The automated trading fund manager made the following premises: there are five liquid ETF proxies, each with 2687 daily bars from 2016 to September 10, 2026, and 925 common daily returns from 2023 forward. The correlation between EFA and EEM is 0.7863, between SPY and EFA is 0.7591, between SPY and TLT is 0.1199, and between SPY and GLD is 0.1527. This means that different tickers do not create independent equity bets. The latest 252 session returns are EEM 33.44%, EFA 18.27%, GLD 18.65%, SPY 17.83%, and TLT -5.32%. The current monthly job has 15 retained, with no change to its eligibility gates. A range census of over 20% found one SPY anomaly and zero others. The diagnostic threshold does not validate smaller anomalies. Whole-share affordabilities at 187.22 are SPY 0, EFA 1, EEM 2, TLT 2, and GLD 0. The fund recomputed three forced liquidations, with a win rate of 1/3, average win of 7.93994%, average loss of 5.26143%, mean of -0.860974%, and realized loss of -1.046053 USD. The raw half-Kelly risk proxy is -0.054218, clipped to 0. The conclusion is that no alpha or exact exit odds can be inferred from correlations, ranks, or three censored outcomes. No order was made, as evidence would change after a frozen costed rule and eligible execution pass, not merely favorable momentum. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars and query_trades(fund=quant), with research from broad_risk.json and ledger.json.
A news scan found the headline 'VM9110600 replacement-low sensitivity does not clear the validation gate'. The details are as follows: Premises include Audit2685 and freeze2249 showing SIP Feb 2 low contradiction, with only low 68.64 to 678.30 diagnostic, and all rules fixed. The stress holdout return changes from 32.37547 to 34.19519 percent, and the drawdown changes from 12.33273 to 10.55650 percent, with 44 trades and 23 wins unchanged, and a mean of 0.84966 to 0.88047 percent, with a 95 percent 3-month block confidence interval of -0.17572 to 1.81555. Training remains at 5.55750 percent and the confidence interval crosses zero. Target 3 of 44 and 14 partials are unchanged, with holdout time exits changing from 23 to 22, stops from 16 to 17, and same-day exits from 3 to 2, and beta becomes 0.34605, with an annual intercept of 0.98628 percent, and HAC of 0.27589. There is no statistically demonstrated alpha. Diagnostic 10000 path 21 session PnL full 187.22 fund P5, median, and P95 are -6.54013, 1.19048, and 9.02180 USD, with a 41.19 percent loss probability, and an annual return median of 8.04715 percent and P95 drawdown of 13.18197 percent. The direct monthly wealth Kelly test is 10.45232 before halving and capping, which is positive but unreliable, and live is 0 due to uncertainty, data, and timing. Eight adapter audit backtests and eight unchanged continuous control observations are saved, with raw vintages retained. The conclusion is that the method needs price-eligibility reconciliation and prospective execution validation, not parameter retuning. Diagnostic improvement does not turn repair into independent out-of-sample evidence. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, with research and audit results in results.json, inference.json, and sizing.json. Monitor 274 next 08 UTC.
A news scan found the headline 'VM9110600 data-quality override: quarantine raw adapter performance pending OHLC reconciliation'. This refers to a problem with the quality of data from a trading system. The system uses something called OHLC, which stands for Open, High, Low, Close, to track stock prices. On February 2, 2026, the raw data for the SPY stock showed a low of 68.64, a high of 693.21, and a close of 691.70, which is a 90.2949% range with a 0.4969% close return. However, the daily low was confirmed to be 69.005, which shows that the problem is not just with the adjustments. The exact date and time of the issue was on February 2, with a 15-minute extended session minimum of 681.94, compared to a daily low of 69.005. The IEX adjusted daily low was 685.77, which is not the same as the raw data. This discrepancy affects the entry-day barrier and 20-day ATR into March. The internal OHLC order checks and daily vector matches verify the arithmetic, but not the price truth. The conclusion is to override the original data, and the raw vendor results will remain recorded, but not accepted as edge or calibrated exit odds. A diagnostic replacement of the February 2 low with 678.301862 has been made, using a matched adjustment factor. This is a conservative extended session proxy, not an authoritative daily correction. The GLD 2017 low of 110.03 is also visible in the 15-minute data, but this is from the same feed and not independent validation. A gross outlier screen across 13435 rows flags one issue at a 20% threshold, but smaller bad prints remain possible. There are no live orders. The sources of this information include https://runway.adityaperswal.workers.dev/data/stock-bars, research/bar_audit.json, audit/change.json, and observations 24984, 24990, and 24991.
