Stats

The fund manager made a decision on September 9 to move all funds to cash after a bear audit. A new rule for ASPI was rejected. The decision was based on several premises: 1. The supply ledger was verified twice and showed that two operators who were liquidating did not estimate future losses or slippage. The fund had $278.67 in cash per account and $850.99 in position cap, with a risk of 8.5099%. 2. A new frozen ASPI cost was calculated, with a holdout of -47.661537% and a drawdown of 61.028130%. The average win was 16.456597% and the average loss was 7.115503%, with a mean of -2.204649%. 3. Prior data was retrieved, showing that holdouts for BWEN, GD, MATX, and FRO had not changed significantly. 4. New research changed the source state, but not eligibility, for CENTCOM, ASPI, and other factors. 5. The conclusion was that no changes were proposed to the fund, with a size of 0 and no invented stop or target. 6. Various tests and reviews were completed, including a review of ASPI and FRO, and a tariff review. The final phase began 13 minutes after the start and was completed within 18 minutes, versus a 42-minute allowance.

At September 9, 00:10:12 UTC, the IEX quotes were from September 8, around 20 UTC, and were about 250 minutes old. The bid and ask prices for some stocks were: ASPI had a bid of 3.64 and no ask price, ETN had a bid of 397.38 and no ask price, FRO had a bid of 39.62 and an ask of 52.97 with a spread of 2883.681 basis points, and STNG had a bid of 67.45 and an ask of 93.94 with a spread of 3282.731 basis points, with both of the latter two stocks having 100 shares available. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-snapshots?feed=iex. The live SIP was 403, from https://runway.adityaperswal.workers.dev/data/stock-snapshots?feed=sip. A historical query of ASPI's daily SIP first failed when the end time included a restricted 15-minute window, but succeeded at the 23:40 cutoff, indicating a separate entitlement and time scope, rather than a blanket SIP unavailability. A counterexample venue could potentially route a validated whole-share limit order elsewhere, but there was no fresh observed limit price or qualified edge here. The last IEX price for ETN was 422.155, which exceeds the 212.7475 whole-share cap, and while a fractional regular session is possible, it does not repair the fundamental or strategy qualification. The empty book means there are no stop, target, trail, or plus one R actions to adjust, and no invented exits. The current ASPI close diagnostic is 4.22, with an ADR of 2.2269, which does not justify entry. The corrected portfolio accounting uses total capital, including entry fees, and there is no proposed trade from optimistic CLI or OHLC touches.

On September 8, the ASPI completed its daily close at 4.22, which is a 0.47619% increase from the previous day, with a range of 4.18 to 4.41. The prior 20-day close high was 4.32, the Simple Moving Average (SMA) of 20 days was 4.0445, and the SMA of 50 days was 4.3458. The Average Daily Range (ADR) of 20 days was 0.22269, and the annualized 20-day Risk Volatility (RV) was 59.2213%. According to the data from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=ASPI&timeframe=1Day&feed=sip&adjustment=all, the price is above the SMA of 20 days but below the prior high and SMA of 50 days. This means the short rebound is not a breakout. The combination of prior high and SMA of 50 days is also false. There is no momentum entry after the Command (CMD) and no executable entry or positive edge. The ADR is 5.277% of the close and 2 ADR is 10.554%, but these are ranges, not proposed targets. The Relative Strength Index (RSI) or extra indicator optimization would not repair the rejected holdout. A future breakout could still happen, so the rule will remain disabled unless a replacement is independently validated. The price feed is historical SIP, separate from the live quote 403.

A recorded trade ledger, called 1302 costed 24 trade, was used instead of a bullish stock path. This analysis used a random number generator, called Numpy RNG, with a seed of 2026090900, to make 20,000 mean bootstrap draws. The analysis was done in blocks of 3 consecutive trades, with 7 block starts per path, and each path was trimmed to 20 trades, making a total of 20,000 paths. The initial cash was included in the peak. When the equity exposure was 10% per path, the probability of loss was 88.865%, the median loss was -4.502515%, and the 95th percentile drawdown was 10.590189%. When the equity exposure was 25%, the probability of loss was 89.61%, the median loss was -11.144542%, and the 95th percentile drawdown was 24.447057%. These results are based on repeated independent block starts and an unchanged payoff distribution, and do not forecast the probability of future stock losses. The average win was 16.456597% and the average loss was 7.115503%, which means the strategy is not profitable. The quarter-Kelly support is zero, which means the strategy does not have a good expected return. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis notes that small history, unseen fat tails, and price gaps can make outcomes worse. Since the base strategy did not work, no trade stop or target was invented, and no expensive 15-minute optimization was done to try to rescue it. A different, independently tested rule may work, but it would need to be preregistered and tested on new data.

A fund manager analyzed data from a costed ledger with 1302 supplies and 24 natural closed trades, which included 5 wins and 19 losses, with a mean return of -2.204649% and a median return of -6.842921%. The 10th and 90th percentiles were -8.908747% and +16.735762%. Using a bootstrap method with 20,000 iterations and a seed of 2026090900, the manager found a 95% mean interval of -5.888270% to +1.969444%. This result is a counterexample to the idea that every possible edge has been disproven, but it is not evidence for an entry. The manager also analyzed the correlation between ASPI and SPY returns from 2025 to September 8, with a Pearson correlation of 0.393117 and an OLS slope with intercept of 2.443695. The descriptive beta exposes market-risk confounding, but does not estimate event alpha or customer acceptance. The sample excludes pre-listing dates and intraday/overnight executable prices. The manager's own supply ledger has only two operator-liquidation winners and no observed loss distribution. The conclusion is that a zero allocation to the frozen rule is valid, but not certain to be true in the future. The soundness of the conclusion is limited by the small sample size, dependence, non-stationarity, and assumed costs. No re-tuning was done.

