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integrity checks. I'll create a core library module that handles data loading and feature engineering, then write a characterization script on top of it. For the timezone issue, since the data timestamps lack timezone information, I'm planning to infer it by analyzing the hourly volatility profile—the US cash market open should produce a distinctive volatility spike that reveals the session alignment. On costs, I'm working with a 2.0 point spread plus 1.0 point slippage assumption. For the cost model, I'm being explicit and conservative: the spread cost is 2.0 points charged once per round trip (buying at ask, selling at bid), plus 0.5 points slippage per side totaling 1.0 point, giving a baseline of 3.0 points round trip. I also need to account for spread widening during session opens and off-hours periods, so I'll use hour-of-day buckets to apply dynamic spread multipliers—roughly 2x during low-liquidity hours like 22:00-01:00 server time and 1.5x at market open. Now I'm ready to start writing the characterization step. The dataset has 61k bars which is manageable. For US30 index data, the price points range from around 17,887 to 51,685, and the volume appears to be tick volume normalized or a proxy for it, so I'll treat it carefully using rank-based approaches. I'm building a data integrity and market characterization script that parses datetimes, checks for gaps and duplicates, examines weekend bars, computes returns statistics and volatility by hour and weekday, runs autocorrelation and variance ratio tests, identifies structural breaks in yearly volatility and drift, and analyzes candle geometry distributions. For the hypothesis engine, I need to ensure all features are built only from past-closed data with proper shifting, and evaluate signals at the close of bar t with entry at the open of bar t+1 to avoid lookahead bias, then measure MFE and MAE over the simulation horizon. I'm implementing a rigorous statistical framework that simulates stop-loss and take-profit with bar-by-bar resolution, assuming stop-loss fills first if both trigger in the same bar, then computes net expectancy after costs, t-statistics with block bootstrap, and applies multiple-testing corrections using Benjamini-Hochberg and Bonferroni alongside Deflated Sharpe ratios. The data split is in-sample from 2015-01 through 2021-12 (7 years) for hypothesis discovery and out-of-sample from 2022-01 through 2026-06 (4.5 years) for validation, with strict separation to avoid peeking at OOS results. Now I'm building the core infrastructure: a data loader that reads CSV files into datetime-indexed DataFrames with OHLCV data, and a feature engineering module that computes causal indicators using only information available up to and including each bar. I'll also implement a vectorized trade simulator that efficiently loops over trades rather than all bars to evaluate outcomes given stop-loss and take-profit levels specified in ATR or point units. Writing core modules...40:o8,af:T6ad,40:o6ad,I've found exactly 2 corrupt rows in the dataset, but there's also a significant data gap spanning from late July through mid-August 2022—roughly 3 weeks where the timestamps jump directly from 2022-07-28 02:00 to 2022-08-18 08:00. This missing period explains why 2022 only has 5547 bars and August just 222. My sanitization approach sets bad OHLC values to the previous close, though since the corrupt bars are consecutive, the second one inherits the first one's replacement value. The real gap between the repaired close (32138.961) and the next actual bar's open (33951) creates a 1813-point jump—a legitimate gap from the missing data that I should flag. Rather than just sanitizing, I should drop these corrupt rows entirely and add a "session discontinuity" marker whenever the time gap between consecutive bars exceeds 72 hours (accounting for weekends being ~48 hours). This prevents any trades from spanning across these discontinuities. I'm checking why the TR at hour 8/9 remained at 384 despite the large gap. The sanitization flagged both corrupt rows correctly based on the deviation calculation, but the repaired bars still create outsized true range at the following bar due to that 1813-point gap. The cleanest approach is to rewrite the sanitization using a rolling median reference—dropping bars where any OHLC is ≤ 0 or deviates more than 15% from the trailing median of the previous 50 closes. The median is robust to 1-2 outliers, so it won't be skewed by the corrupt neighbors. Then I'll add the discontinuity flag after dropping, marking where the time gap exceeds 3 days, and ensure trades can't be opened across these boundaries. Rewriting the core sanitize function...40:o8,b0:Tddc,40:oddc,I'm seeing strong volatility clustering in the data—the autocorrelation of absolute returns is 0.33 at lag 1 and stays elevated at 0.28 even 24 lags out, making it the most predictable feature. Meanwhile, returns themselves show almost no autocorrelation, and the variance ratio stays below 1 across all horizons, suggesting mean reversion dynamics that could be exploited. The upward drift of roughly 9% annually over 11.5 years creates a structural long bias that any short strategy would fight against. But the most intriguing signal is wick asymmetry—bars with large upper wicks (wick_asym > 0.5) precede forward 12-hour returns of +13.85 points versus a baseline of +6, which is counterintuitive since upper wicks typically signal rejection. Combined with volatility expansion (vol_ratio > 1.4) predicting +17.07 point moves, the pattern suggests buying into volatility spikes and dips could be the edge here. Weak closes (close_pos 0-0.1) also show mean reversion strength with +8.5 point forward returns. Calendar effects are pronounced—Mondays average +21.6 points forward, and certain months like November (+33), July (+23), and October (+14.9) are consistently strong while March, September, and February lag. Hour 12 shows the strongest intraday bullish bias at +3.19 points forward, while hours 21 and 23 tend negative. Now I'm setting up a hypothesis engine to systematically test different market regimes on in-sample data from 2015 through 2021, then validate out-of-sample from 2022 onward. I'll explore three main behavioral families: mean-reversion strategies that capitalize on oversold/overbought extremes, volatility-expansion dip buys that exploit the clustering pattern, and compression-to-breakout trades. I'm also testing session timing effects, momentum persistence on higher timeframes, wick reversals, and gap behavior. For each family I'll run a controlled grid of parameter combinations through the trade simulator with realistic costs and ATR-based stops, then apply multiple-testing correction to avoid false discoveries. The key is a two-stage approach: first measure raw conditional forward returns with block-bootstrap p-values and FDR correction to identify which behaviors have genuine signal, then move to the second stage for the most promising candidates. Now I'm building the initial broad scan to generate and test hundreds of hypotheses systematically. I'll create a library of primitive conditions and test them individually plus pairwise combinations, measuring forward returns across multiple horizons on in-sample data. The implementation will compute features on 60-minute bars with 4-hour context merged in, build a dictionary mapping condition names to boolean series, and calculate forward price movements at different horizons. For each condition, horizon, and direction, I'll compute mean net return (accounting for a 3.0 cost proxy), hit rate, sample size, and bootstrap p-values, then apply Benjamini-Hochberg FDR correction across all hypotheses. With roughly 60 primitives and a few thousand pairwise combinations, this stays manageable with vectorized operations. I'll compute forward arrays once upfront, then for each condition mask calculate statistics vectorized, reserving bootstrap analysis for the top candidates that survive the scan. Writing the scan implementation... For the 4-hour context, I need to merge HTF features by taking the most recent closed 4H bar for each 60-minute timestamp—using merge_asof to align the 4H close time with the 60M bar's close time.40:o8,b1:T9ca,40:o9ca,I'm realizing the core issue: my forward returns are inflated by the market's unconditional upward drift, and using overlapping windows has invalidated my t-statistics since they assume independent observations. I need to compare the conditional mean returns against the unconditional baseline at the same horizon rather than against zero, and account for the drift properly. Let me recalculate with the unconditional mean as my null hypothesis. Over the sample, US30 averaged about 0.55 points per bar, so a 24-bar forward return baseline is roughly 13.3 points. When I look at my signal conditions, F_id_wide shows 29.3 points (excess of 16) and E_mon shows 27.6 points (excess of 14)—still meaningful but much smaller than the raw numbers suggested. The real test is whether these excess returns can survive as an actual trading strategy with stops and targets. For a proper statistical test, I should use a circular block permutation approach where I randomly shift the condition labels relative to the returns while keeping the temporal structure intact, then bootstrap under this null to get a valid p-value that accounts for overlapping windows and autocorrelation. I can compute this efficiently using FFT-based circular cross-correlation to get the conditional mean for all possible shifts at once, giving me the exact null distribution in O(n log n) time. With about 8000 hypotheses and 35k data points, running FFT for each candidate takes roughly 16 seconds total—very manageable. Now I'm refocusing the analysis: I'll normalize forward returns by ATR to get risk-adjusted units instead of raw points, so recent high-volatility periods don't dominate the results. Then I'll restructure stage 2 to compute the observed conditional mean, run the FFT circular-shift test to get a two-sided p-value, and apply Benjamini-Hochberg FDR correction across all candidates. Computing the FFT cross-correlation... Now I'm implementing the FFT-based approach using rfft and irfft on the full length. For the circular shift test, I need to be careful: demeaning globally isn't necessary since the null distribution already centers around the unconditional mean, so observing an extreme value naturally indicates genuine excess. I'll report the excess directly for clarity. When testing hour and day-of-week conditions, the circular shift will intentionally break time alignment—that's the point of the test—but I need to handle the mixing of hours carefully when shifting by non-multiples of 24.40:o9,b2:T1005,40:o1005,This is a very important result. Zero of 8,076 hypotheses survive BH-FDR at 5%. This is the honest result of a properly-controlled multiple-comparisons test on directional bar-return prediction. Let me interpret this carefully and not panic. What this tells me: - Directional edge in raw forward returns (in ATR units) is NOT detectable above the noise floor once I control for: - multiple comparisons (8,076 tests) - autocorrelation (circular shift test preserving ACF) - the unconditional long drift baseline - The max |z_shift| is only ~2.7 (A_pos48_high h8). With 8076 tests, expected max |z| under pure null ≈ 3.9. So nothing even reaches the noise ceiling. This is a genuine null result for directional prediction. - BUT — and this is crucial — the s1 characterization found something VERY strong: volatility is highly predictable (acf|ret| = 0.33 at lag 1, 0.28 at lag 24), and variance ratio < 1 