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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:o8,af:T603,40:o603,I've identified a strong core strategy using E5 pin-bar fades at Donchian extremes with a flat MA200 filter, ADX below 25, and 3.5 ATR stops capped at 24 bars—it's delivering solid metrics with a 1.67 profit factor, 56% win rate, and 90 trades per year, though the out-of-sample performance dips slightly to 1.49. The main issue is that two years show minor losses: 2011 dropped 1.47R and 2021 fell 0.38R, but otherwise the strategy holds up well across the dataset. When I tried to fix those two years in phase 16 using exit engineering and diversifiers, I couldn't find a configuration that passed all constraints—the best attempt actually fixed 2011 and 2021 but broke a different year and tanked the win rate below the 40% threshold. There's also a structural consideration: the ensemble averages five correlated Donchian lookback strategies (members 8, 10, 14, 20, 28), which in Pine Script would collapse into a single strategy rather than five separate ones. Looking at the data range, the dataset extends through 2025 with partial 2026 data, so the 16-year requirement covers 2010–2025. Now I need to explore genuinely new statistical approaches to try hitting every-year profitability, and if that's not feasible, I'll document it clearly. After that comes full validation with walk-forward analysis, out-of-sample testing, Monte Carlo, cost analysis, the year-by-year table, Pine v6 code, and final notes. Let me scan through the earlier phase files to see what ideas have already been tested so I don't duplicate effort.40:o8,b0:T8f9,40:o8f9,I've done extensive research across sixteen phases covering reversion, breakout patterns, session dynamics, calendar effects, gaps, sweeps, pin bars, machine learning ridge workflows, portfolio construction, ensembles, gates, exits, and diversifiers. Now I'm looking at what genuinely new dimension I haven't explored yet—multi-instrument approaches won't work since I only have EURUSD data, but position sizing and risk normalization through volatility targeting could be a promising direction to investigate. The key untested idea is dynamic regime-switched allocation—only deploying the trend sleeve when the causal regime signals it's appropriate. I'm also considering a few other mechanisms like failed breakouts at weekly anchors, volatility-of-volatility, and time-based exits at specific session hours, though I recognize that asymmetric long/short parameters risk becoming a fitted filter. The honest reality is that achieving positive returns across all sixteen years is statistically unlikely even with a genuine edge, but before accepting that conclusion, I should run a proper phase 17 test with the regime-switching approach properly implemented. volatility-of-volatility contraction, consecutive same-direction H4 closes, weekly opening-range breakouts, and London-open fades of Asian extremes. I should also verify whether the losing years fall within statistical noise and run a bootstrap to estimate the probability that all years are actually profitable given the true edge. One more thing to explore: the ensemble currently weights all five lookback periods equally, so testing whether adding more ensemble members could improve robustness. The idea is that expanding the ensemble across both lookback parameters and exit parameters (stop-loss and max-bars) would reduce per-year variance and potentially flip those marginal losing years like 2021 (-0.38R) and 2011 (-1.47R). Phase 15 tested E7 with sl3.5 mb18 and got 12/16 positive years, which was worse, but a fuller grid across both dimensions might work better. I'm going to write phase17.py with a fast vectorized simulator to test this systematically. I see the data file exists at that path, but pandas isn't available in the environment — only numpy is installed, so I need to add pandas as a dependency first.40:o8,b1:T6bb,40:o6bb,The sl3.0 / mb30 exit is showing real consistency—it's positive across all four lookback periods, which suggests this isn't just noise. The metrics are holding steady around a 1.59-1.63 profit factor and 52.4% win rate, so I need to dig deeper into whether this is a genuine plateau or just a localized spike. I'm going to map out the sensitivity across different stop-loss and max bars combinations to see how robust this signal actually is. But here's the tension: mb30 isn't improving the out-of-sample profit factor (it actually dropped from 1.490 to 1.412 on L5), and the in-sample metrics declined slightly too. So I'm not selecting it because it inflates historical returns—I'm selecting it because it delivers year-round consistency. That's a different argument, but I need to verify it holds across a neighborhood of parameters, not just at this single point. Let me build a fine-grained grid across stop-loss and max bars values for L5 and L6 to check if there's a real plateau where the strategy stays robust. Now I'm setting up phase18 to map the full parameter space systematically—testing multiple stop-loss and max-bars combinations while tracking position yield, profit factor, win rate, trades per year, and out-of-sample performance. Once I identify the plateau center, I'll run the complete validation suite: walk-forward testing with both anchored and rolling windows, Monte Carlo simulations with different reshuffling methods, cost sensitivity curves, regime breakdowns, yearly tables, and sensitivity sweeps across all the tuning knobs like wick fraction, ADX threshold, tolerance, minimum R-value, slope gate, and moving average length, plus individual member statistics. Writing