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.
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 three sessions at the weekly boundary. It bought the worst performing ETF at the next open if its own 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-day mean high-low range, the target at 1.5 times this range, and half of the position was closed at 1 times this range. The test used data from 2016 to 2020 for training, 2021 to 2025 for testing, and 2026 for a retrospective extension. Late boundary-crossing trades were omitted. The test used all 10 series of 2685 aligned sessions from January 2016 to September 8, 2026, with no missing values and adjusted open, high, low, and close prices. 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. The final capital was 8497.19. The test used a custom event to verify cash and equity points, and stop priority was used on daily collisions. The test was not an exact replication of the Nagel study, which used 48 industry and individual stocks, a long-short strategy, and a conditional beta hedge. The test used ETFs instead of individual stocks and did not include a VIX dependence. The premises of the test were to record returns from a fixed rule, to ensure a positive edge survived costs and uncertainty, and to test the whole family of strategies under stress and confidence intervals. The conclusion of the test was for research purposes only and did not recommend allocation. The sample size was 225, with a p-value of 0.52 and a W-value of 1.60. The mean return was -0.0524%, and the years 2021 to 2025 had returns of -14.63%, -6.11%, 16.13%, -10.18%, and 1.62%, respectively. The exit counts were 114 for stop losses, 39 for targets, 35 for breakeven, 26 for time, and 11 for target gaps. 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 historical windows used were previously used by other fund studies and were not pristine out-of-sample. No winner was chosen from a sweep of parameters. The sources used were a working paper from the National Bureau of Economic Research and a stock bars feed from a website, as well as local research and observations 16049-16076, and a Python script called reversal.py.
A preregistered note, number 1253, was written before data was retrieved. The strategy involves nine sector ETFs, which are ranked based on their adjusted close returns over the prior five sessions at the weekly boundary. The worst performing ETF is bought at the next open if its return is negative. This is called a reversal strategy, meaning it goes against the recent trend. The SPY, a common stock market index, subtraction does not change the cross-sectional ranks, which means the comparison between the ETFs remains the same. The initial stop, target, and other parameters are set based on the prior 20 session mean high-low range, which is a measure of price volatility. The training period is from 2016 to 2020, the testing period is from 2021 to 2025, and there is a retrospective extension to 2026. Late boundary-crossing trades are omitted, which means trades that occur after the weekly boundary are not included. All ten series have 2685 aligned sessions from January 2016 to September 8, 2026, with no missing values, and the ETF set has survived. The normalized capital is $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 have been verified by vectorbt from orders, with an exact final value of $11,078.92. The stop priority is on daily collisions, with an adverse gap stop at open, target gap recognized, and partial or breakeven ambiguous days assigned adverse sequence. Continuous touch is optimistic versus 15-minute polling, even though the collision rule is conservative. The premises of the research are: recorded returns from a fixed rule, a 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, with no allocation. The sample size is 35, with a p-value of 0.6857, a win rate of 1.3326%, a loss rate of 1.9266%, and a mean return of 0.3083%. The years of return are listed, with 2026 having a return of 10.7892%. The exit counts are 14 for stop, 9 for breakeven, 8 for target, 3 for time, and 1 for target gap. The trade sample counts are completed round trips, with partial legs aggregated. The return and drawdown are for full research capital and daily close marks, not account performance or worst intraday drawdown. No fixed monthly platform charges are deducted, which would worsen the net economics. All historical windows previously used by other fund studies are not pristine out-of-sample, and no winner is chosen from a sweep. The sources of the data are listed, including a working paper from the National Bureau of Economic Research and a stock bars dataset. The observations are from 16049 to 16076, and the local research is from reversal.py.
