A fund manager analyzed restaurant stocks and found that the average return was -2.099319% over a certain period. This was calculated using a method called bootstrap resampling, which is a way of estimating the average return by resampling the data. The manager also used a statistical method called ordinary least squares, or OLS, to compare the stock returns to the overall market, represented by the SPY index. The results showed that the restaurant stocks underperformed the market by 2.798827 percentage points on average. The manager also found that removing two pre-announced events from the data reduced the average return, but still found that one restaurant stock, TXRH, had a positive average return of 0.467328%. The manager notes that this result is interesting and deserves further study, but does not necessarily mean that the stock will continue to perform well in the future. The data for this analysis came from a source called runway.adityaperswal.workers.dev and covered a period of time from 17518 to 17594. The manager warns that it is not valid to conclude that restaurant stocks are fundamentally bad or that an inverse strategy would be profitable based on this analysis.
The fund manager selected 10 paired cases from 6 existing fund issuers, with 7 goals in 2025 and 3 in 2026. These cases were chosen from a bounded search, but the search was not exhaustive. The lead days for these cases were 3, -2, 38, 0, -1, -1, 6, 4, 58, and 32, with a median of 3.5 days and 6 positive leads. Correcting the MIRM to an earlier FDA public release gave 35 days, and Australian US dates are intervals. There were 3 robust cases with 35 days or more: RAREUX11138, MIRMchenodiol35, and NUVLzidesamtinib58. The fund manager used a method called ordinary least squares, or OLS, which had zero residual degrees of freedom, meaning the correlation and fitted beta have no value for making inferences. A different method, called quarter Kelly, was used to measure the payoff, but it was contaminated by selected payoffs. The fund manager found that the early rule had no wins and no positive edge, and the fund ledger had 3 losses, including 2 operator liquidations. The fund manager concluded that one verified counterexample can refute guaranteed timing safety, and that sampled counts cannot quantify population risk. To move allocation, the fund manager needs a preregistered prospective full calendar cohort, earliest news timestamps, all pipeline overlaps, independent actual exit-cost returns, and executable risk. The sources used were paired observations 17167-88 and 17285-90, as well as data from the FDA website and stock prices from a website. A news scan found the FDA website https://www.fda.gov/media/151712/download and stock prices https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all.
A fund manager is analyzing Darden Restaurants, ticker DRI, and the SPY index. The manager's model has a 50% chance of a positive outcome, with a 3.216773% win and a 1.314447% loss, and a mean return of 0.951163%. A bootstrap analysis shows that 82.19% of sample means are positive, but this is not the probability of a successful trade. The manager also ran a regression analysis on DRI and SPY returns, finding a correlation of -0.730141 and a beta of -1.272867. The mean return of DRI minus SPY is 1.571702%, but this result is not stable. The manager calculated the Kelly fraction, a measure of optimal bet size, to be 0.073922, but due to uncertainty, the operational fraction is set to 0. The manager's ledger shows three losses, with a mean loss of -16.3806%, and no winners to calibrate the average win. A loss of 45.86% shows that stops are not hard loss bounds. The analysis is based on 16251-74 events from https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/query_trades. The results have limited validity due to the selected issuer and incomplete training notice history. The manager will change the allocation if independent future events support a positive costed actual-window expectancy with adequate uncertainty and executable risk plan.
An audit of a trading fund's payoff has kept the position size at zero. The audit is based on several premises. First, the fund's trade history shows three losses: MIRM lost 0.439321%, IONS lost 2.842420%, and RARE lost 45.860062%, with an average loss of 16.380601%. Two of these losses were due to the operator liquidating the positions, so this is not a representative sample of the strategy's performance. There were no winning trades, so the average winning payoff is unknown. Second, the loss on RARE was 6.440348 times the distance from the entry point to the listed stop, showing that the stop did not limit the loss. Third, a backtest of the AVO stock using the vectorbt system with 10 basis point fees and 10 basis point slippage per side showed a negative net return of 7.331815% and a drawdown of 9.371961%. All 16 variants of the backtest lost money. A Python recalculation confirmed that the quarter-Kelly strategy should have a position size of zero. Fourth, an audit of the TLX event showed no valid training events and only one positive holdout, and the momentum503 strategy failed to predict the outcome. The sources of this information include the Catalyst trade history, backtest analyses, and stock price data. The valid inference from this audit is that there is no evidence of a positive edge to support a position size greater than zero. However, it is possible that future price increases could result in wins, and these small samples do not prove that the market is always efficient. No new backtests or data collection are claimed, and existing failed methods have not been revised. The calculations from Observation16039 have been preserved.
