The automated trading fund manager reviewed the trading premises and found that the prior rejection still stands. There are four premises for entry: an open venue and fresh quotes are required, a proposed long position must be cheap and clear certain costs and exit gates, the actual cost of holding an option must support a positive return, and the whole contract premium must fit within 1% equity and book caps. If any of these premises fail, there will be no proposal. The exchange calendar was re-read on September 7 and confirmed a holiday, which means there is no executable window. The manager reviewed prior analyses from 650 and 607 and found 187 affordable legs, but no new evidence was found to overturn the prior rejection. The risk arithmetic was calculated, with an equity value of 850.99 and a 1% value of 8.5099. The fund value is 127.65 and the position size is 25%, which is 212.7475. The manager will start flat and aim for a repeatable net option edge, with binding constraints including a closed venue and unvalidated option payoff history. The facts end at the live calendar and prior recorded measurements, and fees, future volatility, and option payoff history remain unknown. The consequences of the holiday are that fresh venue execution is prevented and Friday quotes remain stale. The manager will preserve the cash value of 127.65 and make no orders, exits, or adjustments.
The final decision for QIM90708 is to reject intraday transfers and retain $245.69 in cash before Tuesday. The reasons for this decision are as follows: 1. The Frozen360rowexperiment showed no positive results at 5 basis points per side or higher. The main 10 basis points holdouts were SPY with a loss of 77.7773%, EEM with a loss of 71.3673%, EFA with a loss of 70.4248%, and TLT with a loss of 59.6151%, all with negative training results and a Kelly criterion of 0.2. 2. The TLT 2 basis points holdout had a gain of 0.852% but failed training with a loss of 21.009% and a loss of 3.937% in 2026. The HAC test did not establish a stable net edge. 3. The mean 95% block intervals were below zero, and the official daily close endpoint audit remained negative. 4. There were no quant positions or pending orders, and the Tuesday intraday signal was unknown, with SPY whole-share capacity at 0, and exact time-exit, stop, and target management not validated. The conclusion is that there are no proposals, and the actual size is 0, so the $245.69 in cash will be retained. The validity of this decision is supported by pre-declared adoption gates that failed. The soundness of this decision is also supported by cash reconciliation, PDT checks, and price and calendar audits, which reject these feed-based transfers due to data, auction, and selection limitations. The strongest counterexample, the TLT cheap cost positive holdout, is acknowledged but cannot repair the failed training. The goal is to achieve a repeatable net edge, with a binding constraint validated executable payoff, not an account shortfall. The primary sources for this decision are https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf, with data from https://runway.adityaperswal.workers.dev/data/stock-bars. The pre-open calendar analysis, financial baseline, and own trade calibration are all publicly recorded. Six quant monitors are now armed, with none before the next run, and job 35 collects data at 09:15 UTC before 12:30 UTC manager. The New Census QFR and September 9 macro reviews have both branches, with no automatic orders. Lessons 249, 250, and 251 have been saved, and archive 689, 690, 691, 692, and 686 readback has been verified, with raw data refetch possibly revising the results.
A news scan found the headline 'QIM90708 full sensitivity and closing-price audit: rejection survives endpoint changes'. The audit examined the performance of 4 ETFs, including SPY, EEM, EFA, and TLT, with 3 splits, 5 rules, and 6 one-way costs of 0, 1, 2, 5, 10, and 20 basis points. The main results showed holdout returns at 10 basis points: SPY -77.7773%, EEM -71.3673%, EFA -70.4248%, and TLT -59.6151%. At 5 basis points, all 20 ETF/rule holdout cells were negative. The TLT main 2 basis point holdout return was +0.8522% over 5 years, with a training return of -21.0091% and a 2026 return of -3.937%. The ROD 2 basis point holdout return was +3.769%, with a training return of -17.864% and a recent return of -3.526%. No cheap cost variant was adopted. The audit excluded early closes using the exchange_calendars 4.13.2 reconstructed NYSE calendar, which includes more than the 13 observed bars, to prevent extended trading from mimicking a full session. A discrepancy was found between the 30-minute close and daily close for SPY, with a maximum of 279.40 basis points on March 12, 2020, and similar discrepancies were found for EEM and EFA. The official daily close diagnostic for SPY holdout at 10 basis points was -77.1045%, with a gross return of -6.4085%. The audit concluded that the tested transfers were rejected, with no stop or target adaptation or parameter retuning. The sources for the data include https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nyse.com/trade/hours-calendars, as well as primary papers in analysis 660.
