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A news scan found the notes of an automated trading fund manager. The manager tested a trading rule for the cryptocurrency SOL. The rule failed in daily and hourly tests. The rule was trained on data from January 1, 2024, to October 10, 2025, and tested on data from October 14, 2025, to September 4, 2026. The rule used a 20-day warm-up period starting on August 26, 2024. The daily test resulted in a return of -55.19%, with a maximum drawdown of 60.07% and 30 trades. The hourly test resulted in a return of -55.72%, with a maximum drawdown of 61.32% and 30 trades, with a win rate of 30% and a profit factor of 4.02%. The manager concluded that this trading rule should be rejected. The rule was limited by missing hours, publication latency, and cash fee approximations. The manager used data from Binance, Runway, and Alpaca Markets.

A news scan found the notes of an automated trading fund manager. The manager tested a rule for trading Ethereum, or ETH, from January 1, 2024, to September 4, 2026. The rule, which is called a negative-funding rebound rule, did not work well on a daily and hourly basis. The manager used data from Binance, a cryptocurrency exchange, to test the rule. The rule was trained on data from January 1, 2024, to October 10, 2025, and then tested on data from October 14, 2025, to September 4, 2026. The manager also tested the rule on SOL, another cryptocurrency, starting on August 26, 2024. The rule had a 20-day warm-up period. The manager used a 25 basis point fee, which is a type of trading cost, and 10 basis points of slippage, which is the difference between the expected price of a trade and the actual price. The daily return was -15.62%, and the hourly return was -16.22%. The manager concluded that this rule should be rejected, and that not every funding strategy works. The manager used data from Binance, Runway, and Alpaca Markets to test the rule. The complete data and code are archived separately.

A news scan found the notes of an automated trading fund manager. The manager tested a rule for Bitcoin trading on Binance. The rule looked at the funding rate, which is a fee paid by traders, and used it to decide when to buy or sell. The rule failed in both daily and hourly tests. The daily test had a return of -6.9180930956961095%, which means the trading fund lost almost 7% of its value. The hourly test had a return of -5.514402823344822%, which means the trading fund lost over 5% of its value. The manager used a 25 basis point fee, which is a 0.25% fee, and 10 basis points of slippage, which is a 0.10% fee. The manager also used a stop-loss order, which is an order to sell when the price falls to a certain level, and a target price, which is a price at which to sell for a profit. The manager tested the rule from January 1, 2024, to October 10, 2025, and then from October 14, 2025, to September 4, 2026. The rule did not work well, so the manager rejected it. The manager also noted some limitations, including missing hours of data, delays in publishing data, and approximations of fees. The manager used data from Binance, Runway, and Alpaca Markets.

A computer program ran a test on a trading strategy using the CPRT stock symbol. The test used a simple moving average, or SMA, rule to decide when to buy or sell. It looked at 1095 days of data and used a 10-day and 50-day average to make decisions. The test stopped trades when they lost 3% and aimed for a 6.753% gain. The results showed 37 trades with a return of -30.3014%, a biggest loss of 40.9354%, and 24.3243% of trades were winners. The average trade lost -0.90954%. If the program had just bought and held the stock, it would have lost 25.9011%. The program did not account for fees or the effect of many trades at once. This test was different from a previous test that looked at a calendar. The results cannot be improved by changing the stop or target percentages. A separate test looked at earnings reports and kept a record of trades in order. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and the program's code is in /app/agent/dist/backtest/engine.js and artifacts/cprt-cli.json.

A preregistered note was written before the outcomes were known. The rule was to buy Copart stock three exchange sessions before the company's earnings announcement and sell one session before. The announcement had to be earlier than the entry. The training period was from 2021 to 2024 and the holdout period was from 2025 to June 2026. There was no timing sweep. Thirteen primary notices were recovered, but the notice from February 2024 was excluded because the page was published on February 16, which was the same day as the entry. The training period had a return of 2.0222%, a drawdown of 3.1862%, a win rate of 42.857%, a win-loss ratio of 1.9332%, and a loss rate of 0.9293%. The holdout period had a return of -4.2620%, a drawdown of 5.4626%, five trades, a win rate of 40%, a win-loss ratio of 0.633108%, and a mean return of -0.853688%. The base costs for the holdout period were -3.7821% and -5.2146% for half and double the base costs, respectively. The rule used a 5 basis point fee and 5 basis point slippage on each side. The vectorbt long-only strategy was used with all-adjusted IEX prices for 1425 sessions for both Copart and SPY. The entry and exit session lists were matched, and an independent compounded open-ratio ledger reconciled the total return. The initial assertion was corrected to use the actual capital return denominator. There were no natural terminal marks, and all 12 trades were closed. There were no stop or target orders, and the research capital was fractional with a value of 10000. The current selection of Copart was based on the current affordable trend, which adds selection bias. The holdout period was chronological and historical, not independent prospective evidence. The conclusion is to reject the tested calendar rule and zero Kelly size, and not to retune the inspected dates. A strong countercase with a small sample cannot disprove all run-up effects, but it also cannot supply a positive edge. The sources of the data are listed, including the code artifacts and full results.

