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ae:Tc48,40:oc48, https://v0.app/chat/usdjpy-trading-strategy-d0aTUaAHlLs Finish from where we left off # Mandatory Performance Requirements Every candidate strategy must satisfy: - Profit Factor ≥ 1.30 - Win Rate ≥ 40% - Minimum 50 completed trades per year on average across the entire dataset. - Positive expectancy. - Large statistical sample. - Profitable in every individual calendar year contained in the supplied historical dataset. - Stable yearly equity growth. - Robust across different market regimes. - Low parameter sensitivity. If no strategy satisfies every requirement: - Continue researching new statistical edges. - Continue generating new hypotheses. - Continue testing alternative execution logic. - Continue exploring different market behaviors. Only after exhausting genuinely different statistical ideas may you introduce additional filters or refinements. Any added filter must improve: - Out-of-sample performance. - Walk-forward performance. - Robustness. Never add filters whose only purpose is increasing historical Profit Factor. If no statistically honest solution exists after exhaustive research, explicitly state that no such strategy exists. --- # Research Process Continue searching until one of the following occurs: - A strategy satisfies every requirement. - Exhaustive statistical research indicates that no such strategy exists. Do not stop after finding the first profitable strategy. Keep searching for stronger and more robust alternatives. --- # Optimization Objective The objective is not to maximize historical Profit Factor. The objective is to maximize the probability that the strategy survives real live trading. When two candidate strategies achieve similar performance, always prefer the one with: - Lower execution costs. - Higher average profit per trade after all costs. - Better walk-forward performance. - Better out-of-sample performance. - Lower parameter sensitivity. - More stable yearly returns. - Greater robustness across different market regimes. Always prioritize long-term robustness over historical optimization. --- # Output Efficiency Prioritize research over presentation. Generate only lightweight reports. Avoid: - Fancy dashboards. - Decorative charts. - Interactive UI. - Complex layouts. - Large visualizations. - Animations. - Excessive formatting. - Repeated tables. - Any non-essential graphics. Use simple markdown and compact tables only. Allocate the maximum possible computation, context, and tokens to: - Statistical discovery. - Edge generation. - Validation. - Robustness testing. - Portfolio construction (if applicable). - Pine Script generation. Minimize token usage devoted to presentation. --- # Deliverables Provide only: - Research summary. - Statistical findings. - Final strategy (or portfolio if appropriate). - Validation results. - Walk-forward results. - Out-of-sample results. - Monte Carlo results. - Execution-cost analysis. - Year-by-year performance table. - Pine Script Version 6. - Essential implementation notes. Do not generate dashboards, charts, UI components, or visual reports unless explicitly requested.40:o4e2,af:T4d2,The baseline reproduces successfully, but there's a critical issue: the portfolio shows one losing year in 2016 with a -5.99% return, while all other years are profitable. I also need to account for 2026 being a partial year since the data only extends through mid-June, which technically still counts as a calendar year in the dataset even though it's incomplete. For 2016 specifically, the dip-buying strategy took heavy losses (-7.12R on S3) because USDJPY crashed from 120 to 100 in a sustained downtrend—buying dips in a strong bear market naturally produces losses. To address this, I'm considering several structurally different strategy sleeves: a trend-following breakout system that would profit in downtrends like 2016, a symmetric mean reversion approach that shorts rallies in addition to buying dips, a Tokyo session range breakout, day-of-week seasonality patterns, and volatility-regime-conditioned momentum. These aren't filters but genuine diversifiers that would naturally offset the dip-buy weakness during bear markets. Before adding new sleeves, I should check what trend-following and other families were already explored in the existing scan files to avoid duplication and understand what's been exhausted.b0:Tbd0,40:obd0,I should verify the baseline portfolio works first, then search for a fourth strategy component that's structurally distinct, profitable in 2016, and has positive expectancy on out-of-sample terms. Actually, a more rigorous approach would be to enforce profitability across every calendar year in the 17-year sample at the portfolio level, rather than trying to patch specific years like 2016. The real insight is that requiring zero negative years in-sample is statistically unreasonable given the math: with an expected return of 0.083R per trade and volatility around 0.9R, a 17-year sample with roughly 117 trades annually produces a mean annual return of about 9.7R with standard deviation of 9.7R, which means roughly a 16% probability of a negative year—so the probability of all 17 years being positive is only about 5%. That's essentially forcing overfitting. The honest solution is to increase the number of trades per year through more diversified sleeves, which raises the annual t-statistic. To achieve all-years-positive with reasonable probability, I'd need an annual Sharpe ratio around 2, which requires roughly 470 trades per year given the current expectancy-to-volatility ratio. So the constructive approach is to build a portfolio of many genuinely different sleeves—maybe 6 to 10—each independently validated, targeting 400+ trades annually with similar edge per trade. This way the annual Sharpe reaches around 2 and all-years-positive becomes plausible rather than a sign of overfitting. I'll build a broad library of causal sleeve generators using both existing strategies and new ones, evaluate each on statistical significance in the discovery window (2010-2017), then combine and test out-of-sample on 2018-2026. That's honest walk-forward selection. Let me verify the baseline runs first, then write the code to implement stage 12. I'm designing a helper function that takes a dataframe, list of signals, costs, and stop parameters, then simulates trades with entries at the next open, pessimistic stop checking along the path, and exits at close after the holding period or optional take-profit. I'll implement generators across daily and intraday frames, starting with a mean reversion family that includes the existing dipbuy long and rally-fade short as symmetric counterparts, plus H1 late-day reversals and other variants. Continuing with the full sleeve lineup across mean reversion, trend/momentum, and seasonality/flow categories—roughly 14 strategies total. Now I need to set up the backtesting pipeline: fit each sleeve on the in-sample window, apply greedy subset selection to maximize the annual t-stat while ensuring each strategy individually clears the significance threshold, then validate out-of-sample. I'll write stage12.py to handle this, keeping the computation tractable and being mindful of pandas 3.0 compatibility with groupby operations. First, let me run a quick sanity check on the existing portfolio.py to make sure the environment is set up correctly.40:o8,b1:Tc85,40:oc85,The Donchian trend strategy fails out-of-sample, so that's ruled out. Gap fade shows promise—it works in-sample but the out-of-sample edge is real though minimal at 9 trades per year. Dip buy is interesting: the family average is flat in-sample but turns positive out-of-sample, with certain parameter combinations performing well in both regimes. The h1 reversal strategy has me concerned—the family mean is negative overall, yet that specific (18,19) L3 configuration holds up strongly across both periods, which suggests I might be overfitting to parameter selection rather than finding a genuine edge. Looking at the other candidates, h1 reversal's broader family includes momentum hours that consistently lose money, so I'd need to isolate just the late-NY window to see if there's a real subfamily edge. The NR breakout strategy shows mild positive returns in both samples, making it a potential diversifier. Meanwhile h4 momentum, tpull, and spike fade all show noise patterns—positive in one regime, negative in the other—so I'm discarding those. Realistically, I'm left with maybe three or four families that have genuine edges: daily range-position reversion, gap fade, late-NY h1 reversal, and possibly NR compression breakout. But here's the hard constraint: I need 300–400 trades per year of similar quality to hit the 17/17 target, and I'm only at 117. The obvious levers to increase trade count—expanding to other currency pairs or loosening thresholds—either violate the rules or just dilute the same edge across more correlated trades, which doesn't actually help. I'm starting to accept that 17 consecutive positive years with 50+ trades per year on a single instrument at these timeframes might not be achievable without overfitting. I should probably demonstrate this rigorously by showing my best portfolio configuration, then proving through sample-space search that no honest setup satisfies all five criteria. But before I give up, let me try a few genuinely different directions—maybe a session-based mean reversion strategy that captures London or Asian session reversals as independent trade flow. I'm also considering volatility-scaled position sizing as a robustness improvement rather than a filter—halving risk during extreme ATR regimes could smooth out bad years and improve annual Sharpe. Then there's a higher-frequency H1 Bollinger Band fade using z-scores across all hours with ATR normalization, which could generate hundreds of trades per year and deserves an in-sample and out-of-sample test. I'm also thinking about range-position reversion on H4 instead of daily, which might give me more trade opportunities while keeping the structure simpler. My plan is to test a batch of strategies across different timeframes: H1 z-score fades for high frequency, H4 and H1 range-position reversions, a daily two-day reversal on close-to-close, and a broad intraday reversion family with no additional conditioning. The goal is to find a high-frequency reversion sleeve that actually has decent per-trade edge. Now I'm setting up stage13 to systematically explore these reversion strategies across timeframes with in-sample and out-of-sample testing.40:o8,b2:T5c0,40:o5c0,The backtesting shows that high-frequency reversions at shorter timeframes get wiped out by transaction costs, leaving only daily-horizon mean reversion trades viable at around 50-66 trades per year. This caps the realistic trade frequency at 120-160 annually, confirming the 17/17 target isn't achievable. I'm now exploring whether widening stop losses to reduce the cost-to-risk ratio could improve the economics. Testing a longer-horizon version on the H4 timeframe with looser parameters yielded strong results (158 trades per year with solid out-of-sample Sharpe), though it's essentially the same dip-buy edge at a different resolution. Adding this to the portfolio could increase trade count through overlapping entries, but I need to verify empirically whether it actually raises annual Sharpe or just adds correlated noise. I'm moving into stage 14 to construct a portfolio from the surviving edges: the intraday reversions, gap fades, daily dips, and this H4 variant, plus testing volatility-scaled risk and per-year risk normalization to see if those improve the overall results. Now I'm setting up a combination search across these pre-specified sleeves, evaluating all subsets and measuring annual Sharpe, negative years, profit factor, win rate, trade count, and in-sample versus out-of-sample performance. The key discipline here is selecting the best combo on the in-sample window only, then reporting out-of-sample results honestly to avoid overfitting.40:o8,b3:T57c,40:o57c,I need to pull the rev_pos signature from stage13 for S1, S2, S3, and S5 since S4 is excluded from the final combination. Now I'm writing stage15.py to validate that PORT equals the sum of those four components, starting with headline statistics and the mandatory requirement check, followed by a year-by-year breakdown and walk-forward analysis using rolling 4-year in-sample periods. For the walk-forward with parameter re-selection, I'm precomputing all parameter variants once across the full history—S1 tests 9 combinations of k and hold values, S2 tests 3 k values, S3 tests 9 combinations of threshold and hold, and S5 tests 3 nr values, giving 24 total sleeve runs. Then I slice by year for each fold rather than recomputing, which is much more efficient. I'll also add parameter sensitivity analysis by perturbing each parameter, execution cost sensitivity from 0.5 to 4.0 pips, Monte Carlo bootstrap tests for maximum drawdown and probability of outperformance, regime splits by volatility tercile and trend/range, and per-sleeve contribution tracking with drawdown analysis. I'll include cost sensitivity and parameter sensitivity across all 729 combinations to see how performance distributes, then layer in Monte Carlo and regime analysis. Since the sleeve series are precomputed, running the full grid is manageable. I'll track drawdown based on signal timing through the trade sequence.40:o8,b4:T606,40:o606,The validation checks out across the board. The performance factor of 1.329 sits at the 98th percentile within the parameter grid, though the mean hovers around 1.252, and when I account for per-fold reselection in the workflow, I'm seeing 1.322 to 1.379. The Monte Carlo analysis suggests there's roughly a 63-67% probability that the live performance factor stays above 1.30—essentially a coin flip favoring the positive outcome. What's worth noting is that achieving zero negative years across all 17 folds was a fortunate draw at just 6.2% probability, and the weakest performance shows up in high volatility environments around 2025-26 with a t-statistic of 1.16. Now I'm moving into the deliverables: Pine Script v6 and the report. I'm setting up four sleeves within a single strategy script. The first sleeve trades hourly reversals during specific hours, using prior close movements relative to ATR as the entry signal, with a 3-bar hold. The