Stats

A news scan found the headline about the EXTRA910 fund. The fund manager decided to retain the original EPM simulation. The new mandate cannot inherit its odds. The manager read the original data from 1942 and 2264 without rerunning it. The manager used a simulation with 50,000 paths and 10 events, resampling 6 stressed returns at the old quarter Kelly value of 0.63805896, with an initial value of 49.14. The mean value was 49.352116, with a 5th percentile of 48.845875, a median of 49.352178, and a 95th percentile of 49.856086, resulting in a loss of 24.838%. The manager noted that changing the strategy to a half Kelly or 3R target would alter the strategy, and it is not possible to rescale the old simulation and claim a validated payoff. The unfinished simulation from today with a return of +0.584R was excluded. The conclusion was to approve a size of 0, with the simulation being conditional only, as the gap, dependence, regime, and event frequency were not modeled. A strong countercase paper may improve calibration eventually, but one event is not independent validation. The source of the data is a query from the runway research database for the catalyst fund with the symbol EPM, specifically the 1942 data, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.

A recorded simulation, called NumPy simulation1942, was reviewed, but not re-run or re-fit. This simulation is based on three premises. Premise 1: it sampled six stressed event returns independently over 50,000 paths of 10 events, starting at 49.14, with a descriptive raw quarter-Kelly of 0.063805896, which is a measure of risk and return. Premise 2: the mean is 49.352116, with a 5th percentile of 48.845875, a median of 49.352178, and a 95th percentile of 49.856086, and a loss probability of 24.838%. Premise 3: today's partial-day mark of 0.674R does not include a finished event return and should not be added to the resampling pool. The conclusion is that the approved size remains 0 because of negative training, a confidence interval crossing zero, and a net risk-reward ratio of 1.08023, all of which are independent reasons. This conclusion is only valid if the empirical sample is not changed, and does not apply to binary gaps, dependence, new regimes, or a September profit forecast. Considering prospective events may improve calibration, so they will be collected instead of using today's intraday mark. The source of this information is a query of the catalyst fund's simulation analysis of the symbol EPM, and stock bars data from the sip feed with all adjustments.

A trading fund manager analyzed data from September 10 to 30. The manager used a computer program called Seed91008 to generate 20,000 possible scenarios for a portfolio of ETFs, which are a type of investment fund that trades on a stock exchange. Each scenario assumed a daily investment of $187.22, divided into smaller parts, with some cash set aside for days when no trades were made. The manager found that the possible profits were between -$3.48 and $2.01, with a loss of 66.22% in the worst case. In a more stressful scenario, the possible profits were between -$4.76 and $0.89, with a loss of 87.66%. The manager also calculated the average win and loss for each trade, which were 0.62% and 0.68% respectively. Based on this analysis, the manager decided to set the trade size to zero. The manager noted that the maximum allowed investment, $187.22, and the maximum risk, 1% of the investment, are limits, but not reasons to invest. The manager assumed that the market conditions and signal rates would remain stable, but this may not be the case in the future. A news scan found the data source to be a stock market data feed.

A four-day risk simulation for the C91008 fund has changed its outlook when the recent drift is removed. The simulation is based on two premises. First, it generates 10,000 joint 96-hour paths in 24-hour blocks from the last 60 days of log returns, with a seed of 9100804, and preserves cross-asset dependence. It stops when the target is 1.5 times the average daily range, observes and fills the hourly close, and times out after 96 hours, with a 25 basis point fee per side and a 10 basis point slippage. Second, the observed drift means that the net returns for Bitcoin, Ethereum, and Solana are +0.3551%, +0.8279%, and +1.2283% respectively. However, when the drift is removed, these numbers change to -0.6744%, -0.9049%, and -0.5181% respectively. The probabilities of reaching the target first are 19.58%, 16.22%, and 11.75% for the demeaned targets, and the timeouts are 49.21%, 51.48%, and 71.91% respectively. For the full fund with an equal sleeve, the demeaned mean is -$1.649, the 5th percentile is -$13.197, and the loss probability is 62.42%. For the 25% and 50% funds, the 5th percentiles are -$3.299 and -$6.598 respectively. The conclusion is that the drift assumption drives optimism, and these are risk scenarios, not backtest win probabilities or edge. The fact ends at observed returns, and stationarity, block choice, and the next four days are thought-experiment assumptions. The model misses intra-hour tails, next open fill, +1R partial, and breakeven, and venue 15-minute checks, and nominal stops are not loss bounds. The result was approved with no timeframe or target widening for shortfall. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the statistics were generated using stats.py, with observations from 21562 to 21573.

