USDJPY Edge Discovery — Falsification Report

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9d:T14adf3,USDJPY Edge Discovery — Falsification Report

Your v0 generation will show here.

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Pipeline You are an elite quantitative researcher specializing in statistical edge discovery, market microstructure, systematic trading, and robust quantitative validation. Your objective is NOT to optimize existing trading strategies or brute-force parameter combinations. Your objective is to discover original, statistically significant market inefficiencies directly from the supplied historical data and convert them into production-ready algorithmic trading systems capable of surviving realistic live execution. This framework must work for any liquid market, including: - Forex - Indices - Commodities - Metals - Cryptocurrencies - Futures - ETFs The framework must automatically adapt to the supplied asset rather than assuming every market behaves the same. --- Data Analysis Read and analyze the complete historical dataset before generating any hypothesis. If multiple timeframes are provided (15M, 1H, 4H, Daily, etc.), analyze all of them simultaneously. Do not automatically assign one timeframe to entries and another to confirmation. Allow the historical data itself to determine whether each timeframe is best suited for: - Signal generation - Market regime detection - Context - Trade management - Exit optimization - Or not used at all The final architecture must be entirely data-driven. --- Objective Discover statistically recurring market behaviors that can be converted into fully mechanical trading strategies. Do NOT begin with common indicator combinations such as: - EMA - SMA - RSI - MACD - ATR - Bollinger Bands - Stochastic - Supertrend - Ichimoku Indicators may only be introduced after a statistical edge has already been discovered, and only if they measurably improve robustness. The statistical edge must originate from the historical data itself. --- Research Philosophy Never search for profitable backtests. Search for genuine market inefficiencies. Prefer: - Simplicity - Stability - Repeatability - Statistical significance - Robustness - Live survivability Reject: - Curve fitting - Parameter optimization - Data snooping - Accidental historical patterns - Fragile edges If no statistically valid edge exists, report that honestly. --- Statistical Discovery Search for hidden statistical relationships including, but not limited to: - Multi-candle dependencies - Conditional probabilities - Higher-order probability chains - Mean reversion - Momentum persistence - Trend continuation - Trend exhaustion - Volatility expansion - Volatility contraction - Volatility clustering - Candle geometry - Wick asymmetry - Body/range relationships - Gap behavior - Session effects - Day-of-week effects - Month effects - Regime transitions - Liquidity behavior - Cross-timeframe dependencies - Price acceleration - Price deceleration - Distribution shifts - Any statistically significant recurring behavior Generate hundreds or thousands of hypotheses if necessary. Reject weak ideas. Keep only statistically meaningful patterns. --- Market Understanding Before designing any strategy automatically determine: - Asset class - Typical volatility - Typical spread - Liquidity profile - Trend tendency - Mean-reversion tendency - Session behavior - Structural changes - Regime characteristics Adapt every decision to the specific market. Never assume every asset behaves similarly. --- Strategy Discovery Search for multiple independent candidate strategies. Each strategy should exploit a different market behavior. Possible examples: - Trend following - Pullback continuation - Mean reversion - Volatility breakout - Compression breakout - Session behavior - Hidden probability structures - Statistical reversal These are examples only. Allow the data to determine what truly exists. --- Hard Requirements Every candidate strategy must satisfy: - Profit Factor ≥ 1.30 - Win Rate ≥ 40% - Positive expectancy - Large statistical sample - Minimum trade frequency requested by the user - Stable yearly profitability - Robust across different market regimes - Low parameter sensitivity If these requirements cannot be achieved honestly, report failure. Never overfit to satisfy metrics. --- Execution Quality Optimization Execution quality is a primary optimization objective. Do not evaluate profitability without considering real execution. For every strategy measure: - Average spread at entry - Median spread at entry - Spread variability - Commission - Estimated slippage - Average execution latency impact - Entry session distribution - Percentage of trades executed during spread spikes - Average net profit after execution costs - Cost as a percentage of average trade Prefer strategies that naturally execute when: - Spread is below the asset's normal median - Liquidity is high - Slippage is historically low - Transaction costs consume only a small fraction of the statistical edge If the same edge can be captured by: - Waiting one additional candle - Delaying entry - Using another execution trigger - Entering during a more liquid period while maintaining similar performance, prefer that implementation. Do NOT simply reject strategies because they occasionally trade during high spreads. Instead, actively search for more execution-efficient implementations of the same edge. Execution efficiency should improve the strategy rather than unnecessarily restricting it. --- Execution Cost Awareness Execution costs are part of the strategy itself. Always model: - Variable spread - Commission - Random slippage - Entry latency - Exit latency - Spread spikes - Partial fills when appropriate Reject strategies whose edge disappears under realistic execution. Average net profit per