A preregistered note, number 1253, was written before data was retrieved. The strategy involves nine sector ETFs, which are ranked based on their adjusted close returns over the prior five sessions at the weekly boundary. The worst performing ETF is bought at the next open if its return is negative, a process known as a reversal strategy, which is a trading approach that involves buying assets that have performed poorly and selling those that have performed well. The SPY subtraction, which refers to the removal of the impact of the SPY ETF, does not change the cross-sectional ranks, meaning it does not affect the relative performance of the ETFs. The parent exits at the next weekly open without protective exits, which are trades designed to limit losses, and is not eligible for venue, a term that refers to the location or platform where trades are executed. The data used for training the strategy spans from 2016 to 2020, and the testing period is from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades, which occur when a trade is executed after the designated boundary, are omitted. The strategy was tested on all 10 series, with 2685 aligned sessions from January 2016 to September 8, 2026, with no missing values filled, and using adjusted OHLC, or open, high, low, and close prices. The initial capital was $10,000, with cash only, and fees of 1 basis point, or 0.01%, and slippage of 5 basis points, or 0.05%, each side, including partials. The final value was $13,996.98, verified by vectorbt from orders. The strategy prioritizes stop-loss orders on daily collisions, which occur when multiple trades are executed at the same time, and uses an adverse gap stop at open, which is a stop-loss order that is triggered when the price gaps below a certain level. The target gap is recognized, and partial or breakeven ambiguous days are assigned an adverse sequence. The strategy uses continuous touch, which is an optimistic approach, versus 15-minute polling, which is a more conservative approach. The results show a sample size of 35, with a p-value of 0.6857142857142857, and a W-score of 2.19947912344991%, and an L-score of 1.6359066382048224%, and a mean return of 0.9940721697869942%. The year 2026 had a return of 39.96983495286044%. The exit counts were 35, and the trade sample counts were completed round trips, with partial legs aggregated. The return and drawdown 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 worsen the net economics. All historical windows previously used by other fund studies were used, and 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.
A preregistered note, numbered 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 five 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. Subtracting the return of the SPY ETF did not change the cross-sectional ranks. The prior 20-day mean high-low range was used to set the initial stop loss at 1R, the target at 1.5R, and the half off at 1R, with the remaining stop entry 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. The full normalized capital was $10,000, with cash only and fees of 1 basis point and slippage of 5 basis points on each side, including partials. Custom events, including cash and every equity point, were verified by vectorbt from orders, with an exact final value of $11,248.15. 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 optimistic versus 15-minute polling, even though the collision rule is conservative. The test did not exactly replicate the Nagel study, which used 48 industry and individual stocks, long-short, 5 daily sleeves, conditional beta hedge, closing liquidity supplier, and VIX dependence. The ETF cash adaptation tested 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 and not for allocation. The sample size was 219, with a p-value of 0.5525, a W-value of 1.5862%, and an L-value of 1.7960%. The mean return was 0.0727%. The returns by year were: 2021, -1.2578%; 2022, 10.7856%; 2023, 5.3258%; 2024, -5.3318%; and 2025, 3.1232%. The exit counts were: stop, 104; target, 41; breakeven, 38; time, 29; 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 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 using 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 type of ETF that tracks the S&P 500 stock market index, subtraction did not change the cross-sectional ranks. The parent exits at the next weekly open without protective exits, and is not eligible for a specific trading venue. The test was trained on data from 2016 to 2020, tested on data from 2021 to 2025, and retrospectively extended to 2026, with late boundary-crossing trades omitted. The test used all ten series and 2685 aligned sessions from January 2016 to September 8, 2026, with no missing value fill, adjusted OHLC, which stands for Open, High, Low, 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 5 basis points each side, including partials. A basis point is a unit of measurement for interest rates and fees. The test was verified by vectorbt from orders, with an exact final value of 10,550.62. The test had a stop priority on daily collisions, with an adverse gap stop at open, and a target gap recognized, with partial and break-even ambiguous days 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 exactly replicate the Nagel replication, which is a type of investment strategy, as it used ETF cash adaptation tests with different payoffs. The premises of the test were: recorded returns from a fixed rule, a 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, with no allocation. The sample size was 219, with a p-value of 0.5479, a W-value of 2.3086%, and an L-value of 2.6229%, with a mean of 0.0793%. The test had annual returns of 17.56% in 2021, -17.78% in 2022, 12.30% in 2023, 2.56% in 2024, and -5.22% in 2025. The test had 219 exit counts, with trade sample counts completed round trips and partial legs aggregated. 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, and not pristine out-of-sample, with no winner 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, numbered 1253, was written before data was retrieved. The strategy involves nine sector ETFs, ranking them by their adjusted close returns over the prior five sessions at the weekly boundary. The worst performer is bought at the next open if its return is negative. Subtracting SPY, which is a type of stock market index fund, does not change the rankings. The initial stop loss is set at one times the prior 20 session mean high-low range, the target is set at one and a half times this range, and half of the position is closed at one times this range. The remaining position is stopped out at the next weekly open. This strategy was tested on data from 2016 to 2020, and then applied to data from 2021 to 2025, with a retrospective extension to 2026. Late boundary-crossing trades were omitted. The test used all 10 series of 2685 aligned sessions from January 2016 to September 8, 2026, with no missing values filled, and adjusted open, high, low, and close prices. The test started with a normalized capital of 10000, using cash only, with 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 results were verified by vectorbt, a backtesting framework, and the final value was 9321.38. The strategy prioritizes stop losses on daily collisions, and uses an adverse gap stop at the open, with target gap recognition. The results show a sample size of 219, with a p-value of 0.5297, a win rate of 1.2919%, and a loss rate of 1.4892%, with a mean return of -0.0161%. The returns by year were -6.4886% in 2016, -11.2381% in 2017, -4.0142% in 2018, -0.5455% in 2019, and 17.6408% in 2020. The exit counts were 103 for stop losses, 38 for targets, 37 for time stops, 37 for breakeven stops, and 4 for target gaps. The return and drawdown are based on 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. The historical windows used were previously used by other fund studies, and not pristine out-of-sample. No winner was chosen from a sweep. The sources used include a research paper from the National Bureau of Economic Research and a stock bars dataset from a website. The local research script used was reversal.py, with observations from 16049 to 16076.