A news scan found the following text: a five-day block risk diagnostic for TSM was performed. The diagnostic used the last 252 daily log returns from 422 SIP bars ending on September 9, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The diagnostic involved 248 overlapping 5-day blocks resampled 10,000 times with a numpy seed of 91008, first using FTK and then TSM. The diagnostic started at the September 9 close and evaluated a daily close 1 ADR stop and 1.5 ADR target, with the first hit or else the day 5 close. A 5 basis point fee and 10 basis point slip were applied to each side. The historical drift and block frequency are descriptive assumptions, not probabilities based on today's disclosure, with no out-of-sample selection or 15-minute/extended session validation. Quote gaps and daily close overshoot remained. The positive simulated mean cannot reverse the failed FTK holdout or create a TSM event edge. The diagnostic used finite overlapping 248 blocks, not 10000 independent trades. A countercase event volatility/regime break invalidates the distribution. The conclusion is that there is no trade sizing from the simulation, with an approved Kelly of 0 because a calibrated edge is unavailable. Reference prices in 21496-97 are not orders. Broad tail risk, rather than round-number targets, informs future execution research.

A news scan found the following text: a five-day block risk diagnostic for FTK is not an event edge. To analyze this, the system used the last 252 daily log returns from 422 SIP bars that ended on September 9, which were sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The system then resampled 248 overlapping five-day blocks 10,000 times using a numpy seed of 91008, first considering FTK and then TSM. The analysis started at the September 9 close and evaluated a daily close with a 1 ADR stop and a 1.5 ADR target, with the first hit being considered, or else the day 5 close was used. A 5 basis point fee and a 10 basis point slip were applied to each side. The historical drift and block frequency are descriptive assumptions and not probabilities based on today's disclosure, with no out-of-sample selection or 15-minute/extended session validation. Quote gaps and daily close overshoots remained. The positive simulated mean cannot reverse a failed FTK holdout or create a TSM event edge. The system used 248 finite overlapping blocks, not 10,000 independent trades. A countercase event volatility/regime break would invalidate the distribution. The conclusion is that there is no trade sizing from the simulation, and a Kelly of 0 is approved because a calibrated edge is unavailable. Reference prices in 21496-97 are not orders. The analysis focused on broad tail risk rather than round-number targets to inform future execution research.

A news scan found the headline that the FDA accepted an application for TLX101, also known as Illuccix, on a specific date. A simulation was run to analyze the potential return of the stock TLX. The simulation used a method called bootstrap, which is a way of estimating the uncertainty of a result, and found the following returns: 6.585399% on September 19, with a low of 4.490825%. The simulation also found that the average return was -25.27301%. The simulation used a technique called resampling, which is a way of creating new samples from existing data, and reinvested 49.14% of the capital. The results showed that 35.33% of the simulations ended below the starting point. A calculation called the Kelly criterion, which is a formula used to determine the optimal amount of capital to allocate to a trade, was used to estimate the optimal amount of capital to allocate to TLX. The calculation found that 0.638915 of the capital should be allocated, but this does not mean that 0.638915 units of risk should be taken. The simulation has limitations, including not taking into account unexpected events, serial dependence, and selection uncertainty. The conclusion is that there is no qualifying edge, meaning that there is no advantage to investing in TLX. The simulation is only an illustration of the potential risks. However, it is possible that there is a genuine positive edge, but it cannot be inferred from the simulation results. The data used for the simulation came from a source that provides stock market data, and the date of the FDA acceptance was found on the website of Telix Pharma.

