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prompt request : US500 Quantitative Strategy Discovery You are an elite quantitative researcher specializing in statistical edge discovery. Your task is NOT to optimize existing trading strategies or brute-force parameter combinations. Your task is to discover an original statistical edge directly from the attached US500 historical data. --- Objective Analyze the US500 dataset from first principles and discover recurring market behaviors that can be converted into a fully mechanical trading strategy. Do not begin by combining common technical indicators or searching for familiar indicator-based systems (such as EMA, SMA, RSI, MACD, Stochastic, Bollinger Bands, ATR, etc.). Instead, let the historical data guide the research. Search for statistically significant patterns, relationships, and behaviors in the raw market data first. If the discovered statistical edge is later strengthened or filtered by one or more indicators, that is acceptable, but indicators should validate an existing edge rather than create one. Remember that equity indices have a long-term bullish bias, volatility clustering, and occasional high-impact bear markets. Your objective is to discover behaviors that remain statistically robust across both bull and bear market regimes rather than exploiting one specific period. --- Research Method Read and analyze the entire dataset before proposing any strategy. Search for statistically significant relationships such as: - Multi-candle dependencies - Conditional probabilities - Mean reversion - Momentum persistence - Trend continuation - Trend exhaustion - Volatility expansion and contraction - Candle geometry - Wick asymmetry - Body-to-range relationships - Gap behavior - Opening range characteristics - Intraday session effects - Day-of-week effects - Volatility clustering - Regime-dependent behavior - Liquidity behavior - Any hidden recurring behavior present in the historical data Generate a large number of candidate hypotheses. Reject weak ideas. Keep only statistically robust patterns. Never force a strategy if no meaningful edge exists. --- Hard Performance Requirements The final strategy should satisfy ALL of the following on the supplied US500 dataset: - Profit Factor ≥ 1.30 - Win Rate ≥ 40% - Minimum 60 trades per year - Profitable across the entire available historical period - Stable long-term equity growth - Robust across bull markets, bear markets, and high-volatility environments - Low parameter sensitivity If a strategy cannot remain profitable throughout the entire historical period because of structural market changes, attempt to discover one that maximizes robustness instead of overfitting. If no strategy satisfies these requirements, explicitly state that no such strategy exists instead of inventing one. --- Anti-Overfitting Rules Do NOT optimize until the desired metrics are reached. Instead: - Generate many candidate edges. - Rank them by statistical robustness. - Prefer simple rules over complicated ones. - Reject strategies with small sample sizes. - Reject strategies that only work with narrowly optimized parameters. - Reject strategies whose edge disappears after small parameter changes. - Reject strategies that exploit accidental historical quirks. --- Validation Before selecting the final strategy perform: - Walk-forward validation - Out-of-sample testing - Rolling-window testing - Monte Carlo simulation - Spread sensitivity analysis - Slippage sensitivity analysis - Execution delay simulation - Parameter perturbation - Long-only analysis - Short-only analysis - Year-by-year performance analysis Explicitly verify there is: - No lookahead bias - No repainting - No future data leakage - No survivorship bias - No unrealistic fills - No same-bar TP/SL cheating - No hidden optimization bias --- Final Report Provide: - Complete entry rules - Complete exit rules - Stop-loss logic - Take-profit logic - Risk management assumptions - Profit Factor - Win Rate - Expectancy - Trades Per Year - Total number of trades - Maximum Drawdown - Average trade - Average win - Average loss - Long vs Short statistics - Monthly statistics - Year-by-year performance table - Explanation of why the statistical edge may exist - Explanation of why the edge survives different market regimes - Weaknesses and market conditions where the edge is likely to fail --- Pine Script After selecting the best validated strategy, create a complete TradingView Pine Script v6 strategy. Requirements: - Pine Script Version 6 - strategy() implementation - No repainting - No lookahead bias - Entries only after candle close - Configurable inputs - Accurate stop-loss and take-profit implementation - Fixed risk-reward support where applicable - Clean, modular, production-quality code - Well-commented for readability - TradingView backtest results should closely match the research results The Pine Script must faithfully implement the discovered strategy without simplification or omitted logic. 40:o8,af:T669,40:o669,Looking at the performance metrics, V4 fails both the profit factor and yearly consistency tests, while V2 shows solid results with a 1.353 profit factor, 56.2% win rate, and positive returns in 10 out of 12 years. V6 has a slightly better profit factor at 1.402 but underperforms on annual returns and consistency. Now I need to finalize the candidate selection—V2 is looking like the strongest option—then run the complete validation suite and prepare the final report along with the Pine Script implementation and web interface. V2's core logic filters for trades during the 10 to 15 hour RTH window and... V2 uses a volume ratio below 1.15 and prior-day CLV below 0.35 as entry signals, with take-profit at 2.0× ATR, stop-loss at 1.5× ATR, a 5-day hold period, maximum 6 units, and 0.6 points spread plus 0.2 slippage. The prompt requires profitability across the entire historical period, and V2 has two losing years—likely 2015 and 2022, both bear markets—so I'll acknowledge this limitation and focus on maximizing robustness instead. Now I'm building a comprehensive validation script that tests the final V2 configuration across multiple dimensions: overall and monthly statistics, yearly breakdowns, walk-forward analysis with parameter stability checks across folds, rolling 250-trade and 12-month windows, Monte Carlo bootstrap resampling with random-entry null hypothesis, spread and slippage sensitivity grids, execution delay scenarios from 0 to 3 bars, and a full parameter perturbation grid covering take-profit, stop-loss, hold period, unit limits, and the calm and weak thresholds. Writing validation script...40:o9,b0:T1678,40:o1678,I'm laying out the Pine Script definitions for this trading system. A session spans from 9 AM to 3:59 PM in New York time with seven hourly bars, and daily values are calculated only from regular trading hours—the high is the maximum, low is the minimum, and close is the final price. I'm defining the range as high minus low, the close location value as the proportional distance of close within that range, and the average true range as a simple 14-period average of session ranges. The unit for position sizing comes from the previous session's ATR14, shifted forward so it's available at the start of each trading day. The volatility ratio divides the unit by a 60-period rolling mean of ATR14 values, also shifted back one session. Entry signals occur during New York hours 10 through 15 within regular trading hours, with a maximum of six concurrent positions, each sized to one unit with a stop loss at 1.5 times the unit and a profit target at 2.0 times the unit. Positions expire after five sessions from entry, exiting at the close of the final bar at 3 PM on that expiration day, and fills occur at the next bar's open. For the Pine Script implementation on a 1-hour chart, I'm tracking the RTH session aggregates directly in the script using the hour of day to identify session boundaries rather than relying on custom sessions. Strategy orders fill at the next bar open by default, giving a one-bar delay. I'll use pyramiding to allow up to six independent units, each with its own exit order tracked by unique IDs. For the time exit, I'm tracking entry IDs and their target session expiry (five sessions from entry), then closing positions when the session index reaches that threshold on the final RTH bar of the day. On barrier priority, the stop loss wins ties since TradingView's backtester assumes the worst case when both stop and limit hit within the same bar—I'll set `calc_on_every_tick=false` and use bracket orders with explicit stop and limit parameters. For costs, I'm accounting for a 0.6 spread plus 0.2 slippage, which translates to 5 ticks total per side on US500 (0.1 tick minimum), so I'll configure the commission and slippage settings in Pine accordingly. Now I'm working on position sizing: I'll calculate quantity based on 1% risk per trade using the formula `qty = (equity * riskPct) / (stopLoss * unit)`, floored to a 0.01 minimum since index CFDs support fractional shares, and I'll also add a fixed-quantity option for flexibility. I need to restrict entries to allowed trading hours and avoid the last bar to prevent fills at the next session's open. About 200 trades come from the 15:00 signals and they perform slightly worse, so the cleanest approach is to lock the entry hours to 10–14 in Pine and re-validate the research with those same hours to eliminate the overnight-fill ambiguity. Running that validation gives a profit factor of 1.376 with an expectancy of 0.1484 and trades per year around 165, still well above the 60 threshold. Now I need to verify the session-end exit logic in the research engine. slight difference versus the research baseline (exit at 15:00 close versus next bar open). Or I could use `strategy.close()` with `process_orders_on_close=true` and delay entries by one bar using a shifted signal, but that would fill at the close of the bar after the signal rather than its open, which also diverges from research. The simplest approach might be to align the research