Stats

The automated trading fund manager has generated a signal table for nine assets as of 08UTC. The table is based on the following premises: the fund has accepted a fixed momentum rule, daily bars are complete through September 11, there is no stop or monthly rerank trigger, and a whole-share cap applies. The columns in the table show the symbol of each asset, its 12-month return percentage, close price, 200-day simple moving average (SMA200), 20-day average true range (ATR20), qualified rank, chosen quantity, 60-day 90th percentile price, and target price. The assets listed are EEM, QQQ, IWM, EFA, GLD, SPY, TLT, BTC/USD, and ETH/USD. The data sources are https://runway.adityaperswal.workers.dev/data/stock-bars and /data/crypto-bars. The fund started a warm-up period on June 1, 2021, and will target endpoints from September 12, 2021, onward, including 60-bar gains and losses. The returns are dividend-adjusted for ETFs over a 252-interval and 365 days for cryptocurrencies. Seven assets qualify, and EEM, QQQ, and IWM are retained. There is a pending order for EEM at $67.84 with a stop at $65.93 and a target at $75.11, expiring on September 15 at 02:06UTC. The target price for EEM is $7.271066 per share, with an initial stop at $1.90528. The maximum profitable ratchet is $61.76035, but it is inactive until the order is filled or a profit is made. QQQ and IWM prices exceed the $212.7475 cap, so there are 79.426667 equal slots with no substitution or concentration in EEM. There is $170.44 in uncommitted cash and $67.84 reserved pending, with no filled exposure. The current signal table reproduces the prior table exactly, with no need for retesting.

The fund has increased its capital by 18.18%. The current equity is $850.99 with a capital of $221.26, which replaces the previous capital of $187.22. The fund is divided into three equal parts of $73.75, $73.75, and $73.76. The worst split-adjusted gaps from 2016 to September 11 are retained for the stocks QQQ, IWM, and EFA, with percentages of 9.457211%, 9.301937%, and 11.623512% respectively, all as of March 16, 2020. A scenario is used to calculate the stress, where L equals N times the current rate plus 0.005, and the current rate is the current average true range divided by the close price. The assets in the fund are QQQ, IWM, and EFA, with sizes of $73.75, $73.75, and $73.76 respectively. The joint stress is 5.81% of the equity, and the gap beyond the stop needed to lose 3% of the account is $32.63, $32.69, and $33.01. The correlations between the assets range from 0.779 to 0.817, which means they are not independent. The gross target is $28.49, and the ordinary stop is $4.41. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. A prior simulation from 2905 is retained, but it is not a live win odds or Kelly, and no new backtest is required due to the fixed mandate.

A news scan found the following signal table: Premises were refreshed and completed on September 11 with daily bars. The table includes ETFs with a SIP adjustment and crypto data from September 12, but with some exclusions. The return is calculated as the close price divided by the close price 252 intervals prior, minus 1, with a different calculation for crypto. The SMA200 is an arithmetic mean and the ATR20 is a simple mean of true ranges. The table ranks assets based on their return over a 60-day period, with endpoints starting from September 12, 2021. The Core EEM is queued based on the current context, but there are no trades to query. The table includes the following symbols and data: SPY with a return of 18.4891%, an SMA200 of 712.04520, an ATR20 of 5.45286, a rank of 5, and a proposed investment of $0; QQQ with a return of 23.6988%, an SMA200 of 659.25650, an ATR20 of 7.86842, a rank of 2, and a proposed investment of $73.75; IWM with a return of 23.4573%, an SMA200 of 273.16515, an ATR20 of 3.07052, a rank of 3, and a proposed investment of $73.75; EFA with a return of 19.4581%, an SMA200 of 100.67335, an ATR20 of 0.88087, a rank of 4, and a proposed investment of $73.76; EEM with a return of 34.2038%, an SMA200 of 61.76035, an ATR20 of 0.95264, a rank of 1, and a proposed investment of $0; GLD with a return of 18.9435%, an SMA200 of 415.93450, an ATR20 of 7.95245, a rank of below SMA, and a proposed investment of $0; TLT with a return of -5.7569%, an SMA200 of 84.50050, an ATR20 of 0.69113, a rank of below SMA, and a proposed investment of $0; BTC/USD with a return of -33.1666%, an SMA200 of 70029.04267, an ATR20 of 2437.14123, a rank of 6, and a proposed investment of $0; and ETH/USD with a return of -43.6228%, an SMA200 of 2052.46758, an ATR20 of 98.98817, a rank of 7, and a proposed investment of $0. The conclusion is to invest in QQQ, IWM, and EFA, with no additional investment in assets with positive returns and no substitution with crypto assets. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars and /data/crypto-bars. All checks on the data pass, and the prices are adjusted to reflect total return, not just dividend reconstruction. The rank is initially flat and will be reranked monthly or when stopped.