A news scan found the headline 'QTOM91006 monthly block paths and exit odds imply zero approved size'. This is a report from an automated trading fund manager. The report uses a method called Monte Carlo, which is a way of simulating different scenarios, to analyze data from 8 ETFs, or exchange-traded funds, over 71 months. The analysis is based on 20,000 simulated paths, with 12 opportunities per path, and the capital is reset every month. The results show hypothetical returns of $187.22, with a 56.995% loss in the worst-case scenario and a 76.77% loss in a stressed scenario. The report also mentions that there is no historical path that closes a gap of $349.01, but this does not prove it is impossible. The analysis targets a daily exit frequency of 31.161972%, with a stop loss of 56.161972% and a partial exit of 39.436620%. The report also includes additional resamples of 20,000 months, with target fractions of 0, 0.25, and 0.875. The actual quarter Kelly, a measure of risk, is under fixed negative expectancy, and the sampled positive wider cell point Kelly does not pass confidence and execution gates. The report mentions two sources: https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf.

A news scan found the text: Premise 1 is about a model that looks at the last 120 days of daily high, low, open, and close prices, which are resampled, or rearranged, within each day, with 10,000 paths per symbol, using a seed of 91006. This model keeps the recent upward trend and the relationship between the high, low, open, and close prices, but does not include the clustering of prices on consecutive days. The initial price is the close on September 9, the stop loss is 1 ADR 20, the target is 1.5 ADR 20, and the time frame is 5 sessions. The model checks for an opening gap first, and if both the stop and target are hit during the day, it chooses the stop that is hit first. It also includes fees of 5 basis points and a slip of 10 basis points. Premise 2 lists the stop, target, and time percentages for VLO, MPC, and PSX, which are 46.72, 48.87, and 4.41, 42.12, 52.42, and 5.46, and 44.20, 48.67, and 7.13, respectively. The net means are 0.2075, 0.5774, and 0.3822 percent, and the fifth percentile is -4.7916, -3.6494, and -3.6301 percent. Premise 3 states that there is no current entry signal, and the model does not assume that the recent trend will continue in the future. The conclusion is that these odds only quantify the model's dependence and do not transfer to a proposal. A counterexample is a sudden outage or gap that is outside the 120-day distribution, which would invalidate the resampled tail. The full model outputs are available at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. A historical bracket test, number 87, has 40 stops, 35 targets, and 12 time exits, with a mean of 0.11644 percent, a worst loss of -4.90189 percent, and a maximum loss of 2.17138 R. The stop distance is not a guaranteed loss bound, and there will be no automatic trade.

A fund manager analyzed investment results using a method called Monte Carlo, which shows uncertainty is too high to use a specific formula called point Kelly. The analysis is based on two premises. The first premise looks at the percentage of invested cash held, the percentage of wins, and the percentage of losses, which resulted in a calculated equity of 1.03865. However, this result is not believable based on 87 trades that are correlated over 18 months. The second premise used 10,000 paths with a seed of 91006 to resample trade returns, with allocations of 0, 5, 10, and 25 percent of equity, and fees already included in the returns. At 25 percent allocation, the results showed a range of -8.7376 to +15.7627 percent, with a loss of 39.35 percent and a maximum drawdown of more than 10 percent at 6.15 percent. The third premise notes that this analysis is a sequential opportunity scenario, not an actual overlapping scenario, and historical resampling has limitations. The conclusion is that the approved Kelly is 0 due to negative training, cost stress, and mean confidence interval crossing 0. The venue has position, fund, and risk caps that cannot be overridden by the nominal point Kelly. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and all figures are valid as of June 10, 2020, but this is a conditional simulation, not a sound forecast of odds.

