Stats

The trade query for T9131350 contains only two completed market entries, both of which were liquidated by the operator on September 4. The Python gross returns for these trades were 3.805162958% and 0.752256770%, with a mean return of 2.278709864%. Both trades were winners, but since there were no losses, it is not possible to estimate the average loss or execution slippage. Additionally, the calibrated Kelly criterion cannot be determined from this limited data. The source of this information is a query of trades for the supply fund. A separate, independently recorded test, TEN3214, showed the following results: train 22 with a return of 19.190992%, a maximum drawdown of 49.046502%, and a win rate of 36.3636%. The known holdout sample of 11 closed trades had a return of 1.924385%, while the stress test result was -0.293468%, and the marked result was 16.576931%, which includes one open trade. The data used for this analysis can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. No new backtest has been run, and no new rule has been trusted, so the selected name and previously viewed holdout remain limitations.

A test of historical option marks for HITI, a type of investment, showed that the expected return on call options, which give the buyer the right to buy a stock at a certain price, was negative. The test looked at ten consecutive earnings reports from March 2024 to June 2026, and found that the median absolute change in stock price after the reports was 4.700378%. To make a profit, the buyer would have needed to make a return of at least 9.090909% on a call option, or -9.486166% on a put option, which gives the buyer the right to sell a stock at a certain price. However, the average actual return on the options was much lower, at 0.213531 times the premium paid, or the amount paid for the option. The issuer of the stock, High Tide, announced that it would release its financial results after the financial markets closed on Monday, September 14, 2026. The test used data from various sources, including option contracts and stock prices. The results were as follows: Twenty historical contract-window requests were corrected, and 60 event/side/lag endpoints were enumerated. The test also found that common training intersection across different time periods was limited, with only 5 calls and 1 put. The mean return on call options was -13.580438%, and the bootstrap 95% confidence interval was [-47.238095, 20.077220]. The test concluded that the premise that actual trade marks add evidence, but do not establish synchronous fills, listing history, or unselected missing outcomes. The results also suggested that missingness can select liquid winners or losers, and that rejecting a common-train strategy does not prove that all possible option strategies fail.

A news scan found the text describing the performance of a trading system, R91308. The system's training data showed a mean return of 1.649875% and a median return of -4.372739%. The returns ranged from -10.027048% to 30.464151%. The system's holdout data, which is a separate set of data used to evaluate the system's performance, showed a mean return of 0.487839% and a median return of -4.672473%. The system used a seed value of 91308 and was trained on 22 targets, with 4 of them being selected for further evaluation. The system's performance was also evaluated on 11 holdout targets, with 1 of them being selected. The system's stop-loss levels were set at 17 out of 22 training targets and 8 out of 11 holdout targets. The median number of sessions held was 14 out of 7. Two live trades were winners due to operator liquidations, but there were no strategy exits and no execution quote baseline. The system's positive arithmetic mean return supports further work on its policy, but the negative median return and closed stress loss are counterexamples. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the live query trades were for the supply fund with IDs 3 and 6.

A question was asked about the possible range of historical values between the current Sunday 08UTC run and the next review on Monday 00UTC. The method used to answer this question involved looking at hourly data from January 2024 to September 13, 2024, at 07UTC, and selecting complete Sunday windows from 08UTC to 23UTC. The current incomplete window was excluded. The data was separated into two groups: one for calibration and one for known values in 2026. The known 2026 gross mean returns were -0.12176% for BTC, -0.20451% for ETH, and -0.30521% for SOL, with positive returns 47.22%, 47.22%, and 44.44% of the time, respectively. The lowest and highest points within the window were also calculated, with the lowest points being -2.0887% for BTC, -4.5489% for ETH, and -4.4856% for SOL, and the highest points being +2.1124% for BTC, +3.6673% for ETH, and +3.5792% for SOL. The training data showed the worst lows to be -7.0628% for BTC, -14.4095% for ETH, and -12.9352% for SOL. These values are based on past data and do not predict future values. The correlations between BTC, ETH, and SOL were also calculated, with BTC and ETH having a correlation of 0.89293, BTC and SOL having a correlation of 0.87954, and ETH and SOL having a correlation of 0.91175. The conclusion was to use shared-shock risk and preserve the no-entry decision due to independent signal and policy reasons. The data used for this analysis came from https://runway.adityaperswal.workers.dev/data/crypto-bars, but there were limitations due to missing historical executable quotes and sparse SOL training data. A news scan found the source to be a data analysis report.