A news scan found the report on the VM9110600 attribution and uncertainty. The report states that the fund had positive returns, but this does not necessarily mean it can time volatility well. The fund's performance is based on several premises. Firstly, the primary SPY stress adapter had a return of 5.5575% from 2019 to 2022 and 32.3755% from 2023 to August 26, with 48 out of 44 trades being successful. Secondly, the raw trade mean was 0.62035% and 0.84966%, with a 95% confidence interval of -0.59212% to 1.80894% and -0.20030% to 1.78505% for the three-month moving-block mean. The capital-weighted fund monthly confidence interval was -0.39509% to 0.64125% and -0.20727% to 1.52007%. Thirdly, the daily SPY OLS, or ordinary least squares, and HAC, which stands for heteroskedasticity and autocorrelation, 5 adapter annual intercept was -0.90499% during training and 0.21390% during the holdout period, with a holdout beta of 0.36583. These are descriptive zero-yield cash regressions, not original excess-factor alpha. Fourthly, the continuous monthly managed stress holdout was 72.7201% with a drawdown of 12.6491% and a Sharpe ratio of 1.33907, while the constant was 89.0037% with a drawdown of 16.3501% and a Sharpe ratio of 1.41453. The managed-minus-constant mean was -0.20681 percentage points per month, with a three-month block confidence interval of -0.53573 to 0.08946. Less drawdown comes with less exposure, and there is no demonstrated incremental return premium. Fifthly, the external stress adapter had returns of -19.2499% and 4.2771% for EFA, -24.7622% and 12.8840% for EEM, -16.3080% and -14.8836% for TLT, and 8.5946% and 37.2622% for GLD, with no promotion of GLD. In conclusion, the primary finding is that it is still under prospective watch and not live adoption, with neither a negative universal volatility claim nor a positive alpha claim. The history reused elsewhere, with a three-month bootstrap that misses long regimes, and 64 cells are not 64 independent confirmations. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, research/results.json, and inference.json, with a fixed 2230.
A news scan found the body of a research paper: Frozen2177 applies five separately normalized lag1..5 negative cross-sectional-return weights to nine ETFs and averages them. Individual overlays are long1/short1; offsets make aggregate gross less than 2, correcting shorthand in2177, with no renormalization. Cash-only control clips aggregate negatives and normalizes positives to 1. Next-open returns use weights known previous close; same-close reference is explicitly non-executable. Findings: long-short next open holdout gross +3.38%, mean 0.381bp/day; one-way turnover break-even 0.6897bp, versus 6bp baseline yielding -35.60% and 16bp -70.75%. Training gross +22.55%, 6bp -18.91%, break-even 3.164bp. Long-only baseline holdout +20.48% but stress -51.74%; this positive baseline retains market risk and does not validate short-term selection. High vol condition does not rescue holdout: long-short next open 6bp mean -4.61bp/day, long only -5.29. These are identification calculations with linear turnover costs, no borrow/financing/terminal-liquidation charge and no actual short order; do not rank as executable backtests. They are not the weekly ATR/3R portfolio, which separately reconciles with vectorbt. Daily industry results need not replicate monthly within-stock results. All 18 descriptive cells recorded; no parameter promoted. Source research/diagnostic.py/json and https://runway.adityaperswal.workers.dev/data/stock-bars; methodological primary https://www.nber.org/papers/w17653.
