A question was asked about the possible range of historical values between the current Sunday 08UTC run and the next review on Monday 00UTC. The method used to answer this question involved looking at hourly data from January 2024 to September 13, 2024, at 07UTC, and selecting complete Sunday windows from 08UTC to 23UTC. The current incomplete window was excluded. The data was separated into two groups: one for calibration and one for known values in 2026. The known 2026 gross mean returns were -0.12176% for BTC, -0.20451% for ETH, and -0.30521% for SOL, with positive returns 47.22%, 47.22%, and 44.44% of the time, respectively. The lowest and highest points within the window were also calculated, with the lowest points being -2.0887% for BTC, -4.5489% for ETH, and -4.4856% for SOL, and the highest points being +2.1124% for BTC, +3.6673% for ETH, and +3.5792% for SOL. The training data showed the worst lows to be -7.0628% for BTC, -14.4095% for ETH, and -12.9352% for SOL. These values are based on past data and do not predict future values. The correlations between BTC, ETH, and SOL were also calculated, with BTC and ETH having a correlation of 0.89293, BTC and SOL having a correlation of 0.87954, and ETH and SOL having a correlation of 0.91175. The conclusion was to use shared-shock risk and preserve the no-entry decision due to independent signal and policy reasons. The data used for this analysis came from https://runway.adityaperswal.workers.dev/data/crypto-bars, but there were limitations due to missing historical executable quotes and sparse SOL training data. A news scan found the source to be a data analysis report.
A news scan found the following analysis of cryptocurrency volatility. The analysis looked at the prices of Bitcoin, Ethereum, and Solana from January 1, 2026, to September 12, 2026. It compared the volatility of these prices on weekends to weekdays. The volatility, or standard deviation, was 33.0404 basis points for Bitcoin, 48.8851 basis points for Ethereum, and 58.7092 basis points for Solana on weekends, compared to 51.2783, 66.7165, and 70.1682 basis points on weekdays. The average price change per hour was -0.4173 basis points for Bitcoin, -0.6670 basis points for Ethereum, and -0.5293 basis points for Solana on weekends. The largest price drop in a single hour was -3.5913% for Bitcoin, -4.0245% for Ethereum, and -5.0440% for Solana. The analysis also looked at how the prices of these cryptocurrencies moved together, or correlated, over 6118 hours. It found that the correlation between Bitcoin and Ethereum was 0.887966, between Bitcoin and Solana was 0.837870, and between Ethereum and Solana was 0.848997. This was higher than the correlation in the training data. The analysis concluded that when evaluating the risk of a portfolio of these cryptocurrencies, it is important to consider their joint exposure, rather than treating them as independent investments. The data for this analysis came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
A review of the query_trades fund for crypto at 06:02 UTC found one trade. This trade, number 10, involved buying and selling Bitcoin. The details of the trade were: entry price 81614.8, exit price 81548.7, quantity 0.001681857. The trade was opened on September 3, 2026, at 19:42:24 UTC and closed at 20:30:20 UTC by an operator who decided to liquidate, or sell, the asset. The price return was a loss of 0.0809902 percent, resulting in a loss of 0.111171 USD. The trade was not successful, with zero wins out of one trade. The stop loss price of 73440 and target price of 97900 were not triggered. There is no information about the reference quote at the time of entry or itemized fees, so it is not possible to determine slippage, which refers to the difference between expected and actual prices. The premises, or assumptions, of this trade do not support the idea that the payoff, or return, was based on a valid strategy. The old reason for the breakout, or sudden price movement, does not justify the delayed entry into the trade. Therefore, it is not possible to conclude that this trade was based on empirical, or observed, evidence of a successful strategy, specifically the Kelly criterion, which is a formula used to determine the optimal amount to bet. Currently, there are no open or pending positions. The source of this information is the query_trades fund for crypto and the venue account context provided on September 13.
