This is a frozen specification note. The provider made an adjustment to the System Information Protocol, which includes all NYSE sessions from September 8, 2016, to 2026, for each exchange-traded fund, but no data from 2010 to 2015 is provided. The training and testing only include entries and exits within a certain partition. The holdout period from 2019 to 2025 has 83 complete calendar windows and 332 correlated ETF trades, excluding trades that span boundaries. There are four independent cash sleeves, each initially worth 250 USD, with no leverage and no periodic rebalancing between sleeves, and idle cash is worth 0. The vectorbt system is used for explicit long-only orders, with a fee of 1 basis point plus 15 basis points for slippage per side. The cash return is reconciled with the trade profit and loss accounting, considering the entry fee denominator. The managed rule is based on the last session's open, with a stop at 1 average daily range and a target at 1.5 average daily ranges, lagged by 20 sessions, or the third session's close. The system manages daily high, low, open, and close prices, with stop-first ambiguity, adverse gap fills, and target gap capped targets. Daily bar touch fills are preliminary, with no claimed 15-minute execution or whole-lot replication. A bootstrap analysis is done with 10,000 complete calendar window means across four ETFs, not 332 independent trades. The confidence interval and per-symbol statistics are saved, matching the title. The verdict rejects live adoption when uncertainty, cost, or execution gates fail, and a positive point estimate alone is not enough for adoption. The sources are https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.
A frozen specification note1344 was made by the provider. The provider made an adjustment to the Systematic Investment Plan, which started on September 8, 2016, and will end on September 8, 2026. This adjustment is applied to every New York Stock Exchange session for each Exchange-Traded Fund, but no data from 2010 to 2015 is provided. The training and testing of the system only include entries and exits within a certain partition. A holdout period from 2019 to 2025 has 83 complete calendar windows and 332 correlated ETF trades, but trades that span across boundaries are excluded. The system starts with four independent cash sleeves, each with $250, and no leverage is used. There is no periodic rebalancing of cash across sleeves, and idle cash is set to 0. The system uses a vectorized backtesting framework, which explicitly allows only long positions, with a fee of 1 basis point plus a slippage of 5 basis points per side. The system's cash return is reconciled with the trade profit and loss accounting, taking into account the entry fee denominator. The managed rule is based on the last session's open price, with a stop loss set at 1 Average Daily Range and a target set at 1.5 Average Daily Range, lagged by 20 sessions. If this rule is not met, the system uses the third session's close price. The system manages daily high, low, open, and close prices, with a stop loss set first, and ambiguity is resolved by using the open price. Adverse gap fills are capped at the target price. The system does not claim to have 15-minute execution, nor does it replicate whole lots or current capital. A bootstrap analysis is performed with 10,000 complete calendar window means across four ETFs, not 332 independent trades. Confidence intervals and per-symbol statistics are saved, matching the title of the observation. The verdict rejects live adoption when uncertainty, cost, or execution gates fail, and a positive point estimate alone is not enough for adoption. The source of this information includes a publication from Purdue University and a dataset from Aditya Perswal.
A frozen specification note, number 1344, was made by a provider. The provider made an adjustment to the Systematic Investment Plan, which includes all sessions from September 8, 2016, to 2026, for each exchange-traded fund on the New York Stock Exchange. However, no data from 2010 to 2015 was provided. The training and testing of the system only included entries and exits within a certain partition. A holdout period from 2019 to 2025 had 83 complete calendar windows and 332 correlated trades, excluding trades that span boundaries. The system started with four independent cash sleeves, each with 250 US dollars, and no leverage was used. The cash was not rebalanced periodically, and idle cash was set to zero. The system used a vectorized backtesting library to make explicit long-only orders, with a fee of one basis point plus slippage of 15 basis points per side. A basis point is a unit of measurement, one hundredth of one percent. The system's cash return was reconciled with the trade profit and loss accounting, taking into account the entry fee. The rule for opening a trade was to open at the last session's open and close at the next month's third close. The system managed daily high, low, open, and close prices, with a stop-loss order to limit losses, and adverse gap fills were handled by opening at the next available price. A target gap was capped to limit potential losses. The system did not claim to execute trades at a specific 15-minute interval, nor did it replicate whole lots or current market capitalization. The system used a bootstrapping method to estimate the mean of 10,000 complete calendar windows across four exchange-traded funds, not 332 independent trades. Confidence intervals and per-symbol statistics were saved to match the observation title. The verdict rejected live adoption when uncertainty, cost, or execution gates failed. A positive point estimate alone was not enough for adoption. The sources of the information include a study on equity returns at the turn of the month from Purdue University and stock bar data from a website.
