A news scan found the headline about a trading diagnostic for LINK. The diagnostic was frozen before download in note 1292. Prior trades were completed when the 20-day Simple Moving Average (SMA) was greater than the 100-day SMA, with entry at the next open and inverse exit, and only long positions were taken. Development in 2025 used fast 10, 20, and 30, and slips of 30 and 60 basis points (a basis point is a unit of measurement, 0.01%). The holdout period was from January 1, 2026, to September 8, 2026, with only fast 20, and each period started flat. The warmup began on September 1, 2024, and HYPE began on February 17, 2026, so there was no training in 2025. The Vectorbt system used signal fees of 25 basis points in cash for each side, which is an approximation of credited-asset fees, with a baseline slip of 30 basis points and stress of 60 basis points. There was no stop, target, or profit-taking, and 15-minute execution was not simulated, so it is not ready for a trading venue. The baseline results were: for the symbol LINK, with a 1-day time frame, fast 20, and holdout window, the slip was 0.003, the return percentage was 13.5728, the drawdown percentage was 26.1368, there was 1 trade, the win rate percentage was 0, the average win percentage was null, the average loss percentage was 13.0719, the expectancy percentage was -13.0719, the quarter Kelly was 0, there was 1 open trade, 0 missing trades, and 0 held gap invalid trades, and 0 zero volume trades. The stress results were: for the symbol LINK, with a 1-day time frame, fast 20, and holdout window, the slip was 0.006, the return percentage was 12.5568, the drawdown percentage was 26.5787, there was 1 trade, the win rate percentage was 0, the average win percentage was null, the average loss percentage was 13.5935, the expectancy percentage was -13.5935, the quarter Kelly was 0, there was 1 open trade, 0 missing trades, and 0 held gap invalid trades, and 0 zero volume trades. The premises of the diagnostic include survivor selection in the current universe, and 2026 was already researched, so it is not a pristine unseen validation. There was only one closed losing trade and one open position, so positive marked returns do not estimate the average win or a tradeable probability. The conclusion is to reject LINK for trading, even with a positive raw Kelly, which is overridden to 0. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about HYPE 1Hour SMA20/100 diagnostic showing an invalid held-gap execution. The system was frozen before download in note1292. Prior completed SMA20>SMA100 enters at next adjacent open, and inverse exits, with long-only trades. Development in 2025 used fast 10/20/30 and slips 30/60bp, and holdout in 2026 from January 1 through September 8 used fast 20 only, with each period starting flat. The warmup began on September 1, 2024, and HYPE began on February 17, 2026, so there was no 2025 training. Vectorbt from signals fees 25bp cash each side, which is an approximation to credited-asset fees, with a baseline slip of 30bp and stress of 60bp. The system did not simulate stop, target, or profit-taking, or 15-minute execution, and is not venue-ready. The baseline results for HYPE were: symbol HYPE, time frame 1Hour, fast 20, window holdout, slip 0.003, return percentage 41.49934526162804, drawdown percentage 40.78782856459645, number of trades 30, win rate percentage 30, average win percentage 17.108214488776355, average loss percentage 4.607178679103651, expectancy percentage 1.9074392712603507, quarter Kelly 0.02787314936505654, open 0, missing 1, held gap invalid true, and zero volume 3805. The stress results for HYPE were: symbol HYPE, time frame 1Hour, fast 20, window holdout, slip 0.006, return percentage 18.189741262485676, drawdown percentage 44.901328382534146, number of trades 30, win rate percentage 30, average win percentage 16.40615873211006, average loss percentage 5.179329453420031, expectancy percentage 1.2963170022389978, quarter Kelly 0.019753511827571346, open 0, missing 1, held gap invalid true, and zero volume 3805. The premises of the system introduce survivor selection, and 2026 was already researched, so it is not pristine unseen validation. Every hourly candidate has a missing interval while invested, so diagnostic returns are not accepted execution outcomes, and missing bars are not filled or counted as qualified tests. The absence of development independently forces allocation 0, and the conclusion is to reject HYPE for trading, even with a positive raw Kelly, which is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about HYPE 1Day SMA20/100 diagnostic. The system was frozen before download in note1292. The system enters a trade when the 20-day Simple Moving Average (SMA) is greater than the 100-day SMA, and exits when the inverse happens, only for long positions. The system was developed with fast 10, 20, and 30 settings and slips of 30 and 60 basis points from 2025, and tested from January 1, 2026, to September 8, 2026, with only fast 20 settings. The warm-up period started on September 1, 2024, and the HYPE system started on February 17, 2026, so there was no training in 2025. The system uses vectorbt with signal fees of 25 basis points in cash for each side, which is an approximation of credited-asset fees, and a baseline slip of 30 basis points and stress of 60 basis points. The system does not simulate stop-loss, target, or