feat: shadow book + shadow-vs-manual performance comparison
The manual paper book only contains trades taken by hand, inside a 20
minute window, on days someone was available. The backtest that validated
this strategy auto-takes the top-ranked qualified setups up to capacity
every session. The forward record was therefore measuring strategy plus
discretion plus availability -- and degrading silently on busy days.
The shadow book closes that gap: it mirrors the backtest's selection rule
(top strategy_rank qualified, up to capacity, 1% fixed-fractional risk)
and shares the manual book's exit policy, so the only difference between
the two books is which setups get taken. Selection ordering reuses the
strategy_rank the scanner already stores rather than recomputing it, so
the two cannot drift apart. It runs as a near-close pipeline step right
after the scan, marking entries at the same prices a human would see.
Gate-reset re-entry state is now scoped per book -- the books diverge as
soon as their entries differ, and each must see only its own stops.
Performance view rewritten around the comparison:
- three series (shadow, manual, SPY) from a new endpoint
- SPY changes from a per-trade cost-basis counterfactual to plain
buy-and-hold %, since one line has to serve two books
- headline stats are R-multiples, not currency: the books size
differently, so only R compares across them
- configurable start date, because the strategy has been revised
repeatedly and pre-cutover trades ran under rules that no longer
exist
Migration 024 also repairs the numeric weekday crons written by 023,
rewriting only rows still holding the broken form so hand-corrected
settings survive. Its literals are inlined because bound parameters
render as NULL under 'alembic upgrade --sql'.
The shadow book is opt-in and writes nothing until enabled. Verify its
first selections match a backtest of that day's cross-section before
trusting any point on the curve.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
Claude Fable 5
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import { useEffect, useState } from 'react';
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import { useMutation, useQuery, useQueryClient } from '@tanstack/react-query';
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import {
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getPerformanceSettings,
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getShadowBookSettings,
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updatePerformanceSettings,
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updateShadowBookSettings,
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type ShadowBookConfig,
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} from '../../api/admin';
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import { SkeletonCard } from '../ui/Skeleton';
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/** Performance window + the auto-traded shadow book.
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*
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* These belong together: the shadow book is what the comparison measures, and
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* the start date is what keeps the comparison inside a single strategy
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* configuration.
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*/
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export function PerformanceSettings() {
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const qc = useQueryClient();
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const window = useQuery({ queryKey: ['admin', 'performance'], queryFn: getPerformanceSettings });
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const shadow = useQuery({ queryKey: ['admin', 'shadow-book'], queryFn: getShadowBookSettings });
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const [startDate, setStartDate] = useState('');
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const [book, setBook] = useState<ShadowBookConfig | null>(null);
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useEffect(() => {
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if (window.data) setStartDate(window.data.start_date ?? '');
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}, [window.data]);
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useEffect(() => {
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if (shadow.data) setBook(shadow.data);
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}, [shadow.data]);
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const saveWindow = useMutation({
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mutationFn: () => updatePerformanceSettings({ start_date: startDate }),
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onSuccess: () => {
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qc.invalidateQueries({ queryKey: ['admin', 'performance'] });
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qc.invalidateQueries({ queryKey: ['paper-trades', 'performance'] });
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},
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});
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const saveBook = useMutation({
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mutationFn: (payload: Partial<ShadowBookConfig>) => updateShadowBookSettings(payload),
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onSuccess: (data) => {
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setBook(data);
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qc.invalidateQueries({ queryKey: ['admin', 'shadow-book'] });
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qc.invalidateQueries({ queryKey: ['paper-trades', 'performance'] });
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},
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});
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if (window.isLoading || shadow.isLoading || !book) return <SkeletonCard />;
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return (
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<div className="glass space-y-5 p-5">
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<div>
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<h3 className="text-sm font-semibold text-gray-200">Performance & Shadow Book</h3>
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<p className="mt-1 text-xs leading-relaxed text-gray-500">
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The <span className="text-gray-300">shadow book</span> trades the validated strategy with no
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human input: top-ranked qualified setups up to capacity, sized to a fixed risk, entered right
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after the near-close scan. It shares the paper exit policy with your own trades, so the only
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difference between the two books is <span className="text-gray-300">which setups get taken</span>.
