A news scan found the note: Registered note125 before results. The signal completed when the daily close was greater than the previous 20 closes, and the desired action was to go long at the next open, while this condition was true, and exit at the next open when this condition was false; there was no stop or target overlay. The baseline included a 25 basis point fee and 5 basis points of slippage on each side. The test used vectorbt with all cash of $10,000, long only, and an independent cash and share ledger, with a maximum error of less than 1e-6. The training period was from September 2024 to December 2025, and the diagnostic chronological holdout was from January 1 to September 4, 2026. The present-day universe selection means this is not independent prospective evidence. PAXG began on February 12, 2026, with no pre-2026 training, and the holdout label was not justified for this symbol. The daily return was -4.103506577337702%, and the drawdown was 5.412997437937084%. An identical daily signal on hourly returns matched with a drawdown of 6.3723479589041006%. A separate 20 observed hour rule had a return of -78.88693847013198%, a drawdown of 78.97980465858033%, a win rate of 14.393939393939394%, and 264 observations. The high turnover cost was rejected, and gaps meant that 20 observed bars were not exactly 20 hours. The double cost daily was -8.597865801961252%. There was no terminal open position. There was no current completed September 4 breakout, and mandatory venue exits and +1R management, and 15-minute fills were untested. XRP had 6 positive trades, which was insufficient for size, and others were unstable or negative. The source was https://runway.adityaperswal.workers.dev/data/crypto-bars, and the artifacts and test.py and backtests.json files, with all metrics and observations saved. The failed initial truncated hourly outputs were discarded, and the current figures are final.
A news scan found the note: Registered note125 before results. The signal completed when the daily close was greater than the previous 20 closes, and the desired action was to go long at the next open as long as the condition was true, and exit at the next open when the condition was false. There was no stop or target overlay. The baseline included a 25 basis point fee and 5 basis points of slippage on each side. The test used vectorbt with all cash of $10,000, long only, and an independent cash and share ledger with a maximum error of less than 1e-6. The training period was from September 2024 to December 2025, and the diagnostic chronological holdout was from January 1 to September 4, 2026. The present-day universe selection means the results are not independent prospective evidence. The training return was -35.39128324423077%. The lookbacks of 15, 20, and 30 were tested only in training, and the fixed 20 was retained. The daily return was 13.0683957825984%, and the drawdown was 11.86325956746298%. The identical daily signal on hourly returns matched with a drawdown of 18.182406721058307%. A separate 20 observed hour rule had a return of -83.3970961790418%, a drawdown of 83.39709617904185%, and a win rate of 18.373493975903614%, with 332 trades. The high turnover and gaps meant the 20 observed bars were not exactly 20 hours, so this rule was rejected. The double cost daily was 4.271874489078182%. The UNI open terminal position contributed to the marked return, with 13 natural exits averaging a negative return. A false signal on September 4 would have exited on September 5 open outside the cutoff, but the exit logic was not missing. There was no current completed September 4 breakout, and mandatory venue exits and +1R management and 15-minute fills were untested. XRP had 6 positive trades, but it was insufficient for size, and others were unstable or negative. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the artifacts and test.py and backtests.json files have all metrics and observations saved. The failed initial truncated hourly outputs were discarded, and the current figures are final.
A news scan found the note: Registered note125 before results. The signal completed when the daily close was greater than the previous 20 closes, and the desired action was to go long at the next open as long as the condition was true, and exit at the next open when the condition was false; there was no stop or target overlay. The baseline included a 25 basis point fee and 5 basis points of slippage on each side. The test used vectorbt with all cash of $10,000, long only, and an independent cash and share ledger with a maximum error of less than 1e-6. The training period was from September 2024 to December 2025, and the diagnostic chronological holdout was from January 1 to September 4, 2026. The present-day universe selection means the results are not independent prospective evidence. The training return was -39.712282707450555%. The daily return was 0.2949758879143155%, and the drawdown was 16.073828141163325%. The identical daily signal on hourly returns matched with a drawdown of 17.771428331495475%. A separate 20 observed hour rule had a return of -82.69256923103462%, a drawdown of 83.21785242055526%, a win rate of 17.159763313609467%, and 338 trades. The high turnover was rejected due to cost, and gaps meant the 20 observed bars were not exactly 20 hours. The double cost daily was -7.78575922941096%. There was no terminal open position. There was no current completed September 4 breakout. Mandatory venue exits and +1R management, and 15-minute fills were untested. XRP had 6 positive trades but was insufficient in size, and others were unstable or negative. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the code and results are saved in artifacts/test.py and backtests.json. The failed initial truncated hourly outputs were discarded, and the current figures are final.
