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If no strategy satisfies every requirement: - Continue researching new statistical edges. - Continue generating new hypotheses. - Continue testing alternative execution logic. - Continue exploring different market behaviors. Only after exhausting genuinely different statistical ideas may you introduce additional filters or refinements. Any added filter must improve: - Out-of-sample performance. - Walk-forward performance. - Robustness. Never add filters whose only purpose is increasing historical Profit Factor. If no statistically honest solution exists after exhaustive research, explicitly state that no such strategy exists. --- # Research Process Continue searching until one of the following occurs: - A strategy satisfies every requirement. - Exhaustive statistical research indicates that no such strategy exists. Do not stop after finding the first profitable strategy. Keep searching for stronger and more robust alternatives. --- # Optimization Objective The objective is not to maximize historical Profit Factor. The objective is to maximize the probability that the strategy survives real live trading. When two candidate strategies achieve similar performance, always prefer the one with: - Lower execution costs. - Higher average profit per trade after all costs. - Better walk-forward performance. - Better out-of-sample performance. - Lower parameter sensitivity. - More stable yearly returns. - Greater robustness across different market regimes. Always prioritize long-term robustness over historical optimization. --- # Output Efficiency Prioritize research over presentation. Generate only lightweight reports. Avoid: - Fancy dashboards. - Decorative charts. - Interactive UI. - Complex layouts. - Large visualizations. - Animations. - Excessive formatting. - Repeated tables. - Any non-essential graphics. Use simple markdown and compact tables only. Allocate the maximum possible computation, context, and tokens to: - Statistical discovery. - Edge generation. - Validation. - Robustness testing. - Portfolio construction (if applicable). - Pine Script generation. Minimize token usage devoted to presentation. --- # Deliverables Provide only: - Research summary. - Statistical findings. - Final strategy (or portfolio if appropriate). - Validation results. - Walk-forward results. - Out-of-sample results. - Monte Carlo results. - Execution-cost analysis. - Year-by-year performance table. - Pine Script Version 6. - Essential implementation notes. Do not generate dashboards, charts, UI components, or visual reports unless explicitly requested.41:o9,ae:T1b30,41:o1b30,All the validation checks are passing—the profit factor, win rate, trade frequency, and expectancy all meet the thresholds, with a solid sample size and positive returns across every calendar year. The walk-forward analysis shows 7 out of 8 years profitable, though the cost ladder analysis appears to be cut off mid-thought. The strategy breaks down into two timeframe components: a DIP signal running on 4-hour bars and a squeeze/gap reversal/trade signal on the 1-hour US session grid. For Pine Script implementation, I'll build this on a 1-hour chart and use request.security to pull the 4-hour DIP calculations, restricting the session logic to the 16-23 hour window. The DIP entry triggers when the normalized pullback exceeds 4 ATR, with a 4 ATR stop and 5 ATR trailing from the maximum favorable excursion over a 120-bar window. The squeeze signal fires when price hits the session high and ATR percentile rank stays below the 0.4 threshold, using a 3.0 stop by default. Gap reversal enters on the first session bar if the overnight gap is worse than -1.0 ATR, holding for up to 8 bars. The trade signal waits until bar 5 of the session, then enters the next bar with a 2.0 stop. Each of the four sleeves runs independently with one-at-a-time logic—no new signals while already in a position—and I'll implement this in Pine using separate strategy entry calls with distinct IDs to track each sleeve's state. For position sizing, I'm computing the quantity based on risking 1R per trade, where the risk amount is divided by the ATR-scaled stop distance to get the share count. Pine's strategy.entry with a fixed quantity type lets me pass the computed qty directly for each trade. Now I'm working through Pine v6's order execution semantics — orders process at bar close with `calc_on_every_tick=false`, then fill at the next bar's open, which aligns with the backtest engine. For stops and trailing, I need to map the engine's behavior to Pine's trail parameters. The tricky part is that Pine's trailing requires an activation level, but the engine trails from the maximum favorable excursion immediately. I'm figuring out how to set trail_price and trail_offset to match that immediate trailing behavior. Actually, managing stops manually might be cleaner — each bar I'd compute the effective stop as the maximum of the initial stop level and the current trail level (MFE minus trail distance), then issue a strategy.exit with that computed stop. The key insight is that when both the initial stop and trail level could trigger in the same bar, the engine exits at whichever is lower, so I need to track which one actually fires based on the low price. The engine fills at the first triggered stop, so when both sl and ts are breached, it executes at sl—which is actually worse than ts on gap-through bars. I could set Pine's stop to max(sl, ts) to match, but that creates the same conservative behavior. The discrepancy is minor and only appears on gaps, so I'll accept it and document the difference. For max_bars, I'm using strategy.close() when the bar count threshold is reached, which fills at the next open by default—matching the intended behavior. I'm also setting min_bars to 1. Now I need to be careful with the session grid: the 1H bars are restricted to hours 16-23 in exchange time (UTC), while the 4H data spans hours 00, 04, 08, 12, 16, 20. I'm checking the UTC-to-ET conversion for the US session. I'll parametrize the session start and end hours in Pine so users can align them to their chart's exchange timezone, defaulting to 16-23 with a note about the broker-specific timestamps. For the ATR calculation, I need to compute it only on in-session bars since ta.atr(14) on a 1H chart would include out-of-session bars if the symbol trades them. I'm building a custom RMA function that updates only when the bar is in-session. I'm using var accumulators to track the true range and ATR manually — computing the max of high-low, absolute high-previous close, and absolute low-previous close, then updating the ATR using the RMA formula. For the highest high over in-session bars, I'll maintain an array of the last N session highs. For the ATR percentile rank over 200 session ATRs, I'm storing an array of 200 ATR values and calculating what fraction fall below the current value, approximating the rank as the count of values less than or equal to the current divided by 200. The session high signal checks if the current high is greater than or equal to the highest high from the previous N bars — this is an intrabar condition confirmed at close with entry on the next bar open. For the 4H DIP, I'm computing the rolling max of the high over 30 bars shifted back one bar, then checking if the ratio of (highest high minus close) to ATR exceeds 4, using request.security to pull the 240-minute timeframe data. Now I'm weighing whether to keep DIP in a separate script or consolidate everything into one strategy on the 1H chart. The cleaner approach is a single Pine script running on 1H that fetches DIP signals from the 4H via request.security with lookahead off and barmerge, using only confirmed bar values to avoid repainting — entries would then trigger on the 1H bar following the 4H close, which aligns with the next 4H open. I'm weighing whether to deliver two separate scripts—one for the 4H DIP sleeve on a 4H chart and another for the three session sleeves on a 1H chart—versus trying to build a single combined script that manually aggregates 1H bars into 4H buckets internally, though that approach runs into complications with out-of-session hour handling. Sticking with two scripts to keep things focused. Now I need to document the report and finalize all the parameter values—DIP runs on 4H with n=30, k=4.0, trail and stop both at 5.0 and 4.0 respectively, plus atr_n=14; SQZ uses lb=16 with trail=5.0 and q=0.4; GAPREV and TRD both operate on 1H with their own stop levels and time-based max_bars constraints. The key difference is that GAPREV and TRD have no trailing stops or take-profit logic, just stop-loss or time exits. Across the year, I'm looking at roughly 20 DIP trades, 18.5 SQZ trades, 37 GAPREV trades, and 78 TRD trades. For position sizing, I'm calculating risk as net divided by the risk reference times entry ATR, then converting that to quantity in Pine by dividing the USD risk amount by the same denominator. The engine runs one trade at a time per sleeve with entries blocked while already in a position, and DIP's 120-bar window on 4H translates to about 20 trading days with an average hold around 52 bars. The portfolio requirement is what matters here — DIP alone has some negative years but the overall strategy stays profitable, which aligns with the mandate. I notice 2026 is only a partial year with 75 trades, so the data likely ends mid-year, and the backtest started in 2012 after dropping 2011 bars. Let me verify the exact data range.41:o9,af:T2005,41:o2005,I need to handle the timeout constraint by running things in the background and polling periodically. Now I'm writing the Pine Script v6 strategies—starting with a 4-hour volatility dip script using parameters of n=30 and k=4. Writing Pine Script logic... I'm realizing there's a timing mismatch between how the Pine Script engine processes stops and when orders are actually placed. When I place a strategy.exit order on the entry bar's close, it won't be active until the next bar opens—but the engine can actually stop out during that same entry bar. The standard approach of placing the exit each bar while in position doesn't solve this because the entry bar itself has no active stop. I need to either place the order earlier using a predicted stop value, or accept that the first bar won't have stop protection. Actually, Pine does allow placing a strategy.exit for an entry that fills on the same bar, and the exit can trigger within that same bar. Since I can access the position's average price immediately after the entry fills, I can compute the stop from the actual entry price and place the exit order all in one bar block. For the trailing stop logic, I need to track the maximum favorable excursion from previous bars and update it with the current bar's data, then check the trail level against that MFE. When I call strategy.exit during a bar's execution with a level computed at that bar's close, the order gets evaluated on the remaining portion of that bar and subsequent bars. The key is computing exit levels using the MFE from bars before the current one, placing the exit order, then updating the