Stats

A news scan found the text describing the performance of a trading fund. The fund's mean net account trade was 0.8849% for the training period and 1.4939% for the heldout period. The training period had 30 trades and the heldout period had 11 trades. The results show a positive geometric test, which is evidence for a watch, but with uncertainty. The fund had 13 stop trades, 5 gap stop trades, 7 target trades, 1 gap target trade, and 4 time trades during the training period. During the heldout period, the fund had 5 stop trades, 2 gap stop trades, 1 target trade, 2 gap target trades, and 1 time trade. The correlation between CNQ and XLE was 0.7702 and between CNQ and FRO was 0.1724. A statistical model of CNQ using XLE and SPY gave a beta of 1.0133 for XLE and -0.0273 for SPY, with an R2 of 0.5934. The daily intercept was 2.9233 basis points. The fund had two trades, FRO and STNG, which were liquidated by the operator on September 4. The FRO trade had a return of 3.8052% and the STNG trade had a return of 0.7523%. The source of the data is a query of trades from a fund, specifically the supply fund.

The automated trading fund manager reported the following results: 1. The mean return of 31 trades was 1.4456%, with a 95% confidence interval of -2.3253% to 5.6540% based on 20,000 bootstrap samples. The holdout set of 16 trades had a mean return of 3.50445%, with a confidence interval of -1.93285% to 9.08670%. Although the confidence intervals cross zero, the positive geometric results suggest that the strategy is worth watching. 2. A comparison of daily and intraday results over 1429 sessions found a median absolute close difference of 2.2852 basis points, with the 90th percentile at 11.2613 and the 99th percentile at 18.5117. The largest difference of 379.10 basis points occurred on March 8, 2022, which had incomplete data. Three training sessions were missing 25 samples each, but the actual next-day stop remained in place. 3. The fund's own record shows only two winning trades, with a total return of $4.977287 and a mean return of 2.278710%. The source of the data is https://runway.adityaperswal.workers.dev, and the bar data is from https://runway.adityaperswal.workers.dev/data/stock-bars. A news scan found the headline was not available, but the prior moves were adjusted from a daily watch to a positive raw sampled watch. New implementation losses or changes to paid net rates could lower confidence, and dividend accounting and event-specific economics are still unresolved.

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

A fund manager analyzed the stock FTK. The manager used daily stock prices from September 11 and earlier, and also used prices for the stocks SPY and ACDC. The manager found that on September 9, FTK's price changed by 14.402954% while SPY's price changed by -0.464776%. This difference of 15.659472 percentage points is a residual, which is a measure of how much a stock's price changed compared to what was expected. The manager also looked at how closely the prices of FTK and other stocks were related over the last 60 days, and found correlations of 0.406972 with ACDC and 0.320440 with SPY. The manager then looked at the results of some trades, but these trades were not reliable for estimating losses. The manager also tested a strategy using FTK's prices over the last 20 days, and found that it would have lost an average of 6.461891% per trade. The manager decided not to use this strategy because it did not seem to work well.

A news scan found the headline is not available, but a data analysis was performed. The analysis used a source from https://runway.adityaperswal.workers.dev/data/stock-bars. It applied a statistical method called Ordinary Least Squares, or OLS, to daily returns of HWM and SPY over 60 sessions ending on September 4 before September 8. The results showed a beta of 0.867538 and an alpha of -0.005349% per day. On September 8, HWM had a return of -10.69927%, SPY had a return of -0.549215%, and the residual was -10.21746 percentage points. This residual does not seem to be caused by a specific event called GEdeal or a forecast of a subsequent reversal. Additionally, the analysis found that the returns of FRO and STNG over the last 60 days had a correlation of 0.778, and the returns of LPG and FRO had a correlation of 0.690, indicating that shipping positions share similar risks. A query of trades found that the actual returns of FRO and STNG were 3.80516% and 0.75226%, respectively, with a mean return of 2.27871% and a win rate of 100%. However, this assumes that these positions are independent, which they are not. Since there are no losing trades in the sample, it is not possible to identify the average loss or edge. The original targets were 2.61446R and 2.15126R, but these are below the current 3R policy, and the positions have already been closed. In conclusion, the analysis did not find any evidence of a causal relationship between the event and the returns, and the 100% win rate is likely due to chance rather than any underlying edge. The analysis is based on records 25267 and two queried lots.