at all horizons (0.88 at q=48) meaning mean reversion in the path. The key insight: I've been testing the wrong objective. Fixed-horizon directional return is the hardest thing to predict and is dominated by noise. What the data actually says is predictable: - Volatility / range (huge ACF) - Mean reversion in path (VR < 1) A mean-reversion edge does NOT show up well in fixed-horizon forward returns because the reversion happens at an unknown time within the window and then decays. It shows up in path-dependent statistics: P(touch +X ATR before -X ATR), i.e., first-passage asymmetry. That's exactly what a bracket order (SL/TP) monetizes, and that's what my simulator computes. So the correct next step is to pivot the objective function from `mean forward return` to first-passage / bracket-outcome asymmetry, which is: (a) what actually gets traded (SL/TP brackets) (b) where mean reversion and vol clustering actually manifest (c) far more statistically powerful for this structure because each trade is a discrete, nearly-independent event with a well-defined outcome Also important: VR < 1 means mean-reverting. A mean-reversion strategy on US30 60M should be tested. And the depth-1 attribution table actually hints at it: `A_pos120_low` (near 120-bar low) → +0.42 ATR excess LONG, `A_newlow120` → +0.39 LONG. Buying weakness = mean reversion. And `B_vol_con` (vol contraction) → SHORT bias -0.34. These are consistent with buy-the-dip mean reversion in an uptrending index. Let me be rigorous and honest here. I should: - Report the null result honestly — this is the professional thing to do. - Pivot to the objective that matches the discovered structure: first-passage bracket asymmetry, tested with the same rigor. This is exactly the scientific process. The null result on directional forecasting is INFORMATION, not failure. It tells me to stop hunting for directional predictors of fixed-horizon return and instead exploit (a) mean reversion via brackets, (b) volatility clustering. Let me write s3 which: - Uses the path simulator with real costs (3pt friction) - Tests bracket configurations (SL/TP in ATR units) over the mean-reversion primitives that showed up in attribution - Uses the shift-based null: for the null distribution, I need to test whether the entry timing has edge, so I'll compare each rule against randomly-timed entries with the same bracket geometry and same hour-of-day/vol distribution. That's a much better matched null. - Applies FDR control again. Actually, the cleanest matched null for bracket trades: conditional bootstrap / permutation on entry timestamps within the same (hour, vol regime) stratum. That way I isolate "does the signal add anything beyond entering at a similar time in a similar vol regime." Let me build that. This is the honest, powerful test. Let me also reconsider trade frequency: ≥50 trades/yr per engine. Over 7 years IS that's ≥350 trades IS. Good. Let me write s3_bracket.py. I need to be efficient with tokens now — I've used a fair amount. Let me be decisive and write a strong, comprehensive script. Let me first re-read core.py to make sure I use the simulator API correctly.40:o8,b3:Tb01,40:ob01,Good, I have the simulator API. Now let me build s3, the bracket / first-passage test with a properly stratified null. Key design: - Objective: net points per trade after 3pt friction, using SL/TP brackets. This is what actually gets traded. - Null: stratified permutation. For each candidate rule with N entries, draw N random entry bars matched on (hour bucket, vol regime tercile) — the exact same distribution of session and volatility — and run the identical bracket. Repeat ~400x. This isolates the signal's contribution beyond "being in the market at that time in that vol state." - FDR control across all tested configs. - IS only (2015-2021). This is the right test. Mean reversion + vol clustering should show up here if it's real. Candidate families driven by s1/s2 findings: - MR-LONG: buy dips (close_pos low, near N-bar low, RSI low, z-score of close vs MA low) — supported by A_pos120_low +0.42, A_newlow120 +0.39 - MR-SHORT: sell rips (mirror) — needed for diversification, fights drift so likely weaker - Vol-expansion breakout: B_vol_exp_hi, B_rv_exp showed +0.25 - Session-based: RTH open behavior Bracket geometry grid: SL in 1.0, 1.5, 2.0, 2.5 ATR, TP in 0.5, 1.0, 1.5, 2.0, 3.0 ATR, max_bars in 12, 24, 48. That's a lot of configs. Let me keep the grid moderate and count tests honestly for FDR. Let me be smart about compute: the simulator is a Python loop. With ~2000 entries per config and 400 null reps, that's expensive. Let me vectorize first-passage with numpy for the null test instead of using `simulate`. Actually — better approach for efficiency and rigor: precompute, for every bar i and every (SL, TP, maxbars) geometry, the bracket outcome. That's a first-passage matrix. For a given geometry, I compute outcome for ALL bars once (vectorized-ish), then any rule is just a selection, and the null is just resampling from the same precomputed array within strata. That's massively faster and exactly right. Let me implement first_passage_all(d, sl_atr, tp_atr, max_bars, dir) -> array of net points per bar. Cost: n * max_bars operations. n=35000, max_bars=48 → 1.7M ops per geometry per direction. In numpy I can do this with a loop over j (max_bars) which is only 48 iterations of vectorized ops. Very fast. Let me write that. This is the key efficiency + rigor unlock. Implementation of vectorized first passage: - entry at open of i+1 - sl_px = entry - dirsl, tp_px = entry + dirtp - for j in 1..max_bars: check H[i+j], L[i+j] - track first hit; pessimistic: SL wins ties within same bar - else exit at close of i+max_bars Vectorized: maintain arrays `done`, `exit_px`, `exit_j`. Loop j from 1 to max_bars, update where not done. Costs: need hour at entry and exit. I'll compute spread cost using hour at entry and hour at exit index. Let me write it.40:o8,b4:T8f2,40:o8f2,Now let me write s3 which uses this. I'll define signal families based on the mean-reversion + vol clustering structure found, run a bracket grid, and apply the stratified null + FDR. Let me keep the number of tests controlled and pre-registered. I need to think about what to test: Signal families (driven by s1 findings, mean reversion + vol clustering): - MR long: buy when stretched down - z-score of close vs SMA(n) below -k - close_pos low (close in bottom of bar range) + near N-bar low - RSI < threshold - consecutive down bars - MR short: mirror (sell stretched up) - Vol-expansion continuation - Session/time filters as conditioners Bracket geometry: I want to reflect mean reversion → TP smaller than SL is typical for MR (high win rate). Grid: sl_atr in 1.0, 1.5, 2.0, 3.0 tp_atr in 0.5, 0.75, 1.0, 1.5, 2.0 max_bars in 12, 24, 48 = 60 geometries. Times ~30 signals × 2 directions... that's a lot of tests but FDR handles it. Compute: each geometry is one first_passage call (fast, 48 vectorized iterations). 60 geometries × 2 dirs = 120 first_passage calls. Fine. Then for each (signal, geometry) pair: selection + stratified null 400 reps. The null loop is Python over strata (15 strata) × 400 reps = 6000 iterations of rng.choice — per hypothesis. If I have 30 signals × 60 geoms = 1800 hypotheses, that's 10.8M rng.choice calls. Too slow. Optimization: the stratified null distribution depends ONLY on (geometry, stratum counts). I can precompute per-stratum mean/var analytically instead of resampling! For a stratified random draw where in stratum c we take k_c bars from pool with mean μ_c and variance σ²_c, the null mean of the overall average is Σ k_c μ_c / N and the variance is Σ k_c² (σ²_c / k_c) / N² = Σ k_c σ²_c / N². That's exact (with replacement) and instant. That's much better than 400 reps — it's the analytic version of the same test. The only thing I lose is the exact empirical shape, but with n_real ≥ 300 the CLT is excellent, and I can still report a normal-approx p. Let me use the analytic stratified null as the primary screen (fast, exact mean/var), and then run the full empirical permutation only on the top survivors for confirmation. That's the right engineering + rigor tradeoff. Let me add that to fp.py.40:o8,b5:Te96,40:oe96,Critical read of these results. Something is very wrong and I need to catch it. Look at `MR_S_rsi70`: exp = **-3.94 points** (NEGATIVE expectancy!) but "edge" = +3.32 and z=3.31. The null is -7.26. So the rule loses less than random. That's not a tradable edge — it's "less bad than random shorting." This is a crucial flaw in my objective: I'm measuring edge vs a stratified null, but the null itself is deeply negative for shorts (because of the structural long drift). A strategy that loses 3.94 pts/trade is NOT tradable, no matter how favorably it compares to a random short. Also `tpy = 420` — that's 420 trades/year, and n=2941 over 7 years. And exp is negative. Not tradable. And `MR_S_rsi70_dn200` has n=120 → 17 trades/yr, below the 50/yr floor, AND doesn't survive FDR (p_adj=0.063). So the honest summary: **BH-FDR survivors = 15/2820, of which 7 meet ≥50 trades/yr — but I must check whether any have POSITIVE net expectancy.** Looking at top 40, every high-z entry with decent n has NEGATIVE exp. The p_adj values shown are all > 0.05 in the top 40 (0.063+), which contradicts "15 survivors". Wait — the 15 survivors must be elsewhere. Sorting by z descending shows p_adj 0.063 at top... but BH-FDR pass was computed with q=0.05. If the top p_adj is 0.063, then NOTHING should pass. Let me check my bh_fdr function — there may be a bug. Actually yes, I think there's a bug in bh_fdr: `crit = ps[order][kmax.max()]` uses the largest index passing, but `out[idx] = ps <= crit` — this is right in principle. But the adjusted p and the pass flag can disagree if... hmm. Let me reconsider: if p_adj min is 0.063 > 0.05, no test should pass. But it says 15 passed. So there IS a bug. Let me look: `passed = ps[order] <= thr`. `kmax = np.flatnonzero(passed)`. If kmax is non-empty, crit = ps[order][kmax.max()]. That's standard BH. And p_adj = cummin from the right of ps*m/rank. These should be consistent: BH passes iff p_adj <= q. If min p_adj = 0.063 and 15 pass, inconsistent → bug. Hmm, wait. The `out` array: `out[idx] = ps <= crit`. But `idx` are positions where finite. `ps = p[idx]`. OK consistent. Let me just recheck the numbers by printing. Actually, the issue might be that the top-40-by-z table is not showing the smallest p_adj because z and p aren't monotonically related... but for a two-sided normal test they ARE monotone: |z| bigger → p smaller. But I sorted by z (signed!) descending, not |z|. So the most negative z values (strong negative edge) have small p too, and they'd be at the BOTTOM of the sort. Those are the 15 survivors — strategies that are *significantly worse* than the null! Those aren't tradable either (they'd be tradable inverted, but inverting a mean-reversion short into a momentum long is a different hypothesis and would need its own test... actually no, if a rule is significantly negative, the inverse is significantly positive, which IS informative). So: the 15 FDR survivors are probably the significantly-NEGATIVE ones. Let me verify and, crucially, restate the objective properly. The real problem: my objective (edge vs stratified null) is the wrong thing to rank on for tradability. I need BOTH: 1. Positive absolute net expectancy (tradable) 2. Statistically distinguishable from the stratified null (not luck) Let me fix the reporting to rank on absolute expectancy with significance as a filter, and also test the *inverse* of significantly-negative rules explicitly. Also — big realization — the fact that shorts have hugely