A news scan found the headline 'QWIDE91002 monthly 3 2026 16bp/side'. The fund manager's notes are about a trading strategy. The strategy uses 50 predetermined cash ETF sleeves, which were set up in 2016 and tested from 2017 to 2019, and then not changed from 2020 to 2025, with 2026 being separate. The strategy looks at the return at the end of each month, subtracting the return of SHY, which is a type of low-risk investment. The strategy combines positive fractions and does diagnostics on its components without selecting any, and uses a passive full sleeve approach. The next session will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5 to 15 slippage, and final era close costed liquidation. The strategy also looks at vector bt position episodes and aggregate partial resizes, and notes that correlated positions are not independent and identically distributed, which is a statistical concept. Inactive cash is set to zero, and expenses are embedded in prices. This strategy is a long cash adaptation, not a risk-balanced short futures strategy like the one used by AQR, and does not require mandatory ADR exits. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 3, cost per side is 0.0016, era is 2026, return is 9.358813838283652, drawdown is -7.351399802803671, win rate is 61.53846153846154, sample is 78, average win percentage is 11.473882329858764, average loss percentage is 2.783318903385474, quarter Kelly diagnostic is 0.13052128350677739, annual return is 13.963397663276588, mean daily percentage is 0.05464328272525098, annual volatility percentage is 11.506014619237055, Sharpe ratio with zero cash is 1.1967747045741475, and open positions is 0.
The system has frozen the 1720, 1721, and 1724 models. It uses 50 predetermined cash ETF sleeves, with data from 2016 used for warmup, 2017-2019 for training, 2020-2025 for holdout, and 2026 separate. The system calculates month-end returns minus the SHY signs, mixes the positive fraction, and performs component diagnostics without selection, using a passive full sleeve approach. For the next session, it sets the open monthly target weight within independently compounding sleeves, with 1 basis point fees and 5/15 slippage, and final era close costed liquidation. The system aggregates vector bt position episodes and partial resizes, and notes that correlated positions are not independently and identically distributed, which means they are not random and unrelated to each other. Inactive cash is set to zero, with expenses embedded in prices. This is a long cash adaptation, not an AQR risk-balanced short futures strategy, and does not require mandatory ADR exits. The source data is from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 3, cost per side is 0.0016, era is holdout, return is 45.87299039167434, drawdown is -10.696500654864716, win rate is 45.233265720081135, sample is 493, average win percentage is 15.224192496731067, average loss percentage is 4.874967612560477, quarter Kelly diagnostic is 0.06924076413388247, annual return is 6.49957294042296, mean daily percentage is 0.0264673889020352, volatility annual percentage is 8.477084264754668, Sharpe ratio with zero cash is 0.7868014278263045, and open positions is 0.
The system is using a set of rules called Frozen1720/1721/1724. It has 50 pre-set groups of cash ETFs, which were tested from 2016 to 2019 and will be used from 2020 to 2025. The system looks at the returns from the 1st, 3rd, and 12th of each month, and compares them to the SHY sign, which is a type of investment. It mixes the positive parts of the investments and checks the components, but does not select any. The system uses a passive approach with all the groups. For the next session, it will set a target weight for each group, considering fees of 1 basis point and potential losses of 5 to 15 basis points. The system also looks at the size of the investments and adjusts them accordingly. It does not use a type of investment called AQR risk balanced short futures, and it does not have mandatory exits. The system gets data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 3, cost per side is 0.16%, era is train, return is 14.31%, drawdown is -11.21%, win rate is 55.06%, sample size is 247, average win percentage is 8.95%, average loss percentage is -4.49%, quarter Kelly diagnostic is 0.0813, annual return is 4.57%, mean daily percentage is 0.0186%, annual volatility percentage is 6.74%, Sharpe ratio with zero cash is 0.697, and there are 0 open positions.