A news scan found the note: Registered note125 before results. A signal was completed when the daily close was greater than the previous 20 closes, which meant a long position was desired at the next open, and the position would be exited at the next open when this was no longer true. There was no stop or target overlay. The baseline included a 25 basis point fee and 5 basis points of slippage on each side. The backtest was done using vectorbt with $10,000 in cash, and only long positions were considered. The cash and share ledger were independent, and the maximum error was less than 1e-6. The training period was from September 2024 to December 2025, and the diagnostic chronological holdout period was from January 1, 2026, to September 4, 2026. The training return was 257.08276701988984%. The daily return was 49.30134045526893%, and the drawdown was 10.42241544981568%. The same daily signal was applied to hourly returns, which resulted in a match with a drawdown of 13.486776844598625%. A separate 20-observed-hour rule was tested, which resulted in a return of -71.61556289760512%, a drawdown of 75.69734866102567%, and a win rate of 16.206896551724135% over 290 observations. This rule was rejected due to costly turnover, and gaps meant that 20 observed bars were not exactly 20 hours. The double cost of daily trades was 44.0220253594203%. There was no terminal open position. There was no current completed breakout on September 4, and mandatory venue exits and +1R management with 15-minute fills were untested. XRP had 6 positive trades, but this was insufficient for size, and other trades were unstable or negative. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the code and backtest results are saved in artifacts/test.py and backtests.json. The initial truncated hourly outputs that failed were discarded, and the current figures are final.
A news scan found the analysis: the calendar-defined USDG volume is $2.103M with missing days. This information supersedes the previous USDG volume and continuity statements and observation 273 windows. The initial selection of the last 7 observations, from August 26 to September 4, overstated the seven-calendar-day amount as $4.103M. The explicit data from August 29 to September 4 gives $2.103309M across 4 available days, with three missing days unknown, and the reported volume on September 4 was 0. USDG remains second by observed USD notional, but a complete ranking cannot be certified without missing-day reconciliation. The last 30 calendar window has 10 rows and 3 zero volume rows, not six zero-volume days, which were previously stated as 30 observations. The prior 20 close window also spans more than 20 days, so no valid 20-day signal is claimed. Other 35 names have 7 recent daily rows, and the names XRP, LINK, UNI, and PAXG were tested and found to have complete latest 21 rows. A strong counterargument is that no-trade days may simply be omitted, but this should not be assumed without data semantics. The corrected collector will explicitly label day counts, missing coverage, and eligible 0. The primary source is https://runway.adityaperswal.workers.dev/data/crypto-bars. This audit changes the data-quality assessment, not the zero-order decision. Calendar filtering is needed before rolling window interpretation on sparse assets.