The starting point is 245.69 in quant cash and 850.99 in account equity, with the goal of achieving a repeatable and executable net payoff. There are certain constraints, including costed edge and exit control. The premises are as follows: 1. Short-gamma hedging can buy rising markets and sell falling ones, according to a Robeco-affiliated study that provides historical context, but not current dealer inventory. 2. The four ETF cash-only time-exit transfers have negative training and holdout results at main costs, with TLT 2bp holdout at +0.852% being small and training at -21.009%. 3. The fund schedule and 15-minute polling cannot guarantee a specific 15:30 entry and 16:00 exit. The first-order effect is that adverse spreads and slippage consume a few basis points of payoff. The second-order effect is that repeated same-day roundtrips compound costs and compete for 3PDT slots, and restricting slots changes the portfolio but remains negative. The third-order effect is that transferring a published futures Sharpe or cherry-picked low-cost holdout to account sizing increases exposure without demonstrated economics. Liquidity providers and brokers benefit from turnover, while hedgers may pay costs to reduce other risks, unlike the outright alpha objective. The strongest counterargument is that advanced low-cost execution can exploit this effect, especially TLT in later periods. This can coexist with the rejection because instruments, costs, periods, and mandates differ. There is no inference of universal momentum failure, and no redesign is made based on the inspected sample. The facts end at observed returns, and future dealer flows and optimal execution are unknown.
The QIM90708 fund manager reviewed the market on Tuesday. The fund looked at the closing prices on September 4 for the following stocks: SPY at 770.19, EEM at 68.70, EFA at 108.35, and TLT at 82.21. The Simple Moving Average, a way to smooth out price changes, for these stocks was 712.1529 for SPY, 61.74135 for EEM, 101.5884 for EFA, and 86.247 for TLT. The returns over the last 20 sessions were -3.970% for SPY, +4.6618% for EEM, -0.1842% for EFA, and -0.6646% for TLT. On Friday, the signals for EEM and TLT were positive, but these signals expired on the same day. For Tuesday, the fund needs the price at 10 ET to make a decision, and the Risk of Drawdown, a measure of potential loss, requires a 15:30 boundary. Because Monday was a holiday, there was no entry on Tuesday. The fund's position cap is 212.7475, which allows for 0, 3, 1, or 2 whole shares of SPY, EEM, EFA, and TLT, respectively. The average dollar volume over 20 days was 27.975 billion for SPY, 1.071 billion for EEM, 0.930 billion for EFA, and 2.328 billion for TLT, showing that the fund's size is not too big. The fund has no held positions, pending orders, or target progress to review. There are no stop or target prices made for a rejected rule. The user scheduled a review for 15:40 ET and the next review for 17 ET, but this does not automatically execute a time exit between 15:30 and 16 ET. A marketable limit order also cannot guarantee an exact formation. In conclusion, there are no pre-open orders or stops, and the actual size is 0. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars and the calendar is https://www.nyse.com/trade/hours-calendars.
A fund manager's analysis, called QIM90708, used a method called Monte Carlo to test investment sizes and 24-day scenarios with no investment, or zero exposure. The analysis looked at different investment options, including SPY and TLT, which are types of investments, and calculated their potential wins and losses. For SPY, the average win was 0.147373% and the average loss was 0.270744%. For TLT, the average win was 0.126794% and the average loss was 0.194806%. The analysis used 20,000 random 24-day periods from the past to test these investments. It found that the potential losses for SPY, EEM, EFA, and TLT investments were -7.2522%, -5.6375%, -5.6374%, and -4.5511%, respectively. The analysis also found that the frequency of losses was high, at 97.66% to 99.855%. The results showed that there were no gains in the sampled periods. The analysis concluded that it is best to protect cash and not invest to make up for the monthly shortfall. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis used a seed of 90708 and a statistics program called stats.py.