A news scan found the report 'R91108 TSM beta and tanker concentration: descriptive risk before CPI'. Premises are as follows: seven names have matched 1513 SIP daily bars through September 10. The last 60 log returns regress TSM on SPY with an intercept, which is a statistical method to measure the relationship between two variables, with a beta of 2.37655 and a correlation of 0.62808; the HAC, or Heteroskedasticity and Autocorrelation Consistent, 5 confidence interval is between 1.62562 and 3.12748. TSM daily mean is -0.05126%, standard deviation is 2.81771%, and the range is from -7.23683 to 7.36599%. The correlation between FRO and STNG is 0.77894, and between LPG and FRO is 0.68235, indicating that tanker positions are joint-risk exposure. The correlation between TSM and LPG is -0.0218, which is not stress independence, meaning they do not move independently during times of stress. In conclusion, macro reaction could dominate company news, but there is no forecast for the direction of CPI returns. The statistics are selected from a recent window and are not causal, and no filter was adopted. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, with a query of trades for the fund with IDs 3 and 6, and there are no orders.

A news scan found the analysis of tanker correlation. The analysis used SIP adjusted daily simple returns from 2021 to 2024 for training and compared them to returns from January 2025 to September 10, 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG,FRO,STNG,SPY&timeframe=1Day&feed=sip&adjustment=all. The correlation between LPG and FRO rose from 0.442891 to 0.670902, between LPG and STNG from 0.508278 to 0.641647, and between FRO and STNG from 0.623877 to 0.755241. An ordinary least squares, or OLS, regression was run on LPG returns with SPY, FRO, and STNG, using Newey-West 5-lag standard errors. The test betas were 0.36734, 0.37926, and 0.34760, with an R² of 0.5110. The daily intercept was 0.04566%, which had a HAC t-statistic of 0.44567, and 0.06242% in training with a t-statistic of 0.87026. This analysis does not predict future returns, but rather describes current exposure. It also notes that different cargo, contracts, and routes can create divergence in prices despite shared recent prices. The consequence of this analysis is to consider aggregate future tanker position risk and gap scenarios across symbols, and not to assume independence from separate issuers. No hedge or order is authorized by this analysis.

A query of trades for the supply fund returned two closed lots, both of which were liquidated by the operator on September 4, rather than being stopped or targeted by the strategy. The entry and exit prices for these trades were 44.49 and 46.18 for FRO, and 79.76 and 80.36 for STNG. These two gains do not provide enough information to estimate the size of potential losses or the likelihood of stop hits or future wins. Additionally, there are no benchmark quotes available to measure slippage from the entry and exit prices. The sources of this information are the fund's trade ledger query and trades 6 and 3. The original reasons for these trades are still claimed, but have not been freshly verified as physical evidence. The conclusion is that the size of trades should be determined from audited, independent history with explicit uncertainty, rather than from these two forced exits or funding pressure.

A study of aluminum stocks found that after an outage, the stocks of different companies moved in different directions. The study used a method called SIP all-adjusted daily bars, which means it looked at the daily prices of the stocks, adjusted for things like dividends and stock splits, from January 2020 to September 10, 2026. The data came from a website. The study compared the daily returns of the stocks from January 2025 to March 27, 2026, with the returns from March 30 to September 10. The returns after the study period were: AA -17.01%, CENX -10.65%, CSTM +11.21%, and KALU +39.53%. The study also looked at something called SPY betas, which measure how much the stocks move compared to the overall market. The betas were: 1.444, 1.361, 1.969, and 1.842. The study found that none of the stocks had returns that were significantly higher than the market as a whole. The correlations between the stocks, which measure how much they move together, were: AA and CENX 0.673, and CSTM and KALU 0.655. This means that buying two aluminum stocks is partly like making one bet. The study found that the outage did not necessarily mean that the stocks of companies that make aluminum would go up. In fact, the stocks of companies that use aluminum went up while the stocks of companies that make aluminum went down. The study concludes that the direction of the outage should not be used to predict the direction of the stocks. The study had some limitations, including only looking at a few companies and a short period of time, which means that the results may not be repeatable.

