A news scan found the following text: this is a test of a managed trend. The test started with eight fixed exchange-traded fund, or ETF, sleeves, each with an initial investment of 1250 USD. The cash from each sleeve was reinvested independently, and the initial weights of the sleeves were equal, but they were allowed to drift over time without being rebalanced every month. A position was entered at the next session's open if the prior month's return was positive, with no short selling or leverage used. The first stop loss was set at the prior 20-day mean high-low, with a target of 2 times the risk, or 2R. The next-day breakeven was set at the prior high plus 1R, with priority given to adverse stops and gaps at the open, and a conservative target fill. There was no reentry in the same month, and the position was liquidated at the end of the month. The test was trained on data from 2017 to 2020, with a chronological holdout from 2021 to 2025, and an extension from January to August 2026. The warm-up period was 2016, with no invented early history. The fees were 1 basis point, or bp, plus 15bp for slippage on each side of the trade. The results were as follows: in 2021, the return was -2.8106851822086165, in 2022 it was -0.3591077686596389, in 2023 it was -3.5023094311812386, in 2024 it was -3.8460042402528427, and in 2025 it was -1.9850996015152056. These results are simulated portfolio returns, not actual account returns, and only include completed trades, with no partial current month. The daily open, high, low, and close, or OHLC, and marks do not identify 15-minute venue exits, gap overshoot, or national best bid and offer, or NBBO. The fractional sizing and no fixed business costs are also not included. The ETF adjustment means that all returns are proxy data, not futures excess returns, inverse-vol scale, or original long-short paper replication. The era was reused in prior research and is not untouched prospective evidence. The conclusion is to reject venue-style transfer, and fixed variants are reported without selecting the best. The sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, as well as QTR9090200 research/trend.py and archived results.
A news scan found the headline 'QTR9090200 test managed trend 252 target2 cost6.000000000000001bp'. This refers to a test of a trading strategy. The strategy has the following rules: it starts with eight fixed exchange-traded fund, or ETF, investments, each worth 1250 USD, and it reinvests any cash earned. The strategy does not use short selling, which means selling a security that the seller does not own, or leverage, which means using borrowed money to invest. It uses a stop-loss order, which is an order to sell a security when it falls to a certain price, to limit losses. The stop-loss price is set at the average high and low prices of the last 20 days. The strategy also has a target price, which is twice the risk, or potential loss, of the trade. If the security's price reaches this target, the strategy sells it. The strategy does not buy back into a security in the same month it sold it, and it sells all its securities at the end of each month. The strategy was tested using data from 2017 to 2020, and the results were evaluated from 2021 to 2025. The test included a warm-up period in 2016, but it did not invent any missing data from that time. The strategy was charged a fee of 1 basis point, which is 0.01 percent, plus a slippage fee of 5 basis points on each trade. The results of the test are as follows: in 2021, the strategy lost 1.3727693505128036 percent; in 2022, it gained 0.5841447710436487 percent; in 2023, it lost 2.3961675408427086 percent; in 2024, it lost 2.027854114076788 percent; and in 2025, it gained 0.0722878235541291 percent. These results are simulated, meaning they are based on historical data and not actual trading. The test did not include any partial trades from the current month, and it did not account for certain costs and risks, such as the cost of buying or selling a security at a certain time of day. The data used in the test were adjusted to reflect the actual returns of the ETFs, rather than the returns of a related futures contract or a long-short strategy, which is a strategy that involves both buying and selling securities. The test was based on a previous study, and it did not use new, untouched data. The conclusion of the test was that the strategy should not be transferred to a real-world trading venue, and that the results should not be used to select the best variant of the strategy. The sources of the data used in the test are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the test was based on a Python script called trend.py and archived results.
