, last health check age), home overview.
* gui/pages/1_📊_Status.py — engine status with colored health banner,
kill switch detail, audit anchor, open positions table.
* gui/pages/2_🔍_Audit.py — live audit log stream (newest-first),
event filters, hash-chain integrity verify button.
* cli.py gui — replaces the placeholder with os.execvpe to
`python -m streamlit run` bound to 127.0.0.1, --browser.gatherUsageStats
false; --db / --audit paths exported via env to the GUI process.
* pyproject.toml — N999 ignore for src/cerbero_bite/gui/pages/* (Streamlit
auto-discovers pages whose filename contains numbers and emoji icons).
Smoke test: GUI launches, HTTP 200 on / and /_stcore/health, data layer
correctly reflects current testnet state (engine=running, kill_switch
disarmed, 0 open positions, audit chain integra 7 entries).
353/353 tests still pass; ruff clean; mypy strict src clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Each bot now manages its own notification + portfolio aggregation:
* TelegramClient calls the public Bot API directly via httpx, reading
CERBERO_BITE_TELEGRAM_BOT_TOKEN / CERBERO_BITE_TELEGRAM_CHAT_ID from
env. No credentials → silent disabled mode.
* PortfolioClient composes DeribitClient + HyperliquidClient + the new
MacroClient.get_asset_price/eur_usd_rate to expose equity (EUR) and
per-asset exposure as the bot's own slice (no cross-bot view).
* mcp-telegram and mcp-portfolio removed from MCP_SERVICES / McpEndpoints
and the cerbero-bite ping CLI; health_check no longer probes portfolio.
Docs (02/04/06/07) and docker-compose updated to reflect the new
architecture.
353/353 tests pass; ruff clean; mypy src clean.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Conclude il doc drift residuo dei tre documenti che ancora
descrivevano il modello di esercizio pre-Fase 4 (memory/brain-bridge,
push_user_instruction, conferma manuale). Aggiornati per riflettere
l'engine autonomo notify-only attuale, con tutti gli ultimi
hardening integrati.
docs/02-architecture.md:
- Diagramma a blocchi: rimosso cerbero-memory ↔ Cerbero core,
aggiunto annotation sull'audit chain con anchor SQLite.
- Tabella stack: httpx pooling al posto dell'SDK mcp, hash chain
con anchor in system_state.
- Layout cartelle: aggiunte runtime/lockfile.py,
runtime/orchestrator.py, runtime/recovery.py, scripts/dead_man.sh,
state/migrations/0002_audit_anchor.sql.
- Sequenze entry/monitor riscritte all'auto-execute via
place_combo_order, niente attesa conferma utente.
- Nuova sezione "Lifecycle del container" con boot order, scheduler,
SIGTERM clean shutdown, lock release.
- Failure modes aggiornati: environment mismatch, audit anchor
mismatch, lock occupato.
docs/05-data-model.md:
- Filosofia estesa con la regola dell'audit chain e l'anchor.
- Schema instructions: payload_json riferito ai response Deribit
(combo_instrument, order_id, state) invece di
push_user_instruction.
- Aggiunta migration 0002_audit_anchor.sql con last_audit_hash.
- Schema log JSONL: campi cycle e cycle_id propagati da
structlog.contextvars.
- Sezione "Audit log" descrive il formato concretamente in uso
(separatori | con prev_hash/hash) ed elenco eventi reali
(ENGINE_START, RECOVERY_DONE, ENTRY_PLACED, HOLD, EXIT_FILLED,
KILL_SWITCH_*, ALERT, KELLY_RECALIBRATED).
- Sezione backup riferita allo job APScheduler ora schedulato
(0 * * * *).
docs/07-risk-controls.md:
- Nuova tabella trigger automatici allineata al codice (column
"Implementato" punta ai moduli runtime/safety reali).
- Sezione "Single-instance lock" introdotta (fcntl.flock,
EngineLock, caveat multi-host).
- Sezione "Anti-truncation" che descrive il flusso anchor: callback
on_append → SQLite → check al boot.
- "Cap di rischio" estesa con i due nuovi filter dealer-gamma e
liquidation-heatmap (§2.8).
- Sezione "Versionamento config" cita execution.environment,
execution.eur_to_usd, dealer_gamma_min, dealer_gamma_filter_enabled,
liquidation_filter_enabled.
