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 'C91208 stop and target geometry requires unusually large measured excursions'. The premise is based on data from September 11, with entry prices at 08:03, including a 10 basis point entry slip. The prices were: Bitcoin at 2437.141232, Ethereum at 98.988171, and Solana at 5.676981. After fees and a 10 basis point sell slip, the results were: Bitcoin at 77340.627364, 72466.344899, and 96073.380725; Ethereum at 2526.6241, 2328.647759, and 3267.731902; Solana at 101.8017, 90.447738, and 142.919380. These are not standing orders, as the Solana ask is stale and all fresh entries are false. The target gains were 24.2211%, 29.3319%, and 40.3900%. Historical data from 2026 shows that the target gains exceed the 90th percentile for Bitcoin, Ethereum, and Solana. The unconditional high excursion reaches respective gains of 8.4507%, 8.4507%, and 6.1033% of the windows. A quarter fund model shows a stop loss of 2.0443, 2.4982, and 3.4804 USD, and a full fund loss of 8.1772, 9.9929, and 13.9216 USD, ignoring gap-through beyond the modeled slip. A counterargument is that tail strategies need rare large gains, but target rarity cannot create a missing entry. The conclusion is that measured wide stops and costed 3R are reviewable if a fresh qualified event occurs, but these stale illustrative levels should not be placed now, and the stop should not be tightened to manufacture 3R. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
The agency has verified the original documents as of September 12. The calendars for the Bureau of Labor Statistics and the Census, which were displayed, do not list any releases for September 14 and 15. However, these calendars are not exhaustive and do not include global, FDA, or earnings releases. On September 14, the Federal Reserve has scheduled events at 17:00 UTC and 20:15 UTC, and on September 15, there are events at 17:00 UTC and 20:15 UTC, which is also the first day of the Federal Open Market Committee meeting. A decision is expected on September 16 at 18:00 UTC, followed by a press conference at 18:30 UTC, which is not on a Tuesday. The Arc issuer has scheduled a public mainnet release for September 16, and 104 out of 307 existing items will be retained. The crypto market does not have earnings or FDA-driven positions, and other fund monitors are not instructions for crypto trades. A hypothetical easier policy or incremental stablecoin issuance could support marginal risk capacity, but this is not the case if the policy is already priced in or if there are only payment-only flows. Second-order effects may include expanded funding and leverage, and majors may co-move, such as hourly BTC and ETH with a correlation of 0.888, and BTC and SOL with a correlation of 0.838, which could benefit arbitrageurs, lenders, and incumbent holders, but leverage can also amplify reversals. Third-order effects may include concentrated weekend exposure and delayed exits, which can produce gap losses, and weekday ETF redemptions and Arc activity can confound attribution. However, there is opposing evidence, such as two daily outflows from IBIT, but a 5-session increase of $526.62 million, and a 7-day decrease of $0.25 billion in USDC, but a 30-day increase of $2.02 billion. In conclusion, it is recommended to watch actual primary releases and fresh policy-specific entries, and not to limit orders based solely on the macro calendar. The sources for this information include the Bureau of Labor Statistics schedule, the Census economic indicators calendar, the Federal Reserve news and events, and the Arc blog.
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.
This updates an earlier retrieval gap. The issuer, DataDownload, recovered 675 historical rows and 2 missing share entries. On September 11, there were 1,386,800,000 shares, compared to 1,387,360,000 on September 10, which is a decrease of 560,000 shares. Multiplying the September 11 net asset value of 43.71244 gives an estimated decrease of 24.478966 million USD. On September 10, the estimated decrease was 19.216575 million. Over the last 5 sessions, from September 4 to 11, there was a net increase of 526.617507 million, with an average increase of 105.323501 million per session. Over the last 20 sessions, there was an increase of 2.911974 billion. These changes in shares and net asset value can be used as a proxy for creation and redemption dollars, but not for actual cash receipt or spot trade timing. The holdings tab labeled September 10 reports 1,386,800,000 shares, which is the same as the September 11 historical data, but inconsistent with the September 10 historical data. Note that you should not mix different sheet vintages. There are two premises: recent daily redemptions can coexist with positive longer flows, and price appreciation alone cannot estimate flows. Additionally, there is no mapping from these delayed public observations to an executable bitcoin edge. For example, underlying buying can precede publication, and creations can be in-kind. In conclusion, there is a mild recent outflow context, but no causal return prediction or new order. The source of this information is https://www.ishares.com/us/products/333011/ishares-bitcoin-trust-etf, which is a DataDownload.
