A frozen protocol note, number 294, was used to analyze the MRGR merger. The actual training data used was from January 2016 to December 2021, as the requested data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The analysis was done on a quarterly basis, with the first session open after the prior quarter's close. The stop loss was set at 1 times the average true range over 20 sessions, and the target was set at 1.5 times the average true range, with a maximum of 20 sessions before the next open. There was no reentry allowed in the same quarter. The analysis was long-only, meaning only buy positions were considered, and cash-only, meaning no leverage was used. The vectorbt order ledger and independent compounded trade returns agreed within a very small margin of 1e-8. The fees were 5 basis points plus slippage of 45 basis points each way. Gap stops were set at the open, and ambiguous daily bars stopped first. There were 19 completed cohorts, but they were not independent merger deals. The training data was split into 10 and 40 variants for sensitivity checks, and the frozen 20 was used later. The net mean return was -1.023470%, the average win was 0.000000%, and the average loss was -1.023470%. The quarter Kelly was 0.000000. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but neither sparse trading nor 15-minute stop/partial-profit execution was validated. This analysis was an exit adaptation, not the exact original-paper trading. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A news scan found the note: Frozen protocol note294. The actual training data used is from January 2016 to December 2021, as the requested 2015 data was unavailable, and the holdout period is from January 2022 to August 2026. The strategy uses a quarterly first-session open after the prior-quarter close, with a stop set at 1 times the average true range over 20 sessions, known as ATR20, and a target at 1.5 times the ATR20, with a maximum of 20 sessions before moving on to the next open, and no reentry is allowed that quarter. This is a long-only strategy, meaning it only buys assets, and it uses cash-only transactions. The vectorbt order ledger and independent compounded trade returns agree within a very small margin of 1e-8. The fee is 5 basis points, or 5bp, plus slippage of 10 basis points, or 10bp, each way. A basis point is a unit of measurement, equal to 0.01%. Gap stops occur at the open, and ambiguous daily bars stop first. There are 19 completed cohorts, which are groups of merger deals, and no unfinished endpoints are counted. The training includes 10 and 40 variants as sensitivity checks, and the frozen 20 is used later. The net mean return is -0.328197%, the average win is 0.162090%, and the average loss is 0.684770%, with a quarter Kelly of 0.000000. The drawdown includes the initial cash and session-ending marks. Daily adjusted bars are used to approximate distributions and fills, but neither sparse trading nor 15-minute stop and partial-profit execution has been validated. This is an adaptation of an exit strategy, not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study is based on study.py, trades.json, and metrics.json.
A frozen protocol note, numbered 294, was used for training. The actual training data used was from January 2016 to December 2021, as data from 2015 was unavailable. The holdout period was from January 2022 to August 2026. The training process involved quarterly sessions, where the first session of each quarter would open after the prior quarter's close. The stop loss and target prices were set based on the prior 20-day average true range, denoted as ATR20, with a stop loss at 1 times the ATR and a target at 1.5 times the ATR. The maximum duration for each trade was 40 sessions, after which the next open would be used, and no reentry was allowed in the same quarter. The training was long-only, meaning only buy trades were considered, and cash-only, meaning all trades were settled in cash. The vectorbt order ledger and independent compounded trade returns were compared and found to agree within a very small margin of 1e-8. A fee of 5 basis points, or 0.05%, and slippage of 10 basis points, or 0.10%, were applied to each trade. The training data included 23 completed cohorts, but these were not independent merger deals. The training process also included sensitivity checks, with 10 out of 40 variants used for this purpose, and the remaining 20 were frozen for later use. The results showed a net mean return of -0.249348%, an average win of 0.703346%, and an average loss of 0.513986%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. The daily adjusted bars used in the training were an approximation of the actual distributions and fills. It is noted that this is an exit adaptation and not the exact original-paper trading strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the local study.py, trades.json, and metrics.json files were used.
A frozen protocol note, number 294, was used for training. The training data was from January 2016 to December 2021, as data from 2015 was not available. The holdout period was from January 2022 to August 2026. The training process used quarterly data, where the first session of each quarter was determined by the open price after the prior quarter's close. The stop loss and target prices were set based on the prior 20-day average true range, denoted as ATR20, with a maximum of 20 sessions before moving on to the next open price, and no reentry was allowed in the same quarter. The training was long-only, meaning only buy orders were considered, and cash-only, meaning all trades were settled in cash. The vectorbt order ledger and independent compounded trade returns were in agreement, with a difference of less than 1e-8. A fee of 5 basis points, or 0.05%, and slippage of 10 basis points, or 0.10%, were applied to each trade. Gap stops were executed at the open price, and ambiguous daily bars stopped at the first opportunity. The training consisted of 23 completed cohorts, which were not independent merger deals, and did not include any unfinished endpoints. Ten out of 40 variants were used for sensitivity checks, and the remaining 20 were used later. The results showed a net mean return of -0.249348%, an average win of 0.703346%, and an average loss of 0.513986%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. Daily adjusted bars were used to approximate distributions and fills, but sparse trading and 15-minute stop/partial-profit execution were not validated. This training was an adaptation of an exit strategy, not the exact original-paper trading strategy. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and local files study.py, trades.json, and metrics.json were used.