The premises were re-examined using frozen vectorbtbacktest1158 and CLI1159. The actual events from 2025-26 were held out after training from 2021-24. The results showed a loss of 7.3318% with 10 basis points fees and 10 basis points slippage on each side, and all 16 variants were negative. A data source from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVO&timeframe=1Day&feed=sip&adjustment=all was used. A Python run reproduced the results, showing a win rate of 1.3184425721%, a loss rate of 3.7436325013%, and a negative expectancy of -1.2125949646% per event, with a clipped quarter-Kelly of 0.3. The exact ledger from the runway query_trades?fund=catalyst showed three losses, with a mean loss of 16.3806011337%, a profit and loss of -31.9372580268, and a RARE of 6.44035Rloss. Two operator liquidations affected the intended horizons, and there were no average win or arrival quotes for slippage. The conclusion is to have zero new allocation under the 49.14 fund cap and 8.5099 equity risk cap. The small selected cohort, missing archival advance notices, structural break, and daily versus 15-minute fills limit generalization. A strong counter case for a new regime could improve the edge, but an independent, prospectively specified, costed holdout is required. There will be no re-run, re-tuning, or new Monte Carlo, as the previous 1164 and 1165 technical and indicator, and 1169 simulation remain frozen and rejected, and fresh price alone cannot repair the statistical gate.
A news scan found the headline '13:50 trading sizing: frozen negative expectancy and live losses'. The automated trading fund manager analyzed two premises. Premise 1 involved a frozen costed AVO vectorbt backtest1158 and CLI1159, which showed a holdout return of -7.3318%, a drawdown of 9.372%, and all 16 variants were negative. The prior-session entry expired. The research/audit.py results were recomputed with a p-value of 0.5, W of 1.3184425721%, L of 3.7436325013%, and a mean of -1.2125949646% per event. The quarterKelly was clipped. The history source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVO&timeframe=1Day&feed=sip&adjustment=all. Premise 2 involved the current query_trades fund, which is catalyst, and observation 15835, showing all 3 losses and two operator liquidations. There was no observed average win and no arrival quotes to estimate market-order slippage. A RARE loss of 6.44035R demonstrates that gaps can defeat nominal stop risk. The conclusion is zero new allocation because the necessary numeric-edge gate fails. The soundness of this conclusion is that the selected small event cohort and daily fills do not establish that all future trades will fail. The strongest countercase is that a new regime could improve expectancy, but an independent notice-aware holdout under stressed costs is needed before sizing, not a higher price today. Existing technical indicators, simulation, and backtest remain frozen, and no new backtest or Monte Carlo is warranted merely to seek a different answer in a trading audit.
A 12:30 sizing audit was performed, which resulted in a frozen AVO expectancy and a censored live record retaining zero allocation. The Premise1 query_trades fund, which is a type of investment fund, returned 3 closed trades and 0 open, pending, or closing trades for the catalyst fund. The exact fill returns were then recomputed, showing returns of -0.439321% for MIRM, -2.842420% for IONS, and -45.860062% for RARE, with a mean loss of 16.380601% and an aggregate loss of $31.937258. Two operator liquidations, which are forced sales of assets, censored the intended horizons, and there were no winning trades to estimate the average win. One stop exit for RARE resulted in a loss of 6.44035%, but there was no arrival quote, so order type slippage, which is the difference between expected and actual trade prices, cannot be estimated. The Premise2 frozen vectorbtbacktest, a type of backtesting, which is a method of evaluating a trading strategy using historical data, and technical indicators, which are tools used to analyze markets, were already recorded, with numbers 1158, 1159, 1164, 1165, and 1169, and there was no new test or parameter search this trading run. AVO, a stock ticker, held out p, which is a statistical term, of 0.5, with a W, or win, of 1.3184425721% and an L, or loss, of 3.7436325013%, resulting in an expectancy of -1.2125949646% per event, and a clipped quarter Kelly, a betting strategy, of 0. There were six events, with a return of -7.3318%, a drawdown, which is a measure of decline, of 9.372%, and all 16 variants were negative. The selected small cohort, a group of traders or investments, and missing historical notice and fill data remain limitations. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=AVO&timeframe=1Day&feed=sip&adjustment=all. The conclusion is zero, with unknown post-release probability of win or loss for morning names also implying zero. The numeric monthly shortfall, which is a measure of deficiency, cannot enter the edge, which is a measure of risk. A counterexample of future events could perform differently, and a prospectively validated new regime, which is a new set of rules or strategies, is needed, rather than retrospective optimism, which is the tendency to view past events more positively than they actually were.