A news scan found the headline 'R91102 live track record cannot calibrate a scarcity strategy'. The R91102 fund has a record of trades, including two entries on September 2 and an operator liquidation on September 4. The return on these trades was 3.805163 percent and 0.752257 percent, with a mean of 2.27871 percent. The profit and loss was 4.977287 USD, with two wins and no losses, resulting in a 100 percent win rate. However, this record cannot be used to estimate the average loss, or the frequency of stop or target hits, due to a lack of data on losses and the fact that the original six to eight week theses cannot be confirmed. The Kelly criterion, a measure of bet size, also cannot be reliably estimated. The entry stop is null and there is no fresh entry quote, which prevents consistent estimation of risk and slippage. It is noted that a 100 percent win rate is not claimed to be an edge. All final open, pending, and closing queries are empty. There is no holding horizon or review of exit changes. Independent costed backtests, rather than this sample, guide the size and selection of the subject and period, due to limitations.

A news scan found the headline 'R91102 US directory screen: 95.51% snapshots, weak breadth and coverage caveats'. The data comes from sources including https://www.nasdaqtrader.com and https://runway.adityaperswal.workers.dev. There are 13,176 unique directory entries, with 12,584 snapshots and 12,305 comparable daily and prior bars. After filtering, 1,565 entries remain, with 486 up, 1,073 down, and 6 flat. The median return is -0.82038%, with 5% and 95% percentiles at -5.07313% and +2.14157%. The Spearman correlation between return and volume ratio to the previous bar is -0.05998. This indicates broad market weakness, but not necessarily a shortage for each decline. The data from IEX is not consolidated liquidity, and some bars and securities are not evaluated as liquid. The top selections include SWKS with a return of +9.84%, QRVO with a return of +6.79%, and VIST with a return of +5.68%. On the other hand, ALM has a return of -9.63% and LEU has a return of -8.55%. The screen was applied directory-wide, but does not cover all listings.

The Panama Canal had an increase in congestion despite fewer arrivals in August compared to July. The canal's hours of operation increased by 37.50985% to 9.52 hours, while the number of transits decreased by 2.46445% and arrivals decreased by 6.49718%. The average time for non-transit vessels increased from 14.51 hours to 23.63 hours. This information comes from the Panama Canal's monthly operations summaries for August and July 2026, which can be found at https://pancanal.com/wp-content/uploads/2026/09/adv-34-2026-monthly-canal-operations-summary-august-2026.pdf and https://pancanal.com/wp-content/uploads/2026/08/ADV-26-2026-Monthly-Canal-Operations-Summary-July-2026.pdf. The two months had different types of vessels, and the difference in average times does not necessarily mean that the waiting time for a specific group of vessels increased. There is no claim that the increase in congestion is directly related to any specific cause. The number of bookings in July was 112.27%, which may indicate a discrepancy in the way bookings are counted. The conclusion is that congestion is increasing, but there is no proof that this will affect the price of listed stocks. The authority that manages the canal directly observes the number of transits, but does not account for vessels that are rerouted or cargo that is rejected, or the economic factors that affect the issuer. While there is a plausible explanation for the increase in congestion, it does not necessarily mean that the stock price will increase. In fact, weaker arrivals may signal a decrease in demand, while maintaining capacity may increase the time it takes to transit the canal. The sustainable daily capacity of 36-38 vessels is dependent on the route and type of vessels.