A fund manager's notes were rewritten for clarity. The manager started with eight fixed exchange-traded fund, or ETF, investments, each worth 1250 USD, and let them grow independently. The manager would add money to each investment, but not move money between them. A session, or trading day, would start with a new investment if the last month's final session had a positive return. The manager would not sell short, or bet against, any investment, and would not use borrowed money to invest. The manager would stop an investment if it did a certain amount worse than its average over the last 20 days, with a target of making twice that amount. If an investment did well, the manager would sell it the next day if its price was at or above its previous high plus a certain amount. The manager would prioritize stopping an investment if its price changed a lot at the start of a session. The manager would not invest in the same thing more than once a month and would sell all investments at the end of each month. The manager tested this strategy using data from 2017 to 2020, and then used data from 2021 to 2025 to see how it would have worked. The manager also used data from 2016 to prepare, but did not invent any missing information. The manager paid a fee of 1 basis point, or 0.01 percent, plus an extra 15 basis points for each buy and sell. The results were tracked using a system called vectorbt, which matched the manager's orders with the cash ledger. The results for each year were: 2021 had a return of -2.417784266178713 percent, 2022 had a return of -1.0144705925223652 percent, 2023 had a return of -3.5597848445372393 percent, 2024 had a return of -4.3470581111143325 percent, and 2025 had a return of -2.7955311821025064 percent. These results are simulated, not actual, and only include completed trades. The manager used daily high, low, open, and close prices, but did not account for 15-minute exits, gaps, or the best available price. The manager also did not account for business costs or fractional investment sizes. The ETF returns are proxy data, not actual returns from futures or other investments. The manager used existing research and did not try to select the best results. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the manager's code and results are archived in QTR9090200 research/trend.py.
A news scan found the headline about QTR9090200 test managed trend 126 target2 cost6.000000000000001bp. The test had eight fixed exchange-traded fund, or ETF, sleeves that started with 1250 USD each and reinvested cash independently, with equal initial weights that changed over time. The test bought when the previous month's last session close was positive, and sold when it stopped losing money or reached a target, with no short selling or borrowing money to invest, which is called leverage. The test stopped losing money when the price went below the average of the last 20 days, and made money when the price went above the previous high plus a target, with a priority to stop losing money if the price gapped down at the open. The test did not buy the same thing in the same month, and sold everything at the end of the month. The test used data from 2017 to 2020 to train, and from 2021 to 2025 to test, with an extension in 2026, and a warm-up in 2016, without inventing missing early history, which is called backfilling. The test paid fees of 1 basis point, or bp, plus slippage of 5 bp for each buy and sell, and used a long-only strategy, which means it only bought and never sold short. The results for the years 2021 to 2025 were -0.9425669160038841, -0.22491987580169903, -2.1567739866022007, -2.412266109670125, and -1.1343689395069356. These are simulated portfolio returns, not actual account returns, and only include completed trades, without partial current month trades. The test used daily high, low, open, and close, or OHLC, data and marks, but did not account for 15-minute venue exits, gap overshoot, or national best bid and offer, or NBBO, and used fractional sizing without fixed business costs. The ETF adjustment means all returns are proxy data, not futures excess returns, inverse-vol scale, or original long-short paper replication. The era was reused in prior research, and not untouched prospective evidence. The conclusion was to reject venue-style transfer, and fixed variants were reported without selecting the best. The source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, QTR9090200 research/trend.py, and archived results.
This is a test of a managed trend. The test started with eight fixed exchange-traded fund, or ETF, sleeves, each with an initial investment of 1250 USD. The sleeves were managed independently, with cash reinvested and no monthly rebalancing across sleeves. The initial weights of the sleeves were equal, but they were allowed to drift over time. A new position was entered at the next session's open if the prior month's return was positive. The test did not use short selling or leverage. The test used a stop-loss, which is a rule to sell a security when it falls to a certain price, set at the prior 20-day mean high-low, and a target of 1.5 times the risk. If the prior high plus 1 times the risk was reached, the next day's entry was set to breakeven. The test prioritized stopping losses over taking gains if there was a large gap at the open. The target was filled conservatively. The test did not allow reentry in the same month, and all positions were liquidated at the end of the month. The test was trained on data from 2017 to 2020, and then tested on data from 2021 to 2025, with an extension from January to August 2026. The warm-up period was 2016, and no missing early history was invented. The fees were 1 basis point, or 0.01%, plus 15 basis points for slippage on each side of a trade. The test used a long-only strategy, meaning it only bought securities, and did not sell short. The results of the test are as follows: in 2021, the return was -2.680782534324222%; in 2022, the return was -1.0804103576898982%; in 2023, the return was -3.404041102964517%; in 2024, the return was -4.454786416045097%; and in 2025, the return was -2.8754301338842936%. These results are simulated portfolio returns, not actual account returns. The test only included completed trades, and did not include partial current month trades. The daily open, high, low, and close prices, and marks, did not identify 15-minute venue exits, gap overshoot, or national best bid and offer, or NBBO. The test used fractional sizing, and did not include fixed business costs. The ETF adjustment means that all returns are proxy data, not futures excess returns, inverse-vol scale, or original long-short paper replication. The era used in this test was reused from prior research, and is not untouched prospective evidence. The conclusion of the test is to reject venue-style transfer, and fixed variants were reported without selecting the best. The sources used in this test include https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, as well as QTR9090200 research/trend.py and archived results.