A query of the trades fund catalyst found that the mean loss was 16.3806% and the total loss was -31.937258. There were two operator liquidations with planned exits, but only 0 out of 3 trades were successful, which does not prove that the trades were independent and identically distributed, a concept known as IID, which refers to a statistical assumption that observations are independent of each other. The average win is unknown. Another analysis of AVO, a stock ticker, found that the mean return was -1.03766% in the training period and -1.21259% in the holdout period, with a bootstrap confidence interval of -3.75774% to 1.05684%. The median holdout return was +0.01312%, which is not enough to offset the loss tail, a term that refers to the potential for extreme losses. A third analysis found that the net return on AVO was -1.03893% in the training period and -0.614015% in the holdout period, with a beta of 0.01461 and 1.07551, respectively, where beta is a measure of volatility. However, the sample size was too small to make any causal inferences. In conclusion, there is no measurable positive edge, and the quarter Kelly, a formula used to determine the optimal bet size, is clipped to 0. A counterexample could be that a new post-merger edge could differ, but this would require prospective data instead of retuning. The sources of the data include a query of trades and stock bars from a website. The event primary URLs had 152 observations, and the sample is one issuer survivor selected for today's date, not a universe estimate.
A study on event dependence and indicator regressions found that it cannot identify a stable sizing edge. The study used data from sources including https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LOVE,SFIX,SPY&timeframe=1Day&feed=sip&adjustment=all. 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. For LOVE, the training set had 16 observations and an R-squared of 0.162, while the holdout set had 7 observations and an R-squared of 0.681, but only had 2 residual degrees of freedom. For SFIX, the training set had 20 observations and an R-squared of 0.526, while the holdout set had 10 observations and an R-squared of 0.080. The study found that the results were not reliable due to the small number of observations and the fact that the SPY trend was constant, which means it did not change much over time. The study also used a method called equal-year bootstrap, which is a way to resample the data to get a sense of the uncertainty of the results. For LOVE, the holdout confidence interval was [0.072, 6.579]%, which looks positive, but the estimates were different from the 7-event confidence interval [-1.048, 6.139]%. The study also found that leaving one observation out of the LOVE data resulted in holdout means ranging from 1.883% to 4.113%, while excluding 2020 from the training data resulted in a mean of 3.525% for LOVE and -1.839% for SFIX. The study concluded that the results were not reliable due to the small number of observations and the lack of independence between the data points. The prior assumptions were also unvalidated, which means they were not tested or proven. The study suggested that the results would only be reliable with a pre-registered prospective calendar, matched cohort, and actual execution check. The study also found that the censored live losses provided no average win, and that biotech and retail should not be pooled together.
A news scan found the headline 'No allocatable event edge from three censored live losses or clinical significance'. This is a report from the Directquery fund, also known as the catalyst fund, as of September 8. The report can be found at https://runway.adityaperswal.workers.dev, specifically at the query_trades page for the catalyst fund. The report states that the total loss from three exact price and quantity losses is $31.9373 USD, with a mean loss of 16.3806%. The MIRM and IONS operator planned exits due to liquidation and censoring. One notable loss was 6.440R, using an entry price of 25.84 and an original stop price of 24, with an exit price of 13.98976. The report notes that there is no claim of precise order slippage without contemporaneous quotes. The fact that there are zero observed wins does not prove that the population has a zero win rate. The report also mentions that the average win is unidentified because there are no winning trades. The EGTX independent generic trend holdout has 56 observations, with a p-value of 0.375, a win rate of 7.383%, and a loss rate of 6.091%, resulting in a negative Kelly criterion that is clipped to 0. This means that the EGTX trend cannot be pooled with FDA trades. The report includes a descriptive one-day autocorrelation of 0.0460. A survival cohort analysis found that 228 out of 484, or 47.107%, of the cohort was retained. The selected older cohort had a hazard ratio of 0.28, which cannot be converted to FDA approval odds, as referenced in a study found at https://www.endocrine-abstracts.org/ea/0101/ea0101op-03-04. The report concludes that the premises mismatch, censoring, and negative expectancy prevent calibration, resulting in a conclusion size of 0. The evidence bar for change requires an independent dated-event holdout that is positive after costs, with a mean uncertainty above zero, plus venue and gap validation, and this is not a single rally.
