Notes

A news scan found the headline 'GLD VM9110600 constant train slip15 scale1'. The results of a trading model on the symbol GLD, which is a type of investment fund, are as follows: the model used was a constant model, which means it did not change over time, and it was trained on a sample of data, which is a subset of information used to teach the model. The slippage, or the difference between the expected and actual price of a trade, was 15 basis points, or 0.15 percent, and the c_multiplier, a factor that affects the model's performance, was 1. The return, or profit, was 34.92247805890547 percent, and the drawdown, or largest loss, was 19.745224675675132 percent. The CAGR, or compound annual growth rate, which is a measure of an investment's performance over time, was 7.775755837676845 percent. The Sharpe ratio, a measure of risk and return, was 0.6085698508698034. The annual volatility, or fluctuation in price, was 13.893560287942913 percent. There were 1008 trading sessions, which is a period of time when trades can be made, and 48 months, or four years, of data. The win rate, or percentage of successful trades, was 100 percent, and there was only one trade made. The percentage of positive months, or months when the investment made a profit, was 43.75 percent. The mean trade, or average profit of a trade, was 36.02176039855154 percent, and the average win, or average profit of a successful trade, was also 36.02176039855154 percent. There were no losses, so the average loss is not applicable. The mean exposure, or average amount of money invested, was 0.8912225379722589. The vectorbt_max_error, a measure of the model's accuracy, was 0. There were 49 orders, or trades, made. The start date of the data was 2019-01-02, and the end date was 2022-12-30. The measurement was taken on September 11, and other details include frozen2230, SIP/all, a fee of 1 basis point, fractional normalized, and daily proxy. https://runway.adityaperswal.workers.dev/data/stock-bars

A constant model test for the symbol GLD showed the following results: the return was 118.07085269270567, the drawdown was 23.903175817429066, and the Compound Annual Growth Rate (CAGR) percentage was 23.8649314826902. The Sharpe ratio, a measure of risk-adjusted return, was 1.2854051533266133, and the annualized volatility percentage was 17.91081078134928. The test ran for 918 sessions over 44 months, with a win rate of 100 percent and only 1 trade. The average win percentage was 121.03520489452558, and there were no losses, so the average loss percentage is not applicable. The mean exposure, or the average amount of capital used, was 0.8917461654198916. The test started on 2023-01-03 and ended on 2026-08-31, with 45 orders placed. The model used a 5 basis point slippage, a constant multiplier of 1, and a daily proxy. A news scan found the note 'Sep11measurement;frozen2230,SIP/all,fee1bp,fractionalnormalized,dailyproxy'. https://runway.adityaperswal.workers.dev/data/stock-bars

A trading model for the symbol GLD, which is a type of investment fund, used a constant strategy on a sample of training data. The model had a slippage of 5 basis points, which is a measure of the difference between expected and actual trade prices, and a multiplier of 1. The return was 35.18%, the drawdown, or largest loss, was 19.74%, and the compound annual growth rate was 7.83%. The Sharpe ratio, a measure of risk and return, was 0.61, and the annualized volatility was 13.89%. Over 1008 trading sessions and 48 months, the model had a win rate of 100%, with 1 trade, and 43.75% of months were positive. The average trade return was 36.32%, and the average win was also 36.32%, with no losses. The model had an average exposure of 89.12%, meaning it was invested 89.12% of the time. There were 49 orders, and the model started on January 2, 2019, and ended on December 30, 2022. This data was measured on September 11, and the model used a daily proxy, or substitute, with a fee of 1 basis point, and allowed fractional and normalized trading. https://runway.adityaperswal.workers.dev/data/stock-bars