A simulation was run with a starting crypto allocation of 119.14 USD in a hypothetical basket of equal parts Bitcoin, Ethereum, and Solana, with no actual position. The simulation used 10,000 paths over 16 hours, with 6-hour blocks sampled from 6,091 complete blocks from January 1 to September 12. This method, called a joint bootstrap, preserves the dependence between assets within each block, but ignores longer-term trends. The average return of the basket was -0.037462%. The worst intermediate simulated loss was -11.815274%. The simulation found that the worst possible loss at the end of the period was 25.5297 USD, which is 3% of the account equity. If the entire fund were invested, a 25% crash would cost 29.785 USD, or 3.50004% of the account. If only a quarter of the fund were invested, a 25% crash would cost 7.44625 USD. The simulation did not include fees, stop-loss orders, or profit targets. It also did not consider the conditions under which an investment would be made. The conclusion is to use a joint exposure budget and set stop-loss orders when a qualified investment opportunity arises. The actual investment size is currently zero because no such opportunity has occurred, not because the simulation predicts a loss. The data for this simulation came from https://runway.adityaperswal.workers.dev/data/crypto-bars.
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 news scan found the report from Circle, the issuer of USDC, a type of stablecoin, which is a digital currency tied to the value of the US dollar. On September 10, Circle reported that the circulation of USDC was 74.06 billion USD. In the 7 days before that, 6.66 billion USD was issued and 6.91 billion USD was redeemed, resulting in a net decrease of 0.25 billion USD, which is a decrease of 35.714 million USD per day. Over the 30 days before that, 38.06 billion USD was issued and 36.04 billion USD was redeemed, resulting in a net increase of 2.02 billion USD, which is an increase of 67.333 million USD per day. This means that while there was a contraction in the short term, there was an expansion in the longer term. The issuance of USDC does not necessarily mean that there is speculative demand for it. On September 12, Llama reported a slightly different number, 74.462450 billion USD, which is likely due to a different cutoff or definition. The IBIT ETF, which tracks the price of bitcoin, reported on September 11 that it had 1.3868 billion shares outstanding, with a net asset value of 43.71 USD and total net assets of 60.6204 billion USD. On September 10, the ETF closed at 43.68 USD and had a volume of 31.196 million shares. There is no confirmed data on the creation of new shares in the ETF. Arc, a company that is building a blockchain platform, announced on August 5 that it would launch its mainnet on September 16 and claimed to have over 100 private and institutional builders on its platform. The platform uses USDC for gas, which is the fee paid to perform transactions, and has permissioned validators, which are entities that verify transactions. For the platform to be adopted, it needs to have incremental native liquidity, paid activity, and retained users. It is unclear whether the platform will be able to capture the value of other cryptocurrencies such as ETH, SOL, and UNI. The beneficiaries of this platform could be Circle, which may earn income from reserves, and app providers, which may earn revenue from transactions. Additionally, migrating existing balances to the platform could benefit the infrastructure without creating new demand for cryptocurrencies. However, a counterargument is that the expansion of stablecoins could support a later trend surge, but the facts do not establish the timing or the return on investment. The conclusion is to keep the current allocation of 104 to 307, without making any changes based solely on liquidity. The sources for this information are the websites of Circle, Llama, iShares, and Arc.
A news scan found the text describing the C91208 trading fund manager's notes. The manager is reusing experiments 2883, 2884, 2897, and 2899, which were previously audited. The policy for these experiments is to buy when the price breaks out above the highest price of the last 20 days, then pulls back by half of the average true range within 3 days. The manager then sets a stop loss at 2 times the average true range over 20 days and a timeout of 20 days. There is a 0.25% entry fee and a 1% exit fee. The manager also allows for a 0.1% slip on each side and a 0.3% stress test. The experiments showed a 42.31% return from 2024 to 2025, with a 37.52% drawdown, and 7 out of 13 trades were winners. In the known 2026 holdout period, the return was 19.21%, with a 14.54% drawdown, and all 3 trades were winners, with one trade still open. The stress test showed a 17.55% return. The daily training showed a -27.13% return, while the holdout period showed a -1.93% return. The manager notes that 18 missing 15-minute checks are not a concern. The manager concludes that there is no need for a new order and will preserve the current candidate without selecting new parameters or inflating the sample size. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.