engine with Pine's natural convention instead. Building engine4 to match Pine's behavior exactly... The time exit submits at the 14:00 bar close of the expiry session and fills at the 15:00 bar open, keeping the hold duration to 5 sessions maximum. Entry happens at the next bar's open following signal, so if the signal fires at 14:00 on session 1, entry is at 15:00 that same session, then the exit triggers 5 sessions later at the 14:00 close of session 5, filling at 15:00 open. Now I'm building engine4 as a modified version of engine3 to implement this exact logic, then running the full test suite to verify the profit factor stays in the 1.35–1.38 range before writing the Pine equivalent. The key detail is that pending fills happen at the next bar index regardless of session boundaries, since the hourly bars are consecutive within the panel. For the time-based exit, I'm flagging each position with an expiry session calculated as entry_session_idx plus hold_days minus one, then at the bar where nyh==14 and the day index reaches expiry, I mark the position to exit at the open of the next bar. When that next bar arrives, the exit executes at open before any barrier checks, since a market order at open fills at open. Now I'm laying out the engine loop: first process any positions flagged for force exit at the current bar, then check barriers for remaining open positions, fill pending signals that have completed their delay, flag time-based exits for positions that've reached expiry, and finally register any new signals that trigger at this bar. I'm also reconsidering the barrier check timing—specifically whether a position entered at bar i should have its stop-loss or take-profit checked on that same bar. In the current approach, exits are processed before fills, so a newly filled position isn't checked until the next bar, which feels optimistic. To match TradingView's behavior where bracket orders activate immediately, I should check barriers starting on the entry bar itself, which is more conservative. I'll reorder engine4 to process exits after fills to achieve this. Now I'm setting up engine4.py with the same structure and imports as engine3, then writing a runner script to test the full suite with this revised logic and eventually match it in Pine.40:o9,b1:T120b,40:o120b,Now I'm setting up the Pine Script strategy for the US500 with specific parameters like pyramiding, capital allocation, and order execution settings, while also factoring in trading costs from spreads and slippage. I need to figure out how to handle the slippage parameter since Pine Script requires it to be a constant integer at compile time, but I want to make it configurable through inputs. I'm checking whether TradingView allows input values in the strategy() declaration—it looks like some parameters do accept input types even though they technically require const, particularly for commission values, so I might be able to use an input for slippage as well, or I'll need to calculate it separately and pass a fixed value. For tracking sessions, I'm setting up timezone-aware hour and date calculations to identify regular trading hours (9 AM to 3 PM New York time) and detect when a new trading day begins. I'll use dayofyear with the New York timezone to track session boundaries, then aggregate OHLC data for each session. When the first RTH bar appears or a new day starts, I initialize the session highs and lows; otherwise I update them with the current bar's extremes. At the close of the last RTH bar (3 PM), I finalize the session by calculating its range and close-to-low value, then append these metrics to rolling arrays—keeping the last 14 sessions for a 14-period ATR and maintaining a 60-period history of those ATR values to compute a longer-term average. The key is getting the timing right: after adding the current session's ATR to the history, I calculate the 60-session average that will be used as the denominator for the volatility ratio in the *next* session, and I store the current ATR as the numerator for that same next session. Setting up the entry logic with a calm market check (volume ratio below threshold), weak price action (close-to-low ratio below threshold), and a time window during RTH hours 10–14, guarding against intra-bar signals with `barstate.isconfirmed`. The strategy requires valid unit sizing and a full 60-bar ATR history before placing trades, then manages up to 6 units per entry. Adding entry management... Defining the exit logic... I'm tracking session index with a counter that increments on the first RTH bar of each new session, storing each trade's entry session index in a map. The key timing detail: a trade entered at the 15:00 open of session s has entry session index s (not s+1), so when the time exit triggers at 14:00 of session s+4, it correctly represents a 5-session hold. For position sizing, I'm implementing risk-based calculations where quantity equals the risk amount divided by the stop loss distance, with an input mode to choose between "Risk % per unit" or "Fixed contracts"—defaulting to 1% risk per unit. With pyramiding up to 6 levels, that caps total risk at 6%, and I'm adding the directional entry filter plus plotting the unit, volume ratio, and price level states with background shading. I also need to guard against insufficient data by skipping the first ~74 sessions until the ATR history is complete, set `calc_on_order_fills=false` by default, and validate the chart timeframe is 60 minutes with a runtime error (though allowing an override input). For the RTH block requirement, I'm handling the edge case where early market closes prevent the session from reaching the 15:00 bar by adding a fallback finalization trigger. I'll finalize pending sessions lazily at the start of the next session's first RTH bar rather than trying to look ahead, which handles early closes robustly and means signals become available at 10:00 as needed. For the 14:00 time exit on early-close days when that bar might not exist, I'm exploring an alternative approach to arm the exit condition. Pine script file setup... For charts, I'll skip external dependencies and hand-roll lightweight SVG components instead—equity curves, bar charts, all custom. I'm copying the results JSON into the app as a typed import and structuring the page with a server component that composes all the report sections: metric cards, equity chart, bar charts, data tables, and the Pine script display. Now I need to check the existing app structure and write out the Pine script, being careful with v6 syntax around strategy configuration, maps, and loop bounds when there are no open trades. Writing Pine script... Setting up the strategy inputs with timezone, volatility thresholds, ATR and volume parameters, take-profit and stop-loss multipliers, position sizing constraints, and feature toggles, then implementing the core logic.40:o887,b2:T431,cd /vercel/share/v0-project && .venv/bin/python -c " import json;r=json.load(open('research/data/results.json')) print('REQ'); [print(' ',x['ok'],x['label'],x['val']) for x in r['req']] print('YEARLY'); [print(' ',x['year'],x['n'],round(x['R'],1),round(x['wr'],3),round(x['pf'],3),round(x['exp'],3)) for x in r['yearly']] print('EXITS'); [print(' ',x) for x in r['exits']] print('HEAD'); print(json.dumps(r['headline'],indent=1,default=float)) print('CHECKS'); [print(' -',x['k'],'::',x['v']) for x in r['checks']] print('FOLDS'); [print(' ',x) for x in r['folds']] print('ROLL'); print(json.dumps(r['rolling'],indent=1,default=float)) print('MC'); print(json.dumps(r['mc'],indent=1,default=float)) print('COSTS'); [print(' ',x) for x in r['costs']] print('PERTURB n',r['perturb_summary']) print('MONTHLY'); [print(' ',x['month'],x['n'],round(x['R'],1),round(x['exp'],3),round(x['wr'],3)) for x in r['monthly']] print('DOW'); [print(' ',x['day'],x['n'],round(x['R'],1),round(x['exp'],3),round(x['wr'],3)) for x in r['dow']] print('WFS'); [print(' ',x) for x in r['wfs']] "b3:T43d,I'm noticing a discrepancy in the slippage configuration—the Pine script sets it to 5 points with a comment suggesting 0.5 per side, but the research data shows a different breakdown of spread and slippage costs that totals 0.8 points round-trip. I need to reconcile this and standardize it to 0.5 points per side to match what the engine actually charges. Looking at the rest of the code, there are a few cleanup items: the `inWindow` input is declared inline in an expression when it should be moved to the inputs section for clarity, the `f_avg` wrapper doesn't add much value, and I'm verifying the time-exit logic is correct—entry happens in session s0, and with a 5-session hold period, the exit triggers at session s0+4, which is the 5th session as intended. The entry and exit blocks execute in the right order so there's no double-counting issue, and the bracket loop's repeated strategy.exit calls are fine since the IDs stay consistent. Now I need to clean up the inWindow placement and the commission comment, then move on to building the report page in app/page.tsx.b4:Te81d,40:oe81d,[V0_FILE]typescriptreact:file="/app/page.tsx" import { CostCurve, EquityCurve, MonteCarloBand, MonthlyBars, RollingBox } from '@/components/charts' import { Bar, Panel, Pill, Rule, Section, Stat, Table, Td, Tr } from '@/components/kit' import { PineBlock } from '@/components/pine-block' import { R, num, pct, rr } from '@/lib/research' import { readFile } from 'node:fs/promises' import path from 'node:path' const NAV = [ ['01', 'discovery', 'Discovery'], ['02', 'edge', 'The edge'], ['03', 'rules', 'Rules'], ['04', 'performance', 'Performance'], ['05', 'validation', 'Validation'], ['06', 'integrity', 'Integrity'], ['07', 'why', 'Why it exists'], ['08', 'weaknesses', 'Weaknesses'], ['09', 'pine', 'Pine Script'], ] as const export default async function Page() { const pine = await readFile( path.join(process.cwd(), 'public', 'us500-calm-weak-close.pine'), 'utf8', ) const h = R.headline const maxYearR = Math.max(...R.yearly.map((y) => Math.abs(y.R))) const maxMonthR = Math.max(...R.monthly.map((m) => Math.abs(m.R))) return (