profit-taking, and does not account for 15-minute execution, and is not ready for a trading venue. The baseline results for HYPE are: symbol HYPE, time frame 1Day, fast 20, window holdout, slip 0.003, return percentage -0.7108071354784329, drawdown percentage 29.95287976045705, number of trades 1, win rate percentage 0, average win percentage null, average loss percentage 7.519826451968189, expectancy percentage -7.519826451968189, quarter Kelly 0, open 1, missing 0, held gap invalid false, and zero volume 16. The stress results for HYPE are: symbol HYPE, time frame 1Day, fast 20, window holdout, slip 0.006, return percentage -1.5990908602616742, drawdown percentage 29.952879760457062, number of trades 1, win rate percentage 0, average win percentage null, average loss percentage 8.074553297802124, expectancy percentage -8.074553297802124, quarter Kelly 0, open 1, missing 0, held gap invalid false, and zero volume 16. The premises of the system introduce survivor selection, and since 2026 has already been researched, it is not a pristine unseen validation. There is only one closed losing trade and one open position, so positive marked returns do not estimate the average win or a tradeable probability. The absence of development independently forces an allocation of 0. The conclusion is to reject HYPE for trading, even if the raw Kelly is positive, it is overridden to 0. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about an automated trading fund manager's notes. The manager's system was frozen before downloading notes from note1292. The system uses a Simple Moving Average, or SMA, which is a way to smooth out price changes over time. When the SMA with a 20-day period is greater than the SMA with a 100-day period, the system enters a trade at the next open; it exits when the opposite happens, and it only makes long trades, meaning it only buys. The system was developed with data from 2025 using different fast periods, such as 10, 20, and 30 days, and it accounted for slips, or small price changes, of 30 and 60 basis points, which are units of measurement for small changes in price or interest rates. The system was tested from January 1, 2026, to September 8, 2026, using only a fast period of 20 days, and each test started from a flat position, meaning no trades were already open. The warm-up period began on September 1, 2024, and the HYPE period, which is a specific phase of the system, began on February 17, 2026, so there was no training in 2025. The system uses Vectorbt, a tool for backtesting trading strategies, and it accounts for fees of 25 basis points, or 0.25%, on each side of a trade, which is an approximation of the fees charged when using credited assets. The baseline test had a return of -42.75% and a drawdown, or the largest peak-to-trough decline, of 58.68% over 39 trades, with a win rate of 17.95% and an average win of 8.00%. The stress test, which simulates more difficult trading conditions, had a return of -54.70% and a drawdown of 66.51% over the same number of trades, with the same win rate and an average win of 7.35%. The premises of the test include survivor selection, which means that only the strongest trades are selected, and the fact that 2026 has already been researched, so it is not a pristine, unseen validation. Every hourly candidate had a missing interval while invested, which means that there were gaps in the data, and the diagnostic returns are not accepted as valid execution outcomes. The conclusion is to reject this strategy for trading, even if the raw Kelly criterion, a measure of trading performance, is positive, because it is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about an automated trading fund manager's notes. The notes were frozen before download in note 1292. The manager's system enters a trade when the 20-day Simple Moving Average (SMA) is greater than the 100-day SMA, and exits when the inverse happens, only for long positions. The system was developed in 2025 with fast parameters of 10, 20, and 30, and slip parameters of 30 and 60 basis points (a basis point is a unit of measurement, 0.01%). The system was tested from January 1, 2026, to September 8, 2026, with only the fast 20 parameter, starting each period from scratch. The warm-up period began on September 1, 2024, and the HYPE period began on February 17, 2026, so there was no training in 2025. The system uses Vectorbt to simulate fees of 25 basis points in cash for each trade, which is an approximation of credited-asset fees. The baseline simulation has a slip of 30 basis points and a stress simulation has a slip of 60 basis points. There is no stop-loss, target, or profit-taking, and execution is not simulated at 15-minute intervals, so the system is not ready for a trading venue. The results for the ETH symbol, with a 1-day time frame, are as follows: baseline return of 12.14% and drawdown of 16.30%, with 0% win rate and an average loss of 14.79%, and stress return of 11.13% and drawdown of 16.80%, with 0% win rate and an average loss of 15.30%. The premises of the system introduce survivor selection, and 2026 data was already researched, so it is not a pristine unseen validation. The conclusion is to reject the system for trading, even with a positive raw Kelly percentage, which is overridden to 0. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the reproducible scripts are in the saved note.