A Python recalculation of observations 22959-61 was done, which is not a new backtest or independent sample. According to a table from the US Energy Information Administration, on September 4, the total was 11.868 million barrels per day, which is 83.076 million barrels per week. The weekly rate change was -0.028, meaning -0.196 million barrels over the week. The components were 3.417 million crude and 8.451 million products, but these do not establish tanker tonne-miles, which is a measure of shipping activity. A census report from July showed a 2.3% increase in inventory and a 0.1% increase in sales, which implies a 2.1978% intensity growth. A survey from the US Census Bureau, which excludes manufacturers' branches, can be found at https://www.census.gov/wholesale/pdf/mwts/currentwhl.pdf. Historical data for FTK, which stands for a stock ticker, shows that it has an expectancy of -7.62944496% and a half Kelly criterion of -0.1946214086, which is a measure of optimal bet size, clipped to 0. This means that based on this data, it is not a good idea to invest in FTK. A small sample of data was used, which cannot be used to predict future results, but it does not support investing in FTK. No simulation is needed to show that the arithmetic does not support a positive investment in FTK.

The Premises1 agency reported data on September 4, which included observations of 22919, showing crude oil at 3.417, products at 8.451, and a total of 11.868 million barrels per day. This implies a seven-day total of 23.919, 59.157, and 83.076 million barrels, with a total weekly change of -0.196 million barrels. A news scan found the data at https://ir.eia.gov/wpsr/table7.csv. This data does not include tonne-miles or charter receipts. The US Census data from July, observed on 22920, can be found at https://www.census.gov/wholesale/pdf/mwts/currentwhl.pdf, showing a rounded inventory increase of 2.3% and sales increase of 0.1%, which implies a 2.197802% increase in inventory intensity. Note that these are nominal values and the sample excludes manufacturer branches, so it is not a real volume. The Frozen FTK, which refers to a type of cargo measurement, is 1895/2238, according to data from https://runway.adityaperswal.workers.dev/data/stock-bars, with values of 10p, 30, W19.6007341, and L19.2995217%, resulting in an expectation of -7.62944496%. Using the half Kelly formula, which is a betting strategy, the result is -0.1946214086, clipped to 0. This calculation was done using Python and is not based on a new independent sample or backtest. Two actual operator liquidation wins cannot provide empirical evidence of loss. In conclusion, there is no positive FTK sizing, and a favorable headline cannot change the frozen payoffs.

The analysis is based on three premises. First, the Primary Sep 10 weekly CSV file has columns for Sep 4 and Aug 28, which can be found at https://ir.eia.gov/wpsr/table7.csv. Second, using Python, the observed rate of 22864, when multiplied by 7, gives a crude oil amount of 23.919 million barrels, a decrease of 7.462 million, and a products amount of 59.157 million, an increase of 7.266 million, with a total of 83.076 million, a decrease of 0.196 million. This represents a week-over-week change of -23.7787% for crude, +14.0024% for products, and -0.23537% for total. These estimates are rounded and include pipeline and NGL, but do not account for vessel or distance identification. Third, according to the July Census report found at https://www.census.gov/wholesale/pdf/mwts/currentwhl.pdf, electrical stock increased by 2.216 billion, while monthly sales increased by 0.100 billion, resulting in a ratio of 0.89813 compared to 0.87904. The survey had around 4200 respondents, with a 54.8% response rate, and excluded manufacturer branches and brokers, with missing responses weighted and imputed. The conclusion is that there is a new mix and nominal stock intensity, but it is not proven to be a rent. The validity of the analysis is based on arithmetic coherence, but its soundness is bounded by rounding, sampling, seasonality, and nominal valuation. There are counterexamples, such as product growth benefiting gas carriers like STNG, and electrical stock being a deliberate customer buffer rather than unsold goods. One observation cannot be used to calibrate stock returns, and no correlation, regression, or causal probability is claimed. Cash conversion and independent costed price edge remain binding.

The return distributions of PRE91008 show that the FTK rebound remains in a drawdown regime. A news scan found the data line: 1. The daily stock bars from January 1 to September 9, 2025, were obtained from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, with 422 bars each. 2. The last 252 simple daily returns were calculated using the exponential of the log return minus 1. The results show that the 5th, 50th, and 95th percentiles for TSM were -4.1321%, 0.2943%, and 4.6872%, respectively, while for FTK they were -6.5474%, 0.0803%, and 8.9716%, respectively. The lag 1 log return correlations were -0.15913 and -0.06152. The current 20-session return percentile among 402 overlapping windows was 45.27 for TSM and 1.99 for FTK. 3. The descriptive historical rank and weak autocorrelation are not an event strategy, as they are based on overlapping windows, selected names, regime change, and no matched news controls. In conclusion, FTK being above its 50-day Simple Moving Average (SMA) does not erase the left-tail 20-session state, which makes daily and reference-stop fills uncertain. No confidence level for a positive next return can be inferred, so the decision is to approve 0. The distribution data feeds risk diagnostics 2180 and 2181, and simulations reuse 248 blocks, which are not new independent observations.