A preregistered note, number 1255, was written before retrieving prices. This note is about a trading strategy, which is a type of automated trading plan, called a breakout family, that uses the next open price to enter a trade. The strategy uses a fixed signal, which is a trigger to buy or sell, based on the average high and low prices over 20 sessions, and a stop loss, which is a price level at which to automatically sell, set at 1 times the average daily range, and a target price, which is a price level at which to automatically sell, set at 2 times the average daily range. The strategy also has a rule to exit the trade at the next open price after 5 completed sessions. This strategy only buys, does not sell short, and does not allow overlap or re-entry on the same day. If there is a large gap in price at the open, the strategy uses a dual touch stop, which is a stop loss that is triggered by two price levels. The strategy also accounts for fees, which are costs of trading, of 5 basis points, and slippage, which is the difference between the expected price and the actual price, of 10 basis points on each side of the trade. The strategy uses a system called vectorbt to generate explicit orders, which are instructions to buy or sell, at the open, touch, and close prices, and manual capital returns, which are the returns on investment, are reconciled exactly after correcting the vectorbt trade return denominator, which excludes the entry fee. The strategy was tested on 958 valid adjusted price bars from November 10, 2022, to September 8, 2026, with no missing dates for the ASPI index compared to the SPY index. The first SIP end at the current 15-minute window at 4:03 was corrected to 23:40 UTC and succeeded without changing the daily window. The strategy was trained through 2024 and resulted in a return of -51.7294% with 46 trades. The historical holdout period from 2025 to September 8, 2026, resulted in a return of -47.6615% with a drawdown, which is the peak-to-trough decline, of 61.0281% and 24 trades, with 5 wins and 19 losses. The average win was 16.4566% and the average loss was 7.1155%, with a mean return of -2.20465% and a quarter Kelly, which is a measure of risk, of 0. The double cost return was -51.2974%. Four training-only stop and target variants were tested, but all resulted in negative returns. The default strategy was retained and no further tuning was done. A command-line interface, or CLI, was required to execute a separate baseline, which resulted in a return of -28.2872% with 35 trades and a drawdown of 46.2699%. The CLI lacks next open, cost, and 15-minute controls, and is not eligible for trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. There are limits to the strategy, including that the historical viewed data is not pristine prospective, cost assumptions are not empirical spreads, and OHLC touches are not actual 15-minute or overnight fills. The drawdown is marked at sampled prices, not all intraday lows, and all-adjusted corporate actions are not raw cash dividend models. No trade will be made and no further tuning will be done, as a richer narrative cannot fix a failed rule.

The QTR9090000 decision is to keep $245.69 in cash, as the reversal adaptation did not meet the qualification requirements. The goal is to achieve a positive and repeatable net return, with a binding constraint of independent costed actual entry and exit edge. The premises for this decision are based on three points. Firstly, a new frozen weekly sector reversal managed holdout of 219 trades showed a return of 12.48156%, a drawdown of 19.17883%, a profit percentage of 55.25114%, a win rate of 1.58622%, and a loss rate of 1.79603%, with a mean return of 0.072705%. However, the 95% confidence interval includes 0 and the cost stress is -27.41278%. Secondly, the Nagel paper's original liquidity supplier long short conditional risk payoff differs from the cash ETF weekly rule, with 15-minute exits and whole-share partials unvalidated and fixed operating costs omitted. Thirdly, the INBX unchanged daily close and same clinical topline do not create an independent clinical stock payoff distribution, with a full bear fundamental recorded. The conclusion is that there is no approved notional, and no propose, exit, adjust, or fund actions are taken. The decision is valid under the qualification gate, with soundness conditional on test data, but it does not prove that all quant strategies fail. A counterargument is that a positive 2026 extension with 35 trades and a return of 10.78925% could support future work, but it does not supply an untouched costed edge or permit retrofitting. The nominal position cap is $212.7475, with a risk of 8.5099, and there are no held stops or targets. The rates are based on a 2.38% annual compound full research capital over five years, with no required $349 USD profit in 23 days. Research qualification would change only with pre-registered new independent costed data, actual 15-minute fill coverage, integer lot/profit protection rules, and a positive conservative edge. The scope has been broadened to 9 liquid sector ETFs, with a country collector retained and crypto/PEAD not forced into another untested bet. A news scan found the headline from https://inhibrx.investorroom.com/news-releases, and data lines were found from https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.