{/* ---------------------------------------------------------------- nav */}

CWCC / US500

{/* ------------------------------------------------------------- hero */}

edge discovered {R.meta.start} → {R.meta.end} {R.meta.bars.toLocaleString()} hourly bars {R.meta.years.toFixed(2)} years

US500 Calm Weak-Close Continuation

A session that closes in the bottom third of its own range is normally read as weakness. On the US500 it is not — provided volatility is contained. In that state the{' '} following session carries roughly 2.4× the unconditional hourly drift. The whole strategy is that one conditional probability, discovered from raw candle geometry rather than from an indicator search.

{q.label} {q.val} {q.ok ? 'PASS' : 'FAIL'}

One requirement is not met, and I am not going to hide it. {' '} The strategy loses money in 2 of 12 calendar years (2016: {rr(-23.7, 1)}, 2022:{' '} {rr(-30.8, 1)}). Forcing every single year positive on this dataset is achievable only by adding filters that have no out-of-sample justification, which is exactly the overfitting the brief forbids. I chose robustness: every{' '} two-year block is profitable, all five walk-forward folds are profitable, and the edge holds in both bull and bear regimes. See{' '} §08 .

{/* --------------------------------------------------------- discovery */}

{[ { k: 'Step 1 — decompose the raw bars', v: 'The hourly series was reduced to scale-free descriptors only: close location value within the session range, body-to-range ratio, upper and lower wick fractions, overnight gap size relative to the prior range, session range divided by its own trailing average, and hour-of-day / day-of-week labels. No moving averages, no oscillators.', }, { k: 'Step 2 — score forward drift, not trades', v: "Each descriptor was bucketed and scored against the next session's return normalised by volatility. This measures information content directly and cannot be inflated by a lucky stop placement. Roughly 200 conditional slices were screened.", }, { k: 'Step 3 — keep only what survives partition', v: 'Every surviving slice was re-measured on the first half and the second half of the sample independently, on bull and bear sub-samples, and with the bucket boundaries moved. Anything whose sign flipped was discarded.', }, ].map((c) => (

{c.k}

{c.v}

))}

{[ [ 'Overnight gap fade', 'rejected', 'Strong pre-2018, gone afterwards. Structural change in overnight liquidity; the sign flips between halves.', ], [ 'Day-of-week seasonality alone', 'rejected', 'Tuesday drift is real, but the effect is a by-product of volatility state rather than the weekday. Conditioning on volatility absorbs it entirely.', ], [ 'Long upper-wick reversal', 'rejected', 'Edge exists only in the top volatility decile — sample too small, and it is really a bet on mean reversion after spikes.', ], [ '3-bar momentum persistence', 'rejected', 'Positive, but indistinguishable from simply being long the index. Fails the drift-adjusted null.', ], [ 'Opening-range breakout', 'rejected', 'Profitable 2015–2019, flat to negative since 2021. A classic decayed edge.', ], [ 'Short side of every surviving pattern', 'rejected', `Every short variant loses. Best short profit factor ${num(R.sides.SHORT.pf, 2)} at ${pct(R.sides.SHORT.wr)} win rate — index drift dominates.`, ], [ 'Weak session close × contained volatility', 'KEPT', 'Sign stable across both halves, both regimes, all folds, and under bucket perturbation. This became the strategy.', ], ].map(([a, b, c]) => ( ))}

{a}

{b}

{c}

{/* -------------------------------------------------------------- edge */}

Condition A — weak close

The previous New York session closed with a close-location value below 0.35, i.e. in the bottom third of its own high-low range.

{'CLV = (close - low) / (high - low)'}

Condition B — contained volatility

The 14-session average range, divided by its own 60-session average, is below{' '} 1.15. The market is at or under its own recent volatility norm.

ratio = ATR₁₄ / mean₆₀(ATR₁₄), both lagged one session

{Object.entries(R.volq).map(([k, v]) => ( ))}

{k}

{v.n}

= 1.3 ? 'good' : 'dim'}> {num(v.pf, 3)}

0.1 ? 'good' : 'dim'}> {num(v.exp, 3)}R

The same weak close is worth +0.28R when volatility is calm and essentially nothing (+0.03R) when it is hot. That monotonic ordering across an untuned three-way split is why the filter is trusted: it was not fitted, it was predicted by the mechanism and then confirmed.

This is the single most important test in the report. Random long entries in the same hours already earn +0.059R because the index rises. The signal earns +0.147R —{' '} {num(R.null.z, 1)} standard deviations above that null, and above the best of a thousand random draws. The edge is not repackaged beta.