A frozen protocol note, number 294, was used for training. The training data used was from January 2016 to December 2021, as data from 2015 was not available. The holdout period was from January 2022 to August 2026. The training process involved quarterly sessions, where the first session of each quarter would open after the close of the previous quarter. The stop loss and target prices were set based on the average true range of the previous 20 sessions. The maximum number of sessions was 10, after which the next open would be used. There was no reentry allowed in the same quarter. The training was long-only and used cash-only transactions. The vectorbt order ledger and independent compounded trade returns were in agreement, with a difference of less than 1e-8. The fees were 5 basis points, plus a slippage of 10 basis points each way. The training data consisted of 23 completed cohorts, but these were not independent merger deals. The training process also included sensitivity checks, with 10 out of 40 variants used for training, and 20 frozen for later use. The results showed a net mean return of -0.235721%, an average win of 0.683850%, and an average loss of 0.491158%. The quarter Kelly criterion was 0.000000. The drawdown included the initial cash and session-ending marks. The daily adjusted bars were used to approximate the distributions and fills, but this method did not validate sparse trading or 15-minute stop and partial-profit execution. This training process was an adaptation of an exit strategy, not the exact original paper trading strategy. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, and the local files study.py, trades.json, and metrics.json were also used.
A test of a defensive swing replication strategy for the EEMV fund failed to meet deployment requirements. The strategy is based on the original AQR defensive theory, which focuses on long-term lower-risk equity investments, not short-term swings. The test used an existing factor ETF with a specific set of rules, including a next-open entry on the first session of each month, a stop loss at 1 times the prior 20 session average true range, and a target of 1.5 times the stop loss, capped at 10 sessions. The test also included a daily gap stop at the open, and stopped the first session if both the stop and target were hit. The results of the test from 2016 to 2022 showed a return of -8.2106%, and from January 2023 to August 2026 showed a return of 0.8920%, with a session close drawdown of 8.6201%, and a win rate of 54.5455%. The 3-month bootstrap mean confidence interval was -0.29972% to 0.35449%. The cost of the strategy was 10 basis points one-way split fee and slip, not including venue costs. A 50 basis point stress return test showed a return of -29.0448%. The test excluded the September cohort uniformly, and used a complete history of 18,788 adjusted SIP bars across 7 ETFs through September 4. The conclusion of the test was to reject deployment, as the positive point estimate of the EEMV could not overcome the confidence interval crossing zero and the cost failure. The results were validated using Vectorbt and an independent cash ledger, which agreed within 1e-6. The test also recorded training 5/15 holding sensitivities and 2 basis point later controls, but did not select a winner. The market window used in the test has already been seen in other research, so it is not a pristine out-of-sample test. The prices used in the test were adjusted for corporate actions, but did not include cash dividend, raw stop, 15-minute, or profit management validation. The sources used in the test include a white paper from AQR and data from a stock bars website, as well as protocol note 248. The validity of the test follows the prerequisite of a positive independently executable edge, but this test rejects the adaptation, not the universal anomaly.
A test of the EFAV defensive swing replication strategy has failed to meet the deployment gate criteria. The strategy is based on the original AQR defensive theory, which focuses on long-horizon lower-risk equity, not short-term swings. The test used an existing factor ETF with a next-open entry on the first session of each month, a prior 20-session ATR stop at 1, a target at 1.5, and a maximum of 10 sessions. It also included a daily gap stop at the open, stopping the first session if both the stop and target were hit, with the target capped. The test was trained on data from 2016 to 2022, which showed a return of -7.4152%, and later data from January 2023 to August 2026 showed a return of -8.0622%, with a session close drawdown of 12.1907%, 44 sessions, a win rate of 45.4545%, a mean win of 1.08274%, and a mean loss of 1.23836%. The mean return was -0.18331%. The cost was 10 basis points one-way split fee/slip, not including venue costs. A 50 basis point stress return showed a loss of 35.3420%. The test excluded the right-censored September cohort uniformly, as per note 254, and used a complete history of 18,788 adjusted SIP bars across 7 ETFs through September 4. The conclusion is to reject deployment, as the positive EEMV point estimate cannot overcome the confidence interval crossing zero and cost failure. The Vectorbt and independent cash ledger agree within 1e-6. The training included 5/15 holding sensitivities and 2 basis point later controls, but no winner selection. The market window has already been seen in other research, so it is not pristine out-of-sample. Prices were adjusted for corporate actions, but not for cash dividends, raw stops, 15-minute intervals, or profit management validation. The sources for this test include a white paper from AQR and data from runway.adityaperswal.workers.dev. The validity of the test follows the prerequisite of a positive, independently executable edge, but this test rejects the adaptation, not the universal anomaly.
