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The key constraint is that the strategy needs to be profitable in every single calendar year. Now I'm setting up the backtesting engine to run bar-by-bar on hourly timeframes, with conservative handling for stop-loss and take-profit orders that might both trigger within the same bar—I'll assume the stop executes first. For costs, I'm modeling a round-trip spread of about 1.2 pips, which aligns with typical USDJPY retail spreads in the 0.8 to 1.5 pip range. I'm tracking metrics like profit factor, win rate, trades per year, expectancy, and annual PnL in pips. Looking at the timestamp structure, the data appears to be in UTC with forex market sessions aligned accordingly—Tokyo opens at 00:00 UTC, London at 07:00, and New York at 12:00. I'm now building out a list of strategy hypotheses to test: Asian range breakouts timed with the London open, London opening range breakouts with exits at NY close, momentum trades based on H4 close breakouts of N-bar highs with ATR-based stops, and a few more approaches to evaluate. I'll structure this as a modular engine that handles data loading and trade simulation given signal arrays, then run parameter sweeps to check annual performance across different strategy variants. For the simulation itself, I'm processing H1 bars where trades enter at the open of a signal bar. The logic tracks each trade through its lifecycle: checking if the low hits the stop loss (for longs), if the high reaches the target, or if both occur in the same bar (prioritizing the stop loss conservatively). When a trade reaches its exit index, I close it at the open of that bar. I'll also factor in transaction costs by subtracting a fixed pip spread. Rather than measuring everything in raw pips, I'm shifting to R-multiple accounting where R represents the initial risk distance from entry to stop loss. This normalizes across different price levels and position sizes, making the metrics comparable year-to-year as USDJPY moved from 90 to 160. Profit factor and expectancy will both be calculated in R terms, with costs converted to R as well. Now I'm building the engine.40:o8,ad:T858,40:o858,I'm noticing that USDJPY's strong uptrend from 2010 to 2026 (90 to 161) means any long-biased strategy will appear profitable just from riding that drift—I need to filter for strategies that actually work across different market regimes, including years when the yen strengthened like 2011, 2016, and 2020. The hour 22 signal showing +1.0 pips with a t-stat of 5.8 is suspicious; it's likely the Sunday open effect, and the data suggests this might be an artifact rather than a genuine edge. Looking at the actual patterns: mean reversion on the long side is weak, the H4 80-bar breakout shows +2.78 pips forward over 6 bars but that's confounded by upward drift, Asian range breakouts have high whipsaw at 27.5%, and the momentum signals are mixed—extreme lows continue downward while extreme highs continue upward, suggesting weak momentum rather than mean reversion. The most striking finding is that Sunday bars (hour 22) show +2.2 pips per bar over 2275 bars, which is almost certainly weekend gap risk and not reliably tradable. Now I'm setting up a systematic strategy search with multiple families—starting with Asian-range breakout parameters like session hours, stop placement relative to range or ATR, target sizing, time exits, and range filters. I'm defining several strategy families: London/NY session momentum based on prior session direction, H4 Donchian breakouts with ATR stops, H1 pullbacks aligned with H4 trend, mean reversion fades on extreme H1 z-scores during low-volatility Asian hours, time-of-day drift with regime filters, volatility contraction breakouts on narrow ranges, and weekly seasonality patterns. The constraint is strict—need 50+ trades per year average across all 17 years including 2026 partial, which means roughly 850 trades total and requires high-trade-count strategies with small symmetric edges for stability. Building the search harness now... I need to extend the simulate function to accept an optional entry price parameter, handling cases where a stop order fills at a specific level rather than the bar open, and accounting for gaps where the bar opens beyond the level.40:o8,ae:T5c5,40:o5c5,I'm wrestling with whether a strategy meeting these criteria even exists — a 1.30 profit factor with 50+ trades yearly and positive returns across all 17 calendar years is an incredibly high bar for FX intraday trading, especially when accounting for transaction costs around 1.5 pips. The math is getting clearer though: roughly 850 total trades needed, and at a 1.30 PF, the average winning trade would need to substantially outpace losses to make this work consistently year after year. I'm exploring whether combining fewer, higher-conviction trades might be more realistic than chasing high trade counts — maybe 60-80 trades annually with a 1.3-1.4 PF instead. Donchian breakouts on the 4-hour chart with trend filtering could work in theory, though the challenge is that ranging years tend to kill profitability. Trend-following on USDJPY both directions looks more promising for consistency across the 17-year period, given the strong directional moves in 2013-2015 and 2021-2024, though weaker years like 2017, 2019, and 2020 remain problematic. I'll run parameter sweeps on the Donchian family across both hourly and 4-hour timeframes with EMA trend filters and test pullback entry variations to increase trade frequency. Before diving into the code, I need to verify the data quality — the dataset extends to June 2026, which is technically future data relative to the real world but aligns with the system's current date of August 2026, so that's acceptable.40:o8,af:T72a,40:o72a,Confirmed the leak in family B. `session_momentum(ref=(7,12), entry_h=8, ...)`: - `mv[day]` is computed from the ref window hours 7..12: open of hour 7, close of hour 12. - Entry is at `entry_h=8` open. So the direction `sdir = sign(move)` is derived from the close of hour 12, but the trade is entered at hour 8's open — four hours before that information exists. The strategy is literally "know where the next 4 hours went, then trade in that direction." That fully explains PF 3.69, 74.6% WR, and 0/17 losing years. The sweep in sweep.py apparently pairs ref windows with entry hours inside the ref window. Let me check sweep.py to confirm and see how widespread this is. Also I should check my own sweep.py family A overlap guard — I wrote a guard for A (`win[0] > sess[1]`), but for B there's no such guard. Also let me double check family A for the same issue: sess=(0,6), win=(7,15) — range from hours 0-6, entry during 7-15. No overlap, that's clean. And my guard rejects overlapping combos. But note in log_A the top result is `A s(0,3) w(4,12)` — sess 0-3, win 4-12, fine. Wait — but there's a subtler issue in A: `sd` uses groupby over the whole day's mask hours 0..6, that's fine since win starts after 6. Now let me verify family A more carefully for other leaks, and also check: - Family D: uses close[k] to compute z, enters at open[k+1]. Clean. - Family C: uses `hhN = high.rolling(N).max().shift()` — shifted, so prior bars only. Entry at level within bar k. Clean-ish, though intrabar fill assumptions. - Family E: entry at open of hour, exit later. Clean. So the leak is specific to B (and possibly A if overlaps slip through). Let me check sweep.py. Also there's a second, separate realism problem across the whole engine that I should verify: costs/spread. Let me read engine.py and sweep.py.40:o8,b0:Td96,40:od96,Confirmed both the leak and its exact source in sweep.py: With `ref=(7,12)` and `eh=8`: the ref-session move uses close of hour 12, entry at hour 8 open. Every single one of the top B results in the log is `r(7,12)` with `e8` or `e7`. That's a pure look-ahead. There is NO guard in the B branch, unlike A which I guarded. Let me quantify: which B configs are leaky? - ref=(7,12), eh=7: entry at hour 7's open, ref move = open(7)→close(12). Leak (5 hours ahead). - ref=(7,12), eh=8: leak. - ref=(7,12), eh=12: entry at hour 12 open, ref close is close of hour 12 — still a leak (1 bar). - ref=(7,12), eh=13: clean (ref fully complete before 13). - ref=(0,6), eh=7/8/12/13: clean. - ref=(0,7), eh=7: leak (hour 7 close used, entering at hour 7 open). eh=8,12,13 clean. - ref=(21,6), eh=7+: clean (wraps overnight... actually mask is hh>=21 & hh`<=6 which is EMPTY since no hour is both >`=21 and `<=6! So ref=(21,6) produces an empty mask → no days → zero trades. That's a silent bug too — those configs simply produce nothing. Same for A's sess=(21,6): mask empty, sd empty, no trades. And my A guard `sess[0] >` sess[1] and win[0] > sess[1]` was trying to handle wrap but the mask never wraps.) So two bugs: - Look-ahead leak in B (and potentially A) when the reference window overlaps/extends past the entry bar. This is what produced PF 3.69. - Silent empty wrap-around sessions: `(21,6)` intended as overnight Asia wrap but `(hh>=21)&(hh<=6)` is always False. Those configs are silently dropped (or worse, look valid but with n=0 filtered out). The right fix: make the leak structurally impossible rather than patching the loop lists. I'll add an explicit assertion inside the signal generators that the information window ends strictly before the entry bar, so any future config that violates it raises instead of silently producing fantasy results. And fix the wrap masks. Let me implement: - `hour_mask(hh, lo, hi)` helper that handles wrap correctly (lo>hi → OR instead of AND). - For wrap sessions, "day" grouping is tricky: hours 21-23 belong to the previous session day relative to hours 0-6. I need a "session day" that shifts. Simplest correct approach: for a wrap window ending at hi, assign a session_day = date shifted so that the window is contiguous. E.g. for ref=(21,6), a bar at 21:00 on day T and a bar at 03:00 on day T+1 belong to the same session. Define session_day = (dt + (24-lo) hours).normalize() for wrap windows. Then the entry must be after the session ends. - Add a hard guard: compute, per session-day, the index of the last bar in the reference window (`ref_end_i`), and require `entry_i > ref_end_i`. Assert it, and skip+count violations. This is the robust fix: instead of relying on me reasoning about hour arithmetic in every config, the generator itself refuses to use information from at/after the entry bar. Let me also fix the family A range: it uses `shi/slo` from the session mask, and entry during win. I'll apply the same `ref_end_i < entry_i` guard. Also worth noting: family A's `sd` filter `n >= (sess[1]-sess[0]+1)*0.7` breaks for wrap windows too. Let me rewrite the relevant parts of families.py with a shared, leak-proof session helper. I'll also add a leakage self-test (a "canary") to engine: shuffle/permute future data and confirm performance collapses. Actually a simpler decisive test: re-run the exact top B config with the corrected guard and show it collapses to ~breakeven. Let me write it.40:o51d,b1:T50d,cd /vercel/share/v0-project/research && python3 - <<'EOF' p='sweep.py'; s=open(p).read() s=s.replace(""" run(lb, F.donchian(h1, N=N, stop_k=sk, tp_R=tp, max_bars=mb, revert=rev))""", """ run(lb, lambda N=N, sk=sk, tp=tp, mb=mb, rev=rev: F.donchian(h1, N=N, stop_k=sk, tp_R=tp, max_bars=mb, revert=rev))""") s=s.replace(""" run(lb, F.zfade(h1, z_len=zl, z_th=zt, stop_k=sk, tp_R=tp, max_bars=12, hours=hrs))""", """ run(lb, lambda zl=zl, zt=zt, sk=sk, tp=tp, hrs=hrs: F.zfade(h1, z_len=zl, z_th=zt, stop_k=sk, tp_R=tp, max_bars=12, hours=hrs))""") s=s.replace(""" run(lb, F.tod(h1, entry_h=eh, exit_h=exh, stop_k=3.0, sdir=sd, trend=tr))""", """ run(lb, lambda eh=eh, exh=exh, sd=sd, tr=tr: F.tod(h1, entry_h=eh, exit_h=exh, stop_k=3.0, sdir=sd, trend=tr))""") s=s.replace("""df = pd.DataFrame(rows)""", """if skipped: print(f"--- REJECTED {len(skipped)} look-ahead configs ---") for x in skipped[:8]: print(" " + x) print() df = pd.DataFrame(rows)""") open(p,'w').write(s) EOF grep -n "run(lb, F\." sweep.py; echo "remaining direct calls above (should be none)"b2:T6d6,40:o6d6,Beautifully decisive. The hindsight dose-response is the smoking gun: - 5h hindsight → PF 3.689, WR 74.6%, 0/17 neg years, DD 5.0R - 1h hindsight → PF 1.462, WR 57.5%, 0/17 neg years - 0h (causal) → PF 0.887, WR 48.8%, 15/17 NEGATIVE years, DD 164R So the strategy is not merely "less good" — it's a consistent loser. The entire edge was hindsight, monotonically scaling with the number of leaked bars. And it's actually strongly negative, which makes sense: it's a momentum entry paying 1.5 pips round-trip cost 259 times a year, with no real edge → cost bleed. -0.0358 R/trade × ~259 trades/yr ≈ -9R/yr. Sanity check the cost explanation: 1.5 pips cost, avg risk... exp = -0.0358R and pips = -0.88. So gross pips ≈ -0.88 + 1.5 = +0.62 pips gross. So gross is slightly positive but costs kill it. Good, that's the honest read: no edge beyond noise, costs dominate. Now let me re-run the full B sweep with the guard and confirm nothing else survives, and check family A results are unaffected (A had the overlap guard so should be clean, but the guard was crude - now the assert covers it). Let me re-run A and B. Note: family A results in log_A — my old guard `if not (win[0] > sess[1] or ...)` — for sess=(0,6) win=(7,15): 7>6 true, so kept. sess=(0,3) win=(4,12): 4>3 true, kept. These are fine. But sess=(13,23) win=(7,15): 7>23 false, and sess[0]>sess[1] false → skipped. And (21,6) was empty anyway. So A's reported numbers were causal. Good. Now with wrap-aware masks, A gains new valid configs (21,6) and (13,23)-with-next-day-window... actually (13,23) win (7,15) would still be a leak since the window is on the same calendar day before 13. The assert will catch and reject it. Good. Let me re-run A and B sweeps.40:o8,b3:T816,40:o816,Important: family A's previous top result `A s(0,3) w(4,12)` survived (PF 1.172, 2/17 neg years) — it's causal since sess ends at hour 3 and window starts at 4. Good. But 96 A configs were rejected — including `s(0,6) w(4,12)` which my old crude guard let through! Let me verify: old guard was `if not (win[0] > sess[1] or sess[0] > sess[1] and win[0] > sess[1]): continue`. For sess=(0,6), win=(4,12): win[0]=4 > sess[1]=6? No, 4 > 6 is False. sess[0]>sess[1] = 0>6 False. So `not(False or False)` = True → continue → skipped. So it WAS skipped by the old guard. Hmm, so why is it now "rejected" and logged? Because I removed the crude guard and replaced it with the assert. So these 96 rejections include the ones the old guard silently skipped. Fine — same outcome, but now visible and counted rather than silent. That's the improvement. The new wrap-aware masks unlocked `s(21,6)` configs which are genuinely new and causal (session 21:00→06:00, window 7-12 → clean). Those now appear: PF 1.118, 4/17 neg years. Previously they produced zero trades silently. Now, does anything pass the bar? `passes()` requires PF≥1.30, WR≥40, tpy≥50, 0 neg years. Best is PF 1.172 with 2 neg years. Nothing passes. So the honest conclusion: no robust edge found yet. The A family's `s(0,3) w(4,12)` at PF 1.17 is the only mildly interesting one, but 2/17 negative years and PF 1.17 on an in-sample sweep of 204 configs is well within multiple-testing noise. With 204 configs tested, the best-of-N PF of 1.17 is expected by chance. I should report this honestly rather than dress up PF 1.17 as a finding. Let me quickly run a multiple-testing sanity check to quantify: is best-of-204 PF 1.17 meaningful? A quick way: compare against the distribution of all configs — if the median config is ~1.0 and best is 1.17 out of 204 tries, that's likely noise. Let me do a fast check of the distribution and also verify the top A config with a train/test split (first half vs second half). That's the decisive robustness test. Let me be efficient — one script.40:o8,b4:T8bf,40:o8bf,Interesting — I have to correct my own printed hypothesis. The multiple-testing framing I wrote in the script assumed median PF ~1.0, but the actual median is 0.782 and only 20% of configs are >1.0. That's a different picture: the config space is mostly losing (as expected once costs bite), and the winner isn't drawn from a null centered at 1.0. More importantly, the split-sample result is genuinely encouraging and I should not have pre-written the "it's noise" conclusion: - In-sample 2010-2017: PF 1.178, 1/8 neg years, +171R - Out-of-sample 2018-2026: PF 1.166, 1/9 neg years, +168R That's remarkable stability across a held-out half. The candidate wasn't selected on the OOS period... well, actually it WAS — the sweep ran over the full 2010-2026 span, so the "out-of-sample" half was part of the selection. So this isn't a true holdout. That's an important caveat I must state: the sweep saw all 17 years, so the split is a consistency check, not a clean OOS test. Still, the fact that PF is nearly identical in both halves means the edge isn't concentrated in one regime, which is meaningful and rules out the most common failure mode (one lucky period carrying the whole curve). Year-by-year: 15/17 positive years, only 2011 (-16.7R) and 2026 (-15.9R, partial year, n=116) negative. Consistent. Cost sensitivity: this is the real fragility. Edge decays from PF 1.315 at zero cost to 1.048 at 3 pips. At 1.5 pips assumption → 1.172. So the whole thing lives on a ~1.6 pip/trade average gain against a 1.5 pip cost assumption. Gross is ~3.1 pips/trade. That's thin but not absurd for a session breakout; USDJPY majors typically run 0.6-1.2 pip spreads at London open on retail ECN, so 1.5 round-trip is reasonable-to-conservative. But at 245 trades/year, any slippage degradation matters a lot. And critically: stop-order entries on breakouts suffer negative slippage precisely in fast markets, so the realized cost is likely worse than the symmetric assumption. I should correct the script's stale conclusion text since it asserts something false about the median. Let me summarize honestly. The key deliverable of this turn is the bug fix, so I should lead with that. I'll fix the misleading text in test_robust.py.40:o41c,b5:T40b,I need to model slippage more realistically for the breakout strategy. Entry orders are stop orders that fill at the breakout level, but in reality they slip adversely. Same goes for stop-loss exits which are also stop orders. Take-profit exits use limit orders so they don't slip, though they might not fill at all. I should also factor in how slippage scales with the bar's velocity. The key insight is that if a bar gaps through the trigger level on open, the fill happens at the open price rather than the level itself—that's a real and important effect. I'm planning to add a fill model to the engine where entry slip scales with the bar's range and momentum, stop-loss slip works similarly, and take-profit orders have no slippage. I need to implement this carefully to maintain backwards compatibility, probably by adding an optional `slip_model` parameter to the simulate function. First I should check how `families.session_breakout` currently sets the entry level so I understand what trigger information I'm working with.b6:T1c77,40:o1c77,[V0_FILE]plaintext:file="research/log_be.txt" config: s(0, 3) w(4, 12) sk1.0 tp0 signals: 4042 ============================================================================== (2) Are trigger bars faster than normal? ============================================================================== median range, ALL bars : 13.40 pips median range, window-hour bars : 13.80 pips median range, TRIGGER bars : 16.40 pips mean range, TRIGGER bars : 20.71 pips 90th pct range, TRIGGER bars : 37.59 pips => trigger bars are 1.19x the median window bar ============================================================================== (1) Breakeven cost (holdout 2018-2026, config fixed from train) ============================================================================== total cost model PF_out R_out pips/tr flat symmetric 0.0p 1.300 284.3 3.24 flat symmetric 0.5p 1.253 245.6 2.74 flat symmetric 1.0p 1.209 206.9 2.24 flat symmetric 1.5p 1.166 168.2 1.74 flat symmetric 2.0p 1.125 129.5 1.24 flat symmetric 2.5p 1.086 90.8 0.74 flat symmetric 3.0p 1.048 52.2 0.24 flat symmetric 3.5p 1.012 13.5 -0.26 flat symmetric 4.0p 0.978 -25.2 -0.76 >>> breakeven flat cost ~= 3.68 pips round trip spread 0.8 + base b + k*range PF_out R_out b=0.0 k=0.0 (~0.80p eff) 1.226 222.4 b=0.2 k=0.01 (~1.53p eff) 1.173 173.2 b=0.3 k=0.02 (~2.06p eff) 1.135 137.5 b=0.4 k=0.03 (~2.58p eff) 1.099 102.6 b=0.5 k=0.05 (~3.44p eff) 1.044 46.3 b=0.7 k=0.07 (~4.50p eff) 0.983 -18.6 b=1.0 k=0.1 (~6.08p eff) 0.901 -110.9 ============================================================================== READ THIS BEFORE BELIEVING ANY OF IT ============================================================================== The gross edge is small in absolute terms, so the verdict is decided entirely by the cost assumption -- not by the signal. Trigger bars run 1.19x the median window bar, which is exactly the regime where a retail stop order slips worst. Any backtest of this system that assumes a fixed sub-2-pip round trip is asserting a fill quality it never measured. This is a research artefact, not a tradable strategy. To settle it you need tick/quote data to measure real fills at these levels; H1 OHLC cannot. [V0_FILE]python:file="/research/test_breakeven.py" """How much slippage does the surviving config tolerate, and is that plausible? Two questions the earlier sweeps could not answer: (1) Where exactly is the breakeven cost? (solve for it, don't guess) (2) Are breakout trigger bars actually faster than normal bars? If yes, the harsh slip models are the realistic ones and the edge is cost-fragile. """ import numpy as np, pandas as pd from engine import load, simulate, metrics, PIP import families as F h1 = F.prep(load("h1")) CFG = dict(sess=(0, 3), win=(4, 12), stop_k=1.0, tp_R=0, exit_h=20, revert=False, max_trades=1) sigs = F.session_breakout(h1, **CFG) print(f"config: s{CFG['sess']} w{CFG['win']} sk{CFG['stop_k']} tp{CFG['tp_R']}") print(f"signals: {len(sigs)}\n") # ---------------------------------------------------------------- (2) trigger bars rng_all = ((h1["high"] - h1["low"]) / PIP) trig_i = np.array([s["i"] for s in sigs]) rng_trig = rng_all.values[trig_i] # only bars in the same trading-window hours are a fair comparison wmask = F.hour_mask(h1["hour"].values, CFG["win"][0], CFG["win"][1]) rng_win = rng_all.values[wmask] print("=" * 78) print("(2) Are trigger bars faster than normal?") print("=" * 78) print(f" median range, ALL bars : {np.median(rng_all):>6.2f} pips") print(f" median range, window-hour bars : {np.median(rng_win):>6.2f} pips") print(f" median range, TRIGGER bars : {np.median(rng_trig):>6.2f} pips") print(f" mean range, TRIGGER bars : {rng_trig.mean():>6.2f} pips") print(f" 90th pct range, TRIGGER bars : {np.percentile(rng_trig, 90):>6.2f} pips") ratio = np.median(rng_trig) / np.median(rng_win) print(f" => trigger bars are {ratio:.2f}x the median window bar") print() # ---------------------------------------------------------------- (1) breakeven print("=" * 78) print("(1) Breakeven cost (holdout 2018-2026, config fixed from train)") print("=" * 78) print(f" {'total cost model':<38} {'PF_out':>8} {'R_out':>9} {'pips/tr':>9}") rowsA = [] for c in [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0]: tr = simulate(h1, sigs, cost_pips=c) y = tr["exit_dt"].dt.year mo = metrics(tr[y > 2017]) rowsA.append((c, mo["pf"])) print(f" {'flat symmetric ' + str(c) + 'p':<38} {mo['pf']:>8.3f} " f"{mo['totR']:>9.1f} {mo['pips']:>9.2f}") print() # interpolate breakeven cs = np.array([r[0] for r in rowsA]); pfs = np.array([r[1] for r in rowsA]) above = np.where(pfs >= 1.0)[0] if len(above) and above[-1] + 1 < len(cs): j = above[-1] be = cs[j] + (pfs[j] - 1.0) / (pfs[j] - pfs[j + 1]) * (cs[j + 1] - cs[j]) print(f" >>> breakeven flat cost ~= {be:.2f} pips round trip") print() # k-sweep: how sensitive to the velocity term specifically print(f" {'spread 0.8 + base b + k*range':<38} {'PF_out':>8} {'R_out':>9}") for base, k in [(0.0, 0.0), (0.2, 0.01), (0.3, 0.02), (0.4, 0.03), (0.5, 0.05), (0.7, 0.07), (1.0, 0.10)]: tr = simulate(h1, sigs, cost_pips=0.8, slip=dict(base=base, k=k)) y = tr["exit_dt"].dt.year mo = metrics(tr[y > 2017]) eff = 0.8 + 2 * (base + k * np.median(rng_trig)) # entry+exit, median bar print(f" {'b=' + str(base) + ' k=' + str(k) + f' (~{eff:.2f}p eff)':<38} " f"{mo['pf']:>8.3f} {mo['totR']:>9.1f}") print() print("=" * 78) print("READ THIS BEFORE BELIEVING ANY OF IT") print("=" * 78) print(f""" I expected trigger bars to be dramatically faster than normal, which would have made the harsh slip models the realistic ones. They are not: {ratio:.2f}x the median window bar. That was my assumption, and the data does not support it. The mean is inflated ({rng_trig.mean():.1f}p) by a long right tail, so the slippage problem is concentrated in a minority of fast breakouts, not systemic. Breakeven is ~3.7 pips round trip. USDJPY is a top-3 liquidity pair where a 1-2 pip retail round trip is realistic, so the edge has roughly 2x cost headroom -- more robust than a 3.1-pip gross edge sounds. Remaining honest limits, in order of how much they should worry you: - tp0 means the exit is time-based, so the 164R max drawdown seen on the full sample is real path risk, not an artefact. - 1 losing year in 9 holdout years, but 2018-2026 was a persistent USDJPY trend regime; a range-bound decade is not represented. - H1 OHLC cannot resolve intrabar fills. The stop-wins-ties rule is conservative, but the right tail above still needs tick data to price.""")40:o9,b7:T1c77,40:o1c77,[V0_FILE]plaintext:file="research/log_be.txt" config: s(0, 3) w(4, 12) sk1.0 tp0 signals: 4042 ============================================================================== (2) Are trigger bars faster than normal? ============================================================================== median range, ALL bars : 13.40 pips median range, window-hour bars : 13.80 pips median range, TRIGGER bars : 16.40 pips mean range, TRIGGER bars : 20.71 pips 90th pct range, TRIGGER bars : 37.59 pips => trigger bars are 1.19x the median window bar ============================================================================== (1) Breakeven cost (holdout 2018-2026, config fixed from train) ============================================================================== total cost model PF_out R_out pips/tr flat symmetric 0.0p 1.300 284.3 3.24 flat symmetric 0.5p 1.253 245.6 2.74 flat symmetric 1.0p 1.209 206.9 2.24 flat symmetric 1.5p 1.166 168.2 1.74 flat symmetric 2.0p 1.125 129.5 1.24 flat symmetric 2.5p 1.086 90.8 0.74 flat symmetric 3.0p 1.048 52.2 0.24 flat symmetric 3.5p 1.012 13.5 -0.26 flat symmetric 4.0p 0.978 -25.2 -0.76 >>> breakeven flat cost ~= 3.68 pips round trip spread 0.8 + base b + k*range PF_out R_out b=0.0 k=0.0 (~0.80p eff) 1.226 222.4 b=0.2 k=0.01 (~1.53p eff) 1.173 173.2 b=0.3 k=0.02 (~2.06p eff) 1.135 137.5 b=0.4 k=0.03 (~2.58p eff) 1.099 102.6 b=0.5 k=0.05 (~3.44p eff) 1.044 46.3 b=0.7 k=0.07 (~4.50p eff) 0.983 -18.6 b=1.0 k=0.1 (~6.08p eff) 0.901 -110.9 ============================================================================== READ THIS BEFORE BELIEVING ANY OF IT ============================================================================== The gross edge is small in absolute terms, so the verdict is decided entirely by the cost assumption -- not by the signal. Trigger bars run 1.19x the median window bar, which is exactly the regime where a retail stop order slips worst. Any backtest of this system that assumes a fixed sub-2-pip round trip is asserting a fill quality it never measured. This is a research artefact, not a tradable strategy. To settle it you need tick/quote data to measure real fills at these levels; H1 OHLC cannot. [V0_FILE]python:file="/research/test_breakeven.py" """How much slippage does the surviving config tolerate, and is that plausible? Two questions the earlier sweeps could not answer: (1) Where exactly is the breakeven cost? (solve for it, don't guess) (2) Are breakout trigger bars actually faster than normal bars? If yes, the harsh slip models are the realistic ones and the edge is cost-fragile. """ import numpy as np, pandas as pd from engine import load, simulate, metrics, PIP import families as F h1 = F.prep(load("h1")) CFG = dict(sess=(0, 3), win=(4, 12), stop_k=1.0, tp_R=0, exit_h=20, revert=False, max_trades=1) sigs = F.session_breakout(h1, **CFG) print(f"config: s{CFG['sess']} w{CFG['win']} sk{CFG['stop_k']} tp{CFG['tp_R']}") print(f"signals: {len(sigs)}\n") # ---------------------------------------------------------------- (2) trigger bars rng_all = ((h1["high"] - h1["low"]) / PIP) trig_i = np.array([s["i"] for s in sigs]) rng_trig = rng_all.values[trig_i] # only bars in the same trading-window hours are a fair comparison wmask = F.hour_mask(h1["hour"].values, CFG["win"][0], CFG["win"][1]) rng_win = rng_all.values[wmask] print("=" * 78) print("(2) Are trigger bars faster than normal?") print("=" * 78) print(f" median range, ALL bars : {np.median(rng_all):>6.2f} pips") print(f" median range, window-hour bars : {np.median(rng_win):>6.2f} pips") print(f" median range, TRIGGER bars : {np.median(rng_trig):>6.2f} pips") print(f" mean range, TRIGGER bars : {rng_trig.mean():>6.2f} pips") print(f" 90th pct range, TRIGGER bars : {np.percentile(rng_trig, 90):>6.2f} pips") ratio = np.median(rng_trig) / np.median(rng_win) print(f" => trigger bars are {ratio:.2f}x the median window bar") print() # ---------------------------------------------------------------- (1) breakeven print("=" * 78) print("(1) Breakeven cost (holdout 2018-2026, config fixed from train)") print("=" * 78) print(f" {'total cost model':<38} {'PF_out':>8} {'R_out':>9} {'pips/tr':>9}") rowsA = [] for c in [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0]: tr = simulate(h1, sigs, cost_pips=c) y = tr["exit_dt"].dt.year mo = metrics(tr[y > 2017]) rowsA.append((c, mo["pf"])) print(f" {'flat symmetric ' + str(c) + 'p':<38} {mo['pf']:>8.3f} " f"{mo['totR']:>9.1f} {mo['pips']:>9.2f}") print() # interpolate breakeven cs = np.array([r[0] for r in rowsA]); pfs = np.array([r[1] for r in rowsA]) above = np.where(pfs >= 1.0)[0] if len(above) and above[-1] + 1 < len(cs): j = above[-1] be = cs[j] + (pfs[j] - 1.0) / (pfs[j] - pfs[j + 1]) * (cs[j + 1] - cs[j]) print(f" >>> breakeven flat cost ~= {be:.2f} pips round trip") print() # k-sweep: how sensitive to the velocity term specifically print(f" {'spread 0.8 + base b + k*range':<38} {'PF_out':>8} {'R_out':>9}") for base, k in [(0.0, 0.0), (0.2, 0.01), (0.3, 0.02), (0.4, 0.03), (0.5, 0.05), (0.7, 0.07), (1.0, 0.10)]: tr = simulate(h1, sigs, cost_pips=0.8, slip=dict(base=base, k=k)) y = tr["exit_dt"].dt.year mo = metrics(tr[y > 2017]) eff = 0.8 + 2 * (base + k * np.median(rng_trig)) # entry+exit, median bar print(f" {'b=' + str(base) + ' k=' + str(k) + f' (~{eff:.2f}p eff)':<38} " f"{mo['pf']:>8.3f} {mo['totR']:>9.1f}") print() print("=" * 78) print("READ THIS BEFORE BELIEVING ANY OF IT") print("=" * 78) print(f""" I expected trigger bars to be dramatically faster than normal, which would have made the harsh slip models the realistic ones. They are not: {ratio:.2f}x the median window bar. That was my assumption, and the data does not support it. The mean is inflated ({rng_trig.mean():.1f}p) by a long right tail, so the slippage problem is concentrated in a minority of fast breakouts, not systemic. Breakeven is ~3.7 pips round trip. USDJPY is a top-3 liquidity pair where a 1-2 pip retail round trip is realistic, so the edge has roughly 2x cost headroom -- more robust than a 3.1-pip gross edge sounds. Remaining honest limits, in order of how much they should worry you: - tp0 means the exit is time-based, so the 164R max drawdown seen on the full sample is real path risk, not an artefact. - 1 losing year in 9 holdout years, but 2018-2026 was a persistent USDJPY trend regime; a range-bound decade is not represented. - H1 OHLC cannot resolve intrabar fills. The stop-wins-ties rule is conservative, but the right tail above still needs tick data to price.""")40:o182db,48:["$","$L8d",null,{"params":{"chatId":"uaXTgiFtXpR"},"promises":{"attributes":"$@97","externalMeta":"$@98","latest":"$@99","messagesRange":"$@9a","messages":"$@9b","template":"$@9c","gitStatus":"$@9d"},"children":["$","$L9e",null,{"chatId":"uaXTgiFtXpR","children":["$","$L9f",null,{"seedOnlyKeys":["@\"chat:state\",\"uaXTgiFtXpR\","],"data":{"block:output:initial":{"id":"b_XQlH3MNpSJ1","result":"$@a0"},"/chat/api/snapshots/proxy?chatId=uaXTgiFtXpR&messageId=Lmwi0zEI2cf9hv5ZeFOK7YzyXo7uVuZM&v=v2":"$a1","@\"block:mappings\",\"uaXTgiFtXpR\",":{"sqlBlockExecutionStates":"$Qa2","blocksForkedFrom":"$Qa3","blocksTemplateIds":"$Qa4","blocksCreatedAt":"$Qa5","blocksShared":"$Qa6","blocksDeployed":"$Qa7","blocksSubdomains":"$Qa8","brandedBlocks":"$Qa9","unlistedBlocks":"$Qaa","blockOutputs":"$Qab"},"@\"chat:drafts\",null,":null,"@\"chat:state\",\"uaXTgiFtXpR\",":{"messages":[{"content":{"type":"parts","parts":[{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USDJPY%20240%20MINUTE-CV40r11tjLO5lnqWl1us0duXfhhFao.csv","name":"USDJPY 240 MINUTE.csv","contentType":"text/csv","size":1762791},{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USDJPY%2060%20MINUTE%20%281%29-sjpNQzJP7nORoojZb1ZRCRNdkzroUf.csv","name":"USDJPY 60 MINUTE (1).csv","contentType":"text/csv","size":6819601},{"type":"mdx","content":"\n"},{"type":"attachment","url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/%5BPasted%20118%20lines%5D-MxwbW6MXyAqRfISgDBf5CgIfQCMTns","name":"[Pasted 118 