second sleeve captures daily gaps, entering at the open and exiting at the close. The third sleeve identifies dips using a 10-day position metric, going long when price sits in the lower 15% of the range. The fourth sleeve trades narrow-range days, entering the next day in the direction of the prior day's close, holding for five days. I need to be careful about the exact specifications and timezone handling in the data. Now I need to figure out what timezone the data is in by examining the hour distribution in the CSV and looking for the weekly trading gap to identify the exchange.40:o9,b5:T15a5,40:o15a5,Looking at the trading volume data across different hours, the forex week appears to run from Sunday 22:00 UTC through Friday 21:00 UTC, with partial volume on the opening and closing hours and zero activity after Friday 22:00. The signal fires during the late New York afternoon session—specifically at 19:00 and 20:00 UTC, which corresponds to 2-3pm Eastern time. For the daily bars, I need to be careful about timezone alignment since TradingView typically uses exchange time rather than UTC. The most reliable approach would be to build everything on a 60-minute chart and manually construct UTC-aligned daily bars by detecting when the UTC calendar day changes, ensuring exact consistency with the analysis. Writing Pine strategy code... For USDJPY, the profit calculation works out to qty times the price change divided by the price in USD terms, so I need qty equal to risk in USD times price divided by the stop distance — that gives me the exact position sizing. Now I'm setting up the sleeve logic on the hourly chart, starting with calculating the 24-period ATR using the true range. Writing sleeve logic... I need to decide between using `strategy.exit` with a stop loss and `strategy.close` for the time-based exit, weighing the tradeoff between accuracy and simplicity—since fills happen at the next bar's open in Pine, the difference between closing at bar i+3's close versus the next open is negligible for hourly FX data, so I'll go with `process_orders_on_close=false` and place the time exit order accordingly. With `process_orders_on_close=true`, orders fill at the signal bar's close instead of the next open, which matches research but feels unrealistic. I need to find a faithful compromise that keeps entries filling at the next open like the research does, while handling exits properly—maybe using `strategy.close()` with specific timing on bar i+hold-1 to align the fills correctly. Actually, I realize the key insight: in FX markets with no intraday gaps, the open of the next bar essentially equals the close of the current bar. So if I place an exit order at the close of bar e+hold-1, it fills at the open of bar e+hold, which is approximately the close of e+hold-1—that's the faithful approach I need. For daily sleeves though, there are real gaps between daily close and next open due to weekends and rollovers, creating unavoidable friction of a few pips. I'll note this as a cost, though the sweep analysis shows the strategy tolerates over 5 pips anyway. On the H1 chart, daily sleeve exits need to trigger at the last H1 bar of the UTC day. Since Pine can't look ahead, I'll detect the start of a new UTC day on the current bar and treat the previous bar as the completed day's final bar. This means daily processing happens when the first H1 bar of a new UTC day arrives, using the prior day's finished values. Entry then occurs at the daily open of the next day, which is exactly the open of that first H1 bar. But there's a timing issue: an order placed at a bar's close fills at the next bar's open, not the current bar's open. So I need to shift the logic—process daily signals at the last H1 bar of the previous day instead, so the order fills at the open of the first bar of the new day, which is the daily open. To detect the day's end without look-ahead, I can identify the last H1 bar of each UTC day by checking if utcHour equals 23 (or 21/22 on Friday when the FX week closes). The data has an interesting quirk though: Sunday only has bars at hours 21, 22, and 23, making it a tiny 3-hour daily bar separate from the rest of the week. The research groups by UTC calendar day, so Sunday gets its own aggregation. Let me verify this pattern against the expected count — 17 years times roughly 252 trading days should give around 4,284 daily bars. I'll check the actual length to confirm. Rather than overthink this, I'll replicate the exact behavior in Pine by aggregating strictly by UTC calendar date, where a day ends when the hour reaches 23 or we hit the last bar before a weekend. The key is detecting when a UTC day's final bar occurs — that's the bar after which the UTC date changes. I'm planning to use a pending order mechanism in Pine: at the first bar of each new UTC day, I can determine the signal and place the entry order accordingly. The practical issue is that entering at the open of the second H1 bar of the new UTC day (01:00 UTC) instead of exactly at 00:00 UTC introduces a 1-hour delay. For most daily-horizon sleeves holding 5 days this is negligible, but for S2 gap fade — which is explicitly a same-bar gap trade — that 1-hour delay materially changes the outcome. So I need to make the Pine script cleanly detect day-end: the last bar of each UTC trading day is when utcHour equals 23, except on Friday where it's the final bar of the week. Looking at the data, Friday's last bar varies by season due to DST — most Fridays end at hour 20 (855 out of 856 weeks), with 290 Fridays having an extra hour-21 bar. So Friday close is 21:00 UTC in winter and 20:00 UTC in summer. The real issue is that Sunday micro-bar artifact from Friday signals entering on Sunday. I'm going to rebuild the daily sleeves using a proper daily series where Sunday bars merge into Monday (the standard FX convention), re-verify the results hold, then write Pine code using `request.security` on "1D". This removes the artifact and makes the Pine implementation faithful to the actual logic — the Sunday micro-bar is just a data quirk, not a real edge.40:o8,b6:T874,40:o874,The stage-15 result claiming perfect performance was actually driven by a Sunday micro-bar anomaly. When I apply the tradable convention, the portfolio metrics deteriorate significantly—the Sharpe ratio turns negative at -0.024, the portfolio value drops to 0.935, and I see three negative years. This invalidates the "17/17" finding, which is why testing different market conditions matters. The micro-bar is creating false signals across multiple dimensions: it's triggering NR7 constantly since a 3-hour bar is always the narrowest range, it's distorting gap-fade calculations by measuring against an artificially tight close, and it's shifting the dip-buy entry zones. Only S1, the H1 late-New York reversal strategy, holds up independently of the convention choice—it shows an expR of 0.0447 with a t-stat of 4.42 and a Sharpe of 1.396, though it does have four negative years and only 87 trades per year. I need to rebuild the daily strategies properly using the correct fxwk convention and restart the discovery process from clean daily bars. My plan is to rescan all the daily families—dip buy, gap fade, NR breakout, Donchian, trendpull, turn-of-month, and risk-off—with proper in-sample and out-of-sample discipline, while also validating H4 and H1 which aren't affected by the convention issue. Since H1 and H4 data come natively from the CSV and are convention-safe, I should focus on building more sleeve ideas around those timeframes: H1 reversals at different hour clusters, H1 session breakouts at London and New York opens, H4 range-position dips, H1 mean reversion against Bollinger Bands, H1 momentum continuation in trends, and Asia range breakouts at the London open. Let me scan across all three timeframes with a comprehensive stage 17 that reuses the existing library functions but feeds them the clean daily data. Before I do that, I need to verify the robustness of S1 itself—it's now the backbone strategy, so I should systematically test whether the edge holds across different hour combinations or if it's just lucky in hours 18 and 19. That's a critical check to make sure I'm not overfitting. Now let me write stage 17.40:o8,b7:T733,40:o733,The hour-of-day breakdown shows a clear pattern: reversal strategies consistently underperform during hours 0-17 across all trading sessions, meaning momentum actually persists during Asia, London, and early New York hours. Hours 18-19 flip dramatically with strong positive reversal signals in both in-sample and out-of-sample tests. The persistent negativity through most of the day suggests momentum is the dominant force for the majority of trading hours. Now I'm working through the math on how reversal and continuation returns relate to each other given the asymmetric costs and risk structure. For hour 14 specifically, if reversal shows -0.0499 R with a cost of 1.5 pips against a 30-pip risk level, that means continuation should net around +0.0499 minus twice the cost ratio, which checks out as a real edge once I account for the directional flip and cost structure. But when I actually calculate it out, both reversal and continuation end up negative—the gross reversal is essentially zero, meaning there's no real edge in either direction during hours 0-17, just the drag from paying costs. I should verify this with actual backtesting rather than relying on the algebra, since it's easy to miss something in the math. Moving on to hours 18-19 where reversal shows a positive +0.0495 R at hour 18. Now I'm looking at the magnitude of ATR over 24 periods to properly convert the cost into R terms. The real edges that have held up across in-sample and out-of-sample testing are pretty limited: the H1 reversal at hours 18-19 with a t-stat of 4.42 and 87 trades per year, though it had 4 losing years; the daily dip buy which needs more investigation; and a couple of other signals that are probably just noise given how many I've tested. I'm trying to understand why the wider window on A3 produced zero wins out of 80 trades.40:o8,b8:T7f8,40:o7f8,The Donchian strategy shows severe in-sample to out-of-sample decay with a 44% win rate across 7 losing years—it's essentially a trend-following lottery driven by a spike in 2013, so I'm ruling it out. The h4mom N3 k1.5 h12 setup looks more promising with 269 trades per year, consistent returns (1.72 IS / 2.58 OOS), and a 3.06 t-stat, though the profit factor is weak at 1.123 and it still has 5 negative years . The real issue is that adding a 1.12 PF sleeve to the portfolio would drag down the overall PF below my 1.30 minimum constraint, so I'm rejecting it for the same reason as S4. That leaves me with two solid candidates on clean data: the H1 reversal on hours 18-19 with 87 trades and 1.396 PF, and the daily dip-buy with 34 trades and 1.398 PF—roughly 121 combined trades per year. To hit zero negative years I need more diversification, so I'm planning to re-run the H1 reversal parameters more precisely, hunt for a third convention-immune sleeve using H1 with vol or trend conditioning, and revisit the H4 dip-buy from stage 13 which was native to that timeframe and had around 136 trades. I'm noticing the dip-buy only works long on USDJPY—the short fade-high produced zero survivors out of 48—which reflects the yen carry asymmetry where dips get bought. Now I'm moving into stage 18 to build the portfolio and search for that third sleeve, considering options like an H1 reversal conditioned on daily trend direction, an H4 range-position dip-buy for multi-day reversion, or a volatility-expansion reversal triggered when H1 bars close beyond an N-bar band. I'm also exploring less-tested angles: day-of-week effects on H1, an overnight carry strategy holding long during Asia session hours to capture positive USDJPY carry, and a range-breakout at London open rather than a fade. The priority is to test the carry hypothesis and the London breakout, re-grid sleeve A on hours 18 and 19, hunt for new sleeves, and then audit the full portfolio construction. Now I'm going to write the functions for stage 18.40:o8,b9:Tc7d,40:oc7d,Critical: sleeve C (dipH4) is disastrous when properly R-normalized in the portfolio engine — maxDD 157R, worst year -83R. That's because the H4 dip sleeve apparently has a huge tail (probably no stop / wide stop causing giant losses). It must be excluded. Note in stage 17 it looked OK per-trade but in the portfolio metric it's terrible. Actually the difference: in stage 17's "dipH4" scan the expectancy was measured differently... anyway PF 1.057 → reject. The real result: A+B portfolio: - tpy 120.7 - PF 1.397 (≥1.30 ✓) - WR 59.6% (≥40% ✓) - 120.7 trades/yr (≥50 ✓) - exp +0.068R, t=5.18 → positive expectancy, large sample ✓ - PF_is 1.379, PF_os 1.418 → OOS ≥ IS ✓ - annSh 1.37, maxDD 14.8R - BUT negY = 2 → NOT profitable in every calendar year. worst year -2.43R. So the mandatory requirement "Profitable in every individual calendar year" fails: 2 negative years out of 17. A alone: negY 4. B alone: negY 3. A+B: negY 2. Correlation -0.40 helps. So we need to close the last gap: 2 negative years. The requirement is strict: profitable in EVERY calendar year. Options that are statistically honest: - Find a genuinely different third sleeve that is negatively correlated with A+B and covers exactly the losing years — but "covering the losing years" by selection is curve-fitting. Must be justified independently (robust on its own, both halves). - Accept that 17/17 with only honest edges may not exist and report that. Important: I should be careful — targeting the 2 bad years is exactly the kind of curve-fit the user forbade. But the user's mandate says continue searching genuinely different statistical ideas. Let me identify which years are negative for A+B, then continue searching for genuinely different, independently-validated edges (not year-targeted). Then test whether adding any of them (chosen on standalone merit + low correlation, not on which years they fix) yields 17/17. Ideas not yet tested (genuinely different): - Intraday time-of-day seasonality other than 18-19 (already A is h1rev at hours 18,19). - Weekend/Monday gap (S2 gapfade was in old contaminated set — need to re-test on clean data; gap fade uses Friday close → Monday open which is convention-independent if computed from H1 bars directly, actually the Sunday micro-bar issue affects it. Need care: compute gap from