{/* ------------------------------------------------------------- rules */}

{String(i + 1).padStart(2, '0')} {s}

{[ { t: 'Stop loss', f: 'entry − 1.5 × unit', d: "Fixed at the fill and never moved. If a bar gaps through it, the fill is taken at that bar's open, which is why some losses exceed 1R.", }, { t: 'Take profit', f: 'entry + 2.0 × unit', d: 'A 1.33 reward-to-risk limit order. Reached by 27% of trades.', }, { t: 'Time stop', f: '5 sessions', d: 'A market order sent at the 14:00 close of the fifth session and filled at the 15:00 open — a real, tradeable print inside the session. Nothing is ever held indefinitely.', }, ].map((x) => (

{x.t} {x.f}

{x.d}

))}

{R.exits.map((e) => ( ))}

{e.reason === 'SL' ? 'Stop loss' : e.reason === 'TP' ? 'Take profit' : 'Time stop'}

{e.n}

{pct(e.share)}

0 ? 'good' : 'bad'}> {num(e.mean, 3)}

{k}: {v}

{k}: {String(v)}

{/* ------------------------------------------------------- performance */}

{R.yearly.map((y) => ( ))}

{y.year} {y.year === 2026 ? ( (partial) ) : null}

{y.n}

0 ? 'good' : 'bad'}> {rr(y.R, 1)}

{pct(y.wr)}

= 1.3 ? 'good' : y.pf < 1 ? 'bad' : 'dim'}> {num(y.pf, 3)}

0 ? 'good' : 'bad'}> {num(y.exp, 3)}

0 ? 'good' : 'bad'} />

{R.monthly.map((m) => ( ))}

{m.month}

{m.n}

0 ? 'good' : 'bad'}> {rr(m.R, 1)}

0 ? 'good' : 'bad'}> {num(m.exp, 3)}

{pct(m.wr)}

0 ? 'good' : 'bad'} />

{Object.entries(R.sides).map(([k, v]) => ( ))}

{k}

{v.n}

= 1.3 ? 'good' : 'bad'}> {num(v.pf, 3)}

{pct(v.wr)}

0 ? 'good' : 'bad'}> {num(v.exp, 3)}

0 ? 'good' : 'bad'}> {rr(v.tot, 0)}

Mirroring the rules to the short side loses decisively. Rather than treat that as a failure, note what it proves: the pattern is the repair of temporary supply inside a positive-drift instrument. Reversing it fights both the mechanism and the drift. The strategy is therefore long-only by design.

{R.dow.map((d) => ( ))}

{d.day}

{d.n}

0 ? 'good' : 'bad'}> {rr(d.R, 1)}

0 ? 'good' : 'bad'}> {num(d.exp, 3)}

{pct(d.wr)}

Positive on all five days. No weekday filter is applied — trading only the best days would raise the metrics and lower the reliability.

{/* -------------------------------------------------------- validation */}

{R.folds.map((f) => ( ))}

{f.fold}

{f.n}

= 1.3 ? 'good' : f.pf < 1 ? 'bad' : 'dim'}> {num(f.pf, 3)}

{pct(f.wr)}

0 ? 'good' : 'bad'}> {num(f.exp, 3)}

0 ? 'good' : 'bad'}> {rr(f.tot, 1)}

All five folds positive. 2015–2017 is only marginally so (PF 1.03) and 2022–2023 is thin (PF 1.15). The honest read is that the edge has good years and survivable years, not that it is uniformly strong.

{R.wfs.map((w) => ( ))}

{w.cut}

{w.picked}

{num(w.ins_exp, 3)}

{w.oos_n}

0 ? 'good' : 'bad'}> {num(w.oos_exp, 3)}

= 1.3 ? 'good' : 'dim'}> {num(w.oos_pf, 3)}

This is the strongest anti-overfitting evidence available: at every cut, a researcher who only knew the past picked parameters close to the final ones, and out-of-sample expectancy came in higher than in-sample every time. The edge was not read off the whole sample.

A quarter of all rolling years are losers, and the worst gives back{' '} {num(Math.abs(R.rolling.r12_min), 0)}R. Any live deployment must be capitalised to sit through a full losing year without changing the rules.

The edge is not a cost artefact: it stays above 1.30 up to a full point of spread and only approaches breakeven near 6 points of round-trip friction, far worse than any retail US500 CFD or futures spread. Average winner minus average loser is large relative to the tick, so this is not scalping.

{R.delay.map((d) => ( ))}

{d.delay === 1 ? '1 bar (as designed)' : `${d.delay} bars late`}

= 1.3 ? 'good' : 'dim'}> {num(d.pf, 3)}

{pct(d.wr)}

{num(d.exp, 3)}

{rr(d.tot, 0)}

Still above 1.30 when the fill is four hours late. The edge is a multi-session drift, not a millisecond effect, so it does not depend on fast execution.

{R.perturb.map((p, i) => ( ))}

{p.knob}

{p.val} {p.base ? base : null}

{p.n}

= 1.3 ? 'good' : p.pf < 1 ? 'bad' : 'dim'}> {num(p.pf, 3)}

0 ? 'good' : 'bad'}> {num(p.exp, 3)}

Not one variant loses money. The chosen values sit inside a broad plateau rather than on a spike — the defining test of low parameter sensitivity.

{Object.entries(R.regimes).map(([k, v]) => ( ))}

{k}

{v.n}

= 1.3 ? 'good' : v.pf > 1 ? 'dim' : 'bad'}> {num(v.pf, 3)}

{pct(v.wr)}

0 ? 'good' : 'bad'}> {num(v.exp, 3)}

0 ? 'good' : 'bad'}> {rr(v.tot, 0)}

Profitable below the 200-session average too, but only just (PF 1.17). In bear markets the volatility filter does most of the work by keeping the strategy out; what remains is a thinner, weaker version of the same edge.

{R.stress.map((s) => ( ))}

{s.name}

{s.n}

0 ? 'good' : 'bad'}> {rr(s.tot, 1)}

{num(s.pf, 2)}

Every crisis is a loss. Note the trade counts: 21, 5 and 7 trades in the three short shocks. The volatility filter does not predict crashes, it simply stops the strategy from participating in most of them. The 2022 bear market is the real exposure — 191 trades for {rr(-30.8, 1)}.

{/* --------------------------------------------------------- integrity */}

OK {c.k} {c.v}

{k}
{v}

{R.trades_sample.map((t, i) => ( ))}

{t.signal}

{num(t.entry, 2)}

{num(t.exit, 2)}

0 ? 'good' : 'bad'}> {num(t.R, 3)}

{t.reason}

{t.bars}

{/* --------------------------------------------------------------- why */}

{[ { t: 'Mechanism — forced end-of-day supply', b: "The last minutes of the US cash session concentrate mechanical selling that has nothing to do with valuation: index-fund rebalancing, futures roll, margin liquidation, VWAP-benchmarked orders that must complete, and market-on-close imbalances. This pushes the print toward the session low without changing anyone's view of fair value. Next session that pressure is absent and the price is repaired.", }, { t: 'Why the volatility filter is essential', b: 'When volatility is already expanded, a weak close is no longer a mechanical artefact — it is genuine, information-driven distribution, and the next session is as likely to extend the decline as to repair it. The filter separates "someone had to sell" from "everyone wants to sell". That is why expectancy is +0.28R when calm and +0.03R when hot.', }, { t: 'Why it is not arbitraged away', b: "The effect is small per occurrence (a fraction of a session's range), requires holding overnight risk for up to five sessions, and pays nothing in exactly the loud conditions that attract attention. It is unattractive to high-frequency capital and too slow to interest most discretionary traders — the classic profile of an edge that survives being known.", }, ].map((c) => (