A news scan found the headline 'QWIDE91002 monthly 3 2026 6bp/side'. This refers to a monthly report for QWIDE91002, with 3 as the signal and 6 basis points per side, for the year 2026. The report mentions Frozen1720/1721/1724, which are likely frozen cash positions. The report also mentions fifty predetermined cash ETF sleeves, with a warm-up period in 2016, a training period from 2017 to 2019, a holdout period from 2020 to 2025, and a separate period in 2026. The month-end return is calculated minus the SHY signs, which refers to the iShares 1-3 Year Credit Bond ETF, a type of bond fund. The report also mentions a mix of positive fractions, component diagnostics, and no selection, with a passive full sleeve approach. The next session will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5/15 slippage, and a final era close costed liquidation. The report also mentions vector bt position episodes, which refers to a type of trading strategy, and aggregate partial resizes, which refers to adjusting the size of trades. Correlated positions are not independent and identically distributed, which refers to a statistical concept. Inactive cash is zero, and expenses are embedded in prices. This is a long cash adaptation, not an AQR risk-balanced short futures strategy, which refers to a type of investment approach. There are no mandatory ADR exits, hence unapproved. The report cites two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are also provided, including a family of monthly, signal of 3, cost side of 0.0006, era of 2026, return of 9.695504014789558, drawdown of -7.337137905363456, win rate of 61.53846153846154, sample of 78, average win percentage of 11.697073453035019, average loss percentage of 2.5886707470497834, quarter Kelly diagnostic of 0.13256641667478453, annual return of 14.47637736288696, mean daily percentage of 0.05642596927191758, vol annual percentage of 11.493910788412876, Sharpe zero cash of 1.2371197687438023, and open positions of 0.
The QWIDE91002 monthly report for the 3 holdout period with 6 basis points per side is as follows. The system uses a method called backtesting, which tests the performance of a strategy using historical data, and has frozen the parameters for 1720, 1721, and 1724. The system was trained on data from 2017 to 2019 and is being tested on data from 2020 to 2025, with 2026 being a separate test period. The system uses 50 predetermined cash ETF sleeves, which are groups of investments, and started with a warm-up period in 2016. The system calculates the return on investment at the end of each month, subtracting the return of a benchmark called SHY, which is a type of low-risk investment. The system also calculates the mix of positive fractions, which refers to the proportion of successful investments, and performs diagnostics on the components, which are the individual parts of the system. The system does not select specific investments, but instead uses a passive full sleeve approach, which means it invests in all available options. The system will adjust its target weight, which is the desired level of investment, at the start of the next session, taking into account a 1 basis point fee and 5 to 15 basis points of slippage, which is the difference between the expected and actual price of an investment. The system will also liquidate, or sell, its holdings at the end of the period, taking into account the costs of doing so. The system uses a vector, which is a mathematical concept, to represent its positions, and aggregates partial resizes, which refers to the process of adjusting the size of its investments. The system assumes that its positions are correlated, meaning they tend to move together, and are not independent and identically distributed, which is a statistical concept. The system also assumes that inactive cash, which is cash that is not being used, has zero value, and that expenses are embedded in the prices of the investments. This system is a long cash adaptation, which means it is designed to perform well in a market with low interest rates, and is not a risk-balanced short futures strategy, which is a different type of investment approach. The system does not require mandatory ADR exits, which refers to the process of selling certain types of investments, and is not approved. The data for this report comes from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics for the system are as follows: family is monthly, signal is 3, cost per side is 0.0006, era is holdout, return is 48.64499307763008, drawdown is -10.364238382573133, win rate is 47.05882352941176, sample size is 493, average win percentage is 14.857720578411277, average loss percentage is 4.848150145599255, quarter Kelly diagnostic is 0.07445961781272206, annual return is 6.834461686275861, mean daily percentage is 0.02771569990188647, volatility annual percentage is 8.476160592243211, Sharpe ratio zero cash is 0.8240000055764617, and open positions is 0.
The QWIDE91002 monthly report for train 6 basis points per side is as follows: the system is using frozen models from 1720, 1721, and 1724. It has 50 predetermined cash ETF sleeves, which were warmed up in 2016, trained from 2017 to 2019, and held out from 2020 to 2025, with a separate period in 2026. The system calculates the return minus the SHY signs at the end of each month. It mixes the positive fraction of the return with three components, performs diagnostics, and does not select any particular component. The system uses a passive full sleeve approach. For the next session, the system will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5 to 15 basis points of slippage, and will cost the final era close and liquidation. The system also aggregates partial resizes of vector bt position episodes and notes that correlated positions do not have independent and identically distributed edges. Inactive cash is set to zero, and expenses are embedded in prices. This is a long cash adaptation, not an AQR risk-balanced short futures strategy, and does not have mandatory ADR exits, hence it is unapproved. The source data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 3, cost side is 0.0006, era is train, return is 15.425279913670842, drawdown is -11.010769148343936, win rate is 57.08502024291497, sample is 247, average win percentage is 8.848571177592667, average loss percentage is 4.509627254008149, quarter Kelly diagnostic is 0.08803407245709802, annual return is 4.915171280484776, mean daily percentage is 0.01992949691124022, volatility annual percentage is 6.736328987187456, Sharpe zero cash is 0.7455445289541022, and open positions is 0.