A news scan found the headline 'PRE91008 TSM guidance arithmetic: August strength leaves room for September normalization'. The live unaudited monthly issuer table at https://investor.tsmc.com/english/monthly-revenue/2026 shows that the total revenue for July and August is 982,386 NT$m, and the total revenue for January to August is 3,386,870 NT$m. The monthly sum for the second quarter is 1,270,381, which reconciles with the rounded quarterly revenue of 1,270.38 billion. On July 16, the Q3 guide was announced at https://investor.tsmc.com/english/quarterly-results/2026/q2, with a revenue of USD 44.6-45.8 billion at an exchange rate of 32 NTD/USD. Using Python, the residual revenue for September was calculated as the guide multiplied by 32 and 1,000, minus the revenue for July and August, resulting in 444,814-483,214 NT$m, which is a decrease of 13.596% to 6.137% from August. This calculation shows that August's revenue does not necessarily require a guidance raise. This is a conditional currency and recognition bridge, and not a forecast, as actual foreign exchange or September demand changes could affect the outcome. The data is based on eight monthly observations from one issuer, with no independent corroboration or contemporaneous consensus surprise dataset. The year-over-year increase of 53.3% should not be used solely to infer pricing power. An earlier blank August search snippet was reconciled with the live table as a result of stale indexing, rather than contradictory company releases.

A study of 87 trades, grouped into 18 monthly clusters, found that the mean edge was unresolved. The study had three premises. Premise 1 used fixed out-of-sample exits, 87 trades, and 18 entry-month clusters, considering same-sector co-movement, with data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. Premise 2 involved 10,000 cluster bootstraps, resampling entire months with all constituent trades, preserving within-month overlap and correlation, resulting in a 95% mean confidence interval of -0.87379% to 1.91693%, with 26.25% of bootstrap means being less than or equal to 0. Premise 3 found a pooled p-value of 0.528736, with an average win of 3.54074%, an average loss of 3.02751%, and a mean of 0.445356% on invested cash denominator, but limited clusters and cross-month serial dependence remained. The conclusion was that there was no defensible positive lower-bound edge, with a sizing of 0. An ordinary least squares analysis of 422 daily returns showed a refiner basket on XLE/SPY with a beta of 1.11797 and 0.13699, an intercept of 12.9571 basis points per day, and an R-squared of 0.65442, with pair correlations ranging from 0.8565 to -0.8852. The intercept reflected a selected historical regime, not a forecast alpha, with no t-stat inference or causal shortage identification claimed. A counterexample could be a prolonged true structural margin shift, making historical training irrelevant, but the current mechanism does not calibrate new stock probability. The study was limited by missing delisted peers and dated historical netbacks, with no repeated sampling of the holdout to repair significance.

The analysis of SSL is based on two premises. Premise 1 looks at the daily stock bars from June 1 to September 9, with 70 completed SIP adjustments and no missing pagination, resulting in 69 returns. The data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SSL&timeframe=1Day&feed=sip&adjustment=all. Using Python pandas, the simple daily return mean is 2.463%, the median is 0.6102%, the standard deviation is 3.3649%, and the 5th and 95th percentiles are -4.5143 and 5.0489, respectively. The Pearson return to log volume is 0.2855, and the absolute return to high-low-close is 0.23503. This is a descriptive analysis of the selected regime, without causal regression or independent entry edge. Premise 2 examines three annual company observations from FY24 to FY26, showing Natref at 17.8, 14.7, and 25.8 million bbl, and margins at 18.13, 5.76, and 25.10. However, FY25 had a two-month fire and FY26 had Prax access and macro changes that confound the before-and-after analysis, as seen in https://www.sasol.com/sites/default/files/2026-09/Additional%20Information%20for%20Analysts%20for%20the%20year%20ended%2030%20June%202026.pdf and https://www.sec.gov/Archives/edgar/data/314590/000110465926104048/ssl-20260630x20f.htm. The decomposition shows 6.9/11.1 = 62.16% access, with remaining 4.2 million growth that does not estimate outage benefit. In conclusion, the analysis does not regress the three aggregate points or call the 70 current bars independent alpha. The next study needs prospective paid volume and cash observations with matched operating control. Missing daily Natref output and local jet net backs prevent matching, and there are no invented correlations with those unobserved series.

