Notes

A trading fund manager analyzed SOL/USD using a method called bootstrap, which involves drawing samples from a set of closed trades, in this case, 10,000 times, with a seed of 91204. This means the samples are not 10,000 independent trades. The manager used a 95% win interval, known as Wilson interval, to estimate the probability of winning. However, there are unresolved issues with serial dependence and hypothesis selection. The analysis found that it is not possible to invent losses when all trades are winning. The results show the following statistics for SOL with a 1-Day time frame and 4 trades: the mean percentage change is 7.2145%, with a 95% confidence interval of -5.8902% to 21.5234%, and the 95% Wilson win interval is between 30.0637% and 95.4414%. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A trading fund manager analyzed ETH/USD using a method called bootstrap, which is a way to estimate how reliable the results are. The manager used 10,000 draws of closed trades, with a seed of 91204, but these were not 10,000 independent trades. The results showed a 95% win interval, which is a range of values within which the true win rate is likely to lie, using a statistical method developed by Wilson. However, there are still some unresolved issues, such as the dependence between trades and the selection of hypotheses. The analysis found that the strategy had a mean percentage return of 6.157475017100844%, with a 95% confidence interval of 4.3010838940019225% to 9.728972460743535%. The win rate, according to the Wilson method, was between 0.43849391955098227 and 1. The strategy was applied to a 1-hour time frame, and the analysis used 3 trades, without training the model. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the following data: IID bootstrap with 10000 draws of closed trades, using seed 91204, which are not 10000 independent trades. The Wilson 95% win interval is provided. There is a tiny sample size and serial dependence, with hypothesis selection unresolved, meaning the bootstrap cannot invent losses. The data includes the symbol ETH, a 1 Hour time frame, and a training period. The number of trades is 13, with a mean percentage of 4.1234713333288555, and a mean confidence interval percentage of -3.7683574914868374 to 12.803957819711675. The win Wilson 95% interval is 0.2914343243183205 to 0.7679424569851186. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the headline 'ETH/USD C91204 managed halfATR uncertainty 1Day holdout'. This refers to a report on the Ethereum to US Dollar exchange rate, where a trading strategy called C91204 was used to manage uncertainty. The report is based on a statistical method called bootstrap, which involves drawing samples from a set of closed trades, in this case 10,000 times, with a seed value of 91204. Note that these are not 10,000 independent trades. The report also mentions a 95% win interval, which is a statistical measure of the range of possible win rates, calculated using the Wilson method. There are some unresolved issues with the analysis, including serial dependence and hypothesis selection, which means that the method used to test the strategy cannot invent losses, even if they are possible. The report includes some technical details, including the symbol's' being ETH, the time frame 'tf' being 1 day, and 'train' being set to false. The number of trades 'N' is 3, the mean percentage change'mean_pct' is 0.0247, and the mean confidence interval percentage'mean_CI_pct' is between -6.6375 and 5.0468. The win Wilson 95 percentage 'win_Wilson95' is between 0.2077 and 0.9385. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the headline about ETH/USD C91204 managed halfATR uncertainty 1Day train. This is a report about a trading strategy, where halfATR refers to half of the average true range, a measure of price volatility. The report is based on 10,000 draws of closed trades, with a seed of 91204, which means the trades were selected using a specific random number generator setting. The results show a 95% win interval, according to the Wilson method, which is a statistical technique. However, there are unresolved issues with serial dependence and hypothesis selection. The report also includes a bootstrap analysis, which is a way of estimating the uncertainty of the results. The analysis found that it is not possible to invent losses, even with all-winning bootstrap scenarios. The specific results are as follows: the mean percentage change was -2.73%, with a 95% confidence interval of -9.50% to 6.14%. The Wilson 95% win interval was between 13.68% and 69.43%. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the headline 'BTC/USD C91204 managed halfATR uncertainty 1Hour holdout'. This refers to a Bitcoin to US Dollar trading strategy. The strategy used a method called IID bootstrap with 10000 draws of closed trades, using a seed of 91204, which means it is not based on 10000 completely independent trades. The results showed a 95% win interval using the Wilson method. However, there are still some unresolved issues with the tiny sample size, serial dependence, and hypothesis selection. The bootstrap method cannot create losses even if all trades were winning. The strategy's parameters were: symbol 'BTC', time frame '1Hour', not trained, with 3 trades, a mean percentage change of 1.1100457864260616, and a mean confidence interval percentage of -6.169254005246194 to 8.560152831413115. The win Wilson 95 percentage interval was 0.2076549551264879 to 0.9385096847238394. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the headline 'BTC/USD C91204 managed halfATR uncertainty 1Hour train'. The body of the report is about a trading analysis. It mentions IID bootstrap with 10000 draws of closed trades, with a seed of 91204, noting that these are not 10000 independent trades. The report references a Wilson 95% win interval. It also talks about unresolved issues with serial dependence and hypothesis selection, stating that a bootstrap with all winning trades cannot invent losses. The report includes data: the symbol is BTC, the time frame is 1 Hour, it is a training session, N is 16, the mean percentage is -0.3559763329919273, the mean confidence interval percentage is between -3.957715474047218 and 3.7021122275061433, and the win Wilson 95 percentage is between 0.2309836333518552 and 0.6682180439322347. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the headline 'BTC/USD C91204 managed halfATR uncertainty 1Day holdout'. This refers to a report on the Bitcoin to US Dollar exchange rate. The report mentions a statistical method called bootstrap, which involves taking 10,000 draws of closed trades, with a seed value of 91204. Note that these are not 10,000 independent trades. The report also references a 95% win interval, which is a measure of confidence, named after a statistician. A term called serial dependence, which means the relationship between consecutive values, and hypothesis selection, which is the process of choosing a theory, are mentioned as unresolved issues. The report includes a result from a statistical test, which shows that a certain method cannot invent losses, even with all winning trades. The report provides specific data: the symbol's' is BTC, the time frame 'tf' is 1 Day, 'train' is set to false, 'N' is 3, the mean percentage change'mean_pct' is 2.3256, and the mean confidence interval percentage'mean_CI_pct' is between -1.7447 and 8.4605. Additionally, the win Wilson 95 percentage 'win_Wilson95' is between 0.2077 and 0.9385. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the following data: IID bootstrap with 10000 draws of closed trades, seed 91204, which are not 10000 independent trades. The Wilson 95% win interval is noted. There is a tiny sample size and serial dependence, with hypothesis selection unresolved, and all-winning bootstrap cannot invent losses. The data includes the symbol BTC, time frame 1Day, and train set to true, with the following statistics: N is 5, mean percentage is -2.770194337172638, mean confidence interval percentage is between -8.077825397805778 and 6.310732317388967, and win Wilson 95 is between 0.03622316096978745 and 0.6244717358814613. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.py

A news scan found the following trade data for SOL/USD: the trade used a 1 Hour time frame and a half Average True Range, or half ATR, which is a measure of price volatility. The data showed 2 trades, with no full coverage trades, and a mean percentage change of null, or no value. The trades were selected after they happened, because of missing data, and not because of a specific trading rule. The closed trades were as follows: a trade that started on 2026-01-06 18:00:00+00:00 and ended on 2026-01-25 16:15:00+00:00, with 3 missing data points and a return percentage of -10.69536448973344, and a trade that started on 2026-04-18 19:00:00+00:00 and ended on 2026-05-08 19:00:00+00:00, with 13 missing data points and a return percentage of 6.758158591128516. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the trade coverage report for SOL/USD. The report is for a 1Hour time frame and is a training set. It has 7 trades with full coverage, meaning all trades have complete data, and the average return is 2.23%. The trades were selected based on missing data, not an alternative trading rule. The closed trades are as follows: a trade that started on 2024-09-26 00:00:00+00:00 and ended on 2024-10-16 00:00:00+00:00 with a return of 3.74%, a trade that started on 2024-10-25 18:00:00+00:00 and ended on 2024-11-11 10:45:00+00:00 with a return of 25.18%, a trade that started on 2024-11-12 13:00:00+00:00 and ended on 2024-12-02 13:00:00+00:00 with a return of 10.22%, a trade that started on 2025-05-14 15:00:00+00:00 and ended on 2025-05-19 07:00:00+00:00 with a return of -10.40%, a trade that started on 2025-05-23 12:00:00+00:00 and ended on 2025-05-30 17:00:00+00:00 with a return of -11.92%, a trade that started on 2025-08-15 15:00:00+00:00 and ended on 2025-09-04 15:00:00+00:00 with a return of 8.76%, and a trade that started on 2025-09-15 08:00:00+00:00 and ended on 2025-09-23 02:30:00+00:00 with a return of -10.00%. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the trade coverage report for SOL/USD with the following details: the symbol is SOL, the time frame is 1 day, and the training mode is off. The number of trades is 3, with 2 having full coverage, and the average percentage change for these trades is -10.93%. The trades were selected based on missing data, not as an alternative trading rule. The closed trades are as follows: a trade that started on 2026-03-19 00:00:00+00:00 and ended on 2026-04-02 12:15:00+00:00 with a return percentage of -13.81%, a trade that started on 2026-04-20 00:00:00+00:00 and ended on 2026-05-10 00:00:00+00:00 with a return percentage of 10.80%, and a trade that started on 2026-05-14 00:00:00+00:00 and ended on 2026-05-17 23:45:00+00:00 with a return percentage of -8.05%. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the trade data for SOL/USD. The data shows a 1Day training period with 4 trades. The average return percentage for these trades is 7.214541283781239%. The trades are as follows: a trade that started on 2024-09-26 00:00:00+00:00 and ended on 2024-10-16 00:00:00+00:00 with a return percentage of 3.744669585437377%, a trade that started on 2024-10-26 00:00:00+00:00 and ended on 2024-11-11 10:45:00+00:00 with a return percentage of 27.449582597070776%, a trade that started on 2025-05-15 00:00:00+00:00 and ended on 2025-05-30 16:30:00+00:00 with a return percentage of -10.612382635978857%, and a trade that started on 2025-08-16 00:00:00+00:00 and ended on 2025-09-05 00:00:00+00:00 with a return percentage of 8.276295588595662%. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the following trade data for ETH/USD: the system used a 1 Hour time frame and did not train on the data. The number of trades, N, was 3. The full coverage mean percentage change was not available. The trades were selected after they happened because of missing data, and this was not an alternative trading rule. All closed trades are listed: the first trade started on 2026-03-05 18:00:00+00:00 and ended on 2026-03-25 18:00:00+00:00 with 6 missing data points and a return percentage of 4.3010838940019225, the second trade started on 2026-04-09 02:00:00+00:00 and ended on 2026-04-29 02:00:00+00:00 with 3 missing data points and a return percentage of 4.442368696557075, and the third trade started on 2026-07-07 05:00:00+00:00 and ended on 2026-07-27 05:00:00+00:00 with 9 missing data points and a return percentage of 9.728972460743535. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the following data line: this is a set of notes about a trading strategy for Ethereum, which is a type of digital currency, with the symbol ETH, and the US dollar, with the symbol USD. The strategy is based on a 1-hour time frame, which means the strategy looks at how the price of Ethereum changes every hour. The strategy is called halfATR, which stands for half Average True Range, a measure of how much the price of something changes. The strategy was trained, or tested, on 13 sets of data, and 11 of these sets had complete data, with an average return of 5.7093737993943305 percent. The complete data set was selected after the fact, by looking at which sets had missing data, and it is not an alternative rule for trading. The following trades were closed: a trade that started on February 28, 2024, at 20:00:00, and ended on March 11, 2024, at 18:30:00, with a return of 20.380402041281755 percent, a trade that started on April 9, 2024, at 15:00:00, and ended on April 13, 2024, at 20:00:00, with a return of -13.142577611407802 percent, a trade that started on May 27, 2024, at 20:00:00, and ended on June 11, 2024, at 07:00:00, with 2 missing data points and a return of -9.247903506208887 percent, a trade that started on September 29, 2024, at 08:00:00, and ended on October 2, 2024, at 13:00:00, with a return of -8.565098939413218 percent, a trade that started on October 22, 2024, at 01:00:00, and ended on October 25, 2024, at 23:15:00, with a return of -9.218383017298336 percent, a trade that started on November 24, 2024, at 12:00:00, and ended on December 14, 2024, at 12:00:00, with a return of 15.373131934808626 percent, a trade that started on April 28, 2025, at 02:00:00, and ended on May 10, 2025, at 16:45:00, with a return of 34.92829907300505 percent, a trade that started on May 12, 2025, at 19:00:00, and ended on June 1, 2025, at 19:00:00, with a return of 2.728075854294154 percent, a trade that started on June 11, 2025, at 23:00:00, and ended on June 13, 2025, at 01:15:00, with a return of -10.946225461725323 percent, a trade that started on July 14, 2025, at 19:00:00, and ended on July 20, 2025, at 12:00:00, with a return of 22.236432293746766 percent, a trade that started on July 22, 2025, at 05:00:00, and ended on August 11, 2025, at 05:00:00, with a return of 17.01718696305685 percent, a trade that started on August 25, 2025, at 07:00:00, and ended on September 14, 2025, at 07:00:00, with 1 missing data point and a return of 0.04991904614637388 percent, and a trade that started on October 7, 2025, at 15:00:00. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the following trade data for ETH/USD: the trade coverage is based on a 1-day holdout period. The data shows the following closed trades: a trade that started on 2026-01-09 00:00:00+00:00 and ended on 2026-01-21 12:15:00+00:00 with a return percentage of -6.637508128634185, a trade that started on 2026-03-20 00:00:00+00:00 and ended on 2026-04-09 00:00:00+00:00 with a return percentage of 1.664964347390785, and a trade that started on 2026-04-13 00:00:00+00:00 and ended on 2026-05-03 00:00:00+00:00 with a return percentage of 5.046756887317683. The data also includes information about missing data, with 8, 0, and 3 missing data points for each trade respectively. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the following data line: this is a trade coverage report for Ethereum, denoted as ETH, with a 1-day time frame, or period of analysis, and a training set, which is a collection of data used to develop a trading strategy. The report includes 8 data points, with 7 of them having full coverage, meaning they have complete information, and an average percentage return of -3.774139063767279. The trades were selected based on missing data, which refers to gaps in the information, and not as an alternative trading rule, which is a set of instructions for buying and selling. The list of all closed trades includes the following: a trade that started on April 10, 2024, and ended on April 13, 2024, with a return percentage of -12.43394151285363, a trade that started on October 23, 2024, and ended on October 25, 2024, with a return percentage of -8.796035862114515, a trade that started on November 15, 2024, and ended on December 5, 2024, with a return percentage of 23.753126075717866, a trade that started on December 10, 2024, and ended on December 20, 2024, with a return percentage of -12.180220135206433, a trade that started on May 16, 2025, and ended on June 5, 2025, with a return percentage of 1.6850578820813533, a trade that started on June 13, 2025, and ended on June 20, 2025, with a return percentage of -10.03004555033231, a trade that started on August 26, 2025, and ended on September 15, 2025, with one missing data point and a return percentage of 4.555201103034134, and a trade that started on October 8, 2025, and ended on October 10, 2025, with a return percentage of -8.416914343663285. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the following trade data for Bitcoin, denoted as BTC, with a 1 Hour time frame, which is a period of one hour used to analyze price movements. The data is not used for training, indicated by train being false, and the number of trades, denoted as N, is 3. The full coverage number of trades is also 3, with a mean percentage return of 1.1100457864260616 percent. This full coverage subset was selected after the fact due to missing data and is not an alternative tradable rule. All closed trades are listed: the first trade started on April 12, 2026, at 02:00:00 and ended on May 2, 2026, at 02:00:00 with no missing data and a return percentage of 8.560152831413115 percent, the second trade started on May 7, 2026, at 14:00:00 and ended on May 18, 2026, at 15:30:00 with no missing data and a return percentage of -6.169254005246194 percent, and the third trade started on July 17, 2026, at 02:00:00 and ended on August 6, 2026, at 02:00:00 with no missing data and a return percentage of 0.939238533111264 percent. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the following data line: the symbol is BTC, the time frame is 1 hour, and the training is set to true. The number of data points is 16, the number of full coverage data points is 9, and the mean percentage is -4.739571719364512. The complete coverage subset was selected retrospectively by missing data, which is not an alternative tradable rule. All closed trades are listed below: a trade that started on 2024-01-03 13:00:00+00:00 and ended on 2024-01-23 13:00:00+00:00 with a return percentage of -9.615790849850047, a trade that started on 2024-02-20 16:00:00+00:00 and ended on 2024-02-28 17:15:00+00:00 with a return percentage of 19.8364102501009, a trade that started on 2024-03-05 20:00:00+00:00 and ended on 2024-03-20 04:45:00+00:00 with a return percentage of 3.3764005101297956, a trade that started on 2024-04-09 14:00:00+00:00 and ended on 2024-04-13 20:15:00+00:00 with a return percentage of -10.927119553353082, a trade that started on 2024-07-29 17:00:00+00:00 and ended on 2024-08-02 21:15:00+00:00 with a return percentage of -8.09565176492012, a trade that started on 2024-09-24 01:00:00+00:00 and ended on 2024-10-14 01:00:00+00:00 with a return percentage of -1.3161877193947902, a trade that started on 2024-10-15 15:00:00+00:00 and ended on 2024-11-04 15:00:00+00:00 with a return percentage of 3.805873955025141, a trade that started on 2024-11-14 16:00:00+00:00 and ended on 2024-12-04 16:00:00+00:00 with a return percentage of 8.208221735157473, a trade that started on 2024-12-05 23:00:00+00:00 and ended on 2024-12-25 23:00:00+00:00 with a return percentage of 1.4103870534764917, a trade that started on 2025-01-20 01:00:00+00:00 and ended on 2025-02-09 01:00:00+00:00 with a return percentage of -3.763006530966484, a trade that started on 2025-04-28 02:00:00+00:00 and ended on 2025-05-18 02:00:00+00:00 with a return percentage of 10.358421031833554, a trade that started on 2025-05-18 20:00:00+00:00 and ended on 2025-06-07 20:00:00+00:00 with a return percentage of 1.1660186726421218, and a trade that started on 2025-08-11 21:00:00+00:00 and ended on 2025-08-19 15:15:00+00:00. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A trading system for Bitcoin, denoted as BTC, with a 1-day time frame, was analyzed. The system had 3 trades in its dataset, with a mean percentage return of 2.325622972777101 percent. This dataset was selected because it had no missing data, and it is not an alternative trading rule. The system had three closed trades: one that started on March 19, 2026, and ended on April 8, 2026, with a return of 0.2610328345697077 percent, another that started on April 20, 2026, and ended on May 10, 2026, with a return of 8.460513550704519 percent, and the last one that started on July 25, 2026, and ended on August 14, 2026, with a return of -1.7446774669429232 percent. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A news scan found the data line for a BTC/USD C91204 halfATR managed trade coverage for a 1Day training period. The settings include a short name of BTC, a time frame of 1Day, training set to true, N of 5, full coverage N of 3, and a full coverage mean percentage of -7.387161069945085. A complete coverage subset was selected retrospectively by missing data, which is not an alternative tradable rule. All closed trades are listed as follows: a trade that entered on 2024-01-04 00:00:00+00:00 and exited on 2024-01-24 00:00:00+00:00 with 0 missing data and a return percentage of -7.654863818951196, a trade that entered on 2024-12-19 00:00:00+00:00 and exited on 2024-12-30 15:00:00+00:00 with 0 missing data and a return percentage of -9.113821889596817, a trade that entered on 2025-05-05 00:00:00+00:00 and exited on 2025-05-22 03:30:00+00:00 with 15 missing data and a return percentage of 14.93263853900071, a trade that entered on 2025-05-24 00:00:00+00:00 and exited on 2025-06-05 20:45:00+00:00 with 2 missing data and a return percentage of -6.622127015028645, and a trade that entered on 2025-08-15 00:00:00+00:00 and exited on 2025-08-20 14:00:00+00:00 with 0 missing data and a return percentage of -5.392797501287239. https://runway.adityaperswal.workers.dev/data/crypto-bars ; C91204 research/robust.json

A data scan found the following information about the SOL/USD trading pair: it has 71569 rows of data, starting from 2024-08-26 16:00:00+00:00 and ending at 2026-09-12 04:00:00+00:00. There are 96 missing observations, with 47 of those missing in 2026. The data includes a partial final bar, but the study only uses completed bars before 04:00 UTC and the holdout period ends on September 12 at 00:00 UTC. The data starts in November 2023 as requested, but the SOL data is only available from late 2024. A missing observation means there is no data, not a zero return. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: the SOL/USD trading pair has a 1-hour history with 17910 rows of data, starting from 2024-08-26 16:00:00+00:00 and ending at 2026-09-12 04:00:00+00:00. There are 0 duplicate entries and 7 missing observations, with 1 of those missing observations occurring in 2026. The data includes a partial final bar, but the study only uses completed bars before 04:00 UTC and the holdout period ends on September 12 at 00:00 UTC. The data starts from November 2023 as requested, but SOL data is only available from late 2024. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: it has 748 rows of data, starting from 2024-08-26 00:00:00+00:00 and ending at 2026-09-12 00:00:00+00:00, with no duplicates, and no missing observations, including none in 2026. The data includes a partial final bar, but the study only uses completed data before 04:00 UTC and the holdout period ends on September 12 at 00:00 UTC. The data starts from November 2023 as requested, but SOL data is only available from late 2024. A missing observation means there is no data, not a zero return. https://runway.adityaperswal.workers.dev/data/crypto-bars

A data scan found the following information about the ETH/USD trading history: it has 100,283 rows of data, starting from 2023-11-01 00:00:00+00:00 and ending at 2026-09-12 04:00:00+00:00. There are 0 duplicate entries and 150 missing observations, with 98 of those missing observations occurring in 2026. The data includes a partial final bar, but the study only uses completed bars before 04:00 UTC and the holdout period ends on September 12 at 00:00 UTC. The data starts in November 2023 as requested, and data for SOL is available from late 2024. A missing observation means there is no data for that time period, not that the return is zero. https://runway.adityaperswal.workers.dev/data/crypto-bars