phase18...40:o9,b2:T12a2,40:o12a2,I'm seeing that the "all 16 years positive" property only emerges at a very specific parameter setting—max_bars=30 exactly—where it achieves all 16 positive years, but shifting to nearby values like 27 or 33 drops it to 13-16. With only 11 out of 189 configurations passing this test and just 2 out of 29 entry perturbations maintaining the full 16/16 result, this property is extremely fragile and exists only in a narrow slice of parameter space rather than being a robust characteristic. The real robust finding is that the Profit Factor stays above 1.30 across all 189 exit configurations and 90% of entry perturbations—the edge itself is solid, but requiring every single year to be positive isn't achievable robustly. There's an interesting pattern at max_bars=30 where positive years average 15.24 compared to ~14 elsewhere, showing genuine improvement on that metric, though hitting exactly 16 remains marginal. I'm also noticing some promising parameter combinations like min_range=0.75 with specific settings that improve across all axes—PF, out-of-sample performance, in-sample returns, and average pips—which looks like a real improvement worth exploring further. Now I need to be careful about selection bias and run a proper statistical test: bootstrap the trade distribution to calculate the probability that all 16 years come out positive given the actual per-trade statistics and ~90 trades per year. If that probability is around 20-30%, then finding a config with 16/16 positive years among ~200 candidates is just expected by chance, which means the max_bars=30 result is selection luck rather than evidence of a genuine edge. After that, I should select the final strategy based purely on robustness metrics—focusing on the plateau center where parameters like max_bars in the 21-30 range and stop_loss around 3.0-3.75 show the most stability. So phase 19 will run a neighborhood-averaged selection across a grid of minimum range, stop loss, and max bars at fixed entry parameters, with ADX caps at 22/25 and L6/L7lookbacks, scoring each configuration by the mean of its 3×3 neighborhood across OOS profit factor, position yield, and in-sample profit factor. Then I need to be honest about the calendar year requirement — one config might hit it but it won't be robust, so I'll state clearly that no statistically sound solution exists for that constraint and present the best robust strategy instead. Actually, there is a configuration that satisfies every literal requirement: L5/L7 with stop loss 3.5 and max bars 30 delivers profit factor 1.66–1.70, win rate 55%, trades per year 84–115, positive returns across all 16 years, positive expectancy, and OOS profit factor 1.46–1.49. What's striking is that at max bars 30, the stop loss range from 3.0 to 4.0 forms a plateau where five out of five configurations pass for L5 and four out of five for L7, but the fragility shows up in max bars — mb27 and mb33 both fail. The neighboring configurations have worst years that are marginally negative, just a fraction of a return, so while the every-year constraint sits on a knife-edge, the underlying strategy is stable. I'll present the configuration that satisfies all requirements, chosen from the robust center of this plateau, favoring sl3.5 mb30 for its best out-of-sample profit factor and highest win rate margin, and I'll be explicit about the statistical caveat around the all-years probability so the user understands the edge. Now I'm running the final validation battery on the selected configuration. Among the 11 passing setups, L5 with sl3.5 mb30 edges out L7 on the metrics that matter most: it generates 5.84 pips per trade versus 3.92, and executes 84 trades yearly instead of 115, both favoring lower costs and higher per-trade profit as the requirements specify. L5 also uses the same lookback set as the established core strategy, so there's continuity in the selection history. I'll test a min_range=0.75 variant at these same L5 parameters with ADX 22 to see if it improves in-sample, out-of-sample, and walk-forward performance simultaneously before deciding whether to include it. The full validation suite will cover member statistics, year-by-year breakdowns, anchored walk-forward testing with re-selection on historical data only, rolling 5-year windows, and Monte Carlo analysis across multiple null hypotheses. I also need to capture regime robustness across long/short, intraday hours, day-of-week, volatility buckets, and first/second half splits, plus track maximum concurrent positions for Pine implementation. For the Pine strategy, I'm setting up pyramiding with 5 independent slots each taking 1/5 of the risk, using separate entry IDs for each lookback period. Writing phase19...40:o8,b3:T6c0,40:o6c0,So the probability of getting 16 consecutive positive years with this trade distribution is actually 64.6%, which means the earlier failures were just unlucky variance, not a sign of something fundamentally broken. That's reassuring. The final configuration looks solid: L5 with a 3.5 stop loss and 30-minute breakeven, posting a 1.696 profit factor, 55.6% win rate, 84 trades per year, all 16 years profitable with the worst year still up 0.70R, out-of-sample PF of 1.488, in-sample 1.895, Monte Carlo checks out, and the robustness tests against sign-flips and random entries all hold up. Now I'm finding some refinements that actually improve both in-sample and out-of-sample performance while keeping all 16 years positive. Tightening the minimum range to 0.75 pushes the PF to 1.840 with OOS at 1.629 and 76.6 trades per year. Capping the ADX at 22 gets even better: 1.919 PF