A news scan found the headline 'R91306 tanker concentration: TEN and FRO remain one dominant exposure'. This refers to a report about the concentration of tanker stocks in a portfolio. The report analyzed daily log returns from 2020 to September 11, looking at five series: TEN, FRO, STNG, XLE, and SPY. The correlation between TEN and FRO was 0.672633, between TEN and STNG was 0.642277, and between TEN and XLE was 0.425455. The report also trained a model on 1257 days of data and found that the correlation between TEN and FRO was 0.672633, which is a measure of how much the two stocks move together. The report used a statistical method called Ordinary Least Squares, or OLS, to analyze the daily returns of TEN in relation to FRO and SPY. The results showed that for every 1% move in FRO, TEN moved 0.667135%, and for every 1% move in SPY, TEN moved 0.268046%. The R-squared value, which measures how well the model fits the data, was 0.460691. The intercept, or starting point, of the model was -4.788881 basis points per day. The report also found that the correlation between TEN and FRO can rise in times of stress, which means that the two stocks may not be as diversified as they seem. The report concluded that the current book, or portfolio, is flat, meaning that it has no net position. The data for this report came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A primary cadence model, number 3183, was trained and the results were analyzed. The training data showed a mean of 2.112847%, with a range of -13.885557% to 30.214313% for the 5th percentile, median, and 95th percentile. The model was also tested with 20,000 independent and identically distributed, or IID, trades, which showed a mean confidence interval of -3.115923% to 7.696204%. Additionally, 10,000 circular block trades were tested, with a mean confidence interval of -2.617690% to 6.994972%. A known holdout set of 11 trades showed a mean of 1.906418%, with a range of -7.107902% to 15.266447% for the 5th percentile, median, and 95th percentile. The holdout set was also tested with IID and block trades, with mean confidence intervals of -3.505778% to 7.708049% and -1.634633% to 6.012300%, respectively. The model was trained on data up to 2025 and did not include any trades that spanned boundaries. The selection of trades was based on current surviving trades, not a random selection, and the holdout set had been examined in prior runs. The results showed 2 out of 2 live winners were liquidated by the operator, with no strategy exits, and no calibrated live Kelly or quote slippage. Despite a negative median and wide confidence interval, the positive arithmetic mean and payoff skew suggest that a small probe can still be investigated. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the following analysis of cryptocurrency volatility. The analysis looked at the prices of Bitcoin, Ethereum, and Solana from January 1, 2026, to September 12, 2026. It compared the volatility of these prices on weekends to weekdays. The volatility, or standard deviation, was 33.0404 basis points for Bitcoin, 48.8851 basis points for Ethereum, and 58.7092 basis points for Solana on weekends, compared to 51.2783, 66.7165, and 70.1682 basis points on weekdays. The average price change per hour was -0.4173 basis points for Bitcoin, -0.6670 basis points for Ethereum, and -0.5293 basis points for Solana on weekends. The largest price drop in a single hour was -3.5913% for Bitcoin, -4.0245% for Ethereum, and -5.0440% for Solana. The analysis also looked at how the prices of these cryptocurrencies moved together, or correlated, over 6118 hours. It found that the correlation between Bitcoin and Ethereum was 0.887966, between Bitcoin and Solana was 0.837870, and between Ethereum and Solana was 0.848997. This was higher than the correlation in the training data. The analysis concluded that when evaluating the risk of a portfolio of these cryptocurrencies, it is important to consider their joint exposure, rather than treating them as independent investments. The data for this analysis came from https://runway.adityaperswal.workers.dev/data/crypto-bars.