A news scan found the headline 'USMV defensive swing replication fails deployment gate'. This refers to a test of a trading strategy based on the AQR defensive theory, which is a long-term investment approach that aims to reduce risk. The strategy was adapted from an existing factor-based exchange-traded fund, or ETF, which is a type of investment fund that tracks a specific stock market index. The adaptation used a next-open entry on the first session of each month, with a stop-loss order set at 1 times the average true range, or ATR, of the prior 20 sessions, and a target of 1.5 times the ATR, with a maximum of 10 sessions. A daily gap stop was also used at the open, and the stop was triggered first if both the gap stop and the target were hit, with the target capped. The strategy was trained on data from 2016 to 2022 and returned 14.1636%, with 82 trades. However, when tested on data from January 2023 to August 2026, the strategy returned -4.4143%, with a maximum drawdown of 10.9436%, and a win rate of 43.1818%, with an average win of 1.12446% and an average loss of 1.02416%, resulting in a mean return of -0.09634%. A 3-month bootstrap analysis found a 95% confidence interval of -0.43093% to 0.23567%. The cost of the strategy was estimated to be 10 basis points, or 0.1%, one-way split fee and slippage, not including venue costs, and a 50 basis point stress return was estimated to be -32.7765%. The September cohort was excluded due to right-censoring, as per note 254, and the complete history consisted of 18,788 adjusted SIP bars across 7 ETFs through September 4. The conclusion is to reject the deployment of the strategy, as the positive expected excess return over the market value, or EEMV, point estimate cannot overcome the confidence interval crossing zero and the cost failure. The results were validated using Vectorbt and an independent cash ledger, which agreed within 1e-6. The training data included 5/15 holding sensitivities and 2 basis point later controls, but no winner selection was used. The market window used in the test has already been seen in other research, so it is not a pristine out-of-sample test, and the prices were adjusted for corporate actions, but not for cash dividends, raw stops, 15-minute intervals, or profit management validation. The sources used in the test include a white paper from AQR on defensive equity, a dataset of stock bars from Aditya Perswal, and protocol note 248. The validity of the test follows the prerequisite of a positive independently executable edge, but this test rejects the adaptation, not the universal anomaly.
A news scan found the headline 'SPLV defensive swing replication fails deployment gate'. This refers to a test of a defensive investment strategy based on the SPLV ETF. The strategy is based on a theory by AQR, which is a long-term investment approach that aims to reduce risk. The test used a set of rules, including a stop-loss order, which is an order to sell a security when it falls to a certain price, and a target price, which is the price at which the security is expected to rise. The test also used a daily gap stop, which is a stop-loss order that is triggered when the security's price gaps down at the open. The test was run on historical data from 2016 to 2022 and from January 2023 to August 2026. The results showed a return of 14.5780% from 2016 to 2022, but a return of -15.8303% from January 2023 to August 2026. The test also showed a maximum drawdown, which is the maximum loss from a peak to a trough, of 18.7584%. The strategy was tested using a bootstrap method, which is a statistical method that involves resampling the data, and the results showed that the strategy's return was not statistically significant. The test also took into account the cost of implementing the strategy, which was 10 basis points, or 0.1%, one way, and the stress return, which is the return of the strategy during a period of high market stress, was -40.8052%. The test excluded a cohort of data from September, as per a protocol note. The conclusion of the test was to reject the deployment of the strategy, as the positive expected return was not enough to overcome the costs and the uncertainty of the results. The test was run using a software called Vectorbt and the results were verified using an independent cash ledger. The test also recorded the sensitivities of the strategy to different parameters, but did not select a winner. The market window used in the test was not pristine, meaning that it had been seen before in other research, and the prices used in the test were adjusted for corporate actions, but not for cash dividends or other factors. The sources used in the test include a white paper by AQR and a dataset of stock bars. The validity of the test follows from the prerequisite of a positive, independently executable edge, but this test rejects the adaptation of the strategy, not the universal anomaly.