The QIM90708 automated trading fund manager reported on its morning direction, range, and afternoon confirmation. The manager found that holding out for positive confirmation at a 10 basis point one-way threshold for certain stocks, including SPY with a 52.912% change, EEM with a 43.372% change, EFA with a 44.775% change, and TLT with a 34.105% change, resulted in fewer trades, which reduced fee drag but did not create a positive expectancy. The manager used descriptive range buckets based on training median, not holdout-fitted cutoffs, to analyze the morning range threshold for SPY, which was 0.298693%, and for TLT, which was 0.241432%. The manager also analyzed the mean returns for high and low range trades for SPY and TLT. Additionally, the manager performed a joint regression analysis for SPY, which showed a positive r12 coefficient of 0.153300 and a t-statistic of 2.621, while the r1 coefficient was -0.029226 with a t-statistic of -1.896. However, the manager concluded that no combined indicator rule passed the main costs, and no optimized subgroup was promoted. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the range referred to here is the 30-minute OHLC range, not the minute-realized volatility measure used in the paper, and this comparison is descriptive, not an exact replication.
A predictive model called QIM90708 does not show a costed edge, meaning it does not provide a significant advantage in terms of cost. The model was tested on a holdout period from 2021 to 2025, which included 1245 full sessions per exchange-traded fund (ETF). The results showed that the last half-hour return on a variable called r1 had slopes of -0.028106 for SPY, -0.016425 for EEM, -0.001546 for EFA, and +0.023596 for TLT. The HAC5 test, which is a statistical method used to calculate standard errors, gave t-values of -1.835, -2.647, -0.174, and +3.306. The slope for the rest of the day for TLT was +0.026362, with a t-value of 4.480 and an R-squared value of 0.024079. The main costed trade means had 20-session-block bootstrap 95% intervals that were entirely negative for all ETFs: SPY [-0.233140, -0.193289]%, EEM [-0.216185, -0.193609]%, EFA [-0.202470, -0.178244]%, and TLT [-0.173341, -0.142993]%. The gross main mean for TLT was 4.16227 basis points, which implies an approximate one-way break-even point of 2.08113 basis points before overhead costs. However, the low-cost holdout positivity did not survive training, leading to the conclusion that cash allocation should be rejected. These regressions describe association, not causal dealer gamma, and are limited by exact quotes, fees, and source endpoint anomalies. The results are also limited by the use of survivor-selected ETFs and earlier inspected market periods, which limit claims of independent global discovery. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the statistics.py code uses a seed of 90708 and 20,000 draws. All 36 regression specifications were recorded, but multiple tests were not selected for a trading rule.
A recent test of a trading strategy for the TLT ETF, which is a type of investment fund, showed the following results. The test used historical data from 2016 to 2026, up to September 4, and only considered full trading days on the New York Stock Exchange. The strategy was tested with a 5 basis point fee, which is a type of trading cost, and 5 basis point slippage, which is the difference between the expected and actual price of a trade, on each side of the trade. The results showed a return of -16.151854%, which means the strategy lost value, and a maximum drawdown, which is the largest loss from peak to trough, of 16.151854%. The strategy had a win rate of 1.176471%, a mean return of -0.206994%, an average win of 0.028136%, and an average loss of 0.209793%. These results are based on simulated trades, not actual account performance. The test did not include same-day stop or target orders, or guarantees of auction fill, and excluded intrabar drawdown, which is the loss within a single trading period. The conclusion is to reject this strategy due to its poor performance. The test was run with six different cost variants, but changing the costs did not improve the results. The original research papers used for this test can be found at https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf. This test was a simplified version of the original research and did not validate the strategy for future use.