A direct query of trades for the Catalyst fund on September 8 found three losses and two censored exits. A Python calculation of exact entry, exit, and quantity found a mean loss of 16.3806% and a total loss of 31.9373 USD. This means that 93.322% of losses were rare, with a loss of 6.440 versus the original entry of 25.84 and stop of 24. The MIRM and IONS operators had liquidations that censored their planned horizons, which means their planned exit points were not reached. Because of this, it is not possible to infer the true win probability or the efficacy of the original targets. There is no average winning trade. The premises of this record are that it is biased and censored, and therefore cannot be used to calibrate a positive edge. As a result, the live-record Kelly is unidentifiable, and 0 was adopted. A new test, called NewPLAY, was run with a holdout of 6, and it found 2 wins out of 3, with a yield of 5.972 and a loss of 7.795, which is a positive mechanical Kelly. However, the interval crosses zero, and the training fails. It is not possible to pool different catalysts to pretend they are homogeneous. Slippage by order type and session is unavailable without decision quotes, so the return is calculated as exit minus entry, rather than execution slippage. It is possible that the results are due to bad luck, but this does not establish a probability.
The audit of a fund's performance shows a significant loss. The fund had 3 closed trades, with no wins and no open or pending trades. A calculation using Python found that the total loss was $31.93, with an average loss of 16.38%. One loss of $29.80 made up 93.32% of the total losses, and the actual loss was 6.44 times the planned loss. The stop loss was overshot by $10.01 per share. Two trades were closed by the operator, which affected the intended exit strategy. The conclusion is that the sample does not show a positive edge in terms of sizing, and the average winner is not available. The validity of the results is limited by the small sample size and mixed exit strategies. It is noted that future trades can still be winners, even if none were observed in this sample. The risk of stop-level losses is not the same as the maximum gap loss. To improve the results, a full event map and holdout period should be used instead of just deadline-only entries. The price data used is based on actual venue records, not quotes or modeled fills.
A preregistered note, number 652, analyzed the daily return of ROIV using a linear regression model, also known as ordinary least squares or OLS, where daily ROIV return equals a plus b times SPY return, using all-adjusted IEX bars from January 3, 2022, through September 4, 2026, available at https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis was divided into two periods: a training period from 2022 to 2024 with 752 paired returns, which had a beta of 1.09843, a correlation of 0.32355, an R-squared of 0.10468, and an intercept of 0.04914% per day, and a holdout period from 2025 to September 4, 2026, with 420 paired returns, which had a beta of 0.91825, a correlation of 0.39021, an R-squared of 0.15226, and an intercept of 0.21894% per day. The mean and standard deviation of daily ROIV returns were 0.09205% and 3.73385% for the training period, and 0.28739% and 2.46271% for the holdout period. The premises of the analysis were that the low R-squared and large idiosyncratic dispersion, which refers to the variation in returns that is specific to individual stocks, indicate that the model does not explain much of the daily variance in ROIV returns, and that clinical events, which are significant events that affect a stock's price, are specific to the ticker, or stock symbol. The conclusion is that macroeconomic trends, or macro prints, describe the context, but do not predict the likelihood or forecast of alpha, which refers to the excess return of a stock over the market average. The intercepts are descriptive fits within each partition, but not forecasts or estimates of abnormal returns. A counter case could be that broader risk-on, which refers to a market environment where investors are taking on more risk, could matter, but the beta near 1 supports only some exposure, and only 10-15% of the variance is explained. No statistical significance or regime stability is claimed. The analysis is limited by survivorship bias, which refers to the bias that occurs when only surviving companies are included in the analysis, news contamination, which refers to the impact of news events on stock prices, partial IEX, which refers to the use of only some of the data available from the IEX exchange, and adjustment revisions, which refer to changes made to the data after it was initially released. The analysis requires prospective event data, rather than repeated queries that create false independence, and allocation is 0.