last H1 bar of Friday to first H1 bar of the new week — that's convention-free). - Month-end / turn-of-month flows (real documented FX effect: month-end fixing rebalancing). - Tokyo fixing 9:55 JST effect. - NFP / first-Friday. - Short-term momentum on daily close vs open (open-close range behavior). - RSI(2) extremes on daily with trend filter. - Volatility breakout with time-of-day. - Overnight vs intraday session decomposition (a real structural anomaly: FX USDJPY overnight drift). Let me do stage 19: test these genuinely different ideas on clean/convention-free data, evaluate standalone with both-halves criterion, then portfolio. I need to write carefully. Let me first check lib12/stage17 to reuse loaders. Let me read stage17.py to see the clean sleeve definitions (A = h1rev1819, B = dipD20) and the metric function.40:o8,ba:T918,40:o918,Now stage 19: search genuinely different edge families not yet tried, on convention-safe data, to find a third sleeve for diversification. Crucially, selection must be on standalone merit (both-halves survival) + low correlation, NOT on which years it fixes. New genuinely-different ideas to test: - Overnight vs intraday session decomposition — hold from NY close to Tokyo open (documented FX overnight drift), unconditional and conditioned. - Weekend gap fade computed convention-free from last H1 bar of week to first H1 bar of next week. - Intraday hour-of-day unconditional drift (pure seasonality, long/short per hour) — real but tiny. - RSI(2)-style daily oscillator with 200MA trend filter — different from range-position dip. - Volatility-expansion mean reversion at H1 with session filter (already partly done). - Two-hour opposite-hour reversal pairs (hour A move → hour B fade). - Range-expansion exhaustion on H1: 3-bar consecutive same-direction (streak reversal) — genuinely different mechanics. - H1 opening-range breakout of London/NY with ATR stops. - Month-of-year / seasonal — risky, skip (pure calendar overfit). - Bollinger %b daily with hold-until-mean-touch exit (different exit logic = "alternative execution logic" the user asked for). - Time-based exit variants on sleeve A/B: trailing stop, TP targets — alternative execution logic to improve avg profit/trade. - Streak reversal on daily close (N consecutive down closes). Also I should test alternative execution logic on A and B (TP levels, trailing, ATR-scaled) since the user explicitly listed that as a research avenue, and it could raise expectancy per trade → helps close the 2-negative-year gap honestly by improving the edge, not by filtering years. Let me write stage19 with: - Part 1: streak reversal (daily + H1), RSI2 daily, BB %b daily, overnight-drift, convention-free weekend gap, London/NY ORB, hour-pair reversal. - Part 2: alternative execution logic on A and B (TP multiples, trailing, hold variants) — check whether expectancy and negY improve consistently across the grid (low param sensitivity), not cherry-picked. - Part 3: portfolio combos of A + B + any new survivors, report negY. I need a trade() helper supporting TP and trailing. lib12.trade has tp_R param. Let me check its signature more fully.40:o9,bb:T3a2d,40:o3a2d,[V0_FILE]python:file="/research/stage19.py" """Stage 19 -- new statistical ideas + alternative execution logic. State after stage 18: sleeve A = H1 late-day extension reversal (hours 18-19) PF 1.396 87 tpy sleeve B = clean-daily 20d range-position dip buy PF 1.398 34 tpy A+B PF 1.397 121 tpy A+B passes PF / WR / tpy / expectancy / OOS>IS but has 2 NEGATIVE YEARS. sleeve C (H4 dip) rejected: 157R drawdown, PF 1.06. This stage does NOT go looking for something that patches those two years -- that would be curve fitting. It searches genuinely different mechanics, judges each on standalone both-halves survival, and only then asks what the portfolio looks like. Families here have not been tested at all yet: 1a streak reversal (N consecutive same-direction daily closes) 1b RSI(2) oscillator daily, with and without a 200d trend filter 1c Bollinger %b daily with revert-to-mean exit (different EXIT logic) 1d overnight session drift (hold NY-close -> Tokyo-open) 1e convention-free weekend gap (last H1 bar of week -> first H1 bar of next) 1f London / NY opening-range breakout on H1 1g hour-pair reversal: move in hour X faded in hour Y 1h daily inside-bar / outside-bar follow-through Then: 2 alternative execution logic on A and B (TP targets, breakeven, hold grid) 3 portfolio construction over whatever survives """ import itertools import numpy as np import pandas as pd import lib12 as L pd.set_option("display.width", 280) IS, OS = (2010, 2017), (2018, 2026) PIP = L.PIP # --------------------------------------------------------------------- frames def clean_daily(h): k = h.index.normalize() key = pd.DatetimeIndex(np.where(h.index.dayofweek == 6, k + pd.Timedelta(days=1), k)) d = h.groupby(key).agg(open=("open", "first"), high=("high", "max"), low=("low", "min"), close=("close", "last"), volume=("volume", "sum")).dropna().sort_index() d.index = pd.DatetimeIndex(d.index) d["atr"] = L.atr(d, 14) d["dow"] = d.index.dayofweek return d.dropna(subset=["atr"]).copy() def card(s, name, minn=60): a, b, f = L.summ(s, name, *IS), L.summ(s, name, *OS), L.summ(s, name) if f["n"] < minn: return None return dict(name=name, n=f["n"], tpy=f["tpy"], e_is=a["expR"], t_is=a["t"], e_os=b["expR"], t_os=b["t"], e_all=f["expR"], t_all=f["t"], pf=f["pf"], wr=f["wr"], negY=f["negY"], totR=f["totR"]) def show(rows, title, top=10): R = [r for r in rows if r] if not R: print(f"\n{title}\n (insufficient trades)") return pd.DataFrame() D = pd.DataFrame(R).sort_values("t_all", ascending=False) print(f"\n{title}") print(D.head(top).to_string(index=False)) s = D[(D.e_is > 0) & (D.e_os > 0) & (D.t_is > 1.3) & (D.t_os > 1.3)] print(f" survivors both halves: {len(s)}/{len(D)}") if len(s): print(s.to_string(index=False)) return D # ================================================================ NEW FAMILIES def streak_rev(d, n=3, hold=5, slk=1.5, side=0): c = d["close"].values up = np.r_[False, c[1:] > c[:-1]] dn = np.r_[False, c[1:] < c[:-1]] cand, dirs = [], [] for i in range(n, len(d)): if dn[i - n + 1:i + 1].all() and side >= 0: cand.append(i); dirs.append(1) elif up[i - n + 1:i + 1].all() and side <= 0: cand.append(i); dirs.append(-1) return L.trade(d, cand, dirs, slk, hold, name="streak") def rsi(series, n): dl = series.diff() g = dl.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean() ls = (-dl.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean() return 100 - 100 / (1 + g / ls.replace(0, np.nan)) def rsi_rev(d, n=2, lo=10, hi=90, hold=5, slk=1.5, trend=None, side=0): r = rsi(d["close"], n).values c = d["close"].values ma = d["close"].rolling(trend).mean().values if trend else None cand, dirs = [], [] for i in range(len(d)): if not np.isfinite(r[i]): continue if r[i] <= lo and side >= 0 and (ma is None or (np.isfinite(ma[i]) and c[i] > ma[i])): cand.append(i); dirs.append(1) elif r[i] >= hi and side <= 0 and (ma is None or (np.isfinite(ma[i]) and c[i] < ma[i])): cand.append(i); dirs.append(-1) return L.trade(d, cand, dirs, slk, hold, name="rsi") def bb_meanrev(d, N=20, k=2.0, maxhold=15, slk=1.5, side=0): """Enter at band, EXIT ON MIDBAND TOUCH (not a fixed clock) -- different exit.""" c = d["close"].values; o = d["open"].values hi = d["high"].values; lo = d["low"].values; A = d["atr"].values m = pd.Series(c).rolling(N).mean().values sd = pd.Series(c).rolling(N).std().values R_out, t_out = [], [] for i in range(len(d) - 1): if not (np.isfinite(m[i]) and np.isfinite(sd[i]) and sd[i] > 0 and np.isfinite(A[i])): continue z = (c[i] - m[i]) / sd[i] sdir = 1 if (z <= -k and side >= 0) else (-1 if (z >= k and side <= 0) else 0) if sdir == 0: continue entry = o[i + 1]; risk = slk * A[i]; Rp = risk / PIP stop = entry - sdir * risk px, stopped = None, False for j in range(i + 1, min(i + maxhold, len(d) - 1) + 1): if (lo[j] <= stop) if sdir > 0 else (hi[j] >= stop): px, stopped = stop, True; break tgt = m[j] if np.isfinite(tgt) and ((hi[j] >= tgt) if sdir > 0 else (lo[j] <= tgt)): px = tgt; break px = c[j] pips = sdir * (px - entry) / PIP - L.COST - (L.SLIP_STOP if stopped else 0) R_out.append(pips / Rp); t_out.append(d.index[i]) return pd.Series(R_out, index=pd.DatetimeIndex(t_out), name="bb") def overnight(h, enter_h=21, exit_h=1, slk=2.0, side=1, cost=L.COST): """Structural carry/drift window: enter at enter_h open, exit at exit_h close.""" o = h["open"].values; c = h["close"].values hi = h["high"].values; lo = h["low"].values A = h["atr"].values; hr = h["hour"].values R_out, t_out = [], [] n = len(h) for i in range(n - 1): if hr[i] != enter_h or not np.isfinite(A[i]) or A[i] <= 0: continue j = i + 1 while j < n and hr[j] != exit_h: j += 1 if j - i > 14: break if j >= n or hr[j] != exit_h: continue entry = o[i + 1]; risk = slk * A[i]; Rp = risk / PIP stop = entry - side * risk px, stopped = c[j], False for q in range(i + 1, j + 1): if (lo[q] <= stop) if side > 0 else (hi[q] >= stop): px, stopped = stop, True; break pips = side * (px - entry) / PIP - cost - (L.SLIP_STOP if stopped else 0) R_out.append(pips / Rp); t_out.append(h.index[i]) return pd.Series(R_out, index=pd.DatetimeIndex(t_out), name="on") def wkgap(h, k=0.25, slk=1.5, hold=8, cost=L.COST): """Convention-free weekend gap fade measured on native H1 bars.""" o = h["open"].values; c = h["close"].values; A = h["atr"].values idx = h.index newweek = np.r_[True, (idx[1:] - idx[:-1]) > pd.Timedelta(hours=12)] cand, dirs = [], [] for i in np.where(newweek)[0]: if i == 0 or not np.isfinite(A[i - 1]) or A[i - 1] <= 0: continue gap = o[i] - c[i - 1] if abs(gap) < k * A[i - 1]: continue cand.append(i); dirs.append(-np.sign(gap)) return L.trade(h, cand, dirs, slk, hold, cost, "wkgap") def orb(h, open_h=7, nbars=2, k=0.0, hold=8, slk=1.5, side=0): """Opening-range breakout: range of nbars from open_h, trade the break.""" o = h["open"].values; hi = h["high"].values; lo = h["low"].values c = h["close"].values; A = h["atr"].values; hr = h["hour"].values cand, dirs = [], [] for i in range(len(h)): if hr[i] != (open_h + nbars - 1) % 24 or i < nbars: continue rh = hi[i - nbars + 1:i + 1].max(); rl = lo[i - nbars + 1:i + 1].min() if not np.isfinite(A[i]) or (rh - rl) <= 0: continue if c[i] >= rh - k * A[i] and side >= 0: cand.append(i); dirs.append(1) elif c[i] <= rl + k * A[i] and side <= 0: cand.append(i); dirs.append(-1) return L.trade(h, cand, dirs, slk, hold, name="orb") def hourpair(h, move_h, trade_h, k=1.0, hold=4, slk=2.0): """Move during move_h..trade_h-1 faded by entering at trade_h.""" c = h["close"].values; A = h["atr"].values; hr = h["hour"].values span = (trade_h - move_h) % 24 if span == 0: span = 24 n = len(h) cand, dirs = [], [] for i in range(span, n): if hr[i] != trade_h or not np.isfinite(A[i]) or A[i] <= 0: continue mv = c[i] - c[i - span] if abs(mv) < k * A[i]: continue cand.append(i); dirs.append(-np.sign(mv)) return L.trade(h, cand, dirs, slk, hold, name="hp") def inout_bar(d, kind="inside", hold=5, slk=1.5, side=0): hi = d["high"].values; lo = d["low"].values; c = d["close"].values o = d["open"].values cand, dirs = [], [] for i in range(1, len(d)): ins = hi[i] <= hi[i - 1] and lo[i] >= lo[i - 1] outs = hi[i] > hi[i - 1] and lo[i] < lo[i - 1] ok = ins if kind == "inside" else outs if not ok: continue sd = 1 if c[i] > o[i] else -1 if kind == "outside": sd = -sd if side and sd != side: continue cand.append(i); dirs.append(sd) return L.trade(d, cand, dirs, slk, hold, name=kind) # ==================================================================== PORTFOLIO def yr(s): return s.groupby(s.index.year).sum() def pstats(s, y0=None, y1=None): x = s if y0 is None else s[(s.index.year >= y0) & (s.index.year <= y1)] if len(x) < 10: return {} w, l = x[x > 0], x[x <= 0] Y = yr(x) yrs = max(1e-9, (x.index[-1] - x.index[0]).days / 365.25) eq = x.cumsum() return dict(n=len(x), tpy=round(len(x) / yrs, 1), expR=round(x.mean(), 4), t=round(x.mean() / x.std() * np.sqrt(len(x)), 2), pf=round(w.sum() / abs(l.sum()), 3) if len(l) else np.inf, wr=round(100 * len(w) / len(x), 1), annSh=round(Y.mean() / Y.std(), 2) if len(Y) > 2 else np.nan, maxDD=round((eq.cummax() - eq).max(), 1), negY=int((Y < 0).sum()), nY=len(Y), worst=round(Y.min(), 2), totR=round(x.sum(), 1)) def main(): h = L.load("h1"); h["atr"] = L.atr(h, 24); h = h.dropna(subset=["atr"]).copy() d = clean_daily(h) print(f"H1 {len(h)} bars | clean daily {len(d)}") print("\n" + "=" * 150) print("1. GENUINELY NEW FAMILIES (none of these were tested in stages 12-18)") print("=" * 150) rows = [card(streak_rev(d, n, hd, 1.5, sd), f"streak n{n} h{hd} sd{sd}") for n, hd, sd in itertools.product((2, 3, 4, 5), (3, 5, 8), (0, 1, -1))] show(rows, " 1a. consecutive-close streak reversal") rows = [] for n, lo, hd, tr in itertools.product((2, 3, 5), (10, 20, 30), (3, 5, 8), (None, 200)): rows.append(card(rsi_rev(d, n, lo, 100 - lo, hd, 1.5, tr, 0), f"rsi{n} lo{lo} h{hd} tr{tr}")) show(rows, " 1b. RSI oscillator reversion (daily)") rows = [card(bb_meanrev(d, N, k, mh, 1.5, sd), f"bb N{N} k{k} mh{mh} sd{sd}") for N, k, mh, sd in itertools.product((10, 20, 40), (1.5, 2.0, 2.5), (10, 20), (0, 1, -1))] show(rows, " 1c. Bollinger band -> MIDBAND-TOUCH exit (alternative exit logic)") rows = [card(overnight(h, eh, xh, 2.0, sd), f"on {eh}->{xh} sd{sd}") for eh, xh, sd in itertools.product((20, 21, 22, 23), (1, 3, 5, 7), (1, -1))] show(rows, " 1d. overnight session drift") rows = [card(wkgap(h, k, 1.5, hd), f"wkgap k{k} h{hd}") for k, hd in itertools.product((0.15, 0.25, 0.4), (4, 8, 16))] show(rows, " 1e. convention-free weekend gap fade") rows = [card(orb(h, oh, nb, 0.0, hd, 1.5, sd), f"orb o{oh} nb{nb} h{hd} sd{sd}") for oh, nb, hd, sd in itertools.product((7, 8, 13, 14), (1, 2, 3), (6, 12), (1, -1))] show(rows, " 1f. London / NY opening-range breakout") rows = [card(hourpair(h, mh, th, k, hd, 2.0), f"hp {mh}->{th} k{k} h{hd}") for mh, th, k, hd in itertools.product((6, 8, 12, 14), (13, 16, 20, 22), (1.0, 1.5), (3, 6))] show(rows, " 1g. hour-pair reversal") rows = [card(inout_bar(d, kd, hd, 1.5, sd), f"{kd} h{hd} sd{sd}") for kd, hd, sd in itertools.product(("inside", "outside"), (3, 5, 8), (0, 1, -1))] show(rows, " 1h. inside / outside bar follow-through") # ------------------------------------------------------------------ part 2 print("\n" + "=" * 150) print("2. ALTERNATIVE EXECUTION LOGIC on the two confirmed sleeves") print(" (looking for higher profit-per-trade, judged across the WHOLE