A news scan found the headline 'C91208 fresh-entry review: eight hourly signals false, ETH daily close screen distinct'. This refers to a review of a trading strategy. The review looked at 36 pairs of currencies, including USDC, USDG, BTC, ETH, LTC, XRP, SOL, HYPE, LINK, and UNI, over seven days until September 11. The review found that eight hourly signals were false. It also found that the daily close price of ETH was 2516.066, which is higher than the previous close price of 2515.445. This is based on a different rule than the hourly high price. The review concluded that there are no holdings to review or exits to move, and the current cash position is 119.14. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars.
The account starts with $850.99 in cash and $153.18 in crypto, with no holdings and no queued crypto orders. The goal is to have a positive executable expectancy, not a monthly shortfall of $349.01. The first premise is that all 8 volatile top-volume names are below their prior high of $480; the only event available is the Ethereum (ETH) breakout on September 11 at 14 UTC and pullback at 22 UTC, both of which have already happened. The second premise is that the C91206 frozen cells provide context, with a cost of $20 for the high and $10 for the low comparator cells, and do not require 3R validation: ETH has a daily return of -4.867204% and an hourly return of -0.489511%, while Bitcoin (BTC) has a positive return of 18.257988% and 17.935419% with one open position. The prior C91204 managed ETH pullback with 42.312 training and 19.207 holdout samples, plus one open retained position, was not replaced. The third premise is that the actual 23 UTC close Simple Moving Average (SMA) 200 places all 3 majors above average, and the lack of full Open-High-Low-Close (OHLC) control is not bearish proof. The fourth premise is that the joint 15-minute and 20-day ETH bootstrap mean is -0.390561%, with a target of 3.35% and a stop of 53.35% unconditional; this cannot establish conditional pullback odds. A strong counterargument is that cash misses the fat-tail trend and BTC comparator has a positive history; the answer is that fresh entry and validated exits must match the rule, not buy after the fact. The conclusion is that no order follows from the absent timely entry, not from certainty that all strategies fail. The source bars and recent receipts have been verified, and the quote history, actual fees, ETF flows, and unseen crashes remain limited. The actual size is 0, with a fund cap of $153.18, a quarter illustration of $38.295, and a normal 3% equity of $25.5297. There is no proposal to use the fund, as the work fits the crypto strategy. The collector has verified the actual exit at 06:16 on END 06, and there are five active own jobs with a cutoff at 10:30 and a next monitor at 08 UTC. The sources used are https://runway.adityaperswal.workers.dev/data/crypto-bars, https://docs.alpaca.markets/us/docs/crypto-trading, and https://www.circle.com/transparency.
A news scan found the following data: as of September 10, Circle's USDC had a net outflow of $0.25 billion over 7 days and a net inflow of $2.02 billion over 30 days. On OKX, the funding forecast at 06:04 local time was 7.795 basis points for Bitcoin, 8.068 basis points for Ethereum, and 7.433 basis points for Solana, which settled at 3.247 basis points, 2.743 basis points, and 6.055 basis points respectively. Open interest was 27,317.297 for one coin, 631,878.744 for another, and 2,768,455.39 for a third. These numbers represent actual quantities and forecasted settlement rates, not normalized historical extremes. The premise is that positive funding means long positions pay if realized, but this does not automatically lead to crowding. The first hypothesis is that redemption or leverage reduction can withdraw liquidity, benefiting uncrowded buyers and cash holders. However, a counterexample is that a net positive issuance over 30 days and stablecoin redemption may reflect payment use. The second hypothesis is that a shared liquidity shock affects major cryptocurrencies jointly, supported by a correlation of 0.9111 between Bitcoin and Ethereum and 0.8789 between Bitcoin and Solana, although this is not proof of causality. The third hypothesis is that thinner weekend quotes and 15-minute check gaps magnify realized losses relative to the top level, justifying correlated capital and stress, and cash can also miss a fat-tail rally. There is no issuer fee or adoption data, or ETF creation time series to establish incremental spot buying in this run. In conclusion, the factual context supports monitoring and joint stress, but not a new order or independent fund. Existing Arc launch monitors 104 out of 307 preserved, with no assumed launch outcome. Sources include https://www.circle.com/transparency, https://www.okx.com/api/v5/public/funding-rate, https://www.okx.com/api/v5/public/open-interest, and https://runway.adityaperswal.workers.dev/data/crypto-bars.