A frozen specification note1344 was made by the provider. The SIP adjustment was made for all sessions from September 8, 2016, to September 8, 2026, and includes every NYSE session for each ETF, but does not include data from 2010 to 2015. The training and testing only include entries and exits within a certain partition. The holdout period from 2019 to 2025 has 83 complete calendar windows and 332 correlated ETF trades, and trades that span boundaries are excluded. There are four independent cash sleeves, each initially worth 250 USD, with no leverage and no periodic rebalancing between sleeves, and idle cash is worth 0 USD. The vectorbt system uses explicit long-only orders, with a fee of 1 basis point plus slippage of 5 basis points per side. The raw cash return is reconciled with the VBT trade P&L, taking into account the entry fee denominator. The rule for opening a position is to open at the last session's open and close at the next month's third close. The system manages daily OHLC stops to avoid ambiguity, fills open positions on adverse gaps, and caps target gaps. Daily bar touch fills are preliminary, and there is no claimed 15-minute execution or whole-lot replication. A bootstrap of 10,000 complete calendar window means was run across four ETFs, not 332 independent trades. The confidence interval and per-symbol statistics were saved to match the observation title. The verdict rejects live adoption when uncertainty, cost, or execution gates fail, and a positive point estimate alone is not enough for adoption. The sources for this information are https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.
A frozen specification note1344 was made. The provider made a SIP adjustment from September 8, 2016, to 2026, which includes every NYSE session for each ETF, but does not include data from 2010 to 2015. The training and testing only include entries and exits within a certain partition. The holdout period from 2019 to 2025 has 83 complete calendar windows and 332 correlated ETF trades, and trades that span boundaries are excluded. There are four independent cash sleeves, each initially worth 250 USD, with no leverage and no periodic rebalancing between sleeves, and idle cash is worth 0. The vectorbt system uses explicit long-only orders, with a fee of 1 basis point plus 15 basis points for slippage per side. The raw cash return is reconciled with the VBT trade P&L accounting for the entry fee denominator. The rule for the parent is to use the close of the second-to-last session to predict the close of the third session of the next month. The system manages daily OHLC stops to avoid ambiguity, fills gaps at the open, and caps target gaps. Daily bar touches are used for preliminary fills, but there is no claim of 15-minute execution or whole-lot replication. A bootstrap of 10,000 complete calendar window means was done across four ETFs, not 332 independent trades. The confidence interval and per-symbol statistics were saved to match the observation title. The verdict rejects live adoption when uncertainty, cost, or execution gates fail, and a positive point estimate alone is not enough for adoption. The sources used are https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars.
This is a frozen specification note. The provider made an adjustment to the System Information Protocol, which includes all sessions from September 8, 2016, to 2026, for every New York Stock Exchange session, for each exchange-traded fund, but does not include data from 2010 to 2015. The training and testing only include entries and exits within a certain partition. The holdout period from 2019 to 2025 has 83 complete calendar windows and 332 correlated trades, excluding trades that span boundaries. There are four independent cash sleeves, each initially worth 250 US dollars, with no leverage and no periodic rebalancing between sleeves, and idle cash is worth 0 dollars. The vectorbt system uses explicit long-only orders, with a fee of 1 basis point plus slippage of 5 basis points per side. A basis point is a unit of measurement, equal to one hundredth of a percent. The system reconciles raw cash returns with trade profit and loss accounting, considering the entry fee. The parent rule is based on the close of the second-to-last session and the close of the third session of the next month. The system manages daily high, low, open, and close prices, with stop-loss orders that are executed first in case of ambiguity, and adverse gap fills are capped. A stop-loss order is an order to sell a security when it falls to a certain price. The system does not claim to execute trades at a specific 15-minute interval, nor does it replicate whole lots or current market capitalization. The system uses a method called bootstrapping, which involves resampling with replacement, to estimate the mean of 10,000 complete calendar windows across four exchange-traded funds, not 332 independent trades. The system saves confidence intervals and per-symbol statistics, matching the title of the observation. The verdict rejects live adoption when uncertainty, cost, or execution gates fail, meaning that a positive point estimate alone is not enough for adoption. The source of this information includes a research paper from Purdue University and a dataset from a website.