A news scan found the headline 'QIM90708 TLT pdt holdout: costed final-half-hour cash-only replication'. This is a report about a trading strategy test. The test used historical data from 2016 to 2020 to train and from 2021 to 2025 to validate, and also included recent data up to September 4, 2026. The test was done on full New York Stock Exchange sessions, using a calendar and stock bar data from a specific website. The strategy was tested with fees of 5 basis points and slippage of 5 basis points on each side of a trade. The test results showed a return of -55.191299%, with 11.350294% of trades being winners, and an average win of 0.135632% and an average loss of 0.194284%. The test did not include same-day stop or target orders, and did not guarantee auction-fill orders. The results are simulated and not actual account performance. The test concluded that this strategy should be rejected, and that changing costs did not improve the results. The original research papers used for this test can be found on specific websites, and this test is a transfer of a strategy to an ETF, not a full replication of a research paper. The historical market period used for the test is not a pristine example of prospective validation.
A news scan found the report of an automated trading fund manager. The report is about a trading strategy called QIM90708, which involves buying and selling the TLT ETF. The strategy was tested using historical data from 2016 to 2026, with the most recent data up to September 4, 2026. The test used only full New York Stock Exchange sessions and was calibrated with a tool called exchange_calendars. The data was also adjusted using a method called SIP bars, which is a way of adjusting stock prices to account for certain market activities. The strategy was tested with a 5 basis point fee and 5 basis point slippage, which is a measure of the difference between the expected price of a trade and the actual price. The results showed a return of -60.584141%, with a maximum drawdown of -60.584141%. The strategy had 11.735537% winning trades, with an average win of 0.128209% and an average loss of -0.191110%. These results are based on simulated trades and do not reflect actual account performance. The report concludes that this strategy should be rejected, as changing the costs of the trades did not improve the results. The report cites two academic papers as sources, but notes that the historical market period used in the test is not a perfect representation of future market conditions.
The QIM90708 TLT strategy was tested using historical data from 2016 to 2020 for training and 2021 to 2025 for holdout, with additional recent data up to September 4, 2026. The test only used full New York Stock Exchange sessions and was calibrated with exchange calendars version 4.13.2, using adjusted SIP bars from https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy was tested with 5 basis point fees and 5 basis point slippage on each side of the trade, using a long-only direction and all available strategy capital, with cash reconciled independently. The results showed a return of -59.615132%, with a closed wealth drawdown of 59.615132%, 572 trades, a win rate of 11.363636%, a mean return of -0.158261%, an average win of 0.126794%, and an average loss of 0.194806%. These results are simulated full-capital returns, not actual account performance. The test did not include same-day stop or target orders, or auction-fill guarantees, and excluded intrabar drawdown. The conclusion is to reject this costed transfer, with an actual size of 0. All six cost variants were recorded, but changing costs did not select a winner. The original sources are https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf, and this is an ETF long-only transfer, not a full paper replication. The historical market period used is not a pristine prospective validation.
A trading strategy called QIM90708 TLT was tested. The test used historical data from 2016 to 2020 and held out data from 2021 to 2025, with recent data up to September 4, 2026. The test only used full New York Stock Exchange sessions and was calibrated with a tool called exchange_calendars version 4.13.2. The data was adjusted using SIP bars from a website. The test had fees of 5 basis points and slippage of 5 basis points on each side of a trade. The test only considered long positions, used all available capital, and reconciled cash independently. The results showed a return of -71.383763%, with a maximum drawdown of 71.383763%. The test had 659 trades, with a win rate of 4.552352%, a mean return of -0.189575%, an average win of 0.119127%, and an average loss of 0.204299%. These results are simulated and not actual account performance. The test did not guarantee same-day stops or targets, and did not account for intrabar drawdown. The conclusion is to reject this costed transfer, with a size of 0. Six different cost variants were tested, but changing costs did not select a winner. The original sources of the strategy are two academic papers, and this test is a transfer of an ETF long-only strategy, not a full replication. The historical market period used is not a pristine prospective validation.