trade should ideally remain at least 5–10 times larger than the average total execution cost (spread + commission + slippage). Strategies whose profitability depends on unrealistically low transaction costs should receive a significantly lower robustness score. --- Robustness Testing Every surviving strategy must pass: - Walk-forward validation - Out-of-sample testing - Rolling-window validation - Monte Carlo simulation - Bootstrap testing - Spread stress testing - Commission stress testing - Slippage stress testing - Entry delay testing - Exit delay testing - Parameter perturbation - Sensitivity analysis Reject fragile strategies. --- Anti-Cheating Verification Explicitly verify: - No lookahead bias - No repainting - No future leakage - No survivorship bias - No unrealistic fills - No same-bar TP/SL cheating - No hidden optimization bias - No hindsight bias Every decision must use only information available at that point in time. --- Market Regime Detection Automatically identify: - Strong trend - Weak trend - Range - Compression - Expansion - High volatility - Low volatility - Transitional phases Determine whether regime detection improves performance. If not, do not use it. --- Portfolio Discovery Do not stop after finding one profitable strategy. Continue searching. Discover multiple statistically independent trading engines. For every engine calculate: - Signal correlation - Trade overlap - Return correlation - Equity correlation Reject highly correlated engines. Keep only genuinely independent edges. --- Portfolio Construction Build the smallest portfolio that maximizes robustness. Each engine must: - Be independently profitable - Improve diversification - Improve overall stability - Improve or maintain Profit Factor - Improve or maintain drawdown - Increase trades per year without sacrificing robustness Never include an engine solely to increase trade frequency. --- Portfolio Execution Manager Create one master execution layer responsible for: - One position per asset unless statistically justified - No opposing trades - No duplicate entries - Maximum portfolio exposure - Maximum daily loss - Maximum drawdown protection - Spread filter - Commission-aware execution - Slippage-aware execution - Cooldown periods - Session filters only if statistically justified - News avoidance only if testing demonstrates a measurable benefit --- Strategy Health Monitor Each engine must continuously monitor itself. Track rolling: - Profit Factor - Win Rate - Expectancy - Drawdown - Average trade - Trade frequency Automatically disable an engine if its statistical performance significantly deteriorates. Only re-enable it after predefined recovery criteria are satisfied. --- Ranking System Rank strategies using a composite robustness score instead of Profit Factor alone. Include: - Profit Factor - Net average profit per trade after execution costs - Win Rate - Trades per year - Maximum Drawdown - Expectancy - Walk-forward consistency - Out-of-sample performance - Monte Carlo survival - Parameter stability - Yearly consistency - Execution robustness - Execution efficiency Select the strategy with the highest probability of surviving live trading—not the one with the highest historical Profit Factor. --- Final Report For every candidate strategy provide: - Entry rules - Exit rules - Stop-loss logic - Take-profit logic - Position sizing assumptions - Preferred market regime - Disabled market regimes - Profit Factor - Win Rate - Expectancy - Trades Per Year - Total trades - Average trade (pips, points, ticks or dollars depending on the asset) - Average winning trade - Average losing trade - Average holding time - Maximum Drawdown - Long vs Short performance - Monthly statistics - Year-by-year statistics - Walk-forward results - Out-of-sample results - Monte Carlo results - Sensitivity analysis - Execution cost analysis - Execution quality analysis - Explanation of why the edge likely exists - Conditions where the edge is likely to fail If a portfolio is created additionally provide: - Portfolio Profit Factor - Portfolio Win Rate - Portfolio Drawdown - Portfolio Trades Per Year - Correlation matrix - Signal overlap - Individual engine contribution - Portfolio robustness analysis --- Pine Script v6 After selecting the final validated strategy or portfolio, generate a complete TradingView Pine Script Version 6 implementation. Requirements: - Production-quality architecture - Modular design - No repainting - No lookahead - Entries only after candle close - Correct multi-timeframe implementation using confirmed higher-timeframe data only - Configurable inputs - Accurate stop-loss implementation - Accurate take-profit implementation - Support for fixed and dynamic risk-reward where appropriate - Clean, efficient, well-documented code - TradingView backtest results should closely match the validated research results Do not simplify, approximate, or omit any validated logic from the final strategy. a9:T4ef,The research phase wrapped up with 22 stages analyzed and a NO_VALIDATED_EDGE verdict, and I've got all the components built out—masthead, market profile, beta charts, decomposition, candidate family, hour cost chart, and primitives—but the main page.tsx never got assembled to tie everything together. Now I need to restore the zip contents back into the project and compose the report page that brings all these pieces together. I should verify the research pipeline can reproduce (checking if Python, NumPy, and Pandas are available), though re-running 400 reps might be slow. More importantly, I need to build out the remaining report sections that haven't been created yet: the reality-check null distribution chart, geometry table, MFE/MAE table, scan theme table, audit trail, and the Pine Script section. Since there's no validated edge, I'll be honest about that in the report, but I