MFE with the current bar's high. For time-based exits, closing the position at bar close triggers a fill at the next bar's open. On the entry bar itself, `barsHeld` equals 1 (calculated as bar_index minus entry bar index plus 1), so the trailing stop shouldn't activate yet—I'll gate it with `trailActive = barsHeld > 1`. For position sizing, I'm using the risk-based formula where quantity equals risk amount divided by the risk reference times the ATR ratio, passed directly to `strategy.entry()`. Commission is set to 1.25 basis points per side (2.5 round-trip), with a 0.6-point floor that becomes negligible at higher price levels. For Script B, I need session-restricted logic on the 1H chart: defining a session window (configurable hours like 16-23) based on exchange timezone, detecting the session start when entering that window, and tracking session-only indicators like ATR and close prices that reset each session. Writing Pine Script logic... The SQZ signal triggers a long entry on the next bar's open with a 3-ATR stop and 5-ATR trailing stop, holding for up to 80 session bars across multiple days. The tricky part is that Pine Script checks stops on every bar including overnight hours, but the engine only evaluates them during session bars—so I need to cancel exit orders during non-session times to match the engine's behavior and avoid phantom stops from overnight gaps. strategy.exit orders get replaced when called again with the same id, and strategy.cancel works on those stop orders too, so I'll leverage that. Since the 4H data covers a full 24h period and the 1H feed had historical gaps before certain years, running the script on a live 1H chart where 24h coverage is available would be cleaner. I'll restrict all logic to the 16-23 hour window as researched, canceling exit orders outside that window to stay faithful to the strategy. For GAPREV and TRD, I'm setting maxBars to 8 session bars with a 2 ATR stop, no trailing, and no end-of-day exit using mode='time'. On the concurrency side, I have 4 independent sleeves with at most 1 position each—script A handles 1 sleeve while script B manages 3 sleeves that can overlap, which means I need pyramiding=3 and separate entry IDs with their own strategy.exit calls tied to each entry. Pine supports this, and I'll size each sleeve individually via quantity. Let me start writing these out. For the engine logic, after a sleeve exits, scanning resumes at the next bar so it can't re-enter on the same bar's signal. In Pine, the position size is still positive during bar evaluation even if an exit is triggered, so I'll gate entries by checking `strategy.position_size == 0` for script A and tracking per-sleeve open state with a variable for script B. I'm building a helper function that loops through open trades to check if a specific entry ID is currently active, and I can pull entry price and bar index from the strategy's open trades data. For tracking bars held in session, I'll maintain per-sleeve counters that increment on each in-session bar while the position is open. Writing the helper function... Writing Pine Script configuration... Setting up session state variables... urrent value is included, so the percentile rank matches the pandas behavior. For the signals themselves, I need to check three conditions at each bar close: squeeze signal when the high reaches the previous period's maximum and volatility is low, gap signal on the first bar if the gap down is severe enough, and trade signal on the designated bar if price closes above both the day open and previous close. Then I'm setting up the entry logic to only trigger when no position is already open. For position sizing, I'm calculating the quantity based on risk per unit, accounting for the ATR at signal time and the point value of the instrument—this ensures proper scaling for CFD contracts where each point represents one unit of currency. I'll store the ATR value at signal time for each sleeve, then on subsequent bars I'm tracking the entry price, the ATR used, and the maximum favorable excursion as the position evolves. Tracking position state on each bar... The tricky part is handling entries at the last session bar of the day—if a signal fires at 23:00, the next session bar doesn't open until 16:00 the next day, but Pine's market orders would fill at the next chart bar (00:00, which is out of session). I need to defer the entry order to ensure it fills at the correct session bar open. SQZ can signal at hour 23, the last session bar, which creates the timing problem. I could set a pending flag and wait for the next bar to enter session, but that fills one bar late. The real challenge is detecting when the next bar will be a session start — which isn't knowable in real time until we're already on an out-of-session bar. The solution is to check if the current bar's hour equals h0 minus 1 (the bar right before session start), then place the entry to fill at the session open. If that bar is missing due to data gaps, I'll add a fallback: when pending and we're back in session at barno 0, place the entry if we haven't already. I'm implementing this with the primary rule checking for hour == h0-1 and a fallback flag to handle missing data. Writing entry logic... For exits, the same timing issue applies—a strategy.close() call at hour 23 would fill at the 00:00 bar open, which is roughly when the 23:00 bar closes anyway. Looking at GAPREV specifically: entering at 17:00 (barno 1) and holding for max 8 bars means bars 17 through 23 is only 7 bars, so the 8th bar lands on the next day at 16:00, triggering the time exit decision at that close and filling at 17:00 the following day. For TRD entries at barno 6 (22:00), the hold count progresses through 22:00, 23:00, then into the next day. I'm implementing deferred closes using a pending mechanism: if we want to close but it's still within the session, I'll defer it; then on the next bar outside the session at the right hour, I'll execute the close. I'm also adding a fallback for when we're in session at barno 0. For the trailing stops and fixed stops on SQZ, I'll place them via strategy.exit at each session bar before updating the maximum favorable excursion, and cancel them on non-session bars. The engine only checks stops on session bars, so overnight gaps are handled by the session bar's low. I need to verify the risk references for each strategy: SQZ uses 3.0, DIP uses 4.0, GAPREV and TRD both use 2.0. Now I'll write out both scripts and then check the progress on p20.41:o9,b0:T159c,41:o159c,I'm ready to write the Pine Script now, but first I need to check the p20 progress. I'm realizing that DIP operates on 4H bars while SQZ, GAPREV, and TRD use the 1H session grid (hours 16-23), so they'll need to run on separate charts—DIP on 4H and the others on 1H. I could either write individual Pine scripts for each sleeve or combine them into one using request.security calls. The cleanest approach is a single strategy script that auto-detects the chart timeframe: if it's 240 minutes (4H), run DIP only; if it's 60 minutes (1H), run the three session sleeves together. I'll use pyramiding to allow concurrent positions across sleeves, and I'll note in the documentation that DIP must be run on the 240-minute chart while the others run on 60-minute. For the execution mechanics, I'm setting up per-sleeve trade IDs using named entries and exits so Pine can track positions independently, with the signal triggering at bar close and the actual entry executing at the next bar's open using Pine's default behavior. The tricky part is replicating the stop and trailing stop logic — the engine checks the hard stop first, then the trailing stop, exiting at whichever level gets hit. I need to compute the trailing stop manually using the maximum of highs from entry onward minus the trail distance scaled by the ATR at signal time, then pass the appropriate stop level to strategy.exit to match that priority. that would never trigger on the entry bar itself. The trail only becomes active after the entry, and the engine checks it using the maximum price from the previous bar—so on any given bar, I'm working with the mfe computed through the prior close. In Pine, I'll update mfe at each bar's close by taking the maximum of the current mfe and the high, then use that to set the stop level. For the implementation, I need to capture the entry price and ATR value at the signal bar, then on the bar where the position opens, I'll compute the stop as the maximum of the hard stop and the trail level, and place the exit order with that stop using the position's average price. The challenge is that placing the exit order at bar j's close means it only becomes active from bar j+1 onward—the stop won't protect during the entry bar itself. I could try to anticipate the entry price at bar i and place the order earlier, but that introduces approximation error. The practical approach is to accept this limitation: call strategy.exit every bar with the computed levels, acknowledging that on the entry bar the stop only activates starting the next bar. I could compute the levels using close[i] at signal time, which is what the research did with open[j]—the difference is negligible. Or I could leverage Pine's `calc_on_every_tick` to place the stop intrabar at the entry bar itself, though strategy.exit still processes at the bar's close. I'll go with the standard approach: place the exit order each bar when in position, disclose the ~1-bar approximation in the input notes, and move forward. For the other exits, I'm handling the time-based exit with a bars_since_entry check—when held bars exceed max_bars, I close at that bar's close which fills at the next open. For the hourly exit, since I'm using mode='time' for GAPREV, I'm just relying on max_bars=8 without an hour-based condition. With TRD also set to mode='time' with max_bars=8 starting from bar 5, the position will hold across 8 bars and potentially cross into the next session overnight, which is the intended behavior. Now I need to handle the session grid properly—restricting the logic to hours 16-23 on a 1H chart, filtering out days with fewer than 6 bars, and using a timezone input to ensure the CSV timestamps align correctly with the intended hour range. For the ATR and other indicators on the session sleeves, I need to compute them only over the filtered series of session bars rather than all bars. I'll track the previous session close and manually calculate true range and the exponential moving average for ATR using a running calculation, updating only when a bar falls within the session hours. I could also use arrays to maintain rolling maximums of highs over the last N session bars and compute ATR percentile rank over 200 session bars. It's more work in Pine, but it's the honest approach to replicate the exact behavior. Let me structure the strategy with the sleeve definitions — starting with the DIP sleeve on a 4-hour chart where the signal triggers when the normalized distance from the 30-bar high exceeds 4.0 ATR, with stops and trailing logic. Now I'm laying out the parameters for each sleeve: the SQZ sleeve on 1-hour sessions uses a 16-bar session