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</p>
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</div>
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<label className="block space-y-1">
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<span className="text-xs text-gray-400">Performance since</span>
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<div className="flex gap-2">
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<input
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type="date"
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value={startDate}
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onChange={(e) => setStartDate(e.target.value)}
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className="input-glass w-48 px-3 py-2 text-sm"
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/>
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<button
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type="button"
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onClick={() => saveWindow.mutate()}
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disabled={saveWindow.isPending}
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className="btn-glass px-3 py-2 text-sm"
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>
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{saveWindow.isPending ? 'Saving…' : 'Save'}
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</button>
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{startDate && (
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<button
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type="button"
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onClick={() => {
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setStartDate('');
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updatePerformanceSettings({ start_date: '' }).then(() => {
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qc.invalidateQueries({ queryKey: ['admin', 'performance'] });
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qc.invalidateQueries({ queryKey: ['paper-trades', 'performance'] });
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});
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}}
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className="btn-glass px-3 py-2 text-sm text-gray-400"
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>
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Clear
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</button>
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)}
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</div>
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<span className="block text-[11px] leading-relaxed text-gray-500">
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Trades opened before this date are excluded from the Performance card. The strategy has been
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revised repeatedly — pinning a start keeps the comparison inside one configuration instead of
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averaging across rules that no longer exist. Empty shows all history.
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</span>
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</label>
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<div className="border-t border-white/5 pt-4">
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<label className="flex items-start gap-3">
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<input
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type="checkbox"
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checked={book.enabled}
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onChange={(e) => saveBook.mutate({ enabled: e.target.checked })}
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className="mt-0.5"
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/>
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<span>
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<span className="text-sm text-gray-200">Shadow book enabled</span>
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<span className="block text-[11px] leading-relaxed text-gray-500">
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Starts opening real paper positions automatically on the next near-close scan. Verify its
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first selections match a backtest of that day's cross-section before trusting the curve.
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</span>
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</span>
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</label>
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<div className="mt-4 grid gap-4 md:grid-cols-3">
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<label className="block space-y-1">
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<span className="text-xs text-gray-400">Capacity (positions)</span>
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<input
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type="number"
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min={1}
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max={100}
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value={book.capacity}
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onChange={(e) => setBook({ ...book, capacity: Number(e.target.value) })}
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onBlur={() => saveBook.mutate({ capacity: book.capacity })}
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className="input-glass w-full px-3 py-2 text-sm"
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/>
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</label>
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<label className="block space-y-1">
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<span className="text-xs text-gray-400">Risk per trade (%)</span>
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<input
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type="number"
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step="0.05"
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min={0.05}
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max={10}
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value={book.risk_pct}
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onChange={(e) => setBook({ ...book, risk_pct: Number(e.target.value) })}
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onBlur={() => saveBook.mutate({ risk_pct: book.risk_pct })}
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className="input-glass w-full px-3 py-2 text-sm"
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/>
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</label>
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<label className="block space-y-1">
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<span className="text-xs text-gray-400">Start equity ($)</span>
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<input
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type="number"
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min={1000}
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step={1000}
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value={book.start_equity}
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onChange={(e) => setBook({ ...book, start_equity: Number(e.target.value) })}
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onBlur={() => saveBook.mutate({ start_equity: book.start_equity })}
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className="input-glass w-full px-3 py-2 text-sm"
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/>
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</label>
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</div>
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<p className="mt-2 text-[11px] leading-relaxed text-gray-500">
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Defaults match the validated configuration: 10 positions, 1% fixed-fractional risk. Start
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equity is only a sizing base — the books are compared in R-multiples, not currency.
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</p>
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</div>
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</div>
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);
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}
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