with 1.855 OOS and 65.2 trades per year. Combining both refinements yields 1.948 PF, 1.923 OOS, 61 trades per year, and 57.5% win rate, with average pips improving from 5.84 to 7.38. These are real gains on the metrics that matter, so I need to validate the refined config through walk-forward testing and neighborhood sensitivity analysis before committing to it—checking parameter stability, making sure the trade count stays above 50 across the neighborhood, and confirming walk-forward performance holds up. I also need to flag that 2026 is only a partial year in the dataset (January through June) and it's showing -1.06R with a 31.6% win rate, which is a material concern I should disclose honestly. Let me run the full validation phase to test the refinement thoroughly and make the final call between the baseline and refined versions.40:o8,b4:Tf91,40:of91,Key findings from phase 20: R2 (min_range 0.60, adx_cap 22) vs R3 (min_range 0.75, adx_cap 22): - R2: n=1059, tpy=65.2, wr=58.17%, pf=1.919, pf_is=1.974, pf_oos=1.855, y2026=+1.69, worst_y=+0.19, 16/16 positive years, 8/8 OOS years positive, OOS_totR=27.02 - R3: n=992, tpy=61.0, wr=57.46%, pf=1.948, pf_is=1.969, pf_oos=1.923, y2026=-1.19, worst_y=+0.41, 16/16 positive on full years (2010-2025), but 2026 partial year is negative, 7/8 OOS years positive Critically: R2 has 2026 positive (+1.69) and 8/8 OOS years positive. R3 has 2026 negative. 2026 is a partial year (data ends mid-2026 presumably). The requirement "profitable in every individual calendar year contained in the supplied historical dataset" — 2026 is contained in the dataset. R2 satisfies this for all 17 years including the partial 2026. R3 fails 2026. So R2 is the better choice on the "every calendar year" requirement! The `ok` flag was computed on pos_y==16 (excluding 2026 partial presumably). But R2 gives pos_y=16 AND y2026=+1.69 → all 17 years positive. Also R2 has: - Better fixed-parameter walk-forward: 8/8 OOS years positive, OOS_totR=27.02, OOS_pf=1.855 - Higher trades/year 65.2 (more margin over 50) - Higher win rate 58.17% - Lower y_sd 2.38 vs... wait R3 y_sd=2.21 is lower. R2 y_sd=2.38. - Larger sample n=1059 vs 992 R3 has marginally higher pf (1.948 vs 1.919) and higher pf_oos (1.923 vs 1.855). But the objective explicitly says: NOT to maximize historical PF, prefer stable yearly returns, robustness, all years positive. R2 is positive in every single calendar year including partial 2026. That's decisive. Hmm, but also there's the "mr0.7 adx22" option: n=1019, tpy=62.7, pf=1.863, pf_is=1.876, pf_oos=1.848, worst_y=+0.41, y_sd=1.88 (lowest!), y2026=-1.05. Fails 2026. And "mr0.6 adx23": n=1161, tpy=71.4, pf=1.813, pf_oos=1.651, y2026=-0.15, y_sd=2.47. So R2 (mr0.60, adx22) is the only one in the top group that is positive in all 17 calendar years including 2026, has the best fixed-param walk-forward (8/8), and largest sample among the high-PF group. Let me now verify R2 more thoroughly: full validation battery on R2 specifically (MC, cost curve, plateau in mb/sl at mr0.60/adx22, parameter sensitivity), then decide final and write the Pine Script v6 + report. Let me write phase21 to do the final confirmation on R2, including: - Exit plateau at mr0.60/adx22 (sl x mb) — check pos_y including 2026 (17 years) - Entry sensitivity around mr0.60/adx22 - Full stats, year-by-year with 2026, IS/OOS, MC, cost curve, regime - Leave-one-year-out - Compare R2 vs R3 vs BASE on "all 17 years positive" criterion - Trade list export for Pine cross-check Also I should double check the requirement of ≥50 trades/year in EVERY year vs on average. The prompt says "Minimum 50 completed trades per year on average across the entire dataset." So average ≥50. R2 = 65.2 avg. Good. But individual years vary (2010 partial, 2020 low). Fine — the requirement is average. Also, important: I need to check what the actual data range is — 2010 through 2026? Let me confirm from data. The year-by-year table for R3 shows 2010 n=33 (partial start) through 2026 n=41 (partial end). So 17 calendar years, 2010 and 2026 partial. Wait — for R3, 2010 pf=9.57 with n=33 — that's a partial year. And pos_y==16 counts years 2010-2025. So 2026 excluded from pos_y. R2's y2026=+1.69 means all 17 positive. Let me also reconsider: is it honest to select R2 over R3 partly because 2026 is positive? 2026 is a partial year with n≈45 trades — that's a small sample and this is a selection on ~1 year of data. But the requirement explicitly demands profitability in every calendar year in the dataset. And R2 independently wins on walk-forward (8/8 vs 7/8), sample size, win rate, and trades/year. So it's defensible on multiple independent grounds, not just 2026. Let me run phase21 to confirm R2's robustness thoroughly. Let me read phase20.py first to reuse its structure.40:o8,b5:T6de,40:o6de,Very important honest findings: 1. R2 passes everything on 17 years: pf=1.919, wr=58.17%, tpy=65.2, all 17 years positive, worst year +0.19R, pf_is=1.974, pf_oos=1.855. 2. BUT — critical honesty issue: The `pos17==17` share in the exit plateau is only **0.02 (1 of 48 cells)**. And in the entry map, ALL17 = 0.05 (3 of 64 cells). This means "all 17 years positive" is NOT a robust plateau property — it's essentially knife-edge. The wick_frac sensitivity shows 0.45→15 pos, 0.5→17, 0.55→13, 0.6→12. don_tol 0.10→15, 0.15→17, 0.20→16. Gate thr 0.4→15, 0.5→17, 0.6→16. So: the underlying EDGE is robust (PF ≥ 1.30 in 100% of exit cells and 100% of entry cells; PF median 1.76-1.86; sign-flip p-value ~0). But the specific "every single calendar year positive" requirement is satisfied only at a razor-thin parameter point. That is exactly the kind of thing the brief warns about. 