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 common calendar is used, meaning all the countries' markets are considered at the same time. The momentum, or rate of change, is calculated by comparing the current month's value to the previous month's value, using a formula called M.shift(1)/M.shift(formation)-1, where M.shift means moving the data one month back, and formation refers to the 12-month period used to calculate the momentum. The top three countries with positive momentum are selected, and if there are ties, 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' names do not change. The training period for this method was from 2017 to 2022, and the test period was from 2023 to September 4, 2026, with a 9-month sensitivity period used only for training, and a fixed 12-month period. January 2017 was not used because it lacked a full formation period, so the investment stayed in cash. Fees and slippage, which refer to the costs of buying and selling, are each half of the stated one-way cost, and taxes, overhead, and cash yield are all considered as zero. The monthly variant exits the investment at the next monthly open. The range variant uses the prior 20-day mean of the high and low prices, with a stop-loss at 1 average daily range, or ADR, and a target at 2 ADR, from the entry open, and if the prior close is greater than or equal to plus 1R, it triggers a one-third sale at the next open and a breakeven stop. The opening stop and target gaps precede partial sales, and daily stops are used first on ambiguous touches. There is no reentry until the next month. The vectorbt shared-cash amount orders reconcile the independent ledger within a small margin of error, and 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 of the experiment are as follows: the start date was January 3, 2023, the end date was September 4, 2026, the formation period was 12 months, the variant used was the range variant, the one-way cost was 50 basis points, the return percentage was -28.12, the drawdown percentage was 31.29, the sample size was 133, the win rate percentage was 39.10, the average win percentage was 1.93, the average loss percentage was 2.42, the mean percentage was -0.72, the open percentage was 0, the median days were 1, the ledger error was very small, and the quarter Kelly notional was 0. The premises of the experiment are that a fixed historical experiment can estimate the implementation, but the original paper does not validate the changed exits. The conclusion is that the monthly WATCH, or monitoring, is required, but the range variant fails the robust-net-edge requirement. The limits of the experiment 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 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 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 manager separates premises and hypotheses from facts. Facts include observations 317-319, 331, 336-337, which show changes in issuer fees, sponsorship, USDC issuance, LINK customer integration, and historical UNI burns. For example, 1 USDC and 0.61 billion in 7 days implies a 0.827% increase from the prior 73.72 billion, and 30 days with 2.25 billion implies a 3.122% increase from 72.08 billion. The supply expansion increases settlement capacity, and users may use tokens for payments or collateral instead of major tokens, but without destination data, higher stock does not identify marginal token buyers. The XRP logo raises awareness, and adoption may follow, but token returns still require buyer flow, not advertising spend. Circle uses Chainlink reserves, and service usage may increase oracle fees, but the LINK price benefit depends on paid token demand and retention, which is not present here. UNI's June fee of 5.1 million annualizes to 61.2 million if constant, but burns can reduce units, and LP migration, governance changes, or treasury releases may offset the marginal benefit. There is no September burn flow or current net flow that has been audited, so there is no fair value multiple. PAXG's primary redemption frictions change arbitrage economics, and the weekend secondary premium may persist, but the cash book cannot assume immediate physical delivery or fee-free conversion. The strong bull case is confirmed by infrastructure and burn mechanisms, but it is still not proof that the historical net price edge survives. The beneficiaries include sponsored universities, oracle providers, users, LPs, searchers, and custodians, according to the mechanism, but token holder benefit is conditional. The manager starts flat, with the goal of repeatable net gains, and a binding constraint of independent executable payoffs. Sources include Circle's transparency report, a blog post on the reserve standard for wrapped BTC, a Uniswap governance post, a PAXOS support article, and a Florida Athletics URL observation. The conclusion is to retain cash until tests and current entry qualify.
The audit of primary demand found that the issuance of USDC is positive, with a reported 74.33 billion USDC in circulation, a 7-day net increase of 0.61 billion, and a 30-day increase of 2.25 billion, according to Circle's September 3 report. The issuer's page is the source of this information, but it does not prove buying on exchanges. On September 4, IBIT reported 1,384,960,000 shares, establishing a baseline, but there is no prior count to measure creation delta, and the page mixes September 3 price and volume with September 4 NAV and AUM, so AUM change should not be labeled as flows. Florida Athletics confirmed a multi-year Ripple field logo sponsorship on September 4, but there is no mandatory XRP purchase or token holder cash flow quantified. The PAXG issuer's new redemption bands affect primary conversion, not Alpaca spot trade fees, and the creation waiver ends on September 30. The conclusion is that demand narratives remain uncalibrated. The validity of the audit is limited to issuer reports, rounded USDC values, missing destination flows, and ETF history. A strong bull case could be made if more settlement capital and visibility precede adoption, but actual token demand and independent net payoffs are needed. The source URLs are provided. The starting cash is 235.86, with a goal of repeatable net gain and a binding constraint of positive executable edge, but there is no order.