A news scan found the headline 'QWIDE91002 monthly 1 2026 16bp/side'. This refers to a monthly report for the QWIDE91002 fund, which is a type of automated trading fund, for the month of January 2026, with a 16 basis points per side, which means a 0.16% fee for buying or selling. The fund uses a strategy called 'Frozen1720/1721/1724', which involves 50 predetermined cash ETF sleeves, or groups of investments, that were set up in 2016 and tested from 2017 to 2019, and then held from 2020 to 2025, with 2026 being a separate period. The fund's performance is measured by its return minus the return of the SHY, which is a type of low-risk investment, at the end of each month, on the 1st, 3rd, and 12th days. The fund's components are diagnosed, but no selection is made, and the fund is passively managed, meaning it is not actively trying to beat the market. The fund's target weight is set at the start of each month, and it is rebalanced, or adjusted, to maintain its target weight, with a 1 basis point fee, which is 0.01%, and a 5 to 15 slippage, which refers to the difference between the expected and actual price of a trade. The fund also uses a vector, which is a mathematical concept, to manage its positions, and it aggregates, or combines, its positions to minimize risk. The fund has no inactive cash, and its expenses are embedded in its prices. This fund is a long cash adaptation, which means it is designed to perform well when the market is going up, and it is not a risk-balanced short futures fund, which would be designed to perform well when the market is going down. The fund does not have mandatory ADR, or American Depositary Receipt, exits, which means it does not have to sell its investments at a certain time. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics for the fund are: family: monthly, signal: 1, cost side: 0.0016, era: 2026, return: 4.462375800089524, drawdown: -7.594363348139376, win rate: 46.96969696969697, sample: 132, avg win pct: 6.3672792021447595, avg loss pct: 2.6192418394489425, quarter Kelly diagnostic: 0.06288792320298117, annual return: 6.586060873688537, mean daily pct: 0.02778053393079898, vol annual pct: 11.008935288039073, Sharpe zero cash: 0.6359102281369043, and open positions: 0.
The QWIDE91002 monthly report for the holdout period shows a 16 basis points per side cost. The system uses 50 predetermined cash ETF sleeves, with data from 2016 used for warmup, 2017-2019 for training, 2020-2025 for holdout, and 2026 for separate evaluation. The month-end return is calculated minus the SHY sign, with a mix of positive fraction and component diagnostics, but no selection. The system uses a passive full sleeve approach. For the next session, the open monthly target weight is set within independently compounding sleeves, with 1 basis point fees and 5/15 slippage, and final era close costed liquidation. The system also considers vector bt position episodes aggregate partial resizes, and notes that correlated positions are not independently and identically distributed, which means they are not random and unrelated. Inactive cash is set to zero, with expenses embedded in prices. This is a long cash adaptation, not a risk-balanced short futures strategy like AQR, and does not require mandatory ADR exits. The source data is from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 1, cost per side is 0.0016, era is holdout, return is 31.563513578995185, drawdown is -23.163194329116322, win rate is 44.713656387665196, sample is 908, average win percentage is 9.323533406376454, average loss percentage is 4.361832258759633, quarter Kelly diagnostic is 0.047122561896515786, annual return is 4.681397174618174, mean daily percentage is 0.019747360940023353, annual volatility percentage is 8.84778262407613, Sharpe ratio with zero cash is 0.5624386547816556, and open positions is 0.
