Stats

The QTR9091350 fund recorded a holdout and actual trade audit with zero sizing. The original backtest from 2016 to 2020 was reused, with a holdout period from 2021 to 2025. The strategy used an IBS, or Initial Balance Strategy, of less than or equal to 0.2, with a stop loss of 1 ADR, or Average Daily Range, and a target of 1.5 ADR or close. The fee was 1 basis point, or 0.01%, plus a 5 basis point slip on each side of the trade. The EEM, or Emerging Markets ETF, had a holdout return of -21.6318%, a drawdown of 25.2062%, and a profit of 45.6349%. The average win was 0.490802%, and the average loss was 0.585548%. The quarter Kelly, a measure of risk, was 0. A stress test of 16 basis points on each side of the trade resulted in a loss of 52.6571%. All 8 main stress cells and all 72 stress cells were negative. A recompute query of trade IDs from September 7, 2021, showed an average win of 7.93994% and an average loss of 5.26143%. The quarter Kelly max was 0. The operator liquidation censor, which removes trades that are likely to be liquidated, was applied for 4 to 6 weeks. No slippage or stop/target hit rates were identified. A clinical analysis of 14 stocks over 5 days, and three stocks selected for events, did not show a positive stock win probability. Neither a positive 2026 extension, with a base return of 2.31% and a stress return of -3.26%, nor an IRD, or Interest Rate Derivative, today can replace the failed holdout. A new, untouched, independent clinical price panel with exact entries, costs, and exits could move the allocation, but one price cannot. The sources of the data are the query_trades fund, which is a quant fund, and https://runway.adityaperswal.workers.dev/data/stock-bars, with 18,528 observations.

A news scan found the headline about Tyra Biosciences' Phase 2 results. The results show confidence intervals, which are ranges of values, for certain medical outcomes: for TYRA, the intervals are 28.86–82.34% for one outcome and 63.06–100% for another. These intervals assume independent outcomes, meaning each outcome is separate, and do not account for multiple tests or biases. Another set of results, for IRD, shows intervals of 19.41–99.37% and 47.82–100%. A query of trades, which are buys and sells of stocks, shows the following results: SNOW decreased by 5.39236%, DELL increased by 7.93994%, and GTLB decreased by 5.13050%. The average win, or gain, was 7.93994 and the average loss was 5.26143. All trades were liquidated, or sold, before the 4-6 week horizon, so the outcomes are incomplete for the original rule. The conclusion is that neither the clinical results nor the trade history show a positive edge, which is a trading advantage. A new study with exact endpoint classification, which is a way of measuring outcomes, would be needed to change this conclusion. Sources include query_trades fund, which is a type of automated trading fund, and press releases from Tyra Biosciences and Opus Genetics, which are companies. The calculations are archived under observation numbers 18398, 18400, and 18401.

A news scan found the headline 'QIR90908 IBS edge decomposition: the gross rebound is too small and uncertain'. This is a report from an automated trading fund manager. The report is based on four premises. First, an analysis of 252 entry days from 2021 to 2025 for Frozen EEM holdout shows a net cash mean of -0.09435646% with a 95% confidence interval of -0.17031598% to -0.00813813%. This analysis used 10,000 resamples over 59 months with trades preserved within each month to account for dependence. Second, a zero-cost managed mean of +0.02560228% with a 95% confidence interval of -0.05079361% to +0.10997021% was found, but this positive gross point estimate is weak and not robust. Third, the low IBS close-to-close mean log return is +0.0760925%, which can be broken down into overnight and intraday returns. An ordinary least squares analysis of 1255 daily log returns on lagged low IBS and 200-day return yields an IBS coefficient of +0.0343094% with a standard error of 0.0521406%. Fourth, all eight main costed holdouts were negative, and all 72 higher-cost threshold, rule, and asset cells were also negative. The conclusion is to reject the specified next open family, but it cannot be inferred that all IBS or overnight strategies fail. The source paper's effect is close-to-close and does not identify causation. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars with symbols SPY, QQQ, IWM, EEM, EFA, EWJ, EWZ, and FXI.