A news scan found the headline about QTR9090200 test managed trend 126 target 1.5 cost 6.000000000000001 basis points. The test has the following rules: it starts with eight fixed exchange-traded fund sleeves, each with 1250 USD, and reinvests cash independently. The test does not use shorts or leverage. It uses a stop-loss, which is a rule to sell a security when it falls below a certain price, set at the prior 20-day mean high-low, and a target of 1.5 times the risk. The test also has a breakeven rule, which is to sell a security when it reaches the prior high plus 1 times the risk. The test was trained on data from 2017 to 2020, and tested on data from 2021 to 2025. The test has fees of 1 basis point plus slippage of 5 basis points for each buy and sell. The results of the test are: in 2021, the return was -1.2155288712066614, in 2022, the return was -0.29652594673461596, in 2023, the return was -2.0006126766594523, in 2024, the return was -2.5222562598620657, and in 2025, the return was -1.2271237568091564. These results are simulated portfolio returns, not actual account returns. The test uses a vectorbt long-only strategy, which is a type of trading strategy that only buys securities, and does not sell short. The test also uses a ledger, which is a record of all trades, to match the results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the code is in QTR9090200 research/trend.py and archived results.
The QTR9090200 managed trend has a target of 1.5 and a cost of 16 basis points. The strategy involves eight fixed exchange-traded fund sleeves, each starting with $1250, and independently reinvesting cash. The initial weights are equal, but they can drift over time without monthly rebalancing. A new position is entered at the next session's open if the prior 252-session close return is positive. The strategy does not use shorts or leverage. A stop-loss is set at 1.5 times the prior 20-day mean high-low range, and a breakeven stop is set at the prior high plus 1 times the range. If the market gaps against the position, the stop-loss takes priority. The target is filled conservatively, and there is no reentry in the same month. The position is liquidated at the end of the month. The strategy was trained on data from 2017 to 2020, and tested on data from 2021 to 2025, with an extension from January to August 2026. The fees are 1 basis point plus 15 basis points for slippage on each side. The results show a return of -4.191249393480845 in 2026. The simulated portfolio returns are based on completed trades only and do not include partial current month returns. The data used is proxy data and does not include futures excess returns, inverse-volatility scaling, or original long-short paper replication. The research is based on prior work and not on untouched prospective evidence. The conclusion is to reject venue-style transfer and report fixed variants without selecting the best. The sources used include https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, as well as the QTR9090200 research and archived results.
The QTR9090200 managed trend has a target of 1.5 and a cost of 6.000000000000001 basis points. This trend is based on eight fixed exchange-traded fund (ETF) sleeves, which are investment funds that trade on a stock exchange like stocks, with each sleeve starting at $1250. The cash from these sleeves is reinvested independently, and their initial weights are equal, but they can change over time. A new investment is made at the start of the next session if the previous month's return was positive. The investment strategy does not involve short selling, which means betting against a stock, or using leverage, which means using borrowed money to invest. The strategy includes a stop-loss order, which is an order to sell a stock when it falls to a certain price, set at 1.5 times the risk. If the stock price goes up, the next day's entry price will be the previous high plus 1 times the risk. The strategy prioritizes stopping losses over making gains if the stock price gaps down at the open. The target is to make a conservative gain. The investment is liquidated at the end of the month and cannot be re-entered in the same month. The training data for this strategy is from 2017 to 2020, and the testing data is from 2021 to 2025, with an extension from January to August 2026. The warm-up period is 2016, and no missing early history was invented. The fees are 1 basis point plus 5 basis points for each buy and sell, which is a type of trading cost. The results show a return of -2.7706881044148424% in 2026. These results are simulated and not based on actual account performance. The trades are based on completed months only and do not include partial months. The data used does not account for 15-minute venue exits, gap overshoot, or National Best Bid and Offer (NBBO) prices, which are the best prices at which to buy or sell a stock. The sizing of the investments is fractional, and there are no fixed business costs. The ETF adjustment means that all returns are proxy data, which is substitute data used to represent the actual data, and not actual futures excess returns, inverse-volatility scale, or original long-short paper replication. The era used in this research has been used before and is not new, untouched evidence. The conclusion is to reject transferring this strategy to a different venue, and the fixed variants are reported without selecting the best one. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the research is based on the QTR9090200 research/trend.py and archived results.