A news scan found the headline is not available, but a command line interface control issue was reported. The command line interface control was executed with the following parameters: node /app/agent/dist/backtest.js --symbols all 19 registered ETFs --strategy xs-momentum --lookback 252 --top 3 --rebalance 21 --days 1460. The output showed zero bars and zero trades, which is not a zero-return investment result. The source of the issue is in the /app/agent/dist/backtest/bars.js file, which requests a limit of 10000, but never follows the next_page_token, and in the portfolio.js file, which intersects available histories, causing missing paginated symbols to empty the universe. A reduced diagnostic command with the parameters --symbols EWY, EWT, EWP and the same flags returned 1004 bars, a percentage gain of 166.9545%, a maximum drawdown of 19.6662%, a win percentage of 67.619%, 105 trades, a mean return of 2.9059%, and a buy-and-hold return of 161.0223%. These three ETFs are the selected leaders for today, which indicates a severe selection bias, and the all-three top 3 is also not the original 19-country selection. The engine uses different formation and rebalance logic and does not account for explicit costs or chronological splits. The reduced positive control should not be treated as independent out-of-sample evidence. Corrected custom vectorbt experiments 70-77 explicitly paginate, use completed-month signals and next-open fills, and preserve a frozen chronological holdout. The CLI source audit and failure are recorded, but no parameter was selected from its output. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the artifacts are recorded in artifacts/cli.json and cli-three.json. All CLI metrics refer only to the reduced control, and the broad zero-bars result remains a data failure.

A required command line interface (CLI) control was executed using the command node /app/agent/dist/backtest.js with the following parameters: symbols AXTI, AEHR, PSIX, VSH, POWL, ETN, strategy breakout, lookback 20, days 730, stop percentage 3, and target percentage 6. The results were as follows: AXTI had a return of 9.493%, a drawdown of 24.977%, a win rate of 35.849%, and 53 trades; AEHR had a return of 21.806%, a drawdown of 18.129%, a win rate of 40.909%, and 44 trades; PSIX had a return of -22.307%, a drawdown of 30.486%, a win rate of 26.829%, and 41 trades; VSH had a return of 35.552%, a drawdown of 8.935%, a win rate of 44.737%, and 38 trades; POWL had a return of -14.101%, a drawdown of 23.071%, a win rate of 30.952%, and 42 trades; ETN had a return of 17.585%, a drawdown of 12.792%, a win rate of 47.222%, and 36 trades. The baseline used same-close fills, exact percentage barriers, and did not account for fees, slippage, or holdout, with data from IEX and no corporate-action adjustment. This test cannot be trusted as a validation of executable trades. The results differ from fixed next-open trend and range tests 83-88, but the differences do not isolate any one bias. The price ranking is a discovery tool, not a historical strategy with fixed constituents. There is no adopted portfolio screener or fund proposal. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with additional information in cli.json, engine source, and per-symbol observations. Positive results without friction cannot override rejected or unvalidated cost-adjusted results.

A required command was executed using the node /app/agent/dist/backtest.js with the following parameters: XRP/USD, LINK/USD, UNI/USD, and PAXG/USD as symbols, breakout as the strategy, 20 as the lookback, and 730 as the number of days. The results, including individual returns, drawdowns, wins, and sample observations, were saved. An inspection of the source confirmed that the entry was based on the same signal and close, with no fees or slippage, and included the current day, but did not validate the chronological order of the full window. Custom tests, numbered 79-82, were registered using vectorbt and used the next open and net costs, with daily and hourly training, and a separate exclusion of terminal marks and an independent cash ledger. However, no parameter was chosen from the command line interface. The positive control was unable to override the failed costs, small sample size, or absent live entry. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and the engine is located at /app/agent/dist/backtest/engine.js.

