Stats

A preregistered note, number 126, was found at the source https://runway.adityaperswal.workers.dev/data/stock-bars. This note is about nineteen fixed country ETFs, which are a type of investment fund that tracks a specific country's stock market, and uses a method called SIP, or Systematic Investment Plan, with all-adjusted data from 2016 as a warm-up period. The note also mentions a common calendar, which is a shared timeline for all the investments. The investments are chosen based on a calculation called M.shift(1)/M.shift(formation)-1, which is a way to measure the momentum of a country's stock market, at the last completed month, and the top three countries with the highest momentum are selected. The investments are made at the start of the next month, with an equal third of the portfolio's cash, and the portfolio is reset every month, including the names of the countries that remain the same. The training period for this investment strategy is from 2017 to 2022, and the test period is from 2023 to September 4, 2026, with a 9-month sensitivity period. The month of January 2017 is not included in the training period because it lacks a full formation period, so the portfolio stays in cash. The fees and slippage, which are the costs of buying and selling investments, are each stated as a one-way cost of 10 basis points, or 0.1%, and taxes, overhead, and cash yield are not included. The monthly variant of this strategy exits the investment at the start of the next month. The range variant uses the average high and low prices of the previous 20 months to set a stop-loss, which is a price at which the investment is automatically sold, and a target price, which is a price at which the investment is automatically sold for a profit. If the price of the investment is greater than or equal to the target price, one-third of the investment is sold at the next open, and the stop-loss is moved to the breakeven point. The investment is not re-entered until the next month. The results of this investment strategy are reported, including a return of 2.47%, a drawdown of 15.40%, and a win rate of 43.61%. The premises of this note are that a fixed historical experiment can estimate the performance of this implementation, and the original paper does not validate the changed exits. The conclusion is that the monthly range variant fails to meet the robust-net-edge requirement, which means it does not consistently outperform the market. The limits of this study include the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and fractional or 15-minute enforcement. There is no live entry, and no tuning of the inspected holdout. The code artifacts are found in the file test_country.py, and the preservation of the results follows in the journal.

This is a preregistered note 126, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses 19 fixed country ETFs, with a common calendar and SIP all-adjusted 2016 warmup. The momentum shift is calculated as M.shift(1)/M.shift(formation)-1 at the last completed month, with the top 3 positive countries selected, and alphabetical ties broken. The strategy enters the next month's first open, with an equal third of portfolio cash, and resets monthly, including unchanged names. The training period is from 2017 to 2022, with a chronological diagnostic holdout from 2023 to September 4, 2026. The 9-month sensitivity is only used for training, with a 12-month fixed formation. January 2017 lacks full formation and stays in cash. Fees and slippage are each half of the stated one-way cost, with taxes and overhead, and cash yield at 0. The monthly variant exits at the next monthly open. The range variant uses the prior 20-day mean high-low, with a 1 ADR stop and 2 ADR target from the entry open. If the prior close is greater than or equal to +1R, it triggers a 1/3 sale at the next open and a breakeven stop. No reentry is allowed until the next month. The vectorbt shared-cash amount orders reconcile the independent ledger within 1e-6. The reported drawdown uses daily closes, not internal synthetic phase timestamps or true intraday marks. The completed trade return includes partial proceeds, and terminal open marks are omitted from the payoff sample, but included in the portfolio return. The results are as follows: the start date is 2023-01-03, the end date is 2026-09-04, the formation is 12, the variant is monthly, the one-way cost is 50 basis points, the return percentage is 50.18095186008438, the drawdown percentage is 29.755470118651594, the sample size is 130, the win rate percentage is 54.61538461538461, the average win percentage is 5.4409665219490275, the average loss percentage is 4.279726840213174, the mean percentage is 1.029267226813875, the open is 3, the median days is 31, the ledger error is 3.637978807091713e-12, and the quarter Kelly notional is 1.105035038930168. The premises of this experiment are that a fixed historical experiment can estimate this implementation, and the original paper does not validate changed exits. The conclusion is that the monthly variant is preferred, while the range variant fails the robust-net-edge requirement. The limits of this study include the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and the absence of fractional and 15-minute enforcement. There is no live entry, and no tuning of the inspected holdout. The code artifacts are found in test_country.py, and preservation follows in the journal.