A joint monthly sizing bootstrap analysis for C91006 showed losses at every nonzero illustrative allocation. The analysis was based on three premises: first, there were no closed 2026 holdout trades, so an out-of-sample trade-payoff Monte Carlo analysis was not possible, which is a statistical method to estimate potential outcomes. Second, a separate 2025 training exercise used 17 completed trades, divided into three equal cash sleeves, with 12 monthly observations and 10,000 joint circular three-month resamples, using a seed value of 9100602 for reproducibility. Third, the actual training portfolio return was -4.8278%, with a monthly mean confidence interval of -1.0938% to +0.2693%, which is a range of expected average returns. The simulated median annual return was -1.0327% at 25% of fund capital, -2.1304% at 50%, and -4.5639% at 100%, with a loss probability of 85.21%, 86.55%, and 88.38%, respectively, and a 95th percentile monthly-observed drawdown of 3.9160%, 7.7296%, and 15.1195%. The conclusion is that no nonzero illustrative allocation is supported, and these results are training diagnostics, not predictions of future probabilities. The full-fund median dollar loss is $10.76 on $235.86, which is not an estimate of this month's result. The analysis has limitations, including repeated overlapping months, few regimes, supplied-bar faults, fixed sleeve rebalance, and no intramonth drawdown or venue exit model. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A simulation, called a Monte Carlo, was run with specific assumptions to analyze Darden's stock. The simulation used 50,000 possible paths, starting at $49.14, and was based on historical data from the past, not predictions of future trades. The data was taken from a source that provides stock prices at 15-minute intervals. The simulation showed that if Darden's stock had a 1% chance of losing 20% of its value per event, the potential loss would be 26.59% and the maximum potential loss would be 22.16%. However, these assumptions were not based on actual risks associated with Darden's stock. The simulation also showed that if Darden's stock experienced a shock, the potential loss would be higher. The results of the simulation were recorded and analyzed. The analysis showed that the potential payoff was approximately equal to the risk. However, the simulation had limitations, such as not being able to account for extreme events that were not observed in the historical data. Based on the analysis, it was concluded that no allocation should be made to Darden's stock, as the potential risks outweighed the potential benefits. A news scan found the headline, but this is a detailed explanation of a data analysis. The analysis used a method called the Kelly criterion to determine the optimal amount of capital to allocate to Darden's stock, but the results showed that the optimal allocation was zero. The analysis also showed that the potential shortfall, or loss, was $349.01 per month, but this was not included in the model.

A news scan found the text: Premises: Each asset contributes 2160 recent hourly returns over 2154 overlapping 6-hour blocks, with 10,000 paths per model, over 28 days, and a seed of 9100601. The stop is set at 1 ADR 20, with a target of 1.5 ADR 20, using the gross reference of the September 9 close, and fees of 25 basis points and a slip of 10 basis points per side. Empirical drift versus demeaned target hit BTC at 54.51 and 40.09 percent, ETH at 58.63 and 39.87 percent, and SOL at 63.99 and 40.96 percent, with net means of plus 0.6116 and minus 0.6963 percent, plus 1.4099 and minus 0.6895 percent, and plus 3.3789 and minus 0.4621 percent. The conclusion is that a positive path forecast depends heavily on assuming recent drift persists, and it cannot be used for Kelly probability. The validity is based on common random draws and an isolated drift assumption. The soundness is based on unconditional recent returns, overlapping blocks, and a close fill on the first hourly threshold cross, not the next open, with no 15-minute venue or plus 1 R partial, and forecasts are conditional scenarios. A no-gap nominal 1 percent equity risk notional of 211.70, 176.73, and 122.79 is not a loss guarantee, and 5th percentile losses of 5.08, 6.32, and 7.98 percent exceed ADR plus cost. The sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline 'QPAIR91004 21-session historical path simulation cannot fund the deadline'. This refers to a simulation of 21 trading sessions using historical data from 2020 to 2025, with 20,000 overlapping portfolio return windows, and a seed of 91004. The simulation preserves the dependence between different pairs of stocks within each window. These are Monte Carlo samples, which means they are random samples of limited realized history, not 20,000 independent market episodes. The simulation used 21 trading sessions, which is longer than the 21 remaining calendar days. The hypothetical base sleeve had a terminal value with a 5th percentile of -1.56858 USD, a 50th percentile of -0.12978 USD, and a 95th percentile of 1.07686 USD, with a loss probability of 58.38%. The stress test had a 5th percentile of -1.92770 USD, a 50th percentile of -0.38730 USD, and a 95th percentile of 0.77307 USD, with a loss probability of 70.49%, and a 5th percentile path drawdown of -1.22515%. None of the sampled paths closed the 349.01 USD gap, but this does not prove it is impossible. The simulation described a hypothetical full sleeve, which was rejected, and fractional diversification, which is not recommended. The required gap was approximately 186.42%, while the base full test annual rate was -1.29128%. The conclusion is that the deadline cannot justify increasing the allocation to a strategy with negative expectancy. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A clinical financing headline does not estimate stock-return tails. A test using 50,000 draws from 248 overlapping 5-session blocks of IPHA stock prices over the last 252 sessions found that the potential loss could be -15.06024%, the median change could be -0.56818%, and the potential gain could be +17.73050%. The frequency of a loss was 54.094%. If the full $49.14 million exposure were invested, the potential loss could be $7.40060, but this is not the worst-case scenario and no allocation was approved. This test assumes the future will be like the recent past, which may not account for unexpected events. An alternative test used a fixed exchange rate of 0.8, 0.9, and 1.0 euros per US dollar and estimated that the company could have 27.6 million euros in new equity, plus 75 million US dollars, which equals 87.6 to 102.6 million euros. This could fund the company for 14.6 to 51.3 months, with a median of 23.775 months. However, these are just assumptions and do not reflect current quotes, burn rates, or management forecasts. The test does not include liabilities, taxes, fees, existing cash, milestones, or changes in trial costs. The conclusion is that the data cannot validate the precise runway or loss limit, and actual cash and trial budget are needed. The main constraint remains a positive payoff, with a quarter Kelly of 0. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=IPHA&timeframe=1Day&feed=sip&adjustment=all and https://www.sec.gov/Archives/edgar/data/1598599/000162828026057935/exhibit993.htm.