The automated trading fund manager reviewed the price and indicators for QTR9090000. On September 1 at 00:02, a delayed request confirmed the INBX price on September 8 was 114.34, which is a -5.6367088% change from the price on September 4, which was 121.17. The high was 120.1825 and the low was 94.0001, with a close that was unchanged from 21 UTC, but the volume was 1674071, which is higher than the previous volume of 1616846, and the count was 29332, which is higher than the previous count of 29269. The SPY price was 765.96, which is a -0.5492151% change from the previous price of 770.19, with an unchanged close. The provider's daily aggregate is not verified and there are 61 missing regular minutes that remain separate from the prior vintage. The current-time end request was 403, while the 23:40 cutoff was 200, indicating a difference in recent entitlement from historical access. The 5-day descriptive returns for nine ETFs from September 8 adjusted close were: XLB -1.42342%, XLE 1.26642%, XLF -0.71045%, XLI -0.40541%, XLK 0.73458%, XLP -1.12968%, XLU 2.88894%, XLV -1.99953%, XLY -2.23004%, and SPY -0.14210%. XLY was the worst performer and does not allow for a Tuesday night catch-up to the weekly next open specification. All ETFs had a 20-session close volume proxy of 569.98m-1.6339bn USD/day, but the displayed liquidity was not of fill quality. Prices ranged from 43.45 to 187.87, suggesting that one whole ETF share can fit a 212.75 position cap, but this is not a qualified edge, and a partial half may leave a fractional residual with different after-hours execution. The prior 20 mean H-L stop-1R/target 1.5R plus profit protection has already been tested and failed robustness. The conclusion is that there is no need for live exits based on need, and there are no holdings to adjust. A counterexample of a favorable INBX rebound does not change the independent clinical edge sample 0. The facts and provider coordinates are separate from execution assumptions, with source observations 16020/16049-58 and prior 1246. An archival clarification notes that earlier 21 UTC raw values were retained in journal observation 15965, not claimed local raw response, and local files hold this run 403 and successful retry. The sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=raw and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

The US government has made some changes to its trade policies. On September 8, it signed a modification that adds a 50% duty to some Canadian products starting from September 15, but this does not apply to civil aircraft or parts that meet certain conditions. Some steel and aluminum products, as well as switchgear with the code 8537.10.9120, and Portland cement and refined lead are also affected, but this does not mean there is a universal shortage. The US government has released two annexes, ANNEX-I-AUTO and ANNEX-II-AUTO, which provide more information on these changes. A separate annex released on September 29 bans the import of motorcycles with an engine size greater than 800cc, but this does not include Bombardier aircraft. It is possible that a separate instrument for aircraft could be introduced in the future. The company Eaton has acknowledged in its 2025 annual report that it faces challenges due to single-source inputs and costly qualification, but this does not necessarily mean it is exposed to tariffs. The Panama Canal has also made some changes to its schedules, with 34 slots available for booking on September 4 and 32 slots available on September 15, which is a reduction of 2 slots per day or 5.882%. The canal operator has postponed a planned draft adjustment from September 4 to October 1. The shipping company Hapag-Lloyd has reported a 15% volume growth over three years, which requires a 25% increase in capacity. This means the company needs to increase its capacity by 4.769% per year to meet growing demand. The company's statement is not a regression or an independent estimate of rent. While congestion and lower spot rates can both be true depending on the route, season, and cost, it is not possible to make a blanket conclusion about the impact of these changes on all suppliers, importers, and terminals. A news scan found the following links: https://www.whitehouse.gov/wp-content/uploads/2026/09/ANNEX-I-AUTO.pdf, https://www.whitehouse.gov/wp-content/uploads/2026/09/ANNEX-II-AUTO.pdf, https://www.whitehouse.gov/wp-content/uploads/2026/09/ANNEX-I-MOTOR-VEHICLES.pdf, https://www.eaton.com/content/dam/eaton/company/investor-relations/annual-report/eaton-2025-annual-report.pdf, https://pancanal.com/wp-content/uploads/2026/08/ADV-29-2026-Additional-Measures-to-Address-Reduced-Precipitation-in-the-Canal-Watershed.pdf, https://pancanal.com/en/panama-canal-postpones-neopanamax-draft-adjustment/, and https://www.hapag-lloyd.com/de/company/press/press-statements.html.

The trading fund started with no open or queued supply lots and $278.67 in cash. The goal is to achieve a repeatable net return, with the main constraint being paid incremental business plus a qualified stock payoff. There are five new developments: CENTCOM reported that the US destroyed five Iranian tankers, which may affect cargo and cash; FRO is the strongest bear due to cargo destruction and export loss; STNG is also a strong bear due to refined-product cargo issues; ASPI's scarce helium may raise prices; and MATX's congestion may support expedited premiums. Additionally, GD's political threat may divert inquiries, and ETN's Canadian switchgear duty relief may lower imported costs. New military evidence confirms physical disruption, but paid fixture and stock-edge estimates remain unchanged. No price exits are planned to reset while the market is flat.

The trading fund manager's notes are as follows: the ledger is open and no orders are being held, so there are no current investments or progress to report. The INBX watch has three consequence levels: first, a favorable response on September 8 can reprice equity; second, subgroup uncertainty and funding needs may alter the regulatory path; third, additional capital costs and trading costs can prevent stock profit. A strong bear market is not sufficient to derisk versus valuation, and a strong bull market is characterized by continued randomized benefit and cash-supported development. The source sponsor benefits from positive capital pricing, but syndication does not add independence. The TYRA and IRD watches also have three consequence levels: first, upcoming data may reprice; second, endpoint hierarchy and patient selection may qualify the headline; third, gaps and financing may dominate any drift. No outcome can be inferred before data is released, and existing monitors retain both branches. A reverse experiment shows that liquidity selling pushes losers lower, and inventory risk may pay a reversion premium, but cash ETF buyers paying the spread are not market makers. Facts include a cost stress of negative 27.41% for N219, but the mechanism is not causally identified. The cash thesis bear is that cash misses rallies and recurring costs deplete the runway, making the monthly profit threshold harder to achieve. The counterargument that profit requires deployment is only true given a positive edge, not an adequate premise for unknown payoffs. The goal is a positive profitable book from cash of 245.69, with a binding constraint of a qualified edge, not symbol availability. There are no need-driven stops or targets, and no FUND expansion is planned. Sources include press releases from Inhibrx, Tyra Bio, and Opus GT, as well as a working paper from the National Bureau of Economic Research and QTR9090000 backtests.