A news scan found the report of a trading simulation. The simulation used historical data from 2016 to 2020 to train and 2021 to 2025 to test, with additional recent data up to September 4, 2026. It only considered full New York Stock Exchange sessions and adjusted for certain market factors. The simulation assumed a 5 basis point fee and 5 basis point lippage, which is a measure of the difference between the expected price of a trade and the actual price, on each side of a trade. The strategy was long-only, meaning it only considered buying, and used all available capital. The results showed a return of -72.035476%, with a maximum drawdown of 72.035476%. The simulation had 649 trades, with a win rate of 4.776579%, a mean return of -0.195917%, an average win of 0.145722%, and an average loss of 0.213055%. These results are based on simulated full-capital returns, not actual account performance. The simulation did not guarantee same-day stops or targets and excluded intrabar drawdown, which is a measure of the decline in value of a trade during a single trading period. The conclusion was to reject this costed transfer, with no actual size. Six different cost variants were recorded, but changing costs did not select a winner. The original sources of the data were from academic papers. This simulation was for an ETF long-only transfer, not a full replication of a research paper. The historical market period used was not a pristine prospective validation, meaning it was not a perfect test of the strategy's potential performance in the future.
A recent test of a trading strategy called QIM90708 EFA had the following results. The test used historical data from 2016 to 2026, up to September 4, and only considered full New York Stock Exchange sessions. The data was adjusted using a tool called exchange_calendars version 4.13.2 and stock bars from a website. The test assumed a 5 basis point fee and 5 basis point lippage, which is a type of trading cost, on each buy and sell. The test only considered long positions, meaning it only bought stocks, and used all available capital. The results showed a return of -14.042114 percent, with a maximum drawdown of -14.042114 percent. The test had 91 trades, with a win rate of 6.593407 percent. The average win was 0.210324 percent and the average loss was 0.192614 percent. These results are simulated and do not reflect actual account performance. The test did not use same-day stop or target prices, and did not guarantee auction-fill, which is a type of order. The test also excluded intrabar drawdown, which is a type of risk measure. Based on these results, the strategy was rejected. The test was run with six different cost variants, but changing the costs did not improve the results. The original research papers used for this test can be found at https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf. This test was a simulation of an ETF long-only transfer, not a full replication of a research paper. The historical market period used for the test is not a pristine prospective validation, meaning it is not a perfect test of the strategy's potential performance.
The automated trading fund manager analyzed the EFA product using a costed final-half-hour cash-only replication method. The analysis involved frozen notes 651 and 653 with a positive result, and the earliest signal allowed by the maximum three trades per rolling five sessions. The entry point was at 15:30 bar open and the exit point was at the same 30-minute bar close, resulting in a flat position on the same day. The training period was from 2016 to 2020, the holdout period was from 2021 to 2025, and the recent period was from 2026 through September 4. The analysis only considered full NYSE sessions and was calibrated with exchange calendars version 4.13.2, using adjusted SIP bars from a specific website. A 5 basis point fee and 5 basis point slippage were applied to each side of the trade, with a long-only direction and all available strategy capital, and cash was reconciled independently. The results showed a return of -64.124066%, a closed wealth drawdown of 64.124066%, 538 trades, a win rate of 5.018587%, a mean return of -0.190258%, an average win of 0.160302%, and an average loss of 0.208781%. These results are simulated full-capital returns and not actual account performance. The analysis did not include same-day stop or target, or auction-fill guarantee, and intrabar drawdown was excluded. The PDT throttle was applied per isolated ETF, not as a combined account allocation. The conclusion was to reject this costed transfer, with an actual size of 0. All six cost variants were recorded, but changing costs did not select a winner. The original sources were from specific research papers, and this analysis was an ETF long-only transfer, not a full paper replication. The inspected historical market period was not a pristine prospective validation.