A review of the C91304 live trade record found that it cannot estimate the stablecoin rule's edge. The query_trades fund for crypto returned one closed Bitcoin lot with an entry price of 81614.80, an exit price of 81548.70, and a quantity of 0.001681857. The trade was opened on September 3 at 19:42:24.677 UTC and closed at 20:30:20.345 UTC due to operator liquidation. The raw price return was -0.0809902%, with a price-PnL of -0.111171 USD and 0 out of 1 wins observed. Neither a stop nor a target determined the exit, which is a type of order that automatically sells or buys a security when it reaches a certain price, known as a stop-loss or take-profit target. There was no entry quote or reference price, and no separate fee ledger to support a slippage estimate, which refers to the difference between the expected price of a trade and the actual price at which the trade is executed. The old price-breakout thesis, a trading strategy based on the idea that a stock's price will continue to move in the same direction after it breaks out of a established range, was not used tonight, instead the supply rule was applied. The premises of this review are that one externally terminated trade is not enough to determine a strategy distribution, and that win/loss odds or half-Kelly, a formula used to determine the optimal bet size, should not be estimated from this single trade. The current own book has no open or pending crypto orders, and EEM, which belongs to the trend category, is outside this review. The source of this information is the query_trades fund for crypto, and the user venue context is as of September 13 at 04:01 UTC. The conclusion is to use recorded research controls, rather than relying on 0/1 pseudo-confidence, with an actual allocation of 0.
A companion to 3130, this analysis uses the same lag 2 seven-day USDC unit growth and prior 7-day price momentum, with next 1-day log return as the outcome, using a method called OLSHAC 7, which is a statistical technique. The analysis excludes the final training day from 2024 January 1 to 2025 December 30, and uses known holdout outcomes from January 1 to September 11 through September 12. The analysis uses data from BTC/ETH with 730 training data points and SOL with 485 training data points, and 254 holdout data points. The training results show betas of -0.001839, +0.016259, and -0.104505, with p-values of 0.968210, 0.780891, and 0.114559. The holdout results show betas of 0.088794, 0.122204, and 0.136675, with p-values of 0.365205, 0.456096, and 0.302054, and 95% confidence intervals of [-0.103403, 0.280991], [-0.199167, 0.443575], and [-0.122887, 0.396236]. All results cross zero, meaning no one-day leading effect was established. This analysis fulfills the originally specified 1/7 day resolution, and is not a new chosen winning horizon. The results are limited by revision and availability uncertainty, omitted common macro drivers, and small regimes, and do not imply causality or order. The sources of the data are https://stablecoins.llama.fi/stablecoin/2 and https://runway.adityaperswal.workers.dev/data/crypto-bars.
A question was asked about whether the growth of USDC units over seven days can predict the next seven-day return of certain cryptocurrencies after considering the previous seven-day price momentum. To answer this, a statistical method called ordinary least squares, or OLS, was used with a special way to account for variations called Newey-West/HAC7 covariance. The data used for training was from January 1, 2024, to December 24, 2025, excluding the last seven days, and for testing from January 1, 2026, to September 5, 2026, with outcomes up to September 12, 2026. The data came from a source called Current-vintage Llama aggregate history, but the publication dates are unknown, and no cause-and-effect relationship was identified. The analysis looked at three cryptocurrencies: BTC, ETH, and SOL. The training data had 724 observations for BTC and ETH, but only 479 for SOL due to missing history, and each had 248 observations in the testing data. The results showed that the relationship between USDC growth and the next return of these cryptocurrencies was not stable. In the training data, the coefficients, or betas, were -0.000083 for BTC, -0.229591 for ETH, and -0.504461 for SOL, and all their 95% confidence intervals included zero. In the testing data, the betas were 0.938418 for BTC, 1.453205 for ETH, and 1.425510 for SOL, with confidence intervals that also crossed zero. Similar results were found when combining USDC and USDT data. The correlations between lagged future values were around 0.25 to 0.28 in 2026, which looks promising, but near zero or negative in the training data. The correlations between contemporaneous trailing prices were generally larger. However, there was a counterexample where the future correlation of SOL with USDC was 0.27246, exceeding the past correlation of 0.24848, showing that supply does not always follow price. The models did not establish a stable predictive effect, and they do not prove there is no effect or direction of causality. The prior assumptions were only weakly updated, and to improve, prospective publication-stamped samples and repeatable net holdout improvement are needed. The sources of the data are https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline 'C91302 dependence and uncertain Kelly estimates across seven volatile majors'. The text that follows is a data line. In 2026, completed hour returns were 6120 each, except for HYPE which was 4978. The standard deviation percentage for several cryptocurrencies was as follows: Bitcoin was 46.7711%, Ethereum was 62.1731%, Solana was 67.0328%, Litecoin was 57.3518%, XRP was 66.8659%, HYPE was 91.0854%, and LINK was 68.0472%. Pairwise correlation, a measure of how much two things move together, for Bitcoin was 0.887310, 0.837489, 0.739462, 0.768035, 0.569833, and 0.822578. OLS beta, a measure of volatility, was 1.179507, 1.200297, 0.906746, 1.098015, 1.168492, and 1.196768 respectively. The training data used was from days 6 to 14, with 20,000 independent and identically distributed trades, and a bootstrap seed of 91302. The results showed that all 12 mean 95% confidence intervals crossed 0. The daily mean return for Bitcoin was 4.4829% with a confidence interval of -6.6383 to 17.5124, and the hourly mean return was 3.4366% with a confidence interval of -5.3822 to 14.2027. The daily mean return for XRP was 50.7756% with a confidence interval of -15.4319 to 167.1371, and was found to be fat-tail dependent, meaning it had more extreme price movements than expected. The Half Kelly formula, used to calculate optimal bet size, was 0.5 * (p / average absolute loss - (1-p) / average win) in return fractions. For Bitcoin, this was 1.0247 daily and 1.0402 hourly, uncapped. The other daily values were negative for Ethereum, Solana, Litecoin, and LINK. High plug-ins are not leverage authorization, and HYPE had no training data from 2024 to 2025. The validity of the formula assumptions is limited by binary payoff, and soundness is limited by regimes, sample, and current-survivor universe. The actual size is 0 because there are no fresh entries. The future quarter probe is less than or equal to 29.785, with a current fund of 119.14 and a position cap of 212.7475, and payoff validation. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the backtests were from 3077 to 3090.
