A news scan found the headline 'LINK 1Hour SMA20/100 diagnostic: invalid held-gap execution'. This refers to a problem with a trading strategy for the cryptocurrency LINK. The strategy uses two moving averages, SMA20 and SMA100, which are like trend lines that smooth out price changes over 20 and 100 hours, respectively. A frozen note from September 1, 2024, shows that the strategy was not working correctly. The strategy enters a trade when SMA20 is greater than SMA100 and exits when the opposite happens, but only for long positions, meaning it only buys, not sells. The development of this strategy used data from 2025 with different parameters, such as fast10, fast20, and fast30, and slips of 30 and 60 basis points, which are like small fees. The holdout period, which is like a test period, started on January 1, 2026, and ended on September 8, 2026, using only the fast20 parameter. The warm-up period began on September 1, 2024, and the HYPE period started on February 17, 2026, which means that 2025 data was not used for training. The strategy used a tool called Vectorbt to simulate fees of 25 basis points, which is like a small fee, and slips of 30 and 60 basis points. The results showed that the strategy did not work well, with a return of -47.11% and a drawdown of 64.14%, which means that the value of the investment decreased by 64.14% at some point. The win rate was only 16.67%, and the average win was 7.94%, while the average loss was 3.35%. The expectancy, which is like the average return, was -1.47%, and the quarter Kelly, which is a measure of risk, was 0. The stress test, which simulates more difficult market conditions, showed even worse results, with a return of -59.01% and a drawdown of 71.45%. The premises of the strategy, which are like the assumptions, include survivor selection, which means that only the best strategies are chosen, and the fact that 2026 data was already researched, so it is not a pristine, unseen validation. Every hourly candidate had a missing interval while invested, which means that there were gaps in the data, and the diagnostic returns are not accepted as execution outcomes. The conclusion is to reject this strategy for trading, even if the raw Kelly, which is a measure of risk, is positive, it is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The reproducible scripts are in the saved note.
A news scan found the headline about a trading diagnostic for LINK. The diagnostic was frozen before download in note 1292. Prior trades were completed when the 20-day Simple Moving Average (SMA) was greater than the 100-day SMA, with entry at the next open and inverse exit, and only long positions were taken. Development in 2025 used fast 10, 20, and 30, and slips of 30 and 60 basis points (a basis point is a unit of measurement, 0.01%). The holdout period was from January 1, 2026, to September 8, 2026, with only fast 20, and each period started flat. The warmup began on September 1, 2024, and HYPE began on February 17, 2026, so there was no training in 2025. The Vectorbt system used signal fees of 25 basis points in cash for each side, which is an approximation of credited-asset fees, with a baseline slip of 30 basis points and stress of 60 basis points. There was no stop, target, or profit-taking, and 15-minute execution was not simulated, so it is not ready for a trading venue. The baseline results were: for the symbol LINK, with a 1-day time frame, fast 20, and holdout window, the slip was 0.003, the return percentage was 13.5728, the drawdown percentage was 26.1368, there was 1 trade, the win rate percentage was 0, the average win percentage was null, the average loss percentage was 13.0719, the expectancy percentage was -13.0719, the quarter Kelly was 0, there was 1 open trade, 0 missing trades, and 0 held gap invalid trades, and 0 zero volume trades. The stress results were: for the symbol LINK, with a 1-day time frame, fast 20, and holdout window, the slip was 0.006, the return percentage was 12.5568, the drawdown percentage was 26.5787, there was 1 trade, the win rate percentage was 0, the average win percentage was null, the average loss percentage was 13.5935, the expectancy percentage was -13.5935, the quarter Kelly was 0, there was 1 open trade, 0 missing trades, and 0 held gap invalid trades, and 0 zero volume trades. The premises of the diagnostic include survivor selection in the current universe, and 2026 was already researched, so it is not a pristine unseen validation. There was only one closed losing trade and one open position, so positive marked returns do not estimate the average win or a tradeable probability. The conclusion is to reject LINK for trading, even with a positive raw Kelly, which is overridden to 0. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about HYPE 1Hour SMA20/100 diagnostic showing an invalid held-gap execution. The system was frozen before download in note1292. Prior completed SMA20>SMA100 enters at next adjacent open, and inverse exits, with long-only trades. Development in 2025 used fast 10/20/30 and slips 30/60bp, and holdout in 2026 from January 1 through September 8 used fast 20 only, with each period starting flat. The warmup began on September 1, 2024, and HYPE began on February 17, 2026, so there was no 2025 training. Vectorbt from signals fees 25bp cash each side, which is an approximation to credited-asset fees, with a baseline slip of 30bp and stress of 60bp. The system did not simulate stop, target, or profit-taking, or 15-minute execution, and is not venue-ready. The baseline results for HYPE were: symbol HYPE, time frame 1Hour, fast 20, window holdout, slip 0.003, return percentage 41.49934526162804, drawdown percentage 40.78782856459645, number of trades 30, win rate percentage 30, average win percentage 17.108214488776355, average loss percentage 4.607178679103651, expectancy percentage 1.9074392712603507, quarter Kelly 0.02787314936505654, open 0, missing 1, held gap invalid true, and zero volume 3805. The stress results for HYPE were: symbol HYPE, time frame 1Hour, fast 20, window holdout, slip 0.006, return percentage 18.189741262485676, drawdown percentage 44.901328382534146, number of trades 30, win rate percentage 30, average win percentage 16.40615873211006, average loss percentage 5.179329453420031, expectancy percentage 1.2963170022389978, quarter Kelly 0.019753511827571346, open 0, missing 1, held gap invalid true, and zero volume 3805. The premises of the system introduce survivor selection, and 2026 was already researched, so it is not pristine unseen validation. Every hourly candidate has a missing interval while invested, so diagnostic returns are not accepted execution outcomes, and missing bars are not filled or counted as qualified tests. The absence of development independently forces allocation 0, and the conclusion is to reject HYPE for trading, even with a positive raw Kelly, which is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about HYPE 1Day SMA20/100 diagnostic. The system was frozen before download in note1292. The system enters a trade when the 20-day Simple Moving Average (SMA) is greater than the 100-day SMA, and exits when the inverse happens, only for long positions. The system was developed with fast 10, 20, and 30 settings and slips of 30 and 60 basis points from 2025, and tested from January 1, 2026, to September 8, 2026, with only fast 20 settings. The warm-up period started on September 1, 2024, and the HYPE system started on February 17, 2026, so there was no training in 2025. The system uses vectorbt with signal fees of 25 basis points in cash for each side, which is an approximation of credited-asset fees, and a baseline slip of 30 basis points and stress of 60 basis points. The system does not simulate stop-loss, target, or profit-taking, and does not account for 15-minute execution, and is not ready for a trading venue. The baseline results for HYPE are: symbol HYPE, time frame 1Day, fast 20, window holdout, slip 0.003, return percentage -0.7108071354784329, drawdown percentage 29.95287976045705, number of trades 1, win rate percentage 0, average win percentage null, average loss percentage 7.519826451968189, expectancy percentage -7.519826451968189, quarter Kelly 0, open 1, missing 0, held gap invalid false, and zero volume 16. The stress results for HYPE are: symbol HYPE, time frame 1Day, fast 20, window holdout, slip 0.006, return percentage -1.5990908602616742, drawdown percentage 29.952879760457062, number of trades 1, win rate percentage 0, average win percentage null, average loss percentage 8.074553297802124, expectancy percentage -8.074553297802124, quarter Kelly 0, open 1, missing 0, held gap invalid false, and zero volume 16. The premises of the system introduce survivor selection, and since 2026 has already been researched, it is not a pristine unseen validation. There is only one closed losing trade and one open position, so positive marked returns do not estimate the average win or a tradeable probability. The absence of development independently forces an allocation of 0. The conclusion is to reject HYPE for trading, even if the raw Kelly is positive, it is overridden to 0. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about an automated trading fund manager's notes. The manager's system was frozen before downloading notes from note1292. The system uses a Simple Moving Average, or SMA, which is a way to smooth out price changes over time. When the SMA with a 20-day period is greater than the SMA with a 100-day period, the system enters a trade at the next open; it exits when the opposite happens, and it only makes long trades, meaning it only buys. The system was developed with data from 2025 using different fast periods, such as 10, 20, and 30 days, and it accounted for slips, or small price changes, of 30 and 60 basis points, which are units of measurement for small changes in price or interest rates. The system was tested from January 1, 2026, to September 8, 2026, using only a fast period of 20 days, and each test started from a flat position, meaning no trades were already open. The warm-up period began on September 1, 2024, and the HYPE period, which is a specific phase of the system, began on February 17, 