A news scan found the text: Frozen36experiments: each of 3 symbols has train/later fixed/range conditional and unconditional fixed controls, later 2/50 bp one-way cost variants for fixed/range, and training-only 2/4 session range holding sensitivities. No winner was selected for the later window. The main SPY later fixed was +1.2208%, range was -3.6496%; 2 bp one-way fixed was +4.3452%, range was -0.6756%; 50 bp one-way fixed was -13.0525%, range was -17.2362%. The later unconditional calendar SPY was -6.0678%, EFA was -11.6534%, EEM was -10.2040%; merely being near month end did not establish excess returns. All metrics, drawdown, profit, win, loss, and regime payoffs, and calendar-block confidence interval were recorded in 36 observations. Cost assumptions, split evenly between fees and slippage, are not reported venue fees or measured execution. A counterexample: lower costs help the fixed SPY rule, but its 95% mean confidence interval still crosses zero and mandatory range implementation fails. The main training SPY range 2/3/4 session variations are not fresh holdouts and do not license parameter search. The CLI source audited: the available mean-reversion strategy is rolling price z-score, with IEX and no explicit adjustments or single page fetch; it cannot represent the settlement calendar or validate this rule. Therefore, own vectorbt scripts were used under user permission for own backtests; no different CLI strategy is claimed as evidence. All 8052 bars match NYSE session coverage with valid open, high, low, and close; the maximum terminal ledger difference is 5.820766e-11. There is no raw dividend, bank holiday, 15-minute, extended hour, trail, or whole-share validation; screen rejection does not claim actual tradability. The source is https://runway.adityaperswal.workers.dev/data/stock-bars; /app/agent/dist/backtest/{bars,strategies}.js; and artifacts/test.py and stats.py.
A frozen protocol note was made before inspecting prices. From January 2023 to September 4, 2026, the same patterns were seen for other rules, so this is not a completely independent validation. The deadline for the end of the month is calculated by subtracting the settlement lag plus one from the current date, with a lag of 3, 2, or 1 across SEC changes. A buy order is made at the next open only if the return from the preceding 5 sessions is less than 0, with a maximum of 3 open-to-open sessions. This is for research purposes only, with full-capital normalization, and does not reflect actual fund allocation. The results are compared to a fixed hold strategy and a stop strategy using a 20-day average true range with a target of 1.5 times the average true range. A daily stop is made when both conditions are met, with a stop gap at the open and a favorable target gap capped at the target. Same-day entries and exits are supported through separate synthetic slots. Fees are 5 basis points plus slippage of 5 basis points each way, with a cash return of 0. The SPY is used as a primary US proxy, while EFA and EEM are used for transfer diagnostics, which do not establish underlying local flows. A total of 8052 adjusted bars were used, with 2684 bars for each of the NYSE calendar, and no missing or extra dates were found. The results from vectorbt were reconciled with an independent compounded cash ledger for every 36 experiment terminal equity, with errors less than 1e-6 on an initial capital of 10,000. The drawdown is calculated based on open and close prices, not intraday maximum prices. The fixed return was -2.785644%, with a drawdown of 11.925423%, and other metrics are listed below. A range of 3-calendar-month blocks with a mean of 10000 bootstrap and a 95% confidence interval of [-0.8158713215769833, 0.4405276085542465]% was used. All trial metrics and uncertainty were separately observed, and the code, raw inputs, and full trade and equity ledgers are local artifacts. The conclusions are based on this newly specified proxy and do not exactly reproduce the institutional ANcerno study. Mandatory 15-minute, extended hours, and trailing-profit management, as well as raw cash dividends, bank settlement holiday differences, and whole-share sizing, were not validated, and no execution authorization was given. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content, and https://www.sec.gov/exams/educationhelpguidesfaqs/t1-faq.
A frozen protocol note, number 213, was written before checking prices. This training took place from 2016 to 2022. The deadline for the end of the month is calculated by subtracting the settlement lag plus one from the current date, with lags of 3, 2, and 1 across SEC changes. A buy order is placed at the next open only if the return from the preceding 5 sessions is less than 0, with a maximum of 3 open-to-open sessions. This is for research purposes only, using full capital, and does not reflect actual fund allocation. The results are compared to a fixed hold strategy and a stop strategy using a 20-day average true range with a target of 1.5 times the average true range. A daily stop is applied when both conditions are met, with a stop gap at the open and a favorable target gap capped at the target. Same-day entries and exits are supported through separate synthetic slots. Fees are 5 basis points plus slippage of 5 basis points each way, with a cash return of 0. The SPY is used as a primary US proxy, while EFA and EEM are used for transfer diagnostics, which do not establish underlying local flows. A total of 8052 adjusted SIP bars were completed, with 2684 bars each, using the NYSE calendar with no missing or extra dates, and OHLC and volume checks passed. The results from vectorbt were reconciled with an independent compounded cash ledger for every 36 experiment terminal equity, with errors less than 1e-6 on an initial capital of 10000. The drawdown is calculated based on open and close prices, not intraday maximum. The fixed return was 30.245395%, with a drawdown of 11.055170%, and other metrics include N39, p58.9744%, W2.04843%, and L1.23969%. Range figures are also provided, including a 3-calendar-month block with a 10000 bootstrap mean 95% confidence interval of [-0.3608248045957735, 0.6969985198676744]%. All trial metrics and uncertainty were separately observed, and the code, raw inputs, and full trade and equity ledgers are local artifacts. The conclusions concern this newly specified proxy and do not exactly reproduce the institutional ANcerno study. Mandatory 15-minute, extended hours, and trailing profit management, as well as raw cash dividends, bank settlement holidays, and whole-share sizing, were not validated, and no execution authorization was given. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content, and https://www.sec.gov/exams/educationhelpguidesfaqs/t1-faq.