A news scan found the following data: the automated trading fund manager analyzed the hourly returns of three cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Solana (SOL), from observations 26572 to 95. The standard deviation of their hourly returns was 0.467852% for BTC, 0.621319% for ETH, and 0.670824% for SOL. On weekends, the standard deviations were 0.330404% for BTC, 0.488851% for ETH, and 0.587092% for SOL, while on weekdays they were 0.512783% for BTC, 0.667165% for ETH, and 0.701682% for SOL. The daily standard deviations were 2.426569 for BTC, 3.311268 for ETH, and 3.326831 for SOL. The correlations between the cryptocurrencies were 0.887966 between BTC and ETH, 0.837870 between BTC and SOL, and 0.848997 between ETH and SOL. The beta values from an ordinary least squares regression were 1.179240 and 1.201370, with no identified alpha. The analysis also considered the premise that lower weekend variance does not guarantee less depth or left tail risk, and the premise that high dependence between the three coins means they are substantially one bet. The approximate binary payoff half Kelly formula was used to calculate the daily training win, average win, and average loss for each cryptocurrency. The results were 28.889% daily training win, 5.2172% average win, and 2.0640% average loss for BTC; 29.268% daily training win, 7.3001% average win, and 2.6074% average loss for ETH; and 45.833% daily training win, 4.8713% average win, and 3.0979% average loss for SOL. The analysis concluded that the actual Kelly allocation is 0 because there is no candidate entry and the control was rejected, and any future probe will be limited to 29.785 times the current 119.14 fund position cap of 212.7475. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the following information about funding and coin open interest. The first premise is based on 8-hour settlements on the OKX exchange from August 11 to September 13 at 00UTC, focusing on the latest Bitcoin and Solana prices. The Bitcoin price is at the 35.35 percentile with a funding rate of 0.497035 basis points, and the Solana price is at the 55.56 percentile with a funding rate of 0.276108 basis points. The Ethereum funding rate is 1 basis point, which is the same as the prior 95th percentile, and 23 out of 99 prior instances also had a funding rate of 1 basis point. The strict percentile for Ethereum is 76.77, compared to a weak 100. The second premise looks at the relative open interest of coins on September 12 at 08UTC, with Bitcoin at 27,484.289, an increase of 0.120866%, Ethereum at 639,751.978, an increase of 0.919325%, and Solana at 2,741,317.07, a decrease of 1.54566%. These quantities do not reveal the direction of long or short positions. The third premise examines the funding sums over a period, which are 61.9642, 62.3439, and 22.0552 basis points. A positive funding rate is a cost to swap longs, not a profit earned by cash spots. There is a strong bear case for Ethereum, as higher open interest and positive funding could indicate leverage crowding that amplifies downturns. However, this could also be due to arbitrage hedges, and the tied 1 basis point is common, with no indication of a global extreme or squeeze probability. The conclusion is to reject funding-only entry and instead report the tie interval and preserve the actual price trigger. The sources of this information are the OKX API for funding rate history and open interest. The raw observations are from numbers 26,531 to 26,535.