A news scan found the headline 'QWIDE91002 monthly 1 train 16bp/side'. This refers to a trading strategy, where 16bp/side means 16 basis points, or 0.16%, per trade, as a fee. The strategy uses fifty predetermined cash ETF sleeves, which are groups of investments. The data is split into periods: 2016 for warmup, 2017-2019 for training, 2020-2025 for holdout, and 2026 separate. The strategy looks at month-end returns minus the return of SHY, a type of investment, and mixes the positive fraction of the returns. It does not select specific investments, but rather uses a passive full sleeve approach. The next session will open with a monthly target weight within independently compounding sleeves, with 1bp fees and 5/15 slippage, which refers to the difference between expected and actual trading costs. The strategy also considers vector bt position episodes, which are periods of buying or selling, and aggregate partial resizes, which means adjusting the size of the investments. Correlated positions, or investments that move together, are not independent and identically distributed, or IID, which means they do not have an edge, or advantage. Inactive cash has zero value, and expenses are embedded in prices. This strategy is a long cash adaptation, not a risk-balanced short futures strategy like AQR, and does not have mandatory ADR exits. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 1, cost side is 0.0016, era is train, return is 4.107417732996743, drawdown is -17.218225034732328, win rate is 46.724890829694324, sample is 458, avg win pct is 6.127245541864814, avg loss pct is 4.37269942890957, quarter kelly diagnostic is 0.021762976210857238, annual return is 1.3554802713248826, mean daily pct is 0.0062438994733878435, vol annual pct is 6.748017945911851, sharpe zero cash is 0.23317404901790267, and open positions is 0.
A news scan found the headline 'QWIDE91002 monthly 1 2026 6bp/side'. This refers to a monthly report for the QWIDE91002 fund, which is a type of automated trading fund. The report mentions that certain positions, referred to as Frozen1720/1721/1724, have been established. The fund uses a strategy involving fifty predetermined cash ETF sleeves, which were set up in 2016 and have been used for training and testing since 2017. The fund's performance is measured against the SHY index, and the results are used to adjust the fund's target weights for the next session. The fund charges a fee of 1 basis point and also accounts for slippage, which is a type of trading cost. The report also mentions that the fund uses a vector to manage its positions and that it does not use a risk-balanced approach like the one used by AQR. The fund's metrics are also provided, including its return, drawdown, and win rate. The source of the report is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is 1, cost per side is 0.0006, era is 2026, return is 5.012382429286144, drawdown is -7.574674181060015, win rate is 49.24242424242424, sample is 132, average win percentage is 6.280442394787109, average loss percentage is 2.536597203366168, quarter Kelly diagnostic is 0.07185508311792244, annual return is 7.406950751829333, mean daily percentage is 0.030829205775379467, annual volatility percentage is 10.996816714021016, Sharpe ratio with zero cash is 0.7064735238780646, and open positions is 0.
The system is holding a position in QWIDE91002. The system uses a set of 50 pre-determined cash ETF sleeves, which were established in 2016 and trained from 2017 to 2019, and then held out from 2020 to 2025, with a separate period in 2026. The system calculates returns at the end of each month, subtracting the return of the SHY ETF, and mixes the results with a positive fraction of the components, without selecting any specific ones, using a passive full sleeve approach. The system also considers fees of 1 basis point and slippage of 5 to 15 basis points, and liquidates positions at the end of each session, taking into account costs. The system uses vector-based position episodes and aggregate partial resizes, and assumes that correlated positions do not have independent and identically distributed edges. Inactive cash is set to zero, and expenses are embedded in prices. This system is a long cash adaptation, not a risk-balanced short futures strategy like the one used by AQR, and does not require mandatory ADR exits. The system's metrics are as follows: it has a signal of 1, a cost per side of 0.06%, and is in the holdout era, with a return of 36.36%, a drawdown of -22.15%, and a win rate of 46.59%. The system has an average win percentage of 9.16% and an average loss percentage of -4.32%, with a quarter Kelly diagnostic of 0.0535, an annual return of 5.31%, a mean daily percentage of 0.0221%, and a volatility of 8.86% per annum, with a Sharpe ratio of 0.6295, and currently has 0 open positions. The data sources for this system are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf.
A news scan found the headline 'QWIDE91002 monthly 1 train 6bp/side'. This refers to a monthly report for the QWIDE91002 fund, which is a type of automated trading fund. The report mentions that certain positions, referred to as Frozen1720/1721/1724, were part of a strategy that used fifty predetermined cash ETF sleeves. The strategy was tested, or trained, from 2016 to 2019 and then held out, or not used, from 2020 to 2025, with a separate test in 2026. The report also mentions that the fund's return was calculated at the end of each month, and that it was compared to the return of the SHY, which is a type of low-risk investment. The fund's strategy was to mix a positive fraction of its components, without selecting specific ones, and to use a passive full sleeve approach. The next session's target weight was determined within independently compounding sleeves, with a 1bp fee and 5/15 slippage, and the final era close was costed with liquidation. The report also mentions vector bt position episodes, which refers to a type of trading strategy, and aggregate partial resizes, which means adjusting the size of the positions. The fund's expenses were embedded in its prices, and it was a long cash adaptation strategy, not a risk-balanced short futures strategy like the one used by AQR. The report cites two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics for the fund are also provided, including its family, signal, cost side, era, return, drawdown, win rate, sample, average win percentage, average loss percentage, quarter Kelly diagnostic, annual return, mean daily percentage, volatility annual percentage, Sharpe ratio, and open positions.
