Notes

A news scan found the headline 'ETH/USD C91106 trade attribution hourly trend'. This is a data line showing the results of a trading strategy for Ethereum, a type of digital currency, against the US dollar. The data was computed on September 11 at 06:09 UTC. It is a post-hoc partition, which means it is a review of past data, of a frozen baseline, which is a standard for comparison, and it is not filtered, meaning all data is included. The strategy return, which is the profit or loss of the trades, is for the years 2024 and 2025 for training, and 2026 for testing, also known as the holdout period. The fees, which are the costs of the trades, are 25 basis points, which is 0.25%, and the slippage, which is the difference between the expected and actual price of a trade, is 10 basis points, which is 0.10%. The data is divided into two groups: when the trend, which is the direction of the price, is false, meaning the price is not going up or down in a clear direction, and when it is true, meaning the price is going up or down in a clear direction. The results are as follows: when the trend is false, the mean return, which is the average profit or loss, is -6.14% and the compound return, which is the total profit or loss over time, is -23.20%. When the trend is true, the mean return is 3.45% and the compound return is 44.27%. The win rate, which is the percentage of profitable trades, is 25% when the trend is false and 50% when the trend is true. The training rv median, which is a measure of the risk of the trades, is 0.64. The unknown regime, which is a period of time when the market is not following a clear pattern, is excluded from the labels, which are the categories used to classify the data, but all trades are retained. The compound return is also calculated as a subset payoff product, which means it is the total profit or loss of a subset of the trades. The specific results are: for the training period, when the trend is false, there were 4 trades with a mean return of -6.14% and a compound return of -23.20%, and when the trend is true, there were 14 trades with a mean return of 3.45% and a compound return of 44.27%. For the holdout period, when the trend is false, there were 2 trades with a mean return of 1.97% and a compound return of 3.87%, and when the trend is true, there were 4 trades with a mean return of 3.33% and a compound return of 12.87%. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'ETH/USD C91106 trade attribution daily slope'. This is a report about a trading strategy for Ethereum, a type of cryptocurrency, traded against the US dollar. The report was computed on September 11 at 06:09 UTC. It looks at how well the strategy worked, using data from 2024 and 2025 to train the strategy and 2026 to test it. The strategy had fees of 25 basis points, which is 0.25 percent, and a 10 basis points slip, which is 0.10 percent. A basis point is a unit of measurement for interest rates and fees. The report also mentions a 'compound' payoff, which refers to the total return on investment over time, taking into account the effect of reinvesting earnings. The report lists the results of the strategy in different scenarios, including when the daily slope feature was used and when it was not. The slope feature refers to the rate of change of the price of Ethereum over time. The results are as follows: for the training period, when the slope feature was not used, the mean return was -1.8488518143 percent, and when it was used, the mean return was 0.3244966505 percent. For the test period, when the slope feature was not used, the mean return was -8.3317019201 percent, and when it was used, the mean return was 3.6928601266 percent. The report also includes other metrics, such as the number of trades, the win rate, and the median return. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the trade attribution report for ETH/USD on September 11. The report was computed at 06:09 UTC. It is a post-hoc partition of the frozen baseline, which is a strategy return that is not filtered. The report used data from 2024 to 2025 for training and reused 2026 as a holdout. A 25 basis point fee was credited and a 10 basis point slip or trail was applied. A basis point is a unit of measurement, one hundredth of a percent. The unknown warmup was excluded only from labels, but all trades were retained. Compound refers to a subset payoff product, which is a way of calculating returns. The report includes the following data: for ETH, the daily period, train feature highvol, which means high volatility, had a value of false, with 7 observations, a mean of -0.4739287355, a compound of -8.6968783326, a win rate of 42.8571428571 percent, a training rv median of 0.6430540547, and unknown regime excluded was 0. For ETH, the daily period, train feature highvol had a value of true, with 8 observations, a mean of 2.5312746489, a compound of 11.7477159529, a win rate of 37.5 percent, a training rv median of 0.6430540547, and unknown regime excluded was 0. For ETH, the daily period, reused holdout feature highvol had a value of false, with 3 observations, a mean of -5.037063053, a compound of -14.5234345745, a win rate of 0 percent, a training rv median of 0.6430540547, and unknown regime excluded was 0. For ETH, the daily period, reused holdout feature highvol had a value of true, with 2 observations, a mean of -1.2490981741, a compound of -3.6372909448, a win rate of 50 percent, a training rv median of 0.6430540547, and unknown regime excluded was 0. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following trade attribution data for ETH/USD on September 11, computed at 06:09 UTC. The data is based on a post-hoc partition of a frozen baseline, with a strategy return that is not filtered. The data is from 2024 to 2025 for training and 2026 for holdout, with credited fees of 25 basis points and a 10 basis points lip or trail. A basis point is a unit of measurement, equal to one hundredth of a percent. The unknown warmup is excluded only from labels, and all trades are retained. Compound refers to a subset payoff product. The data includes the following information: for the daily trend feature of ETH during the training period, when the trend value was false, the mean was -11.288723631, the compound was -11.288723631, the win was 0, the training rv median was 0.6430540547, and the unknown regime excluded was 1. When the trend value was true, the mean was 0.549098683, the compound was -5.0135207713, the win was 38.4615384615, the training rv median was 0.6430540547, and the unknown regime excluded was 1. For the reused holdout period, when the trend value was false, there was no data. When the trend value was true, the mean was -3.5218771014, the compound was -17.6324659487, the win was 20, the training rv median was 0.6430540547, and the unknown regime excluded was 0. https://runway.adityaperswal.workers.dev/data/crypto-bars

A computation was made on September 11 at 06:09 UTC. The next day's log returns are described, without including any traded profit or loss. A signal was given when the price before the close was higher than the 50-day Simple Moving Average, and the 20-day realized volatility was above the median for 2024-2025. A regression was done using HAC 7 for 2026, reusing the holdout data. The results are as follows: for ETH, during the reused holdout period, when the trend was 0 and volatility was 0, the mean next day log percentage change was -0.1574549453, the median next day log percentage change was -0.0375827968, and the standard deviation of the next day percentage change was 3.1039070376, based on 52 data points. When the trend was 0 and volatility was 1, the mean next day log percentage change was -0.5307911433, the median next day log percentage change was -0.260760704, and the standard deviation of the next day percentage change was 4.2653475468, based on 63 data points. When the trend was 1 and volatility was 0, the mean next day log percentage change was 0.1096703764, the median next day log percentage change was 0.0972674632, and the standard deviation of the next day percentage change was 2.8540954767, based on 106 data points. When the trend was 1 and volatility was 1, the mean next day log percentage change was 0.2965287123, the median next day log percentage change was 0.1771579996, and the standard deviation of the next day percentage change was 3.0373409041, based on 31 data points. https://runway.adityaperswal.workers.dev/data/crypto-bars

A computation was made on September 11 at 06:09 UTC. The results describe the next day's log returns, without including any trading profits or losses. A signal was given before the market closed, indicating that the price was higher than the 50-day Simple Moving Average, which is a calculation of the average price over the last 50 days, and the 20-day Relative Volatility, a measure of price fluctuations, was above the median for 2024 and 2025. A regression analysis, specifically a Heteroskedasticity-Adjusted Covariance, was used, with a value of 7.2026, and the results from 2026 were reused for testing. The following data was found for Ethereum, or ETH: when the trend was low and volatility was low, the average next day log percentage change was -0.2413823626, the median was 0.0520176651, and the standard deviation was 4.0020313672, based on 170 observations; when the trend was low and volatility was high, the average was -0.1257751485, the median was -0.0899421006, and the standard deviation was 3.7041705184, based on 205 observations; when the trend was high and volatility was low, the average was 0.1393187837, the median was 0.1482321681, and the standard deviation was 3.4304463397, based on 164 observations; and when the trend was high and volatility was high, the average was 0.3153091031, the median was 0.0360431008, and the standard deviation was 3.5907440259, based on 142 observations. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the trade attribution report for BTC/USD on September 11. The report was computed at 06:09 UTC. It is a post-hoc partition of the frozen baseline, which is not a filtered strategy return, for the period from 2024 to 2025 for training and 2026 for holdout. The fees were 25 basis points and the slip or trail was 10 basis points. The unknown warm-up was excluded only from labels, but all trades were retained. Compound refers to a subset payoff product. The report includes the following data: for BTC, with an hourly time frame, in the train period, the feature was slope, with a value of false, the mean was -0.9366771407, the compound was -9.4117978143, there were 25 wins, the training rv median was 0.4364544418, and 2 unknown regimes were excluded. For BTC, with an hourly time frame, in the train period, the feature was slope, with a value of true, the mean was -0.6027855486, the compound was -8.3132491664, there were 33.3333333333 percent wins, the training rv median was 0.4364544418, and 2 unknown regimes were excluded. For BTC, with an hourly time frame, in the reused holdout period, the feature was slope, with a value of false, the mean was -2.5358690131, the compound was -5.0075074081, there were 0 wins, the training rv median was 0.4364544418, and 0 unknown regimes were excluded. For BTC, with an hourly time frame, in the reused holdout period, the feature was slope, with a value of true, the mean was 3.3709189718, the compound was 13.3097470256, there were 75 percent wins, the training rv median was 0.4364544418, and 0 unknown regimes were excluded. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following trade attribution data for BTC/USD on September 11, computed at 06:09 UTC. This is a post-hoc partition of a frozen baseline, which is a previously established standard, using a strategy return that was not filtered. The data is from 2024 to 2025 for training and 2026 for holdout, which means it was used to test the strategy. A 25 basis point fee was credited and a 10 basis point slip or trail was applied. A basis point is a unit of measurement for interest rates or fees, equal to 0.01%. The unknown warmup, which refers to a period of time when a model is learning, was excluded only from labels, but all trades were retained. The compound, which refers to the result of combining multiple values, is a subset payoff product. The data includes the following information: a list of trade results for BTC, with a time frame of hourly, and a feature of highvol, which refers to high volume. The list includes the symbol, time frame, period, feature, value, number of trades, mean, compound, win rate, training return value median, and unknown regime excluded. The list contains four items: for the train period with highvol false, the mean is 4.8829110585, the compound is 45.5781713165, and the win rate is 44.4444444444; for the train period with highvol true, the mean is -1.5853159553, the compound is -17.1866405903, and the win rate is 30; for the reused holdout period with highvol false, the mean is 1.4019896435, the compound is 7.6357530493, and the win rate is 50; and for the reused holdout period with highvol true, there are no trades, so the mean and win rate are null. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'BTC/USD C91106 trade attribution hourly trend'. This is a data line. It shows results from September 11, computed at 06:09 UTC. The results are from a post-hoc partition of a frozen baseline, which is a set of data used as a standard for comparison, and a not filtered strategy return, which means the results have not been selected or changed. The data is from 2024 to 2025 for training and 2026 for holdout, which is a period of time when the system is not used to make decisions. There were 25 basis points, which is a unit of measurement for interest rates or fees, credited fees and 10 basis points lip or trail, which refers to the difference between the price at which a security is bought or sold and the price at which it is offered. The unknown warmup, which is a period of time when a system is starting up, was excluded only from labels, which are used to identify the data, and all trades were retained. The compound, which refers to the result of combining multiple things, is a subset payoff product, which means it is the result of multiplying multiple payoffs together. The data includes the following information: [{"symbol":"BTC","tf":"hourly","period":"train","feature":"trend","value":false,"n":4,"mean":-3.2875709471,"compound":-13.218430589,"win":25,"training_rv_median":0.4364544418,"unknown_regime_excluded":2},{"symbol":"BTC","tf":"hourly","period":"train","feature":"trend","value":true,"n":13,"mean":0.0178305173,"compound":-4.2914529128,"win":30.7692307692,"training_rv_median":0.4364544418,"unknown_regime_excluded":2},{"symbol":"BTC","tf":"hourly","period":"reused_holdout","feature":"trend","value":false,"n":1,"mean":-2.6228738134,"compound":-2.6228738134,"win":0,"training_rv_median":0.4364544418,"unknown_regime_excluded":0},{"symbol":"BTC","tf":"hourly","period":"reused_holdout","feature":"trend","value":true,"n":5,"mean":2.2069623349,"compound":10.5349451811,"win":60,"training_rv_median":0.4364544418,"unknown_regime_excluded":0}]. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'BTC/USD C91106 trade attribution daily slope'. This is a data line showing information about a trading strategy for Bitcoin, denoted by the symbol 'BTC', in US dollars, 'USD'. The data was computed on September 11 at 06:09 UTC. It is a post-hoc partition of a frozen baseline, which means it is a review of past data, not filtered by strategy return. The data is from 2024 to 2025 for training and 2026 for holdout, which is a period of time used to test the strategy. A 25 basis point fee was credited and a 10 basis point slip or trail was applied. A basis point is a unit of measurement, one hundredth of a percent. The unknown warm-up, which is a period of time at the start of the data, was excluded only from labels, but all trades were retained. The compound refers to a subset payoff product, which is a way of calculating the return on investment. The data shows the following results for the slope feature: for the train period with a false value, the mean was -3.8738487402, the compound was -21.75676328, the win rate was 16.6666666667 percent, the training return volatility median was 0.4364544418, and the unknown regime excluded was 1. For the train period with a true value, the mean was 1.0680561584, the compound was 6.2262298661, the win rate was 54.5454545455 percent, the training return volatility median was 0.4364544418, and the unknown regime excluded was 1. For the reused holdout period with a false value, the mean was -5.7320116811, the compound was -16.3642976478, the win rate was 0 percent, the training return volatility median was 0.4364544418, and the unknown regime excluded was 0. For the reused holdout period with a true value, the mean was 5.2260021194, the compound was 15.554398783, the win rate was 66.6666666667 percent, the training return volatility median was 0.4364544418, and the unknown regime excluded was 0. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'BTC/USD C91106 trade attribution daily highvol'. This is a report about a trading strategy for Bitcoin, denoted by the symbol BTC, in US dollars, denoted by USD. The report was computed on September 11 at 06:09 UTC. It looks at the performance of a trading strategy, which is a set of rules for buying and selling, from 2024 to 2025, and then applies those rules to 2026 without changing them. The strategy's return, or profit, is calculated after subtracting 25 basis points, or 0.25%, in fees and 10 basis points, or 0.10%, in costs for buying and selling. A basis point is a unit of measurement for interest rates and fees, equal to 0.01%. The report excludes a period known as the 'unknown warmup', which is a time when the strategy is learning and not making actual trades. The compound return is a measure of how much the strategy's returns have multiplied together over time. The report lists the results for different scenarios, including when the feature 'highvol', or high volatility, is true or false. Volatility refers to how much the price of Bitcoin changes over time. The results are as follows: for the training period from 2024 to 2025, when highvol was false, the strategy had a mean return of 1.2171556572%, and when highvol was true, the mean return was -0.0467036914%. For the reused holdout period in 2026, when highvol was false, the mean return was 0.9330969289%, and when highvol was true, the mean return was -2.6252082004%. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'BTC/USD C91106 trade attribution daily trend'. This is a computed report from September 11 at 06:09 UTC. It is a post-hoc partition of a frozen baseline, which is a set of data used as a reference point, and it is not filtered by a specific strategy return. The report covers the period from 2024 to 2025 for training and 2026 for holdout, which means it is testing the strategy on a separate set of data. The report credits fees of 25 basis points, which is a measure of the cost of trading, and it also considers a 10 basis points lip or trail, which refers to the difference between the expected and actual price of a trade. The unknown warm-up period, which is the initial period of a trading strategy, is excluded only from the labels, but all trades are retained. The compound refers to a subset payoff product, which means it is a measure of the overall return of a set of trades. The report includes the following data: for the daily trend feature of Bitcoin, the value is false, with 0 occurrences, a mean of null, a compound of 0, and a win rate of null, during the training period. For the same feature, the value is true, with 17 occurrences, a mean of -0.6761455705, a compound of -16.8851595071, and a win rate of 41.1764705882, during the training period. For the reused holdout period, the value is false, with 0 occurrences, a mean of null, a compound of 0, and a win rate of null. For the same feature, the value is true, with 6 occurrences, a mean of -0.2530047809, a compound of -3.355266979, and a win rate of 33.3333333333, during the reused holdout period. https://runway.adityaperswal.workers.dev/data/crypto-bars