A monthly test of the GLD symbol showed the following results: the return was 82.09329801816475, the drawdown was 10.650372817766629, and the Compound Annual Growth Rate, which is a measure of investment return, was 17.883560448232384 percent. The Sharpe ratio, a measure of risk, was 1.440555167959301. The annual volatility, which is a measure of investment risk, was 11.918268841507 percent. There were 918 sessions, 44 months, and 1 trade, with a win rate of 100 percent. The average win was 11.522535225548593 percent, and there were no losses. The average exposure, which is the amount of capital invested, was 0.6314885074597985. The test started on 2023-01-03 and ended on 2026-08-31. The test was measured on Sep 11, and the following conditions were used: frozen at 2230, Systematic Investment Plan for all, a fee of 1 basis point, and fractional and normalized daily proxy. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the headline 'GLD VM9110600 monthly train slip15 scale1'. The details are about a trading model for the symbol GLD, which is a monthly model used for training, with a slippage of 15 basis points and a multiplier of 1. The return was 22.780768644632985 percent, and the drawdown was 17.556571194272973 percent. The Compound Annual Growth Rate, or CAGR, was 5.264658768125541 percent. The Sharpe ratio, which is a measure of risk, was 0.49602360732588513. The annual volatility was 11.738225486408275 percent. There were 1008 sessions and 48 months, with a win rate of 100 percent and 1 trade. The percentage of positive months was 47.91666666666667 percent, and the mean trade percentage was 2.9224279399774478 percent. The average win percentage was 2.9224279399774478 percent, and there was no average loss percentage. The mean exposure, which refers to the amount of capital used, was 0.7590836648166478. The vectorbt max error, a measure of calculation accuracy, was 7.275957614183426e-12. There were 40 orders from the start date of 2019-01-02 to the end date of 2022-12-30. This was part of a measurement on September 11, with a note about frozen 2230, SIP, or Systematic Investment Plan, all, a fee of 1 basis point, and fractional normalized daily proxy. https://runway.adityaperswal.workers.dev/data/stock-bars

A monthly test of the GLD symbol showed a return of 84.12249185590099, with a drawdown of 10.591120908468277. The Compound Annual Growth Rate was 18.242724425960354 percent. The Sharpe ratio, a measure of risk, was 1.466423509792895. The annual volatility was 11.915368784507987 percent. There were 918 sessions over 44 months, with a win rate of 100 percent and 1 trade. The average monthly win rate was 63.63636363636363 percent. The mean trade percentage was 11.744966886045404 percent, and the average win percentage was also 11.744966886045404 percent. The average loss percentage was not applicable. The mean exposure, or the amount of capital used, was 0.6313849679716671. The test started on 2023-01-03 and ended on 2026-08-31, with 40 orders. This was a measurement taken on September 11, using a frozen 2230 model, with a Systematic Investment Plan and a fee of 1 basis point, and fractional normalization, using daily proxy data. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data line: the symbol is GLD, which is a type of investment fund, and the model used is monthly. The sample used for training is called train, and the slippage, which is the difference between expected and actual trade prices, is 5 basis points, or 0.05 percent. The c_multiplier, a factor that affects trade size, is 1. The return, or profit, is 24.385365922605274 percent, and the drawdown, or largest loss, is 17.386666203685543 percent. The CAGR, or compound annual growth rate, which is a measure of investment return, is 5.606906703000636 percent. The Sharpe ratio, which is a measure of risk and return, is 0.5234109250812987. The annual volatility, or fluctuation in value, is 11.746062999159982 percent. There were 1008 trading sessions, and 48 months, with a win rate of 100 percent, and only 1 trade. The percentage of positive months is 50 percent, and the mean trade return is 3.1101293239625125 percent. The average win return is 3.1101293239625125 percent, and there is no average loss return. The mean exposure, or average amount invested, is 0.7589874980581475. The vectorbt_max_error, a measure of calculation accuracy, is 7.275957614183426e-12. There were 40 orders, and the trading period started on 2019-01-02 and ended on 2022-12-30. This data is from a measurement on Sep 11, and includes notes about a frozen value of 2230, a type of investment plan called SIP, a fee of 1 basis point, and a type of normalized and daily proxy, or substitute value. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data line for a TLT adapter test: the symbol is TLT, the model is an adapter, and the sample is a test. The test had a slippage of 15 basis points, which is a measure of the difference between expected and actual trade prices, and a c_multiplier of 1. The return was -14.883601597817453 percent, the drawdown was 18.0920572171397 percent, and the Compound Annual Growth Rate, or CAGR, was -4.327318191475882 percent. The Sharpe ratio, which is a measure of risk-adjusted return, was -0.6661570729812645, and the annualized volatility was 6.338377171658322 percent. There were 918 sessions, which is a period of trading, and 44 months in the test. The win rate was 38.63636363636363 percent, and there were 44 trades. The percentage of positive months was also 38.63636363636363 percent. The mean trade, or average trade, was -0.27452659080791236 percent, the average win was 2.9991741086224657 percent, and the average loss was 2.335745549708521 percent. The mean exposure, which is the average amount of capital invested, was 0.31452138224323. The vectorbt_max_error, which is a measure of the maximum error in the test, was 1.8189894035458565e-12. There were 96 orders, and the test started on 2023-01-03 and ended on 2026-08-31. The test also included a Sep11 measurement, and was frozen at 2230, with a Systematic Investment Plan, or SIP, and a fee of 1 basis point, with fractional normalization and daily proxy. https://runway.adityaperswal.workers.dev/data/stock-bars