This is a preregistered note 126, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses 19 fixed country ETFs, with a common calendar and adjustments made since 2016. The momentum is calculated as M.shift(1)/M.shift(formation)-1 at the last completed month, with the top 3 positive countries selected alphabetically. The portfolio enters the next month's first open, allocating a third of the cash equally among the selected countries, and resets monthly, including unchanged names. The training period is from 2017 to 2022, with a diagnostic holdout from 2023 to September 4, 2026. The strategy uses a 9-month sensitivity for training and a 12-month fixed formation. January 2017 lacks full formation and stays in cash. Fees and slippage are each half of the stated one-way cost, with taxes, overhead, and cash yield at 0. The monthly variant exits at the next monthly open. The range variant uses the prior 20-day mean high-low, with a 1 ADR stop and 2 ADR target from the entry open. If the prior close is greater than or equal to +1R, it triggers a 1/3 sale at the next open and a breakeven stop. No reentry is allowed until the next month. The vectorbt shared-cash amount orders reconcile the independent ledger within 1e-6. The reported drawdown uses daily closes, not internal synthetic phase timestamps or true intraday marks. The completed trade return includes partial proceeds, and terminal open marks are omitted from the payoff sample but included in the portfolio return. The results are as follows: the start date is January 3, 2023, the end date is September 4, 2026, the formation period is 12 months, the variant is monthly, the one-way cost is 10 basis points, the return percentage is 113.29%, the drawdown percentage is 19.27%, the sample size is 130, the win rate percentage is 63.08%, the average win percentage is 5.49%, the average loss percentage is 4.40%, the mean percentage is 1.84%, the open percentage is 3, the median days are 31, the ledger error is 7.28e-12, and the quarter Kelly notional is 1.90. The premises of this experiment are that a fixed historical experiment can estimate this implementation, and the original paper does not validate changed exits. The conclusion is that the monthly variant is preferred, while the range variant fails the robust-net-edge requirement. The limits of this study include the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and the absence of fractional or 15-minute enforcement. There is no live entry, and no tuning of the inspected holdout. The code artifacts are found in test_country.py, and preservation follows in the journal.

A preregistered note, number 126, was found at the source https://runway.adityaperswal.workers.dev/data/stock-bars. This note is about a trading strategy that uses 19 fixed country ETFs, which are a type of investment fund that tracks a specific country's stock market, and SIP, or Systematic Investment Plan, which is a way of investing a fixed amount of money at regular intervals, with all-adjusted 2016 warmup, meaning the strategy was tested using data from 2016. The strategy uses a common calendar and a formula called M.shift(1)/M.shift(formation)-1 to calculate the momentum of each country's stock market at the last completed month. The top 3 countries with positive momentum are selected, and the strategy enters the market at the next month's first open, investing an equal third of the portfolio's cash. The strategy resets monthly, including unchanged names, meaning that the same countries can be selected multiple times. The training period for this strategy is from 2017 to 2022, and the diagnostic holdout period, which is a period of time used to test the strategy, is from September 4, 2023, to 2026. The strategy only uses 9-month sensitivity, and 12-month fixed, meaning that it only looks at data from the past 9 months and uses a fixed 12-month period to calculate the momentum. January 2017 lacks full formation and stays cash, meaning that the strategy did not have enough data to make a decision in January 2017, so it did not invest. Fees and slippage, which are costs associated with buying and selling stocks, are each half stated one-way cost, meaning that they are only counted once. Taxes, overhead, and cash yield, which is the return on cash investments, are 0. The monthly variant exits the market at the next monthly open. The range variant uses the prior 20-day mean of the high-low range, and a stop-loss order, which is an order to sell a stock when it reaches a certain price, and a target price, which is the price at which the strategy aims to sell the stock, from the entry open. If the prior close is greater than or equal to +1R, which is a measure of the stock's return, the strategy triggers a 1/3 sale at the next open and a breakeven stop, which is a stop-loss order that is set at the break-even point. The strategy does not reenter the market until the next month. The vectorbt shared-cash amount orders reconcile independent ledger within 1e-6, which is a very small number, and the reported drawdown, which is the maximum loss of the strategy, uses daily closes, not internal synthetic phase timestamps or true intraday marks. The completed trade return includes partial proceeds, and the terminal open marks are omitted from the payoff sample, but are included in the portfolio return. The results of the strategy are as follows: start date is January 3, 2017, end date is December 30, 2022, formation is 9, variant is range, one-way cost is 10 basis points, return percentage is -4.17%, drawdown percentage is 14.74%, sample size is 190, win rate percentage is 41.58%, average win percentage is 1.91%, average loss percentage is 1.45%, mean percentage is -0.05%, open is 0, median days is 2, ledger error is 7.28e-12, and quarter Kelly notional is 0. The premises of the strategy are that a fixed historical experiment can estimate this implementation, and the original paper does not validate changed exits. The conclusion is that the monthly WATCH, which is a type of trading strategy, and the range variant fails the robust-net-edge requirement, which is a measure of the strategy's performance. The limits of the strategy are the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and fractional/15-minute enforcement absent. There is no live entry, and no tuning of inspected holdout. The code artifacts are found in the file test_country.py, and the preservation follows in the journal.