- Escalation tree concretizzata sull'AlertManager con i metodi
reali (low/medium/high/critical).
Test: 335 pass, 1 skip (sqlite3 CLI).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Integra due nuovi filtri dal pacchetto quant indicators rilasciato in
Cerbero_mcp (commit a13e3fe). 335 test pass, mypy strict pulito,
ruff clean.
Filtri (§2.8 — nuovo):
- dealer-gamma: blocca entry quando total_net_dealer_gamma <
dealer_gamma_min (default 0). Long-gamma regime favorisce credit
spread (vol-suppressing dealer flow); short-gamma flow lo amplifica
ed è da evitare.
- liquidation-heatmap: blocca entry quando il segnale euristico di
cerbero-sentiment riporta long o short squeeze risk = "high"
(cluster di liquidations imminenti entro 24h).
Entrambi sono best-effort: se il tool MCP fallisce o restituisce
dati anomali l'entry_cycle popola EntryContext con None e
validate_entry salta il gate per non bloccare entry su problemi
infrastrutturali.
Wrapper:
- DeribitClient.dealer_gamma_profile_eth → DealerGammaSnapshot.
- SentimentClient.liquidation_heatmap → LiquidationHeatmap con
property has_high_squeeze_risk.
Schema:
- EntryConfig.dealer_gamma_min, dealer_gamma_filter_enabled,
liquidation_filter_enabled.
- EntryContext.dealer_net_gamma, liquidation_squeeze_risk_high
opzionali.
- strategy.yaml: nuovi campi documentati con commento + hash
ricalcolato (4c2be4c5...).
Documentazione:
- docs/04-mcp-integration.md riscritto al modello attuale (HTTP
REST, no mcp SDK, no memory/brain-bridge, place_combo_order
documentato, environment_info al boot).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Sei interventi MEDIA priorità sul sistema. 323 test pass, mypy strict
pulito, ruff clean.
1. Docker HEALTHCHECK + cerbero-bite healthcheck:
- nuovo subcommand che esce 0 se kill_switch=0 e last_health_check
entro --max-staleness-s (default 600s);
- HEALTHCHECK direttiva nel Dockerfile (60s interval, 5s timeout,
start_period 120s, retries 3);
- healthcheck definition nel docker-compose.yml.
2. Audit hash chain anti-truncation:
- migration 0002: nuova colonna system_state.last_audit_hash;
- AuditLog accetta callback on_append, dependencies.py la wire al
repository.set_last_audit_hash;
- Orchestrator.boot verifica che il tail file matcha l'anchor
persistito; mismatch → kill switch CRITICAL.
3. return_4h bootstrap da deribit get_historical:
- quando dvol_history è vuoto _fetch_return_4h cade su
deribit.historical_close (1h candle 4h fa);
- alert LOW se anche il fallback fallisce.
4. execution.environment + execution.eur_to_usd in strategy.yaml:
- ExecutionConfig promosso a typed schema con i due campi
consumati al boot;
- CLI start preferisce i valori da config; CLI flag overridano
solo quando differenti dai default.
5. Cycle correlation ID:
- structlog.contextvars.bind_contextvars in run_entry/run_monitor/
run_health propaga cycle_id e cycle nei log strutturati.
6. SIGTERM/SIGINT clean shutdown:
- run_forever installa loop.add_signal_handler per SIGTERM e
SIGINT; il segnale set()ta un asyncio.Event che termina il
blocco principale, scheduler.shutdown e ctx.aclose finalizzano.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Sei interventi mirati sui rischi operativi rilevati nell'audit
post-Fase 4. 317 test pass, mypy strict pulito, ruff clean.
1. status CLI: legge SQLite reale e mostra kill_switch, posizioni
aperte, environment, config_version, last_health_check, started_at.
Sostituisce il placeholder "phase 0 skeleton".
2. Lock file single-instance: runtime/lockfile.py acquisisce
data/.lockfile via fcntl.flock al boot di run_forever; un secondo
container fallisce subito con LockError.
3. Backup orario nello scheduler: nuovo job APScheduler 0 * * * *
chiama scripts.backup.backup_database + prune_backups.