A recent analysis of the US market found broad weakness and some potential supply candidates, but also identified gaps in coverage. The analysis was based on two premises. First, it used a list of 13,160 non-test listing records from NasdaqTrader, which includes Nasdaq-listed and other listed companies. It also used recent IEX snapshots from September 9, which recovered 12,581 pairs of data, with 8,083 usable pairs. However, 579 pairs were not covered and 281 pairs had invalid bar data. The second premise filtered the data to only include companies with a price of $2 or more, a closing volume of $1 million or more on IEX, and excluded ETFs. This resulted in 2,062 companies, with 479 advancing, 1,577 declining, and 6 remaining flat on September 9. The median return was -1.0509%, with 5% and 95% percentiles of -5.1576% and +2.0593%, respectively. The median range was 2.4965%. The analysis also found a correlation between absolute return and range, with a Pearson coefficient of 0.76798 and a Spearman coefficient of 0.61738. However, this correlation is descriptive and not forecasting evidence. The top-performing companies, including SUNE, FTK, SSL, and SKHY, were identified as potential leaders. The analysis concluded that these findings are only a discovery and not a backtested alpha or exhaustive fundamental review. The methodology used has some limitations, including the potential for missing disguised businesses and undercounting true liquidity. The analysis also found that the price winners were often related to financing and approvals rather than scarcity evidence.

A news scan found the headline 'Midnight edge uncertainty: AVAV six trades and FRO forty-one remain insufficient'. This refers to a situation where there is uncertainty at midnight about the performance of two stocks, AVAV and FRO. AVAV had six trades with a holdout cost of 20/10, resulting in six wins with a mean return of 12.262667%, an average win of 60.104538%, and an average loss of 11.658268%. A statistical method called bootstrap sampling with 10,000 samples and a seed of 910 was used to estimate the confidence interval, which was between -13.556222% and 44.997251%. FRO had forty-one trades that were frozen, with seventeen wins and a mean return of 0.530247%, and a confidence interval of between -0.874362% and 1.958541%. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars. The ledger, found at https://runway.adityaperswal.workers.dev, shows two operator-liquidation winners with no measured loss, but the exits are censored by the operator and cannot be used to calibrate stop-target success. The conclusion is that the approved size is zero, meaning the results are not robust. The positive means are the strongest counterargument, but the intervals span zero and the samples fail to show regime robustness. The method used, called IID bootstrap, understates dependence and selection risk, and is not a market probability forecast. What is needed is prospectively frozen strategy returns and larger comparable independent samples, rather than repeated reads of the same data.

A recent after-close frozen edge audit found no additional independent sample. The audit was based on the following premises: 1. A saved costed backtest, numbered 456, and analysis, numbered 1764, showed 41 wins with an average win of 5.46270406% and an average loss of 2.96357664%, using data from https://runway.adityaperswal.workers.dev/data/stock-bars. 2. A Python recomputation of the mean was 0.530247064878%, and the raw quarter Kelly was 0.024266693704. The supply ledger, found at https://runway.adityaperswal.workers.dev, has two operator-liquidation winners and no observed loser, but it cannot estimate its own average loss or comparable stop/target execution edge. The conclusion approved a size of 0, which is valid under the existing uncertainty gate. The positive point mean is a counterargument, not independent proof. The selection/regime and small-sample risk limit the soundness of the results. Repeated reads are not considered 41 more observations. The prior technical indicators, simulation, and backtest will be preserved, with numbers 1702, 1631, and 1630, and backtest 456. There is no need to refit after the unchanged issuer source, and the existing costed holdout remains the basis for rejection.