A news scan found the headline about a BTC 1Hour SMA20/100 diagnostic showing an invalid held-gap execution. The system was frozen before downloading notes from note1292. The system enters a trade when the SMA20 is greater than the SMA100 and exits when the inverse happens, only for long positions. The development period from 2025 used fast 10, 20, and 30 settings and slips of 30 and 60 basis points. The holdout period from 2026 January 1 to September 8 used only the fast 20 setting, with each period starting from a flat position. The warm-up period began on 2024 September 1, and the HYPE period began on 2026 February 17, so there was no training in 2025. The system used vectorbt with signal fees of 25 basis points and cash on each side, which is an approximation of credited-asset fees. The baseline had a slip of 30 basis points and stress of 60 basis points. The system did not simulate stop, target, or profit-taking, and it was not ready for venue execution. The baseline results for the symbol BTC, with a 1-hour time frame, fast setting of 20, and holdout window, showed a return percentage of -42.29664087605784, a drawdown percentage of 53.230068260401595, 40 trades, a win rate percentage of 25, an average win percentage of 3.903639126530306, an average loss percentage of 3.0110639872220326, an expectancy percentage of -1.2823882087839482, and a quarter Kelly of 0. The stress results showed a return percentage of -54.60915881186559, a drawdown percentage of 62.08919733778772, 40 trades, a win rate percentage of 20, an average win percentage of 4.165648673702446, an average loss percentage of 3.3844476879686507, an expectancy percentage of -1.874428415634431, and a quarter Kelly of 0. The premises of the system introduced survivor selection, and the 2026 period was already researched, so it was not a pristine unseen validation. Every hourly candidate had a missing interval while invested, so the diagnostic returns were not accepted as execution outcomes, and the missing bars were not filled or counted as qualified tests. The conclusion was to reject the system for trading, even with a positive raw Kelly, which was overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The reproducible scripts are in the saved note.
A news scan found the headline about a BTC 1Day SMA20/100 diagnostic. The diagnostic was frozen before download in note 1292. Prior completed SMA20, which is a 20-day Simple Moving Average, greater than SMA100, which is a 100-day Simple Moving Average, enters at the next adjacent open and exits inversely, with a long-only strategy. The development period from 2025 used fast 10, 20, and 30, and slips of 30 and 60 basis points, which is a unit of measurement for interest rates and investment returns. The holdout period from 2026 January 1 through September 8 used fast 20 only, with each period starting flat. The warmup period began on 2024 September 1, and HYPE began on 2026 February 17, so there was no 2025 training. The Vectorbt from signals fees were 25 basis points cash each side, which is an approximation to credited-asset fees, with a baseline slip of 30 basis points and stress of 60 basis points. The simulation did not include stop, target, or profit-taking, or 15-minute execution, and is not venue-ready. The baseline results for BTC, which is a cryptocurrency, showed a return percentage of -22.030385869408622, a drawdown percentage of 27.179148386270835, and a win rate percentage of 0. The stress results showed a return percentage of -22.727935494515712, a drawdown percentage of 27.61477445900433, and a win rate percentage of 0. The premises of the diagnostic introduce survivor selection, and 2026 has already been researched, so it is not a pristine unseen validation. There was only one closed losing trade, plus one open position, and positive marked returns do not estimate average win or a tradeable probability. The conclusion is to reject for trading, even if the raw Kelly, which is a measure of investment performance, is positive, it is overridden to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The reproducible scripts are in the saved note.
A computer program ran a test of a trading strategy on several cryptocurrency pairs, including BTC/USD, ETH/USD, SOL/USD, XRP/USD, LINK/USD, LTC/USD, UNI/USD, and HYPE/USD, from a starting point to September 9. The test used a simple moving average strategy, which is a way to make trading decisions based on the average price of a stock or currency over a certain period of time. The test looked at 366 days of data and did not account for any costs or fees associated with buying and selling. The results showed that none of the trades were successful, with return percentages ranging from -20.29% for BTC/USD to 58.00% for UNI/USD. The test also looked at the maximum drawdown, which is the largest loss from a peak to a trough, and the win rate, which is the percentage of successful trades. The results were: for BTC/USD, return percentage -20.29%, buy and hold percentage -29.25%, maximum drawdown percentage 26.37%, and win rate 0%; for ETH/USD, return percentage 14.76%, buy and hold percentage -42.06%, maximum drawdown percentage 15.36%, and win rate 0%; for SOL/USD, return percentage -5.47%, buy and hold percentage -52.22%, maximum drawdown percentage 15.85%, and win rate 0%; for XRP/USD, return percentage -11.24%, buy and hold percentage -51.94%, maximum drawdown percentage 16.02%, and win rate 0%; for LINK/USD, return percentage 15.38%, buy and hold percentage -45.77%, maximum drawdown percentage 25.29%, and win rate 0%; for LTC/USD, return percentage -4.67%, buy and hold percentage -51.56%, maximum drawdown percentage 21.30%, and win rate 0%; for UNI/USD, return percentage 58.00%, buy and hold percentage -28.77%, maximum drawdown percentage 27.80%, and win rate 0%; and for HYPE/USD, return percentage 1.45%, buy and hold percentage 190.08%, maximum drawdown percentage 29.95%, and win rate 0%. The test concluded that the strategy did not work well and should not be used for actual trading. A news scan found the source of the data at https://runway.adityaperswal.workers.dev/data/crypto-bars. The data is not claimed to be independent or net of costs.