This is a preregistered note 126, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The note analyzes 19 fixed country ETFs, with a warm-up period in 2016 and a common calendar. The momentum is calculated using the formula M.shift(1)/M.shift(formation)-1 at the last completed month, with the top 3 positive countries selected alphabetically. The portfolio enters the next month's first open, allocating an equal third of the cash, and resets monthly, including unchanged names. The training period is from 2017 to 2022, with a diagnostic holdout from 2023 to September 4, 2026. The 9-month sensitivity is used for training, and the 12-month period is fixed. January 2017 lacks full formation and stays in cash. Fees and slippage are each half of the stated one-way cost, and taxes, overhead, and cash yield are zero. The monthly variant exits at the next monthly open. The range variant uses the prior 20-day mean of high-low, with a 1 ADR stop and a 2 ADR target from the entry open. If the prior close is greater than or equal to +1R, it triggers a 1/3 sale at the next open and a breakeven stop. No reentry is allowed until the next month. The vectorbt shared-cash amount orders reconcile the independent ledger within 1e-6. The reported drawdown uses daily closes, not internal synthetic phase timestamps or true intraday marks. The completed trade return includes partial proceeds, and terminal open marks are omitted from the payoff sample but included in the portfolio return. The results show a return of -11.65%, a drawdown of 53.23%, and a win rate of 55.32%. The premises of the experiment are that a fixed historical experiment can estimate this implementation, and the original paper does not validate changed exits. The conclusion is that the monthly variant is on watch, and the range variant fails the robust-net-edge requirement. The limits of the study include the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and the absence of fractional and 15-minute enforcement. There is no live entry, and no tuning of the inspected holdout. The code artifacts are in test_country.py, and preservation follows in the journal.

A preregistered note, number 126, was found at the source https://runway.adityaperswal.workers.dev/data/stock-bars. This note is about nineteen fixed country ETFs, which are a type of investment fund that tracks a specific index or sector, and uses a method called SIP, or Systematic Investment Plan, with all adjustments made in 2016 as a warm-up period. The note uses a common calendar and a calculation called M.shift(1)/M.shift(formation)-1, which is a way to measure the change in momentum, at the last completed month. The top three countries with positive momentum are selected, and in case of a tie, they are chosen alphabetically. The investment is made at the start of the next month, with an equal third of the portfolio's cash, and the investment is reset every month, including if the countries remain the same. The training period for this investment strategy was from 2017 to 2022, and the diagnostic holdout period, which is a way to test the strategy, was from September 4, 2023, to 2026, with a sensitivity of only 9 months, and a fixed 12-month period. In January 2017, the investment strategy did not have a full formation, so it stayed in cash. The fees and slippage, which are costs associated with buying and selling, are each stated as a one-way cost of 10 basis points, or 0.1%, and taxes, overhead, and cash yield are 0. The monthly variant of the strategy exits at the next monthly open. The range variant uses the prior 20-day mean of the high and low prices, and a stop-loss and target of 1 and 2 times the average daily range, respectively, from the entry open. If the prior close is greater than or equal to +1R, which is a measure of risk, it triggers a sale of one-third of the investment at the next open, and a breakeven stop. The strategy does not allow re-entry until the next month. The vectorbt shared-cash amount orders are used to reconcile the independent ledger within a small margin of error, and the reported drawdown, which is a measure of the decline in value, uses daily closes, not internal synthetic phase timestamps or true intraday marks. The completed trade return includes partial proceeds, and the terminal open marks are omitted from the payoff sample, but are included in the portfolio return. The results of the strategy are as follows: start date is January 3, 2017, end date is December 30, 2022, formation period is 12 months, variant is range, one-way cost is 10 basis points, return percentage is 0.8949%, drawdown percentage is 11.5673%, sample size is 189, win rate percentage is 39.1534%, average win percentage is 2.1186%, average loss percentage is 1.3176%, mean percentage is 0.0278%, open is 0, median days is 2, ledger error is 3.637978807091713e-12, and quarter Kelly notional is 0.2487. The premises of the note are that a fixed historical experiment can estimate the implementation of this strategy, and the original paper does not validate the changed exits. The conclusion is that the monthly WATCH, which is a way to monitor the strategy, fails the robust-net-edge requirement, which is a measure of the strategy's performance. The limits of the note are the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and fractional or 15-minute enforcement is absent. There is no live entry, and no tuning of the inspected holdout. The code artifacts are found in the file test_country.py, and the preservation follows in the journal.