A preregistered note, number 1253, was written before data was retrieved. This note is about a trading strategy that uses nine sector ETFs. The strategy ranks these ETFs based on their adjusted close returns over the prior five sessions at the weekly boundary. It buys the worst performing ETF if its return is negative. The strategy does not use protective exits and is not eligible for certain trading venues. The strategy was tested on data from 2016 to 2020 and then from 2021 to 2025, with a retrospective extension to 2026. Late trades that crossed boundaries were omitted. The strategy used all ten series of data with 2685 aligned sessions from January 2016 to September 8, 2026, with no missing values. The strategy started with a normalized capital of $10,000, using cash only, with fees of 1 basis point and slippage of 5 basis points per side. A basis point is a unit of measurement, equal to 0.01%. The strategy's performance was verified by vectorbt from orders, with an exact final value of $9,339.54. The strategy used a stop priority on daily collisions, with an adverse gap stop at open and a target gap recognized. Partial and break-even days were assigned an adverse sequence. The strategy used continuous touch, which is a method of monitoring prices, and was optimistic versus 15-minute polling. The strategy was not an exact replication of the Nagel strategy, which used 48 industry and individual stocks, long and short positions, and a conditional beta hedge. The ETF cash adaptation tested a different payoff. The premises of the strategy were: one, recorded returns from a fixed rule; two, a base positive edge must survive costs and uncertainty; and three, 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 219, with a p-value of 0.54337899543379 and a mean return of 0.031509207882245496%. The returns for each year were: 2016, 7.380320007257146%; 2017, -5.6854951288370925%; 2018, 6.974500634784531%; 2019, 21.73115524058902%; and 2020, -29.182516886926045%. The exit counts were 219. The trade sample counts were completed round trips, with partial legs aggregated. The return and drawdown are for 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 were previously used by other fund studies, not pristine out-of-sample. 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.
The QSEC90808 fund uses a fixed strategy, focusing on the top 1 positive sector momentum, with 9 ETFs. The strategy was trained on data from 2017 to 2020, held out from 2021 to 2025, and recently applied from 2026 through September 4. It manages risk with a 1 ATR stop and a 1.5 ATR target, with half of the position plus 1 ATR, then a breakeven point. The daily stop is the first ambiguous path, with no overlap, and the next open is lagged for the signal. There are 5 basis point fees and 20 basis points of lippage per fill. An event ledger, signed independently, reconciles the vectorbt with a maximum USD error of 3.637978807091713e-12. Fees are included on every partial fill. The strategy parameters are: stop at 4, same bar breakeven at 1, gap stop at 1, target at 1, gap target at 1, and boundary at 1. The data used is from 2016, with 2684 bars per symbol, and includes all adjusted total return proxy, cash dividends, exact fees, 15-minute quotes, and whole shares, but is not validated under the Pattern Day Trader rule. The out-of-sample data is chronological, but not pristine, due to previous market window reuse. The parent cannot authorize an order without required exits. This rule was not active on September 8 for the monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with notes frozen on 11/10/11 and 11/14, and strategy notes from 11/21.
