warren618
c090d1fce8
- 4 new tools: extract_shadow_strategy, run_shadow_backtest, render_shadow_report, scan_shadow_signals - src/shadow_account/ package: clustering-based rule extractor, cached cross-market backtester, 8-section HTML/PDF reporter with attribution waterfall, signal scanner, Jinja2-rendered signal engine - src/skills/shadow-account/ ships rules + methodology + attribution semantics - API endpoint GET /shadow-reports/<id>?format=html|pdf - Context routing: Shadow block mandates load_skill first - Reused trade_journal_tool.pair_trades_fifo (promoted from private) - 28 new unit tests - Reporter fix: _chart_attribution_waterfall had 6 undefined refs (_ACCENT/_POS/_NEG/_BG/_GRID/_style_axes) — inlined hex + native mpl - Deps: jinja2, matplotlib, weasyprint (optional; HTML fallback when GTK missing)
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
"""Shadow Account — today signals scanner.
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Deterministic, data-free MVP: for each rule in a profile, pick up to N
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symbols from the market's liquid basket that are "in phase" with the
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rule's holding cadence on the target date. No external price fetches.
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Future work: swap the phase test for a real feature check (e.g. the
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rule's entry condition evaluated on the most recent bar).
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"""
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from __future__ import annotations
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from datetime import date, datetime
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from typing import Any
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from src.shadow_account.backtester import _LIQUID_BASKETS, SUPPORTED_MARKETS
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from src.shadow_account.models import ShadowProfile, ShadowRule
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def scan_today_signals(
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profile: ShadowProfile,
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*,
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target_date: str | date | None = None,
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per_market: int = 3,
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) -> list[dict[str, Any]]:
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"""Return a list of {symbol, market, rule_id, reason} matches.
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Args:
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profile: ShadowProfile holding the rules.
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target_date: ISO "YYYY-MM-DD" string, date object, or None (today).
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per_market: Cap per market bucket.
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Returns:
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Possibly-empty list of matched candidates. Always research-only —
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callers should surface the disclaimer from the skill.
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"""
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if target_date is None:
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d = date.today()
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elif isinstance(target_date, date):
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d = target_date
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else:
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d = datetime.strptime(str(target_date), "%Y-%m-%d").date()
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matches: list[dict[str, Any]] = []
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for rule in profile.rules:
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market = rule.entry_condition.get("market", "other")
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if market not in SUPPORTED_MARKETS:
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continue
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basket = _LIQUID_BASKETS.get(market, [])
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if not _date_in_phase(d, rule):
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continue
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lo, hi = rule.holding_days_range
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reason = f"Within {rule.rule_id} entry window (hold {lo}-{hi}d)"
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for symbol in basket[:per_market]:
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matches.append({
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"symbol": symbol,
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"market": market,
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"rule_id": rule.rule_id,
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"reason": reason,
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})
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return matches
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def _date_in_phase(target: date, rule: ShadowRule) -> bool:
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"""True when `target`'s ordinal mod `2 * hold_median` is inside the entry band.
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This keeps the scanner deterministic and reviewable without needing
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real-time market data.
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"""
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lo, hi = rule.holding_days_range
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hold = max(1, int((lo + hi) / 2))
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period = max(2, 2 * hold)
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phase = target.toordinal() % period
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return phase < hold
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