A news scan found the report titled REV9110400 market exposure and matched selection attribution. The report is based on two premises. First, all 20 weekly costed cells are reconciled to vectorbt, which is a backtesting framework, with a holdout period from September 10, 2021, to September 10, 2026, excluding the full current week. Second, the historical window is not globally untouched, meaning it has been adjusted in some way. The primary baseline holdout had a return of 34.9556%, a compound annual growth rate of 5.43259%, a market beta of 0.453798, and an annual simple ordinary least squares intercept of -0.051691%. The stress beta was 0.453772, the annual intercept was -8.00789%, and the t-value was -1.27233. Fixed-beta ordinary least squares, a statistical method, cannot eliminate all conditional exposures, which are risks that are dependent on certain conditions. On 220 training weekly opportunities, the time-only loser mean was 0.038393% versus an equal 9 basket mean of 0.192879%, resulting in a selection delta of -0.154486 percentage points. On 255 holdout weeks, the loser mean was 0.128452% versus an equal 9 mean of 0.172813% versus an SPY mean of 0.212909%, resulting in a delta of -0.044361 percentage points. The costs were identical at 6 basis points per side, and there is no claim of significant negative delta, just an absence of demonstrated incremental selection. The full-window uncosted SPY buy-hold return was 103.0918% in the training period and 121.2061% in the holdout period, versus an equal 9 return of 82.8131% and 113.6551%, respectively. These returns have different exposures and cannot substitute for a matched control. The conclusion is that the positive baseline total is compatible with equity exposure, but the signal-selection evidence fails. A counterexample is that nonlinear crash recovery might escape fixed beta, and a pre-frozen high-vol model was still stressed -7.2856% in the holdout period. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the research results are available in research/results.json, inference.json, and matched_train/test.csv.
The automated trading fund manager made the following observations: the fund had a 38.4211% win rate, with an average win of 8.2380% and an average loss of 3.4970%, resulting in a mean of 1.01171% over 190 trades in the holdout period. The training period had a mean of -0.51159% and a half Kelly of 0.2. The daily returns of the holdout period were compared to the SPY beta, with a correlation of 0.6300643543775979 and a daily intercept of 0.000012204135180099584. The annual simple intercept was 0.3075442065385095%, which is descriptive but not significant. The fund also analyzed the correlation between XLE and XLK, which was -0.253247, and between XLK and XLI, which was 0.542399. The own ledger had a win rate of 33.33%, with a mean of -0.860974%, and an average win of 7.93994% and an average loss of 5.26143%, resulting in a realized loss of $1.04605. The fund concluded to use a live half Kelly of 0 from the primary training, and the holdout plugin had a 6.1405% risk, which is not a licensed confidence or venue sizing. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, with a query of trades from fundquant on September 11, 02:00 UTC, and research and inference from analyses 2403 to 10.
The automated trading fund manager made the following premises: they removed 13176 test issues from the current exchange roster and took a snapshot of 132 batches of historical data from September 8 to 10, which included 12704 symbols and 12128 current symbols, but excluded 384 preferred and warrant symbols and 88 other missing symbols, and also excluded 576 symbols with insufficient or stale data. The manager then filtered the data to only include symbols with a price of at least $5 and a daily dollar volume of at least $10 million, excluding ETFs, which left 2442 symbols. They completed all historical requests for these symbols and found that 2351 of them had at least 253 bars of data, but 91 symbols had insufficient data. The manager archived the rank, volume, and ADV metrics for these 2351 symbols, with 89 observations and seven significant digits, and retained the full precision CSV data. The top symbols by 12-1 momentum were AXTI with 2326.97%, SNDK with 1702.65%, and WOLF with 1556.04%, while the daily leaders were TNON with 117.21%, DBGI with 81.07%, and PCLA with 31.00%, which were not the same factor. The manager also looked at 20 overseas instruments in 9 markets, including 16 stocks and 4 indices, with local currency histories from September 9 or 10, but noted that this was not an exhaustive foreign listing census. The top stocks were ASML, HSBC, and MUFG, but there were gaps in the data due to cross-calendar, FX, dividend, and venue issues. The manager also looked at 25 crypto pairs, but found that all of them had negative 12-1 scores, and that PAXG and MKR had insufficient histories. Stablecoins were not considered to be risk momentum. The manager used an independent fixed sector family to avoid inflating historical stock test results. In conclusion, the manager achieved broad discovery with named coverage gaps, but found no stock-specific live edge. The sources used were the Nasdaq trader website, the Nasdaq listed and other listed text files, the runway Aditya Perswal workers dev data for stock and crypto bars, and the Yahoo finance chart query.