2026, so there was no training in 2025. The system uses Vectorbt, a tool for backtesting trading strategies, and it accounts for fees of 25 basis points, or 0.25%, on each side of a trade, which is an approximation of the fees charged when using credited assets. The baseline test had a return of -42.75% and a drawdown, or the largest peak-to-trough decline, of 58.68% over 39 trades, with a win rate of 17.95% and an average win of 8.00%. The stress test, which simulates more difficult trading conditions, had a return of -54.70% and a drawdown of 66.51% over the same number of trades, with the same win rate and an average win of 7.35%. The premises of the test include survivor selection, which means that only the strongest trades are selected, and the fact that 2026 has already been researched, so it is not a pristine, unseen validation. Every hourly candidate had a missing interval while invested, which means that there were gaps in the data, and the diagnostic returns are not accepted as valid execution outcomes. The conclusion is to reject this strategy for trading, even if the raw Kelly criterion, a measure of trading performance, is positive, because it is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about an automated trading fund manager's notes. The notes were frozen before download in note 1292. The manager's system enters a trade when the 20-day Simple Moving Average (SMA) is greater than the 100-day SMA, and exits when the inverse happens, only for long positions. The system was developed in 2025 with fast parameters of 10, 20, and 30, and slip parameters of 30 and 60 basis points (a basis point is a unit of measurement, 0.01%). The system was tested from January 1, 2026, to September 8, 2026, with only the fast 20 parameter, starting each period from scratch. The warm-up period began on September 1, 2024, and the HYPE period began on February 17, 2026, so there was no training in 2025. The system uses Vectorbt to simulate fees of 25 basis points in cash for each trade, which is an approximation of credited-asset fees. The baseline simulation has a slip of 30 basis points and a stress simulation has a slip of 60 basis points. There is no stop-loss, target, or profit-taking, and execution is not simulated at 15-minute intervals, so the system is not ready for a trading venue. The results for the ETH symbol, with a 1-day time frame, are as follows: baseline return of 12.14% and drawdown of 16.30%, with 0% win rate and an average loss of 14.79%, and stress return of 11.13% and drawdown of 16.80%, with 0% win rate and an average loss of 15.30%. The premises of the system introduce survivor selection, and 2026 data was already researched, so it is not a pristine unseen validation. The conclusion is to reject the system for trading, even with a positive raw Kelly percentage, which is overridden to 0. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about a BTC 1Hour SMA20/100 diagnostic showing an invalid held-gap execution. The system was frozen before downloading notes from note1292. The system enters a trade when the SMA20 is greater than the SMA100 and exits when the inverse happens, only for long positions. The development period from 2025 used fast 10, 20, and 30 settings and slips of 30 and 60 basis points. The holdout period from 2026 January 1 to September 8 used only the fast 20 setting, with each period starting from a flat position. The warm-up period began on 2024 September 1, and the HYPE period began on 2026 February 17, so there was no training in 2025. The system used vectorbt with signal fees of 25 basis points and cash on each side, which is an approximation of credited-asset fees. The baseline had a slip of 30 basis points and stress of 60 basis points. The system did not simulate stop, target, or profit-taking, and it was not ready for venue execution. The baseline results for the symbol BTC, with a 1-hour time frame, fast setting of 20, and holdout window, showed a return percentage of -42.29664087605784, a drawdown percentage of 53.230068260401595, 40 trades, a win rate percentage of 25, an average win percentage of 3.903639126530306, an average loss percentage of 3.0110639872220326, an expectancy percentage of -1.2823882087839482, and a quarter Kelly of 0. The stress results showed a return percentage of -54.60915881186559, a drawdown percentage of 62.08919733778772, 40 trades, a win rate percentage of 20, an average win percentage of 4.165648673702446, an average loss percentage of 3.3844476879686507, an expectancy percentage of -1.874428415634431, and a quarter Kelly of 0. The premises of the system introduced survivor selection, and the 2026 period was already researched, so it was not a pristine unseen validation. Every hourly candidate had a missing interval while invested, so the diagnostic returns were not accepted as execution outcomes, and the missing bars were not filled or counted as qualified tests. The conclusion was to reject the system for trading, even with a positive raw Kelly, which was overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The reproducible scripts are in the saved note.