grid)") print("=" * 150) rows = [] for hd, tp in itertools.product((2, 3, 4, 6), (None, 0.75, 1.0, 1.5, 2.0)): s = L.trade_wrap_A(h, hold=hd, tp=tp) if hasattr(L, "trade_wrap_A") else None if s is None: c = h["close"].values; A = h["atr"].values; hr = h["hour"].values pr = np.full(len(h), np.nan); pr[3:] = c[3:] - c[:-3] m = np.isin(hr, (18, 19)) & np.isfinite(pr) & (np.abs(pr) >= 1.25 * A) cand = np.where(m)[0]; dirs = [-np.sign(pr[i]) for i in cand] s = L.trade(h, cand, dirs, 2.5, hd, tp_R=tp, name="A") rows.append(card(s, f"A hold{hd} tp{tp}")) show(rows, " 2a. sleeve A: hold x take-profit grid", top=20) rows = [] for hd, tp in itertools.product((3, 5, 8, 12), (None, 0.75, 1.0, 1.5, 2.0)): hiN = d["high"].rolling(20).max().shift().values loN = d["low"].rolling(20).min().shift().values c = d["close"].values pos = (c - loN) / (hiN - loN) cand = [i for i in range(len(d)) if np.isfinite(pos[i]) and pos[i] <= 0.15] s = L.trade(d, cand, [1] * len(cand), 1.5, hd, tp_R=tp, name="B") rows.append(card(s, f"B hold{hd} tp{tp}")) show(rows, " 2b. sleeve B: hold x take-profit grid", top=20) rows = [] for be in (None, 0.5, 1.0): c = h["close"].values; A = h["atr"].values; hr = h["hour"].values pr = np.full(len(h), np.nan); pr[3:] = c[3:] - c[:-3] m = np.isin(hr, (18, 19)) & np.isfinite(pr) & (np.abs(pr) >= 1.25 * A) cand = np.where(m)[0]; dirs = [-np.sign(pr[i]) for i in cand] rows.append(card(L.trade(h, cand, dirs, 2.5, 3, be_at=be, name="A"), f"A be{be}")) show(rows, " 2c. sleeve A: breakeven-stop variants") if __name__ == "__main__": main()40:o9,bc:T3a2d,40:o3a2d,[V0_FILE]python:file="/research/stage19.py" """Stage 19 -- new statistical ideas + alternative execution logic. State after stage 18: sleeve A = H1 late-day extension reversal (hours 18-19) PF 1.396 87 tpy sleeve B = clean-daily 20d range-position dip buy PF 1.398 34 tpy A+B PF 1.397 121 tpy A+B passes PF / WR / tpy / expectancy / OOS>IS but has 2 NEGATIVE YEARS. sleeve C (H4 dip) rejected: 157R drawdown, PF 1.06. This stage does NOT go looking for something that patches those two years -- that would be curve fitting. It searches genuinely different mechanics, judges each on standalone both-halves survival, and only then asks what the portfolio looks like. Families here have not been tested at all yet: 1a streak reversal (N consecutive same-direction daily closes) 1b RSI(2) oscillator daily, with and without a 200d trend filter 1c Bollinger %b daily with revert-to-mean exit (different EXIT logic) 1d overnight session drift (hold NY-close -> Tokyo-open) 1e convention-free weekend gap (last H1 bar of week -> first H1 bar of next) 1f London / NY opening-range breakout on H1 1g hour-pair reversal: move in hour X faded in hour Y 1h daily inside-bar / outside-bar follow-through Then: 2 alternative execution logic on A and B (TP targets, breakeven, hold grid) 3 portfolio construction over whatever survives """ import itertools import numpy as np import pandas as pd import lib12 as L pd.set_option("display.width", 280) IS, OS = (2010, 2017), (2018, 2026) PIP = L.PIP # --------------------------------------------------------------------- frames def clean_daily(h): k = h.index.normalize() key = pd.DatetimeIndex(np.where(h.index.dayofweek == 6, k + pd.Timedelta(days=1), k)) d = h.groupby(key).agg(open=("open", "first"), high=("high", "max"), low=("low", "min"), close=("close", "last"), volume=("volume", "sum")).dropna().sort_index() d.index = pd.DatetimeIndex(d.index) d["atr"] = L.atr(d, 14) d["dow"] = d.index.dayofweek return d.dropna(subset=["atr"]).copy() def card(s, name, minn=60): a, b, f = L.summ(s, name, *IS), L.summ(s, name, *OS), L.summ(s, name) if f["n"] < minn: return None return dict(name=name, n=f["n"], tpy=f["tpy"], e_is=a["expR"], t_is=a["t"], e_os=b["expR"], t_os=b["t"], e_all=f["expR"], t_all=f["t"], pf=f["pf"], wr=f["wr"], negY=f["negY"], totR=f["totR"]) def show(rows, title, top=10): R = [r for r in rows if r] if not R: print(f"\n{title}\n (insufficient trades)") return pd.DataFrame() D = pd.DataFrame(R).sort_values("t_all", ascending=False) print(f"\n{title}") print(D.head(top).to_string(index=False)) s = D[(D.e_is > 0) & (D.e_os > 0) & (D.t_is > 1.3) & (D.t_os > 1.3)] print(f" survivors both halves: {len(s)}/{len(D)}") if len(s): print(s.to_string(index=False)) return D # ================================================================ NEW FAMILIES def streak_rev(d, n=3, hold=5, slk=1.5, side=0): c = d["close"].values up = np.r_[False, c[1:] > c[:-1]] dn = np.r_[False, c[1:] < c[:-1]] cand, dirs = [], [] for i in range(n, len(d)): if dn[i - n + 1:i + 1].all() and side >= 0: cand.append(i); dirs.append(1) elif up[i - n + 1:i + 1].all() and side <= 0: cand.append(i); dirs.append(-1) return L.trade(d, cand, dirs, slk, hold, name="streak") def rsi(series, n): dl = series.diff() g = dl.clip(lower=0).ewm(alpha=1 / n, adjust=False).mean() ls = (-dl.clip(upper=0)).ewm(alpha=1 / n, adjust=False).mean() return 100 - 100 / (1 + g / ls.replace(0, np.nan)) def rsi_rev(d, n=2, lo=10, hi=90, hold=5, slk=1.5, trend=None, side=0): r = rsi(d["close"], n).values c = d["close"].values ma = d["close"].rolling(trend).mean().values if trend else None cand, dirs = [], [] for i in range(len(d)): if not np.isfinite(r[i]): continue if r[i] <= lo and side >= 0 and (ma is None or (np.isfinite(ma[i]) and c[i] > ma[i])): cand.append(i); dirs.append(1) elif r[i] >= hi and side <= 0 and (ma is None or (np.isfinite(ma[i]) and c[i] < ma[i])): cand.append(i); dirs.append(-1) return L.trade(d, cand, dirs, slk, hold, name="rsi") def bb_meanrev(d, N=20, k=2.0, maxhold=15, slk=1.5, side=0): """Enter at band, EXIT ON MIDBAND TOUCH (not a fixed clock) -- different exit.""" c = d["close"].values; o = d["open"].values hi = d["high"].values; lo = d["low"].values; A = d["atr"].values m = pd.Series(c).rolling(N).mean().values sd = pd.Series(c).rolling(N).std().values R_out, t_out = [], [] for i in range(len(d) - 1): if not (np.isfinite(m[i]) and np.isfinite(sd[i]) and sd[i] > 0 and np.isfinite(A[i])): continue z = (c[i] - m[i]) / sd[i] sdir = 1 if (z <= -k and side >= 0) else (-1 if (z >= k and side <= 0) else 0) if sdir == 0: continue entry = o[i + 1]; risk = slk * A[i]; Rp = risk / PIP stop = entry - sdir * risk px, stopped = None, False for j in range(i + 1, min(i + maxhold, len(d) - 1) + 1): if (lo[j] <= stop) if sdir > 0 else (hi[j] >= stop): px, stopped = stop, True; break tgt = m[j] if np.isfinite(tgt) and ((hi[j] >= tgt) if sdir > 0 else (lo[j] <= tgt)): px = tgt; break px = c[j] pips = sdir * (px - entry) / PIP - L.COST - (L.SLIP_STOP if stopped else 0) R_out.append(pips / Rp); t_out.append(d.index[i]) return pd.Series(R_out, index=pd.DatetimeIndex(t_out), name="bb") def overnight(h, enter_h=21, exit_h=1, slk=2.0, side=1, cost=L.COST): """Structural carry/drift window: enter at enter_h open, exit at exit_h close.""" o = h["open"].values; c = h["close"].values hi = h["high"].values; lo = h["low"].values A = h["atr"].values; hr = h["hour"].values R_out, t_out = [], [] n = len(h) for i in range(n - 1): if hr[i] != enter_h or not np.isfinite(A[i]) or A[i] <= 0: continue j = i + 1 while j < n and hr[j] != exit_h: j += 1 if j - i > 14: break if j >= n or hr[j] != exit_h: continue entry = o[i + 1]; risk = slk * A[i]; Rp = risk / PIP stop = entry - side * risk px, stopped = c[j], False for q in range(i + 1, j + 1): if (lo[q] <= stop) if side > 0 else (hi[q] >= stop): px, stopped = stop, True; break pips = side * (px - entry) / PIP - cost - (L.SLIP_STOP if stopped else 0) R_out.append(pips / Rp); t_out.append(h.index[i]) return pd.Series(R_out, index=pd.DatetimeIndex(t_out), name="on") def wkgap(h, k=0.25, slk=1.5, hold=8, cost=L.COST): """Convention-free weekend gap fade measured on native H1 bars.""" o = h["open"].values; c = h["close"].values; A = h["atr"].values idx = h.index newweek = np.r_[True, (idx[1:] - idx[:-1]) > pd.Timedelta(hours=12)] cand, dirs = [], [] for i in np.where(newweek)[0]: if i == 0 or not np.isfinite(A[i - 1]) or A[i - 1] <= 0: continue gap = o[i] - c[i - 1] if abs(gap) < k * A[i - 1]: continue cand.append(i); dirs.append(-np.sign(gap)) return L.trade(h, cand, dirs, slk, hold, cost, "wkgap") def orb(h, open_h=7, nbars=2, k=0.0, hold=8, slk=1.5, side=0): """Opening-range breakout: range of nbars from open_h, trade the break.""" o = h["open"].values; hi = h["high"].values; lo = h["low"].values c = h["close"].values; A = h["atr"].values; hr = h["hour"].values cand, dirs = [], [] for i in range(len(h)): if hr[i] != (open_h + nbars - 1) % 24 or i < nbars: continue rh = hi[i - nbars + 1:i + 1].max(); rl = lo[i - nbars + 1:i + 1].min() if not np.isfinite(A[i]) or (rh - rl) <= 0: continue if c[i] >= rh - k * A[i] and side >= 0: cand.append(i); dirs.append(1) elif c[i] <= rl + k * A[i] and side <= 0: cand.append(i); dirs.append(-1) return L.trade(h, cand, dirs, slk, hold, name="orb") def hourpair(h, move_h, trade_h, k=1.0, hold=4, slk=2.0): """Move during move_h..trade_h-1 faded by entering at trade_h.""" c = h["close"].values; A = h["atr"].values; hr = h["hour"].values span = (trade_h - move_h) % 24 if span == 0: span = 24 n = len(h) cand, dirs = [], [] for i in range(span, n): if hr[i] != trade_h or not np.isfinite(A[i]) or A[i] <= 0: continue mv = c[i] - c[i - span] if abs(mv) < k * A[i]: continue cand.append(i); dirs.append(-np.sign(mv)) return L.trade(h, cand, dirs, slk, hold, name="hp") def inout_bar(d, kind="inside", hold=5, slk=1.5, side=0): hi = d["high"].values; lo = d["low"].values; c = d["close"].values o = d["open"].values cand, dirs = [], [] for i in range(1, len(d)): ins = hi[i] <= hi[i - 1] and lo[i] >= lo[i - 1] outs = hi[i] > hi[i - 1] and lo[i] < lo[i - 1] ok = ins if kind == "inside" else outs if not ok: continue sd = 1 if c[i] > o[i] else -1 if kind == "outside": sd = -sd if side and sd != side: continue cand.append(i); dirs.append(sd) return L.trade(d, cand, dirs, slk, hold, name=kind) # ==================================================================== PORTFOLIO def yr(s): return s.groupby(s.index.year).sum() def pstats(s, y0=None, y1=None): x = s if y0 is None else s[(s.index.year >= y0) & (s.index.year <= y1)] if len(x) < 10: return {} w, l = x[x > 0], x[x <= 0] Y = yr(x) yrs = max(1e-9, (x.index[-1] - x.index[0]).days / 365.25) eq = x.cumsum() return dict(n=len(x), tpy=round(len(x) / yrs, 1), expR=round(x.mean(), 4), t=round(x.mean() / x.std() * np.sqrt(len(x)), 2), pf=round(w.sum() / abs(l.sum()), 3) if len(l) else np.inf, wr=round(100 * len(w) / len(x), 1), annSh=round(Y.mean() / Y.std(), 2) if len(Y) > 2 else np.nan, maxDD=round((eq.cummax() - eq).max(), 1), negY=int((Y < 0).sum()), nY=len(Y), worst=round(Y.min(), 2), totR=round(x.sum(), 1)) def main(): h = L.load("h1"); h["atr"] = L.atr(h, 24); h = h.dropna(subset=["atr"]).copy() d = clean_daily(h) print(f"H1 {len(h)} bars | clean daily {len(d)}") print("\n" + "=" * 150) print("1. GENUINELY NEW FAMILIES (none of these were tested in stages 12-18)") print("=" * 150) rows = [card(streak_rev(d, n, hd, 1.5, sd), f"streak n{n} h{hd} sd{sd}") for n, hd, sd in itertools.product((2, 3, 4, 5), (3, 5, 8), (0, 1, -1))] show(rows, " 1a. consecutive-close streak reversal") rows = [] for n, lo, hd, tr in itertools.product((2, 3, 5), (10, 20, 30), (3, 5, 8), (None, 200)): rows.append(card(rsi_rev(d, n, lo, 100 - lo, hd, 1.5, tr, 0), f"rsi{n} lo{lo} h{hd} tr{tr}")) show(rows, " 1b. RSI oscillator reversion (daily)") rows = [card(bb_meanrev(d, N, k, mh, 1.5, sd), f"bb N{N} k{k} mh{mh} sd{sd}") for N, k, mh, sd in itertools.product((10, 20, 40), (1.5, 2.0, 2.5), (10, 20), (0, 1, -1))] show(rows, " 1c. Bollinger band -> MIDBAND-TOUCH exit (alternative exit logic)") rows = [card(overnight(h, eh, xh, 2.0, sd), f"on {eh}->{xh} sd{sd}") for eh, xh, sd in itertools.product((20, 21, 22, 23), (1, 3, 5, 7), (1, -1))] show(rows, " 1d. overnight session drift") rows = [card(wkgap(h, k, 1.5, hd), f"wkgap k{k} h{hd}") for k, hd in itertools.product((0.15, 0.25, 0.4), (4, 8, 16))] show(rows, " 1e. convention-free weekend gap fade") rows = [card(orb(h, oh, nb, 0.0, hd, 1.5, sd), f"orb o{oh} nb{nb} h{hd} sd{sd}") for oh, nb, hd, sd in itertools.product((7, 8, 13, 14), (1, 2, 3), (6, 12), (1, -1))] show(rows, " 1f. London / NY opening-range breakout") rows = [card(hourpair(h, mh, th, k, hd, 2.0), f"hp {mh}->{th} k{k} h{hd}") for mh, th, k, hd in itertools.product((6, 8, 12, 14), (13, 16, 20, 22), (1.0, 1.5), (3, 6))] show(rows, " 1g. hour-pair reversal") rows = [card(inout_bar(d, kd, hd, 1.5, sd), f"{kd} h{hd} sd{sd}") for kd, hd, sd in itertools.product(("inside", "outside"), (3, 5, 8), (0, 1, -1))] show(rows, " 1h. inside / outside bar follow-through") # ------------------------------------------------------------------ part 2 print("\n" + "=" * 150) print("2. ALTERNATIVE EXECUTION LOGIC on the two confirmed sleeves") print(" (looking for higher profit-per-trade, judged across the WHOLE grid)") print("=" * 150) rows = [] for hd, tp in itertools.product((2, 3, 4, 6), (None, 0.75, 1.0, 1.5, 2.0)): s = L.trade_wrap_A(h, hold=hd, tp=tp) if hasattr(L, "trade_wrap_A") else None if s is None: c = h["close"].values; A = h["atr"].values; hr = h["hour"].values pr = np.full(len(h), np.nan); pr[3:] = c[3:] - c[:-3] m = np.isin(hr, (18, 19)) & np.isfinite(pr) & (np.abs(pr) >= 1.25 * A) cand = np.where(m)[0]; dirs = [-np.sign(pr[i]) for i in cand] s = L.trade(h, cand, dirs, 2.5, hd, tp_R=tp, name="A") rows.append(card(s, f"A hold{hd} tp{tp}")) show(rows, " 2a. sleeve A: hold x take-profit grid", top=20) rows = [] for hd, tp in itertools.product((3, 5, 8, 12), (None, 0.75, 1.0, 1.5, 2.0)): hiN = d["high"].rolling(20).max().shift().values loN = d["low"].rolling(20).min().shift().values c = d["close"].values pos = (c - loN) / (hiN - loN) cand = [i for i in range(len(d)) if np.isfinite(pos[i]) and pos[i] <= 0.15] s = L.trade(d, cand, [1] * len(cand), 1.5, hd, tp_R=tp, name="B") rows.append(card(s, f"B hold{hd} tp{tp}")) show(rows, " 2b. sleeve B: hold x take-profit grid", top=20) rows = [] for be in (None, 0.5, 1.0): c = h["close"].values; A = h["atr"].values; hr = h["hour"].values pr = np.full(len(h), np.nan); pr[3:] = c[3:] - c[:-3] m = np.isin(hr, (18, 19)) & np.isfinite(pr) & (np.abs(pr) >= 1.25 * A) cand = np.where(m)[0]; dirs = [-np.sign(pr[i]) for i in cand] rows.append(card(L.trade(h, cand, dirs, 2.5, 3, be_at=be, name="A"), f"A be{be}")) show(rows, " 2c. sleeve A: breakeven-stop variants") if __name__ == "__main__": 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Since this is a fresh project scaffold, I'll need to set up a backtesting environment—Python would be ideal for the heavy computation required for this research task, so let me check if it's available and extract the archive."