lines]","contentType":"text/plain","size":3055}],"version":1},"role":"user","id":"DOJbaoKFPA787Qqh0TaCsZiY5Kyvj2uN","attachments":[{"url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USDJPY%20240%20MINUTE-CV40r11tjLO5lnqWl1us0duXfhhFao.csv","name":"USDJPY 240 MINUTE.csv","size":1762791,"contentType":"text/csv"},{"url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/USDJPY%2060%20MINUTE%20%281%29-sjpNQzJP7nORoojZb1ZRCRNdkzroUf.csv","name":"USDJPY 60 MINUTE (1).csv","size":6819601,"contentType":"text/csv"},{"url":"https://hebbkx1anhila5yf.public.blob.vercel-storage.com/%5BPasted%20118%20lines%5D-MxwbW6MXyAqRfISgDBf5CgIfQCMTns","name":"[Pasted 118 lines]","size":3055,"contentType":"text/plain"}],"type":"message","authorId":"LIl3SYdavnPBzuaZAQawDWxY","createdAt":"$D2026-08-04T20:11:47.516Z"},{"v0ModelId":"v0-opus-5","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785874309572,"finishedAt":1785874309572,"lastPartSentAt":1785874309572,"id":"LC5k6t6tg35Hsfvh","type":"task-start-v1","parts":[{"type":"launch-tasks"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-grouped-tasks-v1","id":"BA1APLpoQHdPJ3Kr-KyV6ce1UIGH7auEw-JKXpaT83hj8rRIjI","parts":[{"createdAt":1785874309572,"finishedAt":1785874310375,"lastPartSentAt":1785874310338,"taskNameActive":"Reading [Pasted-118-lines]","taskNameComplete":"Read [Pasted-118-lines]","toolCallId":"tool_060gjaBd7sVMbLDp","agentTurnId":"turn_17UBxjI8mMzvKXlb","filePath":null,"id":"BA1APLpoQHdPJ3Kr","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/text_attachments/[Pasted-118-lines]-MxwbW"}]},{"createdAt":1785874309572,"finishedAt":1785874310451,"lastPartSentAt":null,"taskNameActive":"Reading USDJPY-240-MINUTE.csv","taskNameComplete":"Read USDJPY-240-MINUTE.csv","toolCallId":"tool_rQhy9ioSbYmH1MUJ","agentTurnId":"turn_17UBxjI8mMzvKXlb","filePath":null,"id":"KyV6ce1UIGH7auEw","type":"task-read-file-v1","parts":[]},{"createdAt":1785874309572,"finishedAt":1785874310257,"lastPartSentAt":null,"taskNameActive":"Reading USDJPY-60-MINUTE-(1).csv","taskNameComplete":"Read USDJPY-60-MINUTE-(1).csv","toolCallId":"tool_U5ulrSYOq0eL3nvO","agentTurnId":"turn_17UBxjI8mMzvKXlb","filePath":null,"id":"JKXpaT83hj8rRIjI","type":"task-read-file-v1","parts":[]}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Explore • 3 Files","taskNameComplete":"Explore • 3 Files"},"id":"BA1APLpoQHdPJ3Kr-KyV6ce1UIGH7auEw-JKXpaT83hj8rRIjI"}],["AssistantMessageContentPart",{"part":{"createdAt":1785874315359,"finishedAt":1785874320360,"lastPartSentAt":1785874320360,"taskNameActive":null,"id":"DVoJe3ToDaZSpztY","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"I need to build a computational backtesting engine to research a USDJPY strategy on 60m and 240m timeframes that meets the strict criteria, then run walk-forward analysis, Monte Carlo simulations, and generate Pine Script v6 code. 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I'll run an initial edge scan that's computationally efficient but gives us meaningful insights."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_eQM6Ko2fV9G","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/explore.py","baseName":"explore.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/explore.py\" \nGENERATING","latestDeploymentInfo":"$a7:2:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_znkpg_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_eQM6Ko2fV9G","meta":{"taskNameActive":"Writing edge scan","taskNameComplete":"Wrote edge scan","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing edge scan","taskNameComplete":"Wrote edge scan"},"id":"block-b_eQM6Ko2fV9G"}],["AssistantMessageContentPart",{"part":{"createdAt":1785874456754,"finishedAt":1785874459599,"lastPartSentAt":1785874459295,"taskNameActive":"Running edge scan","taskNameComplete":"Ran edge scan","toolCallId":"toolu_0133EXPjSfsUCDSaQ3bE2taB","agentTurnId":"turn_oSSD5h68pB3M93AW","filePath":null,"id":"8ASce0dXA60ezdJU","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python explore.py 2>&1 | tail -110","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"bUP3bCO5TWUa9DhE"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"/vercel/share/v0-project/research/explore.py:78: Pandas4Warning: Sorting by default when concatenating all DatetimeIndex is deprecated. In the future, pandas will respect the default of `sort=False`. Specify `sort=True` or `sort=False` to silence th"},"id":"PR2FGDbzfFaGoZFH"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"is message. If you see this warnings when not directly calling concat, report a bug to pandas.\n al = pd.concat([nyc.rena\n\n...\n\n 10267\n5 0.280 1.300 10266\n6 0.668 1.920 10267\n7 1.115 1.093 10267\n8 1.717 0.349 10267\n9 3.042 1.604 10267\n"},"id":"ehoAVVrxVf0Qy6R2"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874465010,"finishedAt":1785874486248,"lastPartSentAt":1785874486248,"taskNameActive":null,"id":"RELFPm16cfBLbqSS","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":21,"thought":"$ad"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_72uRnR5Co8k","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/engine.py","baseName":"engine.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/engine.py\" \nGENERATING","latestDeploymentInfo":"$a7:3:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_b1m4c_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_72uRnR5Co8k","meta":{"taskNameActive":"Extending engine","taskNameComplete":"Extended engine","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Extending engine","taskNameComplete":"Extended engine"},"id":"block-b_72uRnR5Co8k"}],["AssistantMessageContentPart",{"part":{"createdAt":1785874495941,"finishedAt":1785874497184,"lastPartSentAt":1785874497184,"taskNameActive":null,"id":"VD3YEd1u3mIkd75N","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I need to be careful about look-ahead bias when checking stop loss and take profit on the entry bar itself—if I check both, the TP might hit before the actual breakout occurs, which skews results favorably. The conservative approach is to skip the TP check on the entry bar and only validate the stop loss, which is the standard practice. I'll add a flag to enforce this strict behavior by default."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_n8J9635EYmf","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/engine.py","baseName":"engine.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/engine.py\" \nGENERATING","latestDeploymentInfo":"$a7:4:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_a5ufz_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_n8J9635EYmf","meta":{"taskNameActive":"Hardening fill logic","taskNameComplete":"Hardened fill logic","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Hardening fill logic","taskNameComplete":"Hardened fill logic"},"id":"block-b_n8J9635EYmf"}],["p",{},["text",{},"Now the strategy families and sweep driver."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_4bqhk8Tud9Y","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/families.py","baseName":"families.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/families.py\" \nGENERATING","latestDeploymentInfo":"$a7:5:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_met9b_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_4bqhk8Tud9Y","meta":{"taskNameActive":"Writing strategy families","taskNameComplete":"Wrote strategy families","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing strategy families","taskNameComplete":"Wrote strategy families"},"id":"block-b_4bqhk8Tud9Y"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_XjxbWr8ieSi","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/sweep.py","baseName":"sweep.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/sweep.py\" \nGENERATING","latestDeploymentInfo":"$a7:6:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_iqhcn_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_XjxbWr8ieSi","meta":{"taskNameActive":"Writing sweep driver","taskNameComplete":"Wrote sweep driver","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing sweep driver","taskNameComplete":"Wrote sweep driver"},"id":"block-b_XjxbWr8ieSi"}],["AssistantMessageContentPart",{"part":{"createdAt":1785874570383,"finishedAt":1785874692801,"lastPartSentAt":1785874692583,"taskNameActive":"Sweeping families A and B","taskNameComplete":"Swept families A and B","toolCallId":"toolu_011pd4MPH9EJ53NmqVd9GJoK","agentTurnId":"turn_6tAPb2Y4D0h7mNgn","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"The command took too long to complete and was aborted.","code":"AGENT_GUIDANCE"},"id":"2RrNovqItwH2Q5oT","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python sweep.py A 2>&1 | tail -40 && python sweep.py B 2>&1 | tail -40","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"OZkkELD0tUGrZ18q"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"--- family A: 144 configs, sorted by pf (n>200) ---\nA s(0, 7) w(7, 12) sk0.5 tp0 rev1 n= 3664 pf=1.161 wr= 40.4 exp= 0.0951 pips= 2.34 tpy=222.5 negY=2.0/17.0 worstY= -22.97 totR= 348.4 ddR=54.1\nA s(0, 7) w(7, 15) sk0.5 tp0 rev1 \n\n...\n\n2 exp= 0.038 pips= 1.4 tpy=249.4 negY=6.0/17.0 worstY= -17.99 totR= 156.1 ddR=48.7\nA s(0, 6) w(7, 15) sk1.0 tp0 rev0 n= 4026 pf=1.097 wr= 46.2 exp= 0.0382 pips= 1.38 tpy=244.5 negY=6.0/17.0 worstY= -17.33 totR= 153.7 ddR=49.2\n"},"id":"RIrOYnu9wIeXR9N5"},{"type":"subtask-log","data":{"logType":"output-timeout"},"id":"KLREzZJb2RPH1Wkq"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874699677,"finishedAt":1785874701560,"lastPartSentAt":1785874701560,"taskNameActive":null,"id":"Nd3tcriXFgwhiUCW","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"I'm noticing a data leakage issue where the session window (0,7) overlaps with the trading window at hour 7, which means the model is seeing information about the breakout it's supposed to predict. Family A with the tight stop shows a performance factor of 1.16, but that's artificially inflated because the training data includes the very bar being traded. I need to add a guard to enforce non-overlapping windows so the session range doesn't include the trade bar itself."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874702614,"finishedAt":1785874804301,"lastPartSentAt":1785874803866,"taskNameActive":"Reading sweep B output","taskNameComplete":"Read sweep B output","toolCallId":"toolu_01Dfx1ayB3deoZF9pK4mHbT2","agentTurnId":"turn_2u5dKVox8xC1CUZx","filePath":null,"id":"kwpg7x4GBBOn3Xgl","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"sleep 100; cd /vercel/share/v0-project/research && tail -35 /dev/null; ls out_*.csv","timeout":180000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"XqjKo8aSdL9wDJja"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"out_A.csv\n"},"id":"yrdnUyjdusK5VzFm"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874808767,"finishedAt":1785874810716,"lastPartSentAt":1785874810626,"taskNameActive":"Reading task output","taskNameComplete":"Read task output","toolCallId":"toolu_01HumFuun7otqTCtXSpY5R2t","agentTurnId":"turn_LpzOfgDwuSSepWXv","filePath":null,"id":"Gq23guj9NRcrlsik","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"user_read_only_context/task_output/bash_cmd_86fbcb269fdd4661b2592895af90"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874816887,"finishedAt":1785874821983,"lastPartSentAt":1785874821983,"taskNameActive":null,"id":"8aB2DDctsiU6xAxd","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"Family B is still running slow because I'm doing an O(n) lookup per signal—with 4000 signals and 100k-element arrays, that's 400 million operations. 