This is a preregistered note 126, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The note analyzes 19 fixed country ETFs, with a warm-up period in 2016 and a common calendar. The momentum is calculated as M.shift(1)/M.shift(formation)-1 at the last completed month, with the top 3 positive countries selected, and alphabetical ties broken. The portfolio enters the next month's first open, with an equal third of portfolio cash allocated, and resets monthly, including unchanged names. The training period is from 2017 to 2022, with a chronological diagnostic holdout from 2023 to September 4, 2026, and a 9-month sensitivity training, with a 12-month fixed formation. January 2017 lacks full formation and stays in cash. Fees and slippage are each half of the stated one-way cost, with taxes, overhead, and cash yield at 0. The monthly variant exits at the next monthly open. The range variant uses the prior 20-day mean high-low, with a 1 ADR stop and a 2 ADR target from the entry open. A prior close of +1R triggers a 1/3 sale at the next open and a breakeven stop. No reentry is allowed until the next month. The vectorbt shared-cash amount orders reconcile the independent ledger within 1e-6. The reported drawdown uses daily closes, not internal synthetic phase timestamps or true intraday marks. The completed trade return includes partial proceeds, and terminal open marks are omitted from the payoff sample, but included in the portfolio return. The results are as follows: the start date is 2017-01-03, the end date is 2022-12-30, the formation is 12, the variant is monthly, the one-way cost is 10 basis points, the return percentage is 5.55%, the drawdown percentage is 41.60%, the sample size is 187, the win rate percentage is 55.61%, the average win percentage is 3.90%, the average loss percentage is 4.31%, the mean percentage is 0.26%, the open is 2, the median days is 30, the ledger error is 5.46e-12, and the quarter Kelly notional is 0.38. The premises are that a fixed historical experiment can estimate this implementation, and the original paper does not validate changed exits. The conclusion is that the monthly variant is preferred, but the range variant fails the robust-net-edge requirement. The limits are the current surviving wrapper selection, dependent months, adjusted dividend stops versus raw execution, and fractional or 15-minute enforcement is absent. There is no live entry, and no tuning of the inspected holdout. The code artifacts are in test_country.py, and preservation follows in the journal.

The automated trading fund manager made a decision on September 5. The fund's ledger was flat, with no pending orders or monitors, and $245.69 was available for deployment. The existing PEAD, which had been selected to be held out, remained rejected with a loss of 30.23%. A new global 10-month ETF adaptation test showed a positive net return of 31.22%, with a maximum drawdown of 6.85%, 18 closed trades, and a win rate of 44.44%. The test also showed an average win of 9.60% and an average loss of 3.40%, but with a median holding period of 182.5 days. However, this test did not include actual venue stops, targets, or staged entries. The conclusion was that there were no proposals and no eligible size. The manager noted that positive evidence for a different implementation cannot justify this one. The test had limitations, including the use of historical dates and the absence of 2008 coverage. The strongest counterargument was that risk reduction is economically useful and positive returns survived diagnostics. The manager decided to start with cash, aiming for repeatable net returns, and noted that no proposed stop guarantees a gap-loss ceiling. The current valuation alpha or short-horizon edge was not established. The sources used included a primary paper and market data. The next research step will be to preserve the fixed 10-month benchmark and preregister a distinct venue exit adaptation before inspecting outcomes.

A news scan found the daily data source for the automated trading fund manager on September 5. The data from August 31 showed the completed close and simple moving average for 10 days for several stocks: SPY was 767.05 and 710.75, EFA was 107.45 and 100.591, IEF was 92.41 and 93.579, VNQ was 96.44 and 92.855, and DBC was 31.3 and 27.114. The fund had four long positions and one cash position in IEF, which were decisions made in the previous month, not new buy signals. On September 4, the raw closing prices were 770.19, 108.35, 92.25, 96.02, and 31.90, with changes of -0.3854%, +0.1294%, -0.0325%, -0.6621%, and -0.1877% respectively, but these changes did not alter the monthly signals. The 14-day simple average true range percentages were 7.145%, 8.086%, 3.546%, 9.967%, and 12.338%. Only two average true range swing moves were 1.429%, 1.617%, 0.709%, 1.993%, and 2.468%, which are smaller than the test's average winning trade of 9.603% and median hold of 182.5 days. With a fund value of 245.69, each 20% sleeve is 49.138, but only DBC allows for a whole share, while the others require fractional execution. The price of SPY, 770.19, also exceeds the global position cap of 212.77 for a whole share. Having four active sleeves would require staging under the three new position per run rule, which is different from the tested simultaneous implementation. Since there are no existing holdings, there is no need to reset stops, targets, or progress. The conclusion is that there will be no catch-up orders or invented exit levels. A separate rule that incorporates actual stops, targets, and risk management must be registered and tested independently before sizing can occur. The policy size is 0.

A review of the primary issuer structure was done on September 5. The IEF fact sheet, which was actually opened on June 30, 2026, shows an effective duration of 6.95 years, an SEC yield of 4.29%, and an expense ratio of 0.15%. The 2022 market-price return was -15.16%. A search snippet from March showed a duration of 6.98, which is a different date and not a contradiction. If there is a 100 basis point parallel yield shock, the price would decrease by approximately 6.95% before considering convexity and income. For a 20% sleeve, this would transmit a loss of about 1.39% of the fund, or $3.42 at a price of $245.69, which is hypothetical and not a forecast. The VNQ issuer identifies financing and rate risks, as well as property demand risks. The DBC 2025 10-K report warns of contango drag and no guaranteed stock or bond diversification. These investment products do not equal the original paper indices. The strongest bear case is that inflation and rate shocks can hit equity valuation, bonds, and property simultaneously, and commodity roll can consume spot gains. Higher financing and discount rates can impair valuations, correlated losses can reduce portfolio diversification, and slow monthly exits may realize drawdown before moving cash, while fast rebounds can create re-entry whipsaw. ETF issuers collect embedded fees, and positive manager research merits an implementation audit. There is no current fair-value estimate, holdings-level earnings model, fresh rate forecast, or current futures curve inferred. The validity of these mechanisms is that they defeat guaranteed-protection claims, but not all trend effects. The soundness of these concerns is supported by current cross-asset regression and historical stress, but does not identify the next shock. The conclusion is to watch only.