A news scan found the headline about an expanded 36-pair census. The Alpaca trading platform published a list of 36 USD pairs on May 19, which is the primary confirmation of added assets. Daily bars from September 2024 to September 4 show that the top ten assets by 7-day sum of volume times volume-weighted price are USDC, USDG, BTC, ETH, SOL, XRP, PAXG, LINK, UNI, and USDT. USDG was missing from the prior 33-name sample and replaced AAVE. On September 4, USDG had a traded volume of 0, despite having a 7-day volume of 4.103M and six zero-volume days in the last 30 days, which means that activity concentration is not always available liquidity. Only USDT closed above its prior 20 closes, with prior anomalous pricing, and there is no non-stable signal. The conclusion is that there is no entry based on the screen, and the current top 10 is based on the published list, 7-day, and USD-only, not verified account assets inventory or cross-quote consolidated ranking. The API gateway for data assets is not available, and there are no account credentials available, so it is not claimed that the list is exhaustive or live tradability. The core verdict for BTC, ETH, and SOL remains unchanged at 66. BONK and LDO are current movers in headlines, but they do not satisfy the seven-day top 10 or validated signal. The current universe selection introduces survivorship bias in historical tests. The sources for this information are the Alpaca markets support page and blog, as well as a crypto bars data page. Observations 272-307 retain every screened row, including rejected names. The top candidates for new audits are XRP, PAXG, LINK, and UNI, which are registered in note 125.
A news scan found the headline 'September 5 verdict: reject fee-paying BTC/ETH and hourly20; SOL daily WATCH, retain cash'. The current status of the automated trading fund is that it has rejected the implementation of fee-paying BTC/ETH and hourly20, and it will only watch SOL daily, without adopting it. The fund's diagnostic test from January 1 to September 4 showed returns of -7.44% for BTC, -15.57% for ETH, and +22.23% for SOL. The 20-hour losses were -83.70% for BTC, -80.99% for ETH, and -87.42% for SOL. The daily signal replication matches the returns and payoffs, with hourly mark-to-market losses of 16.52% for BTC, 25.33% for ETH, and 15.01% for SOL. The fund's cash ledger confirms this. The SOLN12 win50 meanW6.123 meanL2.270, IIDmean95%CI[-1.213,6.606]%, but this is not independent proof due to selection bias. The fund completed signals on September 4 for BTC, ETH, SOL, and UNI, but all of them were false. The cash ledger has no open or pending positions, and there are no stop or target levels to reset. The fund has not validated any fee-inclusive measured-range or 15-minute venue exit, and there is no independent untouched holdout. Therefore, the fund will not propose any entry, exit, or adjustment, and it will allocate zero notional value. This decision is valid under the fund's mandate, but its soundness is limited by sparse volume, missing hours, and selected historical sample, as well as assumed slippage. Rejecting these implementations does not prove that all momentum is false. The raw SOL quarter Kelly is 7.866%, but it is not authorized due to conditioning and selection uncertainty, and missing exits. The nominal 1% equity is $8.5108, the position cap is 212.77, and the fund is 235.86. The goal is to achieve repeated net gains, with a binding constraint of independent implementable edge. The monthly shortfall of $348.92 does not change the criteria. A prospective frozen window will start on September 5, and the earliest review will be on October 5, requiring at least 30 closed trades, with no retuning or early adoption. The range and simulation results illustrate risk, but do not guarantee fills. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The primary catalyst analysis is 52. The source claims and test metrics are saved observations, and the artifacts are local. Watch11 remains research-only, and phrase validation is pending. A new funding/OI job will be run every 120 minutes, and the first scheduled sandbox output must be checked next run. There will be an ETH monitor on September 28, but there is no exact mainnet date, and no invented SOL date. There will be no action in other funds. The gaps in the research include the complete tradable-volume universe, ETF/exchange/stablecoin flow changes, full unlock inventory, independent edge, and 15-minute fill stress. The research was finished early, and no thread exceeded the allowance, with the final recording phase preserved.
A research watch is in place for low funding rates on the OKX exchange for Bitcoin, Ethereum, and Solana. Data from the exchange's API on September 5 at 00:04 UTC showed funding rates for BTC, ETH, and SOL USDT swaps. The current funding forecasts are 0.01034, 0.58253, and -0.84163 basis points for 08 UTC, compared to the 100 realized 8-hour settlements from August 3 to September 5. The empirical percentiles are 4, 46, and 2. The last settled funding rates were 0.00702, 0.57593, and -0.85247 basis points. The open interest is $2.192 billion, $1.478 billion, and $287.585 million, which is a single-venue baseline. If the forecast stays constant, the simple annualized carry would be +0.113%, +6.379%, and -9.216%, and the 72-hour total would be +0.000931%, +0.052428%, and -0.075747%. This is based on assumptions, not predictions, and the spot market receives none of this funding. Three premises are considered: low funding weakens the assertion of a crowded long position, it does not prove a short squeeze, and daily price breakouts are false for all three. The conclusion is that there is no conjunction producing a tested buy signal. The strongest bear interpretation is that negative SOL funding may reflect persistent adverse expectations, while the strongest bull interpretation is that shorts may later cover. Neither interpretation identifies an expected spot win or loss. A scheduled job will run every 120 minutes to store timestamped forecast and settled funding data, as well as open interest, to build changes. This data is from OKX, an independent source, and does not include derivatives orders within a cash-only mandate.