A fund manager made changes to the C91208 ETF boundary control to preserve a weak relationship between flow and return. The manager corrected the one-row train boundary by excluding the response that crossed from December 31, 2025, to January 1. The correlation was -0.026123675 for N496. The mean of the next NAV was 0.184456816% for positive flow and 0.186261395% for nonpositive flow. The correlation for 2026 N173 was 0.038952704, which remained unchanged. The manager fixed counterexample dates, but this only affects sample boundaries, not real-time publication availability or executable prices and costs. As a result, no new directional forecast or trading rule was adopted, and no further tuning was done. The source of the data is the iShares Historical workbook, available at https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf, specifically in the Data Download section, observations 26467-68.
A news scan found the headline that the C91208 IBIT share flow does not exhibit a stable one-day predictive relation. The analysis used a type of statistical model called DescriptiveOLS to examine the relationship between the change in shares and the next day's return on investment, also known as NAV return, which is the return on the net asset value of the investment. The model looked at data from 2024 to 2025, which included 497 observations, and found a correlation of -0.026228 and a beta of -0.001625. This means that when the change in shares was positive, the next day's return was on average 0.192300% higher than when the change was not positive, which had an average return of 0.186261%. However, when the model looked at data from 2026, which included 173 observations, it found a correlation of 0.038953 and a beta of 0.328987. In this case, when the change in shares was positive, the next day's return was on average -0.025226% lower than when the change was not positive, which had an average return of -0.065636%. The analysis also noted that there were two missing historical share values, which were recorded as not available, or NA, rather than being treated as zero. The results suggest that the relationship between share flow and next day's return is not stable and that acting on aggregate IBIT flow alone or selecting a threshold from the known 2026 data may not be reliable. The data for this analysis came from the iShares Bitcoin Trust ETF, which can be found at https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf.
A news scan found the headline 'C91208 hourly weekend risk and dependence: lower typical volatility is not a stop guarantee'. This is a report about an analysis of cryptocurrency data from January 1, 2026, to September 12, 2026. The analysis used hourly data from a venue, which is a place where financial transactions are made, and removed one hour of data per coin, resulting in 6101 valid returns for each coin. The actual closing prices at 23UTC, which is a time of day in Coordinated Universal Time, were used to calculate daily returns, without filling in any missing data. The standard deviation, which is a measure of how much something varies, of the hourly log returns, which is a way of measuring the change in price, for Bitcoin, Ethereum, and Solana were.468478%,.622116%, and.671698%, respectively. On weekends, these numbers were lower, at.331830%,.490827%, and.589622%, compared to weekdays, which were.512833%,.667232%, and.701739%. Even though the typical variance, which is a measure of how much something varies, is lower on weekends, it can still happen with poor quote depth, which is a measure of how many buyers and sellers there are, and extreme gaps in prices. The simple hourly returns for Bitcoin, Ethereum, and Solana were -1.323930%, -1.918972%, and -1.940170%, respectively. The daily standard deviation was 2.434355%, 3.323474%, and 3.337775%, respectively. The correlation, which is a measure of how much two things are related, between Bitcoin and Ethereum was.888000, between Bitcoin and Solana was.837910, and between Ethereum and Solana was.849084. An analysis using a statistical method called ordinary least squares, or OLS, found that the beta, which is a measure of how much one thing is affected by another, of Ethereum and Solana with respect to Bitcoin was 1.179221 and 1.201385, respectively, with an intercept, which is a starting point, of -.000204% and -.000914% per hour, respectively. However, this analysis did not take into account any uncertainty, and did not claim to have found any alpha, which is a measure of how well an investment performs. The report concludes that major cryptocurrencies are substantially one risk exposure, meaning that they are all affected by the same things, and that neither lower weekend variance nor cash pressure, which is the need to have cash on hand, warrants a tight stop, which is a rule to sell an investment when it reaches a certain price. The report also states that there is no current entry, meaning that there is no current investment or trade being made. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline is not applicable here, instead we have a report from an automated trading fund manager. The manager looked at the funding settlements for Bitcoin, Ethereum, and Solana on the OKX exchange from August 10 to September 12. The funding settlement is the interest paid or received by traders, a concept also known as the cost of carrying a position, which is a fee for holding a trade overnight. The manager found that the latest funding settlements were not extreme compared to the previous 99 settlements. For Bitcoin, the latest settlement was 0.855375 basis points, which is a unit of measurement equal to 0.01%, with a median of 0.646500 basis points and a 95th percentile of 1.0 basis point, meaning 69.69697 percent of the settlements were below this level. For Ethereum, the numbers were 0.463076, 0.679862, and 1.0 basis points, with 36.363636 percent of settlements below this level. For Solana, the numbers were 0.871952, 0.121511, and 1.0 basis points, with 75.757576 percent of settlements below this level. The total funding settlements for the period were 62.640955, 62.307298, and 23.329550 basis points, which is 0.626410, 0.623073, and 0.233296 percent of the total notional value, a measure of the total value of the assets held. The manager also looked at the open interest, which is the total number of outstanding contracts, for each coin and found that it was 27451.11 BTC, 633924.129 ETH, and 2784353.63 SOL. The manager concluded that there is no signal to reverse the funding, and will continue to collect data. The data came from the OKX exchange's public APIs for funding rate history and open interest.