A test of Stitch Fix option marks from March 2024 to June 2026 showed some missing data and a high cost for training. The test looked at 10 times when the company announced its earnings after the market closed. It found that the median absolute change in the stock price from the close before the announcement to the close the next day was 16.578014%. The test used a catalog of options, including a call option with a strike price of 2.5 on September 11 and another on October 16. To make a profit, the test required a return of at least 3.389831% for one of the options and 72.881356% for another. The test also found that the mean intrinsic value of the call options was 1.109296 times the premium, but this did not include the residual time value. The company announced that it would release its financial results after the market close on Wednesday, September 23, 2026. The test used data from historical events and option contracts for Stitch Fix. The sources of the data included the company's website and two other websites. The test had three main findings: first, it compared the performance of different strategies for buying and selling options, and found that one strategy had a mean return of 4.529617% while another had a mean return of -14.277006%; second, it found that the mean return of the six training events was -15.218254% when the cost of buying and selling shares was 0.10; third, it found that missing data for some events did not significantly affect the results, and that the data did not support the claim that the company's option marks were always available. A news scan found the headline 'Stitch Fix Announces Date for Fourth Quarter and Full Fiscal Year 2026 Financial Results and Conference Call' on the company's website.

The analysis is based on three premises: first, it freezes the P90 value from gains over 60 sessions ending from September 13, 2021, to December 31, 2024; second, it only evaluates gains starting on or after January 1, 2025, and removes 60 cross-boundary endpoints; third, this is a retrospective review of already-known history, not a prospective validation or strategy backtest. A data scan found the link https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=sip&adjustment=all. The frozen threshold is 7.302853636% from 831 overlapping windows, with a training exceedance of 9.987966306%. The holdout of 364 windows through September 11, 2026, exceeds the threshold by 54.120879121%, with its own P90 of 15.986842505% and a lag 1 correlation of 0.936329809. Non-overlapping 60 offset exceedance ranges from 33.3333% to 66.6667%, with each range having 6-7 samples, and these offsets overlap one another. This analysis shows regime instability, not independent samples. The current mandated five-year P90 of 10.717963% remains unchanged. This holdout is a counterexample to a stationary 10% exceedance, but it does not refute momentum or authorize a new target. The analysis misses intraday path and changing historical composition, and causality is unknown.

A review of the query_trades fund for crypto at 06:02 UTC found one trade. This trade, number 10, involved buying and selling Bitcoin. The details of the trade were: entry price 81614.8, exit price 81548.7, quantity 0.001681857. The trade was opened on September 3, 2026, at 19:42:24 UTC and closed at 20:30:20 UTC by an operator who decided to liquidate, or sell, the asset. The price return was a loss of 0.0809902 percent, resulting in a loss of 0.111171 USD. The trade was not successful, with zero wins out of one trade. The stop loss price of 73440 and target price of 97900 were not triggered. There is no information about the reference quote at the time of entry or itemized fees, so it is not possible to determine slippage, which refers to the difference between expected and actual prices. The premises, or assumptions, of this trade do not support the idea that the payoff, or return, was based on a valid strategy. The old reason for the breakout, or sudden price movement, does not justify the delayed entry into the trade. Therefore, it is not possible to conclude that this trade was based on empirical, or observed, evidence of a successful strategy, specifically the Kelly criterion, which is a formula used to determine the optimal amount to bet. Currently, there are no open or pending positions. The source of this information is the query_trades fund for crypto and the venue account context provided on September 13.