3. Monte Carlo: P(all 17 years > 0 | trade distribution) — need to see. In phase20 for R3 it was 0.737 for 16 years. Let me read the rest. This is the crucial honest conclusion: The edge is real and robust (PF ~1.7-1.9 across the entire parameter space, sign-flip p<0.001, all 8 OOS years positive at frozen params, 13/17 LOO held-out years positive). The "every calendar year positive" constraint is satisfiable but is NOT itself a robust property — it holds at only 2-5% of neighboring parameter cells. I must state this explicitly. Also note: worst_y for R2 = +0.19R in 2021 — a hair above zero. 2015 = +0.91R, pf 1.19. So 2021 pf=1.04 is essentially breakeven. Honest framing: "positive in every year" is technically true but 2021 is +0.19R (statistically indistinguishable from zero). Let me read the rest of the output (lines 200-286).40:o8,b6:Tb76,40:ob76,Now a critical honest issue I found: the "all 17 years positive" property holds in only 2% of exit-grid cells and 5% of entry-grid cells. That means the specific all-positive record is knife-edge, NOT the edge itself. However — and this is the key statistical distinction — the Monte Carlo says P(all 17 years > 0 | this trade distribution) = 0.722. That means given the strategy's true per-trade distribution, an all-positive 17-year record is the EXPECTED outcome (72% chance). So the all-positive record is not evidence of overfitting; it's consistent with the edge's strength. The reason neighboring cells fail is that neighbors have slightly lower expR, and at expR ~0.045-0.05 with ~65 trades/year, a marginal year can dip negative by noise. That is the honest interpretation: the EDGE is robust (PF≥1.30 in 100% of 112 tested parameter cells, sign-flip p<1e-5, 8/8 frozen OOS years). The all-positive-year record is partly luck-dependent, but the probability of it is 72% given the edge — so requiring it is not itself overfitting, though selecting the exact cell that achieves it involves a mild selection effect. Important caveat to disclose: 2021 = +0.19R (essentially breakeven) and cost sensitivity shows all-17-positive breaks at 3.0 pips round-turn cost. So the all-positive claim is cost-fragile. PF ≥1.30 survives to 8 pips though. Another important consideration: `drop DON10` and `drop DON14` also give ok17=True with even better PF (2.010, 1.890) and tpy 50.1, 51.9 — barely above 50. And lookbacks [10,12,16,22,30] gives ok17 with pf 1.889, tpy 62.2. And the 8-lookback version [8,10,12,14,16,20,24,28] gives ok17 with pf 1.932, tpy 102.3 — highest trade count AND all 17 positive AND worst_y +0.65 (best worst-year!) AND pf_oos 1.864. That 8-slot variant is interesting: worst_y=+0.65 (vs +0.19), y_sd=2.30, tpy=102.3, pf=1.932, pf_is=1.992, pf_oos=1.864, y2026=+1.92. It dominates R2 on nearly every robustness axis: higher trade count (bigger sample = 1663 trades), better worst year, similar PF, better OOS. More slots = more diversification = lower variance. That's a genuine robustness improvement, not a PF-fitting filter. Let me test the wider-ensemble idea properly — it's a diversification change (more lookbacks), which reduces parameter sensitivity by construction (averaging over the lookback parameter instead of picking 5 values). This aligns perfectly with "lower parameter sensitivity" and "larger statistical sample." Let me run phase22 to test ensemble breadth as the final robustness step, including: - Various lookback sets (5, 8, 12, all integers 6..32) - Check the all-17-positive rate across the exit grid for the wide ensemble (does breadth make all-positive a plateau rather than knife-edge?) - That's the key test: if the wide ensemble makes pos17==17 hold across MANY cells, then it's genuinely robust, not knife-edge. This is the right final test. Let me do 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No strategy assumptions.\"\"\"\nimport numpy as np, pandas as pd\nfrom core import load, features, PIP\n\npd.set_option(\"display.width\", 200)\ndf = features(load())\nprint(\"bars\", len(df), df.inde"},"id":"rghvjggkwgrNfGqZ"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"x[0], \"->\", df.index[-1])\nprint(\"bars per year:\\n\", df.groupby(\"year\").size().to_string())\n\nc = df[\"close\"]\nfwd1 = c.shif\n\n...\n\nload())\nN = len(F)\no = F[\"open\"].to_numpy(); h = F[\"high\"].to_numpy(); l = F[\"low\"].to_numpy(); c = F[\"close\"].to_numpy()\n"},"id":"Nack3RdEr3Ade86X"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826469635,"finishedAt":1785826470365,"lastPartSentAt":1785826470317,"taskNameActive":"Reading phase docstrings rest","taskNameComplete":"Read phase docstrings rest","toolCallId":"toolu_019xVs9BRA31qQifqATdrXsJ","agentTurnId":"turn_3OspSYSKrzzKseAT","filePath":null,"id":"eXzPfT4hLrlPs7E0","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/tool_content/reviewing-prior-phase-docstrings-task-s-nme.txt"},{"type":"reading-file","filePath":"user_read_only_context/tool_content/reviewing-prior-phase-docstrings-task-s-nme.txt","offset":107,"limit":310}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826476536,"finishedAt":1785826506704,"lastPartSentAt":1785826506704,"taskNameActive":null,"id":"XWaUfup8Z0noywwc","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":30,"thought":"$b0"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826507133,"finishedAt":1785826510881,"lastPartSentAt":1785826510673,"taskNameActive":"Installing pandas","taskNameComplete":"Installed pandas","toolCallId":"toolu_01AaquEZSx9BGp9mwwhF1iAD","agentTurnId":"turn_8L9SJapcH3wvV137","filePath":null,"id":"BQeNQvyDnYWQhL0X","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && uv pip install --python .venv/bin/python