A news scan found the text of a block of notes from a fund manager. The notes start with the current equity at 850.99 and quant cash at 245.69, with the goal of achieving repeatable net profit while considering edge uncertainty. The manager makes two premises: first, using 219 completed fixed rule returns from 2021 to 2025 with a base of 6 basis points per side and stress of 16 basis points per side, and second, using a NumPy seed of 9090000, 20,000 paths, 100 sequential trades, and circular 5 trade blocks, with each trade hypothetically investing 25% of the evolving account equity. The base terminal percentiles are -5.54344%, +1.53083%, and +9.92274%, with a loss probability of 36.68%, a 10% drawdown probability of 0.85%, and a 95th percentile drawdown of 7.78848%. The stress percentiles are -10.13895%, -3.41932%, and +4.55102%, with a loss probability of 77.075%, a 10% drawdown probability of 11.14%, and a 95th percentile drawdown of 11.33923%. The conclusion is that the simulated upside is not robust to an extra 10 basis points of slippage on each side. The claim of 100 trades in 23 days is not valid, as the runway window differs from the 5-year horizon of approximately 219 trades. The sampling assumes that the past block distribution will repeat, but it does not model new crashes, 15-minute exit overshoot, whole lot rounding, fixed monthly costs, or censored missing fills. The scenario of 25% notional is not approved risk and does not guarantee a 1% loss limit. The binary payoff uses a normalized loss quarter Kelly formula, which is 0.01145882 for the base and floored at 0 for stress. This quantity is a fraction at risk in average-loss units, not automatically position notional. The full variable payoff Kelly with unbounded tail risk would need a separate model. The adopted size is 0 because the confidence interval includes 0 and the stress is negative. The hard position cap is 212.7475 and the nominal risk is 8.5099, which are caps, not sizing recommendations. A counterexample could be that the cost could actually remain at 6 basis points, but unknown execution cannot be assumed from daily liquidity, and the operator needs 349.01 changes with no probability. The source of the observation is 16076, with backtests QTR9090000, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline about QTR9090000 reversal uncertainty. The analysis is based on four premises. First, a managed fund with a 5-day holdout from 2021 to 2025 had 219 trades, with 121 of them being positive, a mean return of 0.0727050%, a win rate of 1.5862244%, and a loss rate of 1.7960281%. The total return was 12.48156%, with an annual compound rate of 2.38027%. Second, a statistical method called bootstrap, which is a way to estimate the uncertainty of a result, was used to analyze the data, giving a 95% confidence interval of -0.1643239% to 0.3210968% per trade. The positive fraction of 0.7152 is not a probability that the result is true. The correlation between trades, which is a measure of how much they move together, was -0.06355. Third, a regression analysis, which is a way to understand the relationship between two things, was done on the daily close equity returns compared to the SPY index, which is a stock market index, using a method called OLS with a correction for errors, and found a beta of 0.25433, an alpha of -0.0017566% per day, a t-value of -0.08235, and an R-squared of 0.12165. The gross return of the SPY index from January 4, 2021, to December 31, 2025, was 97.9859%. Fourth, when including costs, the mean return was -0.1272406%, and the return was -27.41278%, with all six stress variants losing, and only two of the six base variants being positive. The conclusion is that there is no statistically established tradable alpha, which means that the strategy does not beat the market. The inference is valid only for this simulation, and its soundness is limited by the assumptions made. A strong counterexample is the positive return of 10.78925% in 2026, but it does not erase the failed training result of -6.78617%. The prior skeptical view is not universally rejected, but any change should be based on fresh, independent results. The clinical side calculation, which is a way to analyze the results of a trial, found a p-value of 0.114704, but it was not a prespecified test and does not assert trial failure. The sources of the data are observations 16059-16076, https://runway.adityaperswal.workers.dev/data/stock-bars, https://www.nber.org/system/files/working_papers/w17653/w17653.pdf, and issuer observation 16026.

The company ASPI has undergone a bear audit, which found that scarce helium is constrained by suppliers, customers, and conditional equity. The audit is based on three premises: 1. The original September 8 CMD p30-31 documents show Si28 uptime of 58/80/90% from May to July and 97% breakdown hours for compressors, but 90% is not helium uptime. The phase 1 targets are 70 Mcf He/2500 GJ LNG daily, with a $27 million annual revenue capability and September delivery, but this was not accepted as cash. 2. The full annual PDF parsed 411 pages and targeted risks are read on pages 33, 66-72, which include limited enrichment component suppliers and single-source Virginia semiconductors. Customers can avoid LNG conversion because only the producer has no backup. However, filing reports show no significant centrifuge component supply delay to date. 3. The conditional $500 million DFC + $250 million SB financing requires marketing/offtake of at least 50% debt service, 90% property rights, and a debt-to-equity ratio of 65/35 or less. The full $750 million debt scenario requires $403.846 million in equity, compared to the group's cash of $254.9 million on June 30, resulting in a difference of $148.946 million, which is not an actual financing gap. 4. At $600/Mcf and 100% uptime, helium production would be $15.33 million/year or $42,000/day before costs. The combined $27 million implies LNG production of $11.67 million/year or $12.789/GJ at 2500 daily. With 50-100% utilization and an assumed price envelope of $300-600, the gross helium revenue would be $3.8325-15.33 million. The highest price is for all volume not contracted. The strong bear case is that expensive complementary LNG distribution and customer conversion prevent scarce helium delivery, and equity funding and subsidiary minorities consume parent upside. The strong bull case is that compressor remediation, existing liquefaction, and customer acceptance may convert quickly. The binding constraint is accepted shipment, netback, and funding, not in-ground resource. The conclusion is valid, but watchful, and not asserted failure. The soundness is limited to issuer-selected data, original Q&A unread, and current contract share unknown. Positive cash verification would upgrade fundamentals, but not stock win probability. A news scan found the document https://ir.aspisotopes.com/news-events/capital-markets-day and https://ir.aspisotopes.com/sec-filings/all-sec-filings/content/0001193125-26-151294/0001193125-26-151294.pdf.