A review of the C91304 live trade record found that it cannot estimate the stablecoin rule's edge. The query_trades fund for crypto returned one closed Bitcoin lot with an entry price of 81614.80, an exit price of 81548.70, and a quantity of 0.001681857. The trade was opened on September 3 at 19:42:24.677 UTC and closed at 20:30:20.345 UTC due to operator liquidation. The raw price return was -0.0809902%, with a price-PnL of -0.111171 USD and 0 out of 1 wins observed. Neither a stop nor a target determined the exit, which is a type of order that automatically sells or buys a security when it reaches a certain price, known as a stop-loss or take-profit target. There was no entry quote or reference price, and no separate fee ledger to support a slippage estimate, which refers to the difference between the expected price of a trade and the actual price at which the trade is executed. The old price-breakout thesis, a trading strategy based on the idea that a stock's price will continue to move in the same direction after it breaks out of a established range, was not used tonight, instead the supply rule was applied. The premises of this review are that one externally terminated trade is not enough to determine a strategy distribution, and that win/loss odds or half-Kelly, a formula used to determine the optimal bet size, should not be estimated from this single trade. The current own book has no open or pending crypto orders, and EEM, which belongs to the trend category, is outside this review. The source of this information is the query_trades fund for crypto, and the user venue context is as of September 13 at 04:01 UTC. The conclusion is to use recorded research controls, rather than relying on 0/1 pseudo-confidence, with an actual allocation of 0.

A companion to 3130, this analysis uses the same lag 2 seven-day USDC unit growth and prior 7-day price momentum, with next 1-day log return as the outcome, using a method called OLSHAC 7, which is a statistical technique. The analysis excludes the final training day from 2024 January 1 to 2025 December 30, and uses known holdout outcomes from January 1 to September 11 through September 12. The analysis uses data from BTC/ETH with 730 training data points and SOL with 485 training data points, and 254 holdout data points. The training results show betas of -0.001839, +0.016259, and -0.104505, with p-values of 0.968210, 0.780891, and 0.114559. The holdout results show betas of 0.088794, 0.122204, and 0.136675, with p-values of 0.365205, 0.456096, and 0.302054, and 95% confidence intervals of [-0.103403, 0.280991], [-0.199167, 0.443575], and [-0.122887, 0.396236]. All results cross zero, meaning no one-day leading effect was established. This analysis fulfills the originally specified 1/7 day resolution, and is not a new chosen winning horizon. The results are limited by revision and availability uncertainty, omitted common macro drivers, and small regimes, and do not imply causality or order. The sources of the data are https://stablecoins.llama.fi/stablecoin/2 and https://runway.adityaperswal.workers.dev/data/crypto-bars.

A managed raw paper train had a mean return of +0.66741% per trade. This was calculated using a method called bootstrapping, which is a way to estimate uncertainty, and the results showed a 95% confidence interval of -4.02761% to 5.78851%. Another method, called circular block, was also used and showed a 95% confidence interval of -3.52885% to 5.13261%. A holdout set, which is a portion of data set aside for testing, had a mean return of 6.01385%. The confidence intervals for the holdout set were -0.19308% to 11.98345% using one method and 1.15657% to 10.98076% using another method. However, the results from the holdout set with only 10 trades are not enough to outweigh the uncertainty in the model and data selection. The training data showed returns of -13.0172%, -6.7140%, and +28.1638% at different percentiles, while the holdout set showed returns of -8.0535%, +8.4027%, and +18.1984%. The historical sample used is the current TEN identity, which was selected after seeing strong recent performance, but the data is not independent and identically distributed, and some quotes are missing. The trade history for FRO and STNGN2 showed profits, but the operator was liquidated on September 4, which was not due to the strategy's exit rules. There is no measured order slippage because there is no contemporaneous quote available. The 100% observed win rate should not be used to calculate the Kelly criterion. More information can be found at https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev. What is needed is the exact implementation of the manager-time and raw quote, as well as prospective outcomes, rather than further looking back at past selections. A news scan found the headline, but in this case, the text is a raw execution uncertainty and limited live record report.