A news scan found a report about the General Mills stock, GIS. The report analyzed 23 pre-earnings windows from the issuer date observations 8020 to 8043 and used data from IEX at https://runway.adityaperswal.workers.dev/data/stock-bars. The report found that the holdout 10 events had a mean return of +0.109305%, but the 20,000 calendar-year cluster bootstrap 95% mean confidence interval was [-1.408228, +0.925035]%. This was based on only 3 year clusters, which is not a precise confidence level. The train 13 mean return was +0.708895%, with a confidence interval of [-0.237380, +1.300316]%, based on 4 clusters. The report also found that the holdout net GIS return minus the gross same window SPY return mean was -0.221269%, versus -0.099777% for XLP, indicating a conservative cost asymmetry and no hedge backtest. The holdout net return percentiles were -7.583938%, -5.146350%, 0.169236%, 3.731859%, and 4.767482% for the 0, 5, 50, 95, and 100 percentiles, respectively. The ordinary least squares, or OLS, regression trained on 12 finite events found that the return was equal to -0.10556970 minus 0.02600990 times the prior 20-day momentum plus 6.67488562 times the prior average true range, or ATR, divided by the open price. The holdout 10 R-squared value was -2.732571, compared to the holdout mean benchmark. The momentum correlation was -0.051905 for the train and +0.419999 for the holdout. The coefficients were unstable and not causal or usable for forecasting. The trend simple moving average, or SMA, was undefined in 3 train observations, and the regression ATR and momentum were undefined in 1 observation. The report noted that prior positive event edges were unsupported and that there was no weighted approval probability. It would require independent larger prospective events with a net mean lower bound greater than 0 and realistic execution to change sizing. Statistical resampling does not create new independent events.
A news scan found the headline about TLX daily-return predictability failing a simple chronological regression. The analysis used Alpaca IEX 452 bars from November 2024 to September 2026, excluding zero-volume and prior zero-volume observations. It trained 173 daily returns through 2025 and tested 170 in 2026. The ordinary least squares, or OLS, regression was defined as r_t equals intercept plus lag 20 day momentum plus lag TR close. The coefficients were 0.00031832, -0.00449544, and -0.09196510. The train momentum correlation was -0.0228925. The holdout R2 was -0.0226811, which is worse than the holdout mean benchmark. The holdout daily mean was 0.337241 percent, with a bootstrap of 20,000 resamples and a 95 percent confidence interval of -0.192086 percent to 0.877430 percent. The 1, 5, 50, 95, and 99 percentiles were -8.08934, -4.99560, 0.12338, 6.37649, and 9.70074 percent. This analysis is descriptive, not causal or calibrated event forecast. It did not account for independent market factors or ASX-FX adjustments, and it had issues with selected issuer, regime change, missing prints, and iid bootstrap undercount dependence. The positive 2026 mean failed uncertainty and did not transport to the PDUFA window. Current public FDA data cannot be backdated to 2025 signal formation. The source and data are available, with full figures and observations at 7678 and 7722, and the research is reproducible using analyze_final.py and extra.py.
A review of the Catalyst trades from September 2-4 was done using numpy. The average profit per trade was -16.3806%, with 0 wins out of 3 trades, and a total profit and loss of -31.937258, which matches the venue's record of -31.94. Two trades were stopped by the operator, which affected the intended strategy outcomes, including one RARE stop exit with a loss of 45.8601%. Since there were no winning trades, it is not possible to calculate the average win, and it should not be assumed that future trades will have a 0% chance of winning. As a result, the live trading record cannot be used to calibrate the Kelly criterion. A separate analysis of CHWY events was saved, which showed a net return of -1.719988% and a clipped quarter Kelly of 0. The historical loss stress exceeded the account risk cap, but this does not guarantee that future losses will be limited. The premises of the trading strategy, including the edge and time window, have not been validated, which means that the trade size should be 0. A future rally would not prove that the strategy has an edge. The data sources used include the execution ledger query_trades fund, historical stock bars, and dates from Chewy's financial reports. No new backtest or Monte Carlo simulation is claimed, and resampling the 3 censored trades would not provide any new evidence.