A trading fund manager analyzed a strategy called QIM90708 EFA. The manager used past data from 2016 to 2020 to train the strategy and data from 2021 to 2025 as a holdout to test it. The most recent data used was from 2026 up to September 4. The manager only looked at full trading sessions on the New York Stock Exchange and used a tool called exchange_calendars to adjust the data. A news scan found the data source https://runway.adityaperswal.workers.dev/data/stock-bars. The manager assumed a 5 basis point fee and 5 basis point slippage for each trade, which means a small cost for buying or selling. The strategy was set to only buy, not sell, and used all available capital. The results showed a return of -70.597717 percent and a maximum loss of 70.597717 percent. There were 649 trades, with 5.855162 percent of them being winners. The average win was 0.138181 percent and the average loss was 0.208630 percent. These results are based on simulated trades, not actual account performance. The manager did not use same-day stop or target orders and did not guarantee auction-fill orders. The manager also did not include losses that happened within a single trading period. The conclusion was to reject this strategy due to its costs. The manager tested six different cost variants, but changing the costs did not improve the results. The original research papers used for this analysis can be found at https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf. This strategy is for a long-only ETF transfer, not a full replication of a research paper. The historical market data used is not a perfect test of the strategy's potential future performance.
A news scan found the report of an automated trading fund manager. The manager tested a trading strategy on the EFA ETF, which is a type of investment fund that tracks a stock market index. The test used historical data from 2016 to 2020 to train the strategy and data from 2021 to 2025 to evaluate its performance. The test also used recent data from 2026 up to September 4. The strategy was tested on full New York Stock Exchange sessions, using a calendar of exchange sessions and adjusted data from a website. The test assumed a 5 basis point fee, which is a small cost, and 5 basis points of slippage, which is the difference between the expected and actual price of a trade, on each side of a trade. The test used a long-only strategy, which means it only bought stocks and did not sell them short, and it used all available capital. The test also reconciled cash independently, which means it kept track of cash separately from other investments. The results showed a return of -70.424826%, a closed wealth drawdown of 70.424826%, 639 trades, a win rate of 5.477308%, a mean return of -0.190357%, an average win of 0.154433%, and an average loss of 0.210336%. These results are simulated and do not reflect actual account performance. The test did not guarantee same-day stop or target prices or auction fill, and it excluded intrabar drawdown, which is the loss of value of a trade during a single trading session. The conclusion was to reject this costed transfer, with an actual size of 0. Six cost variants were recorded, but changing costs did not select a winner. The original sources of the strategy were two academic papers, and this test was a transfer of an ETF long-only strategy, not a full replication of a research paper. The historical market period used in the test was not a pristine prospective validation, which means it was not a perfect test of the strategy's potential performance in the future.
A news scan found the report of a trading simulation. The simulation used historical data from 2016 to 2020 to train and 2021 to 2025 to test, with recent data up to September 4, 2026. It only used full New York Stock Exchange sessions and adjusted the data for certain market factors. The simulation had a 5 basis point fee and 5 basis point slippage on each trade, which means a small cost was added to each buy and sell, and slippage refers to the difference between expected and actual trade prices. The simulation only considered long positions, meaning it only bought stocks, and used all available capital. The results showed a return of -70.815321%, a closed wealth drawdown of 70.815321%, 661 trades, a win rate of 6.656581%, a mean return of -0.185949%, an average win of 0.219224%, and an average loss of 0.214843%. These results are simulated and not actual account performance. The simulation did not guarantee same-day stops or targets and did not account for intrabar drawdown, which refers to losses within a single trading period. The conclusion was to reject this transfer method, with no actual size used. Six different cost variants were tested, but changing costs did not select a winner. The original sources of the data are from academic papers, and this simulation is a transfer of an ETF long-only strategy, not a full replication. The historical market period used is not a pristine prospective validation, meaning it is not a perfect test of future performance.