pandas 2>&1|tail -3 && cd research && time ../.venv/bin/python -c \"\nfrom core import load, features\nF=features(load()); print(len(F), F.index[0], F.index[-1]); print(F.groupby('year').size().to_dict())\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"o6dXMrbUh85fEAj8"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" + pandas==3.0.5\n + python-dateutil==2.9.0.post0\n + six==1.17.0\n"},"id":"SOWpN7ROKohhLWXO"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"25698 2010-01-01 00:00:00 2026-06-24 00:00:00\n{2010: 1566, 2011: 1560, 2012: 1566, 2013: 1559, 2014: 1558, 2015: 1558, 2016: 1560, 2017: 1557, 2018: 1557, 2019: 1558, 2020: 1563, 2021: 1"},"id":"Xy5FkUeMi2NNwUsi"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"560, 2022: 1560, 2023: 1557, 2024: 1563, 2025: 1557, 2026: 739}\n\n\nreal\t0m1.320s\nuser\t0m1.277s\nsys\t0m0.107s\n"},"id":"ORFUIJHbusr13cvX"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826515966,"finishedAt":1785826521193,"lastPartSentAt":1785826521193,"taskNameActive":null,"id":"WrmtROTzGg50pHQB","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"I'm working with historical data spanning 2010 through mid-2026, with 16 complete years and a partial 2026. Now I need to write phase17.py with a fast pure-Python simulator that loops over numpy arrays according to the configuration, returning trade entry and exit times as integer bar indices along with their corresponding years. I'll represent each trade as an array containing entry index, exit index, direction, R-value, and pips, using the year from the exit index. I'm planning to start with tests that reproduce the base case and then move through additional validation scenarios.\n\nWriting phase17.py..."}]}}],["p",{},["text",{},"Now let me run a genuinely new research phase (17) targeting the two remaining negative years with ideas not yet tested."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_o4Gby9sDUb4","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/phase17.py","baseName":"phase17.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/phase17.py\" \nGENERATING","latestDeploymentInfo":"$a9:0:1","lastDeployedAt":"2026-08-04T07:03:23.722Z","sourceVersionKey":"v_1_ocyzp_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_o4Gby9sDUb4","meta":{"taskNameActive":"Writing phase 17 research","taskNameComplete":"Wrote phase 17 research","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing phase 17 research","taskNameComplete":"Wrote phase 17 research"},"id":"block-b_o4Gby9sDUb4"}],["AssistantMessageContentPart",{"part":{"createdAt":1785826611771,"finishedAt":1785826614742,"lastPartSentAt":1785826614523,"taskNameActive":"Running phase 17","taskNameComplete":"Ran phase 17","toolCallId":"toolu_014V4mjXQ7BgyKcXBsfbj3Md","agentTurnId":"turn_pDUobOsBWWuCF8K0","filePath":null,"id":"3k2CrWXbStukMjFD","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && ../.venv/bin/python phase17.py > phase17_out.txt 2>&1; tail -5 phase17_out.txt; wc -l phase17_out.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Oa703PwsvWmirIMC"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" label n tpy wr pf expR avg_pips pos_y worst_y y2011 y2021 y_sd pf_is pf_oos ok\nL5 sl3.0 mb30 1395 85.836211 52.473118 1.617121 0.048379 5.229031 16 0.48 2.37 0.88 3.64 1.810 1.412 Tru\n\n...\n\n6.542663 52.581262 1.625871 0.040626 4.448192 16 0.36 2.23 0.98 3.54 1.857 1.387 True\nL8 sl3.0 mb30 2044 125.727686 52.397260 1.607152 0.029715 3.193769 16 0.24 2.05 0.98 3.23 1.829 1.381 True\n176 phase17_out.txt\n"},"id":"p76LO1bKRBOpKvLV"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826620402,"finishedAt":1785826621739,"lastPartSentAt":1785826621739,"taskNameActive":null,"id":"Xygz0crcKhiB5STF","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I found a configuration that passes all tests — using a stop loss of 3.0 with a maximum of 30 bars produces consistently positive returns across every year, with a profit factor of 1.62 and a 52% win rate averaging 86 ticks per year. This parameter set differs from what I tested in phase 16, which used 30 bars but didn't yield the same results, so the 30-bar maximum appears to be the key difference here. Let me examine the full output to understand what's driving these improvements."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826621865,"finishedAt":1785826622466,"lastPartSentAt":null,"taskNameActive":"Reading phase17 output","taskNameComplete":"Read phase17 output","toolCallId":"toolu_01FDBXMzM3V7BzjBqGsZgcNF","agentTurnId":"turn_n4KhD4EB6t5FRZsD","filePath":null,"id":"RRVjBG1PKLPNkHkP","type":"task-read-file-v1","parts":[]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826628835,"finishedAt":1785826641886,"lastPartSentAt":1785826641886,"taskNameActive":null,"id":"t8SYcMpYeNv4xQ9G","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":13,"thought":"$b1"}]}}],["p",{},["text",{},"Phase 17 found full-constraint passes. Now I must verify whether that exit setting is a genuine plateau or a lucky spike."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_XHhWX6zBZtK","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/phase18.py","baseName":"phase18.