Stats

A news scan found the following diagnostic report: Premises: the analysis looks at the difference between the daily opening and prior closing prices of EEM and XLE, which are types of investment funds, from January 2019 to September 18, 2026, and also considers a pandemic. The analysis uses a technique called Monte Carlo, which is a method of simulating random events, to test the effect of gaps in prices on the fund. The test used 20,000 random paths and 63 gap observations, and the results showed a median maximum combined gap loss of 3.71245, a 95th percentile of 16.16735, and a 99th percentile of 16.23047. This means that 34.035% of the paths had a gap loss greater than 4.42. The analysis does not take into account cumulative profit and loss, strategy validation, live exit odds, forecasts, or the probability of a stopped position surviving to another gap. It also does not consider intranight fills, evolving positions, dividends, cash, spreads, or fees. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars with a split adjustment. The analysis preserves the dependence within blocks of events and concentrates on events from 2020. A strong counterargument to the analysis is that it overweights old crises relative to the current regime and ignores beneficial overnight exits. The converse model omits failed limit execution, so the error direction is uncertain. The fund has 0 closed trades, and the empirical Kelly, which is a measure of the optimal fraction of a portfolio to invest, is unidentifiable. The fund has a fixed one-share mandate that governs its size, and the joint 10% and 13.20% account values are 1.32% and 1.623%, respectively. The worst observed gap loss is 16.23%, which is equivalent to 1.623% of the account, and the complete loss is 13.2% of the account's hard cash exposure before fees. There is no doubling or target inflation.

A news scan found the text: Premises include a synchronized gap series and today's 93.33 weights from 3426, resampling common basket rows to preserve cross-asset dependence, which refers to the relationship between different types of assets, and 10,000 draws with a seed of 91904 using the numpy library. The results show that the average difference between weekend and ordinary days is -0.147572 USD, with a 95% confidence interval of -0.486861 to 0.179403. Using 20-day moving blocks, the confidence interval is -0.473665 to 0.229915. Both of these intervals cross zero, meaning that there is no robust average weekend penalty established. The blocks do not establish stationarity, which refers to a stable statistical pattern, or future tails, which refer to extreme values. This is an estimation simulation, not a simulation used to test a trading strategy or predict outcomes. Earlier established envelopes for EEM, QQQ, and IWM remain as stress assumptions, with values of 0.125794155019, 0.109666233203, and 0.138906519177 respectively. These values are not replaced with the latest 5-year observed values, which do not include 2020. The current scenario of stop-plus-double-envelope-plus 50 basis points, which refers to a 0.5% increase, remains at 25.913233, 22.608078, and 27.921384 USD per lot, with a joint value of 76.442695. This is not a guaranteed bound. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

The premises of this analysis are: 1) normalized 1255 rolling 60 returns from https://runway.adityaperswal.workers.dev/data/stock-bars through September 18. 2) A circular block bootstrap of the observed gain sequence, with 3000 replicates per block of 20, 60, and 120, and a seed of 91904 reset per ETF. This means that the data is resampled to preserve the relationships within each block, but not the actual tradable price paths. 3) It is assumed that the empirical distribution is stationary, meaning that it does not change over time, and regime change is not modeled. The block 60 central 95% P90-estimator ranges are 13.469-25.977% for XLK, 14.740-26.412% for XLE, and 7.383-12.587% for XLV. The block 20 ranges are 14.512-24.908%, 16.605-24.986%, and 8.254-12.444%, while the block 120 ranges are 13.679-26.107%, 14.212-25.892%, and 7.377-12.581%. These are model sensitivity ranges, not calibrated confidence intervals, future returns, or hit odds. The sizing uses an existing deterministic stress, calculated as r = 2ATR/E, where ATR is the average true range, and g = max(0.10, 2*abs(worst negative gap)). The current g is 0.125185405, 0.115854679, and 0.10. The common N, or number of shares, is calculated as the floor of the minimum of 260/3, 250, and 19.99 divided by the maximum fraction, which equals 86.66. The losses are 13.796520, 13.486896, and 11.105282, each less than 20. The simultaneous sum is 38.388698, which is 3.83887% of the equity. The nominal stop losses are approximately 2.875, 3.408, and 2.229, while the targets are 15.9127, 17.8762, and 9.2108 per lot. The unseen total loss is 86.66 each, so the stress is not a hard loss guarantee. Historical 3381 path-simulation frequencies remain old capital/vintage diagnostics, not current live odds, and no repeated drift simulation is warranted. There is no funding gap, and 0.02 cash is due to cent rounding.