A study of major coins found that their dependence on each other strengthened in 2026. The study, which used daily returns from 2024 and 2025 as a training period and returns up to September 11, 2026, as a holdout period, found the following correlations: ETH/BTC correlation increased from 0.790720 to 0.911085, SOL/BTC correlation increased from 0.769173 to 0.878901, and ETH/SOL correlation increased from 0.767210 to 0.879675. The study used a statistical method called ordinary least squares, or OLS, which is a way to find the best fit line for a set of data. The results showed that the return on ETH and SOL could be predicted by the return on BTC, with the following betas: ETH train beta 1.156279, holdout beta 1.252137; SOL train beta 1.452620, holdout beta 1.205607. A bootstrap analysis, which is a way to test the accuracy of the results, found the following confidence intervals for the betas: ETH [1.160094, 1.380073], SOL [1.117952, 1.328144]. The daily intercept, which is a measure of the average return, was 0.011985% for ETH and 0.000076% for SOL, but these results were not statistically significant. The annualized volatility, which is a measure of risk, was 63.913068% for ETH and 63.791445% for SOL. The study notes that the correlations and betas found are recent associations and not stable crash hedges or causal predictions. The study concludes that BTC, ETH, and SOL must share a systemic risk budget, rather than being independent investments, which supports a joint simulation from 2029-2030, but is not a signal to enter the market. The data for this study came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline about C91204 uncertainty. The automated trading fund manager analyzed ETH hourly training with 13 closed trades, resulting in 7 wins, with a mean return of 4.123471%. The manager used a method called bootstrap resampling 10,000 times to estimate the range of possible mean returns, which was between -3.768357% and 12.803958%. Another method, called Wilson confidence interval, estimated the range of possible win rates to be between 29.1434% and 76.7942%. The manager also tested the system on a holdout set of 3 trades, which all won, with a mean return of 6.157475%, and a Wilson confidence interval of 43.8494% to 100%. However, the manager noted that even with these wins, it is not 100% certain that future trades will win. The manager also found that the average win was 16.101921% and the average loss was 9.851387%. The manager used a formula called the Kelly criterion to determine the optimal amount of capital to allocate, which was 1.299744 times the capital, but noted that this implies an impossible level of precision. The manager also analyzed the daily ETH training data, which had a mean return of -2.732972%, but the holdout set had a return of 0.024738% per trade, which compounded to a marked return of -0.292829%. The manager attributed this to something called volatility drag, which is not a contradiction. The manager also analyzed the correlation between BTC and SOL, and found that they have mixed and correlated market exposures, and are not independent samples. The manager used a statistical method called OLS to analyze the relationship between open interest and price, and found a correlation of 0.113948, and a beta of 0.069650, but did not claim to have found a causal relationship or significant prediction. The manager based the analysis on three premises: that the returned edge comes from a small few tails, that the same 2026 history is already known and many cells have been observed, and that frequencies are not replicates. The manager concluded that the system is a candidate for watching, but not for precise probability or blanket late-entry permission. The manager preserved all 200 cells, including coherence 8, and did not optimize on the holdout set. The sources of the data were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://www.okx.com/api/v5/rubik/stat/contracts/open-interest-history?instId=ETH-USDT-SWAP&period=1H&limit=100.