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/phase18.py\" \nGENERATING","latestDeploymentInfo":"$a9:1:1","lastDeployedAt":"2026-08-04T07:03:23.722Z","sourceVersionKey":"v_1_eon3f_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_XHhWX6zBZtK","meta":{"taskNameActive":"Writing plateau validation","taskNameComplete":"Wrote plateau validation","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing plateau validation","taskNameComplete":"Wrote plateau validation"},"id":"block-b_XHhWX6zBZtK"}],["AssistantMessageContentPart",{"part":{"createdAt":1785826692440,"finishedAt":1785826698968,"lastPartSentAt":1785826698671,"taskNameActive":"Running plateau validation","taskNameComplete":"Ran plateau validation","toolCallId":"toolu_015MT6Wv9e1yAtnDeKNxYTbf","agentTurnId":"turn_yVRuVomJSoRQerh4","filePath":null,"id":"gOXIkIxuLdQUEHqZ","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && time ../.venv/bin/python phase18.py > phase18_out.txt 2>&1; 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Let me quantify that statistically and run the final validation battery."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_8evPNITJrxh","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/phase19.py","baseName":"phase19.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/phase19.py\" \nGENERATING","latestDeploymentInfo":"$a9:5:1","lastDeployedAt":"2026-08-04T07:03:23.722Z","sourceVersionKey":"v_1_2rjjc_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_8evPNITJrxh","meta":{"taskNameActive":"Writing final validation","taskNameComplete":"Wrote final validation","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing final validation","taskNameComplete":"Wrote final validation"},"id":"block-b_8evPNITJrxh"}],["AssistantMessageContentPart",{"part":{"createdAt":1785826899215,"finishedAt":1785826908994,"lastPartSentAt":1785826908620,"taskNameActive":"Running final validation","taskNameComplete":"Ran final validation","toolCallId":"toolu_01D5kFy4rcFMo442XN7kTgZM","agentTurnId":"turn_X68SssepIL5Xi1ea","filePath":null,"id":"Ll7LOX4gRzPIQLRJ","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 600 ../.venv/bin/python phase19.py > phase19_out.txt 2>&1; echo \"exit=$?\"; tail -20 phase19_out.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"465QvAg9SJJIHpHI"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"exit=0\ncost 5.0 pips 1365 84.0 54.73 1.555 0.0385 5.04 16 0.12 3.05 1.761 1.343 True\n breakeven cost (PF -> 1.0): ~26.0 pips round trip; PF>=1.30 holds to 13.0 pips\n\n=== 10. regime robustness (FINAL) ===\n long n= 676 pf=1.576\n\n...\n\n106h)\n\n=== 11. concurrency (for position sizing / Pine pyramiding) ===\n max concurrent slots=5 mean when in market=3.97 time in market=36.9%\n concurrency histogram: {0: 16219, 1: 568, 2: 1200, 3: 1255, 4: 1341, 5: 5115}\n\ntrades exported: 1365\n"},"id":"ffovuYmSkiCbMD9p"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826913313,"finishedAt":1785826915128,"lastPartSentAt":1785826915107,"taskNameActive":"Reading final validation","taskNameComplete":"Read final validation","toolCallId":"toolu_01GgFBB3GSQ1nwXeRH8rXFpc","agentTurnId":"turn_dU7GAZKOhHoAMIYr","filePath":null,"id":"XTKJrdQERh6CxQep","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase19_out.txt"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785826921124,"finishedAt":1785826932576,"lastPartSentAt":1785826932576,"taskNameActive":null,"id":"s66bDb9hUPz4MOhn","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":11,"thought":"$b3"}]}}],["p",{},["text",{},"The refinement candidates improve both IS and OOS — the rules require they also improve walk-forward and robustness. Let me test that properly."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_3uTXl4wMtll","parts":[{"type":"code-project","isVersioned":true,"isSourceless":false,"changedFiles":[{"fileName":"/research/phase17.py","baseName":"phase17.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/phase18.py","baseName":"phase18.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/phase19.py","baseName":"phase19.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/phase20.py","baseName":"phase20.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/phase20.py\" \nGENERATING","latestDeploymentInfo":"$a9:6:1","lastDeployedAt":"2026-08-04T07:03:23.722Z","sourceVersionKey":"v_1_fjv30_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_3uTXl4wMtll","meta":{"taskNameActive":"Validating refinements","taskNameComplete":"Validated refinements","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Validating refinements","taskNameComplete":"Validated refinements"},"id":"block-b_3uTXl4wMtll"}],["AssistantMessageContentPart",{"part":{"createdAt":1785826998485,"finishedAt":1785826998485,"lastPartSentAt":1785826998485,"id":"DxPr4oUM2orcVXQe","type":"task-stopped-v1","parts":[{"type":"agent-timeout","willContinueAfterTimeout":true}]}}]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"kwh1Zj2yfIm9EuuzfDngNou7dZkPr0N0","gitRevision":{"headSha":"3052b08a53ff3d09063b8870223173447246f9cb","readyAt":"2026-08-04T07:03:23.690Z","version":1},"streaming":false,"finishReason":"tool-calls","parentId":"TvnhTrL0cHS9ACzvh2YNxjXuAVmzMryJ","type":"message","creditCost":3.51434825,"workMetrics":{"diffMap":{},"linesRead":1426,"agentActions":33},"createdAt":"$D2026-08-04T06:52:53.733Z","updatedAt":"$D2026-08-04T07:03:23.714Z"},{"content":{"type":"parts","parts":[{"type":"timeout-continue"}],"version":1},"role":"user","id":"7hOa7BE6heLtqbF7lEPnbNttYaFPSAP1","attachments":[],"parentId":"kwh1Zj2yfIm9EuuzfDngNou7dZkPr0N0","type":"message","authorId":"HYrvwIpwK676ynKh0Ly2zMds","createdAt":"$D2026-08-04T07:03:22.429Z"},{"v0ModelId":"v0-opus-5","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785827009889,"finishedAt":1785827016692,"lastPartSentAt":1785827016557,"taskNameActive":"Running refinement validation","taskNameComplete":"Ran refinement validation","toolCallId":"toolu_01VZPgiNXUBdiunLmWBZDGFy","agentTurnId":"turn_HMkfTKM0CUE0G4LO","filePath":null,"id":"QtBy23GHO0E5iSOm","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 600 ../.venv/bin/python phase20.py > phase20_out.txt 2>&1; echo \"exit=$?\"; wc -l phase20_out.txt; tail -25 phase20_out.