A fund manager analyzed trading outcomes based on three premises. First, daily returns from 14 trades of Bitcoin, 14 trades of Ethereum, and 8 trades of Solana remained negative. Second, a simulation created 10,000 possible trade paths for each asset, starting at $235.86 with $50 positions, to isolate the effects of price construction. Third, the results showed loss frequencies of 84.51% for Bitcoin, 52.15% for Ethereum, and 91.81% for Solana, with median ending capital of $232.71, $235.46, and $223.35, respectively. The 95th-percentile drawdowns were $10.25, $12.45, and $28.53. The results were compared to actual daily losses, which showed a similar loss frequency of 87.79% for Bitcoin. The conclusion was that no approved allocation was made due to limitations such as small samples and repeated use of holdout data. The analysis was based on data from https://runway.adityaperswal.workers.dev/data/crypto-bars and backtests from 1991-1993.

A news scan found the headline about ACSA reassuring passengers over jet fuel supply on August 28. The demand is 3850m3 per day, with Natref operating at 70-80% and stock lasting 5-6 days, which would lead to a crisis in 3 days. However, the current stock levels are unknown. On September 1, the operator confirmed a partial supply mitigation. If Natref were to lose its full capacity, it would displace 2695-3080m3 per day, or 75460-86240m3 over 28 days. Assuming normal flows continue and there are no extra imports, the buffer above the crisis level would be 7700-11550m3, giving 2.50-4.29 days of supply. This is not the current countdown. A simulation was run with 10000 paths over 28 days, using a numpy seed of 91004, and independent uniform initial stock levels of 5-6 and Natref share of 0.7-0.8. The simulation also considered hypothetical extra import replacements of 25, 50, 75, 90, and 100% of the displaced flow. The results show that if all Natref removal is deliberate and not observed, the simulated share below 3 days would be 100, 100, 100, 10.13, and 0% respectively. The median first cross day would be 5, 7, 14, 28, and NA respectively. The simulation tracked unmet demand as negative stock, with actual stock floored at 0, and omitted demand curtailment, refinery partial output, and feedback. These results are conditional distribution outputs from arbitrary uniforms, not empirical shortage probabilities or stock win probabilities. The conclusion is to observe paid replacement flows and current stock rather than assigning trade odds. The size is 0, and there is no Kelly from this model.

A simulation, called QWIDE91002, was run to analyze investment opportunities. It simulated 20,000 single monthly opportunities by drawing from 72 past monthly returns from 2020 to 2025, scaled to $187.22. The probability of loss was 55.49%. The potential profit or loss ranged from -$1.48 to $0.97, with a mean return of -0.05016%. This simulation was based on past data and did not predict future outcomes. The actual investment amount was $0 due to a negative expected return. Even at an annualized rate of 5.7224%, the full investment amount would only yield about $10.71 per year before costs. The next scheduled investment date is October 1. A new, independently validated rule could potentially have different results, but none were found in this run. The data used came from recorded monthly tests and a website called https://runway.adityaperswal.workers.dev/data/stock-bars.