A frozen protocol note was made before checking prices. From January 2023 to September 4, 2026, the same patterns were seen as with other rules, so this is not a completely independent validation. The deadline for the end of the month is calculated by subtracting the settlement lag plus one from the current date, with a lag of 3, 2, or 1 across SEC changes. The next open is bought only if the return from the preceding 5 sessions is less than 0, with a maximum of 3 open-to-open sessions, and this is for research purposes only, not actual fund allocation. The results of holding a position are compared to stopping at a target price, which is 1.5 times the average true range over 20 days, and stopping at a loss, which is also based on the average true range over 20 days. The daily stop is triggered when both conditions are met, with a stop gap at the open, and the target gap is capped at the target price. Same-day entries and exits are allowed through separate synthetic positions. Fees are 5 basis points plus slippage of 5 basis points each way, with a cash return of 0. The SPY is the primary US proxy, and diagnostics for EFA and EEM do not establish underlying local flows. A total of 8052 adjusted bars were used, with 2684 each, and no dates are missing or extra, with OHLC and volume checks passing. The results from vectorbt reconcile to an independent compounded cash ledger for every 36 experiments, with errors less than 1e-6 on an initial capital of 10000. The drawdown is calculated from the open and close, not the intraday maximum. The fixed return is -2.359834%, with a drawdown of 9.357740%, and other metrics are also reported. Range figures are also provided, with a 3-calendar-month block and 10000 bootstrap mean 95% confidence interval of [-0.5488546052653037, 0.6886803891309617]%. All trial metrics and uncertainty are separately observed, and the code, raw inputs, and full trade and equity ledgers are local artifacts. The conclusions are about this newly specified proxy, not an exact reproduction of the institutional ANcerno study. Mandatory 15-minute and extended hours and trailing profit management, raw cash dividends, bank settlement holiday differences, and whole-share sizing are not validated, and there is no execution authorization. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content, and https://www.sec.gov/exams/educationhelpguidesfaqs/t1-faq.
A frozen protocol note, numbered 213, was written before inspecting prices. The training period was from 2016 to 2022. There is a deadline at the end of the month, which is calculated by subtracting the settlement lag plus one from the current date, with lags of 3, 2, and 1 across SEC changes. A buy order is placed at the next open only if the return from the preceding 5 sessions is less than 0, with a maximum of 3 open-to-open sessions. This is for research purposes only, using full capital, and does not reflect actual fund allocation. The strategy compares a fixed hold to a stop using a lagged Average True Range of 20, with a target of 1.5 times the Average True Range of 20. A daily stop is triggered when both conditions are met, with a stop gap at the open, and a favorable target gap capped at the target. Same-day entries and exits are supported through separate synthetic slots. Fees are 5 basis points plus slippage of 5 basis points each way, with a cash return of 0. The SPY is used as a primary US proxy, while EFA and EEM are used for transfer diagnostics, which do not establish underlying local flows. A total of 8052 adjusted SIP bars were used, with 2684 bars each, and no missing or extra dates on the NYSE calendar. The results were reconciled to an independent compounded cash ledger for every 36 experiment terminal equity, with errors less than 1e-6 on an initial capital of 10000. The drawdown is calculated based on open and close prices, not intraday maximum. The fixed return was 3.260950%, with a drawdown of 11.791381%, and other metrics including N31, p54.8387%, W1.23598%, and L1.24099%. Range figures for a 3-calendar-month block had a mean of 95CI [-0.6321942322129009, 0.38874040945891103]%. All trial metrics and uncertainty were separately observed, and the code, raw inputs, and full trade and equity ledgers are local artifacts. The conclusions concern this newly specified proxy, not an exact reproduction of the institutional ANcerno study. Mandatory 15-minute, extended hours, and trailing profit management, raw cash dividends, bank settlement holiday differences, and whole-share sizing were not validated, and no execution authorization was given. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content, and https://www.sec.gov/exams/educationhelpguidesfaqs/t1-faq.