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879412141,"finishedAt":1785879438625,"lastPartSentAt":1785879438459,"taskNameActive":"Fetching previous work","taskNameComplete":"Fetched previous work","toolCallId":"toolu_0179tuHjaGxGCL5T5rL5fWz4","agentTurnId":"turn_PzTOmSjVccOf6Jmk","filePath":null,"id":"sRSMRgmjPK2ioYR6","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"mkdir -p /tmp/prev && cd /tmp/prev && curl -sL -o prev.zip \"https://blobs.vusercontent.net/blob/usdjpy-trading-strategy%20%281%29-pHjqV2pd7T19X5Xzc3eFjG1AOnuLhM.zip\" && unzip -o -q prev.zip && find . -type f -not -path \"*/node_modules/*\" -not -name \"*.zip\" | head -60; echo \"--- python:\"; python3 -c \"import numpy, pandas; print(numpy.__version__, pandas.__version__)\" 2>&1 | tail -2"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"SN9VsDGGvqlrnZ9S"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"./.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-32x32.png\n./p"},"id":"47Ty5DJ0xItmc1Qb"},{"type":"subtask-log","data":{"logType":"poll-terminal-output-error"},"id":"GmCYWxlDdMkS1yUy"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"ublic/icon-light-32x32.png\n./public/icon.svg\n./public/placeholder-logo.png\n./public/placeholder-logo.svg\n./public/placeho\n\n...\n\nesearch/s7_c1t.txt\n./research/s7_c2.txt\n--- python:\n ^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"BWYDg4wn6OxvUKEo"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879443102,"finishedAt":1785879443190,"lastPartSentAt":1785879443190,"taskNameActive":null,"id":"9j2am6Sulwu2ASpG","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to review the complete file list and check the latest test results along with any strategy documentation."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879443842,"finishedAt":1785879454938,"lastPartSentAt":1785879444833,"taskNameActive":"Listing research files","taskNameComplete":"Listed research files","toolCallId":"toolu_01To18WvvUEfYRne6PUbfNWa","agentTurnId":"turn_42gZSAWDbAyhF41t","filePath":null,"id":"LWP91NFwj3TS4NHF","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp/prev && find . -type f -not -path \"*/node_modules/*\" -not -name \"*.zip\" -not -path \"*__pycache__*\" | tail -60; echo \"=== sizes\"; ls -la research | tail -50; pip3 install numpy pandas --quiet 2>&1 | tail -3"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"3XqpBpZ3bfsjH7hj"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"./research/log_A2.txt\n./research/log_B.txt\n./research/log_B2.txt\n./research/log_D.txt\n./research/log_E.txt\n./research/log_be.txt\n./research/out_A.csv\n./research/out_B.csv\n./research/out_D.csv\n./research/out_E.csv\n./research/portfolio.py\n./research/po"},"id":"4kno3q4ViK8L4bQT"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"rtfolio.txt\n./research/s11_audit_S1S2S3.csv\n./research/s11_audit_S1S2S3S4.csv\n./research/s7_c1.txt\n./research/s7_c1t.txt\n\n\n...\n\nl environment found; run `uv venv` to create an environment, or pass `--system` to install into a non-virtual environment\n"},"id":"UdldcMoDNssSIqbT"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879459307,"finishedAt":1785879459590,"lastPartSentAt":1785879459590,"taskNameActive":null,"id":"xMRENk9X81yJBhEu","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to check the output files from the previous run to see where things stand, and verify that numpy installed correctly."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879460164,"finishedAt":1785879467278,"lastPartSentAt":1785879467000,"taskNameActive":"Reading prior results","taskNameComplete":"Read prior results","toolCallId":"toolu_01XaEchuAsBJz1Kr86Gg4VK8","agentTurnId":"turn_DtI5IBt9u6Xml2Yf","filePath":null,"id":"WZpwLj6NkhpyNRQw","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp/prev/research && python3 -c \"import numpy,pandas;print('np',numpy.__version__)\" 2>&1|tail -1; echo \"=== portfolio.txt\"; cat portfolio.txt; echo \"=== final_test.txt\"; cat final_test.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"q6PMOzgRutRhDqc0"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"ModuleNotFoundError: No module named 'numpy'\n=== portfolio.txt\n============================================================================================================================================\nSLEEVES, causal ATR-stop R-multiples, cost\n\n...\n\n17.920 7.178\n2.0 42 1.108 0.575 71.900 8.143\n\n-- H --\n n pf exp tpy negY\nH \n1 59 0.951 -0.194 139.885 10.254\n2 59 1.156 0.722 139.885 6.627\n3 59 1.106 0.590 139.885 7.017\n"},"id":"MmztQzRY87wJrmvV"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879470646,"finishedAt":1785879472234,"lastPartSentAt":1785879472213,"taskNameActive":"Executing Read","taskNameComplete":"Completed Read","toolCallId":"toolu_01Jt5QKtf4ZMoec7EUKfbDbV","agentTurnId":"turn_hhVx2CdUWjXoN43p","filePath":null,"id":"f8v1D6nBZ0e5aLBA","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/tool_content/reading-prior-results-task-nr-mo.txt"},{"type":"reading-file","filePath":"user_read_only_context/tool_content/reading-prior-results-task-nr-mo.txt","offset":77,"limit":111}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879478295,"finishedAt":1785879481460,"lastPartSentAt":1785879481460,"taskNameActive":null,"id":"luF382g3vW4gmVNO","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":3,"thought":"I'm picking up where the previous analysis left off—we have a three-sleeve portfolio that performs well overall but stumbled in 2016 with a -6.0R loss. The goal now is to either refine the strategy to eliminate that negative year or document what we find. I'm starting by restoring the research code into the project and setting up the necessary dependencies."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879483436,"finishedAt":1785879485866,"lastPartSentAt":1785879485713,"taskNameActive":"Setting up research env","taskNameComplete":"Set up research env","toolCallId":"toolu_01Qq9QExFRcSwSXiw4qW2U4H","agentTurnId":"turn_lAXed6j2v2lY2bXp","filePath":null,"id":"hf6fAaKPwgWWNE0j","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/*.csv research/data/ && ls research | head -40 && echo \"=== pip\"; (python3 -m pip install --quiet numpy pandas 2>&1 | tail -5); python3 -c \"import numpy,pandas;print('OK',numpy.__version__,pandas.__version__)\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"GyykNMFue1QydySC"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"cands.py\ncands2.py\ncands3.py\ndata\nengine.py\nexplore.py\nfamF.py\nfamilies.py\nfinal_test.py\ninspectF.py\nlab.py\nportfolio.py\nscan.py\nscan2.py\nscan3.py\nscan4.py\nstage10.py\nstage11.py\nstage7.py\nstage7b.py\nstage7c.py\nstage7d.py\nstage8.py\nstage9.py\nsweep.py\n"},"id":"GBQIpR3y2DUQHXHB"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"sweep2.py\nsweepF.py\ntest_breakeven.py\ntest_fills.py\ntest_holdout.py\ntest_leak.py\ntest_robust.py\nvalidate.py\n=== pip\n× Thi\n\n...\n\ndas;print('OK',numpy.__version__,pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"OMJh0PmsBaX2WJEe"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879489823,"finishedAt":1785879491651,"lastPartSentAt":1785879491509,"taskNameActive":"Installing numpy/pandas","taskNameComplete":"Installed numpy/pandas","toolCallId":"toolu_01QQoupiAPWP7gbEMurvXFYn","agentTurnId":"turn_DzqKDM2rOkJXDixF","filePath":null,"id":"oGArt2aaJ1gzdr11","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && (uv pip install --system numpy pandas 2>&1 | tail -3 || python3 -m pip install --break-system-packages --quiet numpy pandas 2>&1|tail -3); python3 -c \"import numpy,pandas;print('OK',numpy.__version__,pandas.__version__)\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"7ORc3hI9x2UfHdf4"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" + pytz==2026.3.post1\n + six==1.17.0\n + tzdata==2026.3\n\nTraceback (most recent call last):\n File \"\", line 1, in \n import numpy,pandas;print('OK',numpy.__version__,pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"bS7GCtcfjzYUnzAA"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879496098,"finishedAt":1785879496505,"lastPartSentAt":1785879496505,"taskNameActive":null,"id":"4HpudJyWKOHnYXIv","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to figure out where `uv` is installed, probably in a virtual environment. I could check with `uv pip list` or try `uv run --with numpy`, but the simplest approach is just to create a fresh venv."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879497098,"finishedAt":1785879520650,"lastPartSentAt":1785879520509,"taskNameActive":"Creating venv","taskNameComplete":"Created venv","toolCallId":"toolu_01QJYGaNvaPsuVHWhTLV6wkg","agentTurnId":"turn_MRE28zqXWr1nUFuk","filePath":null,"id":"fTCOGuSYcBndb5UG","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && uv venv .venv 2>&1|tail -2 && .venv/bin/python -m ensurepip -q 2>/dev/null; uv pip install --python .venv/bin/python numpy pandas 2>&1|tail -2; .venv/bin/python -c \"import numpy,pandas;print('OK',numpy.__version__,pandas.__version__)\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"2k9Q88iKQVp9zpOj"},{"type":"subtask-log","data":{"logType":"poll-terminal-output-error"},"id":"8GsndPVFOUgbax4V"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Creating virtual environment at: .venv\nActivate with: source .venv/bin/activate\n + python-dateutil==2.9.0.post0\n + six==1.17.0\nOK 2.5.1 3.0.5\n"},"id":"pNiBw9JGvSFA6rR0"}]}}],["p",{},["text",{},"Now let me read the existing engine and portfolio code to reuse the sleeve definitions."]],["AssistantMessageContentPart",{"part":{"type":"task-grouped-tasks-v1","id":"2fmPDrzYeGwZEo4z-N91IBOTdFljc1dOd","parts":[{"createdAt":1785879525001,"finishedAt":1785879526035,"lastPartSentAt":1785879525992,"taskNameActive":"Reading portfolio.py","taskNameComplete":"Read portfolio.py","toolCallId":"toolu_01Pk6By7iVFsahqULQZvQAXZ","agentTurnId":"turn_g0C4brznGkfQChdJ","filePath":null,"id":"2fmPDrzYeGwZEo4z","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/portfolio.py"}]},{"createdAt":1785879525424,"finishedAt":1785879525977,"lastPartSentAt":1785879525941,"taskNameActive":"Reading engine.py","taskNameComplete":"Read engine.py","toolCallId":"toolu_01KVpimnsLHeySEt3aRMQfCK","agentTurnId":"turn_g0C4brznGkfQChdJ","filePath":null,"id":"N91IBOTdFljc1dOd","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/engine.py"}]}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Explore • 2 Files","taskNameComplete":"Explore • 2 Files"},"id":"2fmPDrzYeGwZEo4z-N91IBOTdFljc1dOd"}],["AssistantMessageContentPart",{"part":{"createdAt":1785879531355,"finishedAt":1785879542763,"lastPartSentAt":1785879542763,"taskNameActive":null,"id":"8YMhnSKaULyGTFtT","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":11,"thought":"$af"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879542859,"finishedAt":1785879548376,"lastPartSentAt":1785879548206,"taskNameActive":"Reviewing tried families","taskNameComplete":"Reviewed tried families","toolCallId":"toolu_01MT53tvK4qZMxmaqbJpTk1e","agentTurnId":"turn_x0uA04Xi6X62xNkf","filePath":null,"id":"0IGSgFTipzSMHa9w","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && grep -h \"^def \\|^# ---\\|^\\\"\\\"\\\"\" families.py cands.py cands2.py cands3.py famF.py 2>/dev/null | head -80"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"UjU2KTK9QzTOvxoh"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\"\"\"Signal generators (hypotheses). Each returns a list of signal dicts for engine.simulate.\"\"\"\ndef prep(h1):\ndef exit_map(d, exit_h):\ndef hour_mask(hh, lo, hi):\ndef session_frame(d, lo, hi):\ndef session_stats(d, lo, hi, min_frac=0.7):\ndef assert_\n\n...\n\nily range exhaustion (H1)\ndef d6_rangeexh(h, hour=15, k=1.3, sl=1.0, hold=6, cost=COST):\n\"\"\"Family F -- Late-session displacement reversal (USDJPY H1).\n\"\"\"\ndef prep(d):\ndef signals(d, sig_hours=(18, 19), L=4, mag=1.25, hold=2, sl_k=1.5, tp_k=0.0,\n"},"id":"RlYCHTrBKl3XhNww"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879553879,"finishedAt":1785879575803,"lastPartSentAt":1785879575803,"taskNameActive":null,"id":"lamWV57IS5D2s4je","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":22,"thought":"$b0"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879576516,"finishedAt":1785879616743,"lastPartSentAt":1785879616569,"taskNameActive":"Verifying baseline","taskNameComplete":"Verified baseline","toolCallId":"toolu_018fJyPwbVt3bo5Z251X2Dzb","agentTurnId":"turn_TaNRFXQvkHzvNJlV","filePath":null,"id":"nLo6koBEoZW5s8qv","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 900 ../.venv/bin/python -c \"\nimport portfolio as P, pandas as pd\nh,d=P.frames()\nS1=P.s1_reversal(h);S2=P.s2_gapfade(d);S3=P.s3_dipbuy(d)\nport=pd.concat([S1,S2,S3]).sort_index()\nr,ys=P.summ(port,'PORT');print(r);print(ys.round(2).to_string())\nprint('h1 bars',len(h),h.index.min(),h.index.max(),'daily',len(d))\n\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"gn0wyFoREFIPf4ix"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"{'sleeve': 'PORT', 'n': 1986, 'tpy': 116.8, 'expR': 0.0834, 't': 5.28, 'pf': 1.402, 'wr': 59.3, 'totR': 165.6, 'negY': 1, 'nY': 17}\n2010 11.73\n2011 7.67\n2012 4.98\n2013 23.02\n2014 16.53\n2015 8.45\n2016 -5.99\n2017 9.23\n2018 "},"id":"i26jJBT1lffzdDq3"},{"type":"subtask-log","data":{"logType":"poll-terminal-output-error"},"id":"Vy8CcDZGuscFkG7s"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 9.89\n2019 17.09\n2020 20.27\n2021 7.69\n2022 12.75\n2023 3.74\n2024 3.68\n2025 11.11\n2026 3.75\nh1 bars 102673 2010-01-04 21:00:00 2026-06-23 23:00:00 daily 5139\n"},"id":"xpwpjr1rdWMZtSM5"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879622070,"finishedAt":1785879623607,"lastPartSentAt":1785879623607,"taskNameActive":null,"id":"yo9dJGIpdZMHPDrx","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"Now I'm setting up stage 12 with sleeve generators that return R-series values, and building a generic trade simulator function that takes signals with entry points at the next bar's open, using ATR-scaled stop losses and configurable slippage and take-profit levels.\n\nWriting sleeve generators..."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_LoRykke8FGx","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/lib12.py","baseName":"lib12.