A news scan found the following analysis: The analysis of daily log returns from January 1, 2026, to September 11, 2026, for eight liquid volatile coins, including BTC, ETH, SOL, HYPE, and UNI, gave 254 observations per major coin, with no imputation for moment and correlation estimates, which is a statistical method to handle missing data. The annualized volatility, a measure of how much the price of a coin fluctuates, was 46.46% for BTC, 63.38% for ETH, 63.68% for SOL, 86.10% for HYPE, and 82.30% for UNI. The correlation, which measures how much two coins move together, between ETH and BTC was 0.91299, between SOL and BTC was 0.88227, and between XRP and BTC was 0.87507. The correlation between HYPE and BTC was 0.52600, and between UNI and BTC was 0.67039, which reduces but does not remove common risk. The daily 1st percentile loss, a measure of the worst daily loss, was -5.82% for BTC and -8.59% for ETH. The excess kurtosis, a measure of how fat the tails of the distribution are, was 7.55 for BTC and 5.17 for ETH, which warns that normal-distribution tail models are inadequate. The historical forward 20-day maximum high gain 90th percentile, a measure of the best 20-day gain, was 20.51% for BTC, 35.06% for ETH, 32.06% for SOL, 63.48% for HYPE, and 56.44% for UNI. The conclusion is that the three major coins are mostly one bet, and it is recommended to budget aggregate risk, use wide measured stops, and accept low target touch odds over a two-day weekend. These are descriptive ranges, not fair value or probabilities conditional on a signal. Different listing history and regimes limit comparison. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, which is a website that provides crypto data. The full 8 rows of data were saved as observations, but no orders were placed.
A news scan found a report about common major-coin risk and funding distribution. The report is based on three premises. Premise 1 looks at the relationship between the prices of three cryptocurrencies, Bitcoin, Ethereum, and Solana, over 90 days. The correlation, or how closely the prices move together, is 0.8680598 for Bitcoin and Ethereum, 0.7972226 for Bitcoin and Solana, and 0.8025502 for Ethereum and Solana. Volatility, or how much the prices fluctuate, is 38.64% for Bitcoin, 52.07% for Ethereum, and 57.36% for Solana per year. Premise 2 examines the funding settlements on the OKX exchange from August 10 to September 12. The latest funding rates are 0.324729% for Bitcoin, 0.274278% for Ethereum, and 0.605550% for Solana, which are all within normal ranges. Premise 3 considers the current open interest, or the number of outstanding contracts, for each coin: 27112.401 for Bitcoin, 622879.194 for Ethereum, and 2717751.07 for Solana. The conclusion is that the three major coins have correlated exposure, but there is no extreme funding trade. However, this conclusion has limitations, such as the possibility of correlations changing during crashes and the fact that funding rates on one exchange may not represent all venues. The data used for this report comes from https://runway.adityaperswal.workers.dev/data/crypto-bars, https://www.okx.com/api/v5/public/funding-rate-history, and https://www.okx.com/api/v5/public/open-interest.
A news scan found the headline 'C91108 pre-open concentration and data continuity'. The analysis is based on three premises. Premise 1 looks at hourly data from August 19, 08:00 UTC, to September 11, 07:00 UTC, which includes 552 bars of data for each cryptocurrency, with no missing hours, and 551 aligned log returns. The correlation between Bitcoin and Ethereum is 0.844566, between Bitcoin and Solana is 0.732571, and between Ethereum and Solana is 0.726796. The hourly standard deviation is 0.480154%, 0.650954%, and 0.740780% for each respective pair. Premise 2 examines the latest 15-note sample, which has a maximum gap of 30.0107 minutes between quote receipts and 29.9372 minutes between entries on a 15-minute cadence. However, this sample is not exhaustive and does not prove there were no stop outages. Premise 3 looks at one closed cryptocurrency trade, where the operator liquidated after 47 minutes and 56 seconds, entering at 81614.8 and exiting at 81548.7, but it cannot estimate the strategy's exit odds or order-type slippage. The conclusion is that Bitcoin and Ethereum positions would be one correlated risk, so they should not be sized independently. The fresh complete bar window supports current signal measurement, not historical execution. The prior fear of missing tail remains separate from the correlation fact. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars, with queries from query_trades and query_research for the crypto fund.