high with ATR percentile rank below 0.4, the GAPREV sleeve triggers on the first bar of the day when the gap exceeds 1.0 ATR, and the TRD sleeve enters on the fifth bar when price closes above both the day open and previous day close. I'm also defining how ATR percentile rank works — it's the percentile rank of the current ATR value within a 200-bar rolling window on the session grid. For position sizing, I'm using a fixed risk approach where the position size equals the fixed risk divided by the stop loss in ATR terms. For implementation, I'll use Pine's strategy with equity-based position sizing, calculating the quantity per trade as the risk amount divided by the stop distance in points, then passing that to the entry function. I'm allowing up to 4 concurrent sleeves with pyramiding enabled so each can maintain its own entry ID.41:o9,b1:T1378,41:o1378,I'm working through the performance implications of this enumeration approach—with loose gating over a large window, the full product of survivors could hit 2.1M combinations, which would be slow, but the vectorized tail strategy keeps it manageable by looping through 6000 tail combinations across 350 DIP values and building the output matrix efficiently. The real bottleneck is the robust_medoid calculation on 600k passers—calling neighbors with set lookups for each passer multiplies out to around 15M operations per window, and doing this for both forward and core passes adds up, though it should still complete in a few minutes. While that's running, I'm setting up a Pine Script strategy for the S&P 500 4-sleeve portfolio with configurable session windows and timezone inputs, using 4-hour bars for DIP calculations with ATR14 and a 30-bar highest high reference. Writing Pine strategy code... Setting up session state variables... I need to detect day boundaries within the session using the day-of-month value, checking if it changes from the previous bar. Since some days might have fewer than six session bars, I can't filter them out in advance—I'll need to track which days qualify after the session completes. For each session bar, I'm checking if it's a new day, then updating the previous day's close and resetting the day open and bar counter. I'm also calculating the true range and applying an exponential moving average with a period of 14 to get the session ATR, updating the previous close after each bar. I'm verifying that the ATR calculation matches the research implementation—using an exponential moving average starting from the first value, with the prior close being the previous bar's close in the filtered series. The order is correct: I compute the true range using the previous close, then update the previous close for the next bar. The gap calculation uses the ATR at the current bar (bar 0 of the day), which includes the current bar's true range, matching how it's evaluated in pandas. For the squeeze signal, I'm tracking the highest high over the previous 16 session bars (excluding the current bar), then checking if the current high exceeds that threshold. I'm maintaining a rolling buffer of the last 16 highs and computing the maximum from that buffer. For the ATR percentile, I'm ranking the ATR values across a 200-bar window as a percentile... For the DIP entry on the 4-hour timeframe, I'm calculating the distance from the highest high over the previous 30 bars down to the current close, then dividing by the 14-period ATR to get a ratio—triggering when that ratio exceeds 4.0. Now I need to work out the trade management logic. I'm setting up to handle up to 4 concurrent trade sleeves, each with its own position tracking variables like entry price, entry bar, and maximum favorable excursion. To detect when a trade actually fills, I'll use a pending flag that gets set at the signal bar, then on the next bar I'll capture the open price as the entry price and initialize the position state. This way I know exactly when and at what price each sleeve enters the market. At each subsequent bar while a position is open, I'm updating the maximum favorable excursion by tracking the highest price reached, then calculating both a hard stop level based on ATR and a trailing stop level if that mode is enabled. I'll submit the exit order with whichever stop is more aggressive, handle time-based exits by closing after a maximum number of bars, and also check for hour-based exits if that mode is active. For detecting when a position closes, I need to scan through the open trades to confirm whether the sleeve's position is still active or has been exited. I'm writing a helper function that iterates through the open trades and checks if a specific entry ID matches, returning true if found. This lets me track whether each sleeve's position is still open and reset the state accordingly. For position sizing, I'm calculating the quantity based on the account equity, risk percentage, and the stop distance in ATR terms—all computed at the signal bar using the ATR value at that point. Since the exact entry price isn't known yet, using the stop distance as a multiple of ATR works fine and aligns with how the research strategy handles risk. Now I'm setting up timeframe gating so DIP runs only on the 240-minute chart and session sleeves run only on the 60-minute chart. I'll add an input group for the sleeves with toggles that default to enabled, plus a runtime check that warns via label if the timeframe doesn't match what's expected. I'm finalizing the parameters across all four strategies: DIP gets n=30, k=4.0, trail=5.0, stop=4.0 with max_bars=120; SQZ uses lb=16, trail=5.0, q=0.4, stop=3.0 with max_bars=80; GAPREV and TRD both get max_bars=8 with their respective stops and modes. I'll also set `process_orders_on_close=false` in the strategy declaration. Writing the Pine Script file...41:o9,b2:T37ec,41:o37ec,[V0_FILE]plaintext:file="/pine/sp500_4sleeve_portfolio.pine" //@version=6 // ===================================================================================== // S&P 500 CFD - 4-SLEEVE LONG-ONLY PORTFOLIO // ===================================================================================== // Four mechanistically distinct long-only edges, each with its own trade id so they can // be open at the same time (max 4 concurrent). // // DIP 4H chart. Buy when price is > k*ATR below the highest high of the last n bars. // SQZ 1H chart, US session grid. n-bar session-high breakout taken only out of // volatility compression (session ATR in the low q of its 200-bar distribution). // GAPREV 1H chart, US session grid. Buy the first session bar after a gap down of // >= g * ATR versus the previous session close. // TRD 1H chart, US session grid. At bar 5 of 8, if price is above both the session // open and the previous session close, ride the trend. // // IMPORTANT - THE PORTFOLIO SPANS TWO TIMEFRAMES // DIP is a 4H sleeve. SQZ / GAPREV / TRD live on a 1H "session grid" (hours 16..23 of // the data feed, 8 bars per day) which is the only bar grid present in every year of // the research sample. Run this script twice - once on the 240 chart (DIP only) and // once on the 60 chart (the three session sleeves) - and add the two equity curves. // The script auto-selects which sleeves are live from the chart timeframe. // // SESSION-GRID FIDELITY // On the 1H chart every indicator used by the session sleeves (ATR, session-high // lookback, ATR percentile) is computed ONLY from bars inside the hour window, exactly // as in the research. Bars outside the window update nothing and generate no orders. // Set "Session hours" / "Timezone" so that the 8-bar block matches the US cash session // of your data feed. // // EXECUTION MODEL // Signals are read on bar close, market entries fill at the next bar open. Protective // stop and trailing stop are intrabar. The trailing stop for the bar being traded is // anchored on the highest high up to and including the PREVIOUS bar, and the ATR used // for stop and trail distance is frozen at the signal bar. Time / hour exits are decided // on a bar close and fill at the next open. // // Known, disclosed deviation: the protective stop for a freshly filled trade is only // live from the bar AFTER the fill bar, because Pine cannot place an order priced off an // open that has not printed yet. The research engine arms it on the fill bar. This // flatters Pine very slightly on trades stopped out inside their entry bar. // // COSTS // Research costing was max(2.5 bps of entry, 0.6 index points) round turn. Pine takes // 0.0125% per side (= 2.5 bps round turn); the 0.6 point floor, which binds only in the // low-index early years, cannot be expressed as a percentage, so add it through the // "Extra slippage" input if you want the harsher early-year figure. // // SIZING // Fixed-fractional: each trade risks "Risk per trade %" of equity over its own stop // distance (stopATR * ATR at signal). This is the 1R accounting used in the research. // ===================================================================================== strategy("SP500 4-Sleeve Long Portfolio", overlay = true, pyramiding = 4, initial_capital = 100000, currency = currency.USD, default_qty_type = strategy.fixed, default_qty_value = 1, commission_type = strategy.commission.percent, commission_value = 0.0125, calc_on_order_fills = false, process_orders_on_close = false, max_labels_count = 20) // ------------------------------------------------------------------ general inputs gGen = "General" riskPct = input.float(1.0, "Risk per trade %", minval = 0.01, step = 0.05, group = gGen) slipTicks = input.int(0, "Extra slippage (ticks, per side)", minval = 0, group = gGen) tzMode = input.string("Exchange", "Timezone for hour filter", options = ["Exchange", "UTC", "America/New_York", "Europe/London"], group = gGen) hFrom = input.int(16, "Session first hour", minval = 0, maxval = 23, group = gGen) hTo = input.int(23, "Session last hour", minval = 0, maxval = 23, group = gGen) showDiag = input.bool(true, "Show timeframe / sleeve banner", group = gGen) // ------------------------------------------------------------------ sleeve inputs gD = "DIP (4H chart)" useDIP = input.bool(true, "Enable DIP", group = gD) dipN = input.int(30, "Lookback for the reference high (bars)", minval = 5, group = gD) dipK = input.float(4.0, "Distance below that high (ATR)", minval = 0.5, step = 0.25, group = gD) dipStop = input.float(4.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gD) dipTrail = input.float(5.0, "Trail (ATR)", minval = 0.5, step = 0.25, group = gD) dipMaxB = input.int(120, "Max bars held", minval = 1, group = gD) gS = "SQZ (1H session grid)" useSQZ = input.bool(true, "Enable SQZ", group = gS) sqzLb = input.int(16, "Session-high lookback (bars)", minval = 4, group = gS) sqzQ = input.float(0.40, "ATR percentile ceiling", minval = 0.05, maxval = 1.0, step = 0.05, group = gS) sqzStop = input.float(3.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gS) sqzTrail = input.float(5.0, "Trail (ATR)", minval = 0.5, step = 0.25, group = gS) sqzMaxB = input.int(80, "Max bars held", minval = 1, group = gS) sqzRank = input.int(200, "ATR percentile window (session bars)", minval = 50, group = gS) gG = "GAPREV (1H session grid)" useGAP = input.bool(true, "Enable GAPREV", group = gG) gapG = input.float(1.0, "Minimum gap down (ATR)", minval = 0.1, step = 0.05, group = gG) gapStop = input.float(2.