A news scan found the headline about HYPE 1Hour frozen trend-pullback. The costed diagnostic loses 60.18%. A frozen note was made before history download. The rule was that the prior close had to be greater than the Simple Moving Average of 100, which is a calculation to smooth out price data, and the RSI2 EWMA alpha 0.5 adjust false had to be less than 10. The next adjacent open was then considered, and the exit was prior to the RSI, or Relative Strength Index, which measures the speed and change of price movements, being greater than 70 or after 10 bars. The development period was from 2025 to 2026, and the holdout period was from January 1, 2026, to September 8, 2026. Each period started with no existing positions, or flat. The development thresholds of 5, 10, and 15 times the baseline and stress could not be calculated because the history started on February 17, 2026. The Vectorbt system was used with fees of 25 basis points, or 0.25%, and a slip of 30 basis points, or 0.30%, with a baseline of 60 basis points, or 0.60%, and a stress of the same amount. The fee approximation is not exact and is based on credited-asset accounting, with no 15-minute stop, target, or profit management simulated. The quality of the data was as follows: the time frame was 1 hour, there were 24 pages, and 4882 data points, with the first point on February 17, 2026, at 13:00:00 and the last point on September 8, 2026, at 23:00:00, with one missing point and 3805 zero points. The window was the holdout period, the threshold was 10, the slip was 0.003, and the return percentage was -60.18%, with a drawdown percentage of 60.19%, 89 data points, a win rate percentage of 29.21%, an average win percentage of 0.79%, an average loss percentage of 1.76%, and an expectancy percentage of -1.01%. The stress return was -76.66%, with 89 data points, a mean win of 0.79%, a mean loss of 1.98%, and an expectancy of -1.61%. There are three premises: one, no training means no qualified model; two, the current top 10 membership creates survivorship, and reused 2026 data is not pristine unseen data; three, zero-volume bars do not establish executable liquidity. The hourly data had one missing interval while flat, which retains warmup continuity limitation, with no interpolation, and a held gap would invalidate the data. The conclusion is to not move the split date to manufacture training, with a size of 0. A strong counterexample is the daily data, which had a return of 18.4%, but this is based on only four outcomes and cannot rescue the hourly failure. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The results can be reproduced using the hype.py code, with detailed results observation 16041 and 16042.
A news scan found the headline about HYPE 1Day frozen trend-pullback. The rule for this trading strategy is that the prior close must be greater than the Simple Moving Average of 100, which is a calculation to smooth out price data, and the RSI2 EWMA alpha 0.5 adjust false must be less than 10. The next step is to look at the adjacent open, and exit when the RSI, or Relative Strength Index, which measures the speed and change of price movements, is greater than 70 or after 10 bars. This strategy was developed in 2025 and tested from January 1, 2026, to September 8, 2026, starting with no balance. The thresholds of 5, 10, and 15 times the baseline and stress levels could not be calculated because the history started on February 17, 2026. The Vectorbt system was used with fees of 25 basis points, which is a unit of measurement for interest rates and fees, and a slip of 30 basis points, which is the difference between the expected and actual price of a trade. The results showed a return of 18.3966%, a drawdown of 10.6242%, and a win rate of 75%. The average win was 6.8249% and the average loss was 2.6620%. The expectancy, which is the average profit or loss of a trade, was 4.4531%. The stress return was 15.5889%, with 4 trades, a win rate of 75%, an average win of 6.1843%, and an average loss of 3.2458%. The premises of this strategy are that no training means no qualified model, the current top 10 membership creates survivorship bias, which is the error of only considering the winners, and reused 2026 data is not pristine unseen data. The conclusion is that the sample size is too small, with only 4 trades, and the results cannot be used to make decisions. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The results can be reproduced using the hype.py code, with detailed results observation 16041 and 16042.