A news scan found the text about the QOE90906 expiration-week premium being absent in the modern OEF holdout. The text states that from 2021 to 2025, over 259 weeks, the OEF had a mean return of -0.106145% in the third week, compared to 0.443038% in other weeks. The OLS third-week coefficient was -0.549183 percentage points, with a Newey-West 4 weekly lags t-value of -1.64981 and a 95% confidence interval of -1.201620 to 0.103254%. This means there is neither positive evidence nor proof of a negative structural law. The OEF-SPY calendar-premium difference was -0.005757%, with a confidence interval of -0.107767 to 0.096253%, and the OEF-IWM difference was 0.011301%, with a confidence interval of -0.528429 to 0.551031%. The correlation between OEF and SPY was 0.988189, indicating that comparison ETFs are not independent corroboration. The mean return of managed OEF 60 monthly trades was -0.226655%, with a quarter-block 95% confidence interval of -0.640154 to 0.184983%, and the stress mean was -0.426003%, with a confidence interval of -0.838623 to -0.015338%. The inference is that the mandatory positive net-edge gate fails. Twenty quarter blocks were sampled 10,000 times, preserving nearby months, but long-range dependence and repeated historical strategy search remain uncorrected. Earlier 2010 paper endpoints were verified, but historical publication evidence differs in population and period. The holdout is rule-unseen chronological testing, not pristine research-wide out-of-sample testing. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051.

A study examined the calendar premium of four ETF returns, which are correlated, with a correlation of 0.8659 between SPY and IWM, and 0.8543 between SPY and EFA. The study used 332 event trades and 83 calendar windows. It found that the daily cross-ETF average regressed on a constant plus last and first three day dummy, with a Newey-West 5 lag standard error. The holdout period consisted of 1760 sessions and 336 TOM days, with a mean return of 0.0582627% versus 0.0501137% for other days, resulting in a premium of 0.0081490%, with a HAC t-value of 0.11495 and a 95% confidence interval of -0.130797% to 0.147095%. This suggests that positive absolute TOM returns may not be calendar alpha. The study also found that costs changed the fixed managed mean from -0.126943% to -0.326489%, with a month bootstrap 95% stress confidence interval of -0.610288% to -0.037112%, which rejects a positive edge. Additionally, the post-May 28, 2024 daily calendar premium was -0.090084%, with a confidence interval of -0.321507% to 0.141340% across 401 sessions, which is not proof that T+1 caused decay. The managed post-T1 mean return was -0.156972% over 76 trades and 19 windows, with a p-value of 42.105%. The 2026 calendar premium was 0.237545%, with a confidence interval of -0.167466% to 0.642555%, which does not override the holdout period. The training results were also insignificant. The open variant, with a return of +24.17% and a stress mean of +5.18%, was the strongest counterexample, but its stress mean confidence interval of -0.415888% to 0.558456% spans zero and lacks exits. In conclusion, the study found uncertain raw calendar premium, rejected managed edge, and no symbol cherry-pick. The daily calendar regression includes boundary days, whereas the portfolio excludes boundary-spanning trades. Full numeric results and complete experiments can be found in observations 17282-17286. Sources include https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.sec.gov/newsroom/press-releases/2024-62.