The QSEC90808 primary managed stress holdout uses a fixed strategy based on the top 1 positive sector momentum, which is calculated from 9 ETFs. The strategy was trained on data from 2017 to 2020, and its performance is being tested on data from 2021 to 2025, with the most recent data going up to September 4, 2026. The strategy uses a 1 ATR stop and a 1.5 ATR target, with a goal of beating the market, and it has a daily stop on the first ambiguous path. The strategy also includes a non-overlap rule and uses the next open as a lagged signal. There are fees of 5 basis points and lippage of 20 basis points per fill. The event ledger is independently reconciled and has a maximum USD error of 3.637978807091713e-12. All fees are included, even on partial fills. The strategy has specific rules, including a stop at 27, a target at 13, and a best effort rule at 3. The data used is from 2016, with a floor of 2684 bars per symbol, and includes cash dividends, exact fees, 15-minute quotes, and whole-share data, but has not been validated. The out-of-sample data is chronological but not pristine due to previous market window reuse. The parent strategy cannot authorize an order without required exits, and this rule is not active on September 8 for monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on November 11, 2021, with a strategy date of November 21. The notes were also previously frozen on October 11 and October 14.
The QSEC90808 primary managed stress train uses a fixed strategy with a top 1 positive sector momentum, which is based on the performance of nine exchange-traded funds, or ETFs, over a specific period of time, known as a momentum indicator. The train period is from 2017 to 2020, the holdout period is from 2021 to 2025, and the recent period is from 2026 through September 4. The strategy is managed with a 1 ATR stop, which is a volatility-based stop-loss, and a 1.5 ATR target, where ATR stands for Average True Range, a measure of market volatility. The strategy also includes a half plus 1 ATR then break even, or BE, rule, which means that if the strategy's performance reaches a certain level, it will aim to break even. The daily stop is based on the first ambiguous path, and there is no overlap, with the next open lagged signal. The strategy includes fees of 5 basis points, or 0.05%, and 20 basis points, or 0.2%, of lippage per fill, which is the difference between the expected price of a trade and the actual price. A signed event ledger independently reconciles the vectorbt, a backtesting library, with a maximum USD error of 3.637978807091713e-12. Fees are included on every partial fill. The strategy parameters are: target is 9, stop is 23, same bar break even is 7, gap target is 7, and break even is 2. The data used is from 2016, with a floor of 2684 bars per symbol, and includes all adjusted total return proxy, cash dividends, exact fees, 15-minute quotes, whole-share, and PDT, or pattern day trader, rules, which are unvalidated. The out-of-sample, or OOS, period is chronological, but not pristine, due to previous market window reuse. The parent strategy cannot authorize an order without required exits. This rule is not active on September 8 for the monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on 10/11 and 10/14, with a strategy date of 11/21.
The QSEC90808 primary managed base recently used a fixed strategy with a monthly top 1 positive sector momentum, which included nine exchange-traded funds, or ETFs. The strategy was trained on data from 2017 to 2020, held out from 2021 to 2025, and recently used data from 2026 through September 4. The strategy managed a 1 ATR stop and a 1.5 ATR target, with half of the position and then a break-even, or BE, point, using a daily stop on the first ambiguous path, and a non-overlap with the next open lagged signal. The strategy included 5 basis point fees and 5 basis point slippage per fill. A signed event ledger independently reconciles the vectorbt with a maximum USD error of 1.8189894035458565e-12. Fees were included on every partial fill. The strategy parameters were set as follows: stop at 4, same bar BE at 1, gap stop at 1, target at 1, gap target at 1, and boundary at 1. The data used was from 2016, with a floor of 2684 bars per symbol, and included all adjusted total return proxy, cash dividends, exact fees, 15-minute quotes, whole shares, and PDT unvalidated. The out-of-sample, or OOS, data is chronological but not pristine due to previous market window reuse. The parent cannot authorize an order without required exits. This rule was not active on September 8 for the monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with frozen notes from November 11, 2021, and strategy from November 21, 2021.