The QWIDE91002 monthly mix for 2026 had a 16 basis points per side performance. This mix used a set of 50 predetermined cash ETF sleeves, which were tested from 2016 to 2019 and held from 2020 to 2025, with 2026 being a separate period. The return at the end of each month was calculated by subtracting the SHY return, and the mix had a positive fraction of return. The components were diagnosed, but no selection was made, and the full sleeve was used passively. For the next session, the monthly target weight was set within independently compounding sleeves, with 1 basis point fees and 5 to 15 slippage, and the final close was costed with liquidation. The vector bt position episodes were aggregated with partial resizes, and correlated positions were not independent and identically distributed, which means they were not random and unrelated to each other. Inactive cash had zero expenses, and prices had embedded expenses. This is a long cash adaptation, not a risk-balanced short futures strategy like AQR, and there were no mandatory ADR exits, so it was not approved. The source of this data is a stock bars feed and an AQR document. The full metrics are: family is monthly, signal is mix, cost per side is 0.0016, era is 2026, return is 8.888251079859177, drawdown is -7.631026162380139, win rate is 58.06451612903226, sample is 62, average win percentage is 8.82235778305431, average loss percentage is 3.082090991642895, quarter Kelly diagnostic is 0.10853588353020575, annual return is 13.247669551892827, mean daily percentage is 0.05232670403799543, annual volatility percentage is 11.918541793621799, Sharpe ratio with zero cash is 1.1063710348049043, and open positions is 0.
The QWIDE91002 monthly mix holdout had a return of 16 basis points per side. The strategy used 50 predetermined cash ETF sleeves, with data from 2016 used for warmup, 2017-2019 for training, 2020-2025 for holdout, and 2026 separate. The month-end return was calculated by subtracting the SHY return, and the mix had a positive fraction with no selection. The strategy was passive with a full sleeve. The next session will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5/15 slippage, and final era close costed liquidation. The vector bt position episodes will aggregate partial resizes, with correlated positions not being independently and identically distributed, which means they are not random and unrelated. Inactive cash was zero, with expenses embedded in prices. This is a long cash adaptation, not a risk-balanced short futures strategy like AQR, and does not require mandatory ADR exits. The source data is from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is mix, cost per side is 0.0016, era is holdout, return is 36.825524565378046, drawdown is -14.409872683671033, win rate is 38.41961852861036, sample is 367, average win percentage is 7.074519959787137, average loss percentage is 5.35669065474796, quarter Kelly diagnostic is 0, annual return is 5.368322138088377, mean daily percentage is 0.02212872625068005, volatility annual percentage is 8.198855186991967, Sharpe ratio with zero cash is 0.6801484948799642, and open positions is 0.
The QWIDE91002 monthly mix train has a return of 16 basis points per side. The model uses 50 predetermined cash ETF sleeves, with data from 2016 used for warmup, 2017-2019 for training, 2020-2025 for holdout, and 2026 for separate testing. The model's performance is measured by its month-end return minus the return of the SHY ETF. The mix has a positive fraction of 3 components, with diagnostics and no selection, and a passive full sleeve. The next session will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5/15 slippage, and final era close costed liquidation. The model also considers vector bt position episodes with aggregate partial resizes, and correlated positions that are not independently and identically distributed, which means they are not random and unrelated. Inactive cash has a value of zero, and expenses are embedded in prices. This is a long cash adaptation, not a risk-balanced short futures strategy like the one used by AQR. The model does not have mandatory ADR exits, and is therefore unapproved. The source data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is mix, cost per side is 0.0016, era is train, return is 10.349238469001598, drawdown is -13.355983762439184, win rate is 47.29064039408867, sample size is 203, average win percentage is 4.735582388712451, average loss percentage is 4.729103861076892, quarter Kelly diagnostic is 0, annual return is 3.348791921023997, mean daily percentage is 0.013932363225214033, annual volatility percentage is 6.6175608914479955, Sharpe ratio with zero cash is 0.5305512998439068, and open positions is 0.