A news scan found the headline about QTR9090200 recent parent trend with a 252 target and 1.5 cost at 16 basis points. The details are as follows: the fund has eight fixed exchange-traded fund, or ETF, sleeves that start with 1250 USD each and reinvest cash independently, with equal initial weights that can drift over time without monthly rebalancing. The fund enters a position at the next session's open if the prior month's 252-session close return is positive, without using shorts or leverage, and holds it until the last session of the month without a managed stop or target. The fund does not reenter a position in the same month and liquidates at the end of the month. The training period is from 2017 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026, with a warmup period in 2016. The fees are 1 basis point plus 15 basis points for slippage on each side of a trade. The results show a return of 9.5015 percent in 2026, based on simulated portfolio returns and completed trades only. The data source includes https://runway.adityaperswal.workers.dev/data/stock-bars and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, with code and archived results in QTR9090200 research/trend.py.
A news scan found the headline about QTR9090200 recent parent trend with a 252 target and 1.5 cost of 6.000000000000001 basis points. The details are as follows: the system has eight fixed exchange-traded fund, or ETF, sleeves that start with 1250 USD each and reinvest cash independently, with equal initial weights that can drift over time without monthly rebalancing. The system enters a position at the next session's open if the prior month's 252-session close return is positive, without using shorts or leverage, and holds the position until the last session of the month without a managed stop or target, and does not reenter the same month. The system liquidates the position at the last session of the month. The system was trained from 2017 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026, with a warmup period in 2016 and no invented early history. The fees are 1 basis point plus slippage of 5 basis points for each side of the trade. The vectorbt system, which is a backtesting framework, matches the independent event cash ledger for long-only trades from orders. The results for 2026 show a return of 11.143377216690542. The simulated portfolio returns are based on completed trades only and do not include partial current month trades. The daily open, high, low, and close, or OHLC, and marks do not account for 15-minute venue exits, gap overshoot, or national best bid and offer, or NBBO, and the system uses fractional sizing and does not include fixed business costs. The ETF adjustment means all returns are proxy data and not futures excess returns, inverse-volatility scale, or original long-short paper replication. The era used in the research has been reused from prior research and is not new prospective evidence. The conclusion is that the parent system is diagnostic only and cannot transfer its edge to different exits. The source of the information is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, as well as the QTR9090200 research and trend python file and archived results.
This is a test of a managed trend strategy called QTR9090200. The strategy has the following rules: it starts with eight fixed exchange-traded fund, or ETF, sleeves, each with an initial investment of 1250 USD, and it reinvests any cash earned, but it does not rebalance the sleeves every month. The strategy only buys, or goes long, and does not sell short or use leverage, which means using borrowed money to invest. It enters the market at the start of a session if the return from the previous 252 sessions is positive. The strategy uses a stop-loss order, which is an order to sell when a stock or fund reaches a certain price, in this case, the average high and low prices from the previous 20 days, and it aims to make a return of 1.5 times the risk, or R. If the strategy makes a profit, it will close the position at the next session's open, and if it loses, it will stop the loss at the prior high price plus 1R. The strategy does not re-enter the market in the same month and liquidates its positions at the end of the month. The strategy was trained on data from 2017 to 2020, tested on data from 2021 to 2025, and extended to August 2026, with a warm-up period in 2016. The strategy charges a fee of 1 basis point, or 0.01 percent, plus a slippage fee of 15 basis points, or 0.15 percent, for each buy and sell order. The results of the strategy are as follows: in 2021, it lost 3.373370323894198 percent, in 2022, it lost 0.777860349589854 percent, in 2023, it lost 3.264245863495463 percent, in 2024, it lost 3.944768809858712 percent, and in 2025, it lost 2.5341718390499146 percent. These results are simulated, not actual, and are based on completed trades only. The strategy uses daily high, low, open, and close prices, but does not account for 15-minute venue exits, gap overshoot, or national best bid and offer, or NBBO, prices. The strategy also uses proxy data, not actual futures excess returns, inverse-volatility scale, or original long-short paper replication. The data used for this strategy comes from the following sources: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the code for the strategy is available at QTR9090200 research/trend.py and archived results.