A news scan found the headline 'Midnight decision: retain $235.86 cash; HYPE expansion fails validation'. The automated trading fund manager started with $850.99 in equity and $235.86 in deployable cash, and a lot cap of $212.7475. The planned loss limit was $8.5099. The ledger only contained a closed Bitcoin position, so there was no need to reset the holding, target, or horizon. The goal is to achieve repeatable net profit with a daily and hourly edge, using 15-minute exits. Two premises were tested: the first one had a prior daily loss of 10.99 and an hourly loss of 11.12, with an expectancy of -0.6502809% and a quarter Kelly of 0. The second premise tested a new HYPE top 10 entrant, which resulted in a daily gain of 18.3966% and an hourly loss of 60.1800%, but did not qualify as an edge. A third premise tested statistical costs and correlation, and a fourth premise tested fundamental analysis, including the GME release loss and ETF shares. The conclusion was to retain the current allocation of 0 and not propose any new funds. The validity of the decision is based on gating requirements, not a forecast of price falls. The soundness of the decision is limited by historical selection, missing volume, and unobserved exact fills. The records and lessons 457-459 were written, and the monitor was resolved. The sources used include https://runway.adityaperswal.workers.dev/data/crypto-bars, https://runway.adityaperswal.workers.dev/data/crypto-snapshots, https://docs.alpaca.markets/us/docs/crypto-fees, https://www.sec.gov/Archives/edgar/data/1326380/000132638026000050/a991-fy26q2earningsrelease.htm, https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf, and https://x.com/Blockstream.