negative null (-7.26 pts/trade) while longs presumably have positive null means the drift dominates everything. Let me check the long side numbers, which got truncated. Let me read the rest of the output.40:o8,b6:Te16,40:oe16,Confirmed. The only FDR survivors are `MR_S_z50_20` variants with expectancy of −17 to −21 points per trade. These are significantly WORSE than the null. That's the "significantly negative" set. Shorting a 2-sigma stretch-up on US30 is a reliable way to lose money — the drift crushes it. Important information content: the inverse — BUYING strength at z50 > 2.0 — would be significantly positive. That's a MOMENTUM long, not mean reversion! Interesting. But careful: inverting doesn't exactly work because SL/TP geometry doesn't invert symmetrically (a short with SL 2.0/TP 1.0 inverted is a long with SL 2.0/TP 1.0 at the same bars, but the outcome isn't the negative of the short's outcome — brackets are path-dependent and asymmetric). So I must TEST the long version directly, not infer it. So the key discovery so far: - Mean-reversion shorts are reliably BAD (drift). - Nothing on the long side reached significance in this grid. - The strongest structure remains the drift itself. But wait — I need to check the LONG side properly. The top-40 by z was ALL shorts. That means longs had low z. Why? Because for longs the stratified null is already strongly positive (drift), so beating it is hard. A long rule can have great absolute expectancy but zero edge vs "just be long." This is the crux insight: on US30, "being long" is the edge. Any long rule inherits it. The stratified null correctly strips that out, and once stripped, no timing signal survives. This is an honest and important finding. But it does NOT mean no tradable strategy exists. It means: - A tradable long strategy's expectancy comes from drift + time-in-market, not timing. - To build something with genuine timing edge that passes rigorous tests, I need to look where the drift doesn't dominate. Let me now reconsider. I've used a lot of budget. Let me step back and think about what the deliverable should be. The brief (which I read) asks for multiple engines, portfolio, Pine v6, walk-forward, etc. Given my findings, the honest deliverable is: The research says: directional timing edge on US30 60M is not statistically detectable after proper controls. The tradable structures that ARE real: - Long drift (huge, robust) - Volatility clustering (for position sizing, not direction) - Mean reversion in path (VR 0.88) — but shorts lose to drift, so this must be expressed as LONG-ONLY dip buying So the correct strategy family: long-only dip-buying with vol-scaled brackets, whose absolute expectancy is strongly positive (inherits drift + reversion), sized by vol state. And I must be transparent that its edge vs "always long" is not statistically significant — meaning it's a risk-managed expression of the drift, not an alpha discovery. Let me now run the decisive test on the long side with absolute expectancy as the objective, plus check: does dip-buying beat buy-and-hold on RISK-ADJUSTED terms (Sharpe, MDD)? That's a legitimate and testable claim even if mean-edge isn't significant. Reducing time-in-market while keeping return = real value. Let me run s4 focused on: - Long-only entries, absolute net expectancy positive - Compare to buy&hold on Sharpe/MDD/exposure - Then walk-forward + OOS Let me write s4 to find the best long-only configs by absolute expectancy with significance vs the null reported honestly, and require tpy>=50. I'm going to be efficient now. Let me write one comprehensive s4 that does long-side ranking + WFO + OOS. Actually let me first just quickly look at the long side stats from the saved CSV to ground the next step. That's cheap and decisive.40:o8,b7:Te20,40:oe20,Now I have the complete, honest picture. Let me lay it out: The verdict is unambiguous: - LONG side: 0 rules beat a matched random long at 10% FDR. Best long p_adj = 0.38. Nothing close. - SHORT side: only significant results are significantly NEGATIVE (-17 to -21 pts/trade). - Buy & hold IS: 2,645 pts/year, 38.4% MDD, Sharpe 0.72. Best long rule `MR_L_z20_hivol` (SL 3.0 ATR, TP 2.0 ATR, 48 bars): exp +18.9 pts × 164 trades/yr = ~3,100 pts/year with far less than 100% exposure. But z=1.60, p_adj=0.61 — NOT significant vs a matched random long. This is the crucial honest interpretation: dip-buying with wide brackets produces positive expectancy that is statistically indistinguishable from just being long in the same session/vol conditions. It's beta, not alpha. Its value proposition is risk-adjusted — less exposure, potentially lower drawdown — not timing skill. Now, what do I deliver? The brief asked for a full system with multiple engines, portfolio, Pine v6, walk-forward. But my research says the honest answer is: there is no statistically defensible directional timing alpha on US30 60M/240M in this dataset. I have two choices: (A) Ignore the science and build 3 curve-fit "engines" that look great on IS. This is what most people do and it's exactly what the rigorous brief was designed to prevent. (B) Report the null honestly, and build the ONE thing the data does support, with fully transparent framing. (B) is clearly right. And I must not overclaim. What DOES the data support? - Long drift — massive, robust, the dominant tradable feature. - Volatility clustering — acf|ret| 0.33, highly predictable. This is real and exploitable for POSITION SIZING and for choosing when a bracket is favorable. - Mean reversion in path (VR 0.88) — supports buying dips over buying breakouts, and supports wide-SL/moderate-TP geometry. - Shorting mean-reversion is reliably destructive → strategy should be long-only. So the defensible deliverable: a long-only, dip-buying, vol-conditioned engine whose claim is risk-adjusted improvement over buy & hold, not alpha. And I must validate THAT claim on OOS with walk-forward. That's a testable, honest claim. But before I commit, I should run one more genuinely important test that I haven't: does the strategy beat buy&hold on risk-adjusted terms OOS? And critically — I should test whether reduced exposure + vol targeting improves Sharpe. Vol targeting is the most robust finding in all of quant finance and my data directly supports it (vol clustering acf 0.33). Let me now build the final validation: - Engine: long-only dip buy, vol-conditioned, wide bracket - Walk-forward with parameter re-selection - OOS test 2022-2026 - Compare vs buy&hold on Sharpe, MDD, exposure - Report honestly Let me be efficient. I'll write s5 that does WFO + OOS on the long engine and the buy&hold comparison, then decide what to deliver. Given budget, let me make s5 comprehensive and decisive. I'll test a small, pre-registered set of long-only configs (chosen for robustness — plateau not peak), walk them forward, and evaluate OOS. Key: I should select parameters by PLATEAU robustness, not peak. Looking at the results, SL=3.0/TP=2.0 dominates the top — that's consistent across many signals (z20_hivol, rsi30_us, z20_rth, z50_20, lo50, z20_us, pm_dip). That's a genuine plateau, good sign. mb=48 and 24 both work. Signal families that repeat: z20 < -1.5 (dip), with vol/session conditioning. So: dip-buy (z20 < -1.5), SL 3.0 ATR, TP 2.0 ATR, 48 bars max. Robust plateau. Let me run WFO + OOS now. This is the moment of truth.40:o191fe,48:["$","$L8f",null,{"params":{"chatId":"gMXr9Fzu4zH"},"promises":{"attributes":"$@99","externalMeta":"$@9a","latest":"$@9b","messagesRange":"$@9c","messages":"$@9d","template":"$@9e","gitStatus":"$@9f"},"children":["$","$La0",null,{"chatId":"gMXr9Fzu4zH","children":["$","$La1",null,{"seedOnlyKeys":["@\"chat:state\",\"gMXr9Fzu4zH\","],"data":{"block:output:initial":{"id":"b_k1LdJ8Wg1VR","result":"$@a2"},"/chat/api/snapshots/proxy?chatId=gMXr9Fzu4zH&messageId=WjSwzCSVath42QMx2OdJUCVK8gZrFljK&v=v2":"$a3","@\"block:mappings\",\"gMXr9Fzu4zH\",":{"sqlBlockExecutionStates":"$Qa4","blocksForkedFrom":"$Qa5","blocksTemplateIds":"$Qa6","blocksCreatedAt":"$Qa7","blocksShared":"$Qa8","blocksDeployed":"$Qa9","blocksSubdomains":"$Qaa","brandedBlocks":"$Qab","unlistedBlocks":"$Qac","blockOutputs":"$Qad"},"@\"chat:state\",\"gMXr9Fzu4zH\",":{"messages":[{"content":{"type":"parts","parts":[{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USA30IDXUSD%20240%20MINUTE-RRtImW0c2fSWAhcH3Up8ZXOQHBSAU2.csv","name":"USA30IDXUSD 240 MINUTE.csv","contentType":"text/csv","size":1295839},{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USA30IDXUSD%2060%20%20MINUTE-jabvn5m76b2S5B4SJA8dF1HE2PI44s.csv","name":"USA30IDXUSD 60 MINUTE.csv","contentType":"text/csv","size":4616004},{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/%5BPasted%20438%20lines%5D-V2d0etySeBob9FqvxftnujvKOWntu9","name":"[Pasted 438 lines]","contentType":"text/plain","size":9671}],"version":1},"role":"user","id":"sdFUNavFlI57hED3WRnKun3Y3IRTZWoX","attachments":[{"url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USA30IDXUSD%2060%20%20MINUTE-jabvn5m76b2S5B4SJA8dF1HE2PI44s.csv","name":"USA30IDXUSD 60 MINUTE.csv","size":4616004,"contentType":"text/csv"},{"url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/%5BPasted%20438%20lines%5D-V2d0etySeBob9FqvxftnujvKOWntu9","name":"[Pasted 438 lines]","size":9671,"contentType":"text/plain"},{"url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USA30IDXUSD%20240%20MINUTE-RRtImW0c2fSWAhcH3Up8ZXOQHBSAU2.csv","name":"USA30IDXUSD 240 MINUTE.csv","size":1295839,"contentType":"text/csv"}],"type":"message","authorId":"P3H0yqVnQyqfVXliFPAGnj9n","createdAt":"$D2026-08-04T00:47:42.608Z"},{"v0ModelId":"v0-opus-5","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785804465238,"finishedAt":1785804465238,"lastPartSentAt":1785804465238,"id":"WVfOEsjnBrfwqEDx","type":"task-start-v1","parts":[{"type":"launch-tasks"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804465238,"finishedAt":1785804465689,"lastPartSentAt":1785804465642,"taskNameActive":"Reading [Pasted-438-lines]","taskNameComplete":"Read [Pasted-438-lines]","toolCallId":"tool_FACUM8w3CFnuTe7C","agentTurnId":"turn_Eb3nyrK2YUsINT15","filePath":null,"id":"NmDsDQfjJLQ8ov22","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/text_attachments/[Pasted-438-lines]-V2d0e"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804465238,"finishedAt":1785804465622,"lastPartSentAt":null,"taskNameActive":"Reading USA30IDXUSD-240-MINUTE.csv","taskNameComplete":"Read USA30IDXUSD-240-MINUTE.csv","toolCallId":"tool_bfHGaj12Wgsm0FaY","agentTurnId":"turn_Eb3nyrK2YUsINT15","filePath":null,"id":"yiheRVbanY4g6MgE","type":"task-read-file-v1","parts":[]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804465238,"finishedAt":1785804465631,"lastPartSentAt":null,"taskNameActive":"Reading USA30IDXUSD-60-MINUTE.csv","taskNameComplete":"Read USA30IDXUSD-60-MINUTE.csv","toolCallId":"tool_VAZwpeFxfQqRBytl","agentTurnId":"turn_Eb3nyrK2YUsINT15","filePath":null,"id":"wReiA4PgveITFXdw","type":"task-read-file-v1","parts":[]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804474104,"finishedAt":1785804479114,"lastPartSentAt":1785804479114,"taskNameActive":null,"id":"iGHpCUcNQ01jM5tc","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"I need to set up a computational pipeline for analyzing US30 data across multiple timeframes, so I'll start by organizing the CSV files and building Python scripts to run hypothesis discovery, backtesting with realistic costs, walk-forward validation, and Monte Carlo simulations before generating the Pine Script strategy and final report. First, let me verify Python is available on this Linux VM.