The QSEC90808 fund uses a fixed strategy based on the top 1 positive sector momentum, which is calculated monthly and uses 9 ETFs. The strategy was trained using data from 2017 to 2020, and its performance is being tested on data from 2021 to 2025, with the most recent data going up to September 4, 2026. The fund is managed with a stop-loss of 1 ATR, a target of 1.5 ATR, and a trailing stop. ATR stands for Average True Range, which is a measure of market volatility. The fund also uses a daily stop and a signal that is delayed by one day. There is a fee of 5 basis points, which is 0.05%, and a slippage of 5 basis points per fill. The fund's performance is tracked independently using a ledger that records all events, and the maximum error is 3.637978807091713e-12 USD. The fund's data goes back to 2016 and includes 2684 bars per symbol, with all adjustments made for total return, including cash dividends and exact fees. The data is based on 15-minute quotes and whole shares, but has not been validated. The out-of-sample performance is chronological, but not pristine, due to the reuse of previous market windows. The parent fund cannot authorize an order without the required exits. This rule is not active for the monthly entry on September 8. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on November 11, 2021, with a strategy update on November 21. The specific parameters for the strategy are: there are 27 stops, 13 targets, 7 same-bar break-evens, 5 gap targets, 4 gap stops, and 3 break-evens.
The QSEC90808 fund is a managed base train with a fixed strategy. It uses the top 1 positive sector momentum, which is based on the performance of 9 ETFs, and is trained on data from 2017 to 2020, with a holdout period from 2021 to 2025, and recent data from 2026 up to September 4. The fund has a managed stop loss of 1 ATR and a target of 1.5 ATR, with half of the position sized at 1 ATR and the remaining half at the break even point. The fund also uses a daily stop and a first ambiguous path, with no overlap and a next open lagged signal. There are fees of 5 basis points and slippage of 5 basis points per fill. The event ledger is independently reconciled and has a maximum USD error of 3.637978807091713e-12. Fees are included on every partial fill. The target, stop, same bar break even, gap target, and break even values are 9, 23, 7, 7, and 2 respectively. The data used is from 2016, with a floor of 2684 bars per symbol, and includes all adjusted total return proxy, cash dividends, exact fees, 15 minute quotes, whole share, and PDT unvalidated. The out of sample data is chronological but not pristine due to previous market window reuse. The parent cannot authorize an order without required exits. This rule is not active on September 8 for monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on 11/11 and 11/14, with a strategy date of 11/21. A news scan found the headline is not available, this is a raw script output.
The QSEC90808 fund uses a fixed strategy, focusing on the top 1 positive sector momentum, with 9 ETFs. The strategy was trained on data from 2017 to 2020, held out from 2021 to 2025, and recently applied from 2026 through September 4. It manages risk with a 1 ATR stop and a 1.5 ATR target, with half of the position plus 1 ATR, then a break-even point. The strategy uses daily stops for ambiguous paths and does not allow overlapping signals. There are no fees or slippage in this diagnostic test. An independent event ledger confirms the results, with a maximum error of 1.8189894035458565e-12 USD. Fees are included for every partial trade. The strategy parameters are: stop at 4, same bar break-even at 1, gap stop at 1, target at 1, gap target at 1, and boundary at 1. The data used is from 2016, with 2684 bars per symbol, and includes adjusted total return, cash dividends, exact fees, 15-minute quotes, and whole-share trades, but is not validated. The out-of-sample test is chronological, not pristine, due to previous market window reuse. The parent strategy cannot authorize an order without required exits. This rule was not active for the September 8 monthly entry. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, with notes frozen on 11/11 and 11/14, and strategy updated on 11/21.
The QSEC90808 primary managed fund has a zero holdout. It uses a fixed strategy, called L126/H20, which looks at the top 1 positive sector momentum every month, and invests in nine ETFs. The fund was trained using data from 2017 to 2020, and its performance is being tested using data from 2021 to 2025, with recent data going up to September 4, 2026. The fund uses a stop-loss, which is a strategy to limit losses, set at 1 ATR, and a target, which is a desired profit level, set at 1.5 ATR. If the fund's performance is not good, it will switch to a different strategy, called BE. The fund also uses a daily stop, which is a strategy to limit losses, and a signal, which is a trigger to buy or sell, that is based on the previous day's data. The fund assumes there are no fees or slippage, which is the difference between the expected and actual price of a trade. The fund's performance is tracked using a ledger, which is a record of all trades, and is reconciled, or checked, independently. The maximum error in the fund's tracking is 3.637978807091713e-12 USD. The fund includes fees on every partial trade. The fund's strategy is defined by the following parameters: stop at 27, target at 13, same bar BE at 7, gap target at 5, gap stop at 4, and BE at 3. The fund uses data from 2016, with 2684 bars per symbol, and includes cash dividends, exact fees, 15-minute quotes, and whole-share trades, but this data is not validated. The fund's out-of-sample, or OOS, performance is chronological, but not pristine, due to the reuse of previous market windows. The parent fund cannot authorize an order without required exits. This rule is not active on September 8 for the monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on November 11, 2021, with strategy notes from November 21.