A frozen protocol note was made before inspecting prices. From January 2023 to September 4, 2026, the trading rules were tested, but this was not a completely independent test because it was already used for other rules. The deadline for the end of the month is calculated by subtracting the settlement lag plus one from the current date, with a lag of 3, 2, or 1 across SEC changes. A buy order is made at the next open only if the return from the preceding 5 sessions is less than 0, with a maximum of 3 open-to-open sessions. This is for research purposes only, with full-capital normalization, but it does not reflect the actual fund allocation. The results are compared to a fixed hold strategy and a stop strategy with a 20-day average true range and a target of 1.5 times the average true range. A stop is made when both conditions are met, with a stop gap at the open and a favorable target gap capped at the target. Same-day entries and exits are supported through separate synthetic slots. Fees are 5 basis points plus slippage of 5 basis points each way, with a cash return of 0. SPY is the primary US proxy, and diagnostics for EFA and EEM do not establish underlying local flows. The test used 8052 adjusted bars, with 2684 each, and no missing or extra dates, and the OHLC and volume checks passed. The results from vectorbt reconcile with the independent compounded cash ledger for every 36 experiment terminal equity, with errors less than 1e-6 on an initial capital of 10000. The drawdown is calculated from the open and close prices, not the intraday maximum. The fixed return is 1.220813%, with a drawdown of 11.784379%, and other metrics are listed below. The range figures for a 3-calendar-month block with 10000 bootstrap mean and 95% confidence interval are [-0.9636297394857071, 0.5704831950615763]%. All trial metrics and uncertainty were observed separately, and the code, raw inputs, and full trade and equity ledgers are local artifacts. The conclusions are about this newly specified proxy and do not exactly reproduce the institutional ANcerno study. Mandatory 15-minute, extended hours, and trailing profit management, raw cash dividends, bank settlement holiday differences, and whole-share sizing were not validated, and there is no execution authorization. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content, and https://www.sec.gov/exams/educationhelpguidesfaqs/t1-faq.
A frozen protocol note was made before inspecting prices. The training data is from 2016 to 2022. There is a deadline at the end of the month, which is calculated as D minus the settlement lag plus one, with lags of 3, 2, and 1 across SEC changes. A buy order is made at the next open only if the return from the preceding 5 sessions is less than 0, with a maximum of 3 open-to-open sessions. This is for research purposes only, with full-capital normalization, and not for actual fund allocation. The strategy compares a fixed hold to a stop loss with a 1.5 ATR target and a 20-period ATR, with a daily stop when both are hit. Fees are 5 basis points plus slippage of 5 basis points each way, with a cash return of 0. The SPY is the primary US proxy, and diagnostics from EFA and EEM do not establish underlying local flows. The data consists of 8052 adjusted SIP bars, with 2684 bars each, and no missing or extra dates. The results from Vectorbt reconcile to an independent compounded cash ledger for every 36 experiment terminal equity, with errors less than 1e-6 on an initial capital of 10000. The drawdown is calculated from open to close, not from the intraday maximum. The fixed return is 12.367856%, with a drawdown of 11.143295%, and other metrics are as follows: N is 34, p is 52.9412%, W is 1.81719%, and L is 1.27186%. The range figures are below, with a 3-calendar-month block and a 10000 bootstrap mean 95% CI of [-0.6868827262363107, 0.40455541742822726]%. All trial metrics and uncertainty are separately observed, and the code, raw inputs, and full trade and equity ledgers are local artifacts. The conclusions concern this newly specified proxy, not an exact reproduction of the institutional ANcerno study. Mandatory 15-minute, extended hours, and trailing profit management, as well as raw cash dividends, bank settlement holiday differences, and whole-share sizing, are unvalidated and not authorized for execution. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content, and https://www.sec.gov/exams/educationhelpguidesfaqs/t1-faq.
A news scan found the headline 'CLI mean-reversion control cannot validate sector portfolio reversal'. This is a report about a test of a trading strategy called mean-reversion, which is a way of buying and selling based on the idea that prices will return to normal after a big change. The test used a computer program to look at the prices of 9 stocks, specifically XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, and XLY, over 730 days. The program looked at the prices of these stocks over 20 days to decide when to buy or sell, using a measure called the z-score, which is a way to compare prices to what is normal. The test found that the strategy did not work well, and the returns were flat when the z-score was above the entry point. The program also found that the prices of the stocks changed near the time of corporate actions, such as stock splits, and that these changes made it hard to understand the total returns. The test used data from the website https://runway.adityaperswal.workers.dev/data/stock-bars and the computer program /app/agent/dist/backtest.js.