A news scan found the text describing an analysis of HAIN. The analysis used a method called bootstrap, which is a way to estimate how reliable the results are, with a seed of 60606 and found that the average return was -10.97667%. The results also showed a 95% confidence interval, which is a range of values that the true result is likely to be within, of -23.21708% to 2.91121%. Another calculation, called the Wilson confidence interval, which is a way to estimate the probability of an event, found a 95% confidence interval of 3.00534% to 56.35028%. The analysis noted that the results are not precise because the sample size is small and there may be unknown patterns in the data. The analysis also compared the results to two other stocks, SPY and XLP, and found that HAIN had a lower return. The analysis used a method called ordinary least squares, which is a way to find the best fit line to a set of data, to model the relationship between HAIN and the other two stocks. The results showed that HAIN had a daily return of -0.4030566% and was closely related to SPY and XLP. The analysis noted that the results are based on a small sample size and may not be representative of the entire market. The data used in the analysis came from a source called IEX and covered the period from 2025 to September 4, 2026. The analysis also noted that the results are subject to selection risk, which is the risk that the results are biased because they are based on a selected sample rather than the entire market.
A review of the Catalyst fund's trades from September 6 found three closed trades with no wins. The returns were a loss of 0.439321% for MIRM, a loss of 2.842420% for IONS, and a loss of 45.860062% for RARE. The average loss was 16.380601% and the total dollar loss was $31.937258 before system costs. Two trades were closed by the operator and one trade had a stop sale. The RARE trade was closed at $13.98976, which is 41.709333% below the stop trigger of $24. This was not necessarily due to order routing issues, as there was no executable quote at the time. All trades were fractional market-notional entries, with no limit comparator and no reference quote to estimate entry slippage. The winner payoff was not observed and the quarter Kelly sizing was 0 by mandate. These three trades should not be grouped together as a sample of earnings events or used to infer a true win probability of 0%. The data comes from the query_trades fund, with a status of closed, and provides information on account 850.99 and fund 49.14, with a 1% risk of $8.5099. This is evidence of execution and outcome, not a causal rule backtest.
A news scan found the text: Premises: same stock only one completed FDA deadline; arbitrary stock windows measure background drift, not dated edge. Fixed 15 session non-overlapping windows with 16 session step, IEX 2021–Sep 4 2026, fee 5/slip 10bp each side. Train 61 windows mean 1.2841%, median -1.5474%, win 45.9016%, bootstrap 95% mean CI [-6.0859, 11.4398]%. Holdout 25 windows fully entered/exited 2025+, mean 1.6538%, median 0.8703%, win 56%, CI [-3.6993, 7.1608]%; one Dec 18–Jan 13 boundary window excluded explicitly. 20,000 iid bootstrap resamples, seed 60604; non-overlap reduces mechanical dependence but regime autocorrelation remains. Descriptive 420 daily rows 2025+: corr SRRK/XBI 0.5691, SPY 0.3380; two-factor OLS beta XBI 1.2164, SPY 0.09075, daily intercept -0.06767%, R2 0.3243. No robust SE or causal claim; XBI contains biotech exposure and factors correlated. Conclusion: rising price or positive placebo mean does not identify event premium. Need prospective independently classified manufacturing-resubmission cohort with announcements known before entry and full pipeline overlap. No posterior approval probability assigned; N1 cannot calibrate hazard. Source https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SRRK,XBI,SPY&timeframe=1Day&feed=iex&adjustment=all. All measured rows/metrics archived.
A news scan found the headline about High Tide Inc. announcing preliminary Q3 2025 guidance. The Frozen11 event holdout in 244 had a win probability of 7/11 or 0.636364, with a win of 1.502475% and a loss of 1.655815%, which implies a raw quarter Kelly of 0.058903788. Using a NumPy seed of 2026090602 and 20,000 independent and identically distributed event bootstraps, the mean was 0.354006% with a 95% confidence interval of -0.732594% to 1.532965%. However, the positive bootstrap means of 72.22% do not represent the forward win probability. The same-window SPY gross mean was 0.386645%, while the HITI net excess was -0.032639% with a correlation of 0.093685. An ordinary least squares regression had an intercept of 0.284421%, a beta of 0.179970, and an R-squared of 0.008777, with 11 descriptive but not causal data points. The stress mean was 0.053394% and the raw quarter Kelly was 0.007619052. The binding gate positive 95% lower bound failed, resulting in an authorized size of 0. The own record showed 3 closed trades with 0 wins and a mean loss of 16.380601%, as well as 2 operator liquidations and 1 stop, which cannot estimate the clean run-up edge or average win. One known pre-announced match in September 2025 returned 3.639981% net, but the N1 does not validate the current pre-announced regime. The premises include a positive point estimate, uncertainty including loss, and implementation cost and regime shift remaining. The conclusion 0 is valid under the pre-registered gate, but soundness is limited by the selected issuer, small N, and dependence, and the bootstrap may understate uncertainty. Evidence to change would require prospectively collected matched regime event returns with positive cost-stressed CI and verified execution, not monthly funding pressure. Sources include the price API at https://runway.adityaperswal.workers.dev/data/stock-bars, the issuer at https://hightideinc.com/high-tide-announces-preliminary-q3-2025-guidance/, and the own record at runway:query_trades?fund=catalyst.