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Family A finished in about 100 seconds which is acceptable, but Family B has more configurations to process so the bottleneck is more noticeable.\n\nI'll refactor the prep step to build a lookup structure: for each day, precompute the indices and hours, then cache the exit index per configuration so I only compute it once instead of repeatedly for each signal."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874822025,"finishedAt":1785874823899,"lastPartSentAt":null,"taskNameActive":"Stopping slow sweep","taskNameComplete":"Stopped slow sweep","toolCallId":"toolu_01QoSbKdP5rfT8JqTZKZiypx","agentTurnId":"turn_UWjDgIHo4fnj3byG","filePath":null,"id":"TE1sSTS6pKVwzey9","type":"task-subtask-v1","parts":[]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_KtCflLUQlY1","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/families.py","baseName":"families.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/families.py\" \nGENERATING","latestDeploymentInfo":"$a7:7:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_0t2sc_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_KtCflLUQlY1","meta":{"taskNameActive":"Optimizing exit lookup","taskNameComplete":"Optimized exit lookup","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Optimizing exit lookup","taskNameComplete":"Optimized exit lookup"},"id":"block-b_KtCflLUQlY1"}],["AssistantMessageContentPart",{"part":{"createdAt":1785874836828,"finishedAt":1785874839242,"lastPartSentAt":1785874838811,"taskNameActive":"Patching exit lookups","taskNameComplete":"Patched exit lookups","toolCallId":"toolu_01A4i7Dk8pEfs5FhhTopUaK1","agentTurnId":"turn_Zwv2ivyl3jnJDkhZ","filePath":null,"id":"EA0eyw8z5asG8Jo6","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && grep -n \"ii\\[(days == day)\" families.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"qOCj6Jl9OyHQR2tT"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"82: ex = ii[(days == day) & (hh <= exit_h)].max()\n121: ex = ii[(days == day) & (hh <= exit_h)].max()\n"},"id":"pGp0YwIaQoT4z3ND"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874846620,"finishedAt":1785874849323,"lastPartSentAt":1785874848612,"taskNameActive":"Applying speed patch","taskNameComplete":"Applied speed patch","toolCallId":"toolu_01EZKoPd5hexVoLe5m12FeBm","agentTurnId":"turn_Kzy4eTastch2Utth","filePath":null,"id":"nOPU3ynrIqIhbcea","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && python3 - <<'EOF'\nimport re\np='families.py'; s=open(p).read()\ns=s.replace(\" ex = ii[(days == day) & (hh <= exit_h)].max()\",\" ex = EXM[day]\")\ns=s.replace(\" ex = ii[(days == day) & (hh <= exit_h)].max()\",\" ex = EXM[day]\")\n# insert EXM definitions after signature bodies\ns=s.replace(\" sigs = []\\n # session ranges per day\",\" sigs = []\\n EXM = exit_map(d, exit_h)\\n # session ranges per day\")\ns=s.replace(\" mv = ((sd[\\\"c\\\"] - sd[\\\"o\\\"]) / PIP).to_dict()\\n sigs = []\",\" mv = ((sd[\\\"c\\\"] - sd[\\\"o\\\"]) / PIP).to_dict()\\n EXM = exit_map(d, exit_h)\\n sigs = []\")\n# guard missing day\ns=s.replace(\" if day not in shi or taken >= max_trades:\",\" if day not in shi or day not in EXM or taken >= max_trades:\")\ns=s.replace(\" if day not in mv:\\n continue\",\" if day not in mv or day not in EXM:\\n continue\")\nopen(p,'w').write(s)\nprint(s.count(\"EXM\"))\nEOF\ngrep -n \"EXM\" families.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Zbe2hhV6X2SQMEmZ"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"6\n43: EXM = exit_map(d, exit_h)\n55: if day not in shi or day not in EXM or taken >= max_trades:\n83: ex = EXM[day]\n101: EXM = exit_map(d, exit_h)\n105: if day not in mv or day not in EXM:\n123: ex = EXM[day]\n"},"id":"DvvlRk6gdKhHRDaq"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785874855037,"finishedAt":1785874857776,"lastPartSentAt":1785874857776,"taskNameActive":null,"id":"sRgcNXssbYDRBLbM","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":3,"thought":"I need to tighten the sweep A overlap guard so it checks that the first window value exceeds the second session value, accounting for wrapped sessions, and filter out those contaminated configurations. For the exit index logic, I'm realizing the issue: when exit_i must be greater than entry_i, the engine might set exit_i below the actual index if the exit hour falls outside the window range, causing the loop to produce an empty range that leaves px as NaN, then incorrectly assigns it the value at exit_i instead.\n\nI'll add a guard to skip when exit_i is less than or equal to i, then patch the engine and run the remaining test suites B through E in parallel."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_3K32BiVVJun","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/engine.py","baseName":"engine.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/engine.py\" \nGENERATING","latestDeploymentInfo":"$a7:8:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_z5ago_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_3K32BiVVJun","meta":{"taskNameActive":"Guarding exit index","taskNameComplete":"Guarded exit index","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Guarding exit index","taskNameComplete":"Guarded exit index"},"id":"block-b_3K32BiVVJun"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_6RqajfKwk2a","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/sweep.py","baseName":"sweep.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/sweep.py\" \nGENERATING","latestDeploymentInfo":"$a7:9:1","lastDeployedAt":"2026-08-04T20:23:06.919Z","sourceVersionKey":"v_1_fcamp_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_6RqajfKwk2a","meta":{"taskNameActive":"Fixing overlap guard","taskNameComplete":"Fixed overlap guard","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Fixing overlap guard","taskNameComplete":"Fixed overlap guard"},"id":"block-b_6RqajfKwk2a"}],["AssistantMessageContentPart",{"part":{"createdAt":1785874872895,"finishedAt":1785874875042,"lastPartSentAt":1785874874920,"taskNameActive":"Sweeping B","taskNameComplete":"Swept B","toolCallId":"toolu_019Hk6eRiLZwZz8cqWAoLjk8","agentTurnId":"turn_Jo0khaqfIjZAnhMc","filePath":null,"id":"TZtUqXuXAUZlIujV","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python sweep.py B > log_B.txt 2>&1; 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That is far too good to be true for a breakout strategy on H1 FX. This is a classic look-ahead/leakage red flag. I need to audit family B's signal construction before believing anything.\n\nLikely bug: family B likely computes the range window (7,12) and then enters at 8 — i.e., entry hour is INSIDE the range window, so the range high/low used at entry already contains future information (bars up to hour 12). Notice: \"B r(7,12) e8->15\" — range 7-12, entry from hour 8. That means the breakout level is computed from the full 7-12 window but entering at hour 8, which uses future bars. Definite look-ahead leakage.\n\nLet me read families.py to verify."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-0"}],["p",{},["text",{},"Family B's numbers (PF 3.69, 74.6% win rate, zero losing years in 17 years) are not plausible for an H1 FX breakout. The parameter labels are the tell: "],["code",{},"r(7,12) e8->15"],["text",{}," computes the range over hours 7–12 but enters at hour 8 — the breakout level would be built from bars that haven't happened yet. Let me audit before trusting any of it."]],["AssistantMessageContentPart",{"part":{"createdAt":1785875005008,"finishedAt":1785875005587,"lastPartSentAt":1785875005561,"taskNameActive":"Auditing family B","taskNameComplete":"Audited family B","toolCallId":"toolu_013uWJCJyR4zXpctrct6Cy92","agentTurnId":"turn_283qRwRwaqSDs1KM","filePath":null,"id":"y6TsHMfldqC5UNLc","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/families.py"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-1","parts":[{"type":"thinking-end","duration":10.717647058823529,"thought":"$af"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-1"}],["p",{},["text",{},"Confirmed — family B is leaking. Let me check the sweep config and the engine's cost model."]],["AssistantMessageContentPart",{"part":{"type":"task-grouped-tasks-v1","id":"NuYZ49UF4ofsmS7y-kDNRXXMD9nOcX58p","parts":[{"createdAt":1785875022432,"finishedAt":1785875024593,"lastPartSentAt":1785875024549,"taskNameActive":"Reading sweep config","taskNameComplete":"Read sweep config","toolCallId":"toolu_01C7hcebYWTCXb7iTiM1RhSz","agentTurnId":"turn_ZvVWS8w3Pf6AcHQM","filePath":null,"id":"NuYZ49UF4ofsmS7y","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/sweep.py"}]},{"createdAt":1785875022591,"finishedAt":1785875024742,"lastPartSentAt":1785875024580,"taskNameActive":"Reading engine","taskNameComplete":"Read engine","toolCallId":"toolu_012mFeMYd3QGTe1QekGKaY6z","agentTurnId":"turn_ZvVWS8w3Pf6AcHQM","filePath":null,"id":"kDNRXXMD9nOcX58p","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/engine.py"}]}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Explore • 2 Files","taskNameComplete":"Explore • 2 Files"},"id":"NuYZ49UF4ofsmS7y-kDNRXXMD9nOcX58p"}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-2","parts":[{"type":"thinking-end","duration":20.388235294117646,"thought":"$b0"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-2"}],["p",{},["text",{},"Found it. Two real bugs, one of which manufactured the entire result."]],["p",{},["strong",{},["text",{},"Bug 1 — look-ahead leak (the PF 3.69 illusion)."]],["text",{}," In "],["code",{},"sweep.py"],["text",{},"'s B branch there's no guard tying the reference window to the entry hour:"]],["Codeblock",{"lang":"python","meta":{},"closed":true},"for ref in [(0, 6), (0, 7), (21, 6), (7, 12)]:\n for eh in [7, 8, 12, 13]:"],["p",{},["text",{},"Every top-ranked B row is "],["code",{},"r(7,12) e8"],["text",{}," or "],["code",{},"r(7,12) e7"],["text",{},". "],["code",{},"session_momentum"],["text",{}," derives direction from "],["code",{},"sd[\"c\"] = close of the last bar in hours 7–12"],["text",{},", then enters at hour 8's open. So the signal reads the close of 12:00 and trades on it at 08:00 — four hours of hindsight. \"Know where price went, then bet on it\" trivially yields 74.6% WR and 0/17 losing years."]],["p",{},["strong",{},["text",{},"Bug 2 — wrap-around sessions silently produce nothing."]],["text",{}," "],["code",{},"(21, 6)"],["text",{}," is meant to be overnight Asia, but the mask is "],["code",{},"(hh >= 21) & (hh <= 6)"],["text",{},", which no hour can satisfy. Those configs yield zero trades and vanish into the "],["code",{},"n > 200"],["text",{}," filter instead of erroring."]],["p",{},["text",{},"Rather than patch the parameter lists, I'll make the leak structurally impossible in the generators."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_KijZV41197X","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/families.py","baseName":"families.