4. config_hash enforce su start: il CLI start verifica l'integrità
del file (enforce_hash=True). Mismatch → exit 1 prima di toccare
stato. dry-run resta enforce_hash=False per debug.
5. Connection pooling MCP: RuntimeContext espone un httpx.AsyncClient
long-lived condiviso da tutti i wrapper (limits 20/10
connections/keepalive). aclose() chiamato in run_forever finale.
6. Bias direzionale reale: deribit.historical_close +
deribit.adx_14 popolano TrendContext con spot a 30 giorni e
ADX(14) effettivi. Sblocca bull_put e bear_call. Quando i dati
storici mancano l'engine emette alert MEDIUM e cade su no_entry
in modo deterministico.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Wrapper async tipizzati sui sei servizi MCP HTTP che Cerbero Bite
consuma in autonomia. 277 test pass, copertura clients 93%, mypy
strict pulito, ruff clean.
Base layer:
- clients/_base.py: HttpToolClient con httpx + tenacity (retry
esponenziale 3x, timeout 8s, mapping HTTP→eccezioni tipizzate).
- clients/_exceptions.py: McpAuthError, McpServerError, McpToolError,
McpDataAnomalyError, McpNotFoundError, McpTimeoutError.
- config/mcp_endpoints.py: risoluzione URL via Docker DNS
(mcp-deribit:9011, ...) con override per servizio via env var;
caricamento bearer token da secrets/core.token o
CERBERO_BITE_CORE_TOKEN_FILE.
Wrapper:
- clients/macro.py: next_high_severity_within() per filtro entry §2.5.
- clients/sentiment.py: funding_cross_median_annualized() con
annualizzazione per period nativo per exchange (Binance/Bybit/OKX
1095, Hyperliquid 8760).
- clients/hyperliquid.py: funding_rate_annualized() per filtro §2.6.
- clients/portfolio.py: total_equity_eur(), asset_pct_of_portfolio()
per sizing engine + filtro §2.7.
- clients/telegram.py: notify-only (no callback queue, no
conferme — Bite auto-execute).
- clients/deribit.py: environment_info, index_price_eth,
latest_dvol, options_chain, get_tickers, orderbook_depth_top3,
get_account_summary, get_positions, place_combo_order (combo
atomico), cancel_order.
CLI:
- cerbero-bite ping: health-check parallelo di tutti gli MCP con
tabella rich (OK/FAIL/SKIPPED).
Docker:
- Dockerfile multi-stage Python 3.13 + uv, user non-root.
- docker-compose.yml con rete external "cerbero-suite", secret
core_token montato a /run/secrets/core_token, env per ogni MCP.
- secrets/README.md documenta il setup del token.
Documentazione di intervento:
- docs/12-mcp-deribit-changes.md: spec delle modifiche apportate
al server mcp-deribit (place_combo_order + override testnet via
DERIBIT_TESTNET).
Dipendenze:
- aggiunto pytest-httpx per i test HTTP.
- rimosso mcp>=1.0 (non usiamo l'SDK MCP, parliamo via HTTP REST).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Implementa i sette algoritmi puri di docs/03-algorithms.md con
disciplina TDD: 112 test, copertura statement+branch al 100% su
core/ e config/, mypy --strict pulito, ruff pulito.
Moduli:
- config/schema.py: StrategyConfig Pydantic v2 con validatori di
consistenza (kelly, delta, OTM, spread width, profit/stop).
- core/types.py: OptionQuote e OptionLeg condivisi.
- core/entry_validator.py: validate_entry (accumula motivi) e
compute_bias (bull_put/bear_call/iron_condor/None).
- core/liquidity_gate.py: check OI/volume/spread/depth + slippage
stimato in % del credito.
- core/sizing_engine.py: Quarter Kelly con cap 200/1000 EUR e
bande DVOL.
- core/combo_builder.py: select_strikes (DTE/OTM/delta/width/credit)
e build (ComboProposal con credit/max_loss/breakeven).
- core/greeks_aggregator.py: somma firmata BUY/SELL, theta in USD.
- core/exit_decision.py: 6 trigger ordinati con eccezione skip-time
vicino a profit (mark in (50%,70%] credito).
- core/kelly_recalibration.py: full/quarter Kelly, confidence per
sample size, blend medio in fascia 30-99 trade.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adriano