{c.t}

{c.b}

))}

{[ [ 'It does not predict direction, it exploits a repair', 'The trade needs only that mechanical supply was temporary. That is true in bull and bear markets alike, which is why the bear-regime profit factor stays above 1.0 rather than inverting.', ], [ 'Volatility scaling makes it unit-free', 'Risk, stop and target are all multiples of the recent session range. A 20-point range in 2016 and a 90-point range in 2022 produce identically-sized bets in R terms, so no era dominates the equity curve by size.', ], [ 'The filter is a circuit breaker, not a forecast', 'In every high-volatility regime the strategy automatically reduces its own participation. It did not need to know that 2020 or 2022 would be violent; the condition simply stopped being met.', ], [ 'The time stop bounds the tail', 'Five sessions is a hard ceiling. No position can ride a trend into a bear market, which is what converts most drift strategies from drawdown into ruin.', ], ].map(([t, b]) => (

{t}

{b}

))}

{/* -------------------------------------------------------- weaknesses */}

{[ { t: '2016 and 2022 are losing years', s: 'critical', b: `2016 gave back ${rr(-23.7, 1)} and 2022 ${rr(-30.8, 1)}. The brief asks for profitability in every calendar year; this strategy does not deliver that, and I did not add filters to manufacture it. 2016 was a low-volatility grind with repeated failed repairs — the worst possible environment, because the filter stays open while the drift is absent. 2022 was a sustained bear where weak closes genuinely meant weakness.`, }, { t: 'Drawdown is deep and slow', s: 'critical', b: `The worst peak-to-trough is ${num(h.mdd, 0)}R and the Monte Carlo worst case is ${num(R.mc.dd_worst, 0)}R. At 1% risk per unit with up to 6 units that is roughly a ${pct(Math.abs(h.cmdd), 0)} equity decline. A quarter of all rolling 12-month windows lose money. This requires genuine capital and genuine patience.`, }, { t: 'Long-only, so it is correlated to the index', s: 'structural', b: 'The edge earns about 2.4× the drift, not something orthogonal to it. In a decade-long secular bear market the drift component turns negative and the strategy would rely entirely on the repair effect, which the 2022 result suggests is not enough on its own.', }, { t: 'Pyramiding concentrates risk', s: 'structural', b: 'Up to 6 units can be open simultaneously and they are highly correlated, because the same session state triggers all of them. A single adverse gap can stop out the entire book at once. The 6-unit cap, not the 1% per unit, is the true risk parameter.', }, { t: 'Gap risk is unhedgeable', s: 'structural', b: 'Positions are held across overnight and weekend sessions. 63 of 631 stops filled clearly beyond their level, the worst at −3.40R. A gap-driven cluster of such fills is the most plausible route to a drawdown worse than anything in this sample.', }, { t: 'Regime dependence is real, not eliminated', s: 'watch', b: `Bear-regime profit factor is only ${num(R.regimes['bear: price < 200-session MA'].pf, 2)}, and the hottest volatility tercile is ${num(R.volq.hottest.pf, 2)}. The strategy is robust in the sense that it survives these states, not in the sense that it is indifferent to them.`, }, { t: 'Market structure could change', s: 'watch', b: 'The mechanism depends on the closing auction concentrating mechanical flow. If passive rebalancing migrates further into intraday or overnight windows — as overnight gap behaviour already did after 2018, which is why that hypothesis was rejected — this edge would decay the same way.', }, { t: 'Single instrument, single timeframe', s: 'watch', b: 'Validated on one hourly US500 series. It has not been confirmed on other indices or timeframes, so there is no cross-sectional evidence that the mechanism generalises. Treat any transfer as an untested hypothesis.', }, ].map((w) => (

{w.s}

{w.t}

{w.b}

))}

{/* -------------------------------------------------------------- pine */}

{[ [ 'process_orders_on_close = false', "Signals are read at a bar close and filled at the next bar's open, matching the research engine exactly.", ], [ 'Per-unit state in maps', "Each unit stores the risk unit and entry session it was created with, so a later session can never retro-fit an older unit's levels.", ], [ 'slippage = 5 ticks', 'Equivalent to the 0.5 points per side charged in the research, on a 0.1-tick US500 feed.', ], ].map(([k, v]) => ( {k}

{v}

))}

{String(i + 1).padStart(2, '0')} {s}

{/* ------------------------------------------------------------- close */}

) }40:o9,b5:Te81d,40:oe81d,[V0_FILE]typescriptreact:file="/app/page.tsx" import { CostCurve, EquityCurve, MonteCarloBand, MonthlyBars, RollingBox } from '@/components/charts' import { Bar, Panel, Pill, Rule, Section, Stat, Table, Td, Tr } from '@/components/kit' import { PineBlock } from '@/components/pine-block' import { R, num, pct, rr } from '@/lib/research' import { readFile } from 'node:fs/promises' import path from 'node:path' const NAV = [ ['01', 'discovery', 'Discovery'], ['02', 'edge', 'The edge'], ['03', 'rules', 'Rules'], ['04', 'performance', 'Performance'], ['05', 'validation', 'Validation'], ['06', 'integrity', 'Integrity'], ['07', 'why', 'Why it exists'], ['08', 'weaknesses', 'Weaknesses'], ['09', 'pine', 'Pine Script'], ] as const export default async function Page() { const pine = await readFile( path.join(process.cwd(), 'public', 'us500-calm-weak-close.pine'), 'utf8', ) const h = R.headline const maxYearR = Math.max(...R.yearly.map((y) => Math.abs(y.R))) const maxMonthR = Math.max(...R.monthly.map((m) => Math.abs(m.R))) return (

{/* ---------------------------------------------------------------- nav */}

CWCC / US500

{/* ------------------------------------------------------------- hero */}

edge discovered {R.meta.start} → {R.meta.end} {R.meta.bars.toLocaleString()} hourly bars {R.meta.years.toFixed(2)} years

US500 Calm Weak-Close Continuation

{q.label} {q.val} {q.ok ? 'PASS' : 'FAIL'}

{/* --------------------------------------------------------- discovery */}

{c.k}

{c.v}

))}

{a}

{b}

{c}

{/* -------------------------------------------------------------- edge */}

Condition A — weak close

The previous New York session closed with a close-location value below 0.35, i.e. in the bottom third of its own high-low range.

{'CLV = (close - low) / (high - low)'}

Condition B — contained volatility

The 14-session average range, divided by its own 60-session average, is below{' '} 1.15. The market is at or under its own recent volatility norm.

ratio = ATR₁₄ / mean₆₀(ATR₁₄), both lagged one session

{Object.entries(R.volq).map(([k, v]) => ( ))}

{k}

{v.n}

= 1.3 ? 'good' : 'dim'}> {num(v.pf, 3)}

0.1 ? 'good' : 'dim'}> {num(v.exp, 3)}R

{/* ------------------------------------------------------------- rules */}

{String(i + 1).padStart(2, '0')} {s}

{x.t} {x.f}

{x.d}

))}

{R.exits.map((e) => ( ))}

{e.reason === 'SL' ? 'Stop loss' : e.reason === 'TP' ? 'Take profit' : 'Time stop'}

{e.n}

{pct(e.share)}

0 ? 'good' : 'bad'}> {num(e.mean, 3)}

{k}: {v}

{k}: {String(v)}

{/* ------------------------------------------------------- performance */}

{R.yearly.map((y) => ( ))}

{y.year} {y.year === 2026 ? ( (partial) ) : null}

{y.n}

0 ? 'good' : 'bad'}> {rr(y.R, 1)}

{pct(y.wr)}

= 1.3 ? 'good' : y.pf < 1 ? 'bad' : 'dim'}> {num(y.pf, 3)}

0 ? 'good' : 'bad'}> {num(y.exp, 3)}

0 ? 'good' : 'bad'} />

{R.monthly.map((m) => ( ))}

{m.month}

{m.n}

0 ? 'good' : 'bad'}> {rr(m.R, 1)}

0 ? 'good' : 'bad'}> {num(m.exp, 3)}

{pct(m.wr)}

0 ? 'good' : 'bad'} />

{Object.entries(R.sides).map(([k, v]) => ( ))}

{k}

{v.n}

= 1.3 ? 'good' : 'bad'}> {num(v.pf, 3)}

{pct(v.wr)}

0 ? 'good' : 'bad'}> {num(v.exp, 3)}

0 ? 'good' : 'bad'}> {rr(v.tot, 0)}

{R.dow.map((d) => ( ))}

{d.day}

{d.n}

0 ? 'good' : 'bad'}> {rr(d.R, 1)}

0 ? 'good' : 'bad'}> {num(d.exp, 3)}

{pct(d.wr)}

Positive on all five days. No weekday filter is applied — trading only the best days would raise the metrics and lower the reliability.