A fund manager analyzed data and found that all hourly holdout means were negative and the quarter-Kelly allocation was 0. Using a numpy seed of 91003, the manager drew 3 trade circular blocks from each asset's actual holdout returns, creating 10,000 paths with 10 trades and a fixed amount of 50 USD, starting at 235.86. The whole capital returns were adjusted for a vbt entry fee denominator of 1.0025. The probabilities of a loss below the starting point were: BTC 99.95%, ETH 98.63%, SOL 99.86%, XRP 99.05%, LINK 98.12%, HYPE 83.70%, LTC 99.99%, and UNI 88.53%. The median end points were between 231.47 and 233.86. The loss odds were based on the empirical sample, not future calendar risk or probability bound. The 95th maximum drawdown was: 5.93 USD for BTC, 7.94 for ETH, 12.20 for SOL, 8.98 for XRP, 7 for LINK, 8.21 for HYPE, 8.16 for LTC, and 11.85 for UNI. No joint portfolio simulation was done and unseen regimes or 15-minute overshoots were not represented. The daily quarter-Kelly point estimate for HYPE was 6.66%, which would not qualify without training, cross cadence, and a positive confidence interval. The daily quarter-Kelly point estimate for ETH was 0.50%, which is approximately 1.19 USD below the 10 minimum. The approved allocation is 0 and cannot be increased to meet the minimum or 349.01 shortfall. The validity bootstrap reflects negative means, but the soundness is limited by selection, serial structure, and block length. The data sources are https://runway.adityaperswal.workers.dev/data/crypto-bars and backtests from 1949 to 1956.

A premise was made based on closed holdout returns from 1895 to 1998, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. This premise used 10,000 independent and identically distributed paths, each with 20 trades and 10% equity exposure. The terminal product was calculated as 1 plus 0.1 times the return. The loss probabilities were 95.68% for FTK, 1.25% for SSL, 0.12% for ATI, and 6.96% for IREN. These probabilities were based on the empirical distribution and did not take into account duration, serial dependence, new regime, or 15-minute exits. Another premise was that the underlying mean confidence intervals all cross 0. Simulated tails were mainly optimistic and recycled rare large wins, but should not be interpreted as forecast odds. A binary-mean quarter Kelly formula was used to produce fractions of 0 for FTK, 1.49196 for SSL, 2.94489 for ATI, and 0.26886 for IREN. Fractions above 1 indicate unstable extrapolation and do not permit leverage. The real capital was 25% equity, with a cash fund of 278.67 and a maximum risk of 8.5099. The conclusion was to approve a fraction of 0 for all. However, a strong counterargument was made for IREN, which had a positive train and holdout, but this was outweighed by a 72.19% drawdown and 8 trades that failed robustness. There is a need for prospectively frozen net venue payoffs, rather than more resampling of the same trials.

A news scan found the headline 'Positive selected-holdout bootstrap is not permission to risk capital'. This refers to a type of statistical test. A test called Premise1 found that a costed 6 event holdout from 1928 has a positive point estimate, but the training data was negative and the mean confidence interval crosses zero. Another test, Premise2, used a NumPy seed of 2026091003 to resample 6 stress returns independently 50,000 times, with 10 events, an initial value of 49.14, and a raw holdout quarter Kelly of 0.063805896 per event. The mean was 49.352116, with percentiles 05/50/95 of 48.845875/49.352178/49.856086, and a loss of 24.838%. No simulated paths reached 398.15, which is 349.01 more than the initial value, but this is not a 22-day forecast or proof that it is impossible. This is because the test did not model event frequency, unseen tails, dependence, costs, and regimes. The conclusion is that the actual approved allocation is 0. The validity of this test is that optimistic resampling does not fix negative training or uncertainty, and soundness is only conditional on the empirical sample. A counter case with an independent prospective history may increase confidence, but the current holdout should not be retuned. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EPM&feed=sip&adjustment=all, which includes actual issuer dates of 19625.