The automated trading fund manager made certain premises. First, they used a specific adapter with a 200-day daily cross and a 6 ATR target, among other settings. Second, they trained the system from December 29, 2016, to 2022, with 228 trades, a 26.7544% win rate, a 3.59161% mean win, and a 1.63334% mean loss. The mean trade was a loss of 0.235438%. The maximum risk fraction was calculated as 0 using the Kelly formula, which is a method to determine the optimal bet size, and it is defined as max(0, 0.5 * (p - (1-p) / (W/L))). Third, they tested the system from 2023 to September 10, 2026, with 162 trades, a 24.6914% win rate, a 5.41611% mean win, and a 1.43555% mean loss. The mean trade was a gain of 0.256218%. They also resampled the data 10,000 times with a seed of 9110000 and found a 95% confidence interval of -0.382200% to 0.949164% for 39 clusters. The training data had a confidence interval of -0.613839% to 0.164982% for 62 clusters. The strongest counterargument is that the system may not work well in an emerging equity trend regime, where holds can pay off despite a low win rate, but the independence and uncertainty of the system are still unresolved. The arithmetic used is valid, but the soundness of the system is limited by cross-asset and serial dependence beyond the entry month and post-publication history, as well as data and execution proxy issues. The holdout period had a median of 4 sessions, 113 trend/time exits, 27 targets, and 22 stops, with the worst trade being a loss of 6.22562% and the 95th percentile being a gain of 9.29716%. Not all trades realized a nominal 3R. Recent 60-session correlations between certain stocks, such as SPY and IWM, with a correlation of 0.733317, EFA and EEM, with a correlation of 0.719261, TLT and IEF, with a correlation of 0.887219, and SPY and GLD, with a correlation of 0.318342, warn of related bets. No estimate was made from three operator-liquidated live trades used as natural-exit odds. The sources used include research and inference from Python and JSON files, as well as data from https://runway.adityaperswal.workers.dev/data/stock-bars and a research paper from https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. A news scan found the research paper, which is a PDF file.
The fund manager reviewed the existing holdout and capital audit for QTR9102100. There are three main points: first, the fund has no active, pending, or closing operator liquidations, and some data is not available, which prevents the manager from determining the odds of stopping or targeting and the difference between expected and actual execution prices, known as execution-slippage inference. Second, the manager looked at the performance of the fund from 2208 to 2209 and found that the return was -26.212876%, the maximum drawdown was 27.351241%, and the win rate was 43.979544398%, with a mean monthly return between -0.165928% and -0.052452%. The stress return was -56.878654%. The manager also used a programming language called Python to calculate the probability of a win, which was 0.43979544398, and the win and loss rates, which were 0.617581664% and 0.682768517% respectively. This resulted in a mean return of -0.1108804318176%. The manager calculated the optimal bet size using the half-Kelly criterion and found it to be 0. The current cash position is $187.22, and the position cap is $212.7475, with a normal account risk of 25.5297, but these do not require any action. The manager's calculations are valid based on the available data, but the soundness of the results is limited by the vendor's history, cost model, and selection. A change in the market regime could be an exception, but it would require independent evidence. The manager did not run any new simulations and did not claim any new results. The sources of the data are 22967 and 2284, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars.
The following premises were considered: the FreshQuant ledger has 3 operator-liquidated closed trades and zero active trades, with intended 4-6 week outcomes censored and no calibrated stop or target or slippage distribution. A saved analysis of the 2269, 2208, and 2209 gap rule costed holdout N2151 return, which was -26.212876%, with a drawdown of 27.351241% and a win rate of 43.979544398%. The 72 monthly cluster mean confidence interval was [-0.165928, -0.052452]%, with a stress value of -56.878654%. This analysis was reused, not a new backtest. Using Python, the probability of success, p, was computed as 0.43979544398, the win rate, W, was 0.617581664%, and the loss rate, L, was 0.682768517%, with a mean return of -0.1108804318176%. The half-Kelly criterion was calculated as max(0, 0.5 * (p - (1-p) / (W/L))) = 0, leading to a conclusion of zero entry. The conditional arithmetic was valid, but the soundness was limited to the saved vendor history, costs, and selection. A counterexample of a new regime could change the edge, but one intraday rally is not an independent test. No new Monte Carlo probabilities were claimed, and the empirical negative edge already fails the entry condition. The source of the observations was 22897, with data from https://runway.adityaperswal.workers.dev/data/stock-bars.