\n\nBefore diving into the analysis, I should clarify a couple of parameters to avoid wasting compute cycles on misaligned assumptions — specifically, what minimum trade frequency per year would be acceptable, and whether the focus should be on discovering a single best strategy or building a diversified portfolio approach."}]}}],["p",{},["text",{},"I'll run this as a real research pipeline — actual statistical analysis on your US30 data in Python, then a validated Pine v6 implementation. Two quick calibration questions before I start."]],["AssistantMessageContentPart",{"part":{"createdAt":1785804486834,"finishedAt":1785804487891,"stopped":{"reason":"user-input-required"},"lastPartSentAt":1785804487869,"taskNameActive":"Calibrating research constraints","taskNameComplete":"Calibrated research constraints","toolCallId":"toolu_01WUupDiMwVvMhh9BnMEXGgy","agentTurnId":"turn_Eb3nyrK2YUsINT15","filePath":null,"id":"71O5O1SjIyF3vw4v","type":"task-ask-user-questions-v1","parts":[{"type":"asking-questions","questions":[{"id":"q-0","question":"What minimum trade frequency must each engine (and the portfolio) achieve?","header":"Trade freq","options":[{"id":"q-0-opt-0","label":"≥50 trades/year per engine (Recommended)","description":"Good balance: large sample for statistics, still selective enough to keep edge per trade well above costs."},{"id":"q-0-opt-1","label":"≥100 trades/year per engine","description":"More activity, but each trade's net edge must clear costs — will likely force intraday 60M signals only."},{"id":"q-0-opt-2","label":"≥20 trades/year per engine","description":"Highly selective. Bigger average trade, but slower statistical accumulation per engine."},{"id":"q-0-opt-3","label":"No minimum — maximize robustness","description":"Let the data decide frequency; rank purely on survival probability."}],"multiSelect":false},{"id":"q-1","question":"What execution costs should I model for US30 (Dow CFD)?","header":"Exec costs","options":[{"id":"q-1-opt-0","label":"Typical retail CFD: 2.0pt spread + 1.0pt slippage (Recommended)","description":"Conservative baseline: ~3pt round-trip friction, with spread spikes modeled at session opens and news windows."},{"id":"q-1-opt-1","label":"Tight/institutional: 1.0pt spread + 0.5pt slippage","description":"Optimistic. Only realistic on ECN-style pricing during RTH."},{"id":"q-1-opt-2","label":"Wide/worst case: 3.5pt spread + 1.5pt slippage","description":"Stress-first approach. Only very large-edge strategies survive."},{"id":"q-1-opt-3","label":"I'll specify my broker's actual costs","description":"Tell me your spread, commission per lot, and typical slippage."}],"multiSelect":false}]}]}}]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"HLQ1rMlFXwsQ6IbF9TvXLAwQNADlqn9o","gitRevision":{"headSha":"21faa46f1540c20c2c1e23a0f1877ea1d3c5b341","readyAt":"2026-08-04T00:48:11.021Z","version":1},"streaming":false,"finishReason":"tool-calls","parentId":"sdFUNavFlI57hED3WRnKun3Y3IRTZWoX","type":"message","creditCost":0.302801,"workMetrics":{"diffMap":{},"linesRead":438,"agentActions":4},"createdAt":"$D2026-08-04T00:47:42.609Z","updatedAt":"$D2026-08-04T00:48:11.046Z"},{"content":{"type":"parts","parts":[{"type":"answered-questions","answers":[{"questionId":"q-0","questionText":"What minimum trade frequency must each engine (and the portfolio) achieve?","selectedLabels":["≥50 trades/year per engine (Recommended)"]},{"questionId":"q-1","questionText":"What execution costs should I model for US30 (Dow CFD)?","selectedLabels":["Typical retail CFD: 2.0pt spread + 1.0pt slippage (Recommended)"]}]}],"version":1},"role":"user","id":"zPjCRb7GTP0r251AiY9YszWO05P5zqcy","attachments":[],"parentId":"HLQ1rMlFXwsQ6IbF9TvXLAwQNADlqn9o","type":"answered-questions","authorId":"P3H0yqVnQyqfVXliFPAGnj9n","createdAt":"$D2026-08-04T00:50:11.572Z"},{"v0ModelId":"v0-opus-5","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785804617691,"finishedAt":1785804618127,"lastPartSentAt":1785804618127,"taskNameActive":null,"id":"PwOELUBmTHvQEe3t","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to read the full context from the pasted file since the initial read was truncated, so I'll try fetching it with specific offset and limit parameters to get the complete brief."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804618467,"finishedAt":1785804619582,"lastPartSentAt":1785804619557,"taskNameActive":"Reading the brief","taskNameComplete":"Read the brief","toolCallId":"toolu_011f8HfjBdL4xKV4osmQpGDi","agentTurnId":"turn_w84eImWr7WMmV3Jz","filePath":null,"id":"cYXF2Wo8Zksr278t","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/text_attachments/[Pasted-438-lines]-V2d0e"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804624431,"finishedAt":1785804625516,"lastPartSentAt":1785804625516,"taskNameActive":null,"id":"8FSBJcBhyv6sWXj0","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"Let me set up the research environment by checking Python availability on this Linux VM, then copy the CSV files to a working directory and examine their structure. I'll build out a Python pipeline that generates hypotheses, runs statistical tests, backtests with transaction costs, performs walk-forward validation, and Monte Carlo analysis, then wrap everything up with a report and Pine Script v6 implementation."}]}}],["p",{},["text",{},"Brief loaded. Let me set up the data and check the research environment."]],["AssistantMessageContentPart",{"part":{"createdAt":1785804626340,"finishedAt":1785804630383,"lastPartSentAt":1785804630194,"taskNameActive":"Checking Python environment","taskNameComplete":"Checked Python environment","toolCallId":"toolu_01BnWdbvJTJyhN6TiqopMEPC","agentTurnId":"turn_e2rS4zgIFrVbB7hF","filePath":null,"id":"odH1cxSajLk62pJX","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && python3 --version && python3 -c \"import numpy, pandas; print('numpy',numpy.__version__,'pandas',pandas.__version__)\" 2>&1 | tail -5; ls"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"raiyimBO9rZj7ilM"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Python 3.13.11\nTraceback (most recent call last):\n File \"\", line 1, in \n import numpy, pandas; print('numpy',numpy.__version__,'pandas',pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\napp\n"},"id":"FdoNh2Bm5egArSPv"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"components\ncomponents.json\nlib\nnext-env.d.ts\nnext.config.mjs\nnode_modules\npackage.json\npnpm-lock.yaml\npostcss.config.mjs\npublic\ntsconfig.json\n"},"id":"BbFReRu8TWyA68No"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804628865,"finishedAt":1785804629434,"lastPartSentAt":1785804629386,"taskNameActive":"Inspecting CSV structure","taskNameComplete":"Inspected CSV structure","toolCallId":"toolu_01AHdCWY8hu89DBwF9ApoiHn","agentTurnId":"turn_e2rS4zgIFrVbB7hF","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"Retrying file access","code":"AGENT_GUIDANCE"},"id":"A3KxfgVHOHXzWy0L","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && head -4 \"user_read_only_context/text_attachments/USA30IDXUSD-60-MINUTE-jabvn.csv\" && echo \"---TAIL---\" && tail -2 \"user_read_only_context/text_attachments/USA30IDXUSD-60-MINUTE-jabvn.csv\" && echo \"===240===\" && head -4 \"user_read_only_context/text_attachments/USA30IDXUSD-240-MINUTE-RRtIm.csv\" && echo \"---TAIL---\" && tail -2 \"user_read_only_context/text_attachments/USA30IDXUSD-240-MINUTE-RRtIm.csv\""}}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804634158,"finishedAt":1785804634383,"lastPartSentAt":1785804634383,"taskNameActive":null,"id":"XL5VCOqD5dId5r7p","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to set up the data files first by copying the CSVs into the project directory, then install the required dependencies like numpy and pandas."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_dvy1hJjWJfO","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/data/us30_60m.csv","baseName":"us30_60m.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/data/us30_240m.csv","baseName":"us30_240m.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]csv:file=\"/research/data/us30_60m.csv\" isCopied=\"true\" isCopiedFrom=\"user_read_only_context/text_attachments/USA30IDXUSD-60-MINUTE-jabvn.csv\"\nGENERATING\n[V0_FILE]csv:file=\"/research/data/us30_240m.csv\" isCopied=\"true\" isCopiedFrom=\"user_read_only_context/text_attachments/USA30IDXUSD-240-MINUTE-RRtIm.csv\"\nGENERATING","latestDeploymentInfo":"$a9:0:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_5ejih_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_dvy1hJjWJfO","meta":{"taskNameActive":"Copying 240M data","taskNameComplete":"Copied 240M data","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Copying 240M data","taskNameComplete":"Copied 240M data"},"id":"block-b_dvy1hJjWJfO"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804642703,"finishedAt":1785804646885,"lastPartSentAt":1785804646559,"taskNameActive":"Installing scientific stack","taskNameComplete":"Installed scientific stack","toolCallId":"toolu_01PkfhhNNYueCwdQQGgcn3XV","agentTurnId":"turn_bDkkNdqqjJp3Wtyl","filePath":null,"id":"Xvq1pyZuMXT1hhbq","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && (pip3 install --quiet numpy pandas scipy 2>&1 | tail -3 || pip3 install --quiet --break-system-packages numpy pandas scipy 2>&1 | tail -3); 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For the installation, I'll use uv with the system flag."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804651699,"finishedAt":1785804654318,"lastPartSentAt":1785804654102,"taskNameActive":"Installing scientific stack","taskNameComplete":"Installed scientific stack","toolCallId":"toolu_01C2CgHNZwyxfrCZmXMgaBx6","agentTurnId":"turn_n5KoIAwOZMeutOem","filePath":null,"id":"dRNGh4opQQaC9Psp","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && pip3 install --system --quiet numpy pandas scipy 2>&1 | tail -5; python3 -c \"import numpy,pandas,scipy;print('ok',numpy.__version__,pandas.__version__,scipy.__version__)\"","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"EkvvqzSaFYhdYYc3"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\nTraceback (most recent call last):\n File \"\", line 1, in \n import numpy,pandas,scipy;print('ok',numpy.__version__,pandas.__version__,scipy.__version__)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"390jnHTRVUQYCJts"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804658910,"finishedAt":1785804662860,"lastPartSentAt":1785804662631,"taskNameActive":"Creating venv and installing","taskNameComplete":"Created venv and installed","toolCallId":"toolu_01QrL6UsRxNzdtXmyu8jQ3N8","agentTurnId":"turn_en97Eo48WJTPV9Eb","filePath":null,"id":"4yInUHcXHYvuQ2Dg","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && (uv venv .venv 2>&1 | tail -2) && (uv pip install --python .venv/bin/python numpy pandas scipy 2>&1 | tail -3) && .venv/bin/python -c \"import numpy,pandas,scipy;print('ok',numpy.__version__,pandas.__version__,scipy.__version__)\"","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"1EWJ5SPUVccG1uht"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Creating virtual environment at: .venv\nActivate with: source .venv/bin/activate\n"},"id":"RboQNhehhIEDmoPc"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" + python-dateutil==2.9.0.post0\n + scipy==1.18.0\n + six==1.17.0\n"},"id":"PJ6IpAp1A7oxcIzp"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"ok 2.5.1 3.0.5 1.18.0\n"},"id":"Pk7OnfDOmCdHxOwP"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804668088,"finishedAt":1785804695861,"lastPartSentAt":1785804695861,"taskNameActive":null,"id":"3rfqPv5LCvPg0ElZ","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":28,"thought":"$ae"}]}}],["p",{},["text",{},"Environment ready. Data spans 2015-01-02 → 2026-06-23 (61,031 hourly bars, 17,224 4H bars). Let me build the research core and characterize the asset before forming any hypothesis."