can still provide Pine Script for the best-documented candidate as a research and monitoring tool—clearly labeled as unvalidated but implementing the exact discovered logic without repainting. Before I start building, let me check the JSON data to see the reality check p-value and the best candidate's performance profile so I understand the full story to tell.aa:T4cc,I need to update globals.css with new CSS variables for signal and measure colors, add utility classes, set up theme mappings, and configure the font stack using IBM Plex Sans and Mono. I also need to check if recharts is installed and add it to the project if it's missing. Now I'm structuring the page with seven sections covering market analysis through execution strategy, building out new components like the symmetric scan table, reality check histogram, trade geometry metrics, audit trail, and Pine Script viewer with a copy button. For the Pine Script strategy, I'm planning to create a research instrument in v6 that implements the top-ranked candidate—the T9 H4 volatility-expansion momentum continuation strategy with symmetric long/short positioning and the best statistical t-value. I'll include the full execution framework requirements directly in the Pine Script: single position management, no opposing trades, cooldown periods, spread filtering, daily loss limits, and a health monitor with rolling profit factor disable. To ensure accuracy, I need to extract the exact T9 rule logic from the Python backtesting libraries and translate it into Pine, so the script matches the validated research precisely.ab:Taf9,I'm setting up the Pine Script implementation with the T9 volexp momentum strategy using specific parameters: exponential minimum of 1.2, H4 timeframe momentum, threshold of 0.5, trading during the 8 AM hour, long-only bias, and a 12/24 hour lookback. For the ATR calculation on the H4 timeframe, I need to use a simple moving average of true range rather than the standard ATR function, with periods of 6, 14, and 60. The momentum calculation divides the 2-bar price change by the 14-period ATR to normalize it, and I'm checking if the absolute value meets the 0.5 threshold to determine direction. The entry logic triggers when an H1 bar closes during the 8 AM UTC hour, then fills at the open of the next bar (9 AM). For the H4 values, I need to ensure they come from the last completed H4 bar before the H1 bar opens—so for the 8-9 AM H1 bar, I'm pulling from the 4-8 am H4 bar using request.security with lookahead_off to get the confirmed value at the right timing. The standard Pine idiom uses `close[1]` inside the request.security call to avoid realtime repainting, but that shifts the result back one bar earlier than what the research expects. The core issue is that without the `[1]` offset, request.security with lookahead_off returns the forming (incomplete) HTF bar during realtime, which repaints but historically shows the completed bar — creating a discrepancy between live and backtested behavior. ting approach is to use `[1]` on the expression combined with `lookahead_on`, which returns the previous completed HTF bar's value without any repainting—exactly matching what the research used. This way, at any given chart bar, I'm pulling the prior finished HTF bar's data, eliminating both the repainting issue and lookahead bias. Now I need to define the eligibility criteria: checking that we're not in a post-break gap (weekends/holidays), that the 48-bar ATR is finite and positive, and that the H4 conditions are met. I'll need to pull the 48-bar rolling true range average from the data library and understand how the after-break flag works. For the exit logic, the research tested fixed-horizon exits of 12 or 24 bars, so I'll implement those as the primary exit mechanism. The strategy also needs position management features: ATR-based stops (defaulting to 3× the 48-bar ATR but configurable), optional take-profit levels in ATR multiples, a one-per-day entry limit, cooldown periods, spread-hour filtering, daily loss limits, a max drawdown kill switch, and a health monitor that disables trading if the rolling profit factor drops below a threshold. Since entries only trigger at hour 8, the one-per-day constraint is naturally enforced. Let me check the data library to find the ATR48 calculation, volatility ratio, the after_break definition, and trading day logic.ac:T45e,Before I build the UI, I need to validate the T9 candidate's actual tradeable performance under the full cost model—the geometry table only covered other triggers. I'm writing a stage 23 Pine script that backtests the T9 rule with exp≥1.2 and mom2≥0.5 at hour-8 with symmetric entry, testing both H=12 and H=24 across in-sample and out-of-sample periods, applying the cost model and stop variants to generate the complete metrics: profit factor, win rate, expectancy, and trades per year. I'll also capture average win/loss ratios, holding periods, maximum drawdown, long/short splits, yearly and monthly breakdowns, walk-forward analysis, Monte Carlo simulations, cost stress tests, and parameter perturbation effects. The results get appended to research-data.json as the final_candidate block, which feeds directly into the Final Report's per-strategy section and ensures Pine script parity. I need to organize this into scan, reality check, and final candidate sections with all the supporting tables, plus the Pine script itself. Let me start by verifying the Python environment has the required libraries.ad:T111a,Now I'm moving into the UI work. The existing report uses a monochrome editorial style with signal and measure color tokens, plus grid and tabular number styling. I need to establish a minimal palette—neutrals, a rejection red for signal, and a teal-blue for measure—and I'm settling on specific oklch values for those accent colors. Setting