The QIM90708 EFA training program was tested with a costed replication of the final half-hour of cash-only trading. The test used frozen notes 651 and 653, with an entry point at 15:30 when the price was higher than the previous day's close. The test was run from 2016 to 2020, with a holdout period from 2021 to 2025, and recent data from 2026 up to September 4. The test only used full New York Stock Exchange sessions and was calibrated with exchange calendars version 4.13.2, using adjusted SIP bars from https://runway.adityaperswal.workers.dev/data/stock-bars. The test included 5 basis point fees and 5 basis point lippage on each side of the trade, with a long-only direction and all available strategy capital, and cash was reconciled independently. The results showed a return of -72.435609%, with a closed wealth drawdown of 72.435609%, 666 trades, a win rate of 6.606607%, a mean return of -0.193105%, an average win of 0.212972%, and an average loss of 0.221831%. These results are simulated full-capital returns, not actual account performance. The test did not include same-day stop or target orders, or auction-fill guarantees, and intrabar drawdown was excluded. The conclusion is to reject this costed transfer, with an actual size of 0. All six cost variants were recorded, but changing costs did not select a winner. The original sources are https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf, and this is an ETF long-only transfer, not a full paper replication. The historical market period tested is not a pristine prospective validation.
The QIM90708 EEM recent costed final-half-hour cash-only replication analysis is based on frozen notes 651 and 653. The entry point was at 15:30 bar open, with an exit at the close of that 30-minute bar, and the position was flat by the end of the same day. The analysis was trained on data from 2016 to 2020, held out from 2021 to 2025, and considered recent data up to September 4, 2026. It only included full New York Stock Exchange sessions, calibrated with exchange calendars version 4.13.2, and used adjusted Securities Information Processor bars from https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis included 5 basis point fees and 5 basis point slippage on each side of the trade, with a long-only direction and all available strategy capital, and cash was reconciled independently. The results showed a return of -13.086596%, a closed wealth drawdown of 13.197733%, 96 trades, a win rate of 14.583333%, a mean return of -0.145764%, an average win of 0.219555%, and an average loss of 0.208136%. These results are simulated full-capital returns, not actual account performance. The analysis did not include same-day stop or target orders, or auction-fill guarantees, and intrabar drawdown was excluded. The conclusion is to reject this costed transfer, with an actual size of 0. All six cost variants were recorded, but changing costs did not select a winner. The original sources are https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf, and this analysis is an ETF long-only transfer, not a full paper replication. The inspected historical market period is not a pristine prospective validation.
A news scan found the following notes about an automated trading fund manager's decisions. The manager tested a trading strategy on the EEM fund, which is an exchange-traded fund, a type of investment that trades on a stock exchange like a stock. The test used data from 2016 to 2020 to train the strategy and data from 2021 to 2025, and up to September 4, 2026, to test it. The strategy was only tested on full days when the New York Stock Exchange was open, using a calendar of exchange hours, and was adjusted for differences in how stock prices are reported. The test assumed a 5 basis point fee, which is a type of trading cost, and 5 basis points of slippage, which is the difference between the expected price of a trade and the actual price, on both the buy and sell sides of each trade. The strategy was set to only buy, not sell short, which means selling a security that the trader does not own. The test results showed a return of -65.193973%, which means the strategy lost value, and the largest loss from the highest point to the lowest point was also 65.193973%. The strategy had 521 trades, with a win rate of 5.182342%, a average win of 0.113640%, and an average loss of 0.219525%. These results are based on simulated trades, not actual account performance. The strategy did not use stop-loss orders, which automatically sell a security when it reaches a certain price, or target prices, which automatically sell a security when it reaches a certain price, and did not guarantee that trades would be filled at a certain price. The test was run on a per-ETF basis, not on a combined account basis, which means that the strategy was tested on the EEM fund separately from other funds. The conclusion was to reject this strategy due to its costs. The original research papers that this strategy is based on can be found at https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf. This test was not a full replication of the research papers, but rather a test of a long-only transfer strategy, which means buying and holding a security, on an ETF.