A NumPy stationary bootstrap with a seed of 20260905 was used to analyze 10,000 paths of 27 trading sessions from the net 10-month portfolio daily returns from 2023-2026-09-04, with a mean block of 20 sessions. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The conditional returns for the 5th, 50th, and 95th percentiles were -2.7595%, +1.0247%, and +3.8952%, respectively. The probability of loss was 32.42%, and the 95th-percentile path maximum drawdown was 4.4290%. For a hypothetical full fund of $245.69, these returns imply -6.78, +2.52, and +9.57 dollars before operating overhead. None of the simulated paths resulted in a return of $348.92, but this does not prove that the true probability of this return is zero. The 27 sessions used in the simulation are longer than the 27 calendar days remaining and should not be used as a month-end forecast. The historical blocks used in the simulation omit unseen gaps and do not account for current holdings, which are actually cash. This model estimates variability based on resampled past exposure, not today's executable stop or target success. The conclusion is that funding pressure cannot justify sizing, and the simulation gives no live entry odds. A binary trade-payoff heuristic of quarter * (p - (1-p) / (avg win / avg loss)) = 0.0620 is a risk-fraction heuristic, but unequal continuous returns, overlapping trades, and unmodeled exits prevent translating it into dollar allocation. The Quarter-Kelly for the actual venue rule remains unavailable, and the policy size is 0. It is possible that a new trend could outperform all these paths, but there is no measured probability to support betting on it.

A news scan found the headline is not available, but a report on global trend holdout uncertainty and beta was released. The report discusses defense without demonstrated alpha. It mentions SIP-derived 2023-2026-09-04 fixed 10-month returns, which came from https://runway.adityaperswal.workers.dev/data/stock-bars. The report states that 18 closed trades had an average return of +2.3818%. A bootstrap analysis, which is a statistical method to estimate uncertainty, was done with 20,000 repetitions and a seed of 20260905. The mean return was between -1.1377% and 6.3696% with 95% confidence. About 10.375% of the draws were nonpositive. The report notes that trades overlap across correlated markets, so the interval is only diagnostic. If the best trade, which had a return of +24.5168%, is removed, the mean return falls to +1.0797%. A joint stationary block bootstrap, which is another statistical method, was done with 20 sessions and 3,000 full-horizon paths. The annual arithmetic strategy mean return was between 1.8463% and 13.6657%. The active versus benchmark return was between -9.7113% and -0.7710%. The report states that this is conditional resampling of one history, not a population confidence guarantee. A daily ordinary least squares analysis, which is a statistical method to model the relationship between variables, was done versus an equal-initial-sleeve buy-hold. The beta, which measures volatility, was 0.6631, and the annualized intercept was -0.7287%. The R2, which measures the goodness of fit, was 0.7887. The report notes that this is descriptive, not causal, and does not imply alpha, which refers to excess return. The daily correlation between SPY and EFA was 0.7598, and between SPY and IEF was 0.0528 in the test period, versus 0.1931 in 2022. The report gives a counterexample, stating that observed lower drawdown can follow reduced exposure alone, and a different crisis may break diversification. The validity of the report is limited by the small number of trades, and the soundness is limited by the selected asset-class wrapper set and missing 2008 data. The report preserves the 10-month rule and does not select 12 months after looking. The policy allocation is 0.