A Python script using NumPy with a seed of 20260906 generated 20,000 independent and identically distributed event bootstrap samples for each fixed holdout. The source of the price data is the API at https://runway.adityaperswal.workers.dev/data/stock-bars, with exact issuer URLs found in the calendar archive, and the script's own trades can be queried at runway:query_trades?fund=catalyst. The LEN baseline mean return was -1.176670% with a 95% bootstrap mean interval of -3.803275% to 1.275506%. The overlay mean return was 0.181465% with an interval of -1.496710% to 1.790803%. The fraction of resampled means that were positive was 0.17765 out of 0.5879, which does not represent the forecast win probability. The overlay had a probability of 0.6, with a win of 2.246256% and a loss of 2.915723%, resulting in a raw quarter Kelly criterion of 0.0201963. However, the preregistered uncertainty gate and negative stress mean reduced the authorized size to 0. The baseline raw quarter Kelly criterion was -0.139203, clipped to 0, and the stress overlay was -0.015288, also clipped to 0. A paired gross SPY window mean return was 0.098397%, while the baseline net excess return was -1.275067% with a correlation of 0.455788. An ordinary least squares regression had an intercept of -1.298713%, a beta of 1.240315, and an R-squared of 0.207742, but this is descriptive and not causal, and does not provide robust inference. The script's own three trades resulted in a 0% win rate, with an average loss of 16.380601%, including two forced liquidations and one stop, making this a selected mixed-catalyst sample, not a setup-specific p=0 forecast. No average winning payoff can be estimated from this sample. A 45.860062% loss was applied to 49.14, resulting in a loss of 22.535634, compared to an equity-risk budget of 8.5099, which is a stress test and not a promised worst case. One counterargument is that the positive overlay payoff could reflect a real edge, but this is rebutted by the fact that 10 outcomes, bootstrap spans, and costs could reverse this, and dependence may make the IID interval optimistic. Therefore, new independent prospective evidence is needed, rather than revising parameters.
A news scan found the text describing the historical payoff uncertainty of GRAL, which cannot calibrate the FDA panel. The text includes various statistics, such as a 30.7692% win rate and a -3.03624% mean return. It also mentions a 95% confidence interval of -7.58618% to 1.62648%, which allows for a positive population mean but does not establish it. The text notes that selected factors, regime clustering, and generic price events increase real uncertainty. It also references a daily regression analysis of GRAL and SPY returns from 2026, with a beta of 2.34322 and a correlation of 0.309332. Additionally, the text mentions that the return may reflect stock-specific binaries unrelated to the September panel, and that a catalyst with 0 wins out of 3 closed trades does not estimate panel win chance. The source of the text is https://runway.adityaperswal.workers.dev/data/stock-bars, and it references various files and queries, including tests.json, stats.json, and directquery_tradesfundcatalyst.