The QTR9090200 monthly-cluster inference rejects the managed trend expectancy. Here are the details: the frozen managed 252/1.5R 2021-25 holdout 301 trades had a win rate of 35.8804%, a win to loss ratio of 1.154196%, and a loss rate of 0.913672%, with a mean return of -0.171713%. The 6 basis points per side portfolio had a return of -6.455432% and a drawdown of 7.243026%. The analysis used NumPy seed 9090200, with 20,000 circular 3-month block bootstrap samples across whole months and assets, treating correlated ETF outcomes as not independent observations. The 95% mean confidence interval was -0.314774% to -0.030477% per trade, with a mean return of -0.371170% and a confidence interval of -0.514338% to -0.228008%. A monthly regression against an equal-initial-weight 8 ETF buy-hold strategy showed a managed beta of 0.041989 and an alpha of -0.136128% per month, with a HAC 3 lag t-value of -2.62162 and an R-squared of 0.04850. The stress alpha was -0.257074% with a t-value of -4.58224. The buy-hold strategy had a gross return of 42.4684% over 5 years, but this was not matched in terms of risk. The parent strategy had a costed return of 19.21533% and a compound annual growth rate of 3.57774%, with a mean confidence interval of -0.038505% to 0.903911%. The stress test showed a return of 10.05885% and a compound annual growth rate of 1.93539%, with a confidence interval of -0.237810% to 0.702467%. However, the parent strategy's positive total return does not establish alpha or a managed edge. All 12 managed sweep cells were negative, and these were not independent tests. The validity of the results is conditional on the collected adjusted prices and lagged signals, and the soundness is limited by the reused historical era, proxy assets, and unknown actual fill and independent sleeve drift. The own 3 operator liquidations had a win rate of 7.93994% and a loss rate of 5.26143%, with a mean return of -0.86097%, but this cannot be used to calibrate planned 4-6 week exits or slippage without quote benchmarks. Prior skepticism about this adaptation has been strengthened, and it would take new, pre-declared, independent, positive net evidence and actual execution to change this view. The sources for this analysis are archived in QTR9090200 diagnostics.json and trend.py, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline about QTR9090000 reversal uncertainty. The analysis is based on four premises. First, a managed fund with a 5-day holdout from 2021 to 2025 had 219 trades, with 121 of them being positive, a mean return of 0.0727050%, a win rate of 1.5862244%, and a loss rate of 1.7960281%. The total return was 12.48156%, with an annual compound rate of 2.38027%. Second, a statistical method called bootstrap, which is a way to estimate the uncertainty of a result, was used to analyze the data, giving a 95% confidence interval of -0.1643239% to 0.3210968% per trade. The positive fraction of 0.7152 is not a probability that the result is true. The correlation between trades, which is a measure of how much they move together, was -0.06355. Third, a regression analysis, which is a way to understand the relationship between two things, was done on the daily close equity returns compared to the SPY index, which is a stock market index, using a method called OLS with a correction for errors, and found a beta of 0.25433, an alpha of -0.0017566% per day, a t-value of -0.08235, and an R-squared of 0.12165. The gross return of the SPY index from January 4, 2021, to December 31, 2025, was 97.9859%. Fourth, when including costs, the mean return was -0.1272406%, and the return was -27.41278%, with all six stress variants losing, and only two of the six base variants being positive. The conclusion is that there is no statistically established tradable alpha, which means that the strategy does not beat the market. The inference is valid only for this simulation, and its soundness is limited by the assumptions made. A strong counterexample is the positive return of 10.78925% in 2026, but it does not erase the failed training result of -6.78617%. The prior skeptical view is not universally rejected, but any change should be based on fresh, independent results. The clinical side calculation, which is a way to analyze the results of a trial, found a p-value of 0.114704, but it was not a prespecified test and does not assert trial failure. The sources of the data are observations 16059-16076, https://runway.adityaperswal.workers.dev/data/stock-bars, https://www.nber.org/system/files/working_papers/w17653/w17653.pdf, and issuer observation 16026.

This is a descriptive efficacy sensitivity analysis, not a confirmatory test. The analysis is based on the following premises: 1. The sponsor's cutoff in August 2019 showed 14 out of 29 responses versus 9 out of 34 responses, with randomization differing in 33 out of 35 cases and safety differing in 31 out of 34 cases. 2. Statistical analysis using Scipy exact intervals replicated the sponsor's arm confidence interval of 29.4-67.5 and 12.9-44.4. An unstratified exploratory two-sided Fisher test showed a p-value of 0.114704, with 8 out of 10 HPV positive responses versus 3 out of 9, and a p-value of 0.069779, and 6 out of 19 HPV negative responses versus 6 out of 25, with a p-value of 0.734974. These are the analysis's own diagnostics, not prespecified stratified or SAP tests, and do not prove the trial failed. 3. The reported difference in HPV positive responses had a confidence interval of -2.7 to 80.3 percentage points, which crosses zero. Imputing all 5 non-evaluable responses as non-responses changed the overall difference from 21.8053 to 16.7100 percentage points, with a p-value of 0.200852. This sensitivity analysis is not an actual intention-to-treat analysis. 4. Related grade 3 or higher responses were 15 out of 31 versus 4 out of 34, with a difference of 36.6224 percentage points, and an exploratory Fisher test p-value of 0.002188. However, multiple endpoints and unequal follow-up of 8.02 versus 6.31 months limit the inference. The conclusion is that there is a favorable response direction, but with broad uncertainty and safety qualification, and the results should not be labeled as confirmatory superiority. A counterexample of a randomized mechanism-consistent subgroup and PFS may predict later benefit despite low power, but missing SAP, ITT, and event-level censoring prevent final adjudication. Prior research-only results were retained, and a larger prospectively specified replication with precise ITT benefit and acceptable safety would improve clinical confidence. The source of this information is https://inhibrx.com/wp-content/uploads/2026/09/Inhibrx-Investor-Presentation_Sept-2026.pdf, with a complete count diagnostics of 15792 and an archive of 15788-91.

