feat(state+runtime+gui): market_snapshots — calibrazione soglie da dati
Sistema dedicato di raccolta dati per scegliere le soglie dei filtri sui percentili reali invece di valori a istinto. Nuovi componenti: * state/migrations/0003_market_snapshots.sql — tabella + index, PK composta (timestamp, asset). Ogni colonna numerica è NULL-able per preservare la continuità della serie quando un singolo MCP fallisce. * state/models.py — MarketSnapshotRecord Pydantic. * state/repository.py — record_market_snapshot, list_market_snapshots, _row_to_market_snapshot. * runtime/market_snapshot_cycle.py — collettore best-effort che chiama spot/dvol/realized_vol/dealer_gamma/funding_perp/funding_cross/ liquidation_heatmap/macro per ogni asset; raccoglie gli errori in fetch_errors_json e segna fetch_ok=false ma persiste comunque la riga. * clients/deribit.py — generalizzati dealer_gamma_profile(currency), realized_vol(currency), spot_perp_price(asset). dealer_gamma_profile_eth resta come alias per la chiamata dell'entry cycle. * runtime/orchestrator.py — nuovo job APScheduler `market_snapshot` cron */15 con assets configurabili (default ETH+BTC); il consumer manual_actions ora dispatcha anche kind=run_cycle cycle=market_snapshot per la GUI. * gui/data_layer.py — load_market_snapshots, enqueue_run_cycle accetta market_snapshot; tipo MarketSnapshotRecord esposto. * gui/pages/6_📐_Calibrazione.py — selezione asset+finestra, conteggio fetch_ok, per ogni metrica: istogramma, soglia da strategy.yaml come vline rossa, percentili P5/P10/P25/P50/P75/P90/P95, % di tick che la soglia avrebbe filtrato. * gui/pages/1_📊_Status.py — bottone "📐 Forza snapshot" (4° del pannello Forza ciclo) per popolare la tabella senza aspettare il cron. 5 nuovi test sul collector (happy, fault tolerance, asset switch, macro fail, empty assets); test_orchestrator job set aggiornato. 368/368 tests pass; ruff clean; mypy strict src clean. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""Periodic market-snapshot collector.
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Drives the ``market_snapshots`` table populated by the scheduler job
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``market_snapshot`` (cron */15 by default). For every traded asset the
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collector calls the same MCP feeds the entry/monitor cycles consume,
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but in **best-effort mode**: a single failure leaves the corresponding
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column NULL and the row is still persisted, with an error map in
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``fetch_errors_json`` for debugging. This keeps the time series
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continuous even when one of the feeds is briefly down — the
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distributions are what matters for threshold calibration, not the
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real-time correctness of any single tick.
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"""
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from __future__ import annotations
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import json
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import logging
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from collections.abc import Awaitable, Callable
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from datetime import UTC, datetime
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from decimal import Decimal
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from typing import TYPE_CHECKING, Any
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from cerbero_bite.clients._exceptions import McpError
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from cerbero_bite.state import connect, transaction
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from cerbero_bite.state.models import MarketSnapshotRecord
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if TYPE_CHECKING:
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from cerbero_bite.runtime.dependencies import RuntimeContext
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__all__ = ["DEFAULT_ASSETS", "collect_market_snapshot"]
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_log = logging.getLogger("cerbero_bite.runtime.market_snapshot")
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DEFAULT_ASSETS: tuple[str, ...] = ("ETH", "BTC")
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async def _safe_call(
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label: str,
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factory: Callable[[], Awaitable[Any]],
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errors: dict[str, str],
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) -> Any:
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try:
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return await factory()
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except (McpError, Exception) as exc: # pragma: no branch — best-effort
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errors[label] = f"{type(exc).__name__}: {exc}"
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return None
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def _decimal_or_none(value: Any) -> Decimal | None:
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if value is None:
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return None
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if isinstance(value, Decimal):
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return value
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try:
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return Decimal(str(value))
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except (ValueError, ArithmeticError):
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return None
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async def _collect_one(
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ctx: RuntimeContext, asset: str, *, when: datetime
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) -> MarketSnapshotRecord:
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errors: dict[str, str] = {}
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asset_upper = asset.upper()
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spot = await _safe_call(
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"spot",
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lambda: ctx.deribit.spot_perp_price(asset_upper),
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errors,
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)
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dvol_value = await _safe_call(
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"dvol",
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lambda: ctx.deribit.latest_dvol(currency=asset_upper, now=when),
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errors,
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)
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rv = await _safe_call(
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"realized_vol",
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lambda: ctx.deribit.realized_vol(asset_upper),
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errors,
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)
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gamma = await _safe_call(
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"dealer_gamma",
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lambda: ctx.deribit.dealer_gamma_profile(asset_upper),
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errors,
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)