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.

The final decision for R91206 is to advance FRO to execution watch and retain supply cash. The supply started and finished with $170.20 in cash and $850.99 in the account. A query of trades showed only two already closed lots, with no holdings or pending supply orders. The goal is to achieve affordable bottleneck pricing power with asymmetry. The current binding constraint is implementation, not the $349.01 funding shortfall. There are six points to consider: 1. A new FRO 3R study from 2020 to 2023 is positive, with training results of 48.932% and holdout results of 99.823% for 17+1 open, and stress results of 92.950%. 2. The FRO retained cash thesis is supported by issuer bookings and profits, and the declared $3.41 distribution creates a raw stop problem. 3. The final gap budgeted sizing permits 85.099 full, 21.27475 quarter probe, below $49.21 one FRO share. 4. Some stocks, including STNG, currently fail frozen entry, and the SWKS price breakout does not establish a new RF bottleneck cash. 5. A resolved issue and tested new SIP collector were completed. 6. There are no orders, no exit changes, and no FUND proposal. The strong counterargument is FRO's positive net proxy, which earns implementation priority, not rejection merely because CI crosses zero. The next work is on raw dividend and 15-minute execution and target cash bridge.

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.

The automated trading fund manager has analyzed the outcome uncertainty and own-record calibration. The own supply record shows two operator-liquidation winners with a combined stock profit and loss of 4.977287 and a mean of 2.278710%. However, with 100% wins, it is not possible to estimate average losses or execution advantage. A naive exact 95% binomial lower bound is already 15.8114%, and correlated shipping lots further weaken independence, according to the source venue query trades fund, which can be found at https://runway.adityaperswal.workers.dev. The fund manager also analyzed the FRO fixed training, which had 33 closed trades with a mean of 1.87277%, and a 95% confidence interval of -1.97206 to 6.13580. The test period had 17 trades with a mean of 4.65443% and a confidence interval of -1.35232 to 10.72225. The managed holdout had 18 trades with a mean of 2.97157% and a confidence interval of -2.24030 to 8.46648. The STNG fixed test had 11 trades with a mean of -0.34261% and a confidence interval of -5.19766 to 5.84822. The collection was completed with daily SIP, including prior 2020 warmup, 2021-24 training, and 2025 to September 11 held out for this rule. The test period was already seen for other strategies, and the selected current tankers create a selection bias, not a universe-wide edge. The unfinished trades mark equity but do not enter the closed outcome confidence interval. These distributions and the current correlation of 0.777079 are descriptive, not causal, according to the source https://runway.adityaperswal.workers.dev/data/stock-bars. The prior was shifted from an old 2R inconclusive watch to a provisional 3R positive FRO watch, but not to statistical certainty. A costed prospective 15-minute raw cash implementation passing unchanged rules would strengthen it, but weak cash or normalizing net rates or prospective execution losses would weaken it. No precise Bayesian posterior is claimed.