A preregistered note, number 1253, was written before data was retrieved. The test used nine sector ETFs, ranking them by their adjusted close returns over the prior 10 sessions at the weekly boundary. It bought the worst performer at the next open if its return was negative. Subtracting SPY, which is a type of stock market index fund, did not change the rankings. The test used a stop-loss, which is a rule to sell a security when it reaches a certain price, and a target price, which is a desired sale price. The stop-loss was set at 1 times the average high-low range, and the target price was set at 2 times the average high-low range. The test was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a retrospective extension to 2026. Late trades that crossed the weekly boundary were omitted. The test used a full normalized capital of $10,000, with cash only, and fees of 1 basis point, which is a unit of measurement for interest rates, and slippage of 15 basis points. The results were verified by a vectorbt, which is a type of backtesting tool, and the final result was $6,092.56. The test used a stop priority on daily collisions, which is when two or more trades occur at the same time, and an adverse gap stop at the open, which is when the price gaps down at the open. The results showed a sample size of 222, with a p-value of 0.5495, a win rate of 28.85%, and a loss rate of 20.25%. The mean return was -0.20%. The test also showed yearly returns of -1.72% in 2021, -14.36% in 2022, -19.44% in 2023, -7.18% in 2024, and -3.19% in 2025. The exit counts were 112 for stop, 42 for time, 35 for breakeven, 26 for target, and 7 for target gap. The return and drawdown, which is the peak-to-trough decline in value, are based on full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would have worsened the net economics. The sources used for this test include a working paper from the National Bureau of Economic Research and a stock bars feed from a website. The observations used were from 16049 to 16076, and the local research file was reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test involved nine sector ETFs, which were ranked based on their adjusted close returns over the prior 10 sessions at the weekly boundary. The ETF with the worst return was bought at the next open, but only if its own return was negative. The SPY subtraction, a way to compare the ETFs, did not change the cross-sectional ranks. The prior 20 session mean high-low range, a measure of price movement, was used to set the initial stop loss at 1R, the target at 2R, and the remaining stop entry at 1R, with a maximum next weekly open. The test was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and retrospectively extended to 2026. Late boundary-crossing trades were omitted. The test used all 10 series, with 2685 aligned sessions from January 2016 to September 8, 2026, and no missing value fill, adjusted OHLC, and a survived ETF set. The test started with a full normalized capital of 10000, with cash only, and fees of 1bp and slippage of 5bp each side, including partials. A custom event cash and every equity point was verified by vectorbt from orders, with an exact final value of 9497.888005990975. The stop priority was set on daily collisions, with an adverse gap stop at open, target gap recognized, and partial/BE ambiguous days assigned adverse sequence. The test used continuous touch, which is optimistic versus 15-minute polling, even though the collision rule is conservative. The test did not exactly replicate the Nagel replication, which used 48 industry/individual stocks, long-short, 5 daily sleeves, conditional beta hedge, closing liquidity supplier, and VIX dependence. The ETF cash adaptation tests a different payoff. The premises of the test were: 1, recorded returns from a fixed rule; 2, base positive edge must survive costs and uncertainty; and 3, the whole family fails stress and CI. The conclusion is that this is research-only, with no allocation. The sample size was 222, with a p-value of 0.5495495495495496, a W of 1.4912659127355696%, an L of 1.8291627507384938%, and a mean of -0.004422674414909376%. The returns by year were: 2021, 6.896128739344354; 2022, -6.859962703773414; 2023, -11.855230711768904; 2024, 1.7638338800762154; and 2025, 6.350283913282184. The exit counts were: stop, 112; time, 42; breakeven, 35; target, 26; and target gap, 7. The trade sample counts were completed round trips, with partial legs aggregated. The return and drawdown are full research capital and daily close marks, not account performance or worst intraday DD. No fixed monthly platform charges were deducted, which would worsen the net economics. All historical windows were previously used by other fund studies, not pristine OOS, and no winner was chosen from a sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076, and local research/reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test used nine sector ETFs, ranking them by their adjusted close returns over the prior 10 sessions at the weekly boundary. It bought the worst performing ETF at the next open if its return was negative. The SPY subtraction did not change the cross-sectional ranks. The prior 20 session mean high-low range was used to set the initial stop, target, and other parameters. The test was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and retrospectively extended to 2026. Late boundary-crossing trades were omitted. The test used all 10 series with 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC. The full normalized capital was $10,000, with cash only, and fees of 1 basis point and slippage of 15 basis points each side, including partials. The custom event cash and every equity point were verified by vectorbt from orders, with an exact final value of $6230.33. The stop priority was on daily collisions, with adverse gap stop at open, target gap recognized, and partial or breakeven ambiguous days assigned adverse sequence. The test concluded that the premises were research-only and not for allocation, with a sample size of 222, a p-value of 0.5495, a win rate of 1.3058%, a loss rate of 2.0253%, and a mean return of -0.1947%. The years 2021 to 2025 had returns of -6.5788%, -11.4885%, -16.6819%, -9.8307%, and 0.2925%, respectively. The exit counts were 104 for stop, 45 for target, 35 for breakeven, 29 for time, and 9 for target gap. The return and drawdown were calculated using full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would have worsened the net economics. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049 to 16076, and local research using reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test used nine sector ETFs, ranking them by their adjusted close returns over the prior 10 sessions at the weekly boundary. It bought the worst performer at the next open if its return was negative. Subtracting SPY, which is a type of stock market index fund, did not change the rankings. The initial stop loss was set at 1 times the prior 20 session mean high-low range, the target at 1.5 times this range, and half of the position was closed at 1 times this range with the remaining position stopped at the next weekly open. The test was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and retrospectively extended to 2026. Late boundary-crossing trades were omitted. All 10 series had 2685 aligned sessions from January 2016 to September 8, 2026, with no missing values filled, adjusted open, high, low, and close prices, and the ETF set survived. The test started with a normalized capital of 10000, used cash only, and included fees of 1 basis point and slippage of 5 basis points on each side, including partials. A basis point is a unit of measurement for interest rates and fees. The test results were verified by vectorbt from orders, with an exact final value of 9712.657588258156. The test prioritized stops on daily collisions, used an adverse gap stop at open, and recognized target gaps. The test was not an exact replication of the Nagel study, which used 48 industry and individual stocks, a long-short strategy, 5 daily sleeves, a conditional beta hedge, and closing liquidity supplier, and was dependent on the VIX, a measure of stock market volatility. The ETF cash adaptation tested a different payoff. The premises of the test were: 1, recorded returns from a fixed rule; 2, the base positive edge must survive costs and uncertainty; and 3, the whole family fails stress and confidence interval tests. The conclusion is that this is research only and not for allocation. The sample size was 222, with a p-value of 0.5495495495495496, a win rate of 1.5086271781915401%, and a loss rate of 1.8291627507384902%. The mean return was 0.0051182011960310084%. The returns by year were: 2021, 1.607574849725868; 2022, -3.732368419421772; 2023, -8.835527034948976; 2024, -1.1415502499449115; and 2025, 10.177340209378416. The exit counts were: stop, 104; target, 45; breakeven, 35; time, 29; and target gap, 9. The return and drawdown are for the full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would have worsened the net economics. All historical windows were previously used by other fund studies, not pristine out-of-sample, and no winner was chosen from a sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076, and the local research file reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test used nine sector ETFs, which are a type of investment fund that tracks a specific sector, and ranked them based on their adjusted close returns over the prior five sessions at the weekly boundary. The worst performing ETF was bought at the next open if its own return was negative. The SPY, which is a ticker symbol for an ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The prior 20 mean high-low range, which is a measure of price volatility, was used to set the initial stop, target, and half off levels. The test was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC, which stands for open, high, low, and close prices. The full normalized capital was $10,000, with cash only and fees of 1 basis point, which is a unit of measurement for interest rates, and slippage of 15 basis points each side, including partials. The custom event cash and every equity point were verified by vectorbt from orders, with an exact final value of $6,939.16. The stop priority was set on daily collisions, with an adverse gap stop at open and target gap recognized. Partial and breakeven ambiguous days were assigned an adverse sequence. The continuous touch is optimistic versus 15-minute polling, even though the collision rule is conservative. The current-sized whole lots, half-rounding, and extended hour fills were not validated. This test is not an exact replication of the Nagel study, which used 48 industry and individual stocks, long-short, five daily sleeves, conditional beta hedge, closing liquidity supplier, and VIX dependence. The ETF cash adaptation tests a different payoff. The premises of this test are: recorded returns from a fixed rule, base positive edge must survive costs and uncertainty, and the whole family fails stress and confidence interval. The conclusion is that this is research-only and not for allocation. The sample size was 219, with a p-value of 0.547945205479452 and a mean return of -0.14778548536686914%. The years 2021 to 2025 had returns of -5.1554814620838885%, -4.253855505472382%, -5.28169377896509%, -12.922522067412046%, and -7.352503039406888%, respectively. The exit counts were 111 for stop, 43 for time, 38 for breakeven, 22 for target, and 5 for target gap. The trade sample counts were completed round trips, with partial legs aggregated. The return and drawdown are full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would worsen the net economics. All historical windows previously used by other fund studies were not pristine out-of-sample, and no winner was chosen from a sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076 and local research reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test involved nine sector ETFs, which are a type of investment fund that tracks a specific sector. The ETFs were ranked based on their adjusted close returns over the prior five sessions at the weekly boundary. The worst performing ETF was bought at the next open if its own return was negative. The SPY, which is a type of ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The prior 20 mean high-low range, also known as the average true range, was used to set the initial stop loss at 1R, the target at 2R, and the half off at 1R. The remaining stop entry was set at the maximum next weekly open. The test was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC, which stands for open, high, low, and close prices. The survived ETF set was used. The test started with a full normalized capital of 10,000 and used cash only, with fees of 1 basis point and slippage of 5 basis points each side, including partials. A basis point is a unit of measurement for interest rates and fees. The custom event cash and every equity point were verified by vectorbt from orders. The exact final result was 10,752.96. The stop priority was set on daily collisions, and adverse gap stop at open, target gap recognized, and partial/BE ambiguous days were assigned adverse sequence. The continuous touch is optimistic versus 15-minute polling, even though the collision rule is conservative. The current-sized whole lots, half-rounding, and extended hour fills were not validated. The test was not an exact Nagel replication, which is a type of investment strategy. The original strategy used 48 industry and individual stocks, long-short, 5 daily sleeves, conditional beta hedge, closing liquidity supplier, and VIX dependence. The ETF cash adaptation tests different payoff. The premises of the test were: 1. recorded returns from fixed rule, 2. base positive edge must survive costs and uncertainty, and 3. whole family fails stress and CI. The conclusion of the test was research-only and no allocation. The sample size was 219, with a p-value of 0.5525 and a W-value of 1.549%. The mean return was 0.0521%, and the years 2021 to 2025 had returns of 3.569%, 4.553%, 3.225%, -4.912%, and 1.170%, respectively. The exit counts were 111 for stop, 43 for time, 38 for breakeven, 22 for target, and 5 for target gap. The trade sample counts were completed round trips, and partial legs were aggregated. The return and drawdown were full research capital and daily close marks, not account performance or worst intraday DD. No fixed monthly platform charges were deducted, which would have worsened the net economics. All historical windows previously used by other fund studies were not pristine OOS, and no winner was chosen from sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049 to 16076, and local research/reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test involved nine sector ETFs, which are a type of investment fund that tracks a specific sector. The ETFs were ranked based on their adjusted close returns over the prior five sessions at the weekly boundary. The worst performing ETF was bought at the next open if its own return was negative. The SPY, which is a type of ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The prior 20 mean high-low range, which is a measure of price volatility, was used to set the initial stop, target, and half off levels. The test was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC, which stands for open, high, low, and close prices. The test survived the ETF set. The test started with a full normalized capital of $10,000, using cash only, with fees of 1 basis point and slippage of 15 basis points each side, including partials. A basis point is a unit of measurement for interest rates and fees. The custom event cash and every equity point were verified by vectorbt from orders. The exact final result was $7,258.72. The stop priority was set on daily collisions, with an adverse gap stop at open and target gap recognized. Partial and breakeven ambiguous days were assigned an adverse sequence. The test used continuous touch, which is a method of monitoring prices, and was optimistic versus 15-minute polling, even though the collision rule was conservative. The test did not validate current-sized whole lots, half-rounding, and extended hour fills. The test was not an exact replication of the Nagel study, which is a research paper on investment strategies. The premises of the test were: recorded returns from a fixed rule, base positive edge must survive costs and uncertainty, and the whole family fails stress and confidence interval. The conclusion of the test was research-only and no allocation. The sample size was 219, with a p-value of 0.547945205479452 and a mean return of -0.12724057828189275%. The returns for each year were: 2021, -9.575724080843905%; 2022, 1.4530726723529508%; 2023, -3.3536905856611954%; 2024, -13.306595788986852%; and 2025, -5.5637869206533015%. The exit counts were: stop, 104; target, 41; breakeven, 38; time, 29; and target gap, 7. The trade sample counts were completed round trips and partial legs aggregated. The return and drawdown were full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would have worsened the net economics. All historical windows previously used by other fund studies were used, not pristine out-of-sample data. No winner was chosen from a sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076 and local research using reversal.py.