A trading fund manager's report shows results for the symbol TLT, using an adapter model on a train sample, with 15 basis points of slippage and a multiplier of 1. The return was -16.31%, with a drawdown of 19.12%. The compound annual growth rate was -4.35%. A measure of risk called Sharpe ratio was -0.70, and annual volatility was 6.12%. There were 1008 trading sessions over 48 months, with a win rate of 27.08% and 48 trades. The average monthly win rate was also 27.08%. The average trade loss was -0.92%, with an average win of 3.45% and average loss of -2.54%. The mean exposure was 25.61%. A measurement was taken on September 11, with other notes including frozen 2230, SIP all, a fee of 1 basis point, and daily proxy, using fractional normalization. https://runway.adityaperswal.workers.dev/data/stock-bars

A test of the TLT adapter model, which is a type of computer program that helps make investment decisions, found the following results: the symbol being traded was TLT, the sample was a test, the slippage, which is the difference between expected and actual trade prices, was 5 basis points, and the c_multiplier, a factor that affects trade size, was 1. The return, or profit, was -9.23425662628673, the drawdown, which is the largest decrease in value, was 15.754348610633851, and the CAGR, or compound annual growth rate, which is a measure of investment return, was -2.6246194519581545 percent. The Sharpe ratio, a measure of risk and return, was -0.3799895933479405, and the annual volatility, which is a measure of investment risk, was 6.45130657663208 percent. There were 918 trading sessions, which is a period of time when trades can be made, and 44 months, which is a unit of time, in the test. The win rate, which is the percentage of winning trades, was 40.909090909090914 percent, and there were 44 trades. The percentage of positive months, which is a period of time when the investment made money, was 40.909090909090914 percent. The mean trade, which is the average profit or loss of a trade, was -0.07706331530145157 percent, the average win, which is the average profit of a winning trade, was 3.0153490389354625 percent, and the average loss, which is the average loss of a losing trade, was 2.217964175927008 percent. The mean exposure, which is the average amount of money invested, was 0.34132285772952187. The vectorbt_max_error, which is a measure of the accuracy of the model, was 1.8189894035458565e-12. There were 96 orders, which is a request to buy or sell, and the test started on 2023-01-03 and ended on 2026-08-31. This test was measured on Sep 11 and used a frozen 2230, SIP, which stands for Systematic Investment Plan, all, fee 1 basis point, fractional normalized, and daily proxy, which is a substitute for actual data. https://runway.adityaperswal.workers.dev/data/stock-bars