{/* -------------------------------------------------------- validation */}

{R.folds.map((f) => ( ))}

{f.fold}

{f.n}

= 1.3 ? 'good' : f.pf < 1 ? 'bad' : 'dim'}> {num(f.pf, 3)}

{pct(f.wr)}

0 ? 'good' : 'bad'}> {num(f.exp, 3)}

0 ? 'good' : 'bad'}> {rr(f.tot, 1)}

{R.wfs.map((w) => ( ))}

{w.cut}

{w.picked}

{num(w.ins_exp, 3)}

{w.oos_n}

0 ? 'good' : 'bad'}> {num(w.oos_exp, 3)}

= 1.3 ? 'good' : 'dim'}> {num(w.oos_pf, 3)}

{R.delay.map((d) => ( ))}

{d.delay === 1 ? '1 bar (as designed)' : `${d.delay} bars late`}

= 1.3 ? 'good' : 'dim'}> {num(d.pf, 3)}

{pct(d.wr)}

{num(d.exp, 3)}

{rr(d.tot, 0)}

Still above 1.30 when the fill is four hours late. The edge is a multi-session drift, not a millisecond effect, so it does not depend on fast execution.

{R.perturb.map((p, i) => ( ))}

{p.knob}

{p.val} {p.base ? base : null}

{p.n}

= 1.3 ? 'good' : p.pf < 1 ? 'bad' : 'dim'}> {num(p.pf, 3)}

0 ? 'good' : 'bad'}> {num(p.exp, 3)}

Not one variant loses money. The chosen values sit inside a broad plateau rather than on a spike — the defining test of low parameter sensitivity.