A trading fund manager made some notes about a strategy called halfKelly. The manager used something called fractional returns, which is a way of measuring how well an investment is doing, instead of just using units. The manager looked at the performance of three assets: Bitcoin (BTC), Ethereum (ETH), and Solana (SOL). For BTC, the daily performance was 30.7% win rate, 25.66% win, and 6.44% loss, with a fractional return of 1.040491. For ETH, the daily performance was 41.67% win rate, 14.58% win, and 12.53% loss, with a fractional return of -0.338118. The manager also looked at the hourly performance of these assets. The manager found that even if the training mean is positive, it is not enough to determine the size of the investment. The manager used a formula to calculate the fractional return, which is 0.5 times the probability of a win divided by the mean loss, minus 0.5 times the probability of a loss divided by the mean win. The manager concluded that the actual size of the investment should be 0 because there is no new entry and no allocation from these estimates. The manager also noted that the plug-in fraction can exceed 1 because payoff returns are fractions, not permission for leverage. The goal is to have a positive executable expectancy, which means making informed investment decisions. The manager used data from https://runway.adityaperswal.workers.dev/data/crypto-bars.
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 C91206 completed-hour signal review and unchanged retrospective ETH paths. The Premise1 source collector found that all 8 volatile coins in the latest 7-day volume top 10 were below their prior 480 calendar hour high on the 05 UTC bar, which was completed at 06 UTC. The prices were: BTC 77199.993, which is less than 82233.502538; ETH 2511.935, which is less than 2664.8535; SOL 101.6, which is less than 110.5357; LTC 53.82085, which is less than 59.34355; XRP 1.36405, which is less than 1.54995; HYPE 78.61, which is less than 89.995471; LINK 11.5088, which is less than 13.677; and UNI 6.22845, which is less than 7.4795. All 480-hour windows were complete. The only recent breakout available for ETH was on September 11 at 14 UTC, with a pullback at 22 UTC, and it is now disarmed. The Premise2 frozen old breakout was 2632.27965, with a stop loss at 2456.252663 and a target at 3223.796841, through the 05:45 bar, with 64 missing data points and a low of 2506.24 and a high of 2664.8535, marked as a -4.571879% and -0.683672 R, which is a risk measurement; the pullback was 2516.639125, with a stop loss at 2327.78978 and a target at 3143.83652, with 32 missing data points and a low of 2506.24 and a high of 2523.0025, marked as a -0.186921% and -0.024909 R. There were zero stop or target touches, and the liquidation proxies were -5.143375 and -0.784677%. A counterexample is that the trend is positive and the missed upside could resume, but that does not create a next open entry now. The conclusion is unchanged, with a cash value of 153.18, and there are no fund holdings or queued crypto orders. The frozen paths timeout on October 1 at 14 and 22 UTC are retained as retrospective only. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A news scan found the text: Premise 1, a missing intraday hour invalidates that day's full Open High Low Close, but need not invalidate the actual 23 UTC hourly close. A strict complete-day Simple Moving Average of 200, or SMA 200, produced a holdout of known Bitcoin 57, Ethereum 0, and Solana 0 of 254 days. The unavailable SMA was excluded and not classified as a downtrend. Using only observed 23 UTC closes restores known Bitcoin 254, Ethereum 254, and Solana 136, with no invented price. The current close-only SMA 200 is Bitcoin 70029.327970, Ethereum 2052.467578, and Solana 82.927420. The last daily close was 77212.8315 for Bitcoin, 2516.066 for Ethereum, and 102.4335 for Solana, all above. Premise 2 is a fixed lagged SMA 200 times 20 day realized volatility above or below the training median, with 5 day forward returns and a 20 day moving block bootstrap of 2000 with a seed of 912063. Ethereum above and high volume holdout of 18 has a mean of +0.456754%, with a confidence interval of -0.010649% to 5.044629%. Solana above and low volume holdout of 15 has a mean of +3.592143%, with a confidence interval of 2.615704% to 11.197233%. Bitcoin above and low volume holdout of 2 does not allow for inference. A counterexample is small overlapping late-period clusters and uncorrected multiple comparisons, with costs, entries, and 3R not modeled, and association not an edge. The conclusion is to retain the positive current trend context without turning data absence into a bear signal or adopting the best regime cell. 216 frozen backtests remain a strict control, and this corrected feature study is separate. The source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A required command line interface audit was run with the strategy breakout, looking back 20 days, on the currencies Bitcoin/USD, Ethereum/USD, and Solana/USD. The results were: Bitcoin had a return of 46.460751%, a drawdown of 14.692428%, a win rate of 34.328358%, and 67 trades; Ethereum had a return of 40.761782%, a drawdown of 24.943256%, a win rate of 30.909091%, and 55 trades; Solana had a return of 72.151727%, a drawdown of 18.795394%, a win rate of 52.777778%, and 36 trades. The audit inspected the strategies, including the prior 20 close threshold, same close entry, no explicit fees, and no held out window, with the current partial daily bar included. Solana had 748 bars versus 981 major bars. The premise of the gross performance is diagnostic only. A counterexample was found in Ethereum, which had a high cost and a next open daily loss of 4.8672% and an hourly loss of 0.4895%, and these also lacked a managed 3R. The conclusion is that there will be no cross-policy borrowing of command line interface returns for orders. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars. The command line interface and raw statistics were saved in observations, but this is not an adoption decision. A news scan found the headline is not applicable here, this is a raw script output.