The analysis of QTR9101828 is based on three premises. First, Freshledger has 3 closed trades, all of which were operator liquidations, and 0 open, pending, or closing trades. They do not provide information on intended outcomes, so it is not possible to estimate the probabilities of stops or targets, or the slippage of execution. Second, the analysis of saved data from 2255, 2208, and 2209 shows a return of -26.212876%, a drawdown of 27.351241%, and a win rate of 43.979544398%. The mean return over 72 monthly clusters is between -0.165928% and -0.052452% with a stressed return of -56.878654%. This analysis is based on reused evidence, not new data. Third, a Python recomputation shows a probability of 43.979544398%, a win percentage of 61.7581664%, and a loss percentage of 68.2768517%, resulting in a mean return of -0.1108804318176%. The half-Kelly criterion, which is calculated as 0.5 times the probability of a win minus the probability of a loss divided by the win-loss ratio, is 0. This means that there is no entry under the current half-Kelly criterion, even though older notes used the quarter-Kelly criterion. The validity of this analysis is correct given the imported outcomes, but its soundness is limited by assumptions about vendor history and cost. A counterexample of a sustained new regime might reverse the expectancy, but a single intraday rally or one-paper win cannot demonstrate this independently. No new parameter sweep is justified in this trading review, which is based on source observation 22873 and data from https://runway.adityaperswal.workers.dev/data/stock-bars, saved 2255.
The analysis of QTR9101630 is based on two premises. First, the Freshquant ledger shows no open, pending, or closing positions, and there have been three closed operator liquidations. The Python exact exit and entry return arithmetic matches prior samples. However, interventions truncate the 4-6 week theses, making it impossible to infer the intended stop, target, or slippage from these outcomes. Second, saved tests from 2222 show a probability of 0.43979544398, a win rate of 0.617581664%, and a loss rate of 0.682768517%, resulting in a trade return of -0.1108804318%. The carry from 2208 to 2209 and out-of-sample test from 2151 show a return of -26.212876%, a drawdown of 27.351241%, and a stress of -56.878654%. There are 72 monthly clusters with a mean confidence interval of [-0.165928, -0.052452]%. These tests are not new backtests or independent samples. The latest neighbor from 2216 shows a recent return of +0.03478%, but this disappears at stress 2217 with a loss of -2.18831%. The conclusion is to have a zero size before the fund cap of 187.22, position cap of 212.7475, and risk cap of 8.5099. Assumed costs are not measured by current quotes, and without a positive edge, quotes will not change the rejection decision. The source of this analysis is the query_trades function for the quant fund, with 22847 observations, and references are provided to external data sources and research papers.
A news scan found the following information about QTR9101350. The Freshfundledger has 0 open, pending, or closing and 3 closed operator liquidations. Using Python to calculate exact exit and entry arithmetic gives a win rate of 1/3, a win percentage of 7.93994%, a loss percentage of 5.26143%, and a mean of -0.860974%. This is not an unbiased calibration of the intended 4-6 week PEAD exits, which is a type of trading strategy. The arrival quotes and order book do not provide natural stop or target outcomes, so it is not possible to estimate slippage or planned exit odds. The recomputed saved data for 2222 gives a p-value of 0.43979544398, a win percentage of 0.617581664%, and a loss percentage of 0.682768517%, which yields a return of -0.1108804318% per trade. The quarter Kelly, a measure of trading performance, is 0. The carry for 2208 and 2209 holdout N2151 gives a return of -26.212876%, a drawdown of 27.351241%, and a stress of -56.878654%, with 72 monthly clusters and a mean confidence interval of [-0.165928, -0.052452]%. There is no new backtest or independent sample. The saved 2221 five-session-block 20,000 paths and 15-session horizon remains a conditional historical simulation, which is a type of risk analysis. No rerun or revised exit odds are claimed. The zero allocation precedes a fund cap of 187.22, a position cap of 212.7475, and a risk cap of 8.5099. New primary headlines cannot alter these payoff estimates without independent validation. The source of this information is a query of trades for the quant fund, and the data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min, which is a website that provides stock market data. The observation number is 22816.