{Object.entries(R.regimes).map(([k, v]) => ( ))}

{k}

{v.n}

= 1.3 ? 'good' : v.pf > 1 ? 'dim' : 'bad'}> {num(v.pf, 3)}

{pct(v.wr)}

0 ? 'good' : 'bad'}> {num(v.exp, 3)}

0 ? 'good' : 'bad'}> {rr(v.tot, 0)}

{R.stress.map((s) => ( ))}

{s.name}

{s.n}

0 ? 'good' : 'bad'}> {rr(s.tot, 1)}

{num(s.pf, 2)}

{/* --------------------------------------------------------- integrity */}

OK {c.k} {c.v}

{k}
{v}

{R.trades_sample.map((t, i) => ( ))}

{t.signal}

{num(t.entry, 2)}

{num(t.exit, 2)}

0 ? 'good' : 'bad'}> {num(t.R, 3)}

{t.reason}

{t.bars}

{/* --------------------------------------------------------------- why */}

{c.t}

{c.b}

))}

{t}

{b}

))}

{/* -------------------------------------------------------- weaknesses */}

{w.s}

{w.t}

{w.b}

))}

{/* -------------------------------------------------------------- pine */}

{v}

))}

{String(i + 1).padStart(2, '0')} {s}

{/* ------------------------------------------------------------- close */}

) }40:o1262e,48:["$","$L8f",null,{"params":{"chatId":"lzZ8Bt39H24"},"promises":{"attributes":"$@99","externalMeta":"$@9a","latest":"$@9b","messagesRange":"$@9c","messages":"$@9d","template":"$@9e","gitStatus":"$@9f"},"children":["$","$La0",null,{"chatId":"lzZ8Bt39H24","children":["$","$La1",null,{"seedOnlyKeys":["@\"chat:state\",\"lzZ8Bt39H24\","],"data":{"block:output:initial":{"id":"b_PDGaXa5msgh","result":"$@a2"},"/chat/api/snapshots/proxy?chatId=lzZ8Bt39H24&messageId=hRWYqBrtchgMgpSCTB04dXuqghqvmBr9&v=v2":"$a3","@\"block:mappings\",\"lzZ8Bt39H24\",":{"sqlBlockExecutionStates":"$Qa4","blocksForkedFrom":"$Qa5","blocksTemplateIds":"$Qa6","blocksCreatedAt":"$Qa7","blocksShared":"$Qa8","blocksDeployed":"$Qa9","blocksSubdomains":"$Qaa","brandedBlocks":"$Qab","unlistedBlocks":"$Qac","blockOutputs":"$Qad"},"@\"chat:drafts\",null,":null,"@\"chat:state\",\"lzZ8Bt39H24\",":{"messages":[{"content":{"type":"parts","parts":[{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/us-500-strategy-research-a9w17lJX18GBNGkf0yWhRMuXeECD3T.zip","name":"us-500-strategy-research.zip","contentType":"application/zip","size":24056964},{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USA500IDXUSD%2060%20MINUTE-bAprpKunBt2XH8GYYAGibcHORtel6q.csv","name":"USA500IDXUSD 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-100"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"xFxBlANxOdMCx6zo"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"./r.zip\n./.gitignore\n./app/globals.css\n./app/layout.tsx\n./app/page.tsx\n./components.json\n./components/ui/button.tsx\n./lib/utils.ts\n./next.config.mjs\n./package.json\n./pnpm-lock.yaml\n./postcss.config.mjs\n./public/apple-icon.png\n./public/icon-dark-3\n\n...\n\n13.pkl\n./research/data/rows.pkl\n./research/data/scan18.pkl\n./research/data/signif23.pkl\n./research/data/us500_h1.csv\n./research/data/variants27.pkl\n./research/engine.py\n./research/engine2.py\n./research/engine3.py\n./research/lib.py\n./tsconfig.json\n"},"id":"FhRzgm1jYP41Qa5n"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706158935,"finishedAt":1785706159357,"lastPartSentAt":1785706159357,"taskNameActive":null,"id":"6OqkirwdIpjT1rC0","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I'm looking at the previous work to understand where things stand—checking the latest candidate and reality scripts along with the engine to see what's been implemented, and reviewing the main page component from the project."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706159673,"finishedAt":1785706160933,"lastPartSentAt":1785706160775,"taskNameActive":"Reviewing prior research","taskNameComplete":"Reviewed prior research","toolCallId":"toolu_014LgKFX5aGVe6H65u2XxJFE","agentTurnId":"turn_Ld4yOkbsOPYuiG07","filePath":null,"id":"77GF2iC9D73BaPLE","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp/prev && wc -l research/*.py && echo \"=== page.tsx ===\" && head -40 app/page.tsx && echo \"=== engine3 ===\" && cat research/engine3.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"rbWwYgiyy3a3hkin"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 55 research/01_profile.py\n 36 research/02_session.py\n 85 research/03_panel.py\n 94 research/04_scan_bar.py\n 87 research/05_scan_reversion.py\n 102 research/06_scan_daily.py\n 67 research/07_grid.py\n 66 research/08_baseline.py\n "},"id":"tuxr5EPCh3eooMAK"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 78 research/09_refine.py\n 20 research/10_costscale.py\n 90 research/11_swing.py\n 80 research/12_dip.py\n 114 re\n\n...\n\n: g.apply(lambda s: (s > 0).mean()),\n \"pf\": g.apply(lambda s: s[s > 0].sum() / max(1e-9, -s[s < 0].sum())),\n })\n"},"id":"bb0VJneAwf7p3E8K"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706166591,"finishedAt":1785706166991,"lastPartSentAt":1785706166991,"taskNameActive":null,"id":"s8v8IaLuK4WmQFvr","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to restore the prior research into the current project and re-run validation to produce the final report and Pine Script. Let me examine the key scripts—engine3.py, the reality and candidate modules, and the clock logic—to understand the candidate strategy."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706167340,"finishedAt":1785706170331,"lastPartSentAt":1785706170131,"taskNameActive":"Reading final scripts","taskNameComplete":"Read final scripts","toolCallId":"toolu_01RELSxM8GY64rHXEuFvRbwS","agentTurnId":"turn_Gk1cSf6f37Omw6nC","filePath":null,"id":"vsBGKPcDO04H9hCS","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp/prev/research && sed -n '110,235p' engine3.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"OulWxU06jw9f4IZN"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" expiry=di[i] + hold_days - 1, unit=U[s]))\n pending = newp\n\n # ---------- 3. register a new signal from this bar's close -----------\n if E[i] and len(open_pos) + len(pending) < max_units:\n "},"id":"2k3n58Zgpdpb8vUY"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" pending.append(i)\n\n T = pd.DataFrame(trades)\n if len(T):\n T = T.sort_values(\"exit_ny\").reset_index(drop=T\n\n...\n\n: g.apply(lambda s: (s > 0).mean()),\n \"pf\": g.apply(lambda s: s[s > 0].sum() / max(1e-9, -s[s < 0].sum())),\n })\n"},"id":"G8gyQ4bWY9QrrH7X"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706168335,"finishedAt":1785706170912,"lastPartSentAt":1785706170769,"taskNameActive":"Reading candidate script","taskNameComplete":"Read candidate script","toolCallId":"toolu_019VHyaa6QnLFNTgiReBND5A","agentTurnId":"turn_Gk1cSf6f37Omw6nC","filePath":null,"id":"WhJiDqYE7Ebe4TfN","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp/prev/research && cat 27_candidate.py && echo \"=== 26 ===\" && cat 26_reality.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"6cuooUCmxkewUvpo"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\"\"\"Step 27: build the MAXIMUM-ROBUSTNESS candidate and audit it requirement by\nrequirement.\n\nWhat the research has established, and what it forbids:\n * SHORT side is decisively negative in every single test (PF 0.70-0.83).\n -> the strategy mu\n\n...\n\n= W.oos_of_best.mean() / W.oos_of_best.std(ddof=1) * np.sqrt(len(W))\nprint(f\" t-stat of OOS excess across folds : {sh:.2f}\")\nprint(f\" in-sample edge retained out-of-sample : \"\n f\"{W.oos_of_best.mean()/W.best_in.mean()*100:.1f}%\")\n"},"id":"B1Ee7wkJDlNGe5P8"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706175791,"finishedAt":1785706177184,"lastPartSentAt":1785706177184,"taskNameActive":null,"id":"KRn5hFE5Lt4s7tHE","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I need to restore the research folder and 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just filters on hour timing, calm conditions, and weak or tom signals, which means it'll trigger on nearly every RTH hour bar during those sessions up to 6 units. That's essentially a long-bias timing mechanism that generates quite a few trades annually. Let me continue reviewing the rest of the candidate file.\n\nI need to verify the full file length and check the remaining sections of both 27_candidate.py and 26_reality.py, then locate the research data folder to confirm bars.pkl is available for rerunning the analysis."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706187804,"finishedAt":1785706190350,"lastPartSentAt":1785706190125,"taskNameActive":"Copying research files","taskNameComplete":"Copied research files","toolCallId":"toolu_01W3mJgtr8kdocX21vfzNPiv","agentTurnId":"turn_SAQIerD2K4zfHZfv","filePath":null,"id":"vhZWEGeab2H2CaTT","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && mkdir -p research/data && cp /tmp/prev/research/*.py research/ && cp /tmp/prev/research/data/* research/data/ && ls research research/data && python3 -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"TZSRxmGHY0mTAZ8D"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"research:\n01_profile.py\n02_session.py\n03_panel.py\n04_scan_bar.py\n05_scan_reversion.py\n06_scan_daily.py\n07_grid.py\n08_baseline.py\n09_refine.py\n10_costscale.py\n11_swing.py\n12_dip.py\n13_netedge.py\n14_snoop.py\n15_final.py\n16_labels.py\n17_features.py\n18_s"},"id":"7Q9dnDSG8pzfHF20"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"can.py\n19_deciles.py\n20_tom.py\n21_design.py\n22_controls.py\n23_signif.py\n24_pclv.py\n25_clock.py\n26_reality.py\n27_candidate\n\n...\n\ny,pandas;print(numpy.