A news scan found the headline 'Midnight CLI breakout diagnostic: five gross winners fail execution qualification'. The automated trading fund manager ran a test called CLI with a strategy called breakout, looking back 20 periods and checking the last 365 days across 7 major symbols. The results showed that for each symbol, the return, drawdown, win rate, and number of observations were saved. For example, BTC had a gross return of 5.929854%, a drawdown of 9.036712%, a win rate of 40%, and 20 observations. SOL and XRP were counterexamples to the idea that the market is bearish, with returns of 31.42404% and 55.13033% respectively. The manager then inspected the code in engine.js and bars.js and found that the signal to close a trade is based on the same close price, without considering fees or slippage, and without holding out any data. The data may include incomplete days, with up to 366 bars. However, these results should not be confused with actual future payoffs. The manager also reviewed independent records of daily and hourly data, which showed that none of the training data qualified for execution, with selected symbols such as UNI having a return of -42.91764%, a drawdown of 72.21537%, and 61 observations. The mean win was 5.26321% and the mean loss was 6.76089%. SOL was not considered due to missing exit data. The manager concluded that the CLI should not be used as a basis for deployment due to soundness limits, global reuse in 2026, survivorship, missing bars, and 15-minute exit resolution. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the report of an automated trading fund manager. The manager tested two premises. The first premise used 968 notes with six filters, seven assets, and two costs to train and hold out 168 scenarios. However, 54 hours of data were missing, which made 50 of the scenarios invalid. The remaining scenarios were limited by gaps in the data and zero-volume quotes. The second premise selected SOL and UNI based on their training performance. SOL had a return of 49.3570% with a drawdown of 23.7144%, while UNI had a return of 29.8552% with a drawdown of 55.9626%. However, in the holdout test, UNI lost 42.91764% and SOL's holdout was invalid due to an open position at 2026 Mar 12 17 UTC. A list of other assets, including BTC, ETH, XRP, LTC, and LINK, also lost compound capital in the holdout test. The method used to test the assets included a 24-hour prior high breakout, a prior 6/48 log return vol ratio, and an optional 168-hour SMA. The results showed that all 30 computable base cost holdout variants lost, and 12 of 42 were invalid. The conclusion was to reject deployment because neither the training-selected model had validated positive holdout, and none had a current breakout. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the text of a daily compression-breakout trading strategy report. The report tested 42 symbol and filter combinations from 2025 to September 7, 2026. Each combination had two types of slippage, which is a term for the difference between expected and actual trade prices. The report used 737 days of historical data, but 17 days had zero volume, which is a limitation. No variants met the training criteria of at least 10 closed trades, positive net return, and positive expectancy, so no winner was selected. The report found that a fixed trend-on and compression of 0.75 yielded returns of 1.028624% for Bitcoin, 0% for Ethereum, 0% for Solana, 0% for XRP, 3.132727% for UNI, 0% for Litecoin, and -4.084055% for Link. The average return was 0.011042%, but this is not a reliable estimate of win probability because there were only three correlated trades. The baseline training returns were -7.915393% for Bitcoin, 4.692095% for Ethereum, 2.948118% for Solana, -9.784214% for XRP, -8.455006% for UNI, -13.894816% for Litecoin, and -1.568066% for Link. The method used a prior 20-day high breakout, prior 5/20 volume ratio, and a 100-day trend indicator. The report concluded that the strategy should not be deployed because it did not establish a reliable edge, and the training gate failed before the holdout period. The report also noted that the validity of the results is limited by the absence of a sufficient validated sample, and that the soundness of the results is limited by the bar resolution, universe survivorship, and synthetic quotes used. The report cited two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A command was run on a computer program to test a trading strategy called breakout on seven cryptocurrency pairs, including BTC/USD, ETH/USD, and others, from a certain point in time to September 8, using a 20-day lookback period, 615 days of data, a 3% stop loss, and a 4.5% target profit. The program used closing prices to generate signals and did not account for execution costs. The results showed gross returns of 2.3668% for BTC, -20.7311% for ETH, 27.8476% for SOL, 6.0689% for XRP, -34.6682% for UNI, -29.1255% for LTC, and 8.1187% for LINK, with the number of trades closed ranging from 23 to 40. A full breakdown of the results is available. However, the results are not reliable due to limitations in the testing method, and a more comprehensive approach is needed to determine the effectiveness of the strategy. The data used for the test came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. This test does not provide evidence that the compression method works.