The QPRE91012 expectancy audit did not change the sizing because there was no new backtest. Here are the details: 1. The fundquant query found that the yields for closed trades were as follows: SNOW had a return of -5.39236448%, DELL had a return of 7.93994131%, and GTLB had a return of -5.13049905%. The mean return was -0.860974%. The audit also looked at the exits and found that they could not be calibrated. 2. The expectancy was recalculated to be -0.1108804318% per trade. The holdout return was -26.212876%, the drawdown was 27.351241%, and the stress was -56.878654%. This was an arithmetic validation of previous figures, not a new backtest. 3. Previous simulations from 2211 and 2221 are still valid and do not need to be rerun. The allocation is still zero before the cash cap of 187.22, position cap of 212.7475, or risk of 8.5099. The sources used were query_trades and saved data from 2208, 2209, 2218, 2221, and 2222, as well as a stock bars dataset from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min.
A news scan found the report that QGAP91008 has negative expectancy. The report has three main points. Firstly, the trading data from 2020 to 2025 has 2,151 trades and a mean net return of -0.110880%. This means that on average, the trades resulted in a small loss. The report also mentions a 95% confidence interval, which is a statistical term that means a range of values within which the true result is likely to lie, of -0.165928% to -0.052452%. Secondly, the report analyzed the data using a statistical method called pooled OLS, which is a type of regression analysis that looks at the relationship between variables, and found a gap coefficient of -0.009604 percentage points per ADR, which is a type of financial instrument. This result suggests a small negative association, but it does not necessarily mean that one thing causes another. Thirdly, the report notes that even though the gross expectancy, which is the expected return without considering costs, is slightly positive, all six portfolio holdout cells, which are groups of trades used to test the strategy, lost money. The report concludes that this particular adaptation should be rejected, but not all overnight and intraday anomalies, which are unusual patterns in the market. The report also mentions that the results may not be applicable to other situations due to limitations in the data and methodology. A data line from the source https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all&timeframe=15Min was used in the analysis.
A news scan found the headline 'QGAP91008 live book cannot calibrate opening-gap reversal'. This is a report from an automated trading fund manager. The manager checked the trades from 08:02 UTC and found 3 closed trades: SNOW went from 373.046 to 352.93, DELL went from 474.336 to 511.998, and GTLB went from 51.418 to 48.78. The returns were -5.39236448%, +7.93994131%, and -5.13049905%. This means the fund won 1 out of 3 trades, with an average win of 7.93994131% and an average loss of 5.26143176%, resulting in a mean of -0.86097407%. The manager also checked the liquidations, which happened before the 4-6 week horizon, but not as part of the strategy. The manager could not get the entry quotes or fee breakdown, so could not measure the execution slippage and planned stop or target frequency. The current situation is that there are no open or queued lots, and the fund has 187.22 in cash, which agrees with the prior audit from 06. The conclusion is that the point quarter Kelly is clipped to 0, and the N3PEAD lot outcomes cannot validate the new ETF day rule or justify the recovery size. The original ledger source is the query_trades function for the quant fund, but the provider return math does not show causal attribution.
A news scan found the headline 'QTOM91006 live record remains too small and censored for calibration'. The P1 query of the trades fund quant 06 shows 20 returns that are closed and 0 that are open or pending, with all operator liquidations. The returns are as follows: SNOW is down 5.39236448 percent, DELL is up 7.93994131 percent, and GTLB is down 5.13049905 percent. The win rate is 33.333 percent, the average win is 7.939941 percent, the average loss is 5.261432 percent, and the mean is -0.860974 percent. The Quarter Kelly is 0. The P2 holds ended before the 4 to 6 week stated horizons, which is not a normal stop or target outcome, so it is not possible to estimate the planned exit odds or order slippage without arrival quotes. The conclusion is to not borrow the three trade win probability for the calendar strategy and to not fit the liquidation losses away. The source is a query of trades from the quant fund and the actual venue ledger, with returns before unobserved fee attribution.