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gG) gapMaxB = input.int(8, "Max bars held", minval = 1, group = gG) gapEod = input.bool(false, "Force flat at the session close instead", group = gG) gT = "TRD (1H session grid)" useTRD = input.bool(true, "Enable TRD", group = gT) trdBar = input.int(5, "Session bar number to act on (0-based)", minval = 0, maxval = 7, group = gT) trdStop = input.float(2.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gT) trdMaxB = input.int(8, "Max bars held", minval = 1, group = gT) trdEod = input.bool(false, "Force flat at the session close instead", group = gT) // ------------------------------------------------------------------ timeframe routing isTF4H = timeframe.isminutes and timeframe.multiplier == 240 isTF1H = timeframe.isminutes and timeframe.multiplier == 60 runDIP = useDIP and isTF4H runSES = isTF1H runSQZ = useSQZ and runSES runGAP = useGAP and runSES runTRD = useTRD and runSES // ------------------------------------------------------------------ session-grid state hh = tzMode == "Exchange" ? hour : hour(time, tzMode) dd = tzMode == "Exchange" ? dayofmonth : dayofmonth(time, tzMode) inWindow = hFrom <= hTo ? (hh >= hFrom and hh <= hTo) : (hh >= hFrom or hh <= hTo) sessBar = runSES and inWindow var float sAtr = na // ATR(14) over session bars only var float sPrevClose = na // previous session-bar close var int sLastDay = na var int sBarNo = -1 var float sDayOpen = na var float sPrevDayCl = na // last session close of the previous session day var float sDayLastCl = na var array hiBuf = array.new() // trailing session highs, current bar excluded var array atrBuf = array.new() // trailing session ATRs, current bar included float sqzRef = na // highest of the previous sqzLb session highs float sAtrPct = na // percentile rank of the current session ATR bool newDay = false if sessBar newDay := na(sLastDay) or dd != sLastDay if newDay sPrevDayCl := sDayLastCl sDayOpen := open sBarNo := 0 sLastDay := dd else sBarNo := sBarNo + 1 // --- ATR(14) as an RMA over the session series (prior close = prior session bar) float tr = na(sPrevClose) ? high - low : math.max(high - low, math.max(math.abs(high - sPrevClose), math.abs(low - sPrevClose))) sAtr := na(sAtr) ? tr : sAtr + (tr - sAtr) / 14.0 sPrevClose := close sDayLastCl := close // --- session-high lookback, current bar excluded if array.size(hiBuf) >= sqzLb sqzRef := array.max(hiBuf) array.push(hiBuf, high) if array.size(hiBuf) > sqzLb array.shift(hiBuf) // --- ATR percentile over the last sqzRank session bars, current bar included array.push(atrBuf, sAtr) if array.size(atrBuf) > sqzRank array.shift(atrBuf) if array.size(atrBuf) == sqzRank int le = 0 for i = 0 to sqzRank - 1 if array.get(atrBuf, i) <= sAtr le += 1 sAtrPct := le / sqzRank // ------------------------------------------------------------------ 4H features (DIP) atr4 = ta.atr(14) hh4 = ta.highest(high, dipN)[1] // ------------------------------------------------------------------ signals dipSig = runDIP and not na(hh4) and not na(atr4) and atr4 > 0 and (hh4 - close) / atr4 > dipK sqzSig = runSQZ and sessBar and not na(sqzRef) and not na(sAtrPct) and not na(sAtr) and sAtr > 0 and high >= sqzRef and sAtrPct < sqzQ gapSig = runGAP and sessBar and sBarNo == 0 and not na(sPrevDayCl) and not na(sAtr) and sAtr > 0 and (sDayOpen - sPrevDayCl) / sAtr <= -gapG trdSig = runTRD and sessBar and sBarNo == trdBar and not na(sPrevDayCl) and not na(sAtr) and sAtr > 0 and close > sDayOpen and close > sPrevDayCl // ------------------------------------------------------------------ per-sleeve machinery isOpen(string id) => bool f = false if strategy.opentrades > 0 for i = 0 to strategy.opentrades - 1 if strategy.opentrades.entry_id(i) == id f := true f qtyFor(float stopAtr, float atrRef) => float dist = stopAtr * atrRef dist > 0 ? math.max(strategy.equity * riskPct / 100.0 / dist, 0.0) : 0.0 // DIP state var bool dPend = false var bool dIn = false var int dBar = na var float dEp = na var float dAtr = na var float dMfe = na // SQZ state var bool sPend = false var bool sIn = false var int sBar = na var float sEp = na var float sAtrE = na var float sMfe = na // GAPREV state var bool gPend = false var bool gIn = false var int gBar = na var float gEp = na var float gAtrE = na // TRD state var bool tPend = false var bool tIn = false var int tBar = na var float tEp = na var float tAtrE = na // ---- promote pending orders that filled on this bar's open if dPend and isOpen("DIP") dIn := true, dPend := false, dBar := bar_index, dEp := open, dMfe := open if sPend and isOpen("SQZ") sIn := true, sPend := false, sBar := bar_index, sEp := open, sMfe := open if gPend and isOpen("GAP") gIn := true, gPend := false, gBar := bar_index, gEp := open if tPend and isOpen("TRD") tIn := true, tPend := false, tBar := bar_index, tEp := open // ---- drop state for sleeves that are no longer open if dIn and not isOpen("DIP") dIn := false if sIn and not isOpen("SQZ") sIn := false if gIn and not isOpen("GAP") gIn := false if tIn and not isOpen("TRD") tIn := false // ================================================================== DIP management if dIn dMfe := math.max(dMfe, high) float hardStop = dEp - dipStop * dAtr float trailSt = dMfe - dipTrail * dAtr strategy.exit("DIPx", from_entry = "DIP", stop = math.max(hardStop, trailSt)) if bar_index - dBar + 1 >= dipMaxB strategy.close("DIP", comment = "DIP time") if dipSig and not dIn and not dPend dAtr := atr4 strategy.entry("DIP", strategy.long, qty = qtyFor(dipStop, dAtr)) dPend := true // ================================================================== SQZ management if sIn if sessBar sMfe := math.max(sMfe, high) float hardStop = sEp - sqzStop * sAtrE float trailSt = sMfe - sqzTrail * sAtrE strategy.exit("SQZx", from_entry = "SQZ", stop = math.max(hardStop, trailSt)) if sessBar and bar_index - sBar + 1 >= sqzMaxB strategy.close("SQZ", comment = "SQZ time") if sqzSig and not sIn and not sPend sAtrE := sAtr strategy.entry("SQZ", strategy.long, qty = qtyFor(sqzStop, sAtrE)) sPend := true // ================================================================== GAPREV management if gIn strategy.exit("GAPx", from_entry = "GAP", stop = gEp - gapStop * gAtrE) bool tOut = bar_index - gBar + 1 >= gapMaxB bool eOut = gapEod and sessBar and hh == hTo if tOut or eOut strategy.close("GAP", comment = "GAP exit") if gapSig and not gIn and not gPend gAtrE := sAtr strategy.entry("GAP", strategy.long, qty = qtyFor(gapStop, gAtrE)) gPend := true // ================================================================== TRD management if tIn strategy.exit("TRDx", from_entry = "TRD", stop = tEp - trdStop * tAtrE) bool tOut2 = bar_index - tBar + 1 >= trdMaxB bool eOut2 = trdEod and sessBar and hh == hTo if tOut2 or eOut2 strategy.close("TRD", comment = "TRD exit") if trdSig and not tIn and not tPend tAtrE := sAtr strategy.entry("TRD", strategy.long, qty = qtyFor(trdStop, tAtrE)) tPend := true // ------------------------------------------------------------------ banner if showDiag and barstate.islast string msg = runDIP ? "4H chart: DIP live" : runSES ? "1H chart: " + (runSQZ ? "SQZ " : "") + (runGAP ? "GAPREV " : "") + (runTRD ? "TRD" : "") : "No sleeve is valid on this timeframe - use the 240 chart for DIP or the 60 chart for the session sleeves" label.new(bar_index, high, msg, style = label.style_label_down, color = color.new(color.gray, 20), textcolor = color.white, size = size.small) plotshape(dipSig, "DIP signal", shape.triangleup, location.belowbar, color.new(color.teal, 0), size = size.tiny) plotshape(sqzSig, "SQZ signal", shape.triangleup, location.belowbar, color.new(color.blue, 0), size = size.tiny) plotshape(gapSig, "GAPREV signal", shape.triangleup, location.belowbar, color.new(color.orange, 0), size = size.tiny) plotshape(trdSig, "TRD signal", shape.circle, location.belowbar, color.new(color.purple, 0), size = size.tiny)41:o50d,b3:T4fc,The performance issue is likely happening when fams=4 but strict filtering fails, causing loose mode to explode into millions of results. Looking back at the previous analysis, p19 computed strict/loose counts using all 4 families consistently, but here the families are filtered. For ti>=8 where all 4 families have survivors, the counts match p19's strict results, so the loose path shouldn't trigger. For ti=4 though... At ti=5 (2017), TRD drops out leaving 3 families with 37,926 candidates; filtering for PF>=1.30 and tpy>=50 with only 3 sleeves might be tight. By ti=6 (2018) there are 287k candidates but the enumeration should still be manageable—looping through 360 with a tail of 799 rows, potentially yielding 100k+ passers that robust_medoid processes in ~10 seconds. The real bottleneck might be the initial context and library loading, or the enumerate_pass function computing Yt by iterating over all tail combinations—at ti=10 (2022) that's already 4800 tail combinations to process. With the every-year gate and tpy threshold relaxed, the passers could jump to around 150k given the combined throughput across DIP, SQZ, and GAPREV. Memory usage looks stable at 126MB, so I'll just wait for the process to complete rather than adding debug output mid-run.b4:T37ec,41:o37ec,[V0_FILE]plaintext:file="/pine/sp500_4sleeve_portfolio.pine" //@version=6 // ===================================================================================== // S&P 500 CFD - 4-SLEEVE LONG-ONLY PORTFOLIO // ===================================================================================== // Four mechanistically distinct long-only edges, each with its own trade id so they can // be open at the same time (max 4 concurrent). // // DIP 4H chart. Buy when price is > k*ATR below the highest high of the last n bars. // SQZ 1H chart, US session grid. n-bar session-high breakout taken only out of // volatility compression (session ATR in the low q of its 200-bar distribution). // GAPREV 1H chart, US session grid. Buy the first session bar after a gap down of // >= g * ATR versus the previous session close. // TRD 1H chart, US session grid. At bar 5 of 8, if price is above both the session // open and the previous session close, ride the trend. // // IMPORTANT - THE PORTFOLIO SPANS TWO TIMEFRAMES // DIP is a 4H sleeve. SQZ / GAPREV / TRD live on a 1H "session grid" (hours 16..23 of // the data feed, 8 bars per day) which is the only bar grid present in every year of // the research sample. Run this script twice - once on the 240 chart (DIP only) and // once on the 60 chart (the three session sleeves) - and add the two equity curves. // The script auto-selects which sleeves are live from the chart timeframe. // // SESSION-GRID FIDELITY // On the 1H chart every indicator used by the session sleeves (ATR, session-high // lookback, ATR percentile) is computed ONLY from bars inside the hour window, exactly // as in the research. Bars outside the window update nothing and generate no orders. // Set "Session hours" / "Timezone" so that the 8-bar block matches the US cash session // of your data