up the color tokens in globals.css with the signal, signal-foreground, measure, and grid values, plus dark mode variants. I'll also add the font tokens to the theme configuration, using IBM Plex Sans and Mono as specified in the system pattern, and setting those font families directly in the @theme inline. Now I'm mapping out the report components to build: the scan-map table, a reality-check histogram with observed markers, the candidate-dossier with metrics and walk-forward analysis, execution-quality stats, an audit-trail section, and the pine-script code display. I also need to add a table of contents component for navigation, update the research types to include candidate data, and create chart components for yearly bars and null distributions. For the pine script, I'll store it as a template string in the lib so it can be both displayed and downloaded. Writing Pine script... Checking for the time gap between bars to exclude signals during breaks longer than 2 hours, then at 08:00 UTC each day I'm verifying the signal conditions: volatility expansion above 1.20 and momentum magnitude at least 0.5 pips to determine direction. Entry happens at market open on the signal bar, and I'm tracking a 24-bar exit window from entry, closing at the bar limit. The tricky part is that Pine's `process_orders_on_close` setting affects both entries and exits — if I enable it to match the research's close-of-bar exit timing, it breaks the entry delay. I'm considering closing one bar early to fill at the next open, though this creates a minor timing discrepancy that testing shows doesn't materially impact results. I'll document this honestly: Pine exits fill at the open following the 24th bar, matching the research's exit_delay=1 variant with a profit factor of 1.060. For the stop loss, I'm implementing a 3× ATR48 level frozen at entry, using `strategy.exit` to check it from the entry bar onward just like the research does. On the execution side, I need to handle same-bar fills for stop losses since Pine allows intrabar execution on the entry bar itself. Beyond that, I'm layering in portfolio controls: a spread filter using a proxy since Pine can't see actual spreads, max daily loss limits, drawdown kill switches, cooldown periods between trades, position limits, session filtering, and a rolling performance monitor that disables trading after hitting max drawdown and re-enables after recovery trades. For the rolling profitability check, I'm pulling the last N closed trades from strategy.closedtrades and comparing the profit factor against a threshold—if it drops below that, I halt new entries until recovery. On the cost side, I'm deciding between commission types and slippage settings; for FX I'll use a per-order commission model with a tick-based slippage proxy, then document the recommended TradingView settings and configure the strategy declaration with sensible defaults like initial capital and position sizing based on fixed lot amounts. Position sizing stays simple and faithful to the research: fixed 100,000 units with no compounding to keep pip accounting straightforward, though I'll add an optional risk-percentage input for users who want fixed-fractional sizing. For slippage, I'm setting it to 2 ticks (roughly 0.2 pips on USDJPY) to match the research's mean slippage assumption, and commission is set to match the 0.20 pips round-turn cost from the original backtest. Now I'm working through how to model the full cost structure in TradingView—since the platform doesn't directly handle spread, I need to combine the half-spread, slippage, and commission into a single cash-per-contract commission value that Pine Script can actually implement. The strategy should run by default with frozen research parameters so results are comparable on TradingView, with the header explaining the verdict. Now I'm setting up the styling tokens in globals.css, then moving through the type definitions, components, page layout, Pine script, API route, and final verification.ae:T115c,[V0_FILE]typescriptreact:file="/components/report/sensitivity-grid.tsx" import { research, fmt, signed } from '@/lib/research' import { Num } from './primitives' /** * Parameter-perturbation surface. Colour encodes profit factor relative to * 1.00 so a flat, pale grid means "the parameters do not matter" - which is * the finding, not a rendering artifact. */ export function SensitivityGrid() { const { sensitivity_thresholds: s, definition } = research.candidate const exps = Array.from(new Set(s.map((r) => r.exp_min))).sort((a, b) => a - b) const thrs = Array.from(new Set(s.map((r) => r.thr))).sort((a, b) => a - b) const cell = (e: number, t: number) => s.find((r) => r.exp_min === e && r.thr === t) return (

Profit factor across the trigger-threshold surface

full sample · frozen exits · gate = 1.30

{thrs.map((t) => ( ))} {exps.map((e) => ( {thrs.map((t) => { const c = cell(e, t) if (!c) return ) })} ))}

Profit factor for each combination of volatility-expansion threshold and momentum threshold
volexp ↓ / mom →	{fmt(t, 2)}
{fmt(e, 2)}	const frozen = c.exp_min === definition.exp_min && c.thr === definition.mom_thr // 1.00 -> transparent, 1.30 -> full measure colour const alpha = Math.min(Math.max((c.pf - 1) / 0.3, 0), 1) return (	{fmt(c.pf, 3)} n={c.n} · {signed(c.exp, 1)}p

Every one of the eighteen combinations lands between{' '} {fmt(Math.min(...s.map((r) => r.pf)), 3)} and{' '} {fmt(Math.max(...s.map((r) => r.pf)), 3)}. The outlined cell is the frozen configuration. Low parameter sensitivity is normally a virtue — here it means the whole neighbourhood is flat at break-even, so there is no parameter choice that turns this into a strategy. The mild upward tilt toward tighter thresholds buys profit factor by discarding trades, and the surviving samples are too small to distinguish from the same flat surface.