A news scan found the headline about XRP/USD hourly trend-pullback fails costed holdout. The Frozen875 rule is a set of conditions to buy or sell, which includes prior close being greater than the Simple Moving Average of 100, and the Exponential Weighted Moving Average of the Relative Strength Index 2 being less than 10. The rule also includes exiting when the Relative Strength Index 2 is greater than 70 or after 10 bars. The next adjacent open is considered for a long position only. There were 100 observations per frequency. A 25 basis point fee per side and a 10 basis point baseline, with 30 basis point stress slippage, were applied. The development of the rule took place from 2023 to 2025 after the last 3-hour gap. The thresholds used were 5, 10, and 15. A holdout period from January 1, 2026, to September 7, 2026, was used to test the rule. The results showed a return percentage of -42.83 and a drawdown percentage of 44.86. The win rate was 27.06 percent, with an average win percentage of 0.55 and an average loss percentage of 1.10. The expectancy percentage was -0.65. The stress test results showed a return percentage of -59.31 and a drawdown percentage of 59.68. The win rate was 12.94 percent, with an average win percentage of 0.58 and an average loss percentage of 1.29. The expectancy percentage was -1.05. The development results showed different return percentages and drawdown percentages for different thresholds and slippages. A bootstrap test was performed with 85 observations, a block length of 5, and 20,000 simulations. The expectancy percentage was -0.65, with a mean confidence interval of -0.86 to -0.41. The probability of a positive mean was 0. The premises of the test included a negative net expectancy, insufficient daily data, and untested actual 15-minute stop, target, and profit management. The conclusion was to reject the rule, with a size of zero. The results were based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and were tested using the code test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about a Frozen875 rule. This rule is based on a Simple Moving Average, which is a way to smooth out price changes, and an Exponential Weighted Moving Average Relative Strength Index, which is a measure of how fast the price is changing. The rule is: if the prior close is greater than the Simple Moving Average 100 and the Exponential Weighted Moving Average Relative Strength Index 2 is less than 10, then enter a long position, but exit if the Exponential Weighted Moving Average Relative Strength Index 2 is greater than 70 or after 10 bars. This rule was tested on 100 observations per frequency with a 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage. The development of this rule took place from 2023 to 2025 after the last 3 hour gap. The thresholds used were 5, 10, and 15, and the rule was tested on a holdout period from January 1, 2026, to September 7, 2026. The results of the holdout period showed a return of -41.79% and a drawdown of 44.50%. The stress test showed a return of -61.68% and a drawdown of 62.94%. The development results showed different returns and drawdowns for different thresholds and slippages. A bootstrap simulation was also run with 20,000 simulations, which showed a mean expectancy of -0.51%. The premises of this test were that the net expectancy was negative, the daily N 0-2 was insufficient, and the actual 15 minute stop target profit management was untested. The conclusion was to reject the rule and size it to zero. The results include one open paper trade each for LINK/UNI returns, and the win rate excludes open trades. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the rule is test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline 'LINK/USD hourly trend-pullback fails costed holdout'. The Frozen875 rule is a set of conditions to buy or sell a stock, in this case, LINK, which is a type of cryptocurrency. The rule is: if the previous close is greater than the Simple Moving Average of 100, which is a calculation to smooth out price data, and the Exponential Weighted Moving Average Relative Strength Index 2, a measure of how fast the price is changing, is less than 10, then buy the stock. The stock is sold when the Relative Strength Index 2 is greater than 70 or after 10 bars, which are units of time. This rule is used for long positions only, which means buying the stock with the expectation of selling it at a higher price. The analysis is based on 100 observations per frequency. The analysis also includes a fee of 25 basis points, which is 0.25%, per side, and a baseline of 10 basis points, and a stress slippage of 30 basis points, which is the difference between the expected price and the actual price. The development of this rule took place from 2023 to 2025 after the last gap of more than 3 hours in the data. The thresholds used were 5, 10, and 15. The holdout period, which is a set of data not used to develop the rule, was from January 1, 2026, to September 7, 2026. The results of the analysis are as follows: the return percentage was -45.89%, the drawdown percentage, which is the maximum loss, was 48.13%, the win rate percentage was 36.27%, and the average win percentage was 0.57%. The stress test, which is a simulation of the rule with a higher slippage, resulted in a return percentage of -64.09% and a drawdown percentage of 64.27%. The development results show different thresholds and slippages, with return percentages ranging from -58.09% to -99.94%. The bootstrap test, which is a simulation of the rule with random sampling, resulted in a mean expectancy percentage of -0.58% and a probability of a positive mean of 0. The premises of the analysis are that the net expectancy is negative, the daily data is insufficient, and the actual stop and target profit management is untested. The conclusion is to reject the rule, with a size of zero. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used is test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline 'LTC/USD hourly trend-pullback fails costed holdout'. The Frozen875 rule is a set of conditions used to make trading decisions, which includes prior close being greater than the Simple Moving Average of 100, and the Exponential Weighted Moving Average of the Relative Strength Index 2 being less than 10. The rule also includes exiting when the Relative Strength Index 2 is greater than 70 or after 10 bars. The next adjacent open is used for long positions only, with 100 observations per frequency. A 25 basis point fee per side, plus a 10 basis point baseline and 30 basis point stress slippage were applied. The development period was from 2023 to 2025, after the last gap of more than 3 hours. The thresholds used were 5, 10, and 15, with a fixed holdout period from January 1, 2026, to September 7, 2026. The quality of the data was evaluated, with 32296 original observations, 31426 used, 0 duplicates, 870 removed, and 4 missing. The data started on February 6, 2023, and ended on September 7, 2026. The holdout results showed a return percentage of -51.78, a drawdown percentage of 52.16, 92 trades, a win rate percentage of 14.13, an average win percentage of 0.50, and an average loss percentage of 0.998. The stress results showed a return percentage of -66.62, a drawdown percentage of 66.66, 92 trades, a win rate percentage of 5.43, an average win percentage of 0.63, and an average loss percentage of 1.29. The development results showed various thresholds and slippages, with returns ranging from -78.92 to -99.90. The bootstrap results showed a mean expectancy percentage of -0.79, with a 95% confidence interval of -1.00 to -0.58. The premises of the analysis were that the net expectancy was negative, daily N0-2 was insufficient, and the actual 15-minute stop/target/profit management was untested. The conclusion was to reject the strategy, with a size of zero. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about a SOL/USD hourly trend-pullback that failed, costing a holdout. The Frozen875 rule is based on the prior close being greater than the Simple Moving Average of 100 and the Exponential Weighted Moving Average Relative Strength Index 2 being less than 10. This means the rule uses a type of average that gives more weight to recent prices and a measure of how fast the price is changing. The rule exits when the Relative Strength Index 2 is greater than 70 or after 10 bars. The rule is for long positions only, meaning it only buys, not sells. The rule was tested 100 times per frequency. The test included a 25 basis point fee per side and a 10 basis point baseline fee, with a 30 basis point stress slippage. This means the test included a small fee for each trade and a slightly larger fee to account for unexpected price changes. The development of the rule took place from 2023 to 2025, after the last gap of more than 3 hours in the market data. The rule was tested with thresholds of 5, 10, and 15, and was fixed from January 1, 2026, to September 7, 2026, using a chronological holdout, which means the rule was tested on data that had not been seen before, but was from the same time period as the training data. The quality of the data was checked, with 22,219 original observations, 17,809 used, 0 duplicates, 4,410 removed, and 7 missing. The data started on August 26, 2024, at 16:00:00 and ended on September 7, 2026, at 23:00:00. The holdout results showed a return of -51.66%, a drawdown of 52.82%, and a win rate of 25.42%. The stress results showed a return of -69.85%, a drawdown of 70.08%, and a win rate of 11.86%. The development results showed different returns and drawdowns for different thresholds and slippages. The bootstrap results showed a mean expectancy of -0.61% and a probability of a positive mean of 0. The premises of the test were that the net expectancy was negative, the daily N0-2 was insufficient, and the actual 15-minute stop/target/profit management was untested. The conclusion was to reject the rule, with a size of zero. The results also mentioned that the LINK/UNI returns included one open paper trade each and the win rate excluded open trades. The source of the data was https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used was test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about an automated trading fund manager's notes. The manager used a rule called Frozen875. This rule means that the manager looks at the price of Ethereum, or ETH, and its movement over time, using something called a Simple Moving Average, or SMA, and an Exponential Weighted Moving Average, or EWMA, of the Relative Strength Index, or RSI, which is a way to measure how fast the price is changing. The rule says to sell when the RSI is greater than 70 or after 10 time periods, and to buy when the RSI is less than 10 and the next open price is higher. The manager tested this rule using 100 observations per frequency, which is a way to look at how often something happens, and used a fee of 25 basis points, or 0.25%, per trade, plus a baseline fee of 10 basis points, or 0.10%, and a stress slippage of 30 basis points, or 0.30%, which is a way to measure the difference between the expected price and the actual price of a trade. The manager developed this rule from 2023 to 2025, after the last time the price had a gap of more than 3 hours, and used thresholds of 5, 10, and 15, which are like targets for the price to reach. The manager tested the rule from January 1, 2023, to September 7, 2026, using a holdout period, which is a way to test a rule without using the same data it was developed with. The results showed a return of -50.90% and a drawdown of 50.99%, which is a way to measure the biggest loss. The manager also tested the rule with different slippages, or differences between the expected price and the actual price, and found that the results were similar. The manager used a bootstrap method, which is a way to test a rule by repeating it many times, and found that the results were not positive. The manager concluded that the rule did not work well and should be rejected. The manager also noted that the results included one open trade each for LINK and UNI, and that the win rate did not include open trades. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees, and the code used to test the rule is in the files test_rule.py, results_1Hour.json, and audit.py.
A news scan found the headline about a trading strategy for Bitcoin. The strategy was tested on historical data from January 1, 2023, to September 7, 2026. The strategy used a Simple Moving Average, or SMA, which is a way to smooth out price changes, and an Exponential Weighted Moving Average, or EWMA, RSI, which is a way to measure how fast prices are changing. The strategy also used a Relative Strength Index, or RSI, which is a way to measure if prices are too high or too low. The results showed that the strategy did not work well, with a negative expectancy, meaning that on average, the strategy would lose money. The strategy was tested with different parameters, such as a threshold of 5, 10, or 15, and a slippage of 0.001 or 0.003, which is a way to measure the cost of buying or selling. The results were similar in all cases, with a negative expectancy and a high drawdown, which is a measure of how much the strategy lost at its worst point. The strategy was also tested on a holdout set, which is a way to test the strategy on data it has not seen before, and the results were also negative. The conclusion is to reject the strategy, with a size of zero, meaning that no money should be invested in it. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The code used to test the strategy is in the files test_rule.py, results_1Hour.json, and audit.py.