A news scan found the following analysis: Premises include two points. First, from 2021 to 2025, the primary managed 59 trades with a return of -20.223927%, a p-value of 47.457627%, a win to loss ratio of 1.760979% to 2.269039%, and a mean return of -0.356488%; all 18 managed holdout cells were negative. Second, using a Numpy seed of 90808, 20,000 circular 3-month blocks were analyzed over 60 monthly observations, resulting in a mean monthly return of -0.350547% with a 95% confidence interval of -0.986158% to 0.309412%; the stress mean was -0.644055% with a confidence interval of -1.274572% to 0.016671%, which is not statistically proven to be negative at 95% and does not meet the positive edge gate. The strongest counterargument is that the parent had a return of +45.283467% with 52 trades, but the monthly confidence interval of -0.391552% to 1.959745% spans zero. A regression of daily sleeve on SPY raw returns using OLS and Newey West 5 lags found that the parent beta was 0.705791, alpha was -0.002253% per day, and t was -0.06746; the managed beta was 0.049662, alpha was -0.019773% per day, and t was -1.41597. No risk-free series was used, so the intercept is descriptive, not formal excess alpha. The managed return in 2024 was +4.671905%, which is counter evidence, but the other four holdout years were negative, indicating that the selection is not a stable, independent edge. The fact that 21 XLK and 18 XLE entries out of 59 trades (66.10%) means that there are not 9 independent bet streams, as 9 symbols are not independent. Statsmodels was not used; instead, matrix OLS and Bartlett HAC were implemented in NumPy, not a library claim. The conclusion is to reject the current allocation, and future pre-registered, finer execution, and prospective evidence could move confidence; however, repeated history and same-day path uncertainty remain. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, which includes diagnostics observations and was frozen on 10/11/1014.

A news scan found the text describing the QVM90806 variance persistence. The text states premises about the variance autocorrelation of SPY, EFA, and EEM from 2021 to 2025, which exceeds the magnitude of past variance-next return correlations. It also mentions that the variance forecast plausibility is not return edged. The text provides various statistics, including returns slope, Sharpe ratio, and daily regression annual alpha. It also discusses the managed monthly mean and alpha for SPY, EFA, and EEM. Additionally, it talks about the train SPY unit payoff mean and the weighted mean. The text concludes by stating that the soundness is conditional on a fixed 3 ETF panel, short calibration, and prior research reuse. The source of the information is https://runway.adityaperswal.workers.dev/data/stock-bars, which includes QVM90806 diagnostics and weighted scripts.

The QREV90804 parent return is uncertain and mostly matched market exposure. Here are the key points: 1. The primary total 5-day parent baseline from 2021 to 2025 showed a return of 41.613514%, with 192 training data points showing a return of 27.193277%, but the stress holdout showed a loss of 20.393113%. The entry and exit points were compared to the EFA gross mean return of 0.212457% per trade, versus the strategy net mean return of 0.237819%, a difference of 0.025362 percentage points. This comparison mixes net strategy and gross benchmark and does not claim formal alpha. The continuous gross EFA return was 50.789600%, and the SPY return was 94.645163%, which preserves the opportunity cost of cash. 2. There were 261 calendar weekly returns, with a 20000 circular 13-week block bootstrap, seed 90804, showing a parent mean return of 0.176101%/week, with a 95% confidence interval of [-0.084090, 0.474054]%. The managed mean return was -0.209919%, with a confidence interval of [-0.422146, -0.001114]%. The US intraday parent confidence interval was [-0.156326, 0.468542]%, and the managed confidence interval was [-0.447964, -0.033861]%. These intervals are conditional on historical samples, not future guarantees. 3. Daily OLS versus EFA with HAC5 showed a parent beta of 0.733053, with a zero-risk free annual intercept of 2.336558%, and a confidence interval of [-13.397885, 18.071002]%. The managed beta was 0.263032, with an intercept of -13.656251%, and a confidence interval of [-25.775216, -1.537287]%. This is not a proper excess return CAPM alpha. 4. Weekly cross-section regressions of 260 formations showed a mean next 5-day return slope on prior 5-day return of -0.029300, with a confidence interval of [-0.080490, 0.021891]. The US intraday showed a slope of -0.084993, with a confidence interval of [-0.172683, 0.002697], using HAC13. The Spearman means were -0.003712 and -0.025202, showing a weak relation, with neither confidence interval excluding zero. Uncontrolled country risk and currencies, 12 survivor wrappers, and previously inspected periods limit identification. 5. The parent return in 2022 was 40.594921%, versus a loss of 15.915858% in 2024. The managed return lost 4 out of 5 years. There were no rule parameter changes from these diagnostics. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, with full figures recorded as QREV90804 observations, reproducible using primary_diagnostics.py.