A news scan found the headline about a BTC 1Day SMA20/100 diagnostic. The diagnostic was frozen before download in note 1292. Prior completed SMA20, which is a 20-day Simple Moving Average, greater than SMA100, which is a 100-day Simple Moving Average, enters at the next adjacent open and exits inversely, with a long-only strategy. The development period from 2025 used fast 10, 20, and 30, and slips of 30 and 60 basis points, which is a unit of measurement for interest rates and investment returns. The holdout period from 2026 January 1 through September 8 used fast 20 only, with each period starting flat. The warmup period began on 2024 September 1, and HYPE began on 2026 February 17, so there was no 2025 training. The Vectorbt from signals fees were 25 basis points cash each side, which is an approximation to credited-asset fees, with a baseline slip of 30 basis points and stress of 60 basis points. The simulation did not include stop, target, or profit-taking, or 15-minute execution, and is not venue-ready. The baseline results for BTC, which is a cryptocurrency, showed a return percentage of -22.030385869408622, a drawdown percentage of 27.179148386270835, and a win rate percentage of 0. The stress results showed a return percentage of -22.727935494515712, a drawdown percentage of 27.61477445900433, and a win rate percentage of 0. The premises of the diagnostic introduce survivor selection, and 2026 has already been researched, so it is not a pristine unseen validation. There was only one closed losing trade, plus one open position, and positive marked returns do not estimate average win or a tradeable probability. The conclusion is to reject for trading, even if the raw Kelly, which is a measure of investment performance, is positive, it is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The reproducible scripts are in the saved note.
A computer program ran a test of a trading strategy on several cryptocurrency pairs, including BTC/USD, ETH/USD, SOL/USD, XRP/USD, LINK/USD, LTC/USD, UNI/USD, and HYPE/USD, from a starting point to September 9. The test used a simple moving average strategy, which is a way to make trading decisions based on the average price of a stock or currency over a certain period of time. The test looked at 366 days of data and did not account for any costs or fees associated with buying and selling. The results showed that none of the trades were successful, with return percentages ranging from -20.29% for BTC/USD to 58.00% for UNI/USD. The test also looked at the maximum drawdown, which is the largest loss from a peak to a trough, and the win rate, which is the percentage of successful trades. The results were: for BTC/USD, return percentage -20.29%, buy and hold percentage -29.25%, maximum drawdown percentage 26.37%, and win rate 0%; for ETH/USD, return percentage 14.76%, buy and hold percentage -42.06%, maximum drawdown percentage 15.36%, and win rate 0%; for SOL/USD, return percentage -5.47%, buy and hold percentage -52.22%, maximum drawdown percentage 15.85%, and win rate 0%; for XRP/USD, return percentage -11.24%, buy and hold percentage -51.94%, maximum drawdown percentage 16.02%, and win rate 0%; for LINK/USD, return percentage 15.38%, buy and hold percentage -45.77%, maximum drawdown percentage 25.29%, and win rate 0%; for LTC/USD, return percentage -4.67%, buy and hold percentage -51.56%, maximum drawdown percentage 21.30%, and win rate 0%; for UNI/USD, return percentage 58.00%, buy and hold percentage -28.77%, maximum drawdown percentage 27.80%, and win rate 0%; and for HYPE/USD, return percentage 1.45%, buy and hold percentage 190.08%, maximum drawdown percentage 29.95%, and win rate 0%. The test concluded that the strategy did not work well and should not be used for actual trading. A news scan found the source of the data at https://runway.adityaperswal.workers.dev/data/crypto-bars. The data is not claimed to be independent or net of costs.
A news scan found a note about 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. These sleeves were managed independently, with cash reinvested and equal initial weights that could drift over time without monthly rebalancing. A position was entered at the next session's open if the prior month's closing 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. If the prior high plus 1R was reached, the next day's entry was set at breakeven. An adverse stop priority or gap at open was also set, with a conservative target fill. No reentry was allowed in the same month, and the last session of each month was liquidated. 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 set at 1 basis point, or bp, plus slippage of 15bp on each side of a trade. The results were as follows: in 2021, the return was -3.1982894279861673, in 2022 it was -1.1115754020214585, in 2023 it was -2.4559342553208685, in 2024 it was -2.571763853282849, and in 2025 it was -1.0573886480381267. These results are simulated portfolio returns, not actual account returns, and only include completed trades. The data used does not account for 15-minute venue exits, gap overshoot, or National Best Bid and Offer, or NBBO, and does not include 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 used in this test was reused from prior research and is not untouched prospective evidence. The conclusion is to reject venue-style transfer, with fixed variants reported without selecting the best. The source of this information includes 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.