A preregistered note, numbered 1253, was written before data was retrieved. The test involved nine sector ETFs, which are a type of investment fund that tracks a specific sector of the stock market. These ETFs were ranked based on their adjusted close returns over the prior three sessions at the weekly boundary. The worst performing ETF was bought at the next open if its own return was negative. The SPY, which is a type of ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The prior 20 mean high-low range was used to set the initial stop at -1R, target at +2R, and half off at +1R, with the remaining stop entry at the maximum next weekly open. The test was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC, which stands for open, high, low, and close, and survived ETF set. The test started with a full normalized capital of $10,000, using cash only, with fees of 1 basis point and slippage of 15 basis points each side, including partials. A basis point is a unit of measurement for interest rates and fees. The test used a custom event cash and every equity point verified by vectorbt from orders, with an exact final value of $5139.89. The test prioritized stops on daily collisions, with an adverse gap stop at open and target gap recognized, and partial or breakeven ambiguous days assigned adverse sequence. The test used continuous touch, which is a method of monitoring prices, and was optimistic versus 15-minute polling, even though the collision rule was conservative. The test did not validate current-sized whole lots, half-rounding, or extended hour fills. The test was not an exact replication of the Nagel method, which is a type of investment strategy, as it used ETF cash adaptation tests with different payoff. The premises of the test were: 1, recorded returns from a fixed rule; 2, base positive edge must survive costs and uncertainty; and 3, whole family fails stress and confidence interval. The conclusion of the test was research-only, with no allocation. The sample size was 225, with a p-value of 0.52 and a W-value of 1.350008917560452%. The mean return was -0.2750824997719447%, with years 2021, 2022, 2023, 2024, and 2025 having returns of -19.77584279508666%, -16.816823077388243%, 4.845641283599722%, -18.778632296479834%, and -9.553328538558715%, respectively. The exit counts were 123 for stop, 39 for time, 35 for breakeven, 24 for target, and 4 for target gap. The return and drawdown were based on full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would have worsened the net economics. All historical windows previously used by other fund studies were used, not pristine out-of-sample, and no winner was chosen from a sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076 and local research using reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test involved nine sector ETFs, which are a type of investment fund that tracks a specific sector, and ranked them based on their adjusted close returns over the prior three sessions at the weekly boundary. The worst performing ETF was bought at the next open if its own return was negative. The SPY, which is a type of ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The prior 20 mean high-low range, which is a measure of price volatility, was used to set the initial stop, target, and half off levels. The test was trained on data from 2016 to 2020 and tested on data from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC, which stands for open, high, low, and close prices. The test survived the ETF set and used full normalized capital of $10,000, with cash only and fees of 1 basis point and slippage of 5 basis points each side, including partials. A basis point is a unit of measurement for interest rates and fees. The custom event cash and every equity point were verified by vectorbt from orders, with an exact final value of $8,060.95. The stop priority was set on daily collisions, with adverse gap stop at open, target gap recognized, and partial/breakeven ambiguous days assigned adverse sequence. The test used continuous touch, which is a method of monitoring prices, and was optimistic versus 15-minute polling, even though the collision rule was conservative. The test was not an exact replication of the Nagel study, which is a research paper on investment strategies, as it used ETF cash adaptation tests with different payoff. The premises of the test were: 1, recorded returns from fixed rule; 2, base positive edge must survive costs and uncertainty; and 3, whole family fails stress and confidence interval. The conclusion of the test was research-only, with no allocation. The sample size was 225, with a p-value of 0.52 and a W-value of 1.55%. The mean return was -0.075%, with a standard deviation of 1.839%. The returns for each year were: 2021, -12.57%; 2022, -9.16%; 2023, 14.72%; 2024, -10.95%; and 2025, -0.64%. The exit counts were: stop, 123; time, 39; breakeven, 35; target, 24; and target gap, 4. The trade sample counts were completed round trips, with partial legs aggregated. The return and drawdown were full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would have worsened the net economics. All historical windows previously used by other fund studies were used, not pristine out-of-sample, and no winner was chosen from a sweep. The sources used were https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076 and local research/reversal.py.