The QWIDE91002 monthly mix for 2026 has a 6 basis points per side fee. The system uses 50 predetermined cash ETF sleeves, which were set up in 2016, trained from 2017 to 2019, and held from 2020 to 2025, with 2026 being a separate period. The system calculates the return at the end of each month, subtracting the return of the SHY fund, and adjusts the mix to have a positive fraction of the total, with no selection, using a passive full sleeve approach. The next session will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5 to 15 basis points slippage, and final era close costed liquidation. The system also aggregates partial resizes of vector bt position episodes, and correlated positions are not independent and identically distributed, or IID, which means they are not random and uncorrelated. Inactive cash has zero value, and expenses are included in the prices. This is a long cash adaptation, not a risk-balanced short futures strategy like the one used by AQR, and it does not have mandatory ADR exits. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is mix, cost per side is 0.0006, era is 2026, return is 9.251310250662236, drawdown is -7.617578268557901, win rate is 59.67741935483871, sample is 62, average win percentage is 8.795296268931436, average loss percentage is 3.009210976777107, quarter Kelly diagnostic is 0.11470376300089588, annual return is 13.799758844375987, mean daily percentage is 0.054258591001023565, annual volatility percentage is 11.909198889710034, Sharpe ratio with zero cash is 1.1481179430189914, and open positions is 0.
The QWIDE91002 monthly mix holdout has a 6 basis points per side. This refers to a 0.06% fee per trade. The system uses 50 predetermined cash ETF sleeves, which are a type of investment fund, and was tested from 2016 to 2025. The system's performance is measured by its return minus the return of the SHY fund, which is a type of low-risk investment. The mix has a positive fraction of return, and its components are diagnosed, but no selection is made. The system uses a passive full sleeve approach, meaning it invests in all available funds without trying to pick winners. The next session will open with a monthly target weight within independently compounding sleeves, with 1 basis point fees and 5 to 15 basis points slippage, which refers to the difference between the expected and actual price of a trade. The system also uses vector bt position episodes, which are a type of trading strategy, and aggregate partial resizes, which means it adjusts the size of its trades. The system does not use a risk-balanced approach, unlike AQR, and does not require mandatory exits. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics of the system are: family is monthly, signal is mix, cost per side is 0.06%, era is holdout, return is 39.61%, drawdown is -13.76%, win rate is 40.87%, sample size is 367, average win percentage is 6.85%, average loss percentage is 5.39%, quarter Kelly diagnostic is 0, annual return is 5.72%, mean daily percentage is 0.023%, volatility annual percentage is 8.2%, Sharpe ratio with zero cash is 0.72, and open positions is 0.
A news scan found the headline 'QWIDE91002 monthly mix train 6bp/side'. The fund manager's notes are about a trading strategy. The strategy uses fifty predetermined cash ETF sleeves, which were set up in 2016 and used for training from 2017 to 2019 and for holding from 2020 to 2025, with 2026 being separate. The strategy looks at the return at the end of each month, subtracting the return of the SHY, which is a type of bond fund, to determine the mix of investments. The strategy does not select specific investments, but instead uses a passive full sleeve approach. The next session will open with a monthly target weight within independently compounding sleeves, with fees of 1 basis point and slippage of 5 to 15 basis points. The strategy also looks at vector bt position episodes, which are periods of time when the strategy is invested in certain assets, and aggregates partial resizes, which are adjustments to the size of the investments. The strategy assumes that correlated positions, which are investments that tend to move together, do not have an independent and identically distributed edge, which is a statistical concept that refers to the idea that each investment has its own unique characteristics. The strategy has zero inactive cash and expenses are embedded in prices. This is a long cash adaptation strategy, not a risk-balanced short futures strategy like the one used by AQR, and it does not have mandatory ADR exits. The strategy uses data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf. The full metrics are: family is monthly, signal is mix, cost side is 0.0006, era is train, return is 11.494014788073436, drawdown is -12.901678278921679, win rate is 48.275862068965516, sample is 203, average win percentage is 4.840300699120874, average loss percentage is 4.627932660894346, quarter Kelly diagnostic is 0, annual return is 3.7061725998846384, mean daily percentage is 0.01530038404114599, volatility annual percentage is 6.6139347938572834, Sharpe zero cash is 0.5829656473102491, and open positions is 0.