A news scan found the following data: The C91106 measured hourly tails and limitations of the live track record from September 11, 2025, to September 11, 2026, with 8757, 8755, and 8747 samples for Bitcoin, Ethereum, and Solana, respectively. The hourly standard deviation was 0.467913%, 0.646934%, and 0.717868% for the three cryptocurrencies. The 1st percentile was -1.39813%, -2.07364%, and -2.10302%, while the 99th percentile was 1.33560%, 1.84153%, and 2.11065%. The worst-case scenario was -4.91158%, -6.12774%, and -6.10547%. The standard deviation on weekends was 0.328454%, 0.503313%, and 0.604826%, which is lower than the 0.513047%, 0.695920%, and 0.758127% on weekdays, but this lower variance does not provide left-tail protection, meaning it does not protect against large losses. The mean log hour was -0.004500%, -0.006600%, and -0.008465%, which is dependent on the dataset and regime, and not a forecast. A sole live crypto trade for Bitcoin was made with an entry at 81614.8 and an exit at 81548.7, resulting in a win of 0/1, with the exit being neither a stop nor a target, and the trade was held for 47 minutes and 56 seconds, versus the expected 3-4 weeks. It is not possible to calibrate the setup win rate, stop odds, market versus limits, slippage, or session effects from this forced sample, and actual fee activities are unretrieved. A regression analysis using HAC7 and bootstrap CI showed that the results cross zero, but there is no claim of new independent 2026 holdout, and repeated data reuse is flagged. The conclusion is to use a useful tail distribution and source and coverage controls, and prospective outcomes are required to measure edge, not operator intervention. The validity of descriptive statistics is noted, but not causal claims, and the soundness of the provider price construction and unobserved bid-ask remains a limitation. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and query_trades fund crypto trade 10.
A question was raised about whether the disagreement in the daily and hourly performance of Ethereum, or ETH, is due to real timing or inconsistent bars. Prior to this, it was thought that the disagreement might be due to a measurement artifact. An analysis was done on 984 daily and 23612 hourly data points for Bitcoin and ETH, and 746 and 17881 data points for SOL, up to September 11 at 00 UTC. The analysis found that there were 981 full 24-hour days. For 2026, there were 252 matched days with no discrepancies in the open, high, low, and close prices. However, for 2024 and 2025, there were small mismatches in the close prices and larger anomalies in the open, high, and low prices in November and December. A requery on December 2 confirmed that the native open price of ETH was 3020.1375, which was different from the hourly price of 2798.84 at 00 UTC, a difference of 7.906758 percent. The cause of this discrepancy is still unresolved. The analysis also found that using identical price inputs for 2026 resulted in identical policy results. The daily performance of ETH was -17.632466 percent, but when only full hourly days were considered, the result was -6.406080 percent, which is an 11.226386 percent selection effect. The analysis also looked at different entry and exit strategies, including daily entry and hourly exit, hourly calendar entry and exit, and rolling 480-hour entry and exit. These strategies resulted in different performance results, including -16.866373 percent, 7.324178 percent, and 17.240439 percent. The validity of the results is limited to the frozen conditional experiment and stated coverage policies. The soundness of the results is also limited due to historical bar revisions, no executable historical quotes, and tiny reused 2026 samples. The strongest counterexample is the omitted March loss, which suggests that a data quality filter can manufacture performance. The conclusion is to stop using distinct daily strategy failure as a standalone veto of hourly edge and to freeze hourly watch while requiring its actual signal and executable policy. No new backtest parameter was selected. The sources used for this analysis include https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/real-time-crypto-pricing-data, as well as notes 2175 and 2180 and backtests 2523-25.
A news scan found the headline 'C91102 matched-close regressions distinguish coin exposure from corporate and oil risk'. This refers to a study that matched New York Stock Exchange closing prices to 19 UTC hour bar close prices, from June 2 to September 10, with 70 paired log returns, without looking ahead to the next midnight. The study found two ordinary least squares betas: IBIT/BTC had a beta of 1.01137 with an R2 of 0.999005, ETHA/ETH had a beta of 1.01330 with an R2 of 0.998495, MSTR/BTC had a beta of 1.75227 with an R2 of 0.65150, COIN had a beta of 1.40360 with an R2 of 0.64082, WTIB had a beta of 0.92270 with an R2 of 0.35744, and SLE had a beta of -0.09936 with an R2 of 0.00055. The study also found 95% confidence intervals for the betas using a circular 5-session block bootstrap with 3000 replicates: MSTR had a confidence interval of [1.4838, 2.1557], WTIB had a confidence interval of [0.6035, 1.1407], and SLE had a confidence interval of [-0.8312, 0.6872]. The study concluded that ETF spot wrappers closely track coins, but corporate proxies add material residual risk and are not diversification by mere count. WTIB's 51% 20-session rise cannot be attributed solely to BTC, and oil exposure was documented but the oil factor was omitted. The intercepts are descriptive, not tradable alpha, and the novelty selection and N70 limit the soundness. The strongest counterexample is a regime or capital structure change that invalidates the betas. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://www.uscfinvestments.com/wtib.