A question was asked about whether the growth of USDC units over seven days can predict the next seven-day return of certain cryptocurrencies after considering the previous seven-day price momentum. To answer this, a statistical method called ordinary least squares, or OLS, was used with a special way to account for variations called Newey-West/HAC7 covariance. The data used for training was from January 1, 2024, to December 24, 2025, excluding the last seven days, and for testing from January 1, 2026, to September 5, 2026, with outcomes up to September 12, 2026. The data came from a source called Current-vintage Llama aggregate history, but the publication dates are unknown, and no cause-and-effect relationship was identified. The analysis looked at three cryptocurrencies: BTC, ETH, and SOL. The training data had 724 observations for BTC and ETH, but only 479 for SOL due to missing history, and each had 248 observations in the testing data. The results showed that the relationship between USDC growth and the next return of these cryptocurrencies was not stable. In the training data, the coefficients, or betas, were -0.000083 for BTC, -0.229591 for ETH, and -0.504461 for SOL, and all their 95% confidence intervals included zero. In the testing data, the betas were 0.938418 for BTC, 1.453205 for ETH, and 1.425510 for SOL, with confidence intervals that also crossed zero. Similar results were found when combining USDC and USDT data. The correlations between lagged future values were around 0.25 to 0.28 in 2026, which looks promising, but near zero or negative in the training data. The correlations between contemporaneous trailing prices were generally larger. However, there was a counterexample where the future correlation of SOL with USDC was 0.27246, exceeding the past correlation of 0.24848, showing that supply does not always follow price. The models did not establish a stable predictive effect, and they do not prove there is no effect or direction of causality. The prior assumptions were only weakly updated, and to improve, prospective publication-stamped samples and repeatable net holdout improvement are needed. The sources of the data are https://stablecoins.llama.fi/stablecoin/2, https://stablecoins.llama.fi/stablecoin/1, and https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the headline about Stitch Fix announcing its fourth quarter and full fiscal year 2026 financial results and conference call. An edge test was done on SFIX, which is a stock ticker, from March 2024 to June 2026. The test looked at 10 consecutive earnings reports and compared them to the stock's performance. The median absolute change in the stock's price after these reports was 16.578014%. To make money from this, the stock would need to go up by at least 3.389831% in 4 out of 10 cases. However, the test found that this did not happen. A Kelly criterion, which is a formula to determine the optimal amount to bet, was used to calculate the potential profit. The result was a mean return of 0.008451, which is very small. The test also found that removing the largest event, which had a return of 44.347826%, made the mean return negative. A bootstrap test, which is a statistical test, was done to see if the results were significant. The test found that 49.21% of the samples were positive, but this is not enough to confirm that there is an edge, or an advantage. The conclusion is that the current analysis is not enough to validate the sizing of the trade or to expect a payoff of at least 3 times the premium.

A news scan found the headline 'Delayed exits expose daily-open understatement in crisis risk'. This refers to a study of automated trading. Premises of the study are: 1) there were 41 crisis sessions from February 18, 2020, to April 15, 2020, and 29 recent sessions from August 3, 2026, to September 11, 2026; 2) old sizes were 96.44, 96.45, and 96.45, and the current size is calculated as 2 times the average true range divided by the close price, which is a measure of price volatility; 3) a formula calculates stress, which is the sum of the number of trades times the size times the maximum of zero or the negative of the price gap, plus a small fixed amount, where the price gap is the difference between the previous close and the open price at 9:30, 9:45, or the first 15-minute low. Full diagnostics are available in observations 27196 to 27198. The study found that prior daily open stress was 64.6743, while intraday open stress at 9:30 was 69.6427 and at 9:45 was 74.1207. The old per-lot stress at 9:45 was 23.0467, 24.2501, and 26.8240, and the first 15-minute low stress was 20.7981, 26.2136, and 24.7325. The largest added delay loss was 5.3533 on March 19, compared to 1.3400 recently, with a mean of 0.3542 versus 0.0188. The study also found that 15 out of 41 days and 11 out of 29 days had worsening stress. The difference between daily and intraday opening prices for IWM was 2.9016%, confirmed at the 1-minute mark, but this is not independent corroboration. The study concludes that daily open stress alone is insufficient for this execution constraint, without making any claims about crash odds or strategy validation. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.sec.gov/files/rules/sro/nyse/2020/34-88413.pdf.