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/lib12.py\" \nGENERATING","latestDeploymentInfo":"$a9:0:1","lastDeployedAt":"2026-08-04T21:46:59.041Z","sourceVersionKey":"v_1_ah0yu_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_LoRykke8FGx","meta":{"taskNameActive":"Writing sleeve library","taskNameComplete":"Wrote sleeve library","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing sleeve library","taskNameComplete":"Wrote sleeve library"},"id":"block-b_LoRykke8FGx"}],["AssistantMessageContentPart",{"part":{"createdAt":1785879711906,"finishedAt":1785879712917,"lastPartSentAt":1785879712917,"taskNameActive":null,"id":"72WxvJX546MZEM1X","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I'm setting up stage12.py to frame the \"all years positive\" requirement statistically, then evaluating the sleeve library across a parameter grid using only the 2010-2017 in-sample window and selecting based on t-statistics. After building the portfolio, I'll test it out-of-sample from 2018-2026. I'm writing a driver script that runs this evaluation and produces a table comparing the in-sample and out-of-sample statistics."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_eKjmrrCe6zl","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage12.py","baseName":"stage12.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage12.py\" \nGENERATING","latestDeploymentInfo":"$a9:1:1","lastDeployedAt":"2026-08-04T21:46:59.041Z","sourceVersionKey":"v_1_cvd5u_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_eKjmrrCe6zl","meta":{"taskNameActive":"Writing sleeve scan","taskNameComplete":"Wrote sleeve scan","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing sleeve scan","taskNameComplete":"Wrote sleeve scan"},"id":"block-b_eKjmrrCe6zl"}],["AssistantMessageContentPart",{"part":{"createdAt":1785879767262,"finishedAt":1785879778481,"lastPartSentAt":1785879778270,"taskNameActive":"Running stage 12 scan","taskNameComplete":"Ran stage 12 scan","toolCallId":"toolu_01VgqzRoiPUy1kz97JEkyHWj","agentTurnId":"turn_9lckEHYi4ReQ57ZO","filePath":null,"id":"PZ9SYnktYUmLh7M8","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 3000 ../.venv/bin/python stage12.py > s12.txt 2>&1; tail -c 6000 s12.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"VaKkswYvgNa0GgCf"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"ore filters on the same trades.\n\n==================================================================================================================================\nPART 2 -- HYPOTHESIS LIBRARY, SCORED ON 2010-2017 ONLY (2018+ SEALED)\n==========\n\n...\n\n9 0.410 1.146 0.027 0.060\nriskoff 3 -1.793 -1.690 -0.247 -0.170\nspike 12 -1.033 -4.073 -0.089 -0.259\ntom 2 -0.140 0.120 -0.005 0.005\ntpull 9 -1.176 0.599 -0.037 0.018\nweek 3 0.960 -1.357 0.082 -0.100\n"},"id":"VfIpKwvi2RB6VnBH"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879786259,"finishedAt":1785879790676,"lastPartSentAt":1785879790471,"taskNameActive":"Reading part 1 output","taskNameComplete":"Read part 1 output","toolCallId":"toolu_014j9AQFk2tKUV33i8NiJzgQ","agentTurnId":"turn_mCeyIghSyJ19WOfm","filePath":null,"id":"cfhZzZaFbSWCk3Hf","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '1,40p' s12.txt; echo \".....\"; tail -25 s12.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"ptIplaClPMMDm05e"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"H1 102673 bars 2010-01-04 21:00:00..2026-06-23 23:00:00\nH4 26537 bars | D 5139 bars\n\nreplicating the stage-6 portfolio through the new library: {'sleeve': 'PORT', 'n': 1986, 'tpy': 116.8, 'expR': 0.0834, 't': 5.28, 'pf': 1.402, 'wr': 59.3, 'totR': 16"},"id":"nTa7hWSUrRYAcYPK"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"5.6, 'negY': 1, 'nY': 17}\n===============================================================================================\n\n...\n\n 2 -0.140 0.120 -0.005 0.005\ntpull 9 -1.176 0.599 -0.037 0.018\nweek 3 0.960 -1.357 0.082 -0.100\n"},"id":"EzutIvbK4DYWuLWr"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879796073,"finishedAt":1785879819792,"lastPartSentAt":1785879819792,"taskNameActive":null,"id":"1mVdK7c8NZxEXN5V","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":24,"thought":"$b1"}]}}],["p",{},["text",{},"Part 1 gave a decisive statistical result. Now let me hunt for higher-frequency independent trade flow, which is the only honest route to 17/17."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_GAgFTwnMiP1","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage13.py","baseName":"stage13.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage13.py\" \nGENERATING","latestDeploymentInfo":"$a9:2:1","lastDeployedAt":"2026-08-04T21:46:59.041Z","sourceVersionKey":"v_1_0tity_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_GAgFTwnMiP1","meta":{"taskNameActive":"Writing reversion scan","taskNameComplete":"Wrote reversion scan","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing reversion scan","taskNameComplete":"Wrote reversion scan"},"id":"block-b_GAgFTwnMiP1"}],["AssistantMessageContentPart",{"part":{"createdAt":1785879876158,"finishedAt":1785879951126,"lastPartSentAt":1785879950923,"taskNameActive":"Running reversion scan","taskNameComplete":"Ran reversion scan","toolCallId":"toolu_01V96cDEapcCg5tKzNLovWHJ","agentTurnId":"turn_KKMiYPn4W2OqI7SF","filePath":null,"id":"lJiCM1h9fMdHasgF","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 5000 ../.venv/bin/python stage13.py > s13.txt 2>&1; sed -n '/FAMILY SUMMARY/,$p' s13.txt | head -100"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"FM2kJLBnKcHN4jAv"},{"type":"subtask-log","data":{"logType":"poll-terminal-output-error"},"id":"kXjEgjg1odJFl9Bd"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"FAMILY SUMMARY (mean over the grid; IS-selected, OOS honest)\n n tpy_is t_is t_os exp_is exp_os\nfam \next.D 27 74.667 0.230 -0.481 0.013 -0.026\next.H1 27 1292.\n\n...\n\n 79.3 -0.0112 -0.26 0.977 5 z.D\n ext.D L6 k2.0 h3 1157 67.8 0.0414 1.19 1.138 68.3 -0.0075 -0.24 0.977 3 ext.D\n pos.D N10 t0.05 h4 s1 448 27.9 0.0674 1.14 1.203 25.0 0.1353 2.14 1.417 4 pos.D\n"},"id":"N4hiTPB9dy0jYPHn"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785879956540,"finishedAt":1785879967715,"lastPartSentAt":1785879967715,"taskNameActive":null,"id":"v2eCNcRw1AWUSMhc","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":11,"thought":"$b2"}]}}],["p",{},["text",{},"Key finding: high-frequency reversion is killed by costs. Let me now test whether combining the surviving edges can honestly lift the annual Sharpe."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_W7028vEUkZg","parts":[{"type":"code-project","isVersioned":true,"isSourceless":false,"changedFiles":[{"fileName":"/research/lib12.py","baseName":"lib12.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/stage12.py","baseName":"stage12.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/stage13.py","baseName":"stage13.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/stage14.py","baseName":"stage14.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage14.py\" \nGENERATING","latestDeploymentInfo":"$a9:3:1","lastDeployedAt":"2026-08-04T21:46:59.041Z","sourceVersionKey":"v_1_yj5l6_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_W7028vEUkZg","meta":{"taskNameActive":"Writing portfolio stage","taskNameComplete":"Wrote portfolio stage","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing portfolio stage","taskNameComplete":"Wrote portfolio stage"},"id":"block-b_W7028vEUkZg"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880005258,"finishedAt":1785880005258,"lastPartSentAt":1785880005258,"id":"bLh2MsZGGTE9vC62","type":"task-stopped-v1","parts":[{"type":"agent-timeout","willContinueAfterTimeout":true}]}}]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"bh7iRRufqu2zwbtEFa4V44Xg26DkzrEd","gitRevision":{"headSha":"2bd3d8d51383f69c11d978c61e93b5b6284f194f","readyAt":"2026-08-04T21:46:59.010Z","version":1},"streaming":false,"finishReason":"tool-calls","parentId":"fYOLqE1PPRSvxAjdWtYbBU6lpk7W5zVp","type":"message","creditCost":2.0903625,"workMetrics":{"diffMap":{},"linesRead":541,"agentActions":24},"createdAt":"$D2026-08-04T21:36:14.479Z","updatedAt":"$D2026-08-04T21:46:59.029Z"},{"content":{"type":"parts","parts":[{"type":"timeout-continue"}],"version":1},"role":"user","id":"P1Arc1SVZHg4c65Z001c7AjmCNuWff3U","attachments":[],"parentId":"bh7iRRufqu2zwbtEFa4V44Xg26DkzrEd","type":"message","authorId":"PBXrzlEeGT24UT23gl1XMd0A","createdAt":"$D2026-08-04T21:46:56.192Z"},{"v0ModelId":"v0-opus-5","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785880020094,"finishedAt":1785880020157,"lastPartSentAt":1785880020157,"taskNameActive":null,"id":"Vu4pdw6RZp15QEeK","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to run stage14.py next."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880020458,"finishedAt":1785880030597,"lastPartSentAt":1785880030441,"taskNameActive":"Running portfolio stage","taskNameComplete":"Ran portfolio stage","toolCallId":"toolu_01CJJ18L5uGi9XFkrvTdoWiY","agentTurnId":"turn_zQbOjHVk6loXQNi0","filePath":null,"id":"3eawNB0mXlIG0SZo","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 5000 ../.venv/bin/python stage14.py > s14.txt 2>&1; cat s14.txt | head -160"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"RDC6lJMtLr0zay3y"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"======================================================================================================================================================\nSLEEVE CARD (IS 2010-2017 selection window | OOS 2018-2026 sealed)\n===========================\n\n...\n\nnSharpe=1.47\n bootstrap negative-year count: mean=1.38 p50=1 p95=3 P(zero negative years)=23.41%\n all-5: annual mean=24.45R sd=31.03R annSharpe=0.79\n bootstrap negative-year count: mean=1.91 p50=2 p95=4 P(zero negative years)=13.16%\n"},"id":"02Wai66LafhGkGdF"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_vmEciRosQ8D","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/stage13.cpython-313.pyc","baseName":"stage13.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s14.txt","baseName":"s14.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s14_subsets.csv","baseName":"s14_subsets.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/stage13.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s14.txt\" \nGENERATING\n[V0_FILE]csv:file=\"research/s14_subsets.csv\" \nGENERATING","latestDeploymentInfo":"$a9:4:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_j9nrw_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_vmEciRosQ8D","meta":{"taskNameActive":"Applying changes to 3 files","taskNameComplete":"Applied changes to 3 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 3 files","taskNameComplete":"Applied changes to 3 files"},"id":"block-b_vmEciRosQ8D"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880034891,"finishedAt":1785880036194,"lastPartSentAt":1785880036194,"taskNameActive":null,"id":"qakF3GG8S6RxNMov","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"The S1+S2+S3+S5 combination hits all the targets: 172.4 throughput, 1.329 profit factor (just barely clearing the 1.30 threshold), 57.8% win rate, and crucially, zero losing years across both in-sample and out-of-sample periods with a 1.79 Sharpe ratio. This satisfies every mandatory constraint."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880036481,"finishedAt":1785880037094,"lastPartSentAt":1785880037073,"taskNameActive":"Reading full stage14","taskNameComplete":"Read full stage14","toolCallId":"toolu_01SjFDLmtika7oToYemNSKAT","agentTurnId":"turn_5VibgjHTUbitgtSR","filePath":null,"id":"BU5vVfySyRgtW3jW","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/tool_content/running-portfolio-stage-task-g-e7u.txt"},{"type":"reading-file","filePath":"user_read_only_context/tool_content/running-portfolio-stage-task-g-e7u.txt","offset":41,"limit":54}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880041652,"finishedAt":1785880045444,"lastPartSentAt":1785880045444,"taskNameActive":null,"id":"tpcJUoev6bLlP8M5","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":4,"thought":"Looking at this candidate strategy combining four sleeves, the performance metrics are solid across the board—consistent profitability over all seventeen years, a respectable Sharpe ratio, and notably the out-of-sample results outperforming in-sample, which is a good sign. The main hesitation is that the profit factor of 1.329 is cutting it pretty close to the 1.30 threshold, and I'm noticing that S4 got filtered out, likely due to some issue with its drawdown characteristics.\n\nNow I'm setting up the comprehensive validation suite to stress-test this combination. I need to run walk-forward analysis, Monte Carlo simulations with different bootstrap approaches, parameter sensitivity grids, execution cost scenarios, regime analysis, and a detailed year-by-year breakdown before generating the Pine Script implementation. First, I should check the exact sleeve definitions in the library to make sure I'm working with the right parameters."}]}}],["p",{},["text",{},"Candidate found: "],["strong",{},["text",{},"S1+S2+S3+S5"]],["text",{}," — 172 trades/yr, PF 1.329, 17/17 profitable years, OOS better than IS. Now the full validation battery."]],["AssistantMessageContentPart",{"part":{"createdAt":1785880045636,"finishedAt":1785880046145,"lastPartSentAt":1785880046114,"taskNameActive":"Reading sleeve library","taskNameComplete":"Read sleeve library","toolCallId":"toolu_01XivFWqwcHnGhgA9qzzXLEr","agentTurnId":"turn_apT4x9aas3ZrHf2y","filePath":null,"id":"2oeIbLl0onFFgHnt","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/lib12.py"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880049271,"finishedAt":1785880049716,"lastPartSentAt":1785880049683,"taskNameActive":"Reading stage14 defs","taskNameComplete":"Read stage14 defs","toolCallId":"toolu_01RapeMtCYqsKyvXR1sdWG23","agentTurnId":"turn_MvmBNHhqZtDJhXbe","filePath":null,"id":"8a2QK5Lq8C9S6U5u","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/stage14.py"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880054604,"finishedAt":1785880068515,"lastPartSentAt":1785880068515,"taskNameActive":null,"id":"o3jvP00gdZEpS9SU","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":14,"thought":"$b3"}]}}],["p",{},["text",{},"Now the final validation battery on the S1+S2+S3+S5 portfolio."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_9pd5P0uLh8u","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage15.py","baseName":"stage15.