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funding_perp = await _safe_call(
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"funding_perp",
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lambda: ctx.hyperliquid.funding_rate_annualized(asset_upper),
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errors,
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)
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funding_cross = await _safe_call(
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"funding_cross",
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lambda: ctx.sentiment.funding_cross_median_annualized(asset_upper),
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errors,
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)
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heatmap = await _safe_call(
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"liquidation",
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lambda: ctx.sentiment.liquidation_heatmap(asset_upper),
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errors,
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)
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macro_days = await _safe_call(
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"macro",
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lambda: ctx.macro.next_high_severity_within(
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days=ctx.cfg.structure.dte_target,
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countries=list(ctx.cfg.entry.exclude_macro_countries),
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now=when,
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),
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errors,
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)
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rv_30 = (rv or {}).get("rv_30d") if isinstance(rv, dict) else None
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iv_minus_rv_30 = (
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(rv or {}).get("iv_minus_rv_30d") if isinstance(rv, dict) else None
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)
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return MarketSnapshotRecord(
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timestamp=when,
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asset=asset_upper,
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spot=_decimal_or_none(spot),
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dvol=_decimal_or_none(dvol_value),
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realized_vol_30d=_decimal_or_none(rv_30),
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iv_minus_rv=_decimal_or_none(iv_minus_rv_30),
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funding_perp_annualized=_decimal_or_none(funding_perp),
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funding_cross_annualized=_decimal_or_none(funding_cross),
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dealer_net_gamma=(
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_decimal_or_none(gamma.total_net_dealer_gamma)
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if gamma is not None
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else None
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),
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gamma_flip_level=(
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_decimal_or_none(gamma.gamma_flip_level)
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if gamma is not None
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else None
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),
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oi_delta_pct_4h=(
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_decimal_or_none(heatmap.oi_delta_pct_4h)
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if heatmap is not None
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else None
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),
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liquidation_long_risk=(
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heatmap.long_squeeze_risk if heatmap is not None else None
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),
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liquidation_short_risk=(
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heatmap.short_squeeze_risk if heatmap is not None else None
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),
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macro_days_to_event=(
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int(macro_days) if isinstance(macro_days, int) else None
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),
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fetch_ok=not errors,
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fetch_errors_json=(json.dumps(errors) if errors else None),
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)
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async def collect_market_snapshot(
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ctx: RuntimeContext,
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*,
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assets: tuple[str, ...] = DEFAULT_ASSETS,
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now: datetime | None = None,
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) -> int:
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"""Collect + persist one snapshot per asset. Returns count persisted.
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The function is sync at heart (sequential per asset to keep MCP
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load light) but kept ``async def`` so APScheduler can schedule it
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directly. A single asset failing does not abort the loop — the
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other assets are still snapshotted.
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"""
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when = (now or datetime.now(UTC)).astimezone(UTC)
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persisted = 0
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for asset in assets:
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try:
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record = await _collect_one(ctx, asset, when=when)
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except Exception: # pragma: no cover — defensive
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_log.exception("snapshot for %s failed catastrophically", asset)
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continue
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try:
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conn = connect(ctx.db_path)
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try:
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with transaction(conn):
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ctx.repository.record_market_snapshot(conn, record)
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finally:
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conn.close()
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persisted += 1
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except Exception: # pragma: no cover — defensive
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_log.exception("persist snapshot for %s failed", asset)
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if persisted:
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_log.info("market_snapshot persisted %d row(s)", persisted)
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return persisted
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