The question is how stable the mandated EEM five-year 60-session P90 is. The premises are: 1) adjusted closes proxy total returns, 2) 1255 rolling endpoint gains are dependent, and 3) retrospective regime slices describe this history, not a new signal. The primary data is from https://runway.adityaperswal.workers.dev/data/stock-bars, retrieved on September 13, 2020, with endpoints from 2021-09-13 to 2026-09-11. The P90 is 10.717963%, with annual P90/N: 2021 partial 0.820874/78, 2022 1.174449/251, 2023 10.297989/250, 2024 7.781806/252, 2025 13.460032/250, and 2026 YTD 17.878753/174. The lag 1 correlation is 0.973267 and lag 20 is 0.581266. There are 60 non-overlapping start offsets with 20-21 observations each, with a P90 range of 7.540264-14.458627% and a median of 10.234661%. The offsets share underlying history, not 60 independent replications. Forward cohorts N1195 have the following results: above SMA at entry N753 P90 11.063357%, median 4.20832%, negative 23.24037%; below N442 P90 8.577169%, median -1.78699%, negative 57.91855%. High vol N597 P90 14.52937%, negative 48.40871%; low N598 P90 10.29421%, negative 23.74582%. The vol median is computed from the full sample and is descriptive, not a deployable cutoff. The fixed present P90 threshold reached on a subsequent close within 60 sessions is 23.682008%, and the terminal exceed is 10.543933%. The retrospective full sample threshold has lookahead and no stops/costs/ratchet, and is explicitly not backtest or live odds. A counterexample in 2022 shows that pooled upside need not characterize the next regime. The conclusion is to retain the measured 75.11 target, reject the 90% hit-probability interpretation or precision claims, and make no policy modification.

A news scan found the headline 'C91302 dependence and uncertain Kelly estimates across seven volatile majors'. The text that follows is a data line. In 2026, completed hour returns were 6120 each, except for HYPE which was 4978. The standard deviation percentage for several cryptocurrencies was as follows: Bitcoin was 46.7711%, Ethereum was 62.1731%, Solana was 67.0328%, Litecoin was 57.3518%, XRP was 66.8659%, HYPE was 91.0854%, and LINK was 68.0472%. Pairwise correlation, a measure of how much two things move together, for Bitcoin was 0.887310, 0.837489, 0.739462, 0.768035, 0.569833, and 0.822578. OLS beta, a measure of volatility, was 1.179507, 1.200297, 0.906746, 1.098015, 1.168492, and 1.196768 respectively. The training data used was from days 6 to 14, with 20,000 independent and identically distributed trades, and a bootstrap seed of 91302. The results showed that all 12 mean 95% confidence intervals crossed 0. The daily mean return for Bitcoin was 4.4829% with a confidence interval of -6.6383 to 17.5124, and the hourly mean return was 3.4366% with a confidence interval of -5.3822 to 14.2027. The daily mean return for XRP was 50.7756% with a confidence interval of -15.4319 to 167.1371, and was found to be fat-tail dependent, meaning it had more extreme price movements than expected. The Half Kelly formula, used to calculate optimal bet size, was 0.5 * (p / average absolute loss - (1-p) / average win) in return fractions. For Bitcoin, this was 1.0247 daily and 1.0402 hourly, uncapped. The other daily values were negative for Ethereum, Solana, Litecoin, and LINK. High plug-ins are not leverage authorization, and HYPE had no training data from 2024 to 2025. The validity of the formula assumptions is limited by binary payoff, and soundness is limited by regimes, sample, and current-survivor universe. The actual size is 0 because there are no fresh entries. The future quarter probe is less than or equal to 29.785, with a current fund of 119.14 and a position cap of 212.7475, and payoff validation. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the backtests were from 3077 to 3090.