A trading model called an adapter for the symbol TLT had the following results: the return was -11.494672649605132 percent, the drawdown was 17.750262144248484 percent, and the compound annual growth rate, or CAGR, was -3.0065620656151038 percent. The Sharpe ratio, a measure of risk, was -0.45421580011774826, and the annual volatility was 6.28628330080492 percent. Over 1008 sessions and 48 months, the win rate was 29.166666666666668 percent, with 48 trades made. The average trade was a loss of 0.7719386991450878 percent, with an average win of 3.378416887861852 percent and an average loss of 2.480908646736181 percent. The model had a mean exposure of 0.2746180005122335. This data is from a measurement taken on September 11, using a frozen dataset from 2230, with a fee of 1 basis point, or 0.01 percent, and fractional normalization, using daily proxy data, from January 2, 2019, to December 30, 2022. https://runway.adityaperswal.workers.dev/data/stock-bars

A simulation stress test for LPG R91106 was run. The test used 31 training samples and 12 test samples. The train half Kelly criterion, a measure of betting size, was 0.008821333984219859. The probe fraction, a test of the system, was 0.0022053334960549648, and the probe dollars, the amount used for the test, was 1.8767167518078145. The win percentage was 45.16129032258064 percent. The average win percentage was 8.828523013396552 percent, and the average loss percentage was 6.986517793062479 percent. The test mean confidence interval percentage was between -3.59234742455252 and 6.029506469205379 percent. The probability of a loss path percentage was 48.526 percent. The median path percentage was 0.003966466997462614 percent. The 95th percentile drawdown percentage, a measure of the largest loss, was 0.1780184333232276 percent. The 5th percentile path percentage, a measure of the smallest gain, was -0.14818037276380813 percent. The gap stress probability of loss percentage was 49.775999999999996 percent. The target percentage was 0 percent, and the partial percentage was 33.33333333333333 percent. The bootstrap target any 20 percentage, a measure of the system's performance, was 74.936 percent. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=15Min&feed=sip&adjustment=split

A simulation of LPG R91106 had the following results: it used 31 training data points and 12 test data points. The train half Kelly was 0.042452824838893566. The probe fraction was 0.010613206209723391 and the probe dollars were 9.03173235241251. The win percentage was 48.38709677419355 percent. The average win percentage was 8.875053264849075 percent and the average loss percentage was 6.860374494253116 percent. The test mean confidence interval percentage was between -2.9403233697824325 and 6.674445679806679 percent. The probability of loss path percentage was 38.58 percent. The median path percentage was 0.13656116408498065 percent. The 95th percentile drawdown percentage was 0.76247837490444 percent and the 5th percentile path percentage was -0.5968961444626969 percent. The gap stress probability of loss percentage was 39.932 percent. The target percentage was 0 percent, the partial percentage was 33.33333333333333 percent, and the bootstrap target any 20 percentage was 74.946 percent. This data is from a simulation, which is a computer model used to predict how something will work, in this case, a trading strategy. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=15Min&feed=sip&adjustment=split

A data scan found the following regimes for LPG R91106: during the training period, when SPY was not bullish and volatility was not high, the mean percentage change was 0.1707749935846259 over 5 instances, when SPY was not bullish and volatility was high, the mean percentage change was 1.2891024798325454 over 6 instances, when SPY was bullish and volatility was not high, the mean percentage change was -0.39035933423932484 over 11 instances, and when SPY was bullish and volatility was high, the mean percentage change was 2.118363321600049 over 9 instances. During the test period, when SPY was not bullish and volatility was not high, there were 0 instances, when SPY was not bullish and volatility was high, the mean percentage change was -7.619828372848208 over 2 instances, when SPY was bullish and volatility was not high, the mean percentage change was 0.18202187438238576 over 6 instances, and when SPY was bullish and volatility was high, the mean percentage change was 9.603906157215764 over 4 instances. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=15Min&feed=sip&adjustment=split