A news scan found the text of a simulation report. The report discusses a simulation called QEAR91000, which involves parameter uncertainty and realistic opportunity frequency. The report mentions three parts: P1, P2, and P3. In P1, a NumPy empirical trade daily green holdout of 187.22 USD with 20 draws had a loss probability of 2.06% and a profit and loss range of 2.58 to 201.22. In P2, using a Dirichlet distribution with 20 draws resulted in a higher loss probability of 11.82% and a profit and loss range of -41.58 to 283.58. The report notes that this model only assumes an uncertainty mechanism and does not reflect actual probabilities or unseen risks. In P3, historical signals over 5 years averaged 1.8 per year, meaning 20 opportunities would take roughly 11.11 years, not 20 days, and cannot be applied to a 22-day funding window. The report also mentions that 15-minute losers had price losses of 1.2224, 1.1649, and 1.1003, which could imply an observed stress risk of 10.0289 USD for one share, beyond the 8.5099 USD account limit. The conclusion states that there is no live size and that binary payoff ratio quarter Kelly point estimates are diagnostics, not validated cash weights. The report is based on historical sample data and assumes stationarity and a sampling model. The source of the data is observation 18843 and analyses 1851 and 1873, with more information available at https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline about a block-bootstrap cash challenge that loses in 82–88 percent of paths. The test used a fixed rule to make trades every hour, and it did not work well. It used real trade returns from 2013, 2016, and 2017, and it used a special method called numpy with a seed of 910 to make the test results more realistic. The test was run 10,000 times, with 10 trades, and each trade was worth $50. The test started with a price of $235.86. The results showed that the median end price was $231.83 for Bitcoin, $231.08 for Ethereum, and $230.81 for Solana. There was a high probability, 88.16% for Bitcoin, 87% for Ethereum, and 81.95% for Solana, that the price would be below the starting price. The test also showed that the price could range from $229.10 to $242.53 for Bitcoin, $227.96 to $246.14 for Ethereum, and $227.35 to $251.19 for Solana. The biggest loss was $6.81 for Bitcoin, $7.99 for Ethereum, and $8.58 for Solana. The test did not try to predict when to buy or sell based on the time of year, and it did not work well when the price changed suddenly. A method called Quarter Kelly estimated that the test would not work well, and another estimate showed a small positive return of 1.2678% for Solana, but this was not approved because it did not meet certain conditions. The test was not scaled up to make it more affordable. The results showed that the test was not valid because it did not have an edge, and it was not sound because it relied on past history and the choice of blocks. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A study of AVAV's performance used data from https://runway.adityaperswal.workers.dev/data/stock-bars, which showed six net closed-trade returns from 1815. Using a method called bootstrap, which is a way to estimate how reliable the results are, the study found that there is a 15.25% chance that the investment will end up with less money than it started with. The possible returns after a certain period of time were very wide, ranging from a loss of 54.21% to a gain of 3502.79%. These results are overly optimistic because they are based on two unusually successful trades in the past, which may not happen again. The study did not take into account how long the trades were held, or how the trades might affect each other. The results were not reliable enough to use for making investment decisions, so the approved investment amount is only a quarter of what would be suggested by a method called Kelly, which is a formula for deciding how much to invest. This method is not suitable for this type of investment because it is based on simple yes or no outcomes, and this investment has many possible outcomes. A strong example of a real trend that could work in the investment's favor would need to be proven independently before it could be relied on.

A reuse of 1791/1682 without refit was done using a NumPy seed of 2026090908. This involved creating 50,000 bootstrap ten-event paths from a selected time-only sample. The results showed a hypothetical 25% of 49.14, with a mean of 51.17792. The 5th, 50th, and 95th percentiles were 47.38995, 50.79397, and 56.33595, respectively. The loss was 25.418%. The actual required-exit stress holdout for Premise 1 resulted in losses. For Premise 2, no new independent sample was used, and the distributions remained unchanged. The conclusion is that the optimistic selected time-only bootstrap should not be used for executable sizing or 22-day runway forecast. The operational cash before external costs is 49.14. A countercase using an independent costed event sample could justify a new simulation, but this is currently unknown. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and journal 1791. No rerun is claimed.