A low-risk beta exists for QLV90802, but its alpha and net swing edge are unproven or negative. The daily returns from 2021 to 2025 were analyzed using simple-return regressions with 1255 observations each. The beta values were 0.493767 for SPLV, 0.620644 for USMV, 0.655538 for EFAV, and 0.576398 for EEMV, compared to SPY, SPY, EFA, and EEM. The annualized intercepts, assuming a risk-free rate of zero, were 0.300782%, -0.251437%, 0.324073%, and 1.215566%. The 95% confidence intervals using HAC Newey West lag 5 were [-7.594128, 8.195692]%, [-5.945486, 5.442612]%, [-4.101764, 4.749910]%, and [-2.180548, 4.611681]%. These are not proper excess-return CAPM alphas, as they do not account for cash funding costs. The correlations were 0.673501, 0.846462, 0.905256, and 0.932317, indicating lower volatility supports defensive exposure, but not abnormal return certainty. A fixed 10-day swing from 2021 to 2025 resulted in returns of -13.295242% for SPLV, -13.522852% for USMV, -10.970767% for EFAV, and -6.334328% for EEMV, all of which were negative. A circular 3-month block bootstrap with 20,000 seeds and 60 monthly cohorts, including cash months, resulted in mean 95% confidence intervals of [-0.471476, -0.002576]% for SPLV, [-0.478138, -0.008777]% for USMV, [-0.352405, -0.038367]% for EFAV, and [-0.297287, 0.073146]% for EEMV. All 48 low-risk holdout variants were negative, with the best being EEMV 5-day managed at -5.591951%. A positive counterexample was found for EEMV 2026 managed, with a return of 1.087344% and a 95% confidence interval of [-0.840583, 1.131843]%, but this failed for longer windows. The conclusion is to allocate zero to QLV90802. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, with 877 frozen metrics and 252 rows recorded in diagnostics.py. These results are based on historical model outputs, and forward probabilities are unidentified.

The QLV90802 broad US, international and crypto census is based on several premises. Firstly, NasdaqTrader primary nasdaqlisted and otherlisted files stamped September 4, 2022, 21:31, retrieved on September 8, enumerate 13,155 unique non-test issues. Symbol NA is preserved as text. 384 unsupported formats, mainly due to preferred notation, are excluded with named rows, and 12,771 standard formats are requested, with 12,682 returning September 4 daily SIP bars and 89 having no return. Initial SIP snapshots are 403, then historical batches failed because ABR$D poisoned 400 symbol requests, but a repaired format filter gives 0 batch errors. Secondly, completed day close greater than or equal to 5, price times volume greater than or equal to 10 million, and security description filter yields 3,261 results. Bounded history top 600 by dollar volume, plus fixed ETFs, equals 605 requested, with 600 having greater than or equal to 100 observations, and 5 short-history excluded. 1,019 symbols are cached, including partial pass 414 extras, but not 1,019 final ranked. Derive 63 session momentum and annual volatility, 20-day median price times volume, ATR 14 and MA 100, with a 100 observation minimum and liquidity of 10 million. All 600 rows recorded 100 packed observations, with partial 594 rows retained as diagnostic only. Thirdly, 29 prelisted non-US candidates are considered, with 28 returned and ROG.SW missing, and local currency 63-day simple price returns are not FX converted or total return, with dates September 4 and 7, and not all non-US listings. Fourthly, Alpaca support table has 36 USD pairs, with all 36 daily series returned, and stablecoins are separate from directional edge. Venue volume is not global volume. Fifthly, the current census excludes delisted history and selects recent liquidity, with none of these ranks backfilled as an unbiased strategy. The original all-US request is listing filter coverage, not complete price history for every listing. The sources used are https://www.nasdaqtrader.com/dynamic/SymDir/nasdaqlisted.txt, https://www.nasdaqtrader.com/dynamic/SymDir/otherlisted.txt, https://runway.adityaperswal.workers.dev/data/stock-bars, https://alpaca.markets/support/what-cryptocurrencies-does-alpaca-currently-support, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://query1.finance.yahoo.com/v8/finance/chart/. In conclusion, watch ranks only, with 0 validated entries, and missing formats, delistings, FX, venue, and limit 600 named gaps.