A news scan found the following data: this is a regression analysis of LPG R91106, which is a type of investment fund. The training data, which is used to teach a model, has 1005 entries, and the results are as follows: the daily percentage return, also known as alpha, is 0.0624 percent, the beta, which is a measure of volatility, for the SPY fund is 0.3722, for the FRO fund is 0.1602, and for the STNG fund is 0.3237, the HAC5 t-values, which are a statistical measure, are 0.8703, 4.2952, 4.0148, and 9.8552, and the r-squared value, which measures how well the model fits, is 0.3035. The test data, which is used to evaluate the model, has 423 entries, and the results are as follows: the daily percentage return is 0.0457 percent, the beta for the SPY fund is 0.3673, for the FRO fund is 0.3793, and for the STNG fund is 0.3476, the HAC5 t-values are 0.4457, 3.4195, 7.5135, and 5.9670, and the r-squared value is 0.5110. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=15Min&feed=sip&adjustment=split

A data scan found the following correlations for LPG R91106: the training set shows that FRO has a correlation of 1 with itself, 0.442891 with LPG, 0.218494 with SPY, and 0.623877 with STNG. LPG has a correlation of 0.442891 with FRO, 1 with itself, 0.254393 with SPY, and 0.508278 with STNG. SPY has a correlation of 0.218494 with FRO, 0.254393 with LPG, 1 with itself, and 0.196238 with STNG. STNG has a correlation of 0.623877 with FRO, 0.508278 with LPG, 0.196238 with SPY, and 1 with itself. The test set shows similar correlations: FRO has a correlation of 1 with itself, 0.670902 with LPG, 0.216564 with SPY, and 0.755241 with STNG. LPG has a correlation of 0.670902 with FRO, 1 with itself, 0.278272 with SPY, and 0.641647 with STNG. SPY has a correlation of 0.216564 with FRO, 0.278272 with LPG, 1 with itself, and 0.164153 with STNG. STNG has a correlation of 0.755241 with FRO, 0.641647 with LPG, 0.164153 with SPY, and 1 with itself. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=15Min&feed=sip&adjustment=split

A data scan found the following dividend calendar for LPG R91106: the dates and dividend amounts are as follows: July 27, 2026, to August 12, 2026, is 1, May 18, 2026, to May 28, 2026, is 1, February 9, 2026, to February 24, 2026, is 0.7, November 17, 2025, to December 2, 2025, is 0.65, August 12, 2025, to August 27, 2025, is 0.6, May 16, 2025, to May 30, 2025, is 0.5, February 5, 2025, to February 27, 2025, is 0.7, November 5, 2024, to November 25, 2024, is 1, August 8, 2024, to August 21, 2024, is 1, May 7, 2024, to May 30, 2024, is 1, February 2, 2024, to February 27, 2024, is 1, October 19, 2023, to November 2, 2023, is 1, August 9, 2023, to September 6, 2023, is 1, May 5, 2023, to May 22, 2023, is 1, February 14, 2023, to February 28, 2023, is 1, November 4, 2022, to December 6, 2022, is 1, August 12, 2022, to September 2, 2022, is 1, May 13, 2022, to June 2, 2022, is 2.5, January 13, 2022, to January 25, 2022, is 1, August 6, 2021, to September 8, 2021, is 1. https://dorianlpg.com/investors/stock-analysts/dividend-history/default.aspx

A data scan found the following information about LPG R91106 coverage: it has 1513 daily bars and 42923 intraday bars, with 42923 unique timestamps and 1428 intraday dates. There are 36929 regular trading hour bars, with a factor range of 1 to 2.3492658544204934. The regular trading hour count distribution has a count of 1428, a mean of 25.860644257703083, a standard deviation of 1.5343002411689493, a minimum of 1, a 25th percentile of 26, a 50th percentile of 26, a 75th percentile of 26, and a maximum of 26. There are no missing regular trading hour days. The raw all open, high, low, close factor error, which measures the difference between actual and estimated prices, is 0.0013098260332680134. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=15Min&feed=sip&adjustment=split

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