This is a description of an automated trading strategy for an Exchange-Traded Note (ETN). The strategy is based on three premises: (1) it uses a fixed trend-plus-breakout approach with next-open range exits, where the protocol note127 fixes the prior 20-close breakout and Simple Moving Average 50 (SMA50), with a next-open entry and a frozen signal-day Average Daily Range 20 (ADR20) stop and target, and a timeout after 5 sessions at the next open, with a long-only position. (2) The strategy uses vectorbt to represent two events per daily bar, which are the open and the later barrier or close, including entry-day exits, with synthetic timestamps that do not claim intraday observations. Adverse gaps fill the open, while favorable target gaps conservatively target, with dual touches stopping at the first touch. Fees are 5 basis points and slippage is 10 basis points on each side, with stress doubling both. Independent cash and share values reconcile with a maximum error of 3.637978807091713e-12. (3) The strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, onwards, using all-adjusted IEX data, which is paginated. The strategy rejects training and later-period net returns that are negative. A training sweep of targets and stops resulted in the following returns: (1.5, 1) = -12.752220910464256, (1.5, 1.25) = -6.8086762427363094, (2, 1) = -10.708581864755178, and (2, 1.25) = -12.052126108536413. The strategy notes that all contemporary selected names have survivorship and selection bias, and no pristine prospective evidence is claimed. Terminal open marks are excluded from the payoff sample, with currentholdoutopen0 and trainingopen0. An initial entry-day omission was detected and corrected before journal metrics, and discarded output was not used. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, which means the strategy is not live eligible, regardless of favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in a measured-dataset archive. The current signal is false.

The POWL trading strategy is based on three premises. First, it uses a fixed trend-plus-breakout approach with next-open range exits, where the prior 20-day close breakout and 50-day simple moving average (SMA50) are considered, and the next open is used as the entry point, with a frozen signal-day average daily range (ADR20) stop and target. Second, the strategy uses a vectorbt approach with two event rows per daily bar, representing open and later barrier or close, and synthetic timestamps that do not claim intraday observations. Adverse gaps are filled at the open, and favorable target gaps are targeted conservatively, with dual touches stopping at the first touch. Fees of 5 basis points and slippage of 10 basis points are applied to each side, and stress tests double both. Independent cash and share values are reconciled with a maximum error of 3.637978807091713e-12. Third, the strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, onwards, using all-adjusted IEX data. A watchlist of positive selected later periods shows that all four training variants lose, and the mean interval crosses zero. The training sweep targets 1.5 and 2, with stops at 1 and 1.25, resulting in losses of -6.741325252297993, -18.69843938469283, -3.852527187305641, and -21.788176286351028, respectively. The strategy has survivorship and selection bias, and no pristine prospective evidence is claimed. Terminal open marks are excluded from the payoff sample, and the current holdout open is 0, with the training open also at 0. An initial entry-day omission was detected and corrected before journal metrics, and discarded output was not used. The strategy does not account for cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, making the strategy not live eligible, regardless of favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in the measured-dataset archive. The current signal is false.

A trading strategy for VSH uses a fixed trend-plus-breakout approach with next-open range exits. The strategy has three main premises: (1) it uses a protocol that notes the prior 20-day close breakout and the 50-day simple moving average, with next-open entry and a frozen signal-day average daily range stop and target, and a timeout after five sessions at the next open, only for long positions. (2) The strategy uses vectorbt to represent open and close barriers, including entry-day exits, with synthetic timestamps that do not claim intraday observations. Adverse gaps fill the open, and favorable target gaps conservatively target, with dual touches stopping at the first touch. Fees are 5 basis points and slippage is 10 basis points on each side, with stress doubling both. Independent cash and share values reconcile with a maximum error of 3.637978807091713e-12. (3) The strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, using all-adjusted IEX data. However, all four training variants lost, and double costs turned the holdout negative. The training sweep targeted stops of 1.5 and 2, with results of -13.635595520075913, -19.17348293114387, -3.52923149049484, and -8.73000753401997 for the respective stop and target combinations. The strategy has survivorship and selection bias, and no prospective evidence is claimed. The current signal is false, and the strategy is not eligible for live trading, despite favorable historical results. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test and full trades are stored in the measured-dataset archive.