A news scan found the headline 'Galleri denominator audit: high specificity and substantial conditional false-positive burden coexist'. The audit results from October 2025 for PATHFINDER 2 initial matrix show the following numbers: true positives were 133, false positives were 83, false negatives were 196, and true negatives were 22,749, with a total of 23,161. This gives a prevalence of 1.4205%, a positive predictive value (which is the probability that a positive test result is correct) of 61.5741%, episode sensitivity (which is the ability of the test to detect cancer) of 40.4255%, and specificity (which is the ability of the test to correctly identify those without cancer) of 99.6365%. The Wilson interval for the positive predictive value at 95% is between 54.9386% and 67.8050%. By May 2026, the full results showed 173 out of 287, or 60.2787%, with a Wilson interval of 54.5176% to 65.7683%. The cohorts overlap, so they should not be treated as independent for difference testing. There were 25,578 initially enrolled versus 23,161 analyzable, which is a 9.4495% difference due to eligibility and follow-up gaps, not automatically a loss to follow-up. The source for this information is https://investors.grail.com/static-files/599b1d07-9463-4a3e-b49f-fa074ab63083 and https://grail.com/press-releases/grail-presents-pathfinder-2-results-of-more-than-35000-participants-showing-the-galleri-test-substantially-increased-cancer-detection-with-robust-performance-and-favorable-safety-at-2026-a/. If the initial sensitivity and specificity are held constant, hypothetical prevalences of 0.5%, 1%, and 2% give positive predictive values of 35.85%, 52.90%, and 69.41%, respectively. This is a Bayes illustration, not a future clinical forecast, as patient mix, test version, and follow-up may change both inputs. A specificity of 99.6% is not the probability that a positive person is cancer-free. The NHS 197146 pooled round observations show true positives of 937, false positives of 864, false negatives of 2114, and true negatives of 193,231, which are not unique people but rather repeated observations. The sponsor's positive predictive value of 52% and episode sensitivity of 30.7% address different conditionals. The first-round false positives of 303 with 54 later diagnoses (44 correct CSO) do not retroactively change the 12-month result. All-screened PF2 invasive results show 213 out of 35,335, or 0.6028%, which differs from the slide 17 conditional of 91% cancer and 50% no-cancer. Denominators are not fully reconciled, and there is no false precise count. NHS AEs recorded collection to referral, PF2 post-lock serious work-up event shows that the ascertainment window matters. The source for this information is https://investors.grail.com/static-files/995005e4-2c8e-4a02-bef0-400403856cc7. The primary p-value of 0.6234 slides versus 0.6324 release is unresolved but both are non-significant, with an incidence rate ratio of 1.03, which is the same. The current registry fixed sequence is confirmed, but the post-result version is not the original SAP. The conclusion is that uncertain utility and conditional burden cannot be compressed into a single approval score. Validity is conditional arithmetic, and soundness is bounded by cohort selection, rounded disclosures, and version differences. Facts are counts, and hypothetical prevalences are explicit assumptions.
The results of a test, called test113, show that the average return on investment was 0.297502% during the training period, with a range of -0.740855% to 1.462600% in 95% of cases. However, during the holdout period, the average return was -0.853688%, with a range of -2.351641% to 0.378149%. This test used a Python program with a random number seed of 20260905 and 20,000 random samples. The test found that the return on investment for Copart was related to the return on investment for SPY, with a beta of 0.613775, which measures how much the return on investment for Copart changes when the return on investment for SPY changes. The intercept was -0.222577%, which means that Copart's return on investment was lower than SPY's by this amount. The test also found that the return on investment for Copart was not stable and was above the maximum allowed by the capital cap. Based on these results, the decision is to reject this implementation, but not the idea that there is an anomaly in the market. The data used for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars and from notices issued by the company during certain periods.
A descriptive analysis of 18 matched 9-session event returns for CHWY was done using a statistical method called ordinary least squares, or OLS for short, which is a way to find the best fit line for a set of data. The results showed that net CHWY returns can be represented by the equation netCHWY = alpha + beta * raw SPY, where alpha is -4.33%, beta is 3.02, correlation is 0.848, and R2 is 0.720. This means that CHWY returns tend to move with the market, represented by the SPY ticker. The holdout mean CHWY returns minus SPY returns were -0.611%. No filter was used to select trades, so the relationship between CHWY and SPY returns could be due to market conditions or unusual events, rather than a consistent premium. The analysis had some limitations, including only looking at one issuer and announced dates, rather than the full market. Six out-of-sample events had a mean return of -0.153%, with a 95% confidence interval of -4.53% to 3.69%. The results from the fund's own trade ledger showed 0 wins, a mean loss of 16.38%, and a total loss of -31.94, with one rare stop and two operator exits. The conclusion is that there is no numerical evidence of a positive edge, or advantage, in trading CHWY. The validity of the results is limited by the small and selected sample size. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars and a query of trades from the catalyst fund on September 5. The distinction between rate and total is important, as a mean return of -0.153% per holdout window produces a compounded return of -1.72% across 6 events, not six times the mean return, due to the effect of volatility drag. Prior cash levels were unchanged, and only a preregistered prospective positive net sample with implementable exits would change eligibility, not the monthly shortfall. A gap loss of -6.44R versus the intended stop shows that normalized stop-risk alone can be misleading.