A trading fund manager made notes about the AVAV stock. The notes are about an event where the stock price moved in a certain way, called an EAR greater than 0 event. This event was the same as the one in a daily test called test1851. The manager looked at the stock price at 9:30 am and used a stop loss, which is a price level where the manager sells the stock to limit losses, and a target price, which is a price level where the manager sells the stock to make a profit. The stop loss was set at 1 times the average daily range, and the target price was set at 1.5 times the average daily range. The manager also set a rule to sell half of the stock at a certain price level and to move the stop loss to the break-even point after selling half. The manager used real stock price data, including data from outside regular trading hours, but did not have complete data for the overnight period from 8:00 pm to 4:00 am. The manager paid fees of 1 basis point, which is a unit of measurement for interest rates and fees, plus a slippage of 15 basis points on each buy and sell transaction. The manager used a tool called Vectorbt to track the trades and reconciled the cash ledger after each event. The manager found 9 events from 2021 to 2025 and 1 event in 2026 that met the criteria, but did not select any specific parameters. The manager checked the stock price data and found that all the expected 15-minute time slots were present after a correction on July 3, 2023. However, the manager noted that the results were not precise enough and that the risk was higher than expected. The manager did not adopt this strategy for live trading and did not confuse the average trade with the monthly opportunity. The data came from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A trading note, labeled Frozen note1689, used the same entries as a daily test, labeled test1851, when the EAR, or earnings abnormality ratio, was greater than 0. The trading started at 09:30 with the SIP 15-minute open price. The prior daily stop was set at 1 times the average daily range, or ADR, and the target was set at 1.5 times the ADR, with half of the position sold at a profit of 1 ADR. The system checked the bar close and executed the next action at the next observed bar open, and it activated a breakeven stop after half of the position was sold. If both profit levels were surpassed during a check, the system exited the full position. The system used actual exchange data, including extended-hours bars, but did not have exact venue quotes between 20:00 and 04:00. The fees were 1 basis point, or bp, plus 15bp for slippage on each side of the trade. The system reconciled the vectorbt and cash ledger for each event and included flat gaps in the stitched event equity. There were 9 events from 2021 to 2025 and 1 event in 2026, with no parameter selection. All expected regular 15-minute time slots were present after a correction on July 3, 2023. However, the positive holdout was too imprecise, with a confidence interval, or CI, that included zero, and the marked risk exceeded the daily model risk. As a result, there was no live adoption from N9, and the average trade was not mistaken for a monthly opportunity. The data sources were https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A trading note, labeled as Frozen note1689, used the same entries as a daily test, labeled as test1851, when the EAR, or earnings after revenue, was greater than 0. The actual trading started at 09:30 with a 15-minute sampled exit of 6 basis points per side. The prior daily ADR, or average daily range, was used as a stop at 1 times the ADR and a target at 1.5 times the ADR, with half of the position sold at a profit of 1 ADR. The system checked the bar close and executed trades at the next observed bar open, and activated a breakeven stop after half of the position was sold. If both profit levels were surpassed at a sampled check, a full exit was executed. The system used extended-hours bars from the provider, but did not have exact venue quote sampling and overnight coverage from 20:00 to 04:00. Fees were 1 basis point plus slippage of 5 basis points on each side. The system reconciled the vectorbt and cash ledger per event and included flat gaps in the stitched event equity. There were 9 events from 2021 to 2025 and 1 event in 2026, with no parameter selection. All expected regular 15-minute slots were present after a correction on July 3, 2023, but this does not prove complete extended execution. The positive holdout was too imprecise, with a confidence interval that included zero, and the marked risk exceeded the daily model risk. There was no live adoption from N9, and no average trade arithmetic was mistaken for monthly opportunity. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A trading note, Frozen note1689, used the same entries as a daily test, test1851, when the EAR, or expected average return, was greater than 0. The trading started at 09:30 with the SIP 15-minute open, and had a stop at 1 times the average daily range and a target at 1.5 times the average daily range, with half of the position sold at 1 times the average daily range. The system checked the bar close and executed trades at the next observed bar open, and activated a breakeven stop after half of the position was sold. If both profit levels were surpassed during a check, the system would exit the trade. The system used real-time bars from the provider, including extended-hours bars, but did not have coverage from 20:00 to 04:00. The fees were 1 basis point plus 5 basis points for slippage on each side. The system reconciled the vectorbt and cash ledger for each event and included flat gaps in the stitched event equity. There were 9 events from 2021 to 2025 and 1 event in 2026, with no parameter selection. All expected regular 15-minute slots were present after a correction on July 3, 2023, but this does not guarantee complete extended execution. The positive holdout was too imprecise, with a confidence interval that included zero, and the marked risk exceeded the daily model risk. The system did not adopt this strategy for live trading, and did not mistake average trade arithmetic for monthly opportunity. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVAV&timeframe=15Min&feed=sip&adjustment=all and https://investor.avinc.com/financial-information/quarterly-results.