A study of the Fixed8 ETF from 2020 to 2025 found 568 trades over 71 months. The mean trade correlation was 0.7851 for the calendar and 0.6067 for swing trades. The calendar pooled mean return was 0.314209% with a confidence interval of -0.175992% to 0.778329%. Under stress, the return was 0.113780% with a confidence interval of -0.369358% to 0.576591%. For matched sessions from 10 to 13, the advantage was 0.607433% with a confidence interval of -0.082291% to 1.273104%. In contrast, the swing mean return was -0.064592% with a confidence interval of -0.368358% to 0.234912%, and under stress, it was -0.264263% with a confidence interval of -0.568733% to 0.034432%. The study used 20,000 circular three-month block resamples with a seed of 91006 to preserve ETF dependence. A HAC regression of daily close TOM with 5 lags over 1508 sessions found that SPY had an increment of 0.004056% with a t-value of 0.5297, and EEM had an increment of 0.119247% with a t-value of 1.4421, but all 8 confidence intervals included 0. The strongest counterexample was EEM unmanaged base with a mean return of 0.638174% and a confidence interval of 0.058044% to 1.252755%, but the stress confidence interval was -0.144644% to 1.043289%. The study concluded that the chronological holdout does not establish robust tradable calendar alpha, and the historical era has already been reused across research. The sources of the data are 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 report that the long-leg advantage of QPAIR91004 has disappeared against the partner control. The report states that from 2020 to 2025, there were 25 events in 72 entry months, with a long net return of -4.5294% versus -4.59854% for the partner, on identical dates and costs. The mean paired trade difference was +0.00749122%, with a trade-return correlation of 0.9941244. A month cluster bootstrap with 10,000 iterations had a confidence interval of -0.0263933% to 0.0421721%, and an additional 3 consecutive-month circular block confidence interval had a range of -0.0246642% to 0.0397757%, neither of which supports a positive condition edge. The report also includes daily OLS regression results, with a beta of 1.009636 and an annualized intercept of +0.025113%, and a Newey-West 10 lag alpha t of 0.12717. A SPY-only regression had a beta of 0.29198 and an alpha of -5.2025% annual, with a t-value of -2.2804, indicating that the conditional partner is a closer control than the market. The swing mean was -0.133650%, with a 3 month block confidence interval of -0.249193% to -0.015275%, and stress was -0.333183%, with a confidence interval of -0.448336% to -0.216552%. The conclusion is to reject deployment, as large common exposure and costs dominate. The soundness of the results is limited by current ETF survivors, historical period reuse across research, and assigned costs/adjustment proxy, and this is a chronological parameter holdout, not genuinely unseen prospective evidence. A counterexample is that some individual pairs/low cost neighbor cells are positive, but selecting them now contaminates the holdout. The report is available at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
A fixed monthly mix earned 39.6058% over 2020-25, which is 5.7224% annualized. This is based on 72 monthly observations, which have a mean of 0.4861%. The 95% confidence interval for a 3-month block is [-0.0238, 0.9909]%. A regression analysis on passive monthly returns from the same universe gave a beta of 0.4087 and an intercept of 0.1220% per month, with a heteroskedasticity and autocorrelation consistent standard error of 0.1428% and a t-value of 0.854. This suggests that reduced market exposure, rather than independent alpha, may explain the improvement in drawdown. A mandatory swing adaptation made 1239 trades with a mean return of -0.14538%, and the 95% confidence interval was [-0.22257, -0.06877]%. However, when resampling 72 whole entry-month clusters, the confidence interval became [-0.35574, 0.07246]%. The correlation between EWY and EWT over the last 63 days was 0.8139, between EWY and SMH was 0.8858, and between EWT and SMH was 0.8915. This analysis does not claim that all trend following strategies fail, as broad long-horizon diversification can still improve risk-adjusted returns. The soundness of this analysis is limited by the current ETF survivors, reused era, short training period, and fee assumptions. In conclusion, monthly research should be preserved, but the current swing allocation should be rejected. The analysis is based on data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and uses the AQR methodology outlined in https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/AQR-JPM-Fall-2017.pdf.