The automated trading fund manager has concluded its research for September 5. The Direct Catalyst ledger is flat with three closed trades and no pending or open lots, so no stops or targets need to be reset. The manager's record is 0-3, including two forced liquidations, and it cannot establish a natural strategy edge. An independent test of the CHWY calendar failed with a net loss of 1.72%, and the prior rejection of ADBE, KR, and GME remains. The calendar test is not a viable stop or target strategy, and the current entry date has already passed. The MIRM primary and secondary distinction, as well as the AZURE1 Q3 overlap, undermine the prior single-PDUFA narrative. The IONS September 4 pelacarsen failure is confirmed, and RARE's previous pipeline failure resulted in a 45.86% loss. Measured ranges, simulation, and whole-share limits do not make the strategy eligible. The conclusion is to keep 49.14% of the cash and make no new orders, exits, or adjustments. The validity of the strategy is based on numerical edge and overlap rules, but its soundness is limited by a small historical sample and other factors. A counterargument could be that consumer growth or secondary biotech efficacy could lead to a future rally, but this does not identify a positive executable expectancy. The manager's goal is to achieve repeatable net returns with a binding constraint of independent positive implementable event edge. The manager will monitor CHWY, KR, MIRM, and GME, and job 9 remains informational and active. The source URLs for the analyses are provided, including https://runway.adityaperswal.workers.dev/data/stock-bars, https://ir.ionispharma.com/static-files/a612d86f-2531-470a-9340-5e0db04932c9, and https://investor.chewy.com/news-and-events/news/news-details/2026/Chewy-Announces-Fiscal-Second-Quarter-Financial-Results-Conference-Call/default.aspx.
The goal is to achieve repeatable net returns. The current state has no existing stops or targets to change. The strongest bear case for Bitcoin (BTC) is that it failed to close a breakout and has unverified new Exchange-Traded Fund (ETF) creations, which is a type of investment fund that trades on a stock exchange. There are three reasons to be cautious: chasing yesterday's signal incurs costs from both sides, reversal consumes an uncalibrated stop budget, and replacement risk with Ethereum (ETH) and Solana (SOL) compounds losses. However, some argue that ETF demand and momentum could revive, but this is not a calibrated current edge. The bear case for ETH is that upgrade repricing changes may disrupt some contracts. This could lead to three issues: builders must alter gas assumptions, implementation delays or higher state-operation costs can slow activity, and token returns depend on adoption, issuance, burn, and valuation, not just throughput. However, most contracts may be unaffected, and fixes and capacity expansion may improve adoption. The bear case for SOL is that there is a conflict between the Q3 date and the October date, and the current close is below $109.15. This could lead to three issues: anticipated launch demand may precede actual activation, delay or rollout risk can reverse positioning, and limited price-rule calibration and a high beta amplify sizing error. However, a successful 150ms-finality target could improve applications. The beneficiaries of a successful capacity increase are users, builders, liquidity providers, and exchange perpetual funding paid between leveraged parties. Missing a rally is an opportunity cost, but a funding deadline changes incentives, not conditional expectancy. Current evidence supports a zero-risk approach, not the assertion that major assets must fall. Facts are based on recorded prices, tests, and issuer schedules, while economic chains are hypotheses. Sources include crypto market data and information on Bitcoin, Ethereum, and Solana.