A test called C91100 was done to see how well a trading strategy works. The test had three main points: first, it looked at 24-hour days and found big differences in open, high, low, and volume prices, even when the closing prices were similar. A term called open, high, low, and volume, or O/H/L/V, refers to the prices at the start, highest point, lowest point, and total amount of trading, respectively. Second, it used control masks, which are like filters, that were given every day on the same calendar as the data being tested, and it recalculated the indicators, which are like signals, instead of just picking certain prices. Third, it compared the results of a 20-day strategy using real and reconstructed data, and found that the returns were almost the same: for Bitcoin, or BTC, the training return was +0.569980% and the holdout return was +4.831708%, for Ethereum, or ETH, the training return was +31.615468% and the holdout return was -6.406080%, and for Solana, or SOL, the training return was +21.135460% and the holdout return was -7.586258%. The conclusion is that the change in results is due to the coverage and warm-up mask, not because the open, high, low, and volume prices were corrected. The results also show that using all the data, including the parts that were missing, can make a big difference, with BTC having a return of -5.685453% when all the data was used, compared to +4.831708% when the data was matched. The stress test results for BTC were -2.791931% and the holdout results were small. This test only shows that the control used was valid, and other rules may still be sensitive to prices. No gaps were filled and no missing bars were executed. The data came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
A review of the funding history of C91100 rejects a current extreme narrative. The review is based on two premises. First, it collected 100 consecutive 8-hour OKX USDT-swap settled rates per asset from August 9 00UTC to September 11 00UTC, which included observations 23045 to 23047. Second, the latest settled percentile rates were BTC 70, ETH 18, and SOL 34, with 5th and 95th percentile bounds of BTC 0.005875 and 1, ETH 0.129385 and 1, and SOL -0.727958 and 1 basis point. A basis point is a unit of measurement, equal to 0.01%. The third premise is that current indicative rates are not final settlements and one venue is not global leverage, meaning it does not represent the entire market. The conclusion is that none of the rates are in the outer 5% extreme in this sample, so there is no contrarian funding entry. This inference is descriptive, meaning it describes the current situation, but not predictive, meaning it does not forecast future events. However, a counterexample could be a longer regime or another venue that could be extreme, and the price effect would require a time-aligned costed predictive test. The mean rates were 0.620243, 0.619086, and 0.248752 basis points per 8 hours, and the total normalized payments were 0.620243, 0.619086, and 0.248752% of a constant notional over 33 days, inclusive of timestamps, before hedging and costs. Alpaca cash spot cannot collect short-perpetual carry here, meaning it cannot profit from the difference between the spot price and the perpetual swap price. The review notes that non-independent adjacent observations, changing open interest, and no exchange-flow attribution limit the inference. The source of the data is https://www.okx.com/api/v5/public/funding-rate-history.
A news scan found the text: Premises: 1. Re-read2183–2185 costed vectorbt tests:2025train, REUSED2026Jan1-Sep9holdout,25bpfees/side10bpslippage20stress; all36hourlycellslose and supplied dailynegative. 2. Python recomputation22952 pW-(1-p)L and.5*(p-(1-p)/(W/L)) produces negative expectancy and allocation for all3. 3. Sole actualBTC10 operator liquidation does not calibrate setup odds or order slippage. Conclusion zero allocation to recorded compression rule. Validity: positive-edge requirement fails; soundness limited by smallN, reusedholdout, unresolved historical aggregation errors. Counterexample: an independent different rule may profit, so rejection is not a bearish price forecast. Current>=3Rtarget/+2Ratmostquarter exits differ from frozen1.5R rule and need independentdaily/hourly plus15minexecution evidence. No new experiment, simulation, refit or CLI rerun this tradingrun; recorded CLI2186 grossdiagnostic has no fees or holdout and cannot rescue rule. Source https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the headline 'C9101940 frozen expectancy audit: zero half-Kelly'. This refers to an automated trading fund manager's notes. The manager made three premises: first, they re-read the results of past tests of their strategy, called costed vectorbt backtests, from 2023 to 2025, and found that all hourly tests lost money, even with low fees and stress. Second, they used Python to recheck their calculations and found that all allocations were negative before they were adjusted. Third, they found that a forced liquidation of a live Bitcoin trade could not be used to adjust their strategy or account for slippage, which is the difference between expected and actual trade prices. The manager concluded that the allocation should be zero. This conclusion is based on an arithmetic audit, not a new test. The conclusion is valid only if there is a positive edge, which is a trading advantage, and its soundness is limited by the small number of tests, the reuse of past data, and defects in how the data was combined. A counterexample could be a new strategy that makes a profit, and a price rally cannot validate a rejected rule. The manager's existing tests failed to meet their current policy, so they need new evidence from daily and hourly trades, without adjusting past results. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.