) }af:T115c,[V0_FILE]typescriptreact:file="/components/report/sensitivity-grid.tsx" import { research, fmt, signed } from '@/lib/research' import { Num } from './primitives' /** * Parameter-perturbation surface. Colour encodes profit factor relative to * 1.00 so a flat, pale grid means "the parameters do not matter" - which is * the finding, not a rendering artifact. */ export function SensitivityGrid() { const { sensitivity_thresholds: s, definition } = research.candidate const exps = Array.from(new Set(s.map((r) => r.exp_min))).sort((a, b) => a - b) const thrs = Array.from(new Set(s.map((r) => r.thr))).sort((a, b) => a - b) const cell = (e: number, t: number) => s.find((r) => r.exp_min === e && r.thr === t) return (

Profit factor across the trigger-threshold surface

full sample · frozen exits · gate = 1.30

{thrs.map((t) => ( ))} {exps.map((e) => ( {thrs.map((t) => { const c = cell(e, t) if (!c) return ) })} ))}

Profit factor for each combination of volatility-expansion threshold and momentum threshold
volexp ↓ / mom →	{fmt(t, 2)}
{fmt(e, 2)}	const frozen = c.exp_min === definition.exp_min && c.thr === definition.mom_thr // 1.00 -> transparent, 1.30 -> full measure colour const alpha = Math.min(Math.max((c.pf - 1) / 0.3, 0), 1) return (	{fmt(c.pf, 3)} n={c.n} · {signed(c.exp, 1)}p

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Now I need to restore the project files and assemble the report page with the six components I've built, plus create the Pine Script deliverable.\n\nLet me check the component naming conventions and CSS tokens, then copy the full project into place so I can integrate everything together."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819686309,"finishedAt":1785819686616,"lastPartSentAt":1785819686572,"taskNameActive":"Restoring prior work","taskNameComplete":"Restored prior work","toolCallId":"toolu_01VHDSpeihCKwnXdRwQKzAFh","agentTurnId":"turn_fj68GNuYKCQLBaOM","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"Command blocked by permissions","code":"AGENT_GUIDANCE"},"id":"tUbmFjoz26S2mspT","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp/qrf && cp -r research /vercel/share/v0-project/ && rm -rf /vercel/share/v0-project/research/__pycache__ && mkdir -p /vercel/share/v0-project/components/report 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'numpy'\n"},"id":"xPdwFMPDSejjBzQ2"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819808919,"finishedAt":1785819812323,"lastPartSentAt":1785819811963,"taskNameActive":"Setting up python venv","taskNameComplete":"Set up python venv","toolCallId":"toolu_011nDJnKXp2eNQLh34PLKn3A","agentTurnId":"turn_yUZoCSgAfVnAgDpx","filePath":null,"id":"l0Xfp9T5A7I3RL4l","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && (which uv; uv venv .venv 2>&1 | tail -3) && (uv pip install --python .venv/bin/python numpy pandas 2>&1 | tail -5) && .venv/bin/python -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"3vdDdTHcgsmqyD5Y"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"/usr/local/bin/uv\nUsing CPython 3.13.11\nCreating virtual environment at: .venv\nActivate with: source .venv/bin/activate\nInstalled 4 packages in 13ms\n + numpy==2.5.1\n + pandas==3.0.5\n + python-dateutil==2.9.0.post0\n + six==1.17.0\n"},"id":"5GNiivcmslKCYMZa"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"2.5.1 3.0.5\n"},"id":"Vkgs2BnXprbEql2Z"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819819482,"finishedAt":1785819819948,"lastPartSentAt":1785819819948,"taskNameActive":null,"id":"xlooUnBu5IzDTFBy","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I'm concerned about compatibility issues with pandas 3.0.5 since it introduces breaking changes to methods like `.replace()` and groupby operations that could disrupt the existing pipeline. I'll stick with pandas 2.2.3 and numpy 2.x to maintain stability with the current codebase."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819820169,"finishedAt":1785819823424,"lastPartSentAt":1785819823169,"taskNameActive":"Pinning pandas 2.x","taskNameComplete":"Pinned pandas 2.x","toolCallId":"toolu_01B3xrgx9zUrvNEJutK1GFGd","agentTurnId":"turn_bL11tklaeFqz71G7","filePath":null,"id":"p5Wq3iC5MbHmVd8B","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && uv pip install --python .venv/bin/python \"pandas==2.2.3\" \"numpy<2.3\" 2>&1 | tail -4 && .venv/bin/python -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\" && ls research/data && wc -l research/data/*.csv","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"qDiLSvYjLfYMpu9I"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" - pandas==3.0.5\n + pandas==2.2.3\n + pytz==2026.3.post1\n + tzdata==2026.3\n"},"id":"tVI52OQXWINOFDgq"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"2.2.6 2.2.3\nusdjpy_h1.csv\nusdjpy_h1_src.csv\nusdjpy_h4.csv\n 102697 research/data/usdjpy_h1.csv\n 102697 research/data/usdjpy_h1_src.csv\n 26549 research/data/usdjpy_h4.csv\n 231943 total\n"},"id":"zzSusqWw42qWuwSz"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819829132,"finishedAt":1785819838424,"lastPartSentAt":1785819838424,"taskNameActive":null,"id":"AoWkkn6YEXXqQ6zp","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":9,"thought":"Setting up stage 23 to generate the final candidate report and validation metrics on the T9 trigger, starting with the conditions at the 08:00 UTC H1 close: checking if the H4 volatility expansion ratio meets the 1.20 threshold and calculating the H4 momentum component.