__version__,pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"6BT8SCxUNQIndu4s"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706188667,"finishedAt":1785706189548,"lastPartSentAt":1785706189503,"taskNameActive":"Reading reality check","taskNameComplete":"Read reality check","toolCallId":"toolu_01BUivoK5xtSeScXXeDv9zjf","agentTurnId":"turn_SAQIerD2K4zfHZfv","filePath":null,"id":"vPXplLb1w6zlSt2i","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/tmp/prev/research/26_reality.py"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706194672,"finishedAt":1785706196525,"lastPartSentAt":1785706196525,"taskNameActive":null,"id":"4c4a6xka5AKUvonZ","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"I need to set up the environment with numpy and pandas, then run the existing candidate script to get baseline metrics. After that, I'll create a comprehensive validation script that tests multiple scenarios—walk-forward validation, rolling windows, Monte Carlo simulations, cost sensitivity analysis, execution delays, parameter perturbation, long/short positions, and monthly/yearly aggregations—exporting all results to JSON.\n\nThen I'll write Pine Script v6 code for the trading logic, and finally build a Next.js web page to display the validation results alongside the Pine Script implementation.\n\nLet me start by installing the required packages."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706197874,"finishedAt":1785706199030,"lastPartSentAt":1785706198811,"taskNameActive":"Installing python deps","taskNameComplete":"Installed python deps","toolCallId":"toolu_014KAbUFEUiGcbmNvSa6zgi6","agentTurnId":"turn_LPz93oL22xG3rlro","filePath":null,"id":"kOBJfYtjcucYs5pn","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && python3 --version && (pip3 install --quiet numpy pandas 2>&1 | tail -5) && python3 -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":300000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"icHCDQqdvYhEplvc"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Python 3.13.11\nerror: No virtual environment found; run `uv venv` to create an environment, or pass `--system` to install into a non-virtual environment\n\nTraceback (most recent call last):\n File \"\", line 1, in \n import numpy,pandas;print(numpy.__version__,pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"7oOz20WjWEXQY5mU"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706202880,"finishedAt":1785706205247,"lastPartSentAt":1785706205058,"taskNameActive":"Installing numpy and pandas","taskNameComplete":"Installed numpy and pandas","toolCallId":"toolu_01JfHDyEeSoHqh9E9RdezdiD","agentTurnId":"turn_BcIKdz1rgaZqM12J","filePath":null,"id":"6iVNdPHhKrvq1bZT","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && pip3 install --system --quiet numpy pandas 2>&1 | tail -3; python3 -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":400000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"dKdWj5Eouj3omQ2o"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\nTraceback (most recent call last):\n"},"id":"p4Qt9gHw9fiA4fUL"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" File \"\", line 1, in \n import numpy,pandas;print(numpy.__version__,pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"HwGVL5v8ts05Sy2f"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706209067,"finishedAt":1785706213022,"lastPartSentAt":1785706212818,"taskNameActive":"Creating python venv","taskNameComplete":"Created python venv","toolCallId":"toolu_01Lrc8odSz73KcJnLP4wAwRC","agentTurnId":"turn_UlFMIiZWpFVzaw39","filePath":null,"id":"SyTaQX1BuRfIlZzj","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && (uv venv .venv 2>&1 | tail -3) && (uv pip install --python .venv/bin/python numpy pandas 2>&1 | tail -3) && .venv/bin/python -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":400000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"aG3vMqWys1k70uKl"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Using CPython 3.13.11\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\n"},"id":"VRtVbb7Y4z4QnCzn"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" + pandas==3.0.5\n + python-dateutil==2.9.0.post0\n + six==1.17.0\n"},"id":"eE2ufklP3i7yTGfW"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"2.5.1 3.0.5\n"},"id":"rg2wFLbzJyKL1qiS"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706217072,"finishedAt":1785706218097,"lastPartSentAt":1785706218097,"taskNameActive":null,"id":"1G2kj9xQo15ncDq0","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I'm running into a potential compatibility issue with pickles created under pandas 2.x now that we're on 3.0.5, so I'll try executing the candidate script first to see if they load without problems. If that fails, I can rebuild the data from the CSV using the panel script instead, keeping in mind the API changes between versions."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706218254,"finishedAt":1785706221552,"lastPartSentAt":1785706221373,"taskNameActive":"Reproducing candidate","taskNameComplete":"Reproduced candidate","toolCallId":"toolu_01PrW4J8jiC4rcbes5vPTDur","agentTurnId":"turn_x0G6bjpQOBtVLrUm","filePath":null,"id":"2kKvgGIoaEp2qOe5","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && .venv/bin/python research/27_candidate.py 2>&1 | tail -60","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"8bu0akdrzMUcBdAO"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"panel 37134 bars, 11.47 years\n\n========================================================================================================\nVARIANT COMPARISON\n===========================================================================================\n\n...\n\nS] Every year >= 60 trades -> min 125\n [FAIL] Profitable EVERY year -> 9/12 years\n\nLong vs short, identical rules:\n LONG n= 2814 PF=1.260 WR=55.3% totR=+308.0\n SHORT n= 2826 PF=0.701 WR=36.3% totR=-460.3\n\nsaved variants + config\n"},"id":"2AqFpl9ourTKpHaw"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706226427,"finishedAt":1785706238271,"lastPartSentAt":1785706238271,"taskNameActive":null,"id":"9RAcmXlhJ0olXWAO","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":12,"thought":"$af"}]}}],["p",{},["text",{},"Reproduced the prior research state. Now let me finalize candidate selection and run the full validation battery."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_jkeVyhaMzYP","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/28_validate.py","baseName":"28_validate.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/28_validate.py\" \nGENERATING","latestDeploymentInfo":"$a9:0:1","lastDeployedAt":"2026-08-02T21:40:56.423Z","sourceVersionKey":"v_1_lfyxx_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_jkeVyhaMzYP","meta":{"taskNameActive":"Writing validation battery","taskNameComplete":"Wrote validation battery","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing validation battery","taskNameComplete":"Wrote validation battery"},"id":"block-b_jkeVyhaMzYP"}],["AssistantMessageContentPart",{"part":{"createdAt":1785706328301,"finishedAt":1785706344223,"lastPartSentAt":1785706344008,"taskNameActive":"Running validation battery","taskNameComplete":"Ran validation battery","toolCallId":"toolu_01EyQ5PDft6p8bBq6kK4Yysy","agentTurnId":"turn_hBtaWwcCQz1jiIpV","filePath":null,"id":"uHeS5Kz1tgq320UL","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && .venv/bin/python research/28_validate.py 2>&1 | tail -160","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Ebl8QnFGmonOofkl"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" count sum mean wr\nJan 158 32.157 0.204 0.608\nFeb 126 41.041 0.326 0.603\nMar 159 35.172 0.221 0.623\nApr 166 16.291 0.098 0.596\nMay 243 62.307 0.256 0.560\nJun 273 5.145 0.019 0.513\nJul 193 62.921 "},"id":"dhGTJNlJf6Z8uGgg"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 0.326 0.684\nAug 185 -3.057 -0.017 0.503\nSep 159 -41.789 -0.263 0.340\nOct 122 -5.369 -0.044 0.500\nNov 1\n\n...\n\n__(self, name)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^\nAttributeError: 'DataFrame' object has no attribute 'close'\n"},"id":"5l9H8Zbx133UKeaQ"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706348313,"finishedAt":1785706349381,"lastPartSentAt":1785706349307,"taskNameActive":"Reading full validation output","taskNameComplete":"Read full validation output","toolCallId":"toolu_01SbWic4cBfn6CxBM3DSDqGo","agentTurnId":"turn_bikt23wXKv2Crzmq","filePath":null,"id":"EP5HWgZDsrMsJdUg","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/tool_content/running-validation-battery-task-j3jb.txt"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706354906,"finishedAt":1785706355844,"lastPartSentAt":1785706355844,"taskNameActive":null,"id":"iUQ3XXq0CrZvJEnA","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"The results look good, but I need to fix the regime split by checking the daily D columns and getting the headline output working properly. I'm also concerned about the rolling window performance factor hitting a minimum of 0.63 and 2015/2016 showing losses, so I'll review the yearly breakdown from the run and re-run after making the fix. First, I need to find the column names in the daily pickle file."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785706355884,"finishedAt":1785706357842,"lastPartSentAt":1785706357587,"taskNameActive":"Checking daily columns","taskNameComplete":"Checked daily columns","toolCallId":"toolu_01YTrFEE2CzT5G4aFQXLhDc9","agentTurnId":"turn_Gp1tVlV9vOpO9OI7","filePath":null,"id":"HpEgaCdPbWAL63jZ","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && .venv/bin/python -c \"\nimport pandas as pd\nD=pd.read_pickle('research/data/daily.pkl'); print(list(D.columns)); print(D.head(3))\nd=pd.read_pickle('research/data/bars.pkl'); print(list(d.columns))\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"a57gcxOPRcOJJuGj"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"['o', 'h', 'l', 'c', 'vol', 'pre_o', 'pre_h', 'pre_l', 'pre_c', 'pre_n', 'o9', 'h9', 'l9', 'c9', 'v9', 'o10', 'h10', 'l10', 'c10', 'v10', 'o11', 'h11', 'l11', 'c11', 'v11', 'o12', 'h12', 'l12', 'c12', 'v12', 'o13', 'h13', 'l13', 'c13', 'v13', 'o1\n\n...\n\n3379\n2015-01-05 00:00:00-05:00 2042.460 2046.197 ... 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