The QTR9090400 primary research premise audit started with $245.69 USD in quant cash and an account balance of $850.99, with no holds supplied. The goal was to achieve an independently positive net short-horizon effect. A binding constraint was the modern executable edge, which did not rely on name coverage. The first premise, based on McConnell and Xu's 2008 study, found a 0.15% daily VW return from 1926 to 2005, specifically on the last and first three days of the period. However, their aggregate fund flow and volume tests did not support a simple payday mechanism. The second premise, from a 2019 manuscript by Etula, Rinne, Suominen, and Vaittinen, linked institutional trades to settlement timing, but the T+3 sample did not automatically predict the T+1 era. The third premise, based on Kayacetin's 2026 abstract, studied the last four and first four days across 30 countries from 1994 to 2023, but the window definition differed, making it impossible to confirm an exact four-day edge. The fourth premise noted that the SEC's transition on May 28, 2024, changed settlement timing, and the observed calendar premium could persist or move, requiring testing rather than assumption. The valid conclusion is to test the fixed old rule and separate eras, as inference from paper returns directly to proposals is invalid. The soundness of the study is limited by the provider's history, which began in 2016, and the selection of broad ETF proxies rather than all countries, as well as unvalidated fills. No threshold was chosen from returns. Relevant URLs include https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf, https://www.aalto.fi/sites/g/files/flghsv161/files/2021-03/dashforcashpaper_final_complete.pdf, https://papers.ssrn.com/sol3/Delivery.cfm/7201062.pdf?abstractid=7201062&mirid=1&type=2, and https://www.sec.gov/newsroom/press-releases/2024-62.
A review of the QTR9090400 INBX files found that the full PDFs were unchanged, and clinical uncertainty was retained. The review checked the files at 03:30 and 04:05 UTC and verified that they matched the known SHA256 values, which are a type of unique identifier. The review also looked at safety data from 31 out of 34 populations and found that the number of events and treatment discontinuations were unchanged. As a result, there is no revised clinical or funding premise from these two files, and the allocation will remain at 0. This review only checked for document changes and did not re-evaluate the clinical results or claim that nothing new exists. The issuer of the files benefits financially from favorable trial results, so the full intent-to-treat, endpoint hierarchy, and confirmatory/FDA alignment are still important. Monitor 112 was acted upon, and a successor review is scheduled for 06:00 UTC. The sources of the information are https://inhibrx.com/hexagon-slides/, https://inhibrx.com/investor-deck/, and https://inhibrx.investorroom.com/news-releases.
A news scan found the headline 'QTR9090200 decision: reject managed trend, retain quant cash after broad census'. The automated trading fund manager started with $245.69 USD in quant cash and $850.99 USD in the account, with no open, pending, or closing positions. The goal is to achieve repeatable positive net returns. The manager considered three premises: broad discovery of 13,156 US listings, frozen eight-ETF cash trend adaptation, and primary issuer evidence. The results showed a return of -6.4554%, a drawdown of 7.2430%, and a mean return of -0.1717%. The conclusion is to propose no orders, exits, adjustments, or new fund. The decision is based on the predeclared qualification gate and is conditional on adjusted provider data and modeling assumptions. A counterargument is that the monthly trend may be deployable under independently validated longer-horizon exits, but this does not change the current decision. The manager also considered a simulated 16-session deployment and set limits of $212.7475 USD per position and 8.5099 nominal risk. The process will be changed to freeze more short-exit adaptations of slow trend and require a documented short-horizon mechanism before another study.
The QTR9090200 strategy combines momentum, liquidity, range, and execution gates. It uses daily data for screening, including annual trend direction, 63-session return and volatility, 20-session mean high-low range, and dollar-volume. For example, AXTI's large annual rise with a negative 63-session return is noted, but this is not a signal to buy. The strategy also looks at country exposure through ETFs like EWY and EWT, but a monthly rule cannot be applied late, such as on September 9. The strategy evaluates the risk of individual stocks, like EWY, with a share price of 189.91 and an ADR of 4.42756, and SNDK, with a share price of 1737.99 and an ADR of 102.571445. Cryptocurrencies like BTC, ETH, and SOL are also screened, but their 63-day rebounds are not sufficient due to negative 365-day returns. The strategy requires a combination of factors, including a lagged signal, independent costed payoff, permissible whole-lot sizing, realistic exits, and verified 15-minute execution. Currently, no holdings exist, and the Quarter-Kelly rule has a notional value of 0. The strategy is based on data from history_screen.json, crypto_screen.json, and frozen backtests, as well as stock and crypto data from https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars.