The QSEC90808 primary managed zero train uses a fixed strategy with a monthly top 1 positive sector momentum, focusing on nine exchange-traded funds, or ETFs. The training period for this strategy was from 2017 to 2020, with a holdout period from 2021 to 2025, and recent data up to September 4, 2026. The strategy is managed with a 1 ATR stop and a 1.5 ATR target, with half of the position plus 1 ATR, then a break-even, or BE, point, and a daily stop at the first ambiguous path, with no overlap and the next open lagged signal. There are zero fees and slippage in this diagnostic. A signed event ledger independently reconciles the vectorbt with a maximum USD error of 0. Fees are included on every partial position. The strategy parameters are as follows: target is 9, stop is 23, same bar BE is 7, gap target is 7, and BE is 2. The data used starts from 2016, with 2684 bars per symbol, and includes all adjusted total return proxy, cash dividends, exact fees, 15-minute quotes, whole-share, and PDT, although it is unvalidated. The out-of-sample, or OOS, data is chronological but not pristine due to the reuse of previous market windows. The parent cannot authorize an order without the required exits. This rule was not active on September 8 for the monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with frozen notes on 11/11 and 11/14, and strategy on 11/21.
A news scan found the text: Fixed L126/H20 monthly top1positive sector momentum, nineETFs; train2017-20/holdout2021-25/recent2026throughSep4. This means that a computer program analyzed data from 2017 to 2020, used it to make predictions from 2021 to 2025, and then looked at what happened recently, up to September 4, 2026. The program only sells after 20 trading sessions, and it waits for the next day to buy again after selling. It also accounts for 5 basis point fees and 20 basis point lippage per fill, which are types of trading costs. A basis point is a unit of measurement, where 1% is equal to 100 basis points. The program's ledger, which is a record of all trades, was checked separately and found to have a very small error of 1.8189894035458565e-12 USD. The program includes fees for every partial trade. The settings for this program are time 7 and boundary 1. The data used goes back to 2016, with 2684 bars or pieces of data per symbol, and includes total return, which is the change in value of an investment, as well as cash dividends, exact fees, and 15-minute quotes. The data is not completely clean because it was used before in a different market window. The parent program cannot place an order without first selling what it already owns. This rule was not active on September 8 for the monthly entry. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were last updated on November 11, 2021, with frozen notes from October 11 and October 14.
The QSEC90808 primary parent stress holdout uses a fixed strategy with the top 1 positive sector momentum, which is based on 9 ETFs. The strategy was trained on data from 2017 to 2020, and the holdout period is from 2021 to 2025, with recent data going up to September 4, 2026. The parent time exit is only after 20 sessions, with no overlap, and the next open is lagged signal, meaning the signal to buy or sell is delayed until the next opening. There are fees of 5 basis points and 20 basis points of slippage per fill, which is the difference between the expected price and the actual price of a trade. The signed event ledger, which is a record of all trades, independently reconciles with vectorbt, a trading software, and has a maximum error of 1.8189894035458565e-12 USD. Fees are included on every partial trade. The time is 52, which refers to the number of weeks or sessions. The data used is from 2016, with 2684 bars or data points per symbol, and includes all adjusted total return proxy, which is a measure of an investment's return, cash dividends, exact fees, 15-minute quotes, and whole-share trades, but is not validated under the Pattern Day Trader rule. The out-of-sample, or OOS, data is chronological but not pristine due to previous market window reuse. The parent strategy cannot authorize an order without the required exits. This rule is not active on September 8 for the monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on November 11, 2021, with a strategy date of November 21. The notes were also previously frozen on November 11 and November 14.