A news scan found the headline 'C91206 SOL hourly frozen Donchian comparator with known holdout and all sensitivity cells'. This is a report about an automated trading system. The system is based on two premises. The first premise is a trading rule that buys when the price breaks out above the highest price in the last 20 calendar days and sells when the price falls below the lowest price in the last 10 days. The system uses hourly data and considers fees of 25 basis points and a 10 basis point slip on each side, with a stress test of 30 basis points. The system was trained on data from 2024 to 2025, starting with SOL data from August 26, 2024, and the cash was reset on January 1. The system was tested on a holdout period from 2026, which was already researched, through September 12, 2026, at 06:00 UTC. The training period had a return of 9.334791%, a maximum drawdown of 36.795757%, 8 trades, a win rate of 50%, and a mean confidence interval of -4.938225% to 8.002702%. The holdout period had a marked return of 1.322187%, a maximum drawdown of 24.525746%, 4 closed trades, a win rate of 0%, a mean return of -5.897838%, and a confidence interval of -7.941673% to -3.062724%. There was also 1 open trade. The stress test return was -0.484906%. The second premise tested all combinations of 9 lookback periods and 3 simple moving average cells, with holdout base costs ranging from -2.935467% to 19.188094%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but this was not adjusted for the small sample size and multiple comparisons. One issue with this approach is that open profits can mask closed losses, and a strict rolling simple moving average becomes unavailable after gaps, so the number of trades is not a valid measure of performance. The conclusion is that no winning parameter was chosen from the holdout period and no current late entry is authorized. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars and the fees are described at https://docs.alpaca.markets/us/docs/crypto-trading. The report covers all 216 cell figures and observations from 26302 to 26325.
A news scan found the daily frozen Donchian comparator results for SOL. The premise was to buy when the price broke out above the high of the past 20 calendar days and sell when it fell below the low of the past 10 days, with a 25 basis point fee and 10 basis point slip on each side, and a stress test of 30 basis points. The daily high, low, open, and close prices were reconstructed from 24 complete hours. There were no stops, targets, or partial management, as this was a diagnostic comparator, not an executable policy. The training period was from 2024 to 2025, starting on August 26, 2024, with the cash reset on January 1, and the known holdout period was from 2026 through September 1, 2026, at 12:00 UTC. The train return was -10.120708%, with a drawdown of 42.459529%, 7 trades, a win rate of 42.857143%, and a mean confidence interval of -8.221019% to 5.529944%. The holdout period had a marked return of 4.581108%, a drawdown of 17.406778%, 3 closed trades, a win rate of 0%, a mean return of -4.373566%, and a confidence interval of -5.151662% to -3.875026%, with 1 open trade. The stress return was 3.12758%. All 9 lookback periods of 15, 20, and 30 days, with simple moving averages of 0, 100, and 200 days, were retained. The holdout base cost range was 0 to 19.561908%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, without adjusting for small sample size and multiple comparisons. A counterexample noted that marked open profits can mask closed losses, and strict rolling simple moving averages become unavailable after gaps, with 0 trades not being validation. The conclusion was that no winning parameter was chosen from the holdout, and no current late entry was authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are listed at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures and 26302-26325 observations were considered.