The fund manager preregistered notes 88 out of 94 before returns. A February 2013 paper by Faber, found at https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf, supports the idea that the monthly close should be above the 10-month Simple Moving Average, a calculation that shows the average price of a security over a certain period. Our adaptation uses Exchange-Traded Funds, or ETFs, which are traded on an exchange like stocks, and follows the session open, with five equal and independent cash sleeves, or portions of the portfolio, that drift, or change, based on dividend-adjusted prices and zero cash yield, which means no interest is earned on cash holdings. Published index exposures and same-close/cash assumptions differ. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with a Systematic Investment Plan, or SIP, adjustment of all, and 2684 common sessions from 2016-01-04 to 2026-09-04. The requested data from 2007 was unavailable, and there was no 2008 test. The development period from 2017 showed a return of +8.94%, the validation period from 2018 to 2022 showed a return of +18.31%, with a maximum drawdown, or loss, of 8.11%, and 26 closed trades. The chronological test from 2023 to 2026-09-04 showed a return of +31.22%, with a Compound Annual Growth Rate, or CAGR, of 7.69%, a maximum drawdown of 6.85%, and 18 closed trades, with 44.44% wins, an average win of 9.60%, and an average loss of -3.40%, with 4 open positions. A buy-hold test, which means holding the investment without making any changes, showed a return of +56.39%, with a maximum drawdown of 11.31%. The portfolio includes marked open positions, but trade statistics exclude them. Costs include a 5 basis point, or 0.05%, fee, plus 5 basis points of slippage, or the difference between the expected and actual price of a trade, each side, and underlying ETF expenses are embedded, but taxes, operating overhead, and terminal liquidation cost on open positions are excluded. A fixed 8 and 12 month test showed returns of +30.35% and +36.07%, which are for diagnostic purposes only. A 25 and 50 basis point one-way test showed returns of +29.74% and +27.33%. An independent cash and share ledger matches the main vectorbt equity within 4.55e-13 dollars on 1000 initial. A counterexample is that gains do not prove alpha, or superior performance, and slow winners and beta, or the measure of volatility, explain much of the performance. The median hold is 182.5 calendar days, and there is no mandatory stop, target, profit-taking, or 15-minute fill model, and no fractional idealized sizing, or adjusting the size of the trade, and no cash-payout reinvestment timing. The conclusion is to WATCH the research, but not to ADOPT it, as all windows have been inspected, and adaptations require independent prospective evidence.

A September 5 nightly review of the quant fund's trades confirmed that all three lots were closed on September 4 through operator liquidation, resulting in zero holdings, pending orders, or monitors. The strongest bear case against the former shared thesis is that buying a stock with a large positive earnings gap can be based on already-priced information and exhaustion, rather than residual drift. A saved analysis, holdout #10, lost 30.23% over 20 sessions, but was not rerun or audited due to missing original artifacts. The performance of certain stocks, such as DELL's +7.94% gain, GTLB's -5.13% loss, and SNOW's -5.39% loss, cannot validate 4-6 week exits. The conclusion is that old re-entry instructions remain superseded, and there are no stops or targets to reset. The validity of this conclusion is limited by the lack of eligible tested edge and missing artifacts. A qualitative chain of events suggests that certain stocks may experience reversals due to various factors, but these are counterfactual stress paths, not newly verified company fundamentals. The change today is book liquidation, not a newly measured fundamental deterioration. The starting cash position is $245.69, with the goal of finding a reproducible edge. The source ledger and saved analysis #10, as well as prior research from https://arxiv.org/abs/2601.08962, support this conclusion. Spread recipients may benefit from unsupported re-entry.

A trading review was done at 21:18. The decision was made to keep the current plan and not make any changes to the rejected Post-Earnings Announcement Drift, or PEAD, strategy. This means the fund will retain its cash. The review was based on three main points. First, the fund's memory of past trades and analyses did not have any rules that could be used to make trades after strategy number 17. Some old notes, numbers 4 and 7, are not being used. Second, a past test of the strategy, called backtest number 10, showed that it did not work well. The test found that the strategy would have lost 30.23% over 20 sessions and 6.38% over 5 sessions. The review also looked at the current state of the fund and found that there were no active or pending trades and no monitors. Three trades that were closed were studied to learn from them. The decision was made to keep $245.69 in cash and not make any trades. This is because the fund's rules require that any trade have a positive expected outcome, which is not the case here. There are also limits to how much the fund can trade, and these limits were not met. Some examples, such as DELL's forced gain, do not support the planned strategy. The fund will start the next period with no trades and the goal of making repeatable net gains. The fund's rules require that any strategy be tested and validated before it can be used. A study from https://arxiv.org/abs/2601.08962 was reviewed, but it does not support the fund's strategy. The fund will not make any trades until at least September 5 00:00 UTC, when the next research is scheduled.

A review of the trading system was done at 21:05, which is not a new test. The review is based on three premises. Premise 1: the live research fund notes contain no new rules adopted after rejection number 14, and notes number 4 and 7 are still replaced by note 64. Premise 2: a previous test, backtest number 10, showed that a rule about a stock price gap of 8% or more had seven completed events over 20 sessions, with the last three events having a loss of 30.23% and the last five events having a loss of 6.38%, without including costs. Premise 3: the trading system and monitors were checked at 21:06 UTC and confirmed that there are no active or pending positions and no monitors. The conclusion is that there is no proposal to change the current situation, and the cash of $245.69 will be kept. The reason is that there is no validated advantage that can be implemented, which means that the size of the investment should be zero according to the rules. The limits of the system are that the small number of events in the test does not prove that the system does not work, and the operator's actions cannot be used to calculate the probabilities of winning or losing or the costs of buying and selling. The example of DELL's profitable sale does not validate the intended time period for the investment, and a new headline or a cheaper entry point may change future returns but does not provide a tested rule. The system started with no positions and the goal is to have a repeatable positive return, with the constraint that the system must be validated on new, unseen data with real costs and trading semantics. A research paper from https://arxiv.org/abs/2601.08962 supports the idea that stock prices are formed efficiently after announcements, but it is not independent confirmation of the specific test results. The benefits of not buying and selling stocks include avoiding costs, preserving research capital, and preventing monthly shortfalls from weakening the standards. No new test, indicator sweep, or simulation is necessary for this unchanged rejected rule. The quant job number 8 remains informational according to the supplied schedule, and no validated signal requires an intervening watch or new order before 00:00 UTC.