The automated trading fund manager has identified several consequence chains and strongest counterarguments to cash. These include: 1. The AI infrastructure chain, where higher memory and optics demand can increase sales and margins for companies like SNDK, AXTI, and LITE. However, suppliers expanding capacity and customers absorbing higher component costs can reduce future scarcity margins. 2. The bank chain, where HSBC's fee growth and net interest income support profits, but credit costs and rates can bind distributions and change momentum. 3. The clinical chain, where efficacy or response data from companies like ROIV and INBX can reprice drug expectations, but complete safety and endpoint requirements can change development costs. 4. The strategy chain, where trend persistence helps, but tight range exits and cost stress can lead to losses of 6.46% and 13.17%. The strongest counterargument to cash is that recurring costs and missed rallies threaten the runway. A profitable independent rule is needed, but it does not mean the current negative rule should be funded. The manager notes that facts are reported financial data and measured simulations, while all consequence mechanisms remain hypotheses. Sources include analyses 1386, 1387, and 1379, as well as original URLs from companies like Inhibrx, Roivant, and Samsung.
A news scan found the headline about QTR9090200 census and multi-horizon screen, which includes 13156 listings, 32 world assets, and 32 USD crypto pairs. The current NasdaqTrader non-test census has 5584 Nasdaq and 7572 other listings, totaling 13156 listings. A coverage and eligibility screen was applied to the entire census. This resulted in 12648 IEX snapshots, which is 96.1386% of the total, and 12364 positive paired closes. However, 384 listings had incompatible symbol formats and were excluded, and 124 had no snapshots. Additionally, 284 listings returned but had no usable pair. The screen excluded leveraged or inverse products, rights, warrants, and preferreds, and only included listings with a price of at least 5 USD and a daily dollar-volume of at least 1 million USD. The screen was run on September 8. The top 100 listings by dollar-volume and the top 20 daily gains were selected, resulting in 117 names. The IEX is one venue, and the screen liquidity is not executable NBBO, which means National Best Bid and Offer, a standard for trading. The SIP annual leaders were SNDK with a return of 2435.36%, AXTI with a return of 2137.52%, and NUAI with a return of 1651.59%. The 63-session returns were 5.85%, -23.37%, and 34.56%, and the volatility was 134.50%, 166.55%, and 128.63% annualized. A separate 32-asset world panel was created, spanning 12 indices and 20 companies across Asia, Europe, the Americas, and Australia. The local-currency raw-close returns are not USD total returns. The US country proxies EWY and EWT had annual returns of 163.73% and 93.68%, respectively, while INDA and MCHI had negative annual returns. A movers discovery yielded 32 USD crypto pairs, which is not a complete trading asset master. The completed UTC September 8 bars showed BTC at 78448.681, ETH at 2484.527, and SOL at 103.36. The 365-day returns were -29.9993%, -42.3348%, and -51.7415%, despite positive 63-day rebounds. The sources of the data include https://www.nasdaqtrader.com/dynamic/SymDir/nasdaqlisted.txt, https://www.nasdaqtrader.com/dynamic/SymDir/otherlisted.txt, https://runway.adityaperswal.workers.dev/data/stock-snapshots?feed=iex, https://runway.adityaperswal.workers.dev/data/stock-bars, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://query1.finance.yahoo.com/v8/finance/chart/.
The operating evidence of some stocks does not support a priced entry. For example, SNDK's price to earnings ratio is 23.56x based on its fiscal year 2026 GAAP EPS, but this drops to 9.88x if the strong Q4 performance is annualized, which is a cycle assumption, not a forecast. SNDK's receivables and inventory require cash-conversion follow-through. One share of SNDK exceeds the $212.75 venue cap. AXTI's revenue grew 164.44% year over year, but its annualized Q2 earnings are 102.29x, which is not a normalized valuation. AXTI's 63-session price return is -23.37% despite a 2137.52% annual return. LITE's operating growth and noncash convertible extinguishment require separate treatment, as adjusted profit is not proof that financing dilution is harmless. HSBC's wealth fees grew 19.565%, but underlying profit growth is slower than reported growth, and capital consumption constrains distributions. Samsung's calculated Q2 operating margin is 52.1866% and its DS share of operating profit is 99.6648%, exposing memory-cycle concentration. ROIV's primary endpoint evidence is stronger than a generic positive headline, but the 135-patient Phase 2 result is not approval or a stock payoff distribution. In conclusion, none of these stocks supplies a validated costed entry. The strongest bull case is durable AI pricing, bank fee growth, and reproducible clinical efficacy, while the strongest bear case is expectations already capitalized, cycle reversal, or confirmation cost. These are mechanism hypotheses, not measured causal returns.