The main formation remains frozen for 5 days. Pre-declared training-only observations from 3 to 10 days were recorded from 1773 to 1776 with 892 out of 897 payoffs. These were not selected for holdout. A cost-only attribution note 162 preserves the rule, with 20 total experiment metrics from 1765 to 1784. The one-way fixed holdout is 72.880 or 60.678 percent, with a range of 22.797 to 14.129 percent. The range zero to 2 basis points mean 95 percent confidence interval is -0.0178 to 0.2502 percent or -0.0561 to 0.2102 percent. These optimistic variants improve the headline but not confidence or actual exit eligibility. Costs are assumptions and 50 basis points stress, not asserted venue fees, and tested fees and slippages are symmetric. The gross range average trade is 0.120566 percent, which gives a rough first-order break-even of 6 basis points one way before parameter or estimation risk, but this is not an executable edge estimate. Repeated selected names are liquidated or rebought by the frozen rule, and netting may reduce cost but requires new separate registration, not edited past results. All variants have a vector backtest and independent cash ledger error of less than 1e-6. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, which is a data line.
A frozen note, numbered 161, discusses a trading strategy. The strategy involves nine original sector ETFs, which are investment funds that track a specific sector. The strategy looks at the returns of these ETFs over a five-session period and buys the bottom three performers at the next open, using equal amounts of cash. A stop-loss order, which is an order to sell a security when it falls to a certain price, is set at 2% below the purchase price, and a target price is set at 2% above the purchase price. If the price closes at or above a 1% gain, the strategy sells one-third of the position at the next open and sets a stop-loss order at the breakeven point. This process can happen for a maximum of five sessions. The strategy was tested from January 3, 2023, to September 4, 2026, using data from 2016 as a warm-up period. The test assumed a fee of 0.25% and a slippage of 0.25% for each trade, but these are not actual costs. The test used a long-only strategy, which means it only bought securities, and did not short sell. The results showed a return of -80.2992%, a maximum drawdown of 80.3135%, and other metrics. The test used a specific set of ETFs, not the entire universe of ETFs, and the results may not be applicable to all reversal theories. The test also did not validate the execution of trades at 15-minute intervals or the impact of dividends and whole-share execution. The sources of the data used in the test are https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf.
A frozen note, numbered 161, discusses a strategy for trading sector ETFs. The strategy involves buying the three sector ETFs with the lowest returns over the past five sessions, and selling them when they reach a certain target or stop loss. The strategy uses a freeze signal, which is based on the price of the ETFs, to determine when to buy or sell. The freeze signal has a stop loss of 1 ADR and a target of 2 ADR. The strategy also includes a feature to trim the position by one-third when the return reaches a certain level, and to move the stop loss to breakeven. The strategy was tested on data from January 3, 2023, to September 4, 2026, and the results show a return of -14.8384%, a maximum drawdown of 16.9591%, and other metrics. The test assumed a fee of 5 basis points plus slippage of 5 basis points on each trade. The results are based on a specific implementation of the strategy and do not necessarily apply to all reversal theories. The source of the data is listed as https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf.
A frozen note, numbered 161, describes a trading strategy. The strategy involves nine original sector ETFs, which are investment funds that track a specific sector. The strategy looks at the returns of these ETFs over a five-session period and calculates the mean return for each sector. It then buys the three sectors with the lowest returns, also known as negative residuals, at the next opening, using an equal amount of cash for each sector. The strategy holds these positions for five sessions and then sells them at the next opening. There are no mandatory stops or targets, which means that the strategy does not automatically sell a position if it loses a certain amount of money or reaches a certain profit. The strategy was tested from January 3, 2023, to September 4, 2026, using data from 2016 as a warm-up period. The test assumed a fee of 25 basis points, or 0.25%, plus a slippage cost of 25 basis points, which is the difference between the expected price of a trade and the actual price. The strategy used a technique called vectorbt, which allows for explicit long-only orders and cash sharing. The results showed a return of -72.2625%, a drawdown of 73.0675%, and other metrics. The data used for this test came from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf. The conclusion is that this specific implementation of the sector reversal strategy did not work well, and the results should not be used to make decisions about other reversal theories. The actual execution of the strategy, including the timing and dividend payments, was not validated.
A frozen note, numbered 161, discusses a trading strategy. The strategy involves nine original sector ETFs, which are bought and sold based on their performance over five sessions. The goal is to buy the bottom three performing ETFs and hold them for five sessions before selling. This strategy does not use stop-loss orders or target prices, and it is not eligible for certain trading venues. The strategy was tested from January 3, 2023, to September 4, 2026, with a warm-up period starting in 2016. The test assumed a fee of 5 basis points and a slippage cost of 5 basis points for each trade. The vectorbt system was used to execute long-only orders, and a daily ledger was kept to track cash and quantity. The results showed a return of 19.8968%, a maximum drawdown of 18.4248%, and other metrics including 549 trades, a win rate of 54.4627%, a win-to-loss ratio of 1.8538%, and a loss rate of 1.9576%. The data used for this test came from two sources: https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf. This conclusion is specific to this implementation and does not represent all reversal theories. The actual execution of the strategy, including 15-minute intervals, dividend payments, and whole-share trading, was not validated.