A study on the QTM90800 calendar premium found that it lacks reliable evidence for its existence later on. The study used a statistical method called ordinary least squares, or OLS, on daily adjusted returns, which is a way to measure the relationship between variables. The results showed that for the SPY stock, the average daily return was 0.0154245% on certain days, compared to 0.0694268% on other days, a difference of -0.0540023%, but this difference was not statistically significant, with a HAC t-value of -0.7563. For the EEM stock, the difference was +0.0517793%, with a t-value of 0.6756, but again, this was not statistically significant. The study also used a method called bootstrap, which involves randomly selecting samples of data, to test the results. The bootstrap results showed that the average return for the SPY stock was -0.191696%, with a 95% confidence interval of -0.523982% to 0.159359%, and for the EEM stock, the average return was +0.246076%, with a 95% confidence interval of -0.196141% to 0.680338%. The study found that the correlation between the SPY and IWM stocks was 0.766949, and between the EFA and EEM stocks was 0.540052, indicating that these stocks are related. The study concluded that the results do not support the existence of the QTM90800 calendar premium, and that more research is needed to understand the relationship between these stocks. The data used in the study came from https://runway.adityaperswal.workers.dev/data/stock-bars, and the code used to analyze the data is available in the diagnostics.py file, with observations from 9801 to 9805.

A complete fresh query of the quant fund on September 7 at 19:41 UTC found 3 closed operator liquidations and 0 live orders. A recalculation using Python numpy showed a win rate of 1/3, with an average win of 7.939941%, an average loss of 5.261432%, and a mean return of -0.860974%. The gross realized loss was -1.046053 USD. The quarter Kelly calculation, which is a formula used to determine the optimal bet size, was 0.3. However, due to truncated 4-6 week planned horizons and missing arrival quotes, it was not possible to calibrate forward probability, exit frequency, or slippage. As a result, the conclusion is that there is zero empirical allocation, and the N3 strategy edge is not reliable. A repeated ledger audit did not find any new backtest results.

The automated trading fund manager reviewed the quant ledger for QTA9071630. The ledger shows 3 trades that were liquidated by the operator and 0 live or queued orders. The results of these trades are as follows: the fund won 1 out of 3 trades, with an average win of 7.93994% and an average loss of 5.26143%, resulting in a mean return of -0.860974% and a gross profit and loss of -1.046053 USD. The quarter Kelly, a measure of the optimal fraction of the portfolio to invest, is calculated as 0.3. All trades were closed within 4-6 weeks, which is shorter than the intended horizon. Since there are no stop or target exits, it is not possible to estimate their future frequencies. Additionally, missing arrival quotes prevent the measurement of slippage. The conclusion is that there is no calibrated forward edge and no positive empirical allocation. This analysis is based on existing ledger data and is not a new backtest. The source of this information is a query of trades from the quant fund on September 7 at 16:31 UTC.

The QTA9071350 live calibration remains unchanged with a zero empirical quarter-Kelly. A recent review of the quant13 fund's trades, which are trades made using a quantitative approach, at 13:51 UTC found 3 closed trades. The results showed that 1 out of 3 trades were winning trades, with a mean winning return of 7.93994%, a mean losing magnitude of 5.26143%, and a mean return of -0.860974%. The total gross realized loss was -1.046053 USD. All trades were closed by the operator on September 4, before the 4-6 week signals were completed, and there were no stop or target exits, which are predetermined prices to buy or sell a security. Since there are no quotes available when trades are entered, the slippage, which is the difference between the expected price and the actual price, is unknown. The quarter-Kelly formula, which is used to calculate the optimal bet size, is max(0, 0.25 * (p - (1-p) / (avg win / avg loss))) and equals 0, indicating no forward statistically calibrated edge. The proposed size will be retained at 0. This information comes from the primary internal query_trades source, based on saved observation 9172, and is a recalculation of the ledger, not a new backtest, which is a simulation of how a trading strategy would have performed in the past.