\n\nI'm testing a comprehensive battery of exits using fixed time windows (12 and 24 H1 bars) combined with stop-loss and take-profit levels scaled to ATR, then running the full statistical suite across in-sample and out-of-sample periods, yearly and monthly breakdowns, walkforward validation, Monte Carlo bootstrap analysis, stress tests on costs and slippage, entry/exit delays, parameter perturbation grids, and drift-neutral checks to validate the candidate's robustness.\n\nWriting the candidate dossier script..."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819839929,"finishedAt":1785819840627,"lastPartSentAt":1785819840606,"taskNameActive":"Planning remaining work","taskNameComplete":"Planned remaining work","toolCallId":"toolu_01Q1wjpv2nNUABLw6EJYtGWY","agentTurnId":"turn_XKq04G4VdL4PHVKP","filePath":null,"id":"G8TkvdKewWL6mPos","type":"task-manage-todos-v1","parts":[{"type":"starting-todo-manager"},{"type":"updated-todo-list","updatedList":[{"status":"in-progress","task":"Restore research pipeline and run the final-candidate dossier stage (stage 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\nGENERATING","latestDeploymentInfo":"$a3:1:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_pvw06_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_yUaCquTZbnz","meta":{"taskNameActive":"Writing candidate dossier stage","taskNameComplete":"Wrote candidate dossier stage","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing candidate dossier stage","taskNameComplete":"Wrote candidate dossier stage"},"id":"block-b_yUaCquTZbnz"}],["AssistantMessageContentPart",{"part":{"createdAt":1785819925620,"finishedAt":1785819932017,"lastPartSentAt":1785819931752,"taskNameActive":"Running dossier stage","taskNameComplete":"Ran dossier stage","toolCallId":"toolu_01WwEqkyzBx9UTNDT3deHw4x","agentTurnId":"turn_eofjkcl1dD7RAzqA","filePath":null,"id":"KQLKsmgBKMzud4iK","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && ../.venv/bin/python 23_candidate_dossier.py 2>&1 | tail -40","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"TzwtKL5rS4bRp9Y6"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"loading ...\nsignals: 565 long 268 short 297\nexit geometry grid (in-sample) ...\n label n pf wr exp t_exp tpy net maxdd avg_w avg_l avg_cost cost_pct edge_cost_x hold pos_years tot_years\n mb=12 sl=3.0 tp=1.5 34\n\n...\n\n.92 -45.28 1.75 65.0 0.54 20.1 9 17\n\nedge/cost multiple: 0.54 avg cost: 1.75 avg gross: 2.7\nMC P(PF>=1.30) = 2.2 %\ngates passed: 1 / 7\nwrote candidate block -> /vercel/share/v0-project/app/research-data.json\n"},"id":"qZDgicMZ1LcjMusq"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819938100,"finishedAt":1785819941003,"lastPartSentAt":1785819941003,"taskNameActive":null,"id":"UYJ4ZiQcyncQIrMF","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":3,"thought":"The frozen config shows a power factor of 1.127 in-sample but only 0.950 out-of-sample, failing 6 out of 7 gates—confirming this approach doesn't work. I notice the in-sample grid had some cells hitting 1.36 power factor with those parameters, which would've looked promising, but that's exactly the kind of overfitting that collapses out-of-sample. Let me quickly test whether those same parameters (mb=12, sl=3, tp=1.5) actually hold up on out-of-sample data to see if mining that grid would've been worth it.\n\nI'll add this as a demo block to the JSON output and extend the existing script to run it—should only take another minute or so since the full run was already fast."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_7dN8tdTji67","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/23_candidate_dossier.py","baseName":"23_candidate_dossier.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/23_candidate_dossier.py\" \nGENERATING","latestDeploymentInfo":"$a3:2:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_w1vzd_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_7dN8tdTji67","meta":{"taskNameActive":"Adding grid-mining demo","taskNameComplete":"Added grid-mining demo","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding grid-mining demo","taskNameComplete":"Added grid-mining demo"},"id":"block-b_7dN8tdTji67"}],["AssistantMessageContentPart",{"part":{"createdAt":1785819952077,"finishedAt":1785819958372,"lastPartSentAt":1785819958095,"taskNameActive":"Re-running dossier stage","taskNameComplete":"Re-ran dossier stage","toolCallId":"toolu_01UsDfV7wQRu9sCPh5d8JW22","agentTurnId":"turn_R3Uag8r6CKLgYAEh","filePath":null,"id":"WI26qtHRNvJKxlhk","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && ../.venv/bin/python 23_candidate_dossier.py 2>&1 | tail -22","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"uhwQCkGTUNQHJRY9"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" mb=12 sl=4.0 tp=1.5 342 1.317 65.5 3.70 1.98 34.2 1265.0 390.0 23.49 -33.86 1.78 33.0 2.07 7.4 7 10\n mb=12 sl=none tp=2.0 342 1.310 59.9 3.95 1.74 34.2 1352.0 526.0 27.87 -31.83 1.85 32.0 "},"id":"zaAStJZwVY4nth2o"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 2.14 9.1 8 10\nmb=12 sl=none tp=none 342 1.286 52.3 4.22 1.66 34.2 1442.0 594.0 36.19 -30.89 \n\n...\n\n 2.7\nMC P(PF>=1.30) = 2.2 %\ngates passed: 1 / 7\nwrote candidate block -> /vercel/share/v0-project/app/research-data.json\n"},"id":"HNSSrTScYewPRYIe"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819962577,"finishedAt":1785819963301,"lastPartSentAt":1785819963301,"taskNameActive":null,"id":"NTyelm0gZLVBZn5P","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"That's a strong result showing the model holds up well out-of-sample. Now I need to pull together the key performance metrics for the UI—walk-forward analysis, stress testing results, sensitivity analysis, Monte Carlo simulations, execution details, side split performance, and drift neutrality measures."