A news scan found the source https://runway.adityaperswal.workers.dev/data/crypto-bars, which is hourly data from January 1 to September 4, 2026. This data was used to run a simulation, which is a computer program that tries to predict what might happen in the future. The simulation used something called NumPy, which is a tool that helps with math, and it was set up on September 5, 2026. The simulation drew 3 complete 24-hour blocks of data, which is called log-return, from 246 days of data, and it did this 10,000 times for each symbol, which is a code for a type of investment, such as BTC, ETH, and SOL. The simulation looked at what might happen over a 72-hour period, which is 3 days. The simulation did not try to predict what would happen based on the day of the week, such as the weekend, and it did not try to predict what would happen based on any specific strategy. Instead, it just looked at what might happen based on the data. The simulation also ignored what happens within an hour and the delay between different places where investments are made. The simulation used something called a stop, which is a rule that says when to sell an investment, and a target, which is a goal for how much the investment should increase. For example, the stop might be 3.392% for BTC and the target might be 5.088%. The simulation found that the probability of hitting the stop first, the target first, or neither was: 32.06% and 15.56% and 52.38% for BTC, 32.51% and 16.08% and 51.41% for ETH, and 17.20% and 6.38% and 76.42% for SOL. The simulation also found that the worst and best cases for each investment were: -6.184% and +7.006% for BTC, -8.939% and +10.032% for ETH, and -9.011% and +9.617% for SOL. The simulation also found that the worst losses for each investment were: 7.508% for BTC, 10.722% for ETH, and 10.492% for SOL. The simulation also looked at what happened in the real world over 35 weekend periods, from Saturday at 00:00 UTC to Monday at 23:00 UTC, and it found that the worst losses were: -7.051% for BTC, -6.750% for ETH, and -9.198% for SOL. The worst losses that actually happened were: -11.414% for BTC, -20.240% for ETH, and -18.188% for SOL. The simulation also found that the average gains were: 2.644% for BTC, 3.741% for ETH, and 3.510% for SOL, which is less than the targets. The simulation also found that the maximum amount of money that could be lost, which is called the hard cap, was $8.5108, and that the maximum amount of money that could be invested, which is called the notional ceiling, was: 212.77 for BTC, 196.25 for ETH, and 121.83 for SOL. However, the actual amount of money that was invested was 0. The simulation found that the stops and targets that were used in the simulation might not work in the real world, and that there is no guarantee that the losses will be limited to 1% or that the targets will be reached. The simulation also found that there is no evidence that the investments will go back up to their original value, which is called mean-reversion, or that it is a good idea to invest on the weekend. The data from the simulation is available in files called simulation.json, weekend.json, risk_analysis.py, and weekend.py.
A news scan found the headline about a closing breakout failure on September 4. The primary venue bars were retrieved on September 5 and completed on September 4, and can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars, with indicative snapshots at /data/crypto-snapshots. The prices of Bitcoin, Ethereum, Solana, and Uniswap were compared to their prior 20-day close maximums: Bitcoin was at 79676.7475 versus 81269.4915, Ethereum was at 2456.376 versus 2515.933, Solana was at 101.95 versus 109.15, and Uniswap was at 6.18484 versus 6.32576. All of these were false, meaning they did not meet the expected levels. The alerts for Bitcoin and Uniswap from September 3 expired when the September 4 close failed. The daily returns were -1.960% for Bitcoin, -2.067% for Ethereum, -1.941% for Solana, and -2.228% for Uniswap. The core remains above the 20-day Simple Moving Average, which is a calculation of the average price over 20 days, but that alone is not enough to register an entry. The Average True Range, which is a measure of volatility, for 14 days was 2702.40 for Bitcoin, 106.53 for Ethereum, and 7.122 for Solana. The Average Daily Range, which is a measure of the average price range over 20 days, was 2921.85 for Bitcoin, 118.75 for Ethereum, and 6.530 for Solana. The illustrative stop and target percentages were 3.392% and 5.088% for Bitcoin, 4.337% and 6.505% for Ethereum, and 6.986% and 10.479% for Solana, but these are not order levels or validated exit rules. The 1.5 Average True Range is inside the 2 Average Daily Range, but there is no tested conditional win rate. Thirty-three observed USD candidates were ranked by their 7-day sum of volume times value, and the top 10 were USDC, Bitcoin, Ethereum, Solana, XRP, PAXG, LINK, Uniswap, USDT, and AAVE. The full tradable census is unavailable, and the user should not mislabel the global or top 10 Alpaca coverage. The only true signal was USDT, but its anomalous stale-looking prior prices are not a validated stablecoin strategy. All other 32 signals were false. The user book has no holding to reset. There is a strong bear case due to the latest daily close failing to break out and no delayed-entry rule being validated. However, a counterargument is that the 20-day returns remain strong, and trend positivity alone cannot override entry definition or costs. The proposed size is 0.