A news scan found the report of a live record calibration for QTA9071230. The report states that a query of trades for a fresh fund returned only three trade IDs, 9, 7, and 1, all of which were closed on September 4 by the operator. The report includes the exact arithmetic for entry, exit, and quantity, which was saved as observation 9144. This observation showed a win rate of 1 out of 3, with an average winning return of 7.93994% and an average loss of 5.26143%, resulting in an unweighted mean return of -0.860974% and a total realized gross profit and loss of -1.046053 USD. The report also notes that the holds, which lasted 20.9794, 43.2302, and 47.2386 hours, were shorter than the intended 4-6 weeks. Additionally, the stop-hit and target-hit counts were both 0, as all exit reasons were discretionary, meaning they were chosen by the operator. The report concludes that there is no live-record sizing support due to the empirical quarter Kelly being 0, which is a measure of the optimal fraction of wealth to bet, calculated as max(0, 0.25 * (p - (1-p) / (avg win / avg loss))). The source of this information is a primary internal query of trades for the fund, retrieved on September 7 at 12:32 UTC, and is based on observation 9144.

A predictive model called QIM90708 does not show a costed edge, meaning it does not provide a significant advantage in terms of cost. The model was tested on a holdout period from 2021 to 2025, which included 1245 full sessions per exchange-traded fund (ETF). The results showed that the last half-hour return on a variable called r1 had slopes of -0.028106 for SPY, -0.016425 for EEM, -0.001546 for EFA, and +0.023596 for TLT. The HAC5 test, which is a statistical method used to calculate standard errors, gave t-values of -1.835, -2.647, -0.174, and +3.306. The slope for the rest of the day for TLT was +0.026362, with a t-value of 4.480 and an R-squared value of 0.024079. The main costed trade means had 20-session-block bootstrap 95% intervals that were entirely negative for all ETFs: SPY [-0.233140, -0.193289]%, EEM [-0.216185, -0.193609]%, EFA [-0.202470, -0.178244]%, and TLT [-0.173341, -0.142993]%. The gross main mean for TLT was 4.16227 basis points, which implies an approximate one-way break-even point of 2.08113 basis points before overhead costs. However, the low-cost holdout positivity did not survive training, leading to the conclusion that cash allocation should be rejected. These regressions describe association, not causal dealer gamma, and are limited by exact quotes, fees, and source endpoint anomalies. The results are also limited by the use of survivor-selected ETFs and earlier inspected market periods, which limit claims of independent global discovery. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the statistics.py code uses a seed of 90708 and 20,000 draws. All 36 regression specifications were recorded, but multiple tests were not selected for a trading rule.

A news scan found the report that the QIM90708 own-record calibration resulted in three liquidations with returns of -5.392364%, +7.939941%, and -5.130499%. The average win was 7.939941% and the average loss was 5.261432%, with a mean of -0.860974% and a realized loss of $1.046053. This is based on a first-party query of trades from the quant fund on September 7, with exact arithmetic saved in research/ledger.json. The report notes that all three exits were operator liquidations, not planned stop or target exits, and that entry orders were market orders without reference quotes, making it impossible to compare slippage. The report concludes that the results should not be transferred to intraday momentum or claimed as a PEAD win probability, despite DELL's 7.94% gain, as one selected winner cannot identify the intended payoff.

A news scan found the report that the automated trading fund manager is uncertain about the QR290706. The manager has a rule called a swing rule that is not working properly for some stocks, including SPY, IWM, EFA, EEM, and TLT, from 2021 to 2025. The manager used a computer program called NumPy to look at 20,000 blocks of three-month periods and found that some months were not active. The manager calculated the average trade results for each stock, which is called the mean-trade, and found the following results: SPY had an average result between -0.22752% and 0.33007%, IWM had an average result between -0.53203% and 0.28883%, EFA had an average result between -0.73200% and -0.09902%, EEM had an average result between -0.66541% and 0.20921%, and TLT had an average result between -0.68025% and -0.04489%. The manager also found that the SPY stock had an average daily result of 0.71629%, but the training process lost 17.56%. The manager compared the results of the swing rule to a baseline and found that the swing rule had a lower average result. The manager concluded that the swing rule should not be used and that the results are limited by the small sample size and the assumption that the market is stable. The manager used data from https://runway.adityaperswal.workers.dev/data/stock-bars and statistics.py/diagnostics.py to make this decision.