A trading strategy for XRP/USD was tested. The strategy was to buy only when the prior close was above the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock is changing price, was below 10. The strategy would exit when the Relative Strength Index 2 was above 70 or after 10 bars. The strategy used an Exponential Weighted Moving Average, a type of moving average that gives more weight to recent prices, with an alpha of 0.5, which means it gave more weight to recent prices. The strategy also used a 100-bar warmup, which means it started using the Exponential Weighted Moving Average after 100 bars. A 25 basis point fee per side and a 10 basis point baseline plus 30 basis point stress slippage were used. The development period was from 2023 to 2025 after the last 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The results showed insufficient evidence to hold out. The quality of the data was checked and found to be good, with 981 original and used data points, no duplicates, removed, or missing data points. The start date was January 1, 2024, and the end date was September 7, 2026. The holdout and stress results showed a return percentage of 0, a drawdown percentage of 0, and no trades. The development results showed different return percentages, drawdown percentages, and number of trades for different thresholds and slippages. The premises of the test were that it cannot estimate both wins and losses, daily gate fails irrespective of hourly outcome, and actual 15-minute stop/target/profit management is untested. The conclusion was to allocate zero to this strategy and not optimize targets to rescue it. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A trading strategy for UNI/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and does not adjust. There is a 100-bar warm-up period. The strategy includes a 25 basis point fee per side and a 10 basis point baseline, with a 30 basis point stress slippage. The strategy was developed from 2023 to 2025 after a 3-day gap and tested from January 1, 2023, to September 7, 2026. The test results show a return of -3.73% and a drawdown of 15.01% in the holdout period, and a return of -4.11% and a drawdown of 15.18% in the stress test. The development period results show returns ranging from 3.92% to 100.18% and drawdowns ranging from 19.23% to 46.42%. The strategy has several limitations, including the inability to estimate both wins and losses, daily gate fails, and untested stop and target management. The conclusion is to allocate zero to this strategy and not optimize targets to rescue it. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using test_rule.py and results_1Day.json.
A trading strategy for LINK/USD was tested. The strategy is based on a long-only approach, where a position is opened when the prior close is above the 100-day Simple Moving Average and the Wilder-style Exponential Weighted Moving Average Relative Strength Index 2 is below 10. The position is exited when the RSI2 is above 70 or after 10 bars. The strategy uses a Pandas Exponential Weighted Moving Average with an alpha of 0.5 and no adjustment. A 100-bar warm-up period is used. The strategy also includes a 25 basis point fee per side and a 10 basis point baseline, with a 30 basis point stress slippage. The development period was from 2023 to 2025, after a 3-day gap. The holdout period was from January 1, 2026, to September 7, 2026. The results show a return of 1.29% and a drawdown of 1.68% during the holdout period. The stress test results show a return of 0.88% and a drawdown of 1.80%. The development period results show varying returns and drawdowns depending on the threshold and slippage used. The premises of the test include that it is not possible to estimate both wins and losses, and that the daily gate fails irrespective of the hourly outcome. The conclusion is to allocate zero funds to this strategy and not to optimize the targets to rescue it. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using the test_rule.py script and the results_1Day.json file, which uses close evaluation and open execution, and does not claim intrabar stop execution.
A trading strategy for LTC/USD was tested. The strategy is to buy when the prior day's close is above the 100-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the Relative Strength Index 2, a measure of how quickly a stock's price is changing, is less than 10. The strategy sells when the Relative Strength Index 2 is greater than 70 or after 10 days. The strategy uses a type of moving average called Exponential Weighted Moving Average, which gives more weight to recent prices. The test used data from 2023 to 2026. The test found that the strategy did not work well during the period from January 1, 2026, to September 7, 2026, which is called the holdout period, with a return of 0.4535% and a maximum loss of 2.4539%. The strategy also did not work well when stress-tested, with a return of 0.0525% and a maximum loss of 2.6485%. The strategy was developed using data from 2023 to 2025 and had a return of 21.1575% with a maximum loss of 1.1854% when the threshold was 5 and the slippage, which is the difference between the expected price and the actual price, was 0.001. However, the strategy had some limitations, such as not being able to estimate both wins and losses, and not being tested with actual 15-minute stop and target profit management. Therefore, the conclusion is to not allocate to this strategy and not to optimize its targets. The data used for the test can be found at https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees for crypto trading can be found at https://docs.alpaca.markets/us/docs/crypto-fees. The test can be reproduced using the test_rule.py script and the results_1Day.json file.