This is a description of a test for 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 these investments was reinvested independently, and the initial weights were equal, but they were allowed to drift over time without being rebalanced monthly. A new investment was made at the start of the next session if the return from the previous month was positive. The test did not use short selling or leverage. The first stop-loss was set at the average high and low prices of the previous 20 days, with a target of 2 times the risk. If the price reached a new high plus 1 times the risk, the next day's entry price was set to break even. If there was a large gap at the open, the stop-loss was prioritized. 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 the results from 2021 to 2025 were used as a holdout. The warm-up period was 2016, and no early history was invented. The fees were 1 basis point, or bp, plus 5 bp for slippage on each side of the trade. The results were calculated using a long-only strategy from orders that matched an independent event cash ledger. The results for each year were as follows: 2021 had a return of -1.4266879110777175, 2022 had a return of 0.03282691161201612, 2023 had a return of -1.5404086082613566, 2024 had a return of -0.8817272924663944, and 2025 had a return of 1.1059498552612235. 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, or OHLC, 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 meant that all returns were proxy data, not futures excess returns, inverse-vol scale, or original long-short paper replication. The era used in the test was reused from prior research and was not new, prospective evidence. The conclusion was to reject venue-style transfer, and fixed variants were reported without selecting the best. The sources for the test were 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.
This is a description of a test managed trend for QTR9090200. The test has the following parameters: a target of 1.5 and a cost of 16 basis points. Here are the details: 1. The test started with eight fixed exchange-traded fund sleeves, each with an initial investment of 1250 USD, 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 monthly rebalancing. A new position was entered at the next session's open if the previous month's close return was positive. The test did not use short selling or leverage. A stop-loss was set at the prior 20-day mean high-low, with a target of 1.5 times the risk. If the prior high plus 1 times the risk was reached, the next day's breakeven point was set. An adverse stop priority or gap at the open was also used, with a conservative target fill. The test did not allow re-entry in the same month, and the last session of each month was liquidated. 2. The test was trained on data 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 2016, with no invented early history. Fees were 1 basis point plus slippage of 15 basis points on each side. The vectorbt long-only strategy matched the independent event cash ledger from orders. 3. The results of the test are as follows: the returns for the years 2021, 2022, 2023, 2024, and 2025 were -3.2927516767092824, -0.9664546669718388, -2.139692711344532, -2.815434596853883, and -1.7076777646982344, respectively. These results are simulated portfolio returns, not actual account returns, and only completed trades are included. The test used daily open-high-low-close data and marks, but did not account for 15-minute venue exits, gap overshoot, or national best bid and offer prices. Fractional sizing was used, and there were no fixed business costs. The ETF adjustment means that all returns are proxy data, not futures excess returns, 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 to reject venue-style transfer, and fixed variants are reported without selecting the best. 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 test was run using QTR9090200 research/trend.py and archived results.
A news scan found the headline about QTR9090200 test managed trend 378 target 1.5 cost 6.000000000000001 basis points. The test has two main parts. First, eight fixed exchange-traded fund sleeves started with 1250 USD each and reinvested cash independently. They had equal initial weights, but these weights changed over time because there was no monthly rebalancing across sleeves. The test entered the market at the next session's open if the prior month's return was positive, without using shorts or leverage. It used a stop-loss at the prior 20-day mean high-low, with a target of 1.5 times the risk. If the price reached the prior high plus 1 times the risk, the next day's entry would be at breakeven. The test prioritized stopping losses over filling targets at the open and had a conservative target fill. It did not reenter the market in the same month and liquidated at the end of each month. Second, the test trained on data from 2017 to 2020, held out data from 2021 to 2025, and extended the test from January to August 2026. It used a warm-up period in 2016 and did not invent any missing early history. The fees were 1 basis point plus 5 basis points for slippage on each side of the trade. The results matched the cash ledger and were long-only, meaning the test only bought and never sold short. The results for the years 2021 to 2025 were -1.526994698235573, 0.17326654036462497, -1.2198998112890935, -1.131832654620768, and 0.4228545307626952, respectively. These results are simulated portfolio returns, not actual account returns, and only include completed trades. 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. It also did not include fixed business costs or fractional sizing. The returns are proxy data and not excess returns from futures or original long-short paper replication. The era used in the test was reused from prior research and not new evidence. The conclusion was to reject transferring the results to a different venue and to report fixed variants without selecting the best. The sources for the 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 and archived results.
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.