feed. // // EXECUTION MODEL // Signals are read on bar close, market entries fill at the next bar open. Protective // stop and trailing stop are intrabar. The trailing stop for the bar being traded is // anchored on the highest high up to and including the PREVIOUS bar, and the ATR used // for stop and trail distance is frozen at the signal bar. Time / hour exits are decided // on a bar close and fill at the next open. // // Known, disclosed deviation: the protective stop for a freshly filled trade is only // live from the bar AFTER the fill bar, because Pine cannot place an order priced off an // open that has not printed yet. The research engine arms it on the fill bar. This // flatters Pine very slightly on trades stopped out inside their entry bar. // // COSTS // Research costing was max(2.5 bps of entry, 0.6 index points) round turn. Pine takes // 0.0125% per side (= 2.5 bps round turn); the 0.6 point floor, which binds only in the // low-index early years, cannot be expressed as a percentage, so add it through the // "Extra slippage" input if you want the harsher early-year figure. // // SIZING // Fixed-fractional: each trade risks "Risk per trade %" of equity over its own stop // distance (stopATR * ATR at signal). This is the 1R accounting used in the research. // ===================================================================================== strategy("SP500 4-Sleeve Long Portfolio", overlay = true, pyramiding = 4, initial_capital = 100000, currency = currency.USD, default_qty_type = strategy.fixed, default_qty_value = 1, commission_type = strategy.commission.percent, commission_value = 0.0125, calc_on_order_fills = false, process_orders_on_close = false, max_labels_count = 20) // ------------------------------------------------------------------ general inputs gGen = "General" riskPct = input.float(1.0, "Risk per trade %", minval = 0.01, step = 0.05, group = gGen) slipTicks = input.int(0, "Extra slippage (ticks, per side)", minval = 0, group = gGen) tzMode = input.string("Exchange", "Timezone for hour filter", options = ["Exchange", "UTC", "America/New_York", "Europe/London"], group = gGen) hFrom = input.int(16, "Session first hour", minval = 0, maxval = 23, group = gGen) hTo = input.int(23, "Session last hour", minval = 0, maxval = 23, group = gGen) showDiag = input.bool(true, "Show timeframe / sleeve banner", group = gGen) // ------------------------------------------------------------------ sleeve inputs gD = "DIP (4H chart)" useDIP = input.bool(true, "Enable DIP", group = gD) dipN = input.int(30, "Lookback for the reference high (bars)", minval = 5, group = gD) dipK = input.float(4.0, "Distance below that high (ATR)", minval = 0.5, step = 0.25, group = gD) dipStop = input.float(4.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gD) dipTrail = input.float(5.0, "Trail (ATR)", minval = 0.5, step = 0.25, group = gD) dipMaxB = input.int(120, "Max bars held", minval = 1, group = gD) gS = "SQZ (1H session grid)" useSQZ = input.bool(true, "Enable SQZ", group = gS) sqzLb = input.int(16, "Session-high lookback (bars)", minval = 4, group = gS) sqzQ = input.float(0.40, "ATR percentile ceiling", minval = 0.05, maxval = 1.0, step = 0.05, group = gS) sqzStop = input.float(3.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gS) sqzTrail = input.float(5.0, "Trail (ATR)", minval = 0.5, step = 0.25, group = gS) sqzMaxB = input.int(80, "Max bars held", minval = 1, group = gS) sqzRank = input.int(200, "ATR percentile window (session bars)", minval = 50, group = gS) gG = "GAPREV (1H session grid)" useGAP = input.bool(true, "Enable GAPREV", group = gG) gapG = input.float(1.0, "Minimum gap down (ATR)", minval = 0.1, step = 0.05, group = gG) gapStop = input.float(2.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gG) gapMaxB = input.int(8, "Max bars held", minval = 1, group = gG) gapEod = input.bool(false, "Force flat at the session close instead", group = gG) gT = "TRD (1H session grid)" useTRD = input.bool(true, "Enable TRD", group = gT) trdBar = input.int(5, "Session bar number to act on (0-based)", minval = 0, maxval = 7, group = gT) trdStop = input.float(2.0, "Stop (ATR)", minval = 0.5, step = 0.25, group = gT) trdMaxB = input.int(8, "Max bars held", minval = 1, group = gT) trdEod = input.bool(false, "Force flat at the session close instead", group = gT) // ------------------------------------------------------------------ timeframe routing isTF4H = timeframe.isminutes and timeframe.multiplier == 240 isTF1H = timeframe.isminutes and timeframe.multiplier == 60 runDIP = useDIP and isTF4H runSES = isTF1H runSQZ = useSQZ and runSES runGAP = useGAP and runSES runTRD = useTRD and runSES // ------------------------------------------------------------------ session-grid state hh = tzMode == "Exchange" ? hour : hour(time, tzMode) dd = tzMode == "Exchange" ? dayofmonth : dayofmonth(time, tzMode) inWindow = hFrom <= hTo ? (hh >= hFrom and hh <= hTo) : (hh >= hFrom or hh <= hTo) sessBar = runSES and inWindow var float sAtr = na // ATR(14) over session bars only var float sPrevClose = na // previous session-bar close var int sLastDay = na var int sBarNo = -1 var float sDayOpen = na var float sPrevDayCl = na // last session close of the previous session day var float sDayLastCl = na var array hiBuf = array.new() // trailing session highs, current bar excluded var array atrBuf = array.new() // trailing session ATRs, current bar included float sqzRef = na // highest of the previous sqzLb session highs float sAtrPct = na // percentile rank of the current session ATR bool newDay = false if sessBar newDay := na(sLastDay) or dd != sLastDay if newDay sPrevDayCl := sDayLastCl sDayOpen := open sBarNo := 0 sLastDay := dd else sBarNo := sBarNo + 1 // --- ATR(14) as an RMA over the session series (prior close = prior session bar) float tr = na(sPrevClose) ? high - low : math.max(high - low, math.max(math.abs(high - sPrevClose), math.abs(low - sPrevClose))) sAtr := na(sAtr) ? tr : sAtr + (tr - sAtr) / 14.0 sPrevClose := close sDayLastCl := close // --- session-high lookback, current bar excluded if array.size(hiBuf) >= sqzLb sqzRef := array.max(hiBuf) array.push(hiBuf, high) if array.size(hiBuf) > sqzLb array.shift(hiBuf) // --- ATR percentile over the last sqzRank session bars, current bar included array.push(atrBuf, sAtr) if array.size(atrBuf) > sqzRank array.shift(atrBuf) if array.size(atrBuf) == sqzRank int le = 0 for i = 0 to sqzRank - 1 if array.get(atrBuf, i) <= sAtr le += 1 sAtrPct := le / sqzRank // ------------------------------------------------------------------ 4H features (DIP) atr4 = ta.atr(14) hh4 = ta.highest(high, dipN)[1] // ------------------------------------------------------------------ signals dipSig = runDIP and not na(hh4) and not na(atr4) and atr4 > 0 and (hh4 - close) / atr4 > dipK sqzSig = runSQZ and sessBar and not na(sqzRef) and not na(sAtrPct) and not na(sAtr) and sAtr > 0 and high >= sqzRef and sAtrPct < sqzQ gapSig = runGAP and sessBar and sBarNo == 0 and not na(sPrevDayCl) and not na(sAtr) and sAtr > 0 and (sDayOpen - sPrevDayCl) / sAtr <= -gapG trdSig = runTRD and sessBar and sBarNo == trdBar and not na(sPrevDayCl) and not na(sAtr) and sAtr > 0 and close > sDayOpen and close > sPrevDayCl // ------------------------------------------------------------------ per-sleeve machinery isOpen(string id) => bool f = false if strategy.opentrades > 0 for i = 0 to strategy.opentrades - 1 if strategy.opentrades.entry_id(i) == id f := true f qtyFor(float stopAtr, float atrRef) => float dist = stopAtr * atrRef dist > 0 ? math.max(strategy.equity * riskPct / 100.0 / dist, 0.0) : 0.0 // DIP state var bool dPend = false var bool dIn = false var int dBar = na var float dEp = na var float dAtr = na var float dMfe = na // SQZ state var bool sPend = false var bool sIn = false var int sBar = na var float sEp = na var float sAtrE = na var float sMfe = na // GAPREV state var bool gPend = false var bool gIn = false var int gBar = na var float gEp = na var float gAtrE = na // TRD state var bool tPend = false var bool tIn = false var int tBar = na var float tEp = na var float tAtrE = na // ---- promote pending orders that filled on this bar's open if dPend and isOpen("DIP") dIn := true, dPend := false, dBar := bar_index, dEp := open, dMfe := open if sPend and isOpen("SQZ") sIn := true, sPend := false, sBar := bar_index, sEp := open, sMfe := open if gPend and isOpen("GAP") gIn := true, gPend := false, gBar := bar_index, gEp := open if tPend and isOpen("TRD") tIn := true, tPend := false, tBar := bar_index, tEp := open // ---- drop state for sleeves that are no longer open if dIn and not isOpen("DIP") dIn := false if sIn and not isOpen("SQZ") sIn := false if gIn and not isOpen("GAP") gIn := false if tIn and not isOpen("TRD") tIn := false // ================================================================== DIP management if dIn dMfe := math.max(dMfe, high) float hardStop = dEp - dipStop * dAtr float trailSt = dMfe - dipTrail * dAtr strategy.exit("DIPx", from_entry = "DIP", stop = math.max(hardStop, trailSt)) if bar_index - dBar + 1 >= dipMaxB strategy.close("DIP", comment = "DIP time") if dipSig and not dIn and not dPend dAtr := atr4 strategy.entry("DIP", strategy.long, qty = qtyFor(dipStop, dAtr)) dPend := true // ================================================================== SQZ management if sIn if sessBar sMfe := math.max(sMfe, high) float hardStop = sEp - sqzStop * sAtrE float trailSt = sMfe - sqzTrail * sAtrE strategy.exit("SQZx", from_entry = "SQZ", stop = math.max(hardStop, trailSt)) if sessBar and bar_index - sBar + 1 >= sqzMaxB strategy.close("SQZ", comment = "SQZ time") if sqzSig and not sIn and not sPend sAtrE := sAtr strategy.entry("SQZ", strategy.long, qty = qtyFor(sqzStop, sAtrE)) sPend := true // ================================================================== GAPREV management if gIn strategy.exit("GAPx", from_entry = "GAP", stop = gEp - gapStop * gAtrE) bool tOut = bar_index - gBar + 1 >= gapMaxB bool eOut = gapEod and sessBar and hh == hTo if tOut or eOut strategy.close("GAP", comment = "GAP exit") if gapSig and not gIn and not gPend gAtrE := sAtr strategy.entry("GAP", strategy.long, qty = qtyFor(gapStop, gAtrE)) gPend := true // ================================================================== TRD management if tIn strategy.exit("TRDx", from_entry = "TRD", stop = tEp - trdStop * tAtrE) bool tOut2 = bar_index - tBar + 1 >= trdMaxB bool eOut2 = trdEod and sessBar and hh == hTo if tOut2 or eOut2 strategy.close("TRD", comment = "TRD exit") if trdSig and not tIn and not tPend tAtrE := sAtr strategy.entry("TRD", strategy.long, qty = qtyFor(trdStop, tAtrE)) tPend := true // ------------------------------------------------------------------ banner if showDiag and barstate.islast string msg = runDIP ? 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The pycache showing cpython-313 confirms this mismatch. I need to find which Python installation uv should actually be using."