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/families.py\" \nGENERATING","latestDeploymentInfo":"$a7:11:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_sdcrg_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_KijZV41197X","meta":{"taskNameActive":"Adding leak-proof session helper","taskNameComplete":"Added leak-proof session 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edge."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_BWoxhRE16xm","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/sweep.py","baseName":"sweep.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/test_leak.py","baseName":"test_leak.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"research/sweep.py\" \nGENERATING\n[V0_FILE]py:file=\"/research/test_leak.py\" \nGENERATING","latestDeploymentInfo":"$a7:18:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_2vuba_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_BWoxhRE16xm","meta":{"taskNameActive":"Writing leak test","taskNameComplete":"Wrote leak test","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing leak 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The headline config: B ref(7,12) entry_h=8 -> PF 3.689, WR 74.6%, 0/17 neg years\n==============================================================="},"id":"LhpD8buTkJvnD7lX"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=====================================\nREJECTED by causal guard:\n B ref(7, 12) entry_h=8: signal at bar 10 uses info thr\n\n...\n\nrs <- silently empty, configs vanished\n new wrap-aware hour_mask : 42835 bars\n sessions recovered : 4284\n"},"id":"TlEvVtxms9jN1svH"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_AExTNcYCm8m","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/families.cpython-313.pyc","baseName":"families.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/families.cpython-313.pyc\" \nGENERATING","latestDeploymentInfo":"$a7:19:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_irv21_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_AExTNcYCm8m","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_AExTNcYCm8m"}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-3","parts":[{"type":"thinking-end","duration":10.170588235294117,"thought":"$b2"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-3"}],["p",{},["text",{},"The dose-response curve is conclusive:"]],["table",{"align":[null,null,null,null,null]},["thead",{},["tr",{},["th",{},["text",{},"hindsight"]],["th",{},["text",{},"PF"]],["th",{},["text",{},"WR"]],["th",{},["text",{},"neg years"]],["th",{},["text",{},"maxDD"]]]],["tbody",{},["tr",{},["td",{},["text",{},"+5h ("],["code",{},"e8"],["text",{},", the \"winner\")"]],["td",{},["strong",{},["text",{},"3.689"]]],["td",{},["text",{},"74.6%"]],["td",{},["text",{},"0/17"]],["td",{},["text",{},"5.0R"]]],["tr",{},["td",{},["text",{},"+1h ("],["code",{},"e12"],["text",{},")"]],["td",{},["text",{},"1.462"]],["td",{},["text",{},"57.5%"]],["td",{},["text",{},"0/17"]],["td",{},["text",{},"12.8R"]]],["tr",{},["td",{},["strong",{},["text",{},"0h ("],["code",{},"e13"],["text",{},", causal)"]]],["td",{},["strong",{},["text",{},"0.887"]]],["td",{},["text",{},"48.8%"]],["td",{},["strong",{},["text",{},"15/17"]]],["td",{},["text",{},"164.5R"]]]]],["p",{},["text",{},"Performance scales monotonically with the number of leaked bars, and at zero hindsight the strategy is a "],["em",{},["text",{},"consistent loser"]],["text",{}," — 15 of 17 years negative, −226R total. Let me confirm nothing else in the sweep was riding the same bug."]],["AssistantMessageContentPart",{"part":{"createdAt":1785875175045,"finishedAt":1785875223851,"lastPartSentAt":1785875223468,"taskNameActive":"Re-running guarded sweeps","taskNameComplete":"Re-ran guarded sweeps","toolCallId":"toolu_013tzTkMmANs9sW4VqVYUNyR","agentTurnId":"turn_khnm43rPAK6KyLvj","filePath":null,"id":"gvnApDjWhX79wAaC","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python sweep.py B > log_B2.txt 2>&1; head -30 log_B2.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"0xMERWNJXnfprSbk"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"--- REJECTED 120 look-ahead configs ---\n B r(0, 7) e7->12 sk1.0 tp0 rev0 -> B ref(0, 7) entry_h=7: signal at bar 9 uses info through bar 9 (1 bar(s) of hindsight)\n B r(0, 7) e7->12 sk1.0 tp0 rev1 -> B ref(0, 7) entry_h=7: signal at bar 9 us\n\n...\n\n= 43.8 exp= 0.0104 pips= 0.91 tpy=259.1 negY=10/17 worstY= -44.64 totR= 44.6 ddR=68.2\nB r(21, 6) e7->15 sk1.0 tp0 rev0 n= 4264 pf= 1.02 wr= 32.0 exp= 0.0144 pips= 0.36 tpy=259.0 negY=8/17 worstY= -68.35 totR= 61.4 ddR=117.9\n\n"},"id":"ociHgce7kZejJ2zh"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_OgppwnDg32E","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/out_B.csv","baseName":"out_B.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/log_B2.txt","baseName":"log_B2.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]csv:file=\"research/out_B.csv\" \nGENERATING\n[V0_FILE]txt:file=\"research/log_B2.txt\" \nGENERATING","latestDeploymentInfo":"$a7:20:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_qfwv3_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_OgppwnDg32E","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_OgppwnDg32E"}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-4","parts":[{"type":"thinking-end","duration":5.547058823529412,"thought":"120 leaky configs rejected. Now the best surviving causal B config is PF 1.051 with 6/17 negative years and 73.7R drawdown — i.e. indistinguishable from noise, and nowhere near the `passes()` threshold of PF≥1.30 with 0 negative years.\n\nNote that 120 of 448 configs were leaky — a quarter of the search space was fantasy, and it dominated the top of the ranking because leakage is the strongest \"signal\" available.\n\nLet me also re-run A to confirm A is unaffected and check the wrap-aware sessions didn't introduce new leaks that pass.\n\nAlso note the top B configs are all `tp0` (time exit only) with sk2.0 — and PF ~1.03-1.05. With 259 trades/year and 6-8 negative years out of 17, that's noise. None pass.\n\nLet me re-run A quickly and then write up. Family A previously showed top PF 1.172 (`s(0,3) w(4,12)`) with 2/17 neg years. That's the one plausible-ish candidate but still below threshold. Let me verify A is clean under the new guard."}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-4"}],["p",{},["strong",{},["text",{},"120 of 448 B configs were look-ahead"]],["text",{}," — and because leakage is the strongest \"signal\" available, they monopolized the top of the ranking. Let me verify family A wasn't affected and re-check the survivors."]],["AssistantMessageContentPart",{"part":{"createdAt":1785875240020,"finishedAt":1785875310574,"lastPartSentAt":1785875310335,"taskNameActive":"Re-running sweep A","taskNameComplete":"Re-ran sweep A","toolCallId":"toolu_01X28ATeJTiifunXK9LNRaJC","agentTurnId":"turn_esuoyBWohBhpC4uj","filePath":null,"id":"jdYt6825LhsSUljW","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python sweep.py A > log_A2.txt 2>&1; head -12 log_A2.txt; echo \"...\"; grep -A14 \"few neg years\" log_A2.txt | head -15","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"tWPQ350n1VdBurTa"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"--- REJECTED 96 look-ahead configs ---\n A s(0, 6) w(4, 12) sk0.5 tp0 rev0 -> A sess(0, 6) win(4, 12): signal at bar 6 uses info through bar 8 (3 bar(s) of hindsight)\n A s(0, 6) w(4, 12) sk0.5 tp0 rev1 -> A sess(0, 6) win(4, 12): signal at b\n\n...\n\n wr= 46.2 exp= 0.038 pips= 1.4 tpy=249.4 negY=6/17 worstY= -17.99 totR= 156.1 ddR=48.7\nA s(0, 6) w(7, 15) sk1.0 tp0 rev0 n= 4026 pf=1.097 wr= 46.2 exp= 0.0382 pips= 1.38 tpy=244.5 negY=6/17 worstY= -17.33 totR= 153.7 ddR=49.2\n"},"id":"TSXJwEvZDxzchjsk"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_AudlTM8K9tM","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/out_A.csv","baseName":"out_A.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/log_A2.txt","baseName":"log_A2.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]csv:file=\"research/out_A.csv\" \nGENERATING\n[V0_FILE]txt:file=\"research/log_A2.txt\" \nGENERATING","latestDeploymentInfo":"$a7:21:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_jdbc4_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_AudlTM8K9tM","meta":{"taskNameActive":"Applying changes to 2 files","taskNameComplete":"Applied changes to 2 files","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying changes to 2 files","taskNameComplete":"Applied changes to 2 files"},"id":"block-b_AudlTM8K9tM"}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-5","parts":[{"type":"thinking-end","duration":12.035294117647059,"thought":"$b3"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-5"}],["p",{},["text",{},"Family A's top config survived (it was genuinely causal), and fixing the wrap mask unlocked 4,284 previously-invisible overnight sessions. But 96 A configs were also leaky. Before reporting PF 1.17 as a \"finding,\" let me check whether it survives out-of-sample — best-of-204 configs invites selection bias."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_piVp96Jrkdt","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/out_A.csv","baseName":"out_A.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/log_A2.txt","baseName":"log_A2.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/test_robust.py","baseName":"test_robust.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]csv:file=\"research/out_A.csv\" \nGENERATING\n[V0_FILE]txt:file=\"research/log_A2.txt\" \nGENERATING\n[V0_FILE]py:file=\"/research/test_robust.py\" \nGENERATING","latestDeploymentInfo":"$a7:22:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_qmfv9_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_piVp96Jrkdt","meta":{"taskNameActive":"Writing robustness check","taskNameComplete":"Wrote robustness check","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing robustness check","taskNameComplete":"Wrote robustness check"},"id":"block-b_piVp96Jrkdt"}],["AssistantMessageContentPart",{"part":{"createdAt":1785875341762,"finishedAt":1785875344639,"lastPartSentAt":1785875344460,"taskNameActive":"Running robustness check","taskNameComplete":"Ran robustness check","toolCallId":"toolu_01H19NL4izpg2CHXDFkz7ZPr","agentTurnId":"turn_s3XZbLxj5rnSgJPm","filePath":null,"id":"31dIdcmombmPP8yB","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python test_robust.py 2>&1 | tail -55","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"nsroITXMzd9oY17p"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"================================================================================================\nA. Split-sample: fit period 2010-2017 vs held-out 2018-2026\n=========================================================================================\n\n...\n\nr= 43.4 exp= 0.0643 pips= 1.12 tpy=245.5 negY=4/17 worstY= -23.24 totR= 260.0 ddR=36.5\n cost=3.0 pips n= 4042 pf=1.048 wr= 42.8 exp= 0.0249 pips= 0.12 tpy=245.5 negY=7/17 worstY= -36.27 totR= 100.6 ddR=55.1\n"},"id":"iNBdXxH8Yguy7F7O"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-thinking-v1","id":"thinking-6","parts":[{"type":"thinking-end","duration":13.135294117647058,"thought":"$b4"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1},"id":"thinking-6"}],["p",{},["text",{},"I need to correct a claim I'd pre-written into that script — the data contradicts it."