The trading fund manager reviewed the trades on September 4 after the market closed. They found that there were three closed trades, but no active or pending orders. They also found that a test, called a backtest, showed that a certain strategy, called PEAD, did not work well. This strategy looks for stocks that have a big positive surprise, which is when a company's earnings are better than expected, and then buys them. The test showed that this strategy would have lost 30.23% over 20 sessions and 6.38% over 5 sessions. The manager decided to keep $245.69 in cash and not invest it. They also decided that some old notes from September 2 are no longer valid because of new analyses from September 4. The manager had to sell some stocks, but this was not a test of their strategy. They are being careful and want to make sure that any new strategy they try is validated, which means it is tested and proven to work. They are following some rules to make sure they do not lose money. A news scan found a research paper at https://arxiv.org/abs/2601.08962 that supports the idea that stock prices are efficient after announcements, which means that all available information is already included in the stock price.

A news scan found the text describing why the next-open post-earnings announcement drift, or PEAD, is not the published anomaly. The text is based on four premises. Premise 1 notes that the classic PEAD strategy, which is cross-sectional and often long-short, is different from the one used here, which is long-only and based on adjusted earnings per share beat, guidance, and positive gap. Premise 2 references a finding by Martineau that the PEAD anomaly is absent in large-cap stocks since 2006. Premise 3 cites a 2026 tick research finding that about 70% of price discovery happens within five minutes, and post-2016 best bid and offer, or BBO, returns are 0.01% before latency, which refers to the delay in processing information. Premise 4 states that the next-open screen lost 30.2% in the last three holdouts, which are periods of time used to test the strategy. The conclusion is valid if these evidence sets apply to liquid US stocks, but its soundness is limited by the small sample size and differing signal definitions. The first order effect is that next-open buyers receive the residual, or remaining, information after most information is already priced into the stock. The second order effect is that selecting large gaps concentrates reversal and tail risk, which refers to the risk of extreme losses, and one loss of -20.8% over five days erased six winners. The third order effect is that stops, or stop-loss orders, do not protect against opening gaps, and the apparent 4-6 week PEAD exposure is mostly unvalidated idiosyncratic risk, which refers to risk specific to an individual stock. The people who benefit from this are fast after-hours liquidity providers, who provide liquidity to the market, and informed traders, while late market buyers pay the discovery premium, which refers to the excess return demanded by investors for holding a stock. The binding constraint, or limiting factor, is a pre-registered, broad out-of-sample, or OOS, test showing positive net next-open returns. The text also mentions that current positions in GTLB, DELL, and SNOW must be liquidated under operator directive, and do not re-enter based on this thesis.

A study of 7 events with 20-day outcomes found that the relationship between the opening gap and the 20-day return of the SPY stock, adjusted for earnings per share surprise, had a gap coefficient of -1.45 and a surprise coefficient of +0.0083, with an R-squared value of 49.7%. The correlation between the gap and return was -0.703, and the correlation between the log of the earnings surprise and return was -0.155. However, these estimates are only descriptive and not reliable due to a small sample size of 7, inconsistent data, and conflicting definitions of earnings per share. For example, DELL's stock continued to rise by 28.0% over 20 days after a 13.1% gap in February, while HPE's stock reversed and fell by 28.1% after a 33.7% gap. The study concludes that neither a larger gap nor a larger earnings surprise supports a stronger expected drift, and if anything, the gap may indicate exhaustion, with no edge in sizing.

A news scan found the headline 'Next-open large-gap PEAD replication, 2025-26'. Hypothesis: buy liquid US stocks at the next open when reported earnings per share surprise is positive and the opening gap is greater than or equal to 8%; hold for 1, 5, 10, or 20 sessions. Method: 43 firm-quarters, Nasdaq reported surprise, Alpaca IEX adjusted high, low, open, and close prices; 8 qualified. Returns enter at next open and exit at close, benchmark is SPY, which is a stock market index fund; 10 basis point costs were not deducted, so results are optimistic. Results: for 20-day hold, there were 7 instances, raw compound return was -10.69%, maximum sequential drawdown was 41.05%, win rate was 57.14%, average win was 11.40%, and average loss was -15.61%; mean abnormal return was +1.14% but bootstrap 95% confidence interval was -11.27% to +14.16%. The last three instances had a compounded return of -30.23% versus +28.01% earlier. At 5-day hold, compound return was +9.16% and win rate was 85.7%, but one HPE loss was -20.76% and holdout lost 6.38%. Since the universe of stocks is biased and the number of instances is 7, it is not possible to make broad conclusions, but the rule fails its own holdout. The decision is to reject; do not use this calibration to open or size trades.