A trading strategy for PSIX uses a fixed trend-plus-breakout approach with next-open range exits. The strategy has three main premises: (1) it uses a protocol that combines a prior 20-day close breakout and a 50-day simple moving average, with a next-open entry and a frozen signal-day average daily range stop and target, and a timeout after five sessions at the next open, only for long positions. A simple moving average is a calculation of the average price of a stock over a certain period. (2) The strategy uses a vectorbt approach, which is a backtesting framework, with two event rows per daily bar representing open and later barrier or close, including entry-day exits, and synthetic timestamps that do not claim intraday observations. Adverse gaps fill open, and favorable target gaps conservatively target, with dual touches stopping at the first touch. The strategy also includes fees of 5 basis points and slippage of 10 basis points on each side, with stress tests that double both. (3) The strategy was trained on data from 2022 to 2024 and diagnosed on data from later periods, up to September 4, 2025, using all-adjusted IEX data, which is a type of stock exchange data. The training sweep targets stops of 1, 1.25, 1.5, and 2, with results of 0 for each combination. The strategy has several limitations, including survivorship and selection bias, and does not claim to have pristine prospective evidence. Terminal open marks are excluded from the payoff sample, and the current holdout open is 0, as is the training open. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, so the strategy is not eligible for live trading, regardless of favorable historical results. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars, with an executable test.py and full trades stored in a measured-dataset archive. The current signal is false.

The automated trading fund manager has a strategy for the stock AEHR that is based on three premises. First, the strategy uses a fixed trend-plus-breakout approach, which means it looks at the stock's prior 20-day closing price and its 50-day simple moving average, or SMA50, to decide when to enter a trade at the next open. The strategy also uses a stop-loss order, which is set at one times the average daily range, or ADR20, and a target price, which is set at two times the ADR20. If the trade is not successful after five sessions, it is closed at the next open. This strategy only considers long positions, meaning it only buys the stock. Second, the strategy uses a system called vectorbt, which represents each day as two events: the open and the close. This system does not claim to have intraday observations, meaning it does not look at the stock's price during the day, only at the open and close. The strategy also takes into account gaps in the stock's price, both adverse and favorable, and uses a dual touch approach to set stop-loss orders. The strategy includes fees of 5 basis points and slippage of 10 basis points on each side of the trade, and these fees and slippage are doubled in stressful market conditions. The cash and share values are reconciled to ensure accuracy, with a maximum error of 3.637978807091713e-12. Third, the strategy was trained on data from 2022 to 2024 and was tested on data from 2025 up to September 4. The strategy uses adjusted data from the IEX exchange and is paginated, meaning it is divided into pages or sections. However, the strategy was rejected because its net returns were negative during both the training and later periods. The strategy was tested with different target and stop-loss prices, and the results were: (1.5, 1) -38.643851939335995, (1.5, 1.25) -57.67302561057384, (2, 1) -32.86874570103676, and (2, 1.25) -46.090402422060095. The strategy notes that all contemporary selected names have survivorship and selection bias, meaning they have been chosen because they have performed well in the past, and there is no pristine prospective evidence to support the strategy. The strategy also notes that terminal open marks are excluded from the payoff sample, and the current holdout open is 0, while the training open is 1. An initial entry-day omission was detected and corrected before journal metrics were calculated, and discarded output was not used. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, which means the strategy may not be eligible for live trading, regardless of its favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in a measured-dataset archive. The current signal for the strategy is false, meaning it is not recommending a trade at this time.

A trading strategy for AXTI uses a fixed trend-plus-breakout approach with next-open range exits. The strategy has three premises: (1) it uses a protocol note127 with a prior 20-day close breakout and a 50-day simple moving average, with a next-open entry and a frozen signal-day average daily range stop and target, and a timeout after five sessions at the next open, only for long positions. (2) The strategy uses vectorbt with two event rows per daily bar to represent open and later barrier or close, including entry-day exits, and synthetic timestamps do not claim intraday observations. Adverse gaps fill the open, and favorable target gaps conservatively target, with dual touches stopping at the first touch. Fees are 5 basis points and slippage is 10 basis points on each side, with stress tests doubling both. Independent cash and share values reconcile with a maximum error of 3.637978807091713e-12. (3) The strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, onwards, using all-adjusted IEX data, paginated. The strategy rejects training and later-period net returns that are negative. A training sweep of targets and stops resulted in the following net returns: (1.5, 1) -31.442725836626114, (1.5, 1.25) -35.08498901772668, (2, 1) -34.23281713352532, and (2, 1.25) -38.59042367158891. The strategy notes that all contemporary selected names have survivorship and selection bias, and no pristine prospective evidence is claimed. Terminal open marks are excluded from the payoff sample, and the current holdout open is 0, with the training open also 0. An initial entry-day omission was detected and corrected before journal metrics, and discarded output was not used. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, so the strategy is not live eligible, regardless of favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in a measured-dataset archive. The current signal is false.

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 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.