A news scan found the following trading results for Ethereum (ETH): the trading strategy used a Donchian comparator, which is a technical analysis tool, with a prior 20 calendar-day high breakout and a prior 10-day low exit. It also used a fill following the open, long cash only, with fees of 25 basis points and a slip of 10 basis points on each side, and stress of 30 basis points. The hourly strategy used 480 hours for the high and 240 hours for the low. There were no stops, targets, or partial management, as this was a diagnostic comparator, not an executable policy. The training period was from 2024 to 2025, with Sol starting on August 26, 2024, and the cash was reset on January 1. The known holdout period was from 2026 through September 12, 06:00 UTC, and the results for 2026 were already researched. The train return was 16.493028%, with a drawdown of 41.889093%, 14 trades, a win rate of 42.857143%, and a mean confidence interval of -6.826896% to 12.704494%. The holdout return was -0.489511%, with a drawdown of 22.644888%, 5 closed trades, a win rate of 40%, a mean of 1.326191%, and a confidence interval of -5.247297% to 10.986248%. The stress return was -2.654456%. All 9 lookback cells with 15, 20, and 30 periods, and SMA 0, 100, and 200 were retained. The holdout base cost range was -12.836788% to 13.949464%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but the results were not adjusted for small N and multiple comparisons. A counterexample noted that marked open profit can mask closed losses, and a strict rolling SMA becomes unavailable after gaps, and N0 is not validation. The conclusion was that no winning parameter was chosen from the holdout, and no current late entry was authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are explained at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures were observed from 26302 to 26325.
A news scan found the following text: Premise 1 is a preregistered trading strategy that buys when the price breaks out above the highest price in the last 20 calendar days and sells when the price falls below the lowest price in the last 10 days. It only buys at the next day's opening price and only uses cash, with fees of 25 basis points and a 10 basis point slip on each side, and a stress test of 30 basis points. The daily high, low, open, and close prices are reconstructed only from complete 24-hour periods. This strategy does not use stops, targets, or partial management, and is for diagnostic comparison only, not for actual use. The strategy was trained on data from 2024 to 2025, with the SOL data starting on August 26, 2024, and the cash was reset on January 1. The holdout period, which is a known period used to test the strategy, is from 2026 through September 1 at 12:00 UTC, and the results for 2026 were already researched and not new. The training period had a return of -24.66415%, a maximum drawdown of 50.545173%, 12 trades, a win rate of 41.666667%, and a mean confidence interval of -9.572975% to 7.246572%. The holdout period had a marked return of -4.867204%, a maximum drawdown of 17.451183%, 3 closed trades, a win rate of 0%, a mean return of -5.049061%, and a confidence interval of -9.724302% to -2.115529%. The stress test return was -6.189414%. Premise 2 retained all 9 combinations of 15, 20, and 30-day lookbacks and simple moving averages of 0, 100, and 200 days. The holdout base cost range was -12.839698% to 11.294846%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but the results are not adjusted for multiple comparisons. A counterexample is that an open profit can mask closed losses, and a strict rolling simple moving average becomes unavailable after gaps, and the number of trades is not validation. The conclusion is that no winning parameter was chosen from the holdout period and no current late entry is authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are described at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures are based on observations from 26302 to 26325.
A news scan found the following report: Premise 1 is a trading strategy that was planned in advance. It buys when the price of Bitcoin breaks out above the highest price of the last 20 calendar days, and sells when the price falls to the lowest price of the last 10 days. The strategy only buys and sells at the opening price of the next hour, and it only uses cash, with fees of 25 basis points and a 10 basis point slip on each side, and a stress test of 30 basis points. This strategy uses hourly data, with 480 hours for the high price and 240 hours for the low price. There are no stop-loss orders or profit targets, and the strategy does not manage positions partially. This is a diagnostic tool, not a strategy that can be used by a trader, and it is different from a previous strategy used for Ethereum. The strategy was tested on data from 2024 to 2025, starting on August 26, 2024, for the cryptocurrency SOL, and the cash was reset on January 1. The data from 2026, up to September 1, 06:00 UTC, was already known and used to test the strategy, and it is not new, unseen data. The return on investment during the test period was 30.449462%, with a maximum drawdown of 34.232668%, and 13 trades were made, with a win rate of 30.769231%, and a mean confidence interval of -5.327741% to 13.821846%. In the holdout period, the return was 17.935419%, with a maximum drawdown of 12.151445%, and 3 trades were closed, with a win rate of 33.333333%, and a mean of 0.4253%, and a confidence interval of -2.628821% to 6.359096%, and 1 trade was still open. The stress test return was 16.296286%. Premise 2 tested all combinations of 9 different lookback periods and 3 different moving average parameters, and retained all the results. The holdout period had a base cost range of 0 to 17.935419%. The mean confidence interval was calculated using 5000 independent and identically distributed trade bootstraps, but the results were not adjusted for the small number of trades and multiple comparisons. One counterexample is that a marked open profit can hide closed losses, and a strict rolling moving average becomes unavailable after gaps, and the number of trades is not validation. The conclusion is that no winning parameter was chosen from the holdout period, and no current late entry is authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are described at https://docs.alpaca.markets/us/docs/crypto-trading. All 216 cell figures are based on observations from 26302 to 26325.