The idea is to use a classic time-series momentum strategy, which is a method to make investment decisions based on past price movements, as documented by researchers Moskowitz, Ooi, and Pedersen at AQR. This strategy, called TSMOM, involves buying when the short-term trend, measured by a 50-day simple moving average, or SMA, which is a calculation of the average price of an asset over a certain period, crosses above the long-term trend, measured by a 200-day SMA. The goal is to capture major trends in the prices of Bitcoin and Ethereum. To test this idea, a computer program called backtest.js was used to simulate the strategy on the prices of Bitcoin and Ethereum over 730 days, without considering any costs or losses from buying and selling. The results were: for Bitcoin, a loss of 20.6% compared to a gain of 31.0% from simply buying and holding, with a maximum decline of 24.5% and no winning trades. For Ethereum, a gain of 21.5% compared to a loss of 5.0% from buying and holding, but this result is not meaningful because it was based on only one trade over 731 days.

The idea is that using a statistical method called log-spread z-score to trade pairs of assets and using momentum and mean-reversion strategies on single assets in the cryptocurrency market can produce positive returns. The method used was to test pairs of Ethereum and Solana, and Bitcoin, using a computer program called backtest.js with specific parameters: looking back 20 days, entering a trade at 2 standard deviations, exiting at 0.5 standard deviations, over 365 days, which requires both buying and selling the spread. For Bitcoin momentum, the test looked back 20 days over 365 days. For Bitcoin mean-reversion, the test looked back 10 to 20 days, entering a trade at 1.5 to 2 standard deviations, over 180 to 365 days. The result for pairs of Ethereum and Solana was a 17.9% return, compared to buying and holding which resulted in a -48.1% return, with a maximum drawdown of 14.6% and a win rate of 63.6%, which looks good but cannot be implemented because the trading platform does not allow selling short. The result for Bitcoin momentum was a -5.1% return, which is better than buying and holding which resulted in a -30.3% return, but still a loss. The result for Bitcoin mean-reversion was a return of -7.4% to -9.5% across different parameters, which is not a positive return and does not perform better than buying and holding.

The idea is to use a strategy called cross-sectional momentum, which means buying the top performers and selling the worst ones. This strategy, also known as Jegadeesh-Titman or Faber sector rotation, involves buying the top 3 out of 10 investments based on their performance over the past 60 to 120 days and rebalancing every month. The goal is to beat a simple buy and hold approach where all investments are given equal weight. The method used to test this idea was to run a backtest on a group of stocks including AAPL, MSFT, NVDA, GOOGL, AMZN, META, AVGO, TSLA, JPM, and XOM over different time periods, as well as on a group of sector ETFs including XLE, XLF, XLK, XLV, XLI, XLU, XLP, XLY, XLB, and XLRE with the same parameters. The result for the mega-cap tech stocks was a return of -9.9% over 365 days when using a 90-day lookback period, compared to a +6.8% return for a buy and hold approach. Using a 60-day lookback period resulted in a -5.0% return, compared to a +9.2% return for buy and hold. Even when using a 120-day lookback period over 700 days, the return was +40.5%, which still trailed the +52.6% return for buy and hold. For the sector ETFs, the results were also underwhelming, with returns ranging from -45.7% to -3.5%, compared to buy and hold returns ranging from -12.0% to +9.9%. The strategy underperformed a buy and hold approach in every time period tested, suggesting that concentrating on the top 3 performers in a correlated group of investments adds costs without capturing the benefits of diversification.

The idea was to test if buying the top 2 of 6 major cryptocurrencies (BTC, ETH, SOL, DOGE, XRP, LTC) based on their past performance would work better than buying and holding them. This is called cross-sectional momentum, a type of investment strategy. The test used a computer program called backtest.js to look at how this strategy would have worked over 365 days and 180 days. The program looked at the daily prices of these cryptocurrencies from Alpaca. The results showed that over 365 days, this strategy would have lost 61.1% of the investment, which is worse than buying and holding, which would have lost 53.7%. Over 180 days, the strategy would have lost 1.72%, which is also worse than buying and holding, which would have gained 5.12%. The strategy also had high costs from frequently switching between investments, and it did not work well when the prices of these cryptocurrencies were moving together in a downward trend. The maximum loss at any point was 71.8% over 365 days and 33.8% over 180 days. The strategy was successful 40.4% of the time over 365 days and 47.6% of the time over 180 days.