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 news scan found the text of a corrected dividend simulation. The audit found that the first day started with an economic value of 49.21, which was the prior close, while treating 49.21 as the entry value. To correct this, the first day's open, high, low, and close ratios were adjusted relative to the sampled open, so the entry open is exactly 49.21. The subsequent day's ratios remain based on the prior close. Other specifications remain unchanged, including 20,000 paired 40 session paths, 5-day blocks from 2021-24, seed 91206, 2 ADR stop, 3R target, 1 basis point fee, and 5 basis points lip, with 3.41 ex-session 5 deducted from economic OHLC. A basis point is one hundredth of a percent. The simulation includes dividend receivable only for prior day holders, not cash reinvestment. The static raw stop is 88.865% stop, 10.53% target, with 0.605% tolerance, and 41.96% stopped on one day, with a mean of +0.8081302% and a median of -0.2303364%, and a win rate of 48.645%. Adjusting both levels by 3.41 on one day results in 66.105% stop, 31.15% target, with 2.745% tolerance, and 6.10% stopped on one day, with a mean of +1.5436493% and a median of -5.5940764%, and a win rate of 33.41%. The entitlement is 73.385% for both. The directional mechanics finding survives, but neither policy is calibrated to current event stock expectancy. The adjusted levels are hypothetical and not a venue instruction. The simulation does not model 15-minute checks, trailing or partial management, taxes, market-specific dividend repricing, or the current war regime. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the declaration is https://www.frontlineplc.cy/fro-declares-special-one-time-dividend-after-sale-of-two-vllcs/. There are no orders.

A trading fund manager made decisions about the stock STNG from 2029-20 to 2029-27. They used a method called binary approximation, which is a simple way to estimate risk, to calculate the average win and average loss. The half Kelly risk fraction, a measure of how much risk to take, was calculated as 0.5 times the maximum of 0 and the probability of winning minus the probability of losing divided by the average win divided by the average loss. The manager assumed that one average historical loss corresponds to the prospective risk unit. The full notional, or the total amount of money to invest, was calculated as the minimum of $170.20, $212.75, half Kelly times $850.99 divided by the current risk, 3% of equity divided by the current risk, and 3% of equity divided by 30% gap, which equals $85.09. The gap assumption of 30% is subjective and not based on actual measurements. A quarter probe, a small investment to test the market, of $21.27 was made, which is below one share. The manager did not round up to a full share because the account needs $349.01. The manager also ran 20,000 simulated trade paths to test the strategy. The results showed that the maximum potential loss was 21.875% and the maximum potential drawdown, or loss of capital, was 9.80%. The manager used a sensitivity test, which is not a forecast, to see how the strategy would perform in different scenarios. The final paths supersede previous ones, and the eligible live size remains zero due to negative fixed holdout and managed stress holdout.

A news scan found the headline is not available, but a data line shows the final sizing for R91206 FRO. The training net closed-trade outcomes were from 2920-27 with no holdout fitting. A binary approximation, which is a simple way to estimate, was used with b, or the average win divided by the absolute average loss. The half Kelly risk fraction, a way to measure risk, was calculated as 0.5 times the maximum of 0 and p minus 1-p divided by b. The current stop-percent was assumed to be one average historical loss corresponding to that prospective risk unit. The full notional, or the total amount invested, was calculated as the minimum of fund 170.20, position 212.7475, half Kelly times 850.99 divided by current risk, 3% equity divided by current risk, and 3% equity divided by 30% gap, which equals 85.099. The gap assumption of 30% is subjective and not a measured worst case, meaning larger gaps are still possible. A quarter probe of 21.27475 was below one share for either tanker, and the account needs 349.01. One quarter probe's 30% gap loss is 6.382425, which is 0.75% of the account. The results are based on 20,000 trade paths with a seed of 91206 and a constant fraction of 85.099 divided by 850.99, rebalanced with future allocations ignored. A 5% per trade outcome was replaced with the minimum of the original and -30% as a stress test, which is a sensitivity test, not a forecast. The exact fixed and managed inputs and P05/P50/P95/95th DD are listed. These final paths supersede previous results from 26294-97, with the source being https://runway.adityaperswal.workers.dev/data/stock-bars. Positive training supports a candidate, but pending dividend and raw management and sub-share probe remain implementation tasks.