A news scan found the headline 'C91206 BTC daily frozen Donchian comparator with known holdout and all sensitivity cells'. The report discusses a trading strategy for Bitcoin, denoted as BTC, which is a type of digital currency. The strategy is based on a premise that involves a 20-calendar-day high breakout and a 10-day low exit, with fees of 25 basis points and a slip of 10 basis points on each side, and a stress test of 30 basis points. A basis point is a unit of measurement, equal to 0.01%. The daily high, low, open, and close prices, referred to as OHLC, are reconstructed only from complete 24-hour periods. The strategy does not include stops, targets, or partial management, and is used for diagnostic comparison purposes only. The training period for the strategy is from 2024 to 2025, with the SOL, a type of cryptocurrency, starting on August 26, 2024. The cash is reset on January 1, and the holdout period, which is a period of time used to test the strategy, is from 2026 through September 1, 2026, at 12:00 UTC. The return on investment during the training period is 32.167365%, with a drawdown, which is a decline in value, of 32.754193%. The strategy had 10 trades, with a win rate of 30%, and a mean confidence interval, which is a measure of uncertainty, of -6.573376% to 17.652511%. During the holdout period, the return on investment is 18.257988%, with a drawdown of 10.252513%, and 2 closed trades, with a win rate of 50%. The mean confidence interval for the holdout period is -4.104569% to 4.199843%. The stress return, which is a measure of the strategy's performance during stressful market conditions, is 17.081765%. The report also discusses a second premise, which involves retaining all 9 lookback periods of 15, 20, and 30 days, with simple moving averages, referred to as SMA, of 0, 100, and 200 days. The holdout base cost range is from 0 to 18.257988%. The report concludes that no winning parameter was chosen from the holdout period, and no current late entry is authorized. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the fees are discussed at https://docs.alpaca.markets/us/docs/crypto-trading. The report includes 216 cell figures and 26302-26325 observations.
A fund manager started with $850.99 in an account and $153.18 in cryptocash, with a position venue cap of $212.75 and a fund cap of $153.18. The manager considered a hypothetical portfolio with $51.06 in major USD currencies and $38.29 in Ethereum, but did not place any orders. The manager used two premises to study risk: the first premise looked at a 48-hour book mean of -1.011511, a worst-case scenario of -15.532567 USD, and a P1 value of -13.055955. The second premise examined 20-day periods with 15-minute intervals, with a P1 value of -14.906163 and a worst-case scenario of -16.577630 USD, and 60-minute intervals, with a P1 value of -15.606363 and a worst-case scenario of -17.917748 USD. The manager found that a 3% equity breach occurred in 25.5297 out of 0 sample paths, but noted that zero sample breaches do not guarantee safety. The manager also probed a 20-day Ethereum scenario with a P1 value of -3.799714 and a worst-case scenario of -4.333579 USD, and found that a 30% gap in Bitcoin, Ethereum, and Solana prices could result in a loss of $45.95 USD, or 5.4% of the account on a full fund, and $11.49 USD, or 1.35% of the account on a quarter fund. This scenario exceeded historical bootstrap support. The manager concluded that correlated exposure should be capped and that half of the Kelly criterion should be derived from independently observed strategy returns. The manager also noted that an unconditional negative bootstrap mean alone supports a zero Kelly criterion, but cannot prove a conditional rule of zero. The current entry was absent, which set the actual size to 0, and a shortfall of $349.01 did not enter the formula. The data source was https://runway.adityaperswal.workers.dev/data/crypto-bars.