A news scan found the headline about a test of a managed trend. The test, called QTR9090200, has a target of 1.5 and a cost of 6.000000000000001 basis points, which is a measure of the cost of buying or selling something. The test started with eight fixed exchange-traded fund, or ETF, sleeves, each with an initial value of 1250 USD. An ETF is a type of investment that is traded on a stock exchange. The test reinvested any cash that was made, but did not rebalance the sleeves every month. The test only bought, and did not sell short, which means selling something that the seller does not own. The test used a stop-loss, which is a rule to sell something when it loses a certain amount of value, and a target, which is a price at which to sell something. The test stopped buying if the price went below a certain level, and did not buy again in the same month. The test ended at the end of each month. The test was trained on data from 2017 to 2020, and tested on data from 2021 to 2025. The test had fees of 1 basis point, plus a slippage of 5 basis points, which is the difference between the expected price and the actual price. The results of the test were -1.9505770967010672 in 2021, 0.15055079111834235 in 2022, -2.1492579088145836 in 2023, -2.13749270260134 in 2024, and -0.5190949031960157 in 2025. These results are simulated, which means they are based on historical data, and not actual. The test used data from a website, and a research paper. The test concluded that it should not be transferred to a different venue, which is a place where something is bought or sold.
A news scan found the following text: QTR9090200 is a test of a parent trend with a target of 1.5 and a cost of 16 basis points. The test has two main parts. First, eight fixed exchange-traded fund sleeves were started with 1250 USD each, and cash was reinvested independently in each sleeve. The initial weights of the sleeves were equal, but they were allowed to drift over time without being rebalanced monthly. A new position was entered at the next session's open if the previous month's return was positive, with no short selling or leverage used. The parent trend was held until the last session of the month, with no stop or target, and was liquidated at the end of the month. Second, the test was trained on data from 2017 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. A warm-up period was used in 2016, with no invented early history. Fees were 1 basis point plus 15 basis points for slippage on each side of a trade. The results of the test, which are simulated portfolio returns and not actual account returns, were as follows: in 2021, the return was -2.0080833581230917 percent, in 2022 it was -2.270405986327506 percent, in 2023 it was 0.41805183322796147 percent, in 2024 it was 2.3875767421257965 percent, and in 2025 it was 11.776261979038939 percent. The test used daily open, high, low, and close prices, and did not account for 15-minute venue exits, gap overshoot, or national best bid and offer prices. The test also used fractional sizing and did not include fixed business costs. The exchange-traded fund adjustment used in the test means that all returns are proxy data, and not excess returns from futures, inverse-volatility scale, or original long-short paper replication. The era used in the test was reused from prior research and is not untouched prospective evidence. The conclusion is that the parent trend is diagnostic only and cannot transfer its edge to different exits. The sources of the data used in the test are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the test was run using QTR9090200 research/trend.py and archived results.
This is a test of a trading strategy called QTR9090200. The strategy has the following rules: it starts with eight fixed exchange-traded fund, or ETF, investments, each worth 1250 USD, and it reinvests any cash it receives. The strategy does not use leverage, or borrowing money to invest, and it does not sell stocks short, or bet that their prices will fall. It buys stocks when the previous month's return is positive and holds them until the end of the month. The strategy was tested using data from 2017 to 2026, with some years used for training and others used to see how the strategy would perform in real life. The strategy charges a fee of 1 basis point, or 0.01 percent, plus an additional 5 basis points for buying and selling stocks. The results of the test are as follows: in 2021, the strategy lost 0.51 percent; in 2022, it lost 1.25 percent; in 2023, it gained 1.61 percent; in 2024, it gained 4.48 percent; and in 2025, it gained 14.30 percent. These results are based on simulated trades, not actual investments, and they do not include any partial months. The data used for the test came from several sources, including a website and a research paper. The strategy is only for testing and cannot be used to make actual investment decisions.