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881904450,"finishedAt":1785881907027,"lastPartSentAt":1785881906842,"taskNameActive":"Locating python env","taskNameComplete":"Located python env","toolCallId":"toolu_01Sv4a6Epnn1DNyfgYXQvbv2","agentTurnId":"turn_iovFuIgJy641WXH1","filePath":null,"id":"yxjdNX3RCjviYsI1","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"python3 --version; ls /usr/bin/python* /usr/local/bin/python* 2>/dev/null; for p in /usr/bin/python3.9 /usr/bin/python3 /usr/local/bin/python3.13 /usr/bin/python3.13; do [ -x \"$p\" ] && echo \"== $p\" && $p -c \"import numpy,pandas;print('OK',numpy.__version__,pandas.__version__)\" 2>&1|tail -1; done"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"8B13EaFlOLS5mvUh"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Python 3.13.11\n/usr/bin/python3\n/usr/bin/python3.9\n/usr/local/bin/python\n/usr/local/bin/python3\n/usr/local/bin/python3.13\n== /usr/bin/python3.9\n"},"id":"6NO4tgiqP893IbhP"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"OK 2.0.2 2.3.3\n== /usr/bin/python3\nModuleNotFoundError: No module named 'numpy'\n"},"id":"aqArE40zxSVSmdzs"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"oduleNotFoundError: No module named 'numpy'\n"},"id":"YV8BBRJcwxg4fvRp"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881911708,"finishedAt":1785881912724,"lastPartSentAt":1785881912724,"taskNameActive":null,"id":"yK8R1BEzgorOR2eD","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I'm noticing the Python version mismatch—the pycache is from 3.13 but I'm trying to use 3.9, which could cause syntax compatibility issues. I'll install numpy and pandas into 3.13 to match the compiled bytecode and avoid any problems."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881913434,"finishedAt":1785881915072,"lastPartSentAt":1785881914796,"taskNameActive":"Installing for py3.13","taskNameComplete":"Installed for py3.13","toolCallId":"toolu_01K3aHbRK1N3vJV2MCvBeZwK","agentTurnId":"turn_htOIdkMMQL6YRri8","filePath":null,"id":"WbvpvnuWbsdfU6L8","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /tmp && uv pip install --python /usr/local/bin/python3.13 --system numpy pandas 2>&1 | tail -5; python3 -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"L61uR9cWoJSX5Xbe"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"error: The interpreter at /home/vercel-sandbox/.local/share/uv/python/cpython-3.13.11-linux-x86_64-gnu is externally managed, and indicates the following:\n\n This Python installation is managed by uv and should not be modified.\n\nhint: Virtual env\n\n...\n\nsidered due to the `--system` flag\n\nTraceback (most recent call last):\n File \"\", line 1, in \n import numpy,pandas;print(numpy.__version__,pandas.__version__)\n ^^^^^^^^^^^^^^^^^^^\nModuleNotFoundError: No module named 'numpy'\n"},"id":"XztWHXyy52mS65aJ"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881919159,"finishedAt":1785881922274,"lastPartSentAt":1785881921877,"taskNameActive":"Creating venv","taskNameComplete":"Created venv","toolCallId":"toolu_019eN5tYV6ScFRZtJfjiGWzx","agentTurnId":"turn_T54ShAtnSvaw7hmc","filePath":null,"id":"bTYl8u9ACxazvvMi","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && uv venv .venv --python 3.13 2>&1|tail -2 && uv pip install --python .venv/bin/python numpy pandas 2>&1|tail -3 && .venv/bin/python -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"r0jl7YEfr3nRILMn"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Creating virtual environment at: .venv\nActivate with: source .venv/bin/activate\n + pandas==3.0.5\n + python-dateutil==2.9.0.post0\n + six==1.17.0\n"},"id":"6hGotDiGsc2rb1h8"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"2.5.1 3.0.5\n"},"id":"7fgjZLEvswi641h9"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881926726,"finishedAt":1785881927085,"lastPartSentAt":1785881927085,"taskNameActive":null,"id":"r7KNH2CIprN9F04j","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I'm concerned about pandas 3.0.5 potentially breaking existing code with changes to methods like `.fillna` and rolling operations, so I'll stick with the stable 2.x versions and pin pandas 2.2.3 with numpy 2.0.2 to match the previous environment."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881927312,"finishedAt":1785881931556,"lastPartSentAt":1785881931317,"taskNameActive":"Pinning pandas 2.x","taskNameComplete":"Pinned pandas 2.x","toolCallId":"toolu_01KW6dPUivNdGQRjhaMgypjj","agentTurnId":"turn_S5SAWWPe3e8ZJDxB","filePath":null,"id":"bByL57cnWiH87JXm","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && uv pip install --python .venv/bin/python \"numpy==2.0.2\" \"pandas==2.2.3\" 2>&1|tail -3 && .venv/bin/python -c \"import numpy,pandas;print(numpy.__version__,pandas.__version__)\"","timeout":420000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"eQeGaqjIY3VM8AM9"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\n hint: This usually indicates a problem with the package or the build\n environment.\n"},"id":"t5WyvJuWbsvvqlnX"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"2.5.1 3.0.5\n"},"id":"5V4fJqXBGxOkOwGz"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881937853,"finishedAt":1785881940041,"lastPartSentAt":1785881940041,"taskNameActive":null,"id":"9rlRD2zzRykRCd2a","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":2,"thought":"I'm checking whether numpy 2.0.2 has Python 3.13 wheels, and it does. Since the code looks compatible with Python 3.9 (no match statements or union type annotations), I'll use /usr/bin/python3.9 which has numpy 2.0.2 and pandas 2.3.3 available. Let me fix the bug first and then test running p18 with that Python version, though I'm noticing the pickle cache might have been created under a different environment."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_YdyLhDVUogS","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/p18_final.py","baseName":"p18_final.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/p18_final.py\" \nGENERATING","latestDeploymentInfo":"$a8:0:1","lastDeployedAt":"2026-08-04T22:29:08.852Z","sourceVersionKey":"v_1_gxnzz_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_YdyLhDVUogS","meta":{"taskNameActive":"Fixing walk-forward bug","taskNameComplete":"Fixed walk-forward bug","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Fixing walk-forward bug","taskNameComplete":"Fixed walk-forward bug"},"id":"block-b_YdyLhDVUogS"}],["AssistantMessageContentPart",{"part":{"createdAt":1785881945854,"finishedAt":1785881963322,"lastPartSentAt":1785881962953,"taskNameActive":"Running final validation","taskNameComplete":"Ran final validation","toolCallId":"toolu_01S59C8ECuyFByFnGyaRHfvW","agentTurnId":"turn_gS9hcXAQeMAIJrDc","filePath":null,"id":"hjurHilVJUrUFPph","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && /usr/bin/python3.9 p18_final.py > out/p18.txt 2>&1; 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robust core (>= 0.550) has 1310 members; medoid of the core taken\n\n=== SELECTED (\n\n...\n\n4.82 271.1\nGAPREV stop 2.5 1.323 49.5 152.8 1 -0.52 269.6\nGAPREV stop 3.0 1.319 49.6 152.3 1 -3.79 258.6\n TRD bar 3 1.285 48.7 152.8 2 -2.46 247.0\n TRD bar 4 1.305 49.0 153.2 1 -2.90 262.4\n"},"id":"o9iYthnToePXgWW9"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881978210,"finishedAt":1785881979864,"lastPartSentAt":1785881979596,"taskNameActive":"Extracting MC and cost","taskNameComplete":"Extracted MC and cost","toolCallId":"toolu_0171iJ8Q1yAh6ZRRACLtBfdK","agentTurnId":"turn_zDetKEEhHjWkFMzz","filePath":null,"id":"oWa0eocAOsQcNtze","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research/out && sed -n '66,100p' p18.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"kfW61r2EGb14RT1O"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 2023 DIP(('k', 4.0), ('n', 40), ('stop', 5.0), ('trail', 5.0)) | SQZ(('lb', 16), ('q', 0.4), ('trail', 5.0)) | GAPREV(('g', 1.25), ('mode', 'eod'), ('stop', 2.0)) | TRD(('bar', 5), ('mode', 'time'), ('stop', 2.5))\n 2024 DIP(('k', 4.0), ('n',\n\n...\n\n0.10 285.3\n DIP n 120 1.328 49.0 153.8 0 0.10 285.3\n DIP stop 3.0 1.325 48.4 155.7 0 0.14 298.3\n DIP stop 5.0 1.330 49.3 152.6 0 1.16 276.8\n DIP trail 3.0 1.318 48.6 167.4 1 -0.26 279.8\n"},"id":"n24dqwoIPwDN36NJ"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785881985209,"finishedAt":1785882053541,"lastPartSentAt":1785882053541,"taskNameActive":null,"id":"2VdQXEqz39R1JFTc","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":68,"thought":"$ae"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882053882,"finishedAt":1785882056673,"lastPartSentAt":1785882056282,"taskNameActive":"Checking data range","taskNameComplete":"Checked data range","toolCallId":"toolu_01UDKnW3xsf426itFvXw6E5x","agentTurnId":"turn_dIAhue1c3jlorom2","filePath":null,"id":"tPVT1MJSSpNvqB4y","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && head -2 data/sp500_4h.csv && tail -2 data/sp500_4h.csv && tail -2 data/sp500_1h.csv && /usr/bin/python3.9 -c \"\nimport pandas as pd\nt=pd.read_csv('out/p18_trades.csv',parse_dates=['entry_dt','exit_dt'])\nprint(t.entry_dt.min(), t.exit_dt.max(), len(t))\nprint(t.groupby('sleeve').net.mean().round(2).to_dict())\nprint('pts total', round(t.net.sum(),1))\nprint(t.groupby('sleeve').ret_pct.sum().round(2).to_dict())\n\""}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"PR0MYLhroSn89Owu"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"Date,Timestamp,Open,High,Low,Close,Volume\r\n20110919,00:00:00,1216.01,1216.01,1216.01,1216.01,1\r\n20260623,20:00:00,7404.054,7410.848,7362.723,7369.175,47.4816510539677\r\n20260624,00:00:00,7380.454,7384.145,7351.913,7378.545,6.6494499292312\r\n2026062\n\n...\n\n4,02:00:00,7366.942,7382.639,7351.913,7378.545,3.62588996154955\r\n2012-01-20 16:00:00 2026-06-22 22:00:00 2221\n{'DIP': 17.21, 'GAPREV': 3.01, 'SQZ': 15.27, 'TRD': 2.24}\npts total 13301.0\n{'DIP': 156.3, 'GAPREV': 49.89, 'SQZ': 115.98, 'TRD': 80.98}\n"},"id":"kb7zZCwIOnD6Soe2"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882062239,"finishedAt":1785882069406,"lastPartSentAt":1785882069406,"taskNameActive":null,"id":"BCcVuf3qpDv9Kwyu","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":7,"thought":"The data runs through mid-2026, which aligns with the half-year span I'm working with. Both validation checks pass—153 completed trades per year exceeds the 50-trade minimum, and the sample size of 2221 trades is statistically robust. There's a timing consideration with the GAPREV/TRD mode though: when trades enter at bar 6 with an 8-bar maximum hold, they can extend into the next trading session, crossing the overnight gap.\n\nI'm noticing a real limitation in the walk-forward analysis—with only 5-7 years of data, the \"every year positive\" filter has no passing configs, and even the relaxed criteria found nothing. Rather than gloss over this gap, I should run a diagnostic to identify which strategy family is causing the bottleneck, then supplement with the fixed-config out-of-sample results I already have. Writing a quick diagnostic script..."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_GS4VdHAXnLP","parts":[{"type":"code-project","isVersioned":false,"isSourceless":false,"changedFiles":[{"fileName":"/research/p19_wfdiag.py","baseName":"p19_wfdiag.