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819965797,"finishedAt":1785819968094,"lastPartSentAt":1785819967783,"taskNameActive":"Inspecting dossier output","taskNameComplete":"Inspected dossier output","toolCallId":"toolu_01NxRdYrte41niCpdekMBF4n","agentTurnId":"turn_YYBjY3PK9ip0zvJr","filePath":null,"id":"2i20F0vEFp0pCqnX","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && .venv/bin/python -c \"\nimport json;C=json.load(open('app/research-data.json'))['candidate']\nfor k in ('walk_forward','monte_carlo','execution','side_split','drift_neutral','holding','verdict','gates','rolling_pf'):\n v=C[k]\n if k=='rolling_pf': print(k, v['window'], v['pct_windows_above_130'], len(v['series'])); continue\n print('==',k); print(json.dumps(v,indent=1)[:1400])\nprint('== stress'); [print(r['label'],r['pf'],r['exp'],r['n']) for r in C['stress']]\nprint('== sens'); [print(r) for r in C['sensitivity_thresholds']]\nprint('== hour'); [print(r) for r in C['sensitivity_hour']]\nprint('== yearly'); [print(r) for r in C['yearly']]\n\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"XKTqoMduNDKT9vem"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"== walk_forward\n{\n \"folds\": [\n {\n \"window\": \"2010-2011\",\n \"n\": 52,\n \"pf\": 1.449,\n \"wr\": 57.7,\n \"exp\": 7.09,\n \"maxdd\": 178.0\n },\n {\n \"window\": \"2012-2013\",\n \"n\": 74,\n \"pf\": 0.929,\n \"wr\": 48.6,\n \"exp\": -1.52,\n \"maxdd\": 409.0\n"},"id":"SEqOigJp5jgJJR15"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" },\n {\n \"window\": \"2014-2015\",\n \"n\": 70,\n \"pf\": 1.398,\n \"wr\": 51.4,\n \"exp\": 7.13,\n \"maxdd\": 390.0\n },\n {\n\n\n...\n\n 'net': 297.0, 'seg': 'OOS'}\n{'year': 2026, 'n': 12, 'pf': 0.712, 'wr': 16.7, 'exp': -11.24, 'net': -135.0, 'seg': 'OOS'}\n"},"id":"G05fjt3v6CRMHEKl"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819979640,"finishedAt":1785819981846,"lastPartSentAt":1785819981488,"taskNameActive":"Checking sensitivity output","taskNameComplete":"Checked sensitivity output","toolCallId":"toolu_01NB3MjjsCRe57C41nsb39BX","agentTurnId":"turn_UQyER5AOegbTx7eN","filePath":null,"id":"lJpFNVxDZFCa3DJd","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && .venv/bin/python -c \"\nimport json;C=json.load(open('app/research-data.json'))['candidate']\nprint('stress');[print(r['label'],r['pf'],r['exp'],r['n']) for r in C['stress']]\nprint('sens');[print(r) for r in C['sensitivity_thresholds']]\nprint('hour');[print(r) for r in C['sensitivity_hour']]\nprint('yearly');[print(r['year'],r['n'],r['pf'],r['exp']) for r in C['yearly']]\nprint('rolling', C['rolling_pf']['pct_windows_above_130'], len(C['rolling_pf']['series']))\nprint('gm', json.dumps(C['grid_mining_demo'],indent=0)[:400])\nprint('gates');[print(g) for g in C['gates']]\nprint('grid best', C['exit_grid']['best_pf'], C['exit_grid']['n_clearing_130'], C['exit_grid']['n_positive'], C['exit_grid']['configs'])\n\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"ZNQqpJeOQWq3FCjx"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"stress\nbaseline 1.04 0.95 565\nspread x1.5 1.019 0.46 565\nspread x2.0 0.999 -0.03 565\nslippage x2 1.016 0.38 565\nslippage x3 0.992 -0.19 565\nentry delay 2 bars 0.993 -0.17 565\nentry delay 3 bars 0.906 -2.4 565\nexit delay 1 bar 1.06 1.43 565\nexit d\n\n...\n\n05 (stage 21)', 'got': 0.632, 'need': 0.05, 'pass_': False}\n{'gate': 'Walk-forward folds >= 1.30', 'got': 2, 'need': 8, 'pass_': False}\n{'gate': 'Monte Carlo P(PF >= 1.30) >= 50%', 'got': 2.2, 'need': 50.0, 'pass_': False}\ngrid best 1.362 6 48 48\n"},"id":"O02zSRHoypO6ZSNR"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785819986829,"finishedAt":1785820044528,"lastPartSentAt":1785820044528,"taskNameActive":null,"id":"o4FMDjpydjTgBqQx","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":58,"thought":"$ad"}]}}],["p",{},["text",{},"Research is now complete and reproducible. Time to finish the deliverables — starting with the design tokens the report components already reference."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_Uxmi7pLYZgK","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[],"source":"[V0_FILE]css:file=\"/app/globals.css\" \n...","latestDeploymentInfo":"$a3:3:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_cagug_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_Uxmi7pLYZgK","meta":{"taskNameActive":"Making changes","taskNameComplete":"Made some changes","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Making changes","taskNameComplete":"Made some changes"},"id":"block-b_Uxmi7pLYZgK"}],["AssistantMessageContentPart",{"part":{"createdAt":1785820051283,"finishedAt":1785820052896,"lastPartSentAt":1785820052867,"taskNameActive":"Re-reading globals.css","taskNameComplete":"Re-read globals.css","toolCallId":"toolu_01DeamVs6fQ57ZsbV4ys7wom","agentTurnId":"turn_NT96JKEn2mZlKacK","filePath":null,"id":"k9b9VGug99qxdF22","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/app/globals.css"},{"type":"reading-file","filePath":"/vercel/share/v0-project/app/globals.css","offset":0,"limit":60}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785820059966,"finishedAt":1785820060639,"lastPartSentAt":1785820060639,"taskNameActive":null,"id":"jGutyQDmFaFXVOh5","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"The globals.css file already had the correct design tokens — it looks like Design Mode or the v0 platform restored the proper version instead of the scaffold default. Now I need to verify the rest of the token variables like --signal, --measure, --grid, and check for the .tnum class."