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage15.py\" \nGENERATING","latestDeploymentInfo":"$a9:5:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_badbb_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_9pd5P0uLh8u","meta":{"taskNameActive":"Writing final validation","taskNameComplete":"Wrote final validation","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing final validation","taskNameComplete":"Wrote final validation"},"id":"block-b_9pd5P0uLh8u"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880160548,"finishedAt":1785880172172,"lastPartSentAt":1785880171864,"taskNameActive":"Running final validation","taskNameComplete":"Ran final validation","toolCallId":"toolu_01PGsD1Vdhva1riJ5ufc5DhV","agentTurnId":"turn_zIUqCUGSl10oQNfl","filePath":null,"id":"g2oOqMHzEGBkp8P7","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 5400 ../.venv/bin/python stage15.py > s15.txt 2>&1; echo \"exit=$?\"; sed -n '1,60p' s15.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"q6GNVejUOawsntIK"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"==================================================================================================================================\n1. MANDATORY REQUIREMENT AUDIT portfolio = S1+S2+S3+S5, cost 1.5 pip RT + 0.5 slip on stops\n========================="},"id":"lwTeJnDJQ8243ti4"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"================================================================================================================\n PASS \n\n...\n\n8 193 20.68 1.51 [(1.25, 3), (0.2,), (0.15, 4), (7,)]\n 2020 2010-2019 175 24.77 1.63 [(1.25, 3), (0.2,), (0.15, 5), (7,)]\n"},"id":"bjkXh7q0pG9tgNf6"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_aNy90uHGfhR","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/s15.txt","baseName":"s15.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s15_paramgrid.csv","baseName":"s15_paramgrid.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s15_trades.csv","baseName":"s15_trades.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/s15.txt\" \nGENERATING\n[V0_FILE]csv:file=\"research/s15_paramgrid.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/s15_trades.csv\" \nGENERATING","latestDeploymentInfo":"$a9:6:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_je378_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_aNy90uHGfhR","meta":{"taskNameActive":"Applying changes to 3 files","taskNameComplete":"Applied changes to 3 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 3 files","taskNameComplete":"Applied changes to 3 files"},"id":"block-b_aNy90uHGfhR"}],["p",{},["text",{},"All 8 mandatory requirements pass. Let me read the rest."]],["AssistantMessageContentPart",{"part":{"createdAt":1785880177302,"finishedAt":1785880178329,"lastPartSentAt":1785880178143,"taskNameActive":"Reading validation sections","taskNameComplete":"Read validation sections","toolCallId":"toolu_01BiacvqvUizxMA1AFcCjFFU","agentTurnId":"turn_sYRFGdOilUGJEShk","filePath":null,"id":"DbhfW8D1qdoKLDNC","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '52,140p' s15.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"OyUhspA9WYWLkQh3"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" --- anchored walk-forward ---\n year train n R pf params\n 2014 2010-2013 153 14.64 1.49 [(1.5, 2), (0.2,), (0.15, 5), (7,)]\n 2015 2010-2014 183 16.18 1.46 [(1.25, 3), (0.2,), (0.15, 5), (7,)]\n 2016 201\n\n...\n\n expR pf wr annR annSh negY worst totR\n 0.0 2930 0.1014 1.483 60.5 17.48 2.11 0 5.60 297.2\n 0.5 2930 0.0919 1.430 59.9 15.84 2.02 0 4.82 269.2\n 1.0 2930 0.0823 1.379 58.8 14.19 1.91 0 3.58 241.3\n"},"id":"gBV5pX4QKS9jnOAL"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880183716,"finishedAt":1785880185705,"lastPartSentAt":1785880185705,"taskNameActive":null,"id":"YHcIQyJP2aoHCEIX","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"The chosen parameters hit the 98th percentile, which is a real concern—only 23% of the grid achieves a PF of 1.30 or higher, so I'm working with one of the luckier corners rather than a robust baseline. The grid average sits around 1.252, so that's the honest expectation, not the 1.30 I'm targeting. I need to be transparent about this cherry-pick bias. Now let me see what happens when I apply walk-forward testing with parameter re-selection on each fold."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880185918,"finishedAt":1785880187226,"lastPartSentAt":1785880186937,"taskNameActive":"Reading remaining sections","taskNameComplete":"Read remaining sections","toolCallId":"toolu_015SgNAsfDxw9d2n9FBcP9hs","agentTurnId":"turn_PXa9tKPv3kBwMqWS","filePath":null,"id":"DCufUFi8BYadu2J5","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '114,205p' s15.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"yZ84GHgnH4diYfk9"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" s1 (1.25, 4) tpy= 172.4 PF=1.258 exp=+0.0612 negY=1 annSh=1.32\n s1 (1.5, 2) tpy= 146.8 PF=1.310 exp=+0.0736 negY=0 annSh=1.71\n s1 (1.5, 3) tpy= 146.8 PF=1.324 exp=+0.0785 negY=0 annSh=1.81\n s1 (1.5, 4) tpy= 146.8 P\n\n...\n\n=======================================\n10. DRAWDOWN / EQUITY SHAPE\n==================================================================================================================================\n max DD 22.6R = 1.80 years of average return\n"},"id":"jHX1wjord7XiazC7"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880192124,"finishedAt":1785880194401,"lastPartSentAt":1785880194209,"taskNameActive":"Reading MC and regimes","taskNameComplete":"Read MC and regimes","toolCallId":"toolu_01UMeVoMe7DRErd9d1q3qXa1","agentTurnId":"turn_7y0e8oxouHnWsyUl","filePath":null,"id":"V91Tnt1uwXztb6Nu","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '168,210p' s15.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"YxpqUFJPj9PvNnoj"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" total R p5=150 p50=213 p95=278 P(R>0)=100.00%\n max DD p50=14.4R p95=22.8R p99=28.0R realised=22.6R\n\n (b) monthly BLOCK bootstrap (198 month-blocks, preserves clustering)\n PF p5=1.193 p50=1.331 p95=1.487 P(PF\n\n...\n\n==============\n max DD 22.6R = 1.80 years of average return\n longest time under water: 211 trades\n monthly: 198 months, 64.1% positive, worst -9.9R, best 10.2R\n per-sleeve share of total R: S1 h1rev 31%, S2 gap 6%, S3 dipD 49%, S5 nr 14%\n"},"id":"t7R8XXvuW6sQMIEe"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880201187,"finishedAt":1785880215725,"lastPartSentAt":1785880215725,"taskNameActive":null,"id":"EBiHKmoXnf2f60qL","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":15,"thought":"$b4"}]}}],["p",{},["text",{},"Validation is complete and passes on every axis. Let me pin down the data timezone so the Pine Script's session hours are correct."]],["AssistantMessageContentPart",{"part":{"createdAt":1785880217167,"finishedAt":1785880220096,"lastPartSentAt":1785880219908,"taskNameActive":"Checking data timezone","taskNameComplete":"Checked data timezone","toolCallId":"toolu_01LeeDF6jBzuLNFPgfhXkbz8","agentTurnId":"turn_KjSxTxgggy360zm1","filePath":null,"id":"vqD70ZS4Ie6ypMf9","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && ../.venv/bin/python -c \"\nimport lib12 as L, pandas as pd, numpy as np\nh=L.load('h1')\nprint('range',h.index.min(),h.index.max())\ng=h.groupby([h.dow,h.hour]).size().unstack(fill_value=0)\nprint(g.to_string())\n\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"LrKKl3A9yrgygjtX"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"range 2010-01-03 22:00:00 2026-06-23 23:00:00\nhour 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\ndow \n\n...\n\n 856 856 856 856 856 856 857 857 855 855 855 855 855 855 855 855 855 855 854 855 855 290 0 0\n6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 563 855 858\n"},"id":"mTLx23R0rgOn4cr5"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880224733,"finishedAt":1785880291424,"lastPartSentAt":1785880291424,"taskNameActive":null,"id":"Qm38ZwMas8aVtblF","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":67,"thought":"$b5"}]}}],["p",{},["text",{},"One structural issue to settle honestly: the research aggregates daily bars by UTC calendar date, which creates a 3-hour \"Sunday micro-bar\" each week. Let me verify the edge survives standard FX daily convention."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_EuNakpB8EIK","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage16.py","baseName":"stage16.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage16.py\" \nGENERATING","latestDeploymentInfo":"$a9:7:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_kcmiv_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_EuNakpB8EIK","meta":{"taskNameActive":"Writing daily convention test","taskNameComplete":"Wrote daily convention test","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing daily convention test","taskNameComplete":"Wrote daily convention test"},"id":"block-b_EuNakpB8EIK"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880340674,"finishedAt":1785880346806,"lastPartSentAt":1785880346422,"taskNameActive":"Testing daily conventions","taskNameComplete":"Tested daily conventions","toolCallId":"toolu_01XVFaycPywb5iMd14t6g7D9","agentTurnId":"turn_f1ZIrfc0jFGpcaCw","filePath":null,"id":"vTKxaMSZ55cQMBgq","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 3000 ../.venv/bin/python stage16.py > s16.txt 2>&1; echo \"exit=$?\"; sed -n '1,75p' s16.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"alyyVRoYgv8USgVF"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"exit=0\n====================================================================================================================================\n1. DAILY-BAR CONVENTION ROBUSTNESS\n=======================================================================\n\n...\n\n\n PASS Trades/yr >= 50 = 172.5\n PASS Positive expectancy = 0.0432\n PASS Large sample n>=1000 = 2932\n PASS t-stat >= 3 = 3.36\n FAIL Profitable EVERY year = 14/17\n\n cost sweep on the tradable spec:\n"},"id":"RZjP77iW3CsDqqON"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_0qjA8UX2ABF","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/s16.txt","baseName":"s16.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s16_trades_fxwk.csv","baseName":"s16_trades_fxwk.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/s16.txt\" \nGENERATING\n[V0_FILE]csv:file=\"research/s16_trades_fxwk.csv\" \nGENERATING","latestDeploymentInfo":"$a9:8:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_8o0h5_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_0qjA8UX2ABF","meta":{"taskNameActive":"Applying changes to 2 files","taskNameComplete":"Applied changes to 2 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 2 files","taskNameComplete":"Applied changes to 2 files"},"id":"block-b_0qjA8UX2ABF"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880352558,"finishedAt":1785880367427,"lastPartSentAt":1785880367427,"taskNameActive":null,"id":"EkLo5P06ZbjzdIlZ","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":15,"thought":"$b6"}]}}],["p",{},["text",{},"Critical finding — the 17/17 result was an artifact. Let me see the rest, then act on it."]],["AssistantMessageContentPart",{"part":{"createdAt":1785880367473,"finishedAt":1785880370504,"lastPartSentAt":1785880370100,"taskNameActive":"Reading convention test tail","taskNameComplete":"Read convention test tail","toolCallId":"toolu_011TWrVm2JuFosx6MfijLfz9","agentTurnId":"turn_ChkPobOlEpKn2q3g","filePath":null,"id":"1coNm7MLYmm7QvAU","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '68,79p' s16.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"XmfZW54dOiVNZ9Ke"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" PASS Win rate >= 40% = 55.9\n PASS Trades/yr >= 50 = 172.5\n PASS Positive expectancy = 0.0432\n PASS Large sample n>=1000 = 2932\n PASS t-stat >= 3 = 3.36\n FAIL Profitable EVERY year = 14\n\n...\n\nst=-14.42\n cost 1.0p: expR=+0.0524 pf=1.232 wr=57.0 annR= 9.03 negY=2 worst=-17.02\n cost 1.5p: expR=+0.0432 pf=1.188 wr=55.9 annR= 7.45 negY=3 worst=-18.32\n cost 2.0p: expR=+0.0340 pf=1.146 wr=54.4 annR= 5.87 negY=3 worst=-19.62\n"},"id":"gGFJggKOwoQ6wCoZ"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880377732,"finishedAt":1785880386358,"lastPartSentAt":1785880386358,"taskNameActive":null,"id":"71jonK0UH4oYfNDt","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":9,"thought":"The contaminated stage 12-15 results need to be discarded, so I'm moving to stage 17 with clean data—native H1 and H4 plus the daily built with the correct convention. I'm planning a comprehensive scan with strict in-sample and out-of-sample validation, using S1 as the foundation since it's convention-immune and has solid performance metrics.\n\nMy approach is to test S1's robustness across all 24 hours to see if hours 18 and 19 are genuinely special or part of a broader late-day pattern, then scan the lookback and k parameters for sensitivity. After that I'll run clean daily families with parameter grids, then H4 families, and explore new H1 ideas. I'm writing a Python loop structure that tests each candidate subset efficiently, with a helper to report in-sample, out-of-sample, and combined results compactly, filtering for survivors that show t-stats above 1.5 in both regimes with consistent direction."}]}}],["p",{},["text",{},"The stage-12–15 portfolio was contaminated by a data artifact. Discarding it and re-searching on convention-safe data only."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_rN7JnQhXmw9","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage17.py","baseName":"stage17.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage17.py\" \nGENERATING","latestDeploymentInfo":"$a9:9:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_fatsh_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_rN7JnQhXmw9","meta":{"taskNameActive":"Writing clean re-search","taskNameComplete":"Wrote clean re-search","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing clean re-search","taskNameComplete":"Wrote clean re-search"},"id":"block-b_rN7JnQhXmw9"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880433884,"finishedAt":1785880460263,"lastPartSentAt":1785880459273,"taskNameActive":"Running clean re-search","taskNameComplete":"Ran clean re-search","toolCallId":"toolu_01PRHRBnMPSQDD4vWJ6nc4Fp","agentTurnId":"turn_ijRaIdZHq6flRoyI","filePath":null,"id":"BIM8OQRBlw0djJ0T","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 5400 ../.venv/bin/python stage17.py > s17.txt 2>&1; echo \"exit=$?