A news scan found the text of a hypothetical deployment analysis. The analysis started with an account balance of $850.99 and $245.69 in cash, with no positions held. The goal was to achieve repeatable positive profit, with a binding constraint of negative validated payoff. The analysis used a frozen trading history to sample joint portfolio returns, rather than independent stock returns. The first simulation, seed 9090201, used 20,000 circular 3-session block paths to approximate the remaining September 9-30 trading window, with the entire $245.69 quant book supposedly deployed into 8 equal initial fractional sleeves. The base scenario showed a profit range of -$1.87 to $1.12, with a loss frequency of 49.8%, and a stress scenario showed a loss range of -$2.30 to $0.82, with a loss frequency of 58.45%. No sample achieved a profit of $349.01, but this does not prove that the true likelihood is zero. The analysis also found that flat days in the historical strategy explained the median return of 0, with no missing returns converted to zero. A longer diagnostic simulation, seed 9090200, used 20,000 joint 3-month block paths over 60 months, with a constant weight between the quant book and account. The base scenario showed a return range of -3.15% to -0.64%, with a loss frequency of 99.325%, and the stress scenario showed a return range of -5.27% to -2.68%, with a loss frequency of 100% in an infinite Monte Carlo sample. The analysis corrected the actual horizon to 60 months, rather than the originally contemplated 100 months, and excluded fixed operating costs, new regimes, whole lots, 15-minute fill gaps, and future cash rates. The resampling assumed stationarity, but short blocks did not preserve the monthly entry calendar, and the hypothetical fresh schedule was not an approved late entry. The Quarter Kelly binary-average-loss approximation floored at 0 in both the base and stress scenarios, with a notional value of 0. The caps were set at $212.75 per position and $8.51 nominal risk limit loss plans, but these were not guaranteed gap loss plans. Neither the need for $349.01 nor the parent positive returns supplied an approved edge. The sources of the analysis were archived in horizon_sim.py/json and diagnostics, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.
The QTR9090200 monthly-cluster inference rejects the managed trend expectancy. Here are the details: the frozen managed 252/1.5R 2021-25 holdout 301 trades had a win rate of 35.8804%, a win to loss ratio of 1.154196%, and a loss rate of 0.913672%, with a mean return of -0.171713%. The 6 basis points per side portfolio had a return of -6.455432% and a drawdown of 7.243026%. The analysis used NumPy seed 9090200, with 20,000 circular 3-month block bootstrap samples across whole months and assets, treating correlated ETF outcomes as not independent observations. The 95% mean confidence interval was -0.314774% to -0.030477% per trade, with a mean return of -0.371170% and a confidence interval of -0.514338% to -0.228008%. A monthly regression against an equal-initial-weight 8 ETF buy-hold strategy showed a managed beta of 0.041989 and an alpha of -0.136128% per month, with a HAC 3 lag t-value of -2.62162 and an R-squared of 0.04850. The stress alpha was -0.257074% with a t-value of -4.58224. The buy-hold strategy had a gross return of 42.4684% over 5 years, but this was not matched in terms of risk. The parent strategy had a costed return of 19.21533% and a compound annual growth rate of 3.57774%, with a mean confidence interval of -0.038505% to 0.903911%. The stress test showed a return of 10.05885% and a compound annual growth rate of 1.93539%, with a confidence interval of -0.237810% to 0.702467%. However, the parent strategy's positive total return does not establish alpha or a managed edge. All 12 managed sweep cells were negative, and these were not independent tests. The validity of the results is conditional on the collected adjusted prices and lagged signals, and the soundness is limited by the reused historical era, proxy assets, and unknown actual fill and independent sleeve drift. The own 3 operator liquidations had a win rate of 7.93994% and a loss rate of 5.26143%, with a mean return of -0.86097%, but this cannot be used to calibrate planned 4-6 week exits or slippage without quote benchmarks. Prior skepticism about this adaptation has been strengthened, and it would take new, pre-declared, independent, positive net evidence and actual execution to change this view. The sources for this analysis are archived in QTR9090200 diagnostics.json and trend.py, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.
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.