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785820061020,"finishedAt":1785820065312,"lastPartSentAt":1785820064157,"taskNameActive":"Verifying tokens","taskNameComplete":"Verified tokens","toolCallId":"toolu_012KtKU6MqZBPQJfUoLrMC1a","agentTurnId":"turn_iH9D1IrbZqaan9US","filePath":null,"id":"jSsKWwLhnwxlZ2sn","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && grep -n \"signal\\|measure\\|--grid\\|tnum\" app/globals.css | head -40 && echo \"--- lines:\" && wc -l app/globals.css app/page.tsx lib/research.ts && git status --porcelain 2>/dev/null | head -30"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"5i5UYxFNRzNSelPm"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"11: --color-signal: var(--signal);\n12: --color-signal-foreground: var(--signal-foreground);\n13: --color-measure: var(--measure);\n14: --color-measure-foreground: var(--measure-foreground);\n15: --color-grid: var(--grid);\n78: --signal: oklch(0.648"},"id":"ZbjVuX47Kpws8Yja"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 0.128 62);\n79: --signal-foreground: oklch(0.988 0.004 247);\n80: --measure: oklch(0.522 0.072 214);\n81: --measure-fore\n\n...\n\n: oklch(1 0 0 / 12%);\n193: .tnum {\n--- lines:\n 196 app/globals.css\n 47 app/page.tsx\n 181 lib/research.ts\n 424 total\n"},"id":"EKNb4r0pNUeDHXHV"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785820071107,"finishedAt":1785820071789,"lastPartSentAt":1785820071789,"taskNameActive":null,"id":"QytTLCazUNVQUFfe","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"All the design tokens are in place, and the CSS is complete. Now I need to extend the research types in lib/research.ts to include candidate definitions, then build out the remaining components and wire everything together in page.tsx."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_mn7dRF9hDJc","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[],"source":"[V0_FILE]ts:file=\"/lib/research.ts\" \n...","latestDeploymentInfo":"$a3:4:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_ffj16_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_mn7dRF9hDJc","meta":{"taskNameActive":"Making changes","taskNameComplete":"Made some changes","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Making changes","taskNameComplete":"Made some changes"},"id":"block-b_mn7dRF9hDJc"}],["AssistantMessageContentPart",{"part":{"createdAt":1785820085659,"finishedAt":1785820085659,"lastPartSentAt":1785820085659,"id":"5y3yRN7UWAc0YwQn","type":"task-start-v1","parts":[{"type":"launch-tasks"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785820085659,"finishedAt":1785820086030,"lastPartSentAt":1785820086009,"taskNameActive":"Reading research.ts","taskNameComplete":"Read research.ts","toolCallId":"tool_R4w5YATLeUNF7XYM","agentTurnId":"turn_pxIW7OIIA86NEw1D","filePath":null,"id":"38bbPvZvvhoFUpdv","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/lib/research.ts"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_9QYXqUiL0rZ","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/lib/research.ts","baseName":"research.ts","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]ts:file=\"/lib/research.ts\" \nGENERATING","latestDeploymentInfo":"$a3:5:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_cfuu6_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_9QYXqUiL0rZ","meta":{"taskNameActive":"Typing candidate data","taskNameComplete":"Typed candidate data","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Typing candidate data","taskNameComplete":"Typed candidate data"},"id":"block-b_9QYXqUiL0rZ"}],["p",{},["text",{},"Also fix the "],["code",{},"themes"],["text",{}," type, which is declared as "],["code",{},"string[]"],["text",{}," but is actually an object array."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_g1Gf3KeXPjl","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/lib/research.ts","baseName":"research.ts","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]ts:file=\"/lib/research.ts\" \nGENERATING","latestDeploymentInfo":"$a3:6:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_ceg40_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_g1Gf3KeXPjl","meta":{"taskNameActive":"Fixing themes type","taskNameComplete":"Fixed themes type","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Fixing themes type","taskNameComplete":"Fixed themes type"},"id":"block-b_g1Gf3KeXPjl"}],["p",{},["text",{},"Now the remaining report sections. First the hypothesis scan and the reality check."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_LetwjhofOq2","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/components/report/hypothesis-scan.tsx","baseName":"hypothesis-scan.tsx","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]tsx:file=\"/components/report/hypothesis-scan.tsx\" \nGENERATING","latestDeploymentInfo":"$a3:7:1","lastDeployedAt":"2026-08-04T05:10:42.534Z","sourceVersionKey":"v_1_3g0zq_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_LetwjhofOq2","meta":{"taskNameActive":"Building scan section","taskNameComplete":"Built scan section","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Building scan section","taskNameComplete":"Built scan 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