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_XWu3mWNetMz","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/test_robust.py","baseName":"test_robust.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/test_robust.py\" \nGENERATING","latestDeploymentInfo":"$a7:23:1","lastDeployedAt":"2026-08-04T20:31:20.905Z","sourceVersionKey":"v_1_3t6y1_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_XWu3mWNetMz","meta":{"taskNameActive":"Correcting stale conclusion","taskNameComplete":"Corrected stale conclusion","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Correcting stale conclusion","taskNameComplete":"Corrected stale conclusion"},"id":"block-b_XWu3mWNetMz"}],["p",{},["text",{},"Let me run the true holdout — select the best config using "],["strong",{},["text",{},"only"]],["text",{}," 2010–2017, then evaluate it once on 2018–2026."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_AilJ6USIxm2","parts":[{"type":"code-project","isVersioned":true,"isSourceless":false,"changedFiles":[{"fileName":"/research/families.py","baseName":"families.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/sweep.py","baseName":"sweep.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/test_leak.py","baseName":"test_leak.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/__pycache__/families.cpython-313.pyc","baseName":"families.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/out_B.csv","baseName":"out_B.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/log_B2.txt","baseName":"log_B2.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/out_A.csv","baseName":"out_A.csv","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"research/log_A2.txt","baseName":"log_A2.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/test_robust.py","baseName":"test_robust.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/test_holdout.py","baseName":"test_holdout.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/test_holdout.py\" 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&& . /tmp/.venv/bin/activate && python test_holdout.py 2>&1 | tail -30","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"jLxg1ihXrfrTc00I"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"========================================================================================================\nRanked by TRAIN (2010-2017) PF only. pf_out was not used for selection.\n=====================================================================\n\n...\n\n-10 mean PF: in 1.118 -> holdout 1.081\nOf the train top 10, 8/10 stayed above 1.0 in holdout.\n\nRank correlation of PF_in vs PF_out across all 204 configs: 0.941\n(Positive => the ranking carries information forward. ~0 => in-sample PF is noise.)\n"},"id":"gR6vpYJP9HHimsBX"}]}}],["p",{},["text",{},"Fixed the bug that was fabricating the result, then re-tested everything that depended on it."]],["h2",{},["text",{},"The bug"]],["p",{},["text",{},"Family B's headline (PF 3.69, 74.6% WR, 0/17 losing years) was "],["strong",{},["text",{},"look-ahead bias"]],["text",{},". "],["code",{},"session_momentum"],["text",{}," took its direction from "],["code",{},"close of the last bar in hours 7–12"],["text",{},", but "],["code",{},"sweep.py"],["text",{}," paired "],["code",{},"ref=(7,12)"],["text",{}," with "],["code",{},"entry_h=8"],["text",{}," — reading 12:00's close and trading on it at 08:00. Four hours of hindsight. The B branch had no guard tying the reference window to the entry hour."]],["p",{},["text",{},"The dose-response proves it was the entire edge:"]],["table",{"align":[null,null,null,null,null]},["thead",{},["tr",{},["th",{},["text",{},"hindsight"]],["th",{},["text",{},"PF"]],["th",{},["text",{},"WR"]],["th",{},["text",{},"neg years"]],["th",{},["text",{},"maxDD"]]]],["tbody",{},["tr",{},["td",{},["text",{},"+5h ("],["code",{},"e8"],["text",{},", the \"winner\")"]],["td",{},["text",{},"3.689"]],["td",{},["text",{},"74.6%"]],["td",{},["text",{},"0/17"]],["td",{},["text",{},"5.0R"]]],["tr",{},["td",{},["text",{},"+1h ("],["code",{},"e12"],["text",{},")"]],["td",{},["text",{},"1.462"]],["td",{},["text",{},"57.5%"]],["td",{},["text",{},"0/17"]],["td",{},["text",{},"12.8R"]]],["tr",{},["td",{},["strong",{},["text",{},"0h ("],["code",{},"e13"],["text",{},", causal)"]]],["td",{},["strong",{},["text",{},"0.887"]]],["td",{},["text",{},"48.8%"]],["td",{},["strong",{},["text",{},"15/17"]]],["td",{},["text",{},"164.5R"]]]]],["p",{},["text",{},"Made causal, it's not weaker — it's a reliable loser (−226R). "],["strong",{},["text",{},"120 of 448 B configs were leaky"]],["text",{},", and they monopolized the top of the rankings, since leakage is the strongest \"signal\" in any search space."]],["p",{},["strong",{},["text",{},"Second bug:"]],["text",{}," "],["code",{},"(hh >= 21) & (hh <= 6)"],["text",{}," can never be true, so every overnight-Asia config silently produced zero trades and disappeared into the "],["code",{},"n > 200"],["text",{}," filter. Fixing it recovered 4,284 real sessions."]],["h2",{},["text",{},"The fix"]],["p",{},["text",{},"Rather than patch parameter lists, I made the leak structurally impossible: "],["code",{},"session_stats"],["text",{}," now returns each session's final bar index, and "],["code",{},"assert_causal"],["text",{}," raises "],["code",{},"LookAheadError"],["text",{}," if a signal uses information from at/after its entry bar. "],["code",{},"run()"],["text",{}," takes a thunk, so violations are rejected and "],["em",{},["text",{},"counted"]],["text",{},", not silently ranked. Any future config that reaches forward now fails loudly instead of printing a fantasy PF."]],["h2",{},["text",{},"What survived"]],["p",{},["text",{},"Family A's "],["code",{},"s(0,3) w(4,12)"],["text",{}," was genuinely causal. I ran a true holdout — selected on 2010–2017 only, evaluated once on 2018–2026:"]],["ul",{"start":1},["li",{"index":1},["text",{},"Train PF "],["strong",{},["text",{},"1.178"]],["text",{}," → holdout PF "],["strong",{},["text",{},"1.166"]],["text",{}," (2,079 trades), 15/17 years positive"]],["li",{"index":2},["text",{},"Train-top-10 mean PF 1.118 → 1.081 holdout; 8/10 stayed above 1.0"]],["li",{"index":3},["text",{},"PF_in vs PF_out correlation across all 204 configs: "],["strong",{},["text",{},"0.941"]]]],["p",{},["text",{},"I also had to retract a conclusion I'd pre-written into "],["code",{},"test_robust.py"],["text",{}," claiming the winner was best-of-N noise — the config space median PF is 0.782, not ~1.0, so the winner isn't drawn from a null centered at breakeven."]],["p",{},["text",{},"Honest caveat on the one survivor: it lives on ~3.1 gross pips/trade against a 1.5-pip cost assumption, and decays to PF 1.048 at 3 pips. Since these are "],["em",{},["text",{},"stop-order breakout"]],["text",{}," entries, real slippage is asymmetrically bad in exactly the fast markets that trigger them, so realized cost likely exceeds the symmetric assumption. Worth pressure-testing with a fill model that penalizes breakout entries before treating PF 1.17 as tradable."]]]],[1,{}]]},"context":[],"isContentLoading":false,"role":"assistant","id":"wseQ4Ga65ylerzuFH8UgtwIAKe494YXN","gitRevision":{"baseSha":"defb72bf4c0dfc2ad366a90111a1d0e3e6a133ba","headSha":"0cc871a0ad5d3319b50418ae070b23afe5820c4a","readyAt":"2026-08-04T20:31:20.899Z","version":1},"streaming":false,"finishReason":"stop","parentId":"QPdCpvQfkIar3HFkyjqpVrgCivIfwWKZ","type":"message","creditCost":1.73991225,"workMetrics":{"diffMap":{},"linesRead":424,"agentActions":24},"createdAt":"$D2026-08-04T20:23:04.359Z","updatedAt":"$D2026-08-04T20:31:20.900Z"},{"content":{"type":"parts","parts":[{"type":"mdx","content":"Continue "}],"version":1},"role":"user","id":"pW8T5SfgE5WT4ZI3FuzygopwEbJOT6OF","attachments":[],"parentId":"wseQ4Ga65ylerzuFH8UgtwIAKe494YXN","type":"message","authorId":"LIl3SYdavnPBzuaZAQawDWxY","createdAt":"$D2026-08-04T20:35:00.524Z"},{"v0ModelId":"v0-opus-5","content":{"type":"message-binary-format","value":[[0,[["AssistantMessageContentPart",{"part":{"createdAt":1785875706811,"finishedAt":1785875707046,"lastPartSentAt":1785875707046,"taskNameActive":null,"id":"A7UvvzzfN4qKYIc0","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"Now I'm moving on to pressure-test the config by running it through a slippage model that penalizes breakout entries — this will show how robust the strategy actually is under more realistic conditions."}]}}],["p",{},["text",{},"Continuing with the caveat I flagged: the survivor's edge is ~3.1 gross pips against a 1.5-pip symmetric cost. Since these are stop-order breakout entries, let me test it under a fill model that penalizes them asymmetrically."]],["AssistantMessageContentPart",{"part":{"type":"task-grouped-tasks-v1","id":"UYZqUQe8uB0CSkzP-paGuuxYJCSZUac6Y","parts":[{"createdAt":1785875708292,"finishedAt":1785875710242,"lastPartSentAt":1785875710206,"taskNameActive":"Reading engine","taskNameComplete":"Read engine","toolCallId":"toolu_01EoW5N9pEUdQxstcx4FQzhu","agentTurnId":"turn_LTFWSyEcPRzPRE1w","filePath":null,"id":"UYZqUQe8uB0CSkzP","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/engine.py"}]},{"createdAt":1785875708674,"finishedAt":1785875709887,"lastPartSentAt":1785875709862,"taskNameActive":"Reading holdout test","taskNameComplete":"Read holdout test","toolCallId":"toolu_016ee94qXJs4hH6eFWAt7exq","agentTurnId":"turn_LTFWSyEcPRzPRE1w","filePath":null,"id":"paGuuxYJCSZUac6Y","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/test_holdout.py"}]}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Explore • 2 Files","taskNameComplete":"Explore • 2 Files"},"id":"UYZqUQe8uB0CSkzP-paGuuxYJCSZUac6Y"}],["AssistantMessageContentPart",{"part":{"createdAt":1785875715800,"finishedAt":1785875721715,"lastPartSentAt":1785875721715,"taskNameActive":null,"id":"XRWPqJX8jHpuAJUZ","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":6,"thought":"$b5"}]}}],["p",{},["text",{},"Let me check how family A sets its entry so the fill model can identify the trigger level."]],["AssistantMessageContentPart",{"part":{"createdAt":1785875721829,"finishedAt":1785875723429,"lastPartSentAt":1785875723408,"taskNameActive":"Reading family A","taskNameComplete":"Read family A","toolCallId":"toolu_01MqkYCCYsM6Z3PgVK6vF7ys","agentTurnId":"turn_B5KpU2AOkIvw0YY6","filePath":null,"id":"bQWf3QwI9uBM9EWw","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/families.py"},{"type":"reading-file","filePath":"/vercel/share/v0-project/research/families.py","offset":87,"limit":55}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785875728755,"finishedAt":1785875734120,"lastPartSentAt":1785875734120,"taskNameActive":null,"id":"9BZejYmnStowoWU1","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":5,"thought":"The entry fills at exactly the stop level with no slippage, and I need to track whether it's a stop order with the `entry_is_stop` flag. Now I'm adding an asymmetric fill model where slippage scales with bar velocity—the slip amount increases based on the bar's range in pips, and stop orders fill worse by that calculated slippage.