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_ReEaYdvZzpX","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:1:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_ichov_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_ReEaYdvZzpX","meta":{"taskNameActive":"Writing research core","taskNameComplete":"Wrote research core","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing research core","taskNameComplete":"Wrote research core"},"id":"block-b_ReEaYdvZzpX"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804747357,"finishedAt":1785804747594,"lastPartSentAt":1785804747594,"taskNameActive":null,"id":"SAsmKAuhnFRxLbYD","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"Starting with the characterization phase."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_SZCXmYu5X23","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/s1_characterize.py","baseName":"s1_characterize.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/s1_characterize.py\" \nGENERATING","latestDeploymentInfo":"$a9:2:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_h9u26_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_SZCXmYu5X23","meta":{"taskNameActive":"Writing characterization script","taskNameComplete":"Wrote characterization script","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing characterization script","taskNameComplete":"Wrote characterization script"},"id":"block-b_SZCXmYu5X23"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804777820,"finishedAt":1785804780340,"lastPartSentAt":1785804780050,"taskNameActive":"Characterizing market","taskNameComplete":"Characterized market","toolCallId":"toolu_01C9SVLzZAJxCsp2uSFXc9cv","agentTurnId":"turn_ExRmIW7wDokBiA0C","filePath":null,"id":"kMhMmh6iWywsYEzn","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s1_characterize.py 2>&1 | head -230","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"gz1mQGvgvK1Jntox"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"==============================================================================\nDATA INTEGRITY\n==============================================================================\n60M: 61030 bars 2015-01-02 00:00:00 -> 2026-06-23 23:00:00\n dupes=0 nan=3"},"id":"rFqdqIkywGm6BxSQ"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"05150\n OHLC violations=0\n zero-range bars=0 zero-vol bars=0\n240M: 17224 bars 2015-01-02 00:00:00 -> 2026-06-23 20:0\n\n...\n\n 4932 NaN NaN\n8 4766 NaN NaN\n9 4846 NaN NaN\n10 5047 NaN NaN\n11 4789 NaN NaN\n12 4778 NaN NaN\n"},"id":"HEx0q5iO7xOGrkr6"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_uMfO4ot953D","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING","latestDeploymentInfo":"$a9:3:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_1u566_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_uMfO4ot953D","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_uMfO4ot953D"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804786037,"finishedAt":1785804788512,"lastPartSentAt":1785804788512,"taskNameActive":null,"id":"aD4yvhUywq5qjDUW","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"I'm noticing all 305,150 values are NaN across the entire dataframe — that's 61,030 rows times 5 columns. The CSV loaded but the column references like `df[\"Open\"]` are returning NaN, which suggests either the header has formatting issues like a BOM or trailing spaces, or there's a mismatch between how the Series are being indexed when constructing the dataframe with the datetime index.\n\nThe real culprit is likely that when I'm creating the dataframe from a dictionary of Series and forcing a datetime index, pandas is reindexing those Series to match the new index, causing all values to become NaN since the original RangeIndex doesn't align. I need to either reset the index on those Series first or use `.values` to extract the raw data before constructing the dataframe."}]}}],["p",{},["text",{},"Found a bug — pandas reindexed the Series against the datetime index, nulling everything. Fixing the loader."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_Eu6rZ7Pm5Qf","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:4:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_l6139_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_Eu6rZ7Pm5Qf","meta":{"taskNameActive":"Fixing loader","taskNameComplete":"Fixed loader","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Fixing loader","taskNameComplete":"Fixed loader"},"id":"block-b_Eu6rZ7Pm5Qf"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804795726,"finishedAt":1785804799016,"lastPartSentAt":1785804798757,"taskNameActive":"Characterizing market","taskNameComplete":"Characterized market","toolCallId":"toolu_01Y5tteyAjLV3VAVDsVL6ae9","agentTurnId":"turn_jejNG6BZjQkdm2Nm","filePath":null,"id":"1edbthc44KrtVHmG","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s1_characterize.py > s1_out.txt 2>&1; sed -n '1,110p' s1_out.txt","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"GeTB28SBTpgcODII"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"==============================================================================\nDATA INTEGRITY\n==============================================================================\n60M: 61030 bars 2015-01-02 00:00:00 -> 2026-06-23 23:00:00\n dupes=0 n\n\n...\n\n1: acf(ret)=-0.03306 acf(|ret|)= 0.03366\n lag 2: acf(ret)= 0.00001 acf(|ret|)= 0.00005\n lag 3: acf(ret)=-0.00009 acf(|ret|)= 0.00004\n lag 4: acf(ret)= 0.00003 acf(|ret|)= 0.00001\n lag 6: acf(ret)=-0.00017 acf(|ret|)= 0.00010\n"},"id":"672GfNt3H5Y4O0nZ"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_FTAS7agCrjg","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s1_out.txt","baseName":"s1_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s1_out.txt\" \nGENERATING","latestDeploymentInfo":"$a9:5:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_7wx0u_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_FTAS7agCrjg","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_FTAS7agCrjg"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804804542,"finishedAt":1785804807299,"lastPartSentAt":1785804807299,"taskNameActive":null,"id":"OGLDKv030WNqORvk","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":3,"thought":"I'm spotting some serious data quality issues in hours 2 and 8 — the true range values are hitting around 1000 points with volumes in the 1e18 to 1e19 range, which screams corrupted bars. The mean volume at hour 2 alone is 1.58e18, suggesting a few bars with wildly inflated prices or volumes are skewing everything. I should dig into these outliers to identify and clean up the bad data. I'm also noting that Sunday (weekday 6) shows 717 bars from the session open, and there's something about Friday that got cut off.\n\nThe 4-hour file matches the resampled 60-minute data perfectly with zero difference, which is reassuring — it confirms the alignment at the expected times and consistent timezone handling. Now I need to hunt down those corrupted bars."}]}}],["p",{},["text",{},"Two critical data anomalies at hours 2 and 8 (TR ≈ 1000pts, volume ≈ 1e18). Let me isolate them before any hypothesis work."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_GiBjAvfx98h","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s1_out.txt","baseName":"s1_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/s1b_outliers.py","baseName":"s1b_outliers.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s1_out.txt\" \nGENERATING\n[V0_FILE]py:file=\"/research/s1b_outliers.py\" \nGENERATING","latestDeploymentInfo":"$a9:6:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_k47uc_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_GiBjAvfx98h","meta":{"taskNameActive":"Writing outlier audit","taskNameComplete":"Wrote outlier audit","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing outlier audit","taskNameComplete":"Wrote outlier audit"},"id":"block-b_GiBjAvfx98h"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804821773,"finishedAt":1785804824828,"lastPartSentAt":1785804824494,"taskNameActive":"Auditing outliers","taskNameComplete":"Audited outliers","toolCallId":"toolu_015zc2LUbKReVtgw1nLRGtMw","agentTurnId":"turn_i1qbtpiQ2NGIvmfY","filePath":null,"id":"ByYENQZVGdbFc0kB","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s1b_outliers.py > s1b_out.txt 2>&1; sed -n '1,75p' s1b_out.txt","timeout":300000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"mxrKbFkv2drcuQDv"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=== top 25 bars by RANGE ===\n open high low close vol tr rng hour\ndt "},"id":"QVihUh8PmzKfGUo6"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" \n2022-07-28 02:00:00 3595.089 960282.250 -1107230.666 960282.207 3.449679e+21 2067512.916 2067512.916 \n\n...\n\n 19\n2018-12-27 20:00:00 22545.669 23139.669 22460.669 23139.169 2.834880e+00 679.000 679.000 20\n"},"id":"TPHRythRYCBRkr3U"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_dFX7voD56En","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/s1b_out.txt","baseName":"s1b_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/s1b_out.txt\" \nGENERATING","latestDeploymentInfo":"$a9:7:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_1wroa_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_dFX7voD56En","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_dFX7voD56En"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804830358,"finishedAt":1785804835692,"lastPartSentAt":1785804835692,"taskNameActive":null,"id":"i7INwOSA31T3CHr7","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"I've identified exactly two corrupted data points in the dataset: one from July 2022 and another from August 2022, both showing impossible negative prices and inflated highs. The rest of the data looks legitimate—the COVID dip in 2020 and the tariff-related crash in April 2025 are real market events. Since these two bars are outliers among over 61,000 data points, I'll repair them by clamping their values to a plausible range based on neighboring bars rather than dropping them entirely.\n\nI'm building a sanitization function that detects price deviations exceeding 20% from the previous close, then replaces the corrupted OHLC values with the prior close to create a flat bar, marking it as invalid so no trading signals trigger there. Once I implement this and re-run the analysis, I should get clean statistics without the noise from these feed errors."}]}}],["p",{},["text",{},"Exactly 2 corrupted feed bars (negative prices, 960k highs): 2022-07-28 02:00 and 2022-08-18 08:00. Everything else (COVID 2020, April 2025 tariff crash) is genuine. Adding a sanitizer."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_MArAkSpMKFW","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/s1b_out.txt","baseName":"s1b_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/s1b_out.txt\" \nGENERATING\n[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:8:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_xlbvl_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_MArAkSpMKFW","meta":{"taskNameActive":"Adding sanitizer","taskNameComplete":"Added sanitizer","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding sanitizer","taskNameComplete":"Added sanitizer"},"id":"block-b_MArAkSpMKFW"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_BhHNA0MIlT0","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/s1_characterize.py","baseName":"s1_characterize.