A news scan found the report of an automated trading fund manager. The report is about a costed final-half-hour cash-only replication of EEM, which is an exchange-traded fund. The manager used frozen notes from 651 to 653, and the entry was at 15:30 bar open, with an exit at the close of that 30-minute bar, resulting in a flat position on the same day. The training period was from 2016 to 2020, the holdout period was from 2021 to 2025, and the recent period was from 2026 through September 4. The manager only used full New York Stock Exchange sessions, calibrated with exchange calendars version 4.13.2, and adjusted SIP bars from a specific website. The manager applied 5 basis point fees and 5 basis point slippage on each side of the trade, using a long-only direction with all available strategy capital, and cash was reconciled independently. The return was -72.989801%, the closed wealth drawdown was 72.989801%, with 631 trades, a win rate of 4.754358%, a mean return of -0.207122%, an average win of 0.108031%, and an average loss of 0.222853%. These results are simulated full-capital returns, not actual account performance. The manager did not use same-day stop or target, and there was no guarantee of auction-fill, with intrabar drawdown excluded. The conclusion was to reject this costed transfer, with an actual size of 0. All six cost variants were recorded, but changing costs did not select a winner. The original sources were from specific academic websites, and this is an ETF long-only transfer, not a full paper replication. The inspected historical market period is not a pristine prospective validation.
A news scan found the report of an automated trading fund manager. The manager tested a trading strategy on the EEM ETF, which is a type of investment fund that tracks a specific stock market index. The test used historical data from 2016 to 2020 to train the strategy and data from 2021 to 2025, and up to September 4, 2026, to test it. The strategy was only tested on full trading days on the New York Stock Exchange, using a calendar of exchange trading days, and was adjusted for certain market data. The test assumed a 5 basis point fee, which is a type of trading cost, and 5 basis points of slippage, which is the difference between the expected and actual price of a trade, on each buy and sell transaction. The strategy was set to only buy, not sell, and used all available capital. The results showed a return of -71.367297%, which means the strategy lost value, and the largest loss was also 71.367297%. The strategy had 609 trades, with a win rate of 5.582923%, and the average win was 0.104456%, while the average loss was 0.223336%. These results are simulated and do not reflect actual account performance. The test did not guarantee that trades would be filled at a certain price or that stops and targets would be executed on the same day. The test also did not account for potential losses within a trading period. The conclusion was to reject this trading strategy due to its poor performance. The original research papers that this strategy is based on can be found at https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf. This test was a simulation of an ETF trading strategy and not a full replication of a research paper.
A news scan found the following text: 1. The system analyzed notes 651 and 653. It looked at the stock market from 15:00 to the previous daily close, and if that was more than 1, it entered at 15:30 and exited at the close of that 30-minute period, with no action on the same day. The system was trained on data from 2016 to 2020, held out from 2021 to 2025, and looked at recent data from 2026 up to September 4. It only considered full New York Stock Exchange sessions, using a calendar tool called exchange_calendars version 4.13.2, and adjusted the stock prices using a website https://runway.adityaperswal.workers.dev/data/stock-bars. 2. The system added 5 basis points in fees and 5 basis points in slippage to each buy and sell, using a tool called vectorbt that only considered long positions, using all available capital, and keeping track of cash separately. The results showed a return of -72.776051%, with a maximum drawdown of 72.776051%, 685 trades, a win rate of 10.948905%, a mean return of -0.189558%, an average win of 0.166518%, and an average loss of 0.233338%. These results are simulated and not based on actual account performance. 3. The system did not guarantee same-day stops or targets, or auction fills, and did not consider losses within a trading period. The conclusion was to reject this transfer, with a size of 0. All six cost variants were recorded, but changing costs did not select a winner. The original sources are https://smallake.kr/wp-content/uploads/2015/01/SSRN-id2440866.pdf and https://academicweb.nd.edu/~zda/intramom.pdf. This is a long-only transfer of an ETF, not a full replication of a research paper, and the historical market period analyzed is not a pristine prospective validation.