A computation was made on September 11 at 06:09 UTC. The results are about the next day's log returns, which are descriptive and not about actual trades or profits. A signal was given before the market closed, which was higher than the 50-day Simple Moving Average, and the 20-day Relative Volatility was above the median for 2024-2025. A regression analysis was done using HAC 7 for 2026, reusing a holdout set. The results are as follows: for BTC, during the reused holdout period, when the trend was 0 and volatility was 0, the mean next day log percentage change was -0.3349343318 and the median was -0.0371878756, based on 61 days, with a standard deviation of 2.276267162. When the trend was 0 and volatility was 1, the mean was -0.0590420751 and the median was -0.2716527659, based on 63 days, with a standard deviation of 3.3316305541. When the trend was 1 and volatility was 0, the mean was 0.2090561172 and the median was 0.0465000164, based on 98 days, with a standard deviation of 1.9675392969. When the trend was 1 and volatility was 1, the mean was -0.3716946555 and the median was -0.4291939307, based on 30 days, with a standard deviation of 1.8900469043. https://runway.adityaperswal.workers.dev/data/crypto-bars

A computation was made on September 11 at 06:09 UTC. The results describe the next day's log returns, without including filled orders or traded profits and losses. A signal was given before the close, which was higher than the 50-day Simple Moving Average, and the 20-day Relative Volatility was above the median for 2024 and 2025. A regression analysis was done using HAC, with a holdout from 2026 that was reused. The results are as follows: for BTC, during the training period, when the trend was 0 and volatility was low, the mean next day log percentage change was -0.1199999101 and the median was -0.0057064763, with a standard deviation of 2.398598903. When the trend was 0 and volatility was high, the mean was 0.0793610817 and the median was 0.0024956304, with a standard deviation of 2.7675649671. When the trend was 1 and volatility was low, the mean was 0.1221119431 and the median was 0.006760381, with a standard deviation of 2.062201902. When the trend was 1 and volatility was high, the mean was 0.1582606844 and the median was 0.1578482225, with a standard deviation of 2.7882405982. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline is not available, but a data line was found. Measured on September 11 at 06:09 UTC, a trading analysis was done. The analysis used historical data from 2024 to 2025 for training and data from January 1, 2026, to September 11, 2026, as a holdout. The analysis looked at 365 days of data, divided into moving blocks of 1, 24, and 72 hours, with a 20-day horizon and 10,000 paths for each. The analysis did not use channel or breakout conditioning. It used a type of statistical method called bootstrap with 20,000 independent and identically distributed trades, and a type of regression called HAC with 7 lags. The beta values were calculated as intercept, trend, simple moving average 50, high realized volatility, and cross, with values of 0.196407599, -0.3929430007, -0.6701089172, and 0.4326994521. The standard error values were 0.3811040577, 0.5079321303, 0.5618585456, and 0.6560002997. The interaction confidence interval values were -0.8530611354 and 1.7184600396. The symbol analyzed was SOL, with 252 data points, during the reused holdout period, and a training median realized volatility of 0.7496917283. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about SOL/USD with details as follows: Measured on September 11 at 06:09 UTC, a historical analysis from 2024 to 2025 was reused for training from January 1, 2026, to September 11, with a holdout period. The analysis used moving blocks of 1, 24, and 72 hours, a 20-day horizon, and 10,000 paths each. The paths were shared between static and trail strategies without channel or breakout conditioning. A trade bootstrap of 20,000 independent and identically distributed samples was used with a HAC regression of 7 lags. The beta values were calculated as intercept, trend greater than a 50-day simple moving average, high realized volatility, and cross, with realized volatility calculated as the square root of the annualized value over 365 days. The training median was used. The results included the symbol SOL, the kind of analysis being next day interaction HAC7, the number of observations being 443, the period being train, the realized volatility train median being 0.7496917283, and beta values. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'SOL/USD C91106 hourly_distribution trail=undefined'. Measured on September 11 at 06:09 UTC, the data used a seed from September 11, 2026, and historical data from 2024 to 2025 for training, and from January 1, 2026, to September 11, 2026, as a holdout. The data path source was 365 days, with moving blocks of 1, 24, and 72 hours, a 20-day horizon, and 10,000 paths each. The trade bootstrap was 20,000 IID, with HAC regression using 7 lags, and beta was calculated using the intercept, trend, SMA50, high RV, and cross, with RV20 annual sqrt 365, and training median. The results for the symbol SOL, which is a type of cryptocurrency, showed the following statistics: the mean was -0.0084648365, the standard deviation was 0.7178676777, the first quartile was -2.1030242421, the 99th quartile was 2.1106539498, the worst result was -6.1054684867, and the best result was 5.292513637, with a lag of 0.0024697093, a weekend standard deviation of 0.6048256238, and a weekday standard deviation of 0.7581268113. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about ETH/USD with details as follows: Measured on September 11 at 06:09 UTC, using a seed from the same date. The analysis used historical data from 2024 and 2025 for training, and data from January 1, 2026, to September 11, 2026, as a holdout. The data source was 365 days, with moving blocks of 1, 24, and 72 hours, and a 20-day horizon, with 10,000 paths each. The analysis did not use channel or breakout conditioning. It used a trade bootstrap of 20,000 independent and identically distributed samples, and a HAC regression with 7 lags. The beta values were calculated as follows: intercept, trend greater than SMA50, high realized volatility, and cross, with realized volatility calculated as the square root of 365 times the annual volatility, and the training median was used. The results included the following values for the symbol ETH: kind was nextday_interaction_HAC7, n was 252, period was reused holdout, rv_train_median was 0.6430540547, beta_pp was [-0.1574549453, 0.2671253217, -0.3733361981, 0.5601945339], se_pp was [0.4637045207, 0.5292815432, 0.6537872945, 0.7481238233], and interaction_ci_pp was [-0.9061281598, 2.0265172275]. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'Measured Sep 11'. This refers to a measurement taken on September 11. The details include a seed from September 11, 2024-2025, used for training, and a holdout period from January 1, 2026, to September 11. The path source is 365 days, with moving blocks of 1, 24, and 72 hours, a 20-day horizon, and 10,000 paths each. The analysis used static and trail share paths with no channel or breakout conditioning. It involved a trade bootstrap of 20,000 independent and identically distributed (IID) instances, and a HAC regression with 7 lags. The beta values are given as beta equals intercept, trend greater than Simple Moving Average 50, high realized volatility, and cross, with values provided. Realized volatility is calculated over 20 days, annualized, and then square rooted over 365 days, with a training median of 0.6430540547. The symbol is ETH, the kind is nextday_interaction_HAC7, with n equals 681, and the period is train. The beta_pp values are -0.2413823626, 0.3807011462, 0.115607214, and 0.0603831053. The se_pp values are 0.3083829882, 0.4237409137, 0.381511009, and 0.5399873256. The interaction_ci_pp values are -0.9979920528 and 1.1187582634. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: Measured on September 11 at 06:09 UTC, this data is based on a historical period from 2024 to 2025, with a holdout period from January 1, 2026, to September 11, 2026. The data uses moving blocks of 1, 24, and 72 hours, with a 20-day horizon and 10,000 paths for each. The trade bootstrap is 20,000, using independent and identically distributed data, with a regression that includes 7 lags, which is a statistical method to find relationships between variables over time. The beta values include an intercept, trend, and comparison to a simple moving average over 50 periods, high risk or volatility, and a crossover point, all based on a risk value calculated over 20 days and annualized by taking the square root of 365. The data for the symbol ETH, which represents Ethereum, a type of cryptocurrency, is as follows: it has a mean of -0.0065999211, a standard deviation of 0.6469336784, a first quartile of -2.0736449927, a 99th quartile of 1.8415303543, a worst value of -6.1277394312, and a best value of 5.696966137, with a lag of 0.0210757251, a weekend standard deviation of 0.5033131608, and a weekday standard deviation of 0.6959203711. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about a BTC/USD analysis. Measured on September 11 at 06:09 UTC, the analysis used a seed from September 11, 2024-2025 for training and reused a holdout period from January 1, 2026, to September 11. The analysis looked at the past 365 days, using moving blocks of 1, 24, and 72 hours, with a 20-day horizon and 10,000 paths for each. It used static and trail share paths, with no channel or breakout conditioning. The trade bootstrap used 20,000 independent and identically distributed samples, and HAC regression with 7 lags. The beta values were calculated using the intercept, trend, SMA50, high RV, and cross, with RV20 annual sqrt 365 and training median. The results included the symbol BTC, kind nextday_interaction_HAC7, n 252, period reused_holdout, rv_train_median 0.4364544418, beta_pp [-0.3349343318, 0.543990449, 0.2758922567, -0.8566430295], se_pp [0.3450601773, 0.4057506436, 0.4005963846, 0.5361675579], and interaction_ci_pp [-1.9075314429, 0.194245384]. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for a BTC/USD analysis. Measured on September 11 at 06:09 UTC, the analysis used a seed from September 11, 2024-2025 for training and a holdout period from January 1, 2026, to September 11. The analysis looked at the past 365 days, using moving blocks of 1, 24, and 72 hours, with a 20-day horizon and 10,000 paths each. It used static and trail share paths, with no channel or breakout conditioning. The trade bootstrap used 20,000 independent and identically distributed samples, with a HAC regression of 7 lags. The beta values were calculated using the intercept, trend, simple moving average of 50, high realized volatility, and cross values. The realized volatility was calculated over 20 days, with an annual square root of 365, and the training median was used. The results included the symbol BTC, kind of next day interaction HAC7, number of observations 681, period of train, realized volatility train median of 0.4364544418, beta pp values of -0.1199999101, 0.2421118532, 0.1993609918, and -0.1632122505, se pp values of 0.202019318, 0.2492036359, 0.2829952659, and 0.3621368328, and interaction ci pp values of -0.8730004428 and 0.5465759417. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: Measured on September 11 at 06:09 UTC, this data is based on a seed from September 11, 2026, using historical data from 2024 to 2025 for training and data from January 1, 2026, to September 11, 2026, as a holdout. The data source is a 365-day path with moving blocks of 1, 24, and 72 hours, and a 20-day horizon with 10,000 paths each. The data does not include channel or breakout conditioning. A trade bootstrap of 20,000 was used with a regression of 7 lags. The beta values are based on the intercept, trend, and simple moving average of 50, high risk, and cross values. The risk value is calculated as the square root of 365 times the annual value, with a training median. The data for the symbol BTC shows the following values: mean is -0.0044995353, standard deviation is 0.4679126559, the first quartile is -1.3981270565, the 99th quartile is 1.3356031495, the worst value is -4.9115774346, and the best value is 3.888843194. The lag 1 value is -0.0034204911, the standard deviation on weekends is 0.3284539089, and the standard deviation on weekdays is 0.5130469905. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'SOL/USD C91106 conditional_trade_bootstrap hourly trail=undefined'. Measured on September 11 at 06:09 UTC, the seed is 91106. The historical data used is from 2024 to 2025 for training and from 2026 January 1 to September 11 for holdout. The path source is 365 days with moving blocks of 1, 24, and 72 hours, and a 20-day horizon with 10,000 paths each. The trade bootstrap has 20,000 independent and identically distributed draws, with a HAC regression of 7 lags. The beta includes the intercept, trend, SMA50, high RV, and cross. The RV is 20 annual sqrt 365, with a training median. The results are for the symbol SOL, with a conditional trade bootstrap on an hourly time frame, 6 periods, 20,000 draws, a win rate of 33.3333333333%, a mean of 0.6608554517, an average win of 13.7500032946, an average loss of 5.8837184697, a mean confidence interval low of -6.4738730533, a mean confidence interval high of 9.1989682612, a positive mean percentage of 53.71, a half wealth of 0.356, a full notional of 83.96616, a quarter probe of 20.99154, a terminal loss percentage of 48.165, a terminal 5th percentile of -11.6798558105, a terminal 95th percentile of 17.2757407427, an excess best of -14.9391843866, and an actual notional of 0. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about a conditional trade bootstrap for SOL/USD on September 11. The trade was measured at 06:09 UTC. It used historical data from 2024 to 2025 for training and from January 1, 2026, to September 11, 2026, as a holdout. The data source was 365 days with moving blocks of 1, 24, and 72 hours, and a 20-day horizon with 10,000 paths each. The trade did not use channel or breakout conditioning. It used a type of analysis called regression with 7 lags, which is a way to understand how things are related, and a measure of risk called beta, which includes the intercept, trend, and high risk value. The risk value is calculated as the square root of 365 times the annual risk, and the training median is used. The results of the trade are as follows: the symbol is SOL, the kind of trade is a conditional trade bootstrap, the time frame is daily, the number of trades is 6, the number of draws is 20,000, the win percentage is 16.67%, the mean is -0.64%, the average win is 27.07%, the average loss is 6.18%, the lower confidence interval is -9.06%, the upper confidence interval is 11.40%, the percentage of positive mean is 41.41%, and other values are 0, including half wealth, full notional, quarter probe, terminal loss percentage, terminal 5th percentile, and terminal 95th percentile. The best possible result was -27.77%. https://runway.adityaperswal.workers.dev/data/crypto-bars