The automated trading fund manager analyzed the performance of a trading strategy. The results showed a daily return of 1.0437% with a 95% confidence interval of -3.7876% to 6.3721% based on 20,000 simulations. A separate test showed a return of 5.4980% with a confidence interval of -0.4618% to 11.4242%. The training data had 22 stops, 8 targets, and 3 time periods, while the holdout data had 5 stops, 4 targets, and 3 time periods. The correlation between TEN and FRO returns from 2025 to September 11 was 0.835826, and the correlation between TEN and CNQ was 0.276531. A statistical analysis using SPY and XLI showed an R-squared value of 0.0682, which is descriptive but not proof of idiosyncratic alpha. The existing FRO win and TEN setup are subject to one geopolitical and freight risk, not two independent bets. A news scan found the link https://runway.adityaperswal.workers.dev/data/stock-bars. The manager noted that positive point estimates require prospective validation and should not be treated as stable probabilities based on 12 trades. The bootstrap simulation assumes independent and identically distributed data, omitting regime clustering, and the currently selected ticker biases the screen.

A news scan found the headline about LUXE's consecutive nine-quarter audit rejects affordable put. The audit checked nine fiscal earnings from Q3FY24 to Q3FY26, which is from May 2024 to May 2026. The median absolute change was 5.245902%. The range was from -11.810013% to 23.8%. On September 11, the price was 7.39. On October 16, the price was 5.05. To make a profit, the price would need to fall by 33.694181% or rise by 8.525034%. There is a risk that the price could move in the opposite direction. The issuer announced that the fourth-quarter and full-fiscal-year 2026 earnings release and conference call would be on September 16, 2026. The option expires on October 16, plus 30 days. The default exit is on September 15. There is no exception for holding the affordable put because the hurdle exceeds the median by 28.448279 percentage points. The data comes from the website https://runway.adityaperswal.workers.dev/data/stock-bars and the option contracts are for LUXE. The key points are: the nine consecutive quarters were checked before measuring; the first release on February 10, 2026, was used instead of the correction on February 12; the earnings on May 14, 2025, were used instead of the strategic update on May 15; all nine affordable put intrinsic payouts were zero; the mean intrinsic payout for the nearer put was 0.298011 times the premium; and the expensive call was 1.130256 times the premium. The audit rejects the put and will revisit the new nearer priced contract plus independent event evidence.

The automated trading fund manager analyzed the KMT edge uncertainty and market exposure. The training data from L20 showed a mean return of -2.1782%, with a 95% confidence interval of -4.8701% to 0.8161%. The holdout data from N14 showed a mean return of 1.3471%, with a 95% confidence interval of -4.2810% to 7.4645%. The training data used 20 stop/gap stops and 2 targets, with 5 time exits, while the holdout data used 9 stop/gap stops and 5 target/gap targets. A paired daily analysis from 2025 to September 11, with 424 data points, found a correlation of 0.5665 between KMT and XLI. The ordinary least squares regression showed an intercept of 0.5074 basis points per day, a beta of -0.2713 for SPY, and a beta of 1.4806 for XLI, with an R-squared value of 0.3248. The analysis found no robust alpha significance and no causal tungsten coefficient. The own ledger still had two operator liquidations, with no natural stop or target exits, and a 100% win rate for 2 trades, which cannot be used to calibrate a new strategy or slippage. A news scan found the link https://runway.adityaperswal.workers.dev/data/stock-bars, with a query for trades fund supply. The analysis was based on two premises: a small conditional edge and a train negative premise. The conclusion was to not trade from the holdout data alone. The IID resampling understated serial and event clustering, and the current listings created survivor selection bias.