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/s1_characterize.py\" \nGENERATING","latestDeploymentInfo":"$a9:9:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_glkt7_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_BhHNA0MIlT0","meta":{"taskNameActive":"Applying sanitizer to characterization","taskNameComplete":"Applied sanitizer to characterization","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying sanitizer to characterization","taskNameComplete":"Applied sanitizer to characterization"},"id":"block-b_BhHNA0MIlT0"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804855079,"finishedAt":1785804858929,"lastPartSentAt":1785804858575,"taskNameActive":"Re-characterizing clean data","taskNameComplete":"Re-characterized clean data","toolCallId":"toolu_01AjCij9LhzGeX9drRKrC463","agentTurnId":"turn_SKK10KoLloguTFE4","filePath":null,"id":"Bwb1azDq0XCOoMQb","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s1_characterize.py > s1_out.txt 2>&1; sed -n '55,120p' s1_out.txt","timeout":300000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"PN469S7u0JB66eeG"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"23 2171\n\n==============================================================================\nSESSION / TIMEZONE INFERENCE (mean TR in points by server hour)\n==============================================================================\n tr "},"id":"36wZyc4mlKNEAey1"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" rng absret vol n tr_pctofday\nhour \n0 62.827 62.7\n\n...\n\nR>1 trending, <1 mean-reverting), 60M log-ret\n q= 2: VR=0.5072\n q= 3: VR=0.3437\n q= 4: VR=0.2612\n q= 6: VR=0.1787\n"},"id":"nwPHOl72pDE35dJM"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_fl8RBz8nEq9","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s1_out.txt","baseName":"s1_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s1_out.txt\" \nGENERATING","latestDeploymentInfo":"$a9:10:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_wk9ma_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_fl8RBz8nEq9","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_fl8RBz8nEq9"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804864491,"finishedAt":1785804872935,"lastPartSentAt":1785804872935,"taskNameActive":null,"id":"s1PqxxZo3LJdk15Q","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":8,"thought":"Hours 8 and 9 show inflated true range values around 384 compared to normal range of 69, which suggests the sanitization process repaired a corrupt bar by using the previous close, creating a gap effect. Looking at the data across thousands of bars, this pattern appears systematic—driven by a few enormous gaps at hour 8 where absolute return is 349, much larger than the typical range of 69.\n\nThe 2022 data is particularly problematic with a return standard deviation of 0.388 (roughly 39% volatility) and normal range of 116, indicating corrupt close values still remain. My deviation test flagged the corrupt bar at 2022-08-18 08:00 with close 33951, but there may be other corrupt bars with internally consistent OHLC values around extreme prices like 960282 that wouldn't show up in the top range list. I need to directly scan for close values outside the plausible band of 10000 to 60000."}]}}],["p",{},["text",{},"Still anomalies in 2022 (ret_sd 0.388). There must be corrupt bars that are internally consistent and thus escaped the range test. Let me scan the price band directly."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_fRo4ip8N5Ub","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s1_out.txt","baseName":"s1_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/s1c_band.py","baseName":"s1c_band.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s1_out.txt\" \nGENERATING\n[V0_FILE]py:file=\"/research/s1c_band.py\" \nGENERATING","latestDeploymentInfo":"$a9:11:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_1augo_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_fRo4ip8N5Ub","meta":{"taskNameActive":"Scanning price band","taskNameComplete":"Scanned price band","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Scanning price band","taskNameComplete":"Scanned price band"},"id":"block-b_fRo4ip8N5Ub"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804885310,"finishedAt":1785804887849,"lastPartSentAt":1785804887555,"taskNameActive":"Scanning price band","taskNameComplete":"Scanned price band","toolCallId":"toolu_01KS6VXC5kzAsRQT9m6Ykw88","agentTurnId":"turn_GYbv9ziRDlvW5ICb","filePath":null,"id":"XVTSIaQEape4IOYE","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s1c_band.py 2>&1 | head -80","timeout":300000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Ho2sOh3fTAFbiKmG"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\n===== 60M: 2 bars outside band 10k-70k =====\n\n-- row 38380 @ 2022-07-28 02:00:00 --\n open high low close vol\ndt "},"id":"bcZyir3v9Q7QBX9k"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" \n2022-07-27 17:00:00 31858.289 31874.999 31797.219 31849.209 2.657880e+00\n2022-07-27 18:00:00 31851.219 \n\n...\n\n 33837.209 33953.719 1.272774e+01\n2022-08-18 16:00:00 33953.747 34046.269 33840.239 34008.709 1.165926e+01\n"},"id":"pPHihFUOmHkoyHjp"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804893189,"finishedAt":1785804907204,"lastPartSentAt":1785804907204,"taskNameActive":null,"id":"fuIrbUHZNlp695xl","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":14,"thought":"$af"}]}}],["p",{},["text",{},"Root cause found: the second corrupt bar was repaired against the "],["em",{},["text",{},"first"]],["text",{}," corrupt bar's close. There's also a genuine 3-week data gap (2022-07-28 → 2022-08-18). Rewriting the sanitizer with a robust rolling-median reference plus segment discontinuity handling."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_rbxfkOkM2RU","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:12:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_abch4_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_rbxfkOkM2RU","meta":{"taskNameActive":"Hardening sanitizer","taskNameComplete":"Hardened sanitizer","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Hardening sanitizer","taskNameComplete":"Hardened sanitizer"},"id":"block-b_rbxfkOkM2RU"}],["p",{},["text",{},"Now let me make the simulator segment-aware so no trade can span the data hole."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_oVXkdKI0dTE","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:13:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_gmjf4_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_oVXkdKI0dTE","meta":{"taskNameActive":"Making simulator segment-aware","taskNameComplete":"Made simulator segment-aware","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Making simulator segment-aware","taskNameComplete":"Made simulator segment-aware"},"id":"block-b_oVXkdKI0dTE"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_veYKWTml85y","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:14:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_pie6n_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_veYKWTml85y","meta":{"taskNameActive":"Adding segment guard","taskNameComplete":"Added segment guard","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding segment guard","taskNameComplete":"Added segment guard"},"id":"block-b_veYKWTml85y"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_U8qdieK9ZjI","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:15:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_tlaug_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_U8qdieK9ZjI","meta":{"taskNameActive":"Stopping paths at discontinuities","taskNameComplete":"Stopped paths at discontinuities","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Stopping paths at discontinuities","taskNameComplete":"Stopped paths at discontinuities"},"id":"block-b_U8qdieK9ZjI"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804946844,"finishedAt":1785804950727,"lastPartSentAt":1785804950378,"taskNameActive":"Re-characterizing clean data","taskNameComplete":"Re-characterized clean data","toolCallId":"toolu_01Q3p9svQJLYfDY8UCR8HjBJ","agentTurnId":"turn_baXWdss85tGBXwnZ","filePath":null,"id":"7H8ZB8ckrSGPA7Rw","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s1_characterize.py > s1_out.txt 2>&1; sed -n '1,20p;60,132p' s1_out.txt","timeout":300000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"g6Xmfy5iyvkoJSdz"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"[sanitize] dropped 2 corrupt bars: ['2022-07-28 02:00:00', '2022-08-18 08:00:00']\n[sanitize] timeline gap: 2015-04-02 21:00:00 -> 2015-04-05 23:00:00 (74h)\n[sanitize] timeline gap: 2015-12-24 18:00:00 -> 2015-12-28 07:00:00 (85h)\n[sanitize] timel\n\n...\n\n02 69.723 -1479.501 -5.966 0.306\n2019 5898 23392.469 28567.700 54.525 0.002 53.853 5175.231 22.123 0.191\n2020 5899 28587.000 30603.789 121.156 0.004 119.541 2016.789 7.055 0.396\n"},"id":"B8ULB8yBMFolsE6i"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_lH6EsuaI32M","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s1_out.txt","baseName":"s1_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s1_out.txt\" \nGENERATING","latestDeploymentInfo":"$a9:16:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_jfzvj_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_lH6EsuaI32M","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_lH6EsuaI32M"}],["AssistantMessageContentPart",{"part":{"createdAt":1785804956800,"finishedAt":1785804961858,"lastPartSentAt":1785804961858,"taskNameActive":null,"id":"qmWl5iEwTK9Ed70e","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"The data's cleaned up and the volatility looks reasonable now. Looking at the hourly trading volume pattern, there are clear peaks at hours 13, 14, 15 (with 142 being the highest), and another spike at hour 19. This timing aligns perfectly with UTC server time—hour 14 corresponds to the US cash market open at 14:30 UTC in winter, and that hour 19 spike matches the final hour of US trading around 20:00 UTC, which is 16:00 ET close.\n\nI'm also noticing elevated activity around hours 22-23 with absret hitting 48.2 at hour 22, which looks like the daily rollover and close. There's a smaller uptick around hours 6-7 too, consistent with European market open. Everything points to the timestamps being in UTC or UTC+0 broker time.\n\nNow I need to dig into the autocorrelation patterns and conditional behavior in the rest of the output."}]}}],["p",{},["text",{},"Data is now clean and the session profile is coherent — TR peaks at hour 14 (142pts) with a secondary peak at 19, which pins the timestamps to "],["strong",{},["text",{},"UTC"]],["text",{}," (09:30 ET cash open = 14:30 UTC). Let me read the behavioral statistics."]],["AssistantMessageContentPart",{"part":{"createdAt":1785804962252,"finishedAt":1785804963233,"lastPartSentAt":1785804963212,"taskNameActive":"Reading characterization output","taskNameComplete":"Read characterization output","toolCallId":"toolu_016Ey6ihR1VejUr5K7RvFpit","agentTurnId":"turn_vOwzt1ufQD8ThZiQ","filePath":null,"id":"WriUGDLYI10AB86l","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/s1_out.txt"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/s1_out.txt","offset":95,"limit":130}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804967439,"finishedAt":1785804968473,"lastPartSentAt":1785804968453,"taskNameActive":"Reading session/calendar stats","taskNameComplete":"Read session/calendar stats","toolCallId":"toolu_015yJW5kjcY2uZujfdz2RDSC","agentTurnId":"turn_DRWD1hL5zXtThx4y","filePath":null,"id":"JfI2IfGNRy0x70hK","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/s1_out.txt"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/s1_out.txt","offset":225,"limit":56}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785804973926,"finishedAt":1785805006554,"lastPartSentAt":1785805006554,"taskNameActive":null,"id":"TB4CSPez5SK8xcdh","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":33,"thought":"$b0"}]}}],["p",{},["text",{},"Data is clean and the picture is clear. Key structural findings that will drive hypothesis generation:"]],["ul",{"start":1},["li",{"index":1},["strong",{},["text",{},"Volatility clustering is the dominant structure"]],["text",{},": "],["code",{},"acf(|ret|)"],["text",{}," = 0.33 at lag 1, still 0.28 at lag 24 — volatility is highly predictable while direction is not ("],["code",{},"acf(ret)"],["text",{}," ≈ −0.01)"]],["li",{"index":2},["strong",{},["text",{},"Variance ratio < 1 at every horizon"]],["text",{}," (0.99 → 0.88 at q=48) → US30 60M is "],["strong",{},["text",{},"mean-reverting"]],["text",{},", not trending"]],["li",{"index":3},["strong",{},["text",{},"Structural long drift"]],["text",{}," (+189% over 11.5y) — shorts fight the drift"]],["li",{"index":4},["text",{},"Weak closes ("],["code",{},"close_pos"],["text",{}," "],["code",{},"< 0.1) and vol expansion (`vol_ratio` >"],["text",{}," 1.4) both precede "],["em",{},["text",{},"positive"]],["text",{}," forward returns"]]],["p",{},["text",{},"Let me add confirmed higher-timeframe context, then run a broad hypothesis scan on in-sample data only."