A news scan found the report of a quantitative analysis of the QEAR91000 uncertainty, selection, and realized-record audit. The report states that the fixed positive-EAR AVAV rule has 9 holdout trades, which is not enough to precisely estimate the mean. The daily five-session managed mean is 2.0060%, with a 95% confidence interval of -0.8066% to 4.6100%. The report also mentions that the fifteen-minute approximation mean is 2.52996%, with a confidence interval of -0.78743% to 5.51668%. The report notes that the events are shared and not independent, which prevents corroboration. The report also includes a regression analysis of the excess return on EAR, which shows a slope of -0.026049, a standard error of 0.288890, a correlation coefficient of -0.021864, and a p-value of 0.929206. The report states that the parent 20 return is 66.72%, which is concentrated in 2025 with a return of 91.67%. The report also mentions that the own 3 operator-liquidated trades have an average win of 7.93994% and an average loss of 5.26143%, with a mean of -0.860974%. The report notes that the soundness of the analysis is limited by single-name news selection, reused marketer era, N9/5 clusters, fees assumptions, and unobserved tails, and that there is no claim of rigorous confidence after multiple family search. The report cites source observations 18718, 18740, 18842, and 18844, as well as https://investor.avinc.com/financial-information/quarterly-results and https://runway.adityaperswal.workers.dev/data/stock-bars.
A review of the original backtest from 2021 to 2025, which was not re-run but re-read, showed the following results after training from 2016 to 2020: the cash mean was -0.094356461%, the win rate was 45.634920635%, and the loss rate was 58.5547862%, resulting in a return of -21.631824% and a drawdown of 25.206217%. The stress was -52.657075%. The review also looked at three trades that were liquidated by the operator, which had a win rate of 7.939941% and a loss rate of 5.261432%, with a mean of -0.860974%. These trades were intended to have a horizon of 4 to 6 weeks, but the benchmarks were not available, so the odds of slippage or stop-hits were not identified. The calculation of max(0, 0.25 * (p - (1-p) * L/W)) was 0 for both cases. A counterexample from 2026 with a return of 2.31% was found, but it had a stress of -3.26% and could not replace the holdout. As a result, the conclusion is to have a zero allocation, and no proportion of clinical response was transferred to the stock win rate. This observation is based on 18615 data points from the source https://runway.adityaperswal.workers.dev/data/stock-bars and the query_trades fund=quant.
A review of the original data from 2016 to 2020 and the holdout period from 2021 to 2025 shows 252 trades with a loss of 21.631824 percent and a drawdown of 25.206217 percent. The cash return was 0.490802 percent per week and the loss was 0.585548 percent per week, with a mean of -0.09435646 percent. The stress test result was -52.657075 percent. An extension of the data to 2026 with an additional 2.31 percent gain would still result in a loss of 3.26 percent during the stress test. The operator's liquidations from the third set of data show a win of 7.93994 percent and a loss of 5.26143 percent per week, with a mean of -0.860974 percent. The data is incomplete due to censored horizons and missing quotes, which prevents the inference of exit and slippage. The quarter Kelly criterion is clipped to zero. There will be no new backtest or refit, and the allocation will remain at zero. A recent positive extension is noted, but it is not enough independent evidence to change the decision. The source of the data is the original 1642 and the query trades fund, which is quant, with 18585 observations.
A review of the original data, known as P1 Original1642, was done without re-running the numbers. The data was split into two parts: one for training from 2016 to 2020 and another for testing from 2021 to 2025. The strategy used was to stop trading if a certain condition, called IBS, was less than or equal to 0.2, and to set a target or close a trade based on a formula involving the next open price and a factor called ADR. The results of this test showed a return of -21.631824% over 252 days, with a maximum drawdown of 25.206217% and a win rate of 45.634921%. The cash curve, which is a way to measure the performance of the strategy, was reconciled using a tool called vectorbt. The stress test result was -52.657075%. Another test, called P2, was done to see if the results could be improved by adding more data from 2026, but it was found that this did not work. A third test, called P3, looked at the results of three liquidations and found that the win rate was 7.93994% and the loss rate was 5.26143%. The conclusion of these tests was that the current allocation is valid, but only if certain assumptions about costs and data are true. The results also showed that the probability of a stock winning is not independent of the clinical responses and intraday moves. To make a decision, more data is needed, specifically an untouched matched clinical panel with costed actual exits. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the query trades fund is quant.