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"OKv1dntWR3j8QHtp"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"exit=0\n211 phase20_out.txt\ncost 2.0 992 61.0 56.96 1.908 0.0590 7.18 16 0.30 2.22 1.934 1.876 -1.29 True\ncost 3.0 992 61.0 56.75 1.868 0.0571 6.98 16 0.20 2.22 1.900 1.829 -1.39 True\ncost 4.0 992 61.0 56.45 1\n\n...\n\n.09 2.73 1.999 2.260 0.55 False\nDON20 177 10.9 59.32 2.314 0.3823 46.37 16 1.18 2.72 2.379 2.238 -1.47 False\nDON28 149 9.2 57.72 2.040 0.3239 37.52 15 -0.96 2.45 2.097 1.979 -1.47 False\n\nexported 992 trades\n"},"id":"J5Tkx7Iee2hpf7y9"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_qEevS1LVPtI","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/phase20_out.txt","baseName":"phase20_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/final_trades_v2.csv","baseName":"final_trades_v2.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase20_cost.csv","baseName":"phase20_cost.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase20_entrymap.csv","baseName":"phase20_entrymap.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase20_exitmap.csv","baseName":"phase20_exitmap.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase20_wf_anchored.csv","baseName":"phase20_wf_anchored.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase20_wf_rolling5y.csv","baseName":"phase20_wf_rolling5y.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase20_yearly.csv","baseName":"phase20_yearly.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/phase20_out.txt\" \nGENERATING\n[V0_FILE]csv:file=\"research/final_trades_v2.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase20_cost.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase20_entrymap.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase20_exitmap.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase20_wf_anchored.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase20_wf_rolling5y.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase20_yearly.csv\" \nGENERATING","latestDeploymentInfo":"$a9:7:1","lastDeployedAt":"2026-08-04T07:09:00.758Z","sourceVersionKey":"v_1_rbcdr_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_qEevS1LVPtI","meta":{"taskNameActive":"Applying changes to 8 files","taskNameComplete":"Applied changes to 8 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 8 files","taskNameComplete":"Applied changes to 8 files"},"id":"block-b_qEevS1LVPtI"}],["p",{},["text",{},"Let me read the full output to see the refinement comparison results."]],["AssistantMessageContentPart",{"part":{"createdAt":1785827026584,"finishedAt":1785827026926,"lastPartSentAt":1785827026905,"taskNameActive":"Reading refinement results","taskNameComplete":"Read refinement results","toolCallId":"toolu_01BY3nQkPm94A1eDzpfNWbiF","agentTurnId":"turn_emcJNM695778Bzj4","filePath":null,"id":"MFzI9MLw9elQ2BLX","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase20_out.txt"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-0","parts":[{"type":"thinking-end","duration":23.311764705882354,"thought":"$b4"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-0"}],["p",{},["text",{},"Phase 20 gives a decisive result I need to examine closely: "],["strong",{},["text",{},"R2 (min_range 0.60, adx_cap 22) is positive in all 8 OOS years (8/8) and positive in 2026 (+1.69R)"]],["text",{},", while R3 has higher raw PF but a negative 2026. Since the requirement is profitability in "],["em",{},["text",{},"every"]],["text",{}," calendar year in the dataset, let me run the final confirmation battery on R2."]],["AssistantMessageContentPart",{"part":{"createdAt":1785827053032,"finishedAt":1785827053494,"lastPartSentAt":1785827053462,"taskNameActive":"Reading phase20 script","taskNameComplete":"Read phase20 script","toolCallId":"toolu_01P3azhr9queuKvTG4EzAdX4","agentTurnId":"turn_hZHXaDLyc8aVRsmH","filePath":null,"id":"KG9tSVrHTCErZ1Vg","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase20.py"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-1","parts":[{"type":"thinking-end","duration":4.535294117647059,"thought":"Now let me write phase21: the final adjudication of R2 vs R3 with all 17 years, plus full validation battery on R2, leave-one-year-out, parameter sensitivity, and a bar-by-bar reference simulation that exactly matches what Pine will do (so I can verify the Pine logic).\n\nKey: I need to add a `pos_y17` metric that counts all 17 years including 2026.\n\nAlso important for Pine: the strategy is an ensemble of 5 Donchian lookbacks (8,10,14,20,28) each with its own position, up to 5 concurrent positions. In Pine that's awkward but doable with 5 separate strategy.entry IDs. Let me confirm concurrency: max concurrent=5, so each slot holds at most 1 position — that works cleanly as 5 independent sub-strategies in one script using distinct trade IDs.\n\nLet me write phase21."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-1"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_HSOSYr1wF4n","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/phase21.py","baseName":"phase21.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/phase21.py\" \nGENERATING","latestDeploymentInfo":"$a9:8:1","lastDeployedAt":"2026-08-04T07:09:00.758Z","sourceVersionKey":"v_1_50z7l_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_HSOSYr1wF4n","meta":{"taskNameActive":"Adjudicating R2 vs R3","taskNameComplete":"Adjudicated R2 vs R3","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adjudicating R2 vs R3","taskNameComplete":"Adjudicated R2 vs R3"},"id":"block-b_HSOSYr1wF4n"}],["AssistantMessageContentPart",{"part":{"createdAt":1785827174520,"finishedAt":1785827183605,"lastPartSentAt":1785827183287,"taskNameActive":"Running final adjudication","taskNameComplete":"Ran final adjudication","toolCallId":"toolu_01PMCcSZTjwfwewPTasq3JLV","agentTurnId":"turn_3Tervu8S2CvIDTFp","filePath":null,"id":"WhM1cOWGe8XWlLIt","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && timeout 600 ../.venv/bin/python phase21.py > phase21_out.txt 2>&1; echo \"exit=$?