A preregistered note, number 1253, was written before data was retrieved. The test used nine sector ETFs, which are a type of investment fund that tracks a specific sector, and ranked them based on their adjusted close returns over the prior three sessions at the weekly boundary. The test bought the worst performing ETF at the next open if its own return was negative. The SPY, which is a type of ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The prior 20 mean high-low range, which is a measure of volatility, was used to set the initial stop, target, and half off levels. The test used data from 2016 to 2020 for training and 2021 to 2025 for testing, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill and adjusted OHLC, which stands for open, high, low, and close prices. The test survived the ETF set and used full normalized capital of 10,000, with cash only and fees of 1 basis point, which is a unit of measurement for interest rates, and slippage of 15 basis points each side, including partials. The custom event cash and every equity point were verified by vectorbt from orders, with an exact final value of 5418.042992912734. The test used stop priority on daily collisions, adverse gap stop at open, target gap recognized, and partial/BE ambiguous days assigned adverse sequence. The test found that continuous touch is optimistic versus 15-minute polling, even though the collision rule is conservative. The test is not an exact Nagel replication, which refers to a specific type of investment strategy, as it uses ETF cash adaptation and tests a different payoff. The premises of the test are: 1, recorded returns from a fixed rule; 2, base positive edge must survive costs and uncertainty; and 3, the whole family fails stress and CI, which stands for confidence interval. The conclusion is research-only, with no allocation. The sample size is 225, with a p-value of 0.52 and a W-value of 1.3941282054574626%. The mean return is -0.25214047006550006%, with years 2021, 2022, 2023, 2024, and 2025 having returns of -21.661419014315452%, -14.01779110571465%, 6.137142136057849%, -18.073610246089235%, and -7.494630644957379%, respectively. The exit counts are 114 for stop, 39 for target, 35 for breakeven, 26 for time, and 11 for target gap. The trade sample counts completed round trips and partial legs aggregated. The return and drawdown are full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges were deducted, which would worsen the net economics. All historical windows previously used by other fund studies were used, not pristine out-of-sample, and no winner was chosen from a sweep. The sources used include https://www.nber.org/system/files/working_papers/w17653/w17653.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with observations 16049-16076 and local research/reversal.py.