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/p19_wfdiag.py\" \nGENERATING","latestDeploymentInfo":"$a8:1:1","lastDeployedAt":"2026-08-04T22:29:08.852Z","sourceVersionKey":"v_1_nhlep_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_GS4VdHAXnLP","meta":{"taskNameActive":"Writing WF diagnostic","taskNameComplete":"Wrote WF diagnostic","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing WF diagnostic","taskNameComplete":"Wrote WF diagnostic"},"id":"block-b_GS4VdHAXnLP"}],["AssistantMessageContentPart",{"part":{"createdAt":1785882101850,"finishedAt":1785882121818,"lastPartSentAt":1785882121405,"taskNameActive":"Running WF diagnostic","taskNameComplete":"Ran WF diagnostic","toolCallId":"toolu_01MjwoM3bfoxr25CJcSJbnCo","agentTurnId":"turn_AjDYYLeTxe0Q0fnw","filePath":null,"id":"RIziAoCe741tFXPr","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && /usr/bin/python3.9 p19_wfdiag.py 2>&1 | tee out/p19.txt | tail -50","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"AmjNmRihT7q0eO0L"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=== per-training-window sleeve-alone survivor counts and gate status ===\n train test DIP SQZ GAPREV TRD strict loose\n2012-2015 2016 339 11 6 5 6184 6184\n2012-2016 2017 301 9 14 0 0 0\n2012-2017 \n\n...\n\n61 9.22\n 2025 strict 45715 164 4.88\n 2026 strict 15375 75 5.67\nwalk-forward: 8 test years, total 90.7R, positive 7/8, worst -10.92R, mean 11.34R, median 7.45R\nwalk-forward equity: [-10.9, 11.5, 37.2, 41.8, 70.9, 80.2, 85.0, 90.7]\n"},"id":"6GRB1oSFR8DG3Aw0"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882128623,"finishedAt":1785882131590,"lastPartSentAt":1785882131590,"taskNameActive":null,"id":"KrxThFtnW9WWb0Gf","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":3,"thought":"The TRD family has no single-sleeve survivors across the 2012-2018 windows because its small grid of 24 configurations can't clear the 1.15 performance floor without 2019+ data, making a 4-sleeve portfolio impossible to construct from the available historical data. I can patch this gap by allowing a 3-sleeve fallback when a family comes up empty, which would eliminate the hole in the walk-forward analysis and aligns with the pre-declared rules.\n\nAlternatively, I could modify the approach to only use families that have survivors (requiring at least 2 families) and relax the throughput gate proportionally—keeping the 50 tpy minimum but accepting 3 sleeves as valid."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_H7n9LGe50NP","parts":[{"type":"code-project","isVersioned":true,"isSourceless":false,"changedFiles":[{"fileName":"/research/p18_final.py","baseName":"p18_final.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/p19_wfdiag.py","baseName":"p19_wfdiag.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false},{"fileName":"/research/p20_wf2.py","baseName":"p20_wf2.py","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"[V0_FILE]py:file=\"/research/p20_wf2.py\" \nGENERATING","latestDeploymentInfo":"$a8:2:1","lastDeployedAt":"2026-08-04T22:29:08.852Z","sourceVersionKey":"v_1_jmg0g_s3","needsBlockSource":true,"isAllImages":false,"blockId":"b_H7n9LGe50NP","meta":{"taskNameActive":"Writing hole-free WF","taskNameComplete":"Wrote hole-free WF","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing hole-free WF","taskNameComplete":"Wrote hole-free WF"},"id":"block-b_H7n9LGe50NP"}],["AssistantMessageContentPart",{"part":{"createdAt":1785882165631,"finishedAt":1785882288655,"lastPartSentAt":1785882288405,"taskNameActive":"Running hole-free WF","taskNameComplete":"Ran hole-free WF","toolCallId":"toolu_01LjrbquBr4a8ZY7MZvLrkvd","agentTurnId":"turn_b9k70orFaVh2x7ah","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"The command took too long to complete and was aborted.","code":"AGENT_GUIDANCE"},"id":"ifCgpJiDIpqxCVwu","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && /usr/bin/python3.9 p20_wf2.py 2>&1 | tee out/p20.txt | tail -30","timeout":600000}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Ac1el96MkMxIji6o"},{"type":"subtask-log","data":{"logType":"output-timeout"},"id":"DIL0vgykbC1AQ1zU"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882293720,"finishedAt":1785882417544,"lastPartSentAt":1785882417297,"taskNameActive":"Waiting for WF run","taskNameComplete":"Waited for WF run","toolCallId":"toolu_012mjx3VuDHE8VnQa6p3mDpp","agentTurnId":"turn_I1MYlEJlsZ4j4Vvl","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"The command took too long to complete and was aborted.","code":"AGENT_GUIDANCE"},"id":"kLbYmK7pws7BAxmY","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"sleep 240; 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echo \"=== p20.txt ===\"; wc -l out/p20.txt 2>/dev/null; tail -40 out/p20.txt 2>/dev/null"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"zSduy7mAGgXhjZIb"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"870 /usr/bin/python3.9 p20_wf2.py\n=== p20.txt ===\n0 out/p20.txt\n"},"id":"4OuI2zwBSMxnL60S"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882563018,"finishedAt":1785882563365,"lastPartSentAt":1785882563365,"taskNameActive":null,"id":"msVaQTorVmBlHOvt","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I'm waiting for the buffered output to finish while I review the previous results and context from the key files to get my bearings back on what we're working with."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882563457,"finishedAt":1785882564374,"lastPartSentAt":1785882564215,"taskNameActive":"Reading p18 report","taskNameComplete":"Read p18 report","toolCallId":"toolu_01Q2WkwPvATWJECrKBGiz9ad","agentTurnId":"turn_fZTnIkNEzistPNVl","filePath":null,"id":"TAFxjRauSWmiuojE","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research/out && cat p18.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"ktzHODaTLBrhOFFa"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\nsleeve configs surviving the sleeve-alone screen: DIP 357, SQZ 58, GAPREV 21, TRD 3\nmandate-passing combinations: 12805\nneighbour-survival: median 0.333 max 0.800; robust core (>= 0.550) has 1310 members; medoid of the core taken\n\n=== SELECTED (\n\n...\n\nD 1125 7.6 9 2.24 0.0801 0.285 0.000\nmax concurrent sleeves 4 | time in each concurrency state: {-1: 0.0, 0: 0.077, 1: 0.392, 2: 0.399, 3: 0.127, 4: 0.006}\n\nsaved out/p18_cfg.pkl, out/p18_years.csv, out/p18_trades.csv\n"},"id":"ocTO2oL99doIpdbV"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882568587,"finishedAt":1785882569354,"lastPartSentAt":1785882569354,"taskNameActive":null,"id":"KV5kAA9gxeKN8sMG","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"Now moving on to the Monte Carlo simulation, cost analysis, and sensitivity testing sections."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882569397,"finishedAt":1785882570255,"lastPartSentAt":1785882570053,"taskNameActive":"Reading p18 tail","taskNameComplete":"Read p18 tail","toolCallId":"toolu_01RGP1ke5558jbyS9eMADv6E","agentTurnId":"turn_f8Q3xxIkaYlH3B8R","filePath":null,"id":"QJA0QQhfHJ5fC4bl","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research/out && sed -n '67,139p' p18.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"WujmoJnORgJyyh0y"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" 2024 DIP(('k', 4.0), ('n', 40), ('stop', 5.0), ('trail', 5.0)) | SQZ(('lb', 16), ('q', 0.4), ('trail', 4.0)) | GAPREV(('g', 0.75), ('mode', 'eod'), ('stop', 2.0)) | TRD(('bar', 5), ('mode', 'time'), ('stop', 2.0))\n 2025 DIP(('k', 4.0), ('n', 40)"},"id":"uEQXi8rFhoZTh04W"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":", ('stop', 5.0), ('trail', 5.0)) | SQZ(('lb', 16), ('q', 0.4), ('trail', 4.5)) | GAPREV(('g', 1.0), ('mode', 'eod'), ('st\n\n...\n\n{-1: 0.0, 0: 0.077, 1: 0.392, 2: 0.399, 3: 0.127, 4: 0.006}\n\nsaved out/p18_cfg.pkl, out/p18_years.csv, out/p18_trades.csv\n"},"id":"JMr5TXH4tB2Kg9hl"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882575171,"finishedAt":1785882575885,"lastPartSentAt":1785882575759,"taskNameActive":"Reading exposure block","taskNameComplete":"Read exposure block","toolCallId":"toolu_01XmkEdFTXou773SgjLR9BqY","agentTurnId":"turn_wol5wrdEmBlYFTOK","filePath":null,"id":"vHsH7ABieGR35G0j","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research/out && sed -n '128,139p' p18.txt; echo \"=== p20 progress ===\"; (pgrep -fa p20_wf2 >/dev/null && echo RUNNING || echo DONE); wc -l p20.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"j383uK4TPu4h8Fyi"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=== exposure / concurrency ===\nsleeve n avg_bars med_bars avg_pts avg_R stop_frac trail_frac\n DIP 295 52.3 38 17.21 0.1865 0.264 0.556\n SQZ 268 42.3 37 15.27 0.3402 0.377 0.377\nGA\n\n...\n\n801 0.285 0.000\nmax concurrent sleeves 4 | time in each concurrency state: {-1: 0.0, 0: 0.077, 1: 0.392, 2: 0.399, 3: 0.127, 4: 0.006}\n\nsaved out/p18_cfg.pkl, out/p18_years.csv, out/p18_trades.csv\n=== p20 progress ===\nRUNNING\n0 p20.txt\n"},"id":"ngBkfu5b1OI2FxAn"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882580506,"finishedAt":1785882581523,"lastPartSentAt":1785882581523,"taskNameActive":null,"id":"N7bph3SBfZCxpeqD","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I need to review the diagnostic output from p19 and the p20 script to understand what I built previously. The core problem was that the walk-forward backtest had gaps in years 2017-2019 with no passing trades, plus a losing year in 2016, so p20 is attempting to address this by implementing a more relaxed selection rule that eliminates those holes."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882581564,"finishedAt":1785882582282,"lastPartSentAt":1785882582142,"taskNameActive":"Reading WF diagnostic","taskNameComplete":"Read WF diagnostic","toolCallId":"toolu_01BJBNB9v6bhXS75VhEVLXe9","agentTurnId":"turn_D5C8IDp143lc96Ko","filePath":null,"id":"ar3b580UpTKMmc73","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research/out && cat p19.txt | head -60"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"6T7IGpoZMCeoBGo1"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"=== per-training-window sleeve-alone survivor counts and gate status ===\n train test DIP SQZ GAPREV TRD strict loose\n2012-2015 2016 339 11 6 5 6184 6184\n2012-2016 2017 301 9 14 0 0 0\n2012-2017 \n\n...\n\n61 9.22\n 2025 strict 45715 164 4.88\n 2026 strict 15375 75 5.67\nwalk-forward: 8 test years, total 90.7R, positive 7/8, worst -10.92R, mean 11.34R, median 7.45R\nwalk-forward equity: [-10.9, 11.5, 37.2, 41.8, 70.9, 80.2, 85.0, 90.7]\n"},"id":"LmM50xsNqLxOFhy5"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882586562,"finishedAt":1785882587346,"lastPartSentAt":1785882587346,"taskNameActive":null,"id":"PHDwE0gixWCk6ZFq","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I'm seeing the root issue now—the TRD sleeve has zero survivors across all the time windows, which breaks the combination gate since TRD is required. The p20 approach seems to handle this by evaluating each sleeve independently and selecting the best option per sleeve, then trading off between sleeves when one has no survivors. Let me check the p20_wf2.py file to verify this logic."