\"; wc -l phase21_out.txt; tail -20 phase21_out.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"2koxA2JOpq6F5RBe"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"exit=0\n"},"id":"rwSBJyRgu3vD6USf"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"286 phase21_out.txt\n 2018 sl4.0 mb33 mr0.75 adx25 2.173 92 2.098 4.59\n 2019 sl4.0 mb33 mr0.75 adx22 2.428 72 1.021 0.13\n 2020 sl4.0 mb33 mr0.75 adx22 2.088 39 2.975 2.82\n 2021 sl4.0 mb33 mr0.75 adx22 2.16\n\n...\n\nR2 (mr.60 adx22) 8 8 27.02 1.855 0.19\nR3 (mr.75 adx22) 7 8 26.31 1.923 -1.19\n\nexported 1059 trades -> final_trades_R2.csv\nfirst/last trade: 2010-03-17 12:00:00 2026-06-18 04:00:00\n"},"id":"vmBDiE0ZLBsyJs8P"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_iPfzmVLFZ9y","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/final_trades_R2.csv","baseName":"final_trades_R2.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_cost.csv","baseName":"phase21_cost.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_entrymap.csv","baseName":"phase21_entrymap.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_exitmap.csv","baseName":"phase21_exitmap.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_loo.csv","baseName":"phase21_loo.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_out.txt","baseName":"phase21_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_sens.csv","baseName":"phase21_sens.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_wf_anchored.csv","baseName":"phase21_wf_anchored.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_wf_rolling5y.csv","baseName":"phase21_wf_rolling5y.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/phase21_yearly.csv","baseName":"phase21_yearly.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]csv:file=\"research/final_trades_R2.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_cost.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_entrymap.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_exitmap.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_loo.csv\" \nGENERATING\n[V0_FILE]txt:file=\"research/phase21_out.txt\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_sens.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_wf_anchored.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_wf_rolling5y.csv\" \nGENERATING\n[V0_FILE]csv:file=\"research/phase21_yearly.csv\" \nGENERATING","latestDeploymentInfo":"$a9:9:1","lastDeployedAt":"2026-08-04T07:09:00.758Z","sourceVersionKey":"v_1_y6eju_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_iPfzmVLFZ9y","meta":{"taskNameActive":"Applying changes to 10 files","taskNameComplete":"Applied changes to 10 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 10 files","taskNameComplete":"Applied changes to 10 files"},"id":"block-b_iPfzmVLFZ9y"}],["p",{},["text",{},"R2 confirmed: "],["strong",{},["text",{},"8/8 OOS years positive, worst OOS year +0.19R"]],["text",{},". Let me read the full battery."]],["AssistantMessageContentPart",{"part":{"createdAt":1785827188676,"finishedAt":1785827189077,"lastPartSentAt":1785827189043,"taskNameActive":"Reading validation battery","taskNameComplete":"Read validation battery","toolCallId":"toolu_01BJrJhK3sY2ZdRzo97zV3Mo","agentTurnId":"turn_grRCRwhkgu6vH9Wu","filePath":null,"id":"AIg4EMVw4FhF5n4j","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase21_out.txt"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase21_out.txt","offset":0,"limit":200}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-2","parts":[{"type":"thinking-end","duration":10.152941176470588,"thought":"$b5"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-2"}],["AssistantMessageContentPart",{"part":{"createdAt":1785827207093,"finishedAt":1785827209162,"lastPartSentAt":1785827209102,"taskNameActive":"Reading MC and cost results","taskNameComplete":"Read MC and cost results","toolCallId":"toolu_01FwuDhdc4wmevXhmPZb3y52","agentTurnId":"turn_8HNSZWg5LVvN5iG4","filePath":null,"id":"tiP2yCXbPP6RidEm","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase21_out.txt"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/phase21_out.txt","offset":199,"limit":88}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-3","parts":[{"type":"thinking-end","duration":17.16470588235294,"thought":"$b6"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-3"}],["p",{},["text",{},"Critical finding: the wide-ensemble variant (8 lookbacks) passes all requirements with a "],["strong",{},["text",{},"better worst year (+0.65R vs +0.19R)"]],["text",{}," and a much larger sample. Since averaging over more lookbacks reduces parameter sensitivity by construction, let me test whether ensemble breadth converts the knife-edge \"all years positive\" into a genuine plateau."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_RZB6YYhUvhV","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/phase22.py","baseName":"phase22.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/phase22.py\" \nGENERATING","latestDeploymentInfo":"$a9:10:1","lastDeployedAt":"2026-08-04T07:09:00.758Z","sourceVersionKey":"v_1_i0lm8_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_RZB6YYhUvhV","meta":{"taskNameActive":"Testing ensemble breadth","taskNameComplete":"Tested ensemble 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