The QTR9090200 train managed a trend with a target of 1.5 and a cost of 16 basis points. The strategy started with eight fixed ETF sleeves, each with an initial investment of 1250 USD, and reinvested cash independently without monthly rebalancing. The strategy entered a position at the next session's open if the prior 252-session close return was positive, without using shorts or leverage. A stop-loss was set at 1.5 times the prior 20-day mean high-low range, with a target fill and no reentry in the same month. The strategy was trained from 2017 to 2020, with a holdout period from 2021 to 2025, and an extension from January to August 2026. Fees were 1 basis point plus 15 basis points for slippage on each side. The results for the years 2017, 2018, 2019, and 2020 were -2.8149%, -3.6408%, -1.5477%, and 0.5002%, respectively. These results are simulated portfolio returns, not actual account returns. A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, which are related to the QTR9090200 research and trend analysis.
The QTR9090200 train managed a trend with a target of 1.5 and a cost of 6.000000000000001 basis points. The strategy started with eight fixed exchange-traded fund sleeves, each with an initial investment of 1250 USD, and reinvested cash independently. The weights of the sleeves were equal at the start, but were allowed to drift over time without monthly rebalancing. A position was entered at the next session's open if the previous month's return was positive, with no short selling or leverage used. The strategy used a stop-loss at the prior 20-day mean high-low, with a target of 1.5 times the risk, and a breakeven point at the prior high plus 1 times the risk. The strategy also used an adverse stop priority and gap at open, with a conservative target fill. There was no re-entry in the same month, and the position was liquidated at the end of the month. The training period was from 2017 to 2020, with a chronological holdout period from 2021 to 2025, and an extension from January to August 2026. The warm-up period was in 2016, with no invented early history. The fees were 1 basis point plus 5 basis points for slippage on each side. The results were as follows: in 2017, the return was -1.4841720267347425, in 2018 it was -2.2140289894281184, in 2019 it was -0.0939076822628171, and in 2020 it was 2.293375215453519. These results are simulated portfolio returns, not actual account returns. The data used was from the sources https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, and the research was conducted using the QTR9090200 research/trend.py and archived results.
A news scan found the headline about QTR9090200 train parent trend 252 target 1.5 cost 16bp. The details are as follows: the fund has eight fixed exchange-traded fund, or ETF, sleeves that start with 1250 USD each and independently reinvest cash, with equal initial weights that can drift over time without monthly rebalancing. The fund enters a position at the next session's open if the prior completed month's 252-session close return is positive, without using shorts or leverage. It holds the position through the last session of the month without a managed stop or target, and does not reenter the same month, with liquidation at the end of the month. The training period is from 2017 to 2020, with a chronological holdout from 2021 to 2025, and an extension from January to August 2026, with a warmup in 2016 and no invented early history. The fees are 1 basis point, or bp, plus 15 bp for slippage on each side. The results for the years 2017 to 2020 are 4.8517, -6.9643, 4.8927, and -1.2946, respectively, which are simulated portfolio returns and not actual account returns. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf, with code and archived results in QTR9090200 research/trend.py.
This is a description of a trading strategy. The strategy has eight parts, called ETF sleeves, which start with $1250 each and reinvest any cash they make. They do not rebalance monthly, meaning their weights can change over time. The strategy buys when the previous month's return is positive and sells at the end of the month. It does not use leverage or short selling. A trading period, or trend, is considered long-only, meaning it only buys, not sells. The strategy was trained on data from 2017 to 2020, tested on data from 2021 to 2025, and further tested from January to August 2026. It includes fees of 1 basis point plus slippage of 5 basis points for each buy and sell. The results for each year are as follows: 2017 had a return of 6.355536569709552%, 2018 had a return of -5.562579921964328%, 2019 had a return of 6.404240580754461%, and 2020 had a return of 0.42316346016491035%. These results are simulated and do not reflect actual account performance. The data used is proxy data and does not include some costs. The strategy is for diagnostic purposes only and its success cannot be transferred to different trading exits. The data sources include https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://w4.stern.nyu.edu/facdir/lpederse/papers/TimeSeriesMomentum.pdf.
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.