\n\nFor the implementation, I'll add a `fill` parameter to the simulate function as a dictionary with base and k values, then use an explicit flag to mark stop entries so the fill model only applies entry slippage when appropriate. I'm inferring this from whether an \"entry\" key exists in the signal, which naturally corresponds to family A stop orders versus family B market entries at open."}]}}],["p",{},["text",{},"Now I'll add an asymmetric fill model to the engine. The key realism: breakout entries and stop-losses are both "],["strong",{},["text",{},"stop orders"]],["text",{}," that slip against you and slip most in fast bars, while take-profits are "],["strong",{},["text",{},"limit orders"]],["text",{}," that never slip in your favor."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_POTOKbSwgPp","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/engine.py","baseName":"engine.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/engine.py\" \nGENERATING","latestDeploymentInfo":"$a7:25:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_2p372_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_POTOKbSwgPp","meta":{"taskNameActive":"Adding asymmetric fill model","taskNameComplete":"Added asymmetric fill model","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Adding asymmetric fill model","taskNameComplete":"Added asymmetric fill model"},"id":"block-b_POTOKbSwgPp"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_tc0ZgI0DTYg","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/engine.py","baseName":"engine.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/engine.py\" \nGENERATING","latestDeploymentInfo":"$a7:26:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_qiw5a_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_tc0ZgI0DTYg","meta":{"taskNameActive":"Applying entry slippage","taskNameComplete":"Applied entry slippage","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying entry slippage","taskNameComplete":"Applied entry slippage"},"id":"block-b_tc0ZgI0DTYg"}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_mFvTv0sKlC0","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/engine.py","baseName":"engine.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/engine.py\" \nGENERATING","latestDeploymentInfo":"$a7:27:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_29zyn_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_mFvTv0sKlC0","meta":{"taskNameActive":"Applying stop-loss slippage","taskNameComplete":"Applied stop-loss slippage","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Applying stop-loss slippage","taskNameComplete":"Applied stop-loss slippage"},"id":"block-b_mFvTv0sKlC0"}],["AssistantMessageContentPart",{"part":{"createdAt":1785875768493,"finishedAt":1785875771965,"lastPartSentAt":1785875771965,"taskNameActive":null,"id":"5FMtTGnYqzNiqcuz","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":3,"thought":"When using the slip model, I need to adjust the cost_pips parameter to represent just the spread since slippage is now being modeled separately. I'm writing a test that validates this setup with cost_pips set to spread only. Then I'll create test_fills.py to evaluate the winning configuration across different fill models, extracting the best parameters from the holdout results and re-deriving them on the training set before testing.\n\nRather than just testing a single winning config, I should run the full holdout selection process under each fill model to see if the optimal configuration actually changes depending on how fills are modeled—that's a more realistic and thorough validation."}]}}],["p",{},["text",{},"Now a test that re-runs the whole train/holdout selection under progressively harsher fill models — since realistic costs may also change "],["em",{},["text",{},"which"]],["text",{}," config wins, not just its PF."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_ufiZPU2yUVl","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/test_fills.py","baseName":"test_fills.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/test_fills.py\" \nGENERATING","latestDeploymentInfo":"$a7:28:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_t04vj_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_ufiZPU2yUVl","meta":{"taskNameActive":"Writing fill-model test","taskNameComplete":"Wrote fill-model test","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing fill-model test","taskNameComplete":"Wrote fill-model test"},"id":"block-b_ufiZPU2yUVl"}],["AssistantMessageContentPart",{"part":{"createdAt":1785875797608,"finishedAt":1785875919686,"lastPartSentAt":1785875919628,"taskNameActive":"Running fill-model test","taskNameComplete":"Ran fill-model test","toolCallId":"toolu_019NegN7zPTFXRbiDSqkvfW3","agentTurnId":"turn_cmT8QR5wyYa47RL1","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"The command took too long to complete and was aborted.","code":"AGENT_GUIDANCE"},"id":"Jgj5naWlgPZz9CAT","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python test_fills.py 2>&1 | tail -45","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"aY6WYZ81xpFhzOKY"},{"type":"subtask-log","data":{"logType":"output-timeout"},"id":"Bj8ZThLsCMQrTjfL"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_9f1tKWRV7KZ","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/__pycache__/engine.cpython-313.pyc","baseName":"engine.cpython-313.pyc","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]pyc:file=\"research/__pycache__/engine.cpython-313.pyc\" \nGENERATING","latestDeploymentInfo":"$a7:29:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_512z9_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_9f1tKWRV7KZ","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_9f1tKWRV7KZ"}],["AssistantMessageContentPart",{"part":{"createdAt":1785875923534,"finishedAt":1785876045199,"lastPartSentAt":1785876045109,"taskNameActive":"Waiting for fill test","taskNameComplete":"Waited for fill test","toolCallId":"toolu_01DYBJQZcHkaj5g78UpyFsNV","agentTurnId":"turn_tQU7EZVq3eV42IaW","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"The command took too long to complete and was aborted.","code":"AGENT_GUIDANCE"},"id":"2vVspZ0KLHrbWvt4","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"sleep 200; 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Now I need to measure realistic slippage from the actual data rather than assuming it—specifically by looking at the bar ranges at entry points to determine which model applies, then calculate the breakeven slippage threshold and compare it against what I'm seeing in the data."}]}}],["p",{},["text",{},"Selection is stable (same config wins under every model), but profitability is almost entirely a function of the cost assumption. Rather than guess which model is right, let me measure the trigger bars empirically and solve for breakeven slippage."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_JIL9F7oowke","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/test_breakeven.py","baseName":"test_breakeven.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/test_breakeven.py\" \nGENERATING","latestDeploymentInfo":"$a7:30:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_8vt4t_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_JIL9F7oowke","meta":{"taskNameActive":"Writing breakeven analysis","taskNameComplete":"Wrote breakeven analysis","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing breakeven analysis","taskNameComplete":"Wrote breakeven analysis"},"id":"block-b_JIL9F7oowke"}],["AssistantMessageContentPart",{"part":{"createdAt":1785876087628,"finishedAt":1785876089329,"lastPartSentAt":1785876089306,"taskNameActive":"Running breakeven analysis","taskNameComplete":"Ran breakeven analysis","toolCallId":"toolu_01SnpjPZRavdyRSWW1vk7imC","agentTurnId":"turn_amkbeoDlPn3GItyK","filePath":null,"id":"Ps7SOtpgdEFDqIch","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && . /tmp/.venv/bin/activate && python test_breakeven.py > log_be.txt 2>&1; cat log_be.txt","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"FzayOUb3pn9jcSz2"},{"type":"subtask-running-in-background"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_AuQY8roUYC4","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/log_be.txt","baseName":"log_be.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/log_be.txt\" \nGENERATING","latestDeploymentInfo":"$a7:31:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_fkeon_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_AuQY8roUYC4","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_AuQY8roUYC4"}],["AssistantMessageContentPart",{"part":{"createdAt":1785876093258,"finishedAt":1785876204562,"lastPartSentAt":1785876204360,"taskNameActive":"Reading breakeven results","taskNameComplete":"Read breakeven results","toolCallId":"toolu_011vpj58gRBZCkB8jdArjuS6","agentTurnId":"turn_w5L5MxiXPfwJnHXI","filePath":null,"id":"rB8LDqPNTMlWd2Oz","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"sleep 110; cd /vercel/share/v0-project/research && cat log_be.txt","timeout":180000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"jyMSRCUemLg4f3QI"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"config: s(0, 3) w(4, 12) sk1.0 tp0\nsignals: 4042\n\n==============================================================================\n(2) Are trigger bars faster than normal?\n============================================================================\n\n...\n\ntem that assumes a fixed\n sub-2-pip round trip is asserting a fill quality it never measured.\n\n This is a research artefact, not a tradable strategy. To settle it you need\n tick/quote data to measure real fills at these levels; H1 OHLC cannot.\n"},"id":"9ele6eJKW56YjgLP"}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_FXDSBCMY45G","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"research/log_be.txt","baseName":"log_be.txt","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]txt:file=\"research/log_be.txt\" \nGENERATING","latestDeploymentInfo":"$a7:32:1","lastDeployedAt":"2026-08-04T20:43:54.347Z","sourceVersionKey":"v_1_gg4wl_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_FXDSBCMY45G","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_FXDSBCMY45G"}],["AssistantMessageContentPart",{"part":{"createdAt":1785876212614,"finishedAt":1785876216312,"lastPartSentAt":1785876216312,"taskNameActive":null,"id":"2F5jZWy65ESJkuSl","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":4,"thought":"The breakeven of 3.68 pips round trip is actually quite reasonable—more forgiving than I initially thought. The trigger bars at 1.19x median aren't dramatically faster either, so the harshest 6-pip model seems overly pessimistic for a liquid pair like USDJPY. With realistic retail costs around 1-2 pips round trip, the edge should hold up.\n\nI need to correct my earlier overstated pessimism about asymmetric slippage. At 1.19x, the asymmetry is much milder than I claimed, giving roughly a 2x margin between breakeven and realistic costs. I should update the closing text in the test file to reflect that 1.19x is actually a mild regime, not the worst-case scenario I initially framed it as."}]}}],["p",{},["text",{},"My pre-written conclusion in that script overstates the case — 1.19x is mild, not \"the regime where slippage is worst.\" Let me correct it to match what the data actually 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