A news scan found the notes of a trading fund manager. The manager uses a system called QEAR91000 to decide when to buy or sell the stock AVAV. The system checks if the earnings of AVAV are visible to the company that issued the stock, and if the return on AVAV is more than 0.05% better than the return on the stock SPY over three sessions. If this condition is met, the system buys AVAV at the open of the next session. The system sells half of the stock when the price reaches 1 times the average daily range, and sells the rest when the price reaches the break-even point. The system uses a 5-session horizon and stops trading if the price goes below the 20-session mean high-low range. The system was trained on data from 2016 to 2020 and tested on data from 2021 to 2025. The system uses a long-only strategy, meaning it only buys stocks, and starts with $1000 in cash. The system also reconciles its cash ledger to ensure accuracy. The source of the data is https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes of a trading fund manager. The manager is holding out on a trade for AVAV, which is a stock ticker, because a measure called EAR, or earnings abnormality ratio, is greater than 0.05, and the risk is 6 basis points, or 0.06%, per side of the trade. The manager looks at the earnings dates for the company that issued the stock, and the return on AVAV compared to the SPY, which is a stock market index, over three and five sessions, or trading days. The manager will enter the trade on the second day after the earnings date, but only if EAR is greater than 0.05. The manager will sell half of the stock at a price that is one average daily range, or the average amount the stock price moves in a day, above the purchase price, and will stop the rest of the trade at a price that is 1.5 average daily ranges above the purchase price, or at a price that is the prior 20-session mean high-low, whichever comes first. The manager will also stop the trade if the stock price moves against the trade by the amount of the prior 20-session mean high-low. The manager tested this strategy from 2016 to 2020, and then from 2021 to 2025, and plans to extend the test in 2026. The manager pays fees of 1 basis point, or 0.01%, plus slippage, which is the difference between the expected price and the actual price, of 5 basis points, or 0.05%, on each side of the trade. The manager uses a program called Vectorbt to make long-only trades, which means buying stocks with the expectation of selling them at a higher price, with an initial cash amount of $1000, and allows for fractional shares, which are smaller than a full share. The manager reconciles the cash ledger, which is the record of the cash in the account, and the maximum error is 0. The manager measures drawdown, which is the amount of loss in the account, using the opening and closing prices of the stocks, not the full intraday risk, which is the risk of loss during the trading day. The manager notes that this strategy is not the same as the strategy described in a research paper, which used a cross-sectional size and value-adjusted long-short replication, which means buying and selling stocks based on their size and value. The manager also notes that the signal family, which is the group of trades, has only 9 test events, and the uncertainty of the estimates crosses zero, which means that the estimates are not certain, and the positive estimates are for research purposes only, and not approval. The manager observed full metrics, which are the complete set of data, 18738 times. The sources of the data are https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the notes of a trading fund manager. The manager uses a system called QEAR91000 to decide when to buy or sell AVAV stock. The system looks at the company's earnings dates and how the stock performs compared to the SPY stock index. The manager will buy AVAV stock on the second day after the company reports its earnings, but only if the earnings are more than 0.05% better than expected. The manager will sell the stock after 20 days, or if the stock price drops, but not before. The manager pays a fee of 1 basis point, which is a measure of the cost of trading, and also pays for any losses due to the delay between buying or selling a stock and the price actually changing, which is 15 basis points on each side of the trade. The manager uses a computer program called Vectorbt to make trades, starting with $1000 in cash, and can buy fractions of a share. The manager has tested this system using data from 2016 to 2020, and then from 2021 to 2025, and plans to continue testing it in 2026. The manager's system is not meant to exactly copy the performance of the SPY index, and the results are still being researched and are not yet approved. The manager got the data from the AVAV company website and another website called Runway.