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_VFowmeK4ieg","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:17:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_87k5a_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_VFowmeK4ieg","meta":{"taskNameActive":"Adding HTF merge","taskNameComplete":"Added HTF merge","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding HTF merge","taskNameComplete":"Added HTF merge"},"id":"block-b_VFowmeK4ieg"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_CRlUzGSCvzL","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/s2_scan.py","baseName":"s2_scan.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/s2_scan.py\" \nGENERATING","latestDeploymentInfo":"$a9:18:1","lastDeployedAt":"2026-08-04T01:00:49.188Z","sourceVersionKey":"v_1_2chy7_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_CRlUzGSCvzL","meta":{"taskNameActive":"Building hypothesis scanner","taskNameComplete":"Built hypothesis scanner","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Building hypothesis scanner","taskNameComplete":"Built hypothesis scanner"},"id":"block-b_CRlUzGSCvzL"}],["AssistantMessageContentPart",{"part":{"createdAt":1785805072797,"finishedAt":1785805080023,"lastPartSentAt":1785805079635,"taskNameActive":"Running hypothesis scan","taskNameComplete":"Ran hypothesis scan","toolCallId":"toolu_01MPTk3Qxkd8FyTcaELYq7Lb","agentTurnId":"turn_Rwtj5jUJCCqkxNSB","filePath":null,"id":"F8WRgmtxrFxv1nMI","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s2_scan.py > s2_out.txt 2>&1; 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The t-stats are inflated by US30's unconditional drift plus serial correlation. Let me replace it with an exact circular-shift randomization test (via FFT) that preserves autocorrelation and measures "],["em",{},["text",{},"excess"]],["text",{}," return over baseline."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_ZbTDSvNwpHv","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s2_hypotheses.csv","baseName":"s2_hypotheses.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s2_out.txt","baseName":"s2_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/s2_scan.py","baseName":"s2_scan.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]csv:file=\"research/s2_hypotheses.csv\" \nGENERATING\n[V0_FILE]txt:file=\"research/s2_out.txt\" 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Max |z| was 2.69 where pure chance across 8,076 tests would produce ~3.9. Directional forecasting of fixed-horizon returns on US30 has no detectable edge once I control for multiplicity, autocorrelation, and the long drift."]],["p",{},["text",{},"That is information, not failure. It says I was optimizing the wrong objective. Let me check what my own characterization proved "],["em",{},["text",{},"is"]],["text",{}," predictable."]],["AssistantMessageContentPart",{"part":{"createdAt":1785805286499,"finishedAt":1785805287134,"lastPartSentAt":1785805287021,"taskNameActive":"Reading core simulator API","taskNameComplete":"Read core simulator API","toolCallId":"toolu_01DbW14sqLrmrhLjGG3dEfpA","agentTurnId":"turn_6zQzur0hUU5hCJBb","filePath":null,"id":"vXrVwQ0ueUFel1ju","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/core.py"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/core.py","offset":224,"limit":135}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-1","parts":[{"type":"thinking-end","duration":16.5,"thought":"$b3"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-1"}],["p",{},["text",{},"The characterization already told me what "],["em",{},["text",{},"is"]],["text",{}," predictable: variance ratio 0.88 (mean reversion) and "],["code",{},"acf(|ret|)"],["text",{}," 0.33 (vol clustering). Fixed-horizon mean return is the wrong lens for both — reversion happens at an unknown time inside the window. Bracket first-passage is what actually gets traded and where that structure shows up. Let me build a vectorized first-passage engine with a stratified null."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_ubVkxJTEM8s","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/fp.py","baseName":"fp.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/fp.py\" \nGENERATING","latestDeploymentInfo":"$a9:22:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_7lp8r_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_ubVkxJTEM8s","meta":{"taskNameActive":"Building first-passage engine","taskNameComplete":"Built first-passage engine","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Building first-passage engine","taskNameComplete":"Built first-passage engine"},"id":"block-b_ubVkxJTEM8s"}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-2","parts":[{"type":"thinking-end","duration":13.31764705882353,"thought":"$b4"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-2"}],["p",{},["text",{},"The 400-rep resampling loop would be too slow for a full grid. The stratified null's mean and variance are analytic — let me add that as a fast exact screen, keeping empirical permutation for finalists."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_izBuoWeotNz","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/fp.py","baseName":"fp.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/fp.py\" \nGENERATING","latestDeploymentInfo":"$a9:23:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_l6l32_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_izBuoWeotNz","meta":{"taskNameActive":"Adding analytic stratified null","taskNameComplete":"Added analytic stratified null","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding analytic stratified null","taskNameComplete":"Added analytic stratified null"},"id":"block-b_izBuoWeotNz"}],["p",{},["text",{},"Now the bracket hypothesis scan, pre-registered against the mean-reversion and vol-clustering structure the data actually showed."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_F4tLTELvZoa","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/s3_bracket.py","baseName":"s3_bracket.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/s3_bracket.py\" \nGENERATING","latestDeploymentInfo":"$a9:24:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_acu6j_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_F4tLTELvZoa","meta":{"taskNameActive":"Writing bracket scan","taskNameComplete":"Wrote bracket scan","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing bracket scan","taskNameComplete":"Wrote bracket scan"},"id":"block-b_F4tLTELvZoa"}],["AssistantMessageContentPart",{"part":{"createdAt":1785805456946,"finishedAt":1785805462422,"lastPartSentAt":1785805460784,"taskNameActive":"Running bracket scan","taskNameComplete":"Ran bracket scan","toolCallId":"toolu_016iSJNRtj6wTT9PKyiXYCPv","agentTurnId":"turn_JRigbo0Klqf4zN1f","filePath":null,"id":"8Jlj515LRZQHLP1U","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s3_bracket.py > s3_out.txt 2>&1; sed -n '1,70p' s3_out.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"ckuwgxd1JDMyD8QH"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Traceback (most recent call last):\n File \"/vercel/share/v0-project/research/s3_bracket.py\", line 24, in \n from core import load, sanitize, add_features, add_daily_context, merge_htf, IS_END\nImportError: cannot import name 'IS_END' from 'core' (/vercel/share/v0-project/research/core.py)\n"},"id":"OhvT0sgfmQNt3Clv"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_roFhQrmrUKg","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/s3_out.txt","baseName":"s3_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/s3_out.txt\" \nGENERATING","latestDeploymentInfo":"$a9:25:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_bmu2l_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_roFhQrmrUKg","meta":{"taskNameActive":"Applying changes","taskNameComplete":"Applied changes","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes","taskNameComplete":"Applied changes"},"id":"block-b_roFhQrmrUKg"}],["AssistantMessageContentPart",{"part":{"createdAt":1785805469510,"finishedAt":1785805471464,"lastPartSentAt":1785805470864,"taskNameActive":"Checking core constants","taskNameComplete":"Checked core constants","toolCallId":"toolu_01T7QCQhUeNdhtHfpeZh688h","agentTurnId":"turn_cSFX1dKak8d2hBjE","filePath":null,"id":"MpuupdLLG80Z5VRb","type":"task-grep-repo-v1","parts":[{"type":"grepping-repo","pattern":"^(IS_|OOS|SPREAD_BASE|SLIP_PER_SIDE|def )","path":"/vercel/share/v0-project/research/core.py"},{"type":"grep-file-results","numFileMatches":1}]}}],["p",{},["text",{},"The IS/OOS split lives in s2. Let me put it in core so every script shares one definition."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_06H52f5r6Xa","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:26:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_nxr8e_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_06H52f5r6Xa","meta":{"taskNameActive":"Centralizing IS/OOS split","taskNameComplete":"Centralized IS/OOS split","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Centralizing IS/OOS split","taskNameComplete":"Centralized IS/OOS split"},"id":"block-b_06H52f5r6Xa"}],["AssistantMessageContentPart",{"part":{"createdAt":1785805489137,"finishedAt":1785805491911,"lastPartSentAt":1785805491764,"taskNameActive":"Running bracket scan","taskNameComplete":"Ran bracket scan","toolCallId":"toolu_015G8rK5N6gcPZxDpb8nDh3w","agentTurnId":"turn_wDRRhQcJ8a5gd0Qy","filePath":null,"id":"dHox0VC099LvOtBZ","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s3_bracket.py > s3_out.txt 2>&1; sed -n '1,60p' s3_out.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"VZOLq8X9TsVwLxcX"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Traceback (most recent call last):\n File \"/vercel/share/v0-project/research/s3_bracket.py\", line 34, in \n h1 = merge_htf(h1, h4f, [\"ema_fast\", \"ema_slow\", \"rsi\", \"close\", \"atr\"], 4, \"h4_\")\n File \"/vercel/share/v0-project/research/core.py"},"id":"GNt8jpycsku16xcO"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\", line 217, in merge_htf\n right = htf[cols].copy()\n ~~~^^^^^^\n File \"/vercel/share/v0-project/research/.v\n\n...\n\nf_missing\n raise KeyError(f\"{not_found} not in index\")\nKeyError: \"['ema_fast', 'ema_slow', 'rsi', 'atr'] not in index\"\n"},"id":"tuLyhyGwjlgrd611"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_bmoDfiOUhAK","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s3_out.txt","baseName":"s3_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/__pycache__/fp.cpython-313.pyc","baseName":"fp.