A command was given to run a test on two stocks, FRO and STNG, using a strategy called breakout. This strategy looks at the prices of the stocks over the past 20 days and decides what to do based on that. The test ran for 2080 days, with a stop loss of 10 percent and a target gain of 30 percent. The test used a method that makes decisions based on signals and also considers percentage stops and targets. It did not account for the costs of actually buying or selling the stocks and did not set aside any data to check the results independently. This test was a diagnostic tool, not a way to exactly copy a specific investment policy. The results of this test and another tool were kept, even though they were different, because the methods used were different. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-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 command was given to run a test on a trading strategy using the stocks FRO and STNG. The test used a strategy called breakout, looking back 20 days, over 2080 days, with a stop loss of 10 percent and a target gain of 30 percent. The test returned 1426 sets of stock price data and used a method called signal-driven exits, as well as stop losses and target gains based on percentages. This test did not include costs of executing trades or a separate set of data to test the results. It was a diagnostic test, not a copy of a specific investment policy, and cannot validate that policy. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The results of this test and another method were kept, even if they were not the same, to show the differences between the two methods.

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.

The R91206 market regime does not replace the tanker cash capture or validate a new filter. There are several points to consider: 1. Both tankers passed a conjunction breakout and a 50-day Simple Moving Average (SMA), with the SPY at 764.29 being above the 200-day SMA at 712.0452, which provides market context but is not a requirement. The log return correlation of 0.777079 between FRO and STNG implies common shipping risk, and adding STNG does not provide independent tail diversification. 2. The fixed rule training mean for FRO in the SPY above 200 regime was 0.8941% for 25 periods, versus 4.9311% for 8 periods below; the holdout was 5.1453% for 14 periods above versus 2.3635% for 3 periods below. The direction of the regime advantage reverses. The managed training above was 0.0802% for 25 periods versus 6.3820% for 8 periods below; the holdout above was 3.2246% for 15 periods. 3. The fixed holdout for STNG was -0.5586% for 10 periods above and 1.8169% for 1 period below. One period does not license a bear market filter. All partitions were from 2023-01 to 2023-01, with indicators from 2021-06-15 to 2021-07-02 and correlations from 2021-07-05 to 2021-07-12. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. 4. These are post-test descriptive partitions, not additional independent validation; no Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), or volatility threshold was optimized after the holdout. The current Risk Volatility (RV) for FRO is 20 days at 36.1031% versus 60 days at 45.6912%; the window choice and return definition must remain explicit. A changed regime can move probabilities but does not make a false entry true.

A news scan found the headline is not available, but a data line from September 11 shows that the adjusted signal for FRO is 49.21, which is greater than 48.40, and the 50-day simple moving average is 40.8458. For STNG, the signal is 84.52, which is greater than 84.51, and the 50-day simple moving average is 77.9266. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars. The current signal persists despite the failure of IEX STNG, so there is no need to swap feeds. The reference points for FRO are 46.51089 and 57.30733, and for STNG are 80.51631 and 96.53107, which are examples derived from price ranges, not fundamental analysis. The fund will buy a maximum of 3 FRO or 2 STNG before considering other constraints. On September 18, FRO distributed 3.41, which changes the comparison for the stop price, as reported at https://www.frontlineplc.cy/fro-declares-special-one-time-dividend-after-sale-of-two-vllcs/. There are no holdings to update, and the volumes, average daily range versus average true range, lookbacks, and prices from 26165 to 72 are exact. The 1620-bar period for HWM starts in April 2020, after a corporate structure change, and the other periods are 1682 bars, with no claim that all warm-up periods are identical. The 3R watch for FRO is newly positive, and the holdout failure for STNG is separate. The price breakout for SWKS alone does not establish supply pricing power.