The QSEC90808 strategy uses a fixed approach to identify the top-performing sector, known as momentum, which is a measure of the speed of price changes. It considers the top 1 positive sector out of 9 ETFs, or exchange-traded funds, which are a type of investment fund. The strategy was trained using data from 2017 to 2020, tested on data from 2021 to 2025, and recently updated with data from 2026 up to September 4. The parent strategy will only exit a trade after 20 sessions, and it does not allow overlapping trades, waiting for the next open to send a signal. The strategy also accounts for 5 basis point fees and 20 basis point lippage, or the difference between the expected and actual price of a trade, per fill. A signed event ledger, which is a record of all trades, independently reconciles the strategy's performance, with a maximum error of 1.8189894035458565e-12 in USD. Fees are included for every partial trade. The strategy has been running for 41 time periods. The data used starts from 2016, with 2684 bars, or periods, per symbol, and includes all adjusted total returns, cash dividends, exact fees, 15-minute quotes, and whole-share trades, but has not been validated. The out-of-sample, or OOS, data is chronological but not pristine due to previous market window reuse. The parent strategy cannot authorize an order without the required exits. This rule is not active for the monthly entry on September 8. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on 11/11 and 11/14, with the strategy updated on 11/21.
A news scan found the text: Fixed L126/H20 monthly top1positive sector momentum, nineETFs; train2017-20/holdout2021-25/recent2026throughSep4. This means the system uses a specific method, called momentum, to pick the best performing sector, and it uses nine exchange-traded funds, or ETFs, for this. The system was trained using data from 2017 to 2020, tested on data from 2021 to 2025, and recently used data up to September 4, 2026. The parent system will exit a position after only 20 sessions, and it does not overlap with the next signal, which is given after the market opens. The system also accounts for 5 basis points in fees and 5 basis points in slippage per fill, which are small costs associated with buying and selling. A basis point is a unit of measurement, equal to 0.01%. The system's event ledger, which is a record of all events, is independently reconciled, meaning it is checked separately, and it uses a tool called vectorbt, which has a very small error of 1.8189894035458565e-12 USD. Fees are included for every partial fill, which is when an order is partially executed. The system has a time boundary of 7 and a boundary of 1. The data used goes back to 2016, with 2684 bars per symbol, and it includes all adjusted total returns, cash dividends, exact fees, 15-minute quotes, and whole shares, but it has not been validated for Pattern Day Trader, or PDT, rules. The out-of-sample, or OOS, data is in chronological order, but it is not pristine because the system reuses previous market windows. The parent system cannot authorize an order without the required exits. This rule was not active for the monthly entry on September 8. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on November 11, 2021, for strategy 1121, and on November 14, for notes 1011.
The QSEC90808 primary parent base holdout uses a fixed monthly top 1 positive sector momentum strategy, which selects the top performing sector out of 9 ETFs, based on data from 2017 to 2020, and holds it from 2021 to 2025, with recent data up to September 4, 2026. The parent time exit is set to only exit after 20 sessions, with no overlap, and the next open is lagged by the signal. The strategy includes 5 basis point fees and 5 basis point slippage per fill. A signed event ledger independently reconciles the vectorbt max USD error, which is 1.8189894035458565e-12. Fees are included on every partial fill. The time is set to 52. The data used is from 2016, with 2684 bars per symbol, and includes all adjusted total return proxy, cash dividends, exact fees, 15-minute quotes, whole-share, and PDT unvalidated. The out-of-sample data is chronological, but not pristine, due to previous market window reuse. The parent cannot authorize an order without the required exits. This rule is not active for the September 8 monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the notes were frozen on 11/10/11 and 11/14, with a strategy date of 11/21.
The QSEC90808 primary parent base train uses a fixed strategy with the top 1 positive sector momentum, based on 9 ETFs, with training data from 2017 to 2020, holdout data from 2021 to 2025, and recent data from 2026 up to September 4. The parent time exit is only after 20 sessions, with no overlap, and the next open is lagged for signal. There are 5 basis point fees and 5 basis point slippage per fill. The signed event ledger independently reconciles the vectorbt max USD error of 3.637978807091713e-12. Fees are included on every partial fill. The time is 41. The data from 2016 has a floor of 2684 bars per symbol, with all adjusted total return proxy, including cash dividends, exact fees, 15-minute quotes, whole shares, and PDT, although it is unvalidated. The out-of-sample data is chronological, but not pristine, due to previous market window reuse. The parent cannot authorize an order without the required exits. This rule is not active for the September 8 monthly entry. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with frozen notes from November 11, 2021, and strategy from November 21, 2021.