A news scan found the note: Frozen note 161. There were nine original sector ETFs. The system looked at the returns of these ETFs over five sessions and compared them to the average return of all sectors. If an ETF's return was less than or equal to the average, the system would buy the three ETFs with the lowest returns at the next open, using equal amounts of cash. This was called a freeze signal. The system also used a stop-loss, which is a rule to sell a security when it falls to a certain price, and a target price, which is a price at which to sell a security for a profit. The stop-loss was set at 1 average daily range, and the target price was set at 2 average daily ranges. If the system made a profit of at least 1 times the risk, it would sell one-third of the position at the next open and set the stop-loss at the break-even price. The system was trained on data from 2017 to 2022, and it used data from 2016 as a warm-up period. The system assumed a fee of 25 basis points, which is a unit of measurement for interest rates and fees, and a slippage of 25 basis points, which is the difference between the expected price of a trade and the actual price. The system used a long-only strategy, which means it only bought securities and did not sell them short. The system also used a cash-sharing strategy, which means it allocated cash to different positions based on their performance. The results showed a return of -92.3758%, a maximum drawdown of 92.5984%, and a number of trades of 896. The system had a win rate of 32.4777% and a loss rate of 2.0536%. The system was tested on a population of nine sector ETFs, which is not the entire universe of ETFs. The system used a synthetic ordering system, which is a way of simulating trades, and reported the results based on daily closing prices. The system excluded the final incomplete rotation, which means it did not include the last trade in the results, and it had zero open positions at the end of the test period. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf. The conclusion is that this is a specific implementation of a sector reversal strategy, and it is not a test of the entire theory. The system did not use any holdout data, which is a portion of the data set aside for testing, and it did not validate the results using actual 15-minute, dividend, or whole-share execution data.
A news scan found the note about a trading strategy. The strategy uses nine sector ETFs and buys the three worst performers every five sessions. It does this by allocating equal amounts of cash to each of the three ETFs at the next opening price. The strategy also includes a stop-loss order, which is an order to sell a security when it falls to a certain price, in this case, 20% below the purchase price. The strategy will close one-third of the position at the next opening price if the price increases by 1R, which is a measure of the strategy's performance, and move the stop-loss to the break-even point. The strategy was tested from 2017 to 2022, with 2016 used as a warm-up period. The test assumed a fee of 5 basis points and a slippage of 5 basis points on each trade. The results showed a return of -16.8486%, a maximum drawdown of 31.2305%, and 896 trades, with 44.7545% of trades being winners and 1.9125% being losers. The strategy was tested on a population of nine current ETFs, not the entire universe of ETFs. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf. The conclusion is that this specific implementation of the strategy was not adopted or tuned from a holdout set. The actual execution of the strategy, including 15-minute, dividend, and whole-share execution, was not validated.
A frozen note, number 161, describes a trading strategy. The strategy involves nine original sector ETFs. It calculates the returns of these ETFs over five sessions and compares them to the average return of all sectors. Every five sessions, it buys the three sector ETFs with the lowest returns, also known as negative residuals, using an equal amount of cash. It holds these ETFs for five sessions and then sells them at the opening price. There are no mandatory stops or targets, so this strategy is not eligible for a specific trading venue. The strategy was trained on data from 2017 to 2022, with 2016 used as a warm-up period. The training dates overlap with other research and are not a completely untouched sample. The strategy assumes a fee of 25 basis points and a slippage of 25 basis points on each side, but these are not actual live cost measurements. The strategy uses explicit long-only orders and cash sharing. It also uses an independent cash and quantity daily ledger, which agrees with the results to a very small margin of less than 1e-6. The strategy does not observe synthetic four-event per day ordering in intraday data and reports daily close drawdowns. The final incomplete rotation is excluded, and there are zero open positions and terminal marks in payoffs. The results show returns of -92.7967%, a drawdown of 92.7967%, 896 trades, a win percentage of 36.7188%, a win to loss ratio of 2.0795%, and a loss to win ratio of 2.5193%. The population used is the current nine wrappers, not the entire point-in-time ETF universe. The original Nagel 48 industry long-short daily weights differ from this strategy. The data sources are https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nber.org/system/files/working_papers/w17653/w17653.pdf. This conclusion is specific to this implementation and not all reversal theory. There was no adoption or tuning from the holdout. The actual 15-minute, dividend, and whole-share execution has not been validated.