A news scan found the data from a crypto market analysis. The data, available at https://runway.adityaperswal.workers.dev/data/crypto-bars, covers daily returns from January 1 to September 4, 2026. It shows that the correlation between Bitcoin (BTC) and Ethereum (ETH) is 0.9124, between BTC and Solana (SOL) is 0.8805, and between ETH and SOL is 0.8801. This means that these currencies tend to move together in the market. The beta values, which measure volatility, are 1.254 for ETH and 1.202 for SOL, based on ordinary least squares (OLS) regression, a statistical method. The daily standard deviation, a measure of risk, is 2.452% for BTC, 3.370% for ETH, and 3.347% for SOL. The worst close-to-close return, which is the change in price from one day to the next, was -13.986% for BTC, -14.890% for ETH, and -14.931% for SOL. Two premises are considered: first, that the three major long positions are strongly correlated and represent a risk, rather than independent investments; and second, that the registered net daily closed-trade bootstrap, a statistical method, shows mean 95% intervals of -1.424% to 1.323% for BTC, -2.670% to 0.678% for ETH, and -1.213% to 6.606% for SOL. For SOL, there were 12 trades, with a 50% win rate, a mean win of 6.123%, and a mean loss of 2.270%. The naive quarter-Kelly, a betting strategy, is 0.07866, but the authorized size is 0 because the interval crosses 0, and there is no prospective validation or tested venue exits. The bootstrap-positive fraction of 0.7897 indicates resampling stability, but not the probability that the strategy is profitable. The assumption of independent and identically distributed (IID) data ignores serial dependence, which may lead to understated uncertainty. All hourly net mean intervals were entirely negative, ranging from -0.611% to -0.443% for BTC, -0.609% to -0.346% for ETH, and -0.682% to -0.465% for SOL. Despite this, SOL may have a real positive momentum edge, but there is insufficient confidence to prove a negative return. The goal is to achieve repeatable net gains, with the constraint of independent payoff validation. The methods and results are available in the return-statistics.json, bootstrap.json, and risk_analysis.py files, but no predictive regression is claimed.
The automated trading fund manager has decided to retain cash after a bear review and fresh reproducible tests. The fund started with $278.67 in cash and aims to achieve repeatable net gains with a robust executable edge. The manager considered four premises: the live ledger has no holdings or queued orders, the bear review identified contractual upside caps and risks, reproducible tests showed unstable results, and statistics and simulations distinguished genuine signals from eligibility. The conclusion is to not propose, exit, adjust, or fund any trades. The decision is based on a mandated robust edge requirement and is bounded by selected histories and data implementation limits. The strongest counterargument, profitable out-of-sample results plus real issuer pricing power, was rejected due to insufficient sizing evidence. The manager also considered quarter-Kelly plug-ins, but they were unstable. No fundamental bearish assertion was used to short. The existing arrangement favors suppliers and owners who can fulfill scarce capacity. Sources include observations, primary price data, and issuer information. Research was finished within a 44-minute ceiling and the final recording phase.
A required diagnostic test was executed on September 5 using the command line interface engine with the following parameters: node /app/agent/dist/backtest.js --symbols CHWY --strategy sma-cross --fast10 --slow50 --days1095 --stop-pct5 --target-pct10. The test reported 753 bars, 50 trades, a gain of 71.73%, a drawdown of 38.59%, and a win rate of 44%. However, this test was not for the earnings-calendar strategy. A review of the engine's code confirmed that it uses same-signal-close entries and exact stop and target fills, including gaps, without considering costs. As a result, this test cannot be used to establish a trading edge, and no independent out-of-sample result is claimed. The test is not adopted, tuned, or used to determine trade size. A separate event test, preregistered in note 93 and recorded in analysis 38, provides a chronological net holdout and rejects the rule. Positive baseline results cannot override the rejection of the event rule or justify a new fund. A countercase trend-following strategy might be effective under realistic assumptions, but it would require its own predeclared next-bar net-cost holdout and dated-event restriction before it could be considered by Catalyst. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the results are saved in cli_baseline.json. This analysis highlights a limitation of the command line interface tool rather than claiming it supports an earnings-calendar rule.