\"; grep -n \"survivors\" s17.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"WykzqLnlxKL4ho6t"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"exit=0\n34: survivors (exp>0 and t>1.3 in BOTH halves): 2/24\n51: survivors (exp>0 and t>1.3 in BOTH halves): 3/10\n77: survivors (exp>0 and t>1.3 in BOTH halves): 0/80\n97: survivors (exp>0 and t>1.3 in BOTH halves): 12/48\n126: survivors (\n\n...\n\n3 in BOTH halves): 1/36\n261: survivors (exp>0 and t>1.3 in BOTH halves): 0/12\n273: survivors (exp>0 and t>1.3 in BOTH halves): 0/27\n285: survivors (exp>0 and t>1.3 in BOTH halves): 0/18\n298: survivors (exp>0 and t>1.3 in BOTH halves): 0/9\n"},"id":"G7lu4K7uRE1FBVVS"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_5AwtaISyKiA","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/s17.txt","baseName":"s17.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/s17.txt\" \nGENERATING","latestDeploymentInfo":"$a9:10:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_epu07_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_5AwtaISyKiA","meta":{"taskNameActive":"Applying changes","taskNameComplete":"Applied changes","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes","taskNameComplete":"Applied changes"},"id":"block-b_5AwtaISyKiA"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880467887,"finishedAt":1785880479514,"lastPartSentAt":1785880479514,"taskNameActive":null,"id":"9fCdTw17drkbwfZR","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":12,"thought":"Looking at the backtesting results, most strategies are washing out completely, but a few are showing real promise. The parameter grid approach with a wide hour window is performing strongest at 12 wins out of 48 attempts, while the single-hour scan and hour-cluster variants are picking up a couple wins each. The daily fade strategy and H4 momentum are barely hanging on with just one win apiece, and the daily dip buy setup is a complete bust with zero wins across 80 attempts. Finishing the grid counts through D4, then the key insight: A3's wide hour window has zero survivors out of 80, meaning the H1 reversal edge doesn't hold across that parameter range, and A1 only kept 2 out of 24 hours—suggesting the original hours 18 and 19 were a lucky find rather than robust. B1's daily dip buy long strategy is standing out as the most resilient with 12 survivors from 48 combinations on clean data."}]}}],["p",{},["text",{},"Very different — and much more honest — picture. Let me read the details."]],["AssistantMessageContentPart",{"part":{"createdAt":1785880480164,"finishedAt":1785880484922,"lastPartSentAt":1785880484462,"taskNameActive":"Reading part A results","taskNameComplete":"Read part A results","toolCallId":"toolu_01468UtcdGkijBF7jexs2mwP","agentTurnId":"turn_3nKN8CkkpsGmInk8","filePath":null,"id":"oPxCaLjZG1PxUAGL","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '1,80p' s17.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"qzcVpbc9HhLz5UJh"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"data: H1 102673 bars | H4 26537 | clean daily 4282 (2010-01-21 -> 2026-06-23)\n\n============================================================================================================================================\nA. ANATOMY OF THE CONVENTION-I"},"id":"Xg8lTKR1yojkrRzt"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"MMUNE SURVIVOR: H1 late-day extension reversal\n==========================================================================\n\n...\n\n===============================================================\nB. 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The second one particularly stands out because it survives robustly across different threshold and holding period variations.\n\nCombined, these two strategies would generate about 121 trades annually, which exceeds the 50-trade minimum. The challenge now is that each strategy individually has 3-4 negative years, but since they showed negative correlation earlier (-0.35), combining them might help smooth out those drawdowns and potentially achieve the zero negative years requirement while maintaining the 1.30 profit factor threshold.\n\nI need to quickly examine a couple of other candidate strategies—a Donchian breakout survivor and an H4 momentum survivor—before moving into the portfolio construction phase."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880529527,"finishedAt":1785880532061,"lastPartSentAt":1785880531612,"taskNameActive":"Reading other survivors","taskNameComplete":"Read other survivors","toolCallId":"toolu_01RjzbpgJu5HBhrCS1cs5SqP","agentTurnId":"turn_0cVfPy4rzTrX5lbb","filePath":null,"id":"zQGXxyQdZLgZZ1C1","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && echo \"=== B5 donch surv\"; sed -n '155,170p' s17.txt; echo \"=== C2 h4mom surv\"; sed -n '229,246p' s17.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"FL9tMIAolAJMteKs"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=== B5 donch surv\n B5. Donchian breakout\n name n tpy e_is t_is e_os t_os e_all t_all pf wr negY nY totR\ndonch N40 h20 sd1 272 16.0 0.8936 5.28 0.0037 0.03 0.4029 3.81 1.791 46.7 11 17 109.6\ndonch N60 h20\n\n...\n\n9\n survivors (exp>0 and t>1.3 in BOTH halves): 1/36\n name n tpy e_is t_is e_os t_os e_all t_all pf wr negY nY totR\nh4mom N3 k1.5 h12 4566 268.6 0.0435 1.72 0.0639 2.58 0.0542 3.06 1.123 45.6 5 17 247.6\n\n"},"id":"WMulIwDC4x4HRmP5"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785880538562,"finishedAt":1785880557563,"lastPartSentAt":1785880557563,"taskNameActive":null,"id":"7xOOAaxbBr58Z7h3","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":19,"thought":"$b8"}]}}],["p",{},["text",{},"Two robust, convention-immune families confirmed. Donchian and H4-momentum rejected (IS/OOS decay and PF 1.12 respectively). Now a targeted hunt for a third sleeve plus portfolio construction."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_cu12I31xTRa","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage18.py","baseName":"stage18.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/stage18.py\" \nGENERATING","latestDeploymentInfo":"$a9:11:1","lastDeployedAt":"2026-08-04T21:57:21.918Z","sourceVersionKey":"v_1_ibi2t_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_cu12I31xTRa","meta":{"taskNameActive":"Writing stage 18","taskNameComplete":"Wrote stage 18","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing stage 18","taskNameComplete":"Wrote stage 18"},"id":"block-b_cu12I31xTRa"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880617243,"finishedAt":1785880633892,"lastPartSentAt":1785880633501,"taskNameActive":"Running stage 18","taskNameComplete":"Ran stage 18","toolCallId":"toolu_01NGKBY52hixCDzdXQ4ZfUFA","agentTurnId":"turn_neMdU0tdalIsgW0y","filePath":null,"id":"CXiA5KXHNc3kZiOU","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 5400 ../.venv/bin/python stage18.py > s18.txt 2>&1; echo \"exit=$?\"; sed -n '1,45p' s18.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"aYCQ1oRPfw4LKrga"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"exit=0\n"},"id":"IpeaOg6GaDwQtqP4"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"============================================================================================================================================\n1. SLEEVE A RE-GRID on its own hour window (18,19) -- parameter sensitivity\n==========================\n\n...\n\n.39 -0.0591 -2.77 0.899 39.8 12 -253.3\ncarryL (0, 1, 2) h12 12849 755.8 -0.0858 -4.65 0.0057 0.32 -0.0387 -3.02 0.935 39.2 10 -497.1\n carryL (2,) h8 4283 251.9 -0.0860 -3.53 -0.0226 -0.96 -0.0534 -3.14 0.886 44.8 13 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stage 18 results","taskNameComplete":"Read stage 18 results","toolCallId":"toolu_019up9ZgsCp4NMEXReHzonhT","agentTurnId":"turn_VxObl87PgsjrZCdN","filePath":null,"id":"5EGzYKTipZdqZptT","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && wc -l s18.txt && sed -n '45,120p' s18.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"HJvk86ZP3gBiiSlT"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"146 s18.txt\n carryL (2,) h8 4283 251.9 -0.0860 -3.53 -0.0226 -0.96 -0.0534 -3.14 0.886 44.8 13 -228.6\n carryL (23,) h12 4284 252.0 -0.0847 -2.91 -0.0463 -1.60 -0.0649 -3.17 0.887 40.7 12 -278.1\n carryL (1,) h8 4283 251.9 -0.12"},"id":"T2ot11NOZRvHtCKe"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"50 -5.12 -0.0031 -0.13 -0.0622 -3.66 0.869 44.2 12 -266.5\n carryL (22,) h12 4282 251.9 -0.1166 -4.12 -0.0520 -1.8\n\n...\n\n7 -83.75 153.9 188.6\n C 217.9 0.988 4 1.138 3 1.057 42.0 0.0309 1.40 0.17 7 -83.71 157.0 114.6\n"},"id":"xJ65qYaSxGqi9bnm"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-0","parts":[{"type":"thinking-end","duration":4.6,"thought":"Key findings from stage 18:\n\n- 2a carryL: dead (PF < 0.9)\n- 2b asiabrk: dead (0/27)\n- 2c dipH4: 3/36 survivors, PF ~1.15-1.21, but e_is is weak (t_is 2.04 for the best) — it's basically the same economics as sleeve B at finer resolution. High trade count (135 tpy). This is a candidate third sleeve but IS/OOS asymmetry (0.096 IS vs 0.165 OOS) is fine actually — OOS better. But PF 1.20 alone < 1.30. Could contribute to portfolio.\n- 2d: sleeve A conditioned on vol — all 6 survive, robust across all vol regimes. Good: sleeve A is not a vol-regime artifact. Low param sensitivity.\n- 2e: sleeve B by day-of-week — B works across all dow subsets roughly; dow(4) strongest. Not a pure calendar artifact but noisy per-day.\n\nLet me read the rest (lines 53-80) to see remaining sections."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-0"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880652608,"finishedAt":1785880653302,"lastPartSentAt":1785880653263,"taskNameActive":"Reading stage 18 tail","taskNameComplete":"Read stage 18 tail","toolCallId":"toolu_01GWFxph4V7cVSYuNY1BaaM6","agentTurnId":"turn_7K2iMRYqI9uzaUfu","filePath":null,"id":"DlvafsUnc0ZyohR8","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/tool_content/reading-stage-18-results-task-cy-r8.txt"},{"type":"reading-file","filePath":"user_read_only_context/tool_content/reading-stage-18-results-task-cy-r8.txt","offset":52,"limit":28}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-1","parts":[{"type":"thinking-end","duration":18.58235294117647,"thought":"$b9"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-1"}],["p",{},["text",{},"Sleeve C (H4 dip) is rejected — properly R-normalized it has a 157R drawdown. The honest core is "],["strong",{},["text",{},"A+B: PF 1.397, 121 trades/yr, OOS > IS, but 2 negative years out of 17"]],["text",{},". Let me read the clean sleeve definitions before continuing the search."]],["AssistantMessageContentPart",{"part":{"createdAt":1785880676680,"finishedAt":1785880677288,"lastPartSentAt":1785880677255,"taskNameActive":"Reading stage17 definitions","taskNameComplete":"Read stage17 definitions","toolCallId":"toolu_01KK7gnpsQvmjVYFctB2xweP","agentTurnId":"turn_0ZZvqZu4BZwwuLzK","filePath":null,"id":"f141HNEcGBTZnO3m","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/stage17.py"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-2","parts":[{"type":"thinking-end","duration":0.9235294117647059,"thought":"Now I need the exact sleeve A and B definitions used in stage 18's portfolio. Let me check stage18.py's portfolio section quickly. Actually let me just grep."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-2"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880682991,"finishedAt":1785880684045,"lastPartSentAt":1785880683885,"taskNameActive":"Checking sleeve definitions","taskNameComplete":"Checked sleeve definitions","toolCallId":"toolu_01284ejGd9Ez5eng1MwSTsrs","agentTurnId":"turn_V6D1j1vqRcg5vvoP","filePath":null,"id":"YXYaUVwGAp08FX6g","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '/PORTFOLIO/,$p' stage18.py | head -50; echo \"=== lib12 signatures\"; grep -n \"^def \" lib12.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Totx2hP42RjBKnKY"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" print(\"3. PORTFOLIO -- clean data only\")\n print(\"=\" * 140)\n SA = L.mr_h1rev(h, hours=(18, 19), L=3, k=1.25, hold=3, slk=2.5)\n SB = L.mr_dipbuy(d, N=20, thr=0.15, hold=5, slk=1.5, side=1)\n SC = dip_h4(h4, N=30, thr=0.15, hold=12, slk=1"},"id":"okGdnK704XND9YSH"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":".5)\n SL = {\"A h1rev1819\": SA, \"B dipD20\": SB, \"C dipH4\": SC}\n for nm, s in SL.items():\n print(f\" {nm:14s} \n\n...\n\nh, dow=0, entry_h=0, hold=8, slk=1.5, cost=COST, side=1):\n317:def summ(s, name=\"s\", y0=None, y1=None):\n336:def yearly(s):\n"},"id":"7czWTPUaRk5B19XF"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-3","parts":[{"type":"thinking-end","duration":13.588235294117647,"thought":"$ba"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-3"}],["AssistantMessageContentPart",{"part":{"createdAt":1785880702111,"finishedAt":1785880702798,"lastPartSentAt":1785880702772,"taskNameActive":"Reading trade engine","taskNameComplete":"Read trade engine","toolCallId":"toolu_01CgBnsT5LXqAKFuTjgXupBH","agentTurnId":"turn_zMVcoh5XRYmIIBiR","filePath":null,"id":"m6kIspHJP23yav5D","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/lib12.py"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/lib12.py","offset":0,"limit":160}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-4","parts":[{"type":"thinking-end","duration":0.7764705882352941,"thought":"Good, I have the engine. Now write stage19 with genuinely new families + alternative execution logic + portfolio. Keep it efficient."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-4"}],["p",{},["text",{},"Now stage 19: genuinely new edge families plus alternative execution logic, all on convention-safe data."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_3QIq4bkP0No","parts":[{"type":"code-project","isVersioned":true,"isSourceless":false,"changedFiles":[{"fileName":"/research/stage19.py","baseName":"stage19.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"$bb","latestDeploymentInfo":"$a9:13:1","lastDeployedAt":"2026-08-04T21:59:40.562Z","sourceVersionKey":"v_1_2vvev_s3","needsBlockSource":false,"isAllImages":false,"blockId":"b_3QIq4bkP0No","meta":{"taskNameActive":"Writing stage 19","taskNameComplete":"Wrote stage 19","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing stage 19","taskNameComplete":"Wrote stage 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