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s3_out.txt\" \nGENERATING\n[V0_FILE]pyc:file=\"research/__pycache__/fp.cpython-313.pyc\" \nGENERATING","latestDeploymentInfo":"$a9:27:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_hn4am_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_bmoDfiOUhAK","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_bmoDfiOUhAK"}],["p",{},["text",{},"Let me check the actual feature names produced by "],["code",{},"add_features"],["text",{},"."]],["AssistantMessageContentPart",{"part":{"createdAt":1785805496183,"finishedAt":1785805498042,"lastPartSentAt":1785805498022,"taskNameActive":"Reading add_features","taskNameComplete":"Read add_features","toolCallId":"toolu_01KfdkdzxxfLUrUW3kEbF8kj","agentTurnId":"turn_igAg9osL9hdKPatn","filePath":null,"id":"lQg4McmAgHxzpGAC","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/core.py"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/core.py","offset":105,"limit":98}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-3","parts":[{"type":"thinking-end","duration":3.2,"thought":"So actual names: `atr14` not `atr`, no `rsi`, no `ema_fast`/`ema_slow`, `rv_ratio` not `rv_exp`. Let me fix s3 to use actual column names. I need:\n\n- atr → atr14\n- rsi → compute it myself (or use pos24 as proxy). The brief said \"no classic indicators\" for primitives but RSI is fine as a signal. Let me just add an RSI helper in s3.\n- h4 trend: use h4 close vs h4 rolling mean. Let me merge h4 close and atr14, and compute h4 trend from h4 SMA.\n\nLet me fix fp.py too — it uses atr_col=\"atr\" default. Change to \"atr14\".\n\nLet me make these edits."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-3"}],["p",{},["text",{},"Column names differ ("],["code",{},"atr14"],["text",{},", no RSI/EMA). Let me fix the references."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_GlKOp9XGXvm","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/fp.py","baseName":"fp.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/fp.py\" \nGENERATING","latestDeploymentInfo":"$a9:28:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_u0lqp_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_GlKOp9XGXvm","meta":{"taskNameActive":"Fixing atr column default","taskNameComplete":"Fixed atr column default","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Fixing atr column default","taskNameComplete":"Fixed atr column default"},"id":"block-b_GlKOp9XGXvm"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_Tvr7RXv0Vym","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/fp.py","baseName":"fp.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/fp.py\" \nGENERATING","latestDeploymentInfo":"$a9:29:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_lbeww_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_Tvr7RXv0Vym","meta":{"taskNameActive":"Fixing strata atr reference","taskNameComplete":"Fixed strata atr reference","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Fixing strata atr reference","taskNameComplete":"Fixed strata atr reference"},"id":"block-b_Tvr7RXv0Vym"}],["p",{},["text",{},"Now let me add an RSI helper to core and fix the s3 feature block."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_wQhPe4xkubZ","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/core.py","baseName":"core.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/core.py\" \nGENERATING","latestDeploymentInfo":"$a9:30:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_gq4ks_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_wQhPe4xkubZ","meta":{"taskNameActive":"Adding RSI to features","taskNameComplete":"Added RSI to features","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding RSI to features","taskNameComplete":"Added RSI to 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7\n\n=================================================================\n\n...\n\n edge wr z p_adj\nMR_S_z50_20 -1 2.0 1.0 24 2430 347.380626 -17.325019 -8.748722 -8.576297 0.566667 -3.678259 0.047046\nMR_S_z50_20 -1 2.0 1.0 48 2430 347.380626 -17.414443 -8.545627 -8.868817 0.575309 -3.757875 0.046311\n"},"id":"DL6qzMlH6cCOxRO2"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_s65zTEUJJuy","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/core.cpython-313.pyc","baseName":"core.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/__pycache__/fp.cpython-313.pyc","baseName":"fp.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s3_out.txt","baseName":"s3_out.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/s3_bracket.csv","baseName":"s3_bracket.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/core.cpython-313.pyc\" \nGENERATING\n[V0_FILE]pyc:file=\"research/__pycache__/fp.cpython-313.pyc\" \nGENERATING\n[V0_FILE]txt:file=\"research/s3_out.txt\" \nGENERATING\n[V0_FILE]csv:file=\"research/s3_bracket.csv\" \nGENERATING","latestDeploymentInfo":"$a9:34:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_cnnih_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_s65zTEUJJuy","meta":{"taskNameActive":"Applying changes to 4 files","taskNameComplete":"Applied changes to 4 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 4 files","taskNameComplete":"Applied changes to 4 files"},"id":"block-b_s65zTEUJJuy"}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-4","parts":[{"type":"thinking-end","duration":21.811764705882354,"thought":"$b5"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-4"}],["p",{},["text",{},"Two things demand scrutiny. "],["code",{},"MR_S_rsi70"],["text",{}," shows z=3.31 but expectancy of "],["strong",{},["text",{},"−3.94 points"]],["text",{}," — it merely loses "],["em",{},["text",{},"less"]],["text",{}," than a random short. And the top-40 shows no "],["code",{},"p_adj"],["text",{}," under 0.05 despite \"15 survivors,\" which means I sorted by signed z, so the survivors are the significantly "],["em",{},["text",{},"negative"]],["text",{}," ones. Let me see the rest."]],["AssistantMessageContentPart",{"part":{"createdAt":1785805614549,"finishedAt":1785805625269,"lastPartSentAt":1785805625244,"taskNameActive":"Reading full scan output","taskNameComplete":"Read full scan output","toolCallId":"toolu_01NTgJU8gydXV3NWh6ueiFFY","agentTurnId":"turn_IxfiqO5AsBEVkiwE","filePath":null,"id":"XNgx1Knv3cUxsgAg","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/s3_out.txt"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/s3_out.txt","offset":42,"limit":20}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-5","parts":[{"type":"thinking-end","duration":21.105882352941176,"thought":"$b6"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-5"}],["p",{},["text",{},"Decisive finding confirmed. The "],["strong",{},["text",{},"only"]],["text",{}," FDR survivors are "],["code",{},"MR_S_z50_20"],["text",{}," shorts with expectancy of −17 to −21 pts/trade — significantly "],["em",{},["text",{},"worse"]],["text",{}," than random. Shorting strength on US30 reliably loses to the drift. And every high-z result was a short, which reveals the real structure: for longs, the stratified null is "],["em",{},["text",{},"already"]],["text",{}," strongly positive, so \"being long\" is itself the edge and no timing signal can beat it."]],["p",{},["text",{},"Let me confirm that directly on the long side."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_Wv9ND6Xs33E","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/s4_long.py","baseName":"s4_long.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/s4_long.py\" \nGENERATING","latestDeploymentInfo":"$a9:35:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_vp2z6_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_Wv9ND6Xs33E","meta":{"taskNameActive":"Analyzing long side","taskNameComplete":"Analyzed long side","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Analyzing long side","taskNameComplete":"Analyzed long side"},"id":"block-b_Wv9ND6Xs33E"}],["AssistantMessageContentPart",{"part":{"createdAt":1785805680614,"finishedAt":1785805683510,"lastPartSentAt":1785805683308,"taskNameActive":"Diagnosing long side","taskNameComplete":"Diagnosed long side","toolCallId":"toolu_01LGHPiUwvRntdPde3zQ5XdB","agentTurnId":"turn_rBE5dLW2Zk0Xv47e","filePath":null,"id":"ZhjQXMkFzu7pFv48","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && .venv/bin/python s4_long.py 2>&1 | head -75","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"orLIycrt56oh7pT6"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"==============================================================================================================\nQ1 vs Q2 -- absolute expectancy and null, by direction\n================================================================================\n\n...\n\n=====================================================\n total points : 18,503\n points / year : 2,645\n max drawdown : 38.4%\n Sharpe (ann) : 0.72\n exposure : 100%\n\nAny long-only engine must be judged against THIS, not against zero.\n"},"id":"GnrUeCbJdNXaiZdR"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785805688052,"finishedAt":1785805689103,"lastPartSentAt":1785805689043,"taskNameActive":"Reading benchmark output","taskNameComplete":"Read benchmark output","toolCallId":"toolu_01VXN3WwRuPoCtXvToGfDCNX","agentTurnId":"turn_H0uH83OtERlCXpDE","filePath":null,"id":"xJn1e8pziEF85GBV","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/tool_content/diagnosing-long-side-task-qj69.txt"},{"type":"reading-file","filePath":"user_read_only_context/tool_content/diagnosing-long-side-task-qj69.txt","offset":50,"limit":21}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-6","parts":[{"type":"thinking-end","duration":21.147058823529413,"thought":"$b7"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-6"}],["p",{},["text",{},"Now I have the complete picture, and it is decisive:"]],["table",{"align":[null,null]},["thead",{},["tr",{},["th",{}],["th",{},["text",{},"Result"]]]],["tbody",{},["tr",{},["td",{},["text",{},"Long rules beating matched random long (10% FDR)"]],["td",{},["strong",{},["text",{},"0 of 1,560"]],["text",{}," (best "],["code",{},"p_adj"],["text",{}," = 0.38)"]]],["tr",{},["td",{},["text",{},"Short rules significant"]],["td",{},["text",{},"Only "],["strong",{},["text",{},"negatively"]],["text",{}," (−17 to −21 pts/trade)"]]],["tr",{},["td",{},["text",{},"Buy & hold IS"]],["td",{},["text",{},"2,645 pts/yr, 38.4% MDD, Sharpe 0.72"]]]]],["p",{},["text",{},"The best long config ("],["code",{},"MR_L_z20_hivol"],["text",{},", SL 3.0 / TP 2.0 ATR) earns +18.9 pts/trade — but z=1.60, p_adj=0.61. "],["strong",{},["text",{},"That is beta, not alpha."]],["text",{}," Dip-buying is indistinguishable from being long in the same session/vol state."]],["p",{},["text",{},"One encouraging sign: SL 3.0 / TP 2.0 tops the table across "],["em",{},["text",{},"seven independent signals"]],["text",{}," — a genuine parameter plateau, not a peak. Let me test the only honest claim left: risk-adjusted improvement, validated walk-forward and out-of-sample."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_oAKcbmg4Hjc","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/s5_validate.py","baseName":"s5_validate.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/s5_validate.py\" \nGENERATING","latestDeploymentInfo":"$a9:36:1","lastDeployedAt":"2026-08-04T01:09:35.604Z","sourceVersionKey":"v_1_khtgz_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_oAKcbmg4Hjc","meta":{"taskNameActive":"Building walk-forward validation","taskNameComplete":"Built walk-forward validation","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Building walk-forward validation","taskNameComplete":"Built 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