Notes

A news scan found the data line for ETH/USD with the following information: the symbol is ETH, the time frame is 1 hour, and the window is for training. The sample size is 19, the mean trade percentage is 2.555082441190936, and the mean confidence interval 95 percentage is between -4.388176158497373 and 9.837393598839423. The mean percentage without the best return is -3.3497310305233507. There are 4 targets and 10 stops, with a maximum stop overshoot percentage of 2.3613021249854915. The data is divided into subsets, including weekend and trend groups, with the following characteristics: a weekend group with a flag set to false, a sample size of 16, a mean percentage of 2.032868488954636, and a win percentage of 50; a weekend group with a flag set to true, a sample size of 3, a mean percentage of 5.340223519784538, and a win percentage of 33.33333333333333; a trend group with a flag set to false, a sample size of 6, a mean percentage of 2.4942132159570076, and a win percentage of 33.33333333333333; and a trend group with a flag set to true, a sample size of 13, a mean percentage of 2.5831759297604426, and a win percentage of 53.84615384615385. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. The analysis uses 10,000 independent and identically distributed trade resamples, with a seed of 913061, and non-overlapping positions per coin, but the regimes are not independent. The confidence interval is conditional and descriptive. The simple moving average 200 uses the previous completed day, with no missing trend promotion. The remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is ETH, the time frame is 1 Day, and the window is holdout. The sample size is 5, the mean trade percentage is -2.705731396618394, and the mean confidence interval 95 percentage is between -9.172405977513364 and 4.522173249359829. The mean percentage without the best return is -22.649736583088686. There are 1 target and 2 stops, with a maximum stop overshoot percentage of 2.5202710029793924. The subsets are as follows: a group called weekend with flag false, with 4 trades, a mean percentage of -3.3324697182031904, and a win percentage of 25; a group called weekend with flag true, with 1 trade, a mean percentage of -0.19877811027920744, and a win percentage of 0; a group called trend with flag false, with 4 trades, a mean percentage of -6.0913526250785415, and a win percentage of 0; and a group called trend with flag true, with 1 trade, a mean percentage of 10.836753517222197, and a win percentage of 100. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. There are 10000 independent and identically distributed trade resamples, with a seed of 913061. The positions are non-overlapping per coin, but the regimes are not independent. The confidence interval is conditional and descriptive. The simple moving average 200 uses the previous completed day, with no missing trend promotion. Remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol ETH, time frame 1Day, window train, sample 16, mean trade percentage 0.008970593893272566, mean confidence interval 95 percentage -6.59962821576186 to 7.467911149686474, without best return percentage -32.97817197539621, targets 4, stops 9, max stop overshoot percentage 5.536791081874158. The subsets are: weekend with flag false, N 13, mean percentage 0.4417164354974004, win percentage 38.46153846153847, and weekend with flag true, N 3, mean percentage -1.866261386391285, win percentage 33.33333333333333, and trend with flag false, N 5, mean percentage 3.1563260671232336, win percentage 40, and trend with flag true, N 11, mean percentage -1.421645530302165, win percentage 36.36363636363637. This is based on a simple moving average, or SMA, which is a calculation that uses previous days to determine trends, with a 200-day SMA using the previous completed day, and no promotion of missing trends. The base gross is 3R with a delay of 0 and a slip of 10 basis points, or 0.10 percentage points, with 10000 independent and identically distributed, or IID, trade resamples and a seed of 913061, and non-overlapping positions per coin, but the regimes are not independent, and the confidence interval is conditional and descriptive. Remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is BTC, the time frame is 1 hour, and the window is a holdout period. The sample size is 6, the mean trade percentage is 0.7840122152101595, and the mean confidence interval 95 percentage is between -3.2307465547472325 and 5.674502358601478. The percentage without the best return is -6.464569943295439. There is 1 target and 1 stop, with a maximum stop overshoot percentage of 0.24695096771678784. The subsets are as follows: for the weekend group with the flag set to false, the mean percentage is 1.511084722161129, the win percentage is 60, and the number of trades is 5, for the weekend group with the flag set to true, the mean percentage is -2.8513503195446877, the win percentage is 0, and the number of trades is 1, for the trend group with the flag set to false, the mean percentage is 0.6146832903930187, the win percentage is 40, and the number of trades is 5, and for the trend group with the flag set to true, the mean percentage is 1.6306568392958631, the win percentage is 100, and the number of trades is 1. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. There are 10000 independent and identically distributed trade resamples, with a seed of 913061. The trades are non-overlapping and per coin, but the regimes are not independent, and the confidence interval is conditional and descriptive. The simple moving average 200 uses the previous completed day, with no missing trend promotion. The remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: symbol is BTC, time frame is 1 Hour, and window is train. The sample size is 22, with a mean trade percentage of -1.3218899729572475. The mean confidence interval 95 percentage is between -5.65367989536998 and 3.3571233663827034. Without the best return percentage, it is -46.298114146875044. There are 3 targets and 13 stops, with a maximum stop overshoot percentage of 2.8370082361597144. The subsets are as follows: a group called weekend with flag false, with 21 samples, a mean percentage of -1.113587105813677, and a win percentage of 33.33333333333333, and a group called weekend with flag true, with 1 sample, a mean percentage of -5.6962501829722285, and a win percentage of 0, and a group called trend with flag false, with 4 samples, a mean percentage of -1.6177175706389404, and a win percentage of 25, and a group called trend with flag true, with 18 samples, a mean percentage of -1.2561505068057608, and a win percentage of 33.33333333333333. This is based on a model with a base gross 3R delay 0 and slip 10bp, with 10000 independent and identically distributed trade resamples, and a seed of 913061. The positions are non-overlapping per coin, but the regimes are not independent, and the confidence interval is conditional descriptive. The simple moving average 200 uses the previous completed day, with no missing trend promotion. Remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the symbol is BTC, the time frame is 1 Day, and the window is holdout. The sample size is 6, the mean trade percentage is -1.4488758062713305, and the mean confidence interval 95 percentage is between -7.510742099402358 and 5.136979371523831. The mean percentage without the best return is -19.90881351115046. There are 1 target and 2 stops, with a maximum stop overshoot percentage of 1.3348774334045388. The subsets are as follows: a group called weekend with a flag set to false, a sample size of 6, a mean percentage of -1.448875806271331, and a win percentage of 33.33333333333333, and another group called weekend with a flag set to true but no data, a group called trend with a flag set to false, a sample size of 5, a mean percentage of -4.183727883391557, and a win percentage of 20, and a group called trend with a flag set to true, a sample size of 1, a mean percentage of 12.225384579329802, and a win percentage of 100. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. There are 10000 IID trade resamples with a seed of 913061, non-overlapping per coin positions, but the regimes are not independent. The confidence interval is conditional and descriptive. The SMA200 uses the previous completed day, with no missing trend promotion. The remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line for the BTC/USD C91306 train 1Day indicator subsets and bootstrap. The symbol is BTC, the time frame is 1Day, and the window is train. The sample size is 18, the mean trade percentage is -0.807128911609015, and the mean confidence interval 95 percentage is between -4.969363296747336 and 3.785450523477305. The percentage without the best return is -34.6437716688083. There are 2 targets and 9 stops, with a maximum stop overshoot percentage of 3.1923539639222174. The subsets are as follows: for the weekend group with flag false, the number of trades is 16, the mean percentage is 0.2267944913022571, and the win percentage is 43.75; for the weekend group with flag true, the number of trades is 2, the mean percentage is -9.07851613489919, and the win percentage is 0; for the trend group with flag false, the number of trades is 1, the mean percentage is -0.09127024185187071, and the win percentage is 0; for the trend group with flag true, the number of trades is 17, the mean percentage is -0.8492382451241408, and the win percentage is 41.17647058823529. The base gross is 3R, with a delay of 0 and a slip of 10 basis points. The analysis uses 10000 independent and identically distributed trade resamples with a seed of 913061, with non-overlapping positions per coin but non-independent regimes. The simple moving average 200 uses the previous completed day, with no missing trend promotion. The remove best is a sensitivity test, not an instruction to cut winners. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the data line: a holdout hourly correlation report for C91306. The report shows correlation between cryptocurrency prices, specifically Bitcoin, Ethereum, and Solana, from January 1, 2026, to September 12, 2026. The correlation is calculated using logclose returns, which means the natural logarithm of the ratio of an asset's price at the close of an hour to its price at the close of the previous hour, with no bridging of missing hours. The data was trained from 2024 to 2025 and the holdout period is from 2026. The data source is venue bars downloaded on September 13. The report includes standard deviation and mean, as well as absolute percentage and worst simple percentage. It also includes cross-coin correlation, which is a measure of how the prices of different coins move together, and is based on complete cases, meaning that only hours where all three coins have prices are included. This report is descriptive and does not include a weekend timing strategy, which means it does not try to predict when to buy or sell based on the day of the week. The correlation between the coins is as follows: Bitcoin and Ethereum have a correlation of 0.8879660550913631, Bitcoin and Solana have a correlation of 0.8378700207420143, and Ethereum and Solana have a correlation of 0.8489972465553788. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'SOL/USD C91306 holdout hourly weekend return distribution'. The given data is about the cryptocurrency SOL, with a mean return of -0.52931791542479 basis points and a standard deviation of 58.709200442293614 basis points. The absolute 99th percentile is 2.3959463829631553 percent and the worst hourly return is -5.0440135873292435 percent. This data is based on log close returns, without filling in missing hours, from January 1, 2026, to September 12, 2026, with data downloaded on September 13. The standard deviation and mean are in basis points, while the absolute 99th percentile is in log percentage and the worst hourly return is in simple percentage. This data includes cross-coin correlation and is based on complete cases, and is descriptive, not a strategy for timing weekends. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a fund manager's analysis of SOL/USD returns from January 1, 2026, to September 12, 2026, shows a mean return of -0.24157348918435803 basis points and a standard deviation of 70.16823849191752 basis points per hour on weekdays. The absolute 99th percentile return was 2.4654500999676476 percent and the worst hourly return was -5.922820761083248 percent. This analysis is based on log close returns, without filling in missing hours, using data from 2024 to 2025 for training and 2026 for testing, downloaded from a venue on September 13. The standard deviation and mean are reported in basis points, which are a unit of measurement, and the absolute 99th percentile and worst return are reported as percentages. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a holdout hourly return distribution for ETH/USD from January 1, 2026, to September 12, 2026, with 1752 data points, a mean of -0.6669962314028051 basis points, a standard deviation of 48.885121711561624 basis points, an absolute 99th percentile of 2.0761196362159615 percent, and a worst hour return of -4.024472793543343 percent. This data is based on log close returns, without filling in missing hours, and comes from venue bars downloaded on September 13. The standard deviation and mean are in basis points, which are a unit of measurement, and the absolute 99th percentile and worst hour return are in percentages. The data also includes cross-coin correlation, which is a measure of how different coins move together, and is based on complete cases, meaning that only data points with complete information are included. This data is descriptive and does not represent a weekend timing strategy. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a fund manager's notes about the Ethereum to US Dollar exchange rate, specifically the holdout hourly weekday return distribution. The data includes the following information: the window is a holdout period, the symbol is Ethereum, and the data only includes weekdays. There are 4366 data points, with a mean return of -0.08526962294700854 basis points and a standard deviation of 66.7164947466568 basis points. The absolute 99th percentile return is 2.4634900909502147 percent, and the worst hourly return is -5.943770304572404 percent. The data is based on log close returns, without filling in missing hours, and was trained from 2024 to 2025 and held out from January 1, 2026, to September 12, 2026. The data was downloaded from a venue on September 13. The standard deviation and mean are in basis points, while the absolute 99th percentile is in log percent and the worst return is in simple percent. The data also includes cross-coin correlation, but only for complete cases, and is descriptive, not a weekend timing strategy. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'BTC/USD C91306 holdout hourly weekend return distribution'. The data shows that for the holdout period, which is from January 1, 2026, to September 12, 2026, the mean return in basis points, or a measure of the return as a fraction of 1%, is -0.4173233219015995 and the standard deviation, or a measure of how spread out the returns are, in basis points is 33.040433577362336. The absolute value of the 99th percentile, or the worst 1% of returns, in percentage is 1.2172373475013174% and the worst hour return in percentage is -3.5913058604274073%. This data is based on log close returns, which means the returns are calculated based on the natural logarithm of the closing prices, and does not include any bridging of missing hours. The data was trained from 2024 to 2025 and the holdout period is from 2026. The data source is venue bars downloaded on September 13. The standard deviation and mean are in basis points, while the absolute 99th percentile and worst hour are in percentages. The data also includes cross-coin correlation, which means how the returns of different coins are related, and is based on complete cases, meaning that only data points with complete information are included. This data is descriptive and does not include a weekend timing strategy. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the holdout hourly weekday return distribution for BTC/USD, also known as the distribution of returns for Bitcoin when the US dollar is used, from January 1, 2026, to September 12, 2026, has a mean of -0.10896150733828203 basis points, which is a measure of percentage change, and a standard deviation of 51.27830197417295 basis points. The absolute value of the 99th percentile is 1.9134628546265693 percent, and the worst hour return is -4.792910206779643 percent. This data is based on log close returns, which means returns calculated using the natural logarithm of closing prices, with no bridging of missing hours. The data was trained from 2024 to 2025 and tested from January 1, 2026, to September 12, 2026, using venue bars downloaded on September 13. The standard deviation and mean are reported in basis points, while the absolute 99th percentile and worst hour return are reported in percentages. This analysis also includes cross-coin correlation, which means comparing the returns of different cryptocurrencies, and is based on complete cases, meaning that only data points with complete information are used. This is a descriptive analysis, not a weekend timing strategy, which means it does not involve making decisions based on the day of the week. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data: the train hourly correlation for the window is based on log close returns with no bridging of missing hours. The training period is from 2024 to 2025 and the holdout period is from January 1, 2026, to September 12, 2026. The data was downloaded from the source venue on September 13. The correlation is calculated using standard deviation and mean basis points, with absolute 99th percentile log returns and worst simple returns. The cross-coin correlation is complete case. The data is descriptive and does not include a weekend timing strategy. The correlation between the coins is as follows: BTC and BTC have a correlation of 1, BTC and ETH have a correlation of 0.7928990409242241, BTC and SOL have a correlation of 0.7500430338147436, ETH and BTC have a correlation of 0.7928990409242241, ETH and ETH have a correlation of 1, ETH and SOL have a correlation of 0.7700799529011811, SOL and BTC have a correlation of 0.7500430338147436, SOL and ETH have a correlation of 0.7700799529011811, and SOL and SOL have a correlation of 1. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train hourly weekend return distribution for SOL/USD, also known as a statistical summary of how the price of SOL, a cryptocurrency, changed over time when traded against the US dollar. The data was collected from a training period, which is a set of data used to train a model, from 2024 to 2025, and a holdout period, which is a set of data used to test a model, from January 1, 2026, to September 12, 2026. The data was downloaded from a source venue, which is a place where data is collected, on September 13. The statistical summary includes the following information: the mean, or average, return was 0.9175053273220327 basis points, the standard deviation, or a measure of how spread out the returns were, was 79.58650774885527 basis points, the absolute value of the 99th percentile, or the worst 1% of returns, was 2.9454653321229753%, and the worst hourly return was -8.327465168248683%. The data was collected using log close returns, which means the returns were calculated based on the logarithm of the closing prices, and no bridging of missing hours was done, which means that if there was a missing hour of data, it was not filled in. The data also includes a cross-coin correlation, which is a measure of how the returns of different cryptocurrencies are related, and is based on complete cases, which means that only data points where all the necessary information was available were used. This data is descriptive, meaning it is used to describe what happened, and is not a weekend timing strategy, which means it is not a plan for when to buy or sell based on the day of the week. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'SOL/USD C91306 train hourly weekday return distribution'. This is a data line about the cryptocurrency SOL, which is traded in US dollars. The data was collected from a training period, which is a time frame used to analyze data, from 2024 to 2025, and a holdout period, which is a time frame used to test the analysis, from January 1, 2026, to September 12, 2026. The data was downloaded from a source venue on September 13. The results show a mean, which is an average, of -0.5477294850647224 basis points, a standard deviation, which is a measure of how spread out the data is, of 94.93517679281598 basis points, an absolute 99th percentile, which is a measure of the worst 1% of returns, of 3.2530051049109923%, and a worst hour return of -6.973020271991171%. The data is based on log close returns, which means the returns are calculated based on the natural logarithm of the closing prices, and does not include weekend data or bridging of missing hours. The analysis also includes cross-coin correlation, which means comparing the returns of different cryptocurrencies, and is based on complete cases, which means only including data points where all information is available. This analysis is descriptive, meaning it is used to describe the data, and is not a weekend timing strategy, which means it is not used to make decisions about when to buy or sell based on the day of the week. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train hourly weekend return distribution for ETH/USD, also known as Ethereum to US Dollar, with the following details: it is a training window, the symbol is ETH, and it includes weekends. The number of data points, or N, is 4990. The mean, which is the average, in basis points, or bp, is 0.7362459829681438. The standard deviation, or sd, in basis points is 55.69152753530754. The absolute value of the 99th percentile, or P99, in percentage is 1.9388093972977607. The worst hour percentage is -5.551052887520069. This data is based on log close returns, which means the natural logarithm of the ratio of the current price to the previous price, with no bridging of missing hours. The data is split into a training period from 2024 to 2025 and a holdout period from January 1, 2026, to September 12, 2026. The source of the data is venue bars downloaded on September 13. The standard deviation and mean are in basis points, and the absolute P99 is in percentage, with worst hour in simple percentage. The data also includes cross-coin correlation, which means how the price of one cryptocurrency moves in relation to another, and is based on complete cases, meaning there are no missing values. This data is descriptive and does not represent a weekend timing strategy. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train hourly weekday return distribution for ETH/USD, also known as Ethereum to US Dollar, from a trading log. The data has 12549 entries, with a mean of -0.08981049595994141 and a standard deviation, which is a measure of how spread out the numbers are, of 79.11106180433606. The absolute value of the 99th percentile, which is the value below which 99% of the data falls, is 2.782750938117098 percent. The worst hour had a return of -11.864281880454818 percent. The data is based on log close returns, which means the returns are calculated based on the logarithm of the closing prices, and does not include weekends. The data was split into a training period from 2024 to 2025 and a holdout period from January 1, 2026 to September 12, 2026. The data was downloaded from a venue, which is a place where trading happens, on September 13. The standard deviation and mean are measured in basis points, which are units of measurement for percentages, and the absolute 99th percentile is measured in log percent, while the worst hour is measured in simple percent. The data also includes cross-coin correlation, which means how the returns of different coins are related, and is based on complete cases, meaning that only data points with complete information are included. This data is descriptive and does not include a weekend timing strategy, which means it does not try to predict when to buy or sell based on the day of the week. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the following data line: a train hourly weekend return distribution for BTC/USD, also known as Bitcoin to US Dollar, with the following details. The data was collected from a training window, which is a period of time used to train a model, with 4990 data points, from 2024 to 2025, and a holdout period, which is a period of time used to test a model, from January 1, 2026, to September 12, 2026. The data was downloaded from a source venue on September 13. The mean, or average, return was 0.38835636790084094, and the standard deviation, which is a measure of how spread out the data is, was 35.60555933003448. The absolute value of the 99th percentile, which is a measure of the worst 1% of returns, was 1.3497227486760532%, and the worst hourly return was -4.290659668464924%. The data includes log close returns, which are returns calculated based on the natural logarithm of the closing prices, with no bridging of missing hours, and cross-coin correlation, which is a measure of how different coins move together, was calculated using complete cases, which means that only data points with complete information were used. This data is descriptive, meaning it describes what happened, and is not a strategy for timing weekend trades. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline 'BTC/USD C91306 train hourly weekday return distribution'. The data shows the return distribution for Bitcoin, symbol BTC, from a training dataset. This dataset has 12549 entries and only includes weekdays. The average hourly return is 0.4318934484333188, with a standard deviation of 57.65163458883257. Standard deviation is a measure of how spread out the returns are. The absolute 99th percentile return is 2.112540798162487 percent, and the worst hourly return is -4.895889045284707 percent. The data was collected from log close returns, without filling in missing hours, and was split into training and holdout periods, from 2024-2025 and 2026 January 1 to September 12, respectively. The data source is venue bars downloaded on September 13. The standard deviation and mean are given in basis points, which are a unit of measurement for returns. The absolute 99th percentile and worst returns are given as percentages. The correlation with other coins is also complete. This data is descriptive and does not include a weekend timing strategy. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about the SOL/USD C91306 calendar history quality for a 1 Hour time frame. The symbol is SOL and the time frame is 1 Hour. There are 32424 rows of data, with 10085 rows missing. The first piece of data is from 2023-01-01 00:00:00+00:00 and the last piece of data is from 2026-09-12 23:00:00+00:00, with 100 events. This information was retrieved on September 13 at 06:03-04 UTC. Some data from September 13 is not included. Missing calendar slots mean that rolling high and Average True Range, which is a measure of price volatility, signals are not used, and only mark-to-market, which is a way of valuing assets, is used to fill in missing close prices. There are 418 missing daily spans of data from 2023 to 2024, so the data from 2024 to 2025 should not be used for full training. Duplicate timestamps, which are repeated time stamps, have been removed. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline is not available, but a data line was retrieved on September 13 at 06:03-04UTC. The data line is about the SOL symbol with a 1Day time frame, which has 1351 rows of data, but 418 rows are missing. The first piece of data is from January 1, 2023, at 00:00:00+00:00, and the last piece of data is from September 12, 2026, at 00:00:00+00:00. There are 29 events in the data. The data is incomplete, with 418 missing calendar slots from 2023 to 2024, which affects the rolling high and Average True Range, or ATR, signals. The ATR is a measure of how much a stock's price moves. The missing data only allows for mark-to-market calculations going forward, using the close price. The data from 2024 to 2025 should not be used for full training because it is incomplete. Any duplicate timestamps have been removed. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline about the ETH/USD C91306 calendar history quality for a 1 Hour time frame. The data shows the symbol is ETH, with a time frame of 1 Hour, and it has 32424 rows of data, but 6 rows are missing. The first piece of data is from 2023-01-01 00:00:00+00:00 and the last piece of data is from 2026-09-12 23:00:00+00:00, with 172 events in total. This information was retrieved on September 13 at 06:03-04 UTC. Some data from September 13 is not included, and the missing calendar slots mean that rolling high and Average True Range, which is a measure of price volatility, signals are not used, and only mark-to-market, which is the value of assets at current prices, is used to fill in the missing close prices. There are also 418 missing daily spans for SOL from 2023 to 2024, so the full 2024 to 2025 training data should not be used. Any duplicate timestamps have been removed to ensure the data is accurate. https://runway.adityaperswal.workers.dev/data/crypto-bars

A news scan found the headline is not available, but a data line was retrieved on September 13 at 06:03-04UTC. The data line is about the Ethereum symbol, with a 1-day time frame, and it has 1351 rows of data with no missing rows. The first data point is from January 1, 2023, at 00:00:00, and the last data point is from September 12, 2026, at 00:00:00. There are 46 events in the data. The data quality is affected by missing calendar slots, which suppresses certain signals, and only allows forward filling of close prices. The Solana daily data has 418 missing spans from 2023 to 2024, so the 2024 to 2025 training data should not be used. Duplicate timestamps have been removed. https://runway.adityaperswal.workers.dev/data/crypto-bars