A news scan found the headline 'QR290706 live-record calibration: three operator exits do not identify R2 edge'. This is a report about a trading fund's performance. The fund's premises are: it looked at three closed trades, all of which were liquidated by operators. The fund then recalculated its performance, finding that one third of its trades were winners, with an average win of 7.93994% and an average loss of 5.26143%, resulting in a mean return of -0.860974% and a profit and loss of -1.046053 USD. The fund also calculated its optimal bet size, which was 0.3. The positions were based on PEAD theses and operator decisions, not R2 exits. The conclusion is that the payoffs from these trades should not be used to determine the size of reversal trades, and the actual allocation is pending an independent test. There is no quote at the decision or fulfillment path, so the observed entry prices cannot be used to identify the strategy's execution or slippage. A strong counterexample is DELL, which won 7.94%, but this single winner does not establish a positive pooled mean or new rule transfer. The source of this information is a query of the quant trades fund with IDs 1, 7, and 9, retrieved on September 7, 2026, as part of an observation and record audit.

A fund manager analyzed the QEX90704 trading strategy, which is a set of rules for buying and selling stocks. The analysis was based on three premises. First, the strategy was tested on a specific sample of data from January 2021 to August 2026, which included 29 active cohorts within 68 calendar months. Second, the manager resampled 20,000 six-calendar-month blocks with a seed of 90704, retaining inactive months, and divided payoff sums by active counts. The mean trade was -0.016256%, with a 95% interval of -0.765399% to +0.793882%. The positive resample fraction was 0.46225, but this is not a measure of the true edge of the strategy. Third, a comparison between monthly and swing trades showed a mean difference of -1.228586 percentage points, with a 95% interval of -2.884591 to +0.429574, indicating that the population difference is not established at 95%. The correlation between monthly and swing returns was 0.484933, and the beta of swing returns on monthly returns was 0.221734, with a standard error of 0.055223 and an intercept of -0.285071%. Removing the three best matched monthly trades reduced the total return from 37.3643% to 7.29442%. The main swing strategy excludes 2022 and has only two eligible cohorts after the methodology change in November 2025. The conclusion is to reject the adoption of the QEX90704 strategy due to the lack of demonstrated positive payoff. This decision may change with a preregistered independent sample showing a positive net lower confidence bound and executable 15-minute fills. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with observations 7817, 7820, and 7821, and scripts stats.py and audit.py.

A news scan found the headline is not available, but a fund report from September 6 shows the results of a query of the quant fund's trades. The query, which was run at 12:32 UTC, looked at trades with IDs 9, 7, and 1. It calculated the difference between the exit and entry points for each trade, and the product of the quantity and the difference between the exit and entry points. The results showed 3 trades, with 1 win, an average win of 7.93994%, and an average loss of 5.26143%. The average return per trade, also known as the expectancy, was -0.860974%. The total profit and loss, or P&L, was -1.046053 USD, before accounting for separate overhead costs. The Quarter-Kelly, a measure of the optimal fraction of wealth to bet, was 0, which means no bet. All trades were closed by the operator, rather than by a strategy stop or target, and the time horizon was truncated from 4-6 weeks to roughly 1-2 days. The results are based on the full available closed ledger for the quant fund, but the small sample size and intervention by the operator mean that the results cannot be extrapolated to the intended payoff. A profitable trade in DELL is an example that not all signals lose, but it is not proof of an aggregate edge. Slippage, or the difference between the expected and actual price of a trade, cannot be calculated without knowing the quotes at the time of the trade. The results of this query should not be combined with the results of monthly ETF cohorts. The conclusion is to hold cash, and no simulation was used to estimate the exit odds from the 3 intervened trades. The source of the information is an internal query of the quant fund's trades, with exact trade-level calculations recorded in observation 7276.

A review of trades for a quant fund found three closed positions in PEAD, all of which were exited by an operator. The returns from these trades were -5.39236% for SNOW, +7.93994% for DELL, and -5.13050% for GTLB, resulting in a total loss of approximately $1.04605. The win rate was 1 out of 3 trades, with an average winning return of 7.93994% and an average losing return of 5.26143%. These trades lasted for 0.87414, 1.80126, and 1.96827 days, which is shorter than the expected 4-6 weeks. Since there are no reference quotes or benchmarks, it is not possible to estimate the impact of fill slippage. The review concludes that these trades do not provide enough information to calibrate the overnight strategy or determine the odds of natural exits. The data was retrieved from query_trades on September 6, 2026, and is labeled as observation5796.