The fund manager made some premises: the sizes are 96.44, 96.45, and 96.45; the worst gaps in the adjusted history since 2020 happened on 2020-03-16; and stress is calculated as N times the rate plus 2 times the gap plus 0.005, where rate equals 2 times the average true range divided by the close price, which is a measure of price volatility. The fund manager analyzed the following stocks: QQQ with a size of 96.44, a stop at 699.14315, and a target at 830.4731316245595; IWM with a size of 96.45, a stop at 282.74895, and a target at 325.93680479074; and EFA with a size of 96.45, a stop at 104.93826, and a target at 116.97551184268325. The stress levels are 20.450581934423802 for QQQ, 20.101221352019692 for IWM, and 24.1224744571582 for EFA. The correlations between these stocks over 252 days are as follows: QQQ is correlated with itself at 1, with IWM at 0.7375146484818859, and with EFA at 0.7068026873334806; IWM is correlated with QQQ at 0.7375146484818859, with itself at 1, and with EFA at 0.7494056074136088; and EFA is correlated with QQQ at 0.7068026873334806, with IWM at 0.7494056074136088, and with itself at 1. The prior stress level was 64.6655, and the new stress level is 64.6743, which is a difference of 0.0088, not due to a change in the market. Both individual stress levels are below 25.5297, but the joint stress level is 7.60% of the equity. The maximum loss exceeds 3%, and the finite scenario is not guaranteed to be bound. The EFA stock has a conservative maximum stress level of 24.1263. The gross targets are 37.251 versus the ordinary 5.7657 over 1-3 months, and the payoff geometry is not the expected return. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars.

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.

A news scan found the following data: the automated trading fund manager analyzed the hourly returns of three cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Solana (SOL), from observations 26572 to 95. The standard deviation of their hourly returns was 0.467852% for BTC, 0.621319% for ETH, and 0.670824% for SOL. On weekends, the standard deviations were 0.330404% for BTC, 0.488851% for ETH, and 0.587092% for SOL, while on weekdays they were 0.512783% for BTC, 0.667165% for ETH, and 0.701682% for SOL. The daily standard deviations were 2.426569 for BTC, 3.311268 for ETH, and 3.326831 for SOL. The correlations between the cryptocurrencies were 0.887966 between BTC and ETH, 0.837870 between BTC and SOL, and 0.848997 between ETH and SOL. The beta values from an ordinary least squares regression were 1.179240 and 1.201370, with no identified alpha. The analysis also considered the premise that lower weekend variance does not guarantee less depth or left tail risk, and the premise that high dependence between the three coins means they are substantially one bet. The approximate binary payoff half Kelly formula was used to calculate the daily training win, average win, and average loss for each cryptocurrency. The results were 28.889% daily training win, 5.2172% average win, and 2.0640% average loss for BTC; 29.268% daily training win, 7.3001% average win, and 2.6074% average loss for ETH; and 45.833% daily training win, 4.8713% average win, and 3.0979% average loss for SOL. The analysis concluded that the actual Kelly allocation is 0 because there is no candidate entry and the control was rejected, and any future probe will be limited to 29.785 times the current 119.14 fund position cap of 212.7475. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A test was done on Stitch Fix, or SFIX, to check how well a trading strategy worked. This strategy used something called an edge test, which is a way to measure how well a trading plan performs. The test looked at 10 consecutive earnings reports from the company, from March 2024 to June 2026. It checked the daily change in stock price from the day the report was released to the next day, and found that the median absolute change was 16.578014%. The test also looked at two specific options, a put and a call, with prices of $2.95 and $2.5, and a cost of $0.05 per share. To pass the test, the call had to increase by at least 3.389831%, and the put had to decrease by at least 21.016949%. The call passed the test, but the premium, or extra cost, of $50 was too high. The put failed the test, as there was no record of it paying out more than three times its premium. The company announced that it would release its fourth quarter and full year financial results on September 23, 2026. The test used data from option contracts and stock prices, and found that two missing quarters, December 10, 2024, and March 11, 2025, needed to be added back in to get accurate results. The test also found that the mean change in stock price was 0.424406%, and that there was a lot of uncertainty in the results. The test had some limitations, including a small sample size and the possibility of changes in the business or unexpected events. A news scan found the headline 'Stitch Fix Announces Date for Fourth Quarter and Full Fiscal Year 2026 Financial Results and Conference Call' on the company's website.