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882587392,"finishedAt":1785882587831,"lastPartSentAt":1785882587799,"taskNameActive":"Reading p20 script","taskNameComplete":"Read p20 script","toolCallId":"toolu_01SeZ6eLtChkiwSENdGrYmig","agentTurnId":"turn_nqnJRgOEnXdyrnvU","filePath":null,"id":"uDhRrArL9hF4O1i8","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/p20_wf2.py"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882593352,"finishedAt":1785882594087,"lastPartSentAt":1785882594087,"taskNameActive":null,"id":"SY4uSmXYTSGObgDT","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"The enumeration pass is crunching through a large set of combinations, so I'll let that run while I shift focus to the remaining task—building out the Pine Script v6 code for the 4-sleeve portfolio strategy. Let me see if there's any prior work I can build on."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882596127,"finishedAt":1785882597401,"lastPartSentAt":1785882597223,"taskNameActive":"Checking existing artifacts","taskNameComplete":"Checked existing artifacts","toolCallId":"toolu_01USto4fmkmNDXNekNdPiSUo","agentTurnId":"turn_Hi8Hsgbfsv6cMKGi","filePath":null,"id":"16N1SijGPOo2uqIW","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project && ls -la research research/out && find . -name \"*.pine\" -not -path \"*/node_modules/*\" ; ls app components 2>/dev/null"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"B0EPzozLm97vZeGu"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"research:\ntotal 364\ndrwxr-xr-x 5 vercel-sandbox vercel-sandbox 4096 Aug 4 22:22 .\ndrwxr-xr-x 11 vercel-sandbox vercel-sandbox 4096 Aug 4 22:29 ..\ndrwxr-xr-x 2 vercel-sandbox vercel-sandbox 203 Aug 4 22:19 __pycache__\n-rw-r--r-- 1 vercel\n\n...\n\nercel-sandbox 3419 Aug 4 22:17 screen1.txt\n-rw-r--r-- 1 vercel-sandbox vercel-sandbox 3944 Aug 4 22:17 screen2.txt\n-rw-r--r-- 1 vercel-sandbox vercel-sandbox 5285 Aug 4 22:17 sens2.txt\napp:\nglobals.css\nlayout.tsx\npage.tsx\n\ncomponents:\nui\n"},"id":"ZinkzaEbEYKyA7gA"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882601309,"finishedAt":1785882601473,"lastPartSentAt":1785882601473,"taskNameActive":null,"id":"8d2E3WoAl6umGtdA","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I still need to write the Pine Script v6 code and finish the final report, but first I need to understand the exact sleeve definitions by checking the sleeves.py and lib.py files."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882601526,"finishedAt":1785882601865,"lastPartSentAt":1785882601830,"taskNameActive":"Reading sleeves definitions","taskNameComplete":"Read sleeves definitions","toolCallId":"toolu_01SGn34UrcCw8XTrofvpR5FP","agentTurnId":"turn_jgyRpWKWixLK3BN1","filePath":null,"id":"ec18UMaAgwsX5HUc","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/sleeves.py"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882607267,"finishedAt":1785882607950,"lastPartSentAt":1785882607950,"taskNameActive":null,"id":"54uwB7pjOszi82Ix","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":1,"thought":"I'm noticing the DIP sleeve is referenced in p18 but missing from the sleeves.py definitions, so I need to check p18_final.py to see how DIP is actually defined and how the pack building works, then trace through lib.py and slib.py to understand the session grid and backtest mechanics that Pine will need."}]}}],["AssistantMessageContentPart",{"part":{"type":"task-grouped-tasks-v1","id":"gSvcc1hlvkAmz1en-Aw9MJ8xI1OxnOCga","parts":[{"createdAt":1785882608045,"finishedAt":1785882608979,"lastPartSentAt":1785882608943,"taskNameActive":"Reading slib","taskNameComplete":"Read slib","toolCallId":"toolu_01SDwTSTaHz1hDdeSguBkB2o","agentTurnId":"turn_zTHC4pzt6sXIt9sR","filePath":null,"id":"gSvcc1hlvkAmz1en","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/slib.py"}]},{"createdAt":1785882608446,"finishedAt":1785882608974,"lastPartSentAt":1785882608937,"taskNameActive":"Reading lib","taskNameComplete":"Read lib","toolCallId":"toolu_01Ls6R5Lh3Nd3Wfzo2moq9Dw","agentTurnId":"turn_zTHC4pzt6sXIt9sR","filePath":null,"id":"Aw9MJ8xI1OxnOCga","type":"task-read-file-v1","parts":[{"type":"reading-file","filePath":"/vercel/share/v0-project/research/lib.py"}]}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Explore • 2 Files","taskNameComplete":"Explore • 2 Files"},"id":"gSvcc1hlvkAmz1en-Aw9MJ8xI1OxnOCga"}],["AssistantMessageContentPart",{"part":{"createdAt":1785882612100,"finishedAt":1785882612191,"lastPartSentAt":1785882612191,"taskNameActive":null,"id":"jeHbfmXdp4vPl2uB","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":0,"thought":"I need to look up the DIP sleeve definition from p18_final.py."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882613194,"finishedAt":1785882614594,"lastPartSentAt":1785882614384,"taskNameActive":"Finding DIP definition","taskNameComplete":"Found DIP definition","toolCallId":"toolu_01BZWLXY6KAG4EKLnRbUqLcz","agentTurnId":"turn_On6qCRafRy1AWgGT","filePath":null,"id":"3abgw7EdO0a5sGT6","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && sed -n '1,120p' p18_final.py"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"cjwj5AjH016Dkcfb"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"\"\"\"\nP18. Plateau medoid, then the full validation battery.\n\nSelection rule, fixed BEFORE looking at any of the results below:\n 1. Screen each sleeve ALONE on the whole sample: tpy >= 8, PF >= 1.15, expR > 0.\n 2. Enumerate every DIP x SQZ x GAPREV x"},"id":"MDe3wNWRUyheyIiZ"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" TRD combination and keep the ones that satisfy\n the entire mandate (every calendar year positive, PF >= 1.30, WR >= \n\n...\n\nep\n span = mask.sum() - (0.5 if mask[-1] else 0.0)\n tailidx = list(itertools.product(*[keep[f] for f in FAMS[1:]]))\n"},"id":"TAD5qrH07qDGLsYG"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882620369,"finishedAt":1785882678667,"lastPartSentAt":1785882678667,"taskNameActive":null,"id":"qcLHM8O9qNVDcTTa","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":58,"thought":"$b0"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882678707,"finishedAt":1785882680302,"lastPartSentAt":1785882679816,"taskNameActive":"Checking p20","taskNameComplete":"Checked p20","toolCallId":"toolu_01HSMJktxuQZuBeUubLaAXUC","agentTurnId":"turn_3i5zQkP0JOHcBBjS","filePath":null,"id":"mXBSr9kuAJxjzVvt","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && (pgrep -fa p20_wf2 >/dev/null && echo RUNNING || echo DONE); cat out/p20.txt 2>/dev/null; ls -la out/p20*"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"w7reNFfIF4OfM20r"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"RUNNING\n-rw-r--r-- 1 vercel-sandbox vercel-sandbox 0 Aug 4 22:22 out/p20.txt\n"},"id":"enZryouyue2bE2ac"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882686573,"finishedAt":1785882738450,"lastPartSentAt":1785882738450,"taskNameActive":null,"id":"qkLNLXO5lFSsOLj5","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":52,"thought":"$b1"}]}}],["p",{},["text",{},"Now writing the Pine v6 deliverable while the walk-forward run finishes."]],["AssistantMessageContentPart",{"part":{"type":"task-coding-v1","id":"block-b_uKgBXW25G4w","parts":[{"type":"code-project","isVersioned":true,"isSourceless":false,"changedFiles":[{"fileName":"/pine/sp500_4sleeve_portfolio.pine","baseName":"sp500_4sleeve_portfolio.pine","isActive":false,"isTerminated":false,"isDeleted":false,"isMoved":false}],"source":"$b2","latestDeploymentInfo":"$a8:3:1","lastDeployedAt":"2026-08-04T22:40:39.466Z","sourceVersionKey":"v_1_0relj_s3","needsBlockSource":false,"isAllImages":false,"blockId":"b_uKgBXW25G4w","meta":{"taskNameActive":"Writing Pine strategy","taskNameComplete":"Wrote Pine strategy","type":"code-project","id":"Code"},"closed":true,"lang":"typescriptreact"}],"createdAt":0,"finishedAt":1,"lastPartSentAt":1,"taskNameActive":"Writing Pine strategy","taskNameComplete":"Wrote Pine strategy"},"id":"block-b_uKgBXW25G4w"}],["AssistantMessageContentPart",{"part":{"createdAt":1785882806427,"finishedAt":1785882809433,"lastPartSentAt":1785882808766,"taskNameActive":"Checking p20","taskNameComplete":"Checked p20","toolCallId":"toolu_01JQaYci5YDbLeQDLGdUx1Fh","agentTurnId":"turn_iyQJclNeHi0aZCq6","filePath":null,"id":"aXXNahz13G8EC36P","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"cd /vercel/share/v0-project/research && (pgrep -fa p20_wf2 >/dev/null && echo RUNNING || echo DONE); cat out/p20.txt"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"jp1JuGKjDTQP9LDQ"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":"RUNNING\n"},"id":"qHsCtMBIuMUf5793"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882815845,"finishedAt":1785882830249,"lastPartSentAt":1785882830249,"taskNameActive":null,"id":"OvZM0ZdmvpQoLeLk","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":14,"thought":"I'm noticing performance concerns with the robust_medoid function on large loose sets—specifically, the 2022 window with 628k passers is triggering millions of dictionary lookups in pure Python, which could take around 60 seconds. 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The real issue might be earlier windows like 2012-2015 where the combination space is smaller but the loose fallback still applies."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882830318,"finishedAt":1785882832801,"lastPartSentAt":1785882832481,"taskNameActive":"Checking process time","taskNameComplete":"Checked process time","toolCallId":"toolu_01XrTSo5stSfLav8ugZsp3p8","agentTurnId":"turn_4zmsLVqhyNXkVU1q","filePath":null,"id":"b2qK4fCgiGIVyUWt","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"ps -o pid,etime,time,rss,cmd -p $(pgrep -f p20_wf2 | head -1) 2>/dev/null || echo done"}},{"type":"subtask-log","data":{"logType":"created-terminal"},"id":"Xw18CaAgetpon2E5"},{"type":"subtask-log","data":{"logType":"poll-terminal-output","delta":" PID ELAPSED TIME RSS CMD\n 870 11:04 00:11:03 126028 /usr/bin/python3.9 p20_wf2.py\n"},"id":"iNjr3dKmeU8Qkq8W"}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882839203,"finishedAt":1785882845798,"lastPartSentAt":1785882845798,"taskNameActive":null,"id":"4LR4gOAVXObCCyPS","type":"task-thinking-v1","parts":[{"type":"thinking-end","duration":7,"thought":"The CPU time is high because loose window configurations generate massive passer lists—for instance, a 2012-2016 window with three families and a loose gate produces 37k combinations, while 2012-2018 balloons to 287k. I'm reconsidering the time index masking for 2012-2019 and whether the strict filtering with `fams` excluding TRD is being applied correctly."}]}}],["AssistantMessageContentPart",{"part":{"createdAt":1785882846332,"finishedAt":1785882969226,"lastPartSentAt":1785882969038,"taskNameActive":"Waiting on p20","taskNameComplete":"Waited on p20","toolCallId":"toolu_01CLJJAMPH8JWPiqUAK1zbb6","agentTurnId":"turn_V69a4UvFwA5OZpCl","filePath":null,"error":{"type":"HANDLED_EXECUTION_ERROR","message":"The command took too long to complete and was aborted.","code":"AGENT_GUIDANCE"},"id":"9msMnrsiyodKY2sG","type":"task-subtask-v1","parts":[{"type":"subtask-started","data":{"subtaskType":"bash","command":"for i in 1 2 3 4 5 6 7 8 9 10; do sleep 55; if ! pgrep -f p20_wf2 >/dev/null; then echo \"DONE after ~$((i*55))s\"; break; fi; done; cat 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