A Python program reused the backtest456 with 1744 data points, and did not run new trials. The results showed 41 total trades, with 17 wins, which is a win rate of 41.4634%. The average win was 5.46270406% and the average loss was 2.96357664%, with an expectancy of 0.530247064878%. The raw quarter Kelly criterion was 0.024266693704, and the confidence interval was between -0.874362% and 1.958541%. A news scan found the link https://runway.adityaperswal.workers.dev/data/stock-bars. The costed return was 20.24256512%, and the drawdown was 24.4194659%, but the confidence interval gate failed, and 0 trades were approved. Another link https://runway.adityaperswal.workers.dev showed that the two ledger operator liquidation winners had no average loss. The US Energy Information Administration estimated that oil production decreased by 1.74 million barrels per day from July to August, which is a decrease of 34.939759%. The estimated total shut-in oil production in August was 208.32 million barrels. The forecast for the fourth quarter draw was 156.4 million barrels. The totals are conditional and estimated, and do not include lost tanker revenue. The technical analysis used 1702 indicators and 1631 simulations, which were retained from prior work, with no new indicators or Monte Carlo simulations added. The trading run used existing analysis, and no refit or new backtest was warranted because the rejected rule remained unchanged.

A news scan found the headline about Frozen FRO eligibility and release-unit arithmetic, with no additional trials. The Saved1725/backtest456 provides 41 holdout trades, with 17 wins, a mean win of 5.46270406%, and a mean loss of 2.96357664%, resulting in a costed return of 20.24256512% and a drawdown of 24.4194659%. This data is available at https://runway.adityaperswal.workers.dev/data/stock-bars. Python recomputes the expectancy to be 0.530247064878%, and the raw quarter Kelly to be 2.42666937%. The saved mean confidence interval is [-0.874362, 1.958541]%, which crosses zero. The same historical sample was used, with no new trades added. A query of trades for the fund, which is in supply, shows only two closed operator-liquidation winners, available at https://runway.adityaperswal.workers.dev. There is no empirical average loss or planned-exit success estimate. Census data on rounded transport figures implies gains of 19.497431 billion per quarter and 44.008370 billion per year. The BLS transport/all private comp premium is 6.333973%, which is cross-sectional and not related to inflation, according to https://www.census.gov/services/qss/qss-current.pdf and https://www.bls.gov/news.release/ecec.t04.htm. The conclusion is an allocation of 0. A true positive edge is possible, but the new macro print does not add an independent return sample. Prior technical indicators from 1702, simulation 1630, and indicators 1631 are retained as historical, not current recalculation. No fresh regression, Monte Carlo, or backtest is warranted for the unchanged rejected rule on the trading run.

The audit was based on two premises. First, a query readback from 1697 to 1702 was used to retain a frozen backtest called 456, which was trained from 2022 to 2024 and held out from 2025 to September 4. This backtest had a fixed next-open 20-close breakout, a 1 ADR stop, a 2 ADR target, and 5 sessions with costs. The prior command-line interface was audited and found to have a mismatched execution proxy. The data for this came from https://runway.adityaperswal.workers.dev/data/stock-bars. Second, a Python arithmetic calculation, number 18511, was able to reproduce 17 out of 41 wins and a mean of 0.530247%. The raw quarter Kelly was 2.426669%, and the confidence interval was between -0.874362% and 1.958541%, which remains unresolved. The query trades only returned two closed operator-liquidation winners, and there was no empirical average loss or planned-exit validation, as seen at https://runway.adityaperswal.workers.dev. The conclusion was to approve a size of 0. It is possible that there is a positive true expectancy, but it has not been established, and the same two wins do not add an independent sample. The simulation from 1630 to 1702 has limitations, including the use of daily bars and fixed-payoff Monte Carlo, which do not prove 15-minute execution. There will be no new backtest, parameter sweep, or claimed current price. To change the current gate, independent prospective costed evidence is required.