A Monte Carlo simulation, which is a method of estimating the outcome of a process using random sampling, was run for each stock symbol. The simulation used a numpy seed of 91902 to sample the costed training trades into 20,000 independent and identically distributed paths of 20 trades, investing 12% of the account per trade. Every result was saved as an observation. For GLW, the terminal values were -5.67% for the 5th percentile, +0.54% for the median, and +8.10% for the 95th percentile, with 44.95% of paths resulting in a loss. The 95th percentile maximum drawdown was 6.93%. For OCC, the median was -7.54%, with 83.74% of paths resulting in a loss. For FEAM, all training trades resulted in a loss, so all empirical paths also resulted in a loss, which is not a 100% forecast certainty. The simulation for BTDR produced implausibly tight and optimistic paths despite a negative holdout, so this distribution was rejected as adequate for live calibration. For FCEL, a positive arithmetic mean can coexist with a negative compounded full investment return and negative holdout, which shows that skew and volatility drag matter. These paths count trades, not 20 days or 30-day funding runway. A stress test was also applied, with a stress gap of 2 times the observed worst negative gap, and a stop loss plus residual gap and 50 basis point cushion. For GLW, the loss fraction was 0.376921, which resulted in a normal risk budget of 79.59 USD notional ceiling. The quarter probe was 19.90, before training Kelly, concentration, and fractional exit constraints. A whole GLW share of 150.13 exceeds the 120 fund cap. The OCC ceiling was 48.59, with a quarter of 12.15 below one 14.82 share, but the actual allocation was zero because the entry and historical edge failed. Other ceilings recorded are not recommended positions. Training half Kelly, which is a fraction of the risk bank, can constrain risk below 3% of the account, and the allocation should never be increased from holdout optimism. The current account is 1000, with a fund of 120 and cash of 120. No probabilistic left tail bound is claimed, and gaps larger than the stress test are possible. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The model does not validate the 15-minute off-hours fill policy or +2R quarter/trail exits. No order is generated from this simulation.

A diagnostic test was run on a trading system. The test used the last 1260 daily returns, divided into blocks of 5 sessions, starting from a specific point, and ran 20,000 simulated paths of 63 sessions. The test used proposed limits of 67.37 and 64.63. It assumed that trades would be filled exactly at the target or stop thresholds, with no fees or gaps. The test was not a live trading test, but rather a backtest. The results for EEM were a stop loss of 63.91%, a target of 23.625%, and neither of these was reached 12.465% of the time. The average gain was $0.84, and the 5th percentile was a loss of $1.89. For XLE, the results were a stop loss of 55.43%, a target of 19.76%, and neither of these was reached 24.81% of the time. The average gain was $2.70, and the 5th percentile was a loss of $2.53. The data used was from https://runway.adityaperswal.workers.dev/data/stock-bars and was adjusted for SIP, completed on September 18. The results cannot be used to claim stop bounds due to model mismatch. A hypothetical prior probability of 0.25 was used to illustrate a payoff of 3.82 or 5.27, which implies a Kelly fraction of 2.68% or 5.38% of risk capital. However, this is not an empirical estimate and was not used to override the planned one-share sizing. The actual planned risk is 0.189% or 0.253% of the account, which is below the illustrative fractions. A 10% entry gap of 13.20, or 1.32% of equity, and a total loss of 132, or 13.2%, were also calculated. These are not probabilistic bounds. The plan is to maintain one share of each, with a focus on tail payoffs rather than maximizing the hit rate.

This is an updated diagnostic of a five-day trading path. The premises of this diagnostic are: 1) a planned return of 93.33 each; 2) the last 756 common daily returns were completed; and 3) a special math tool called numpy was used with a seed of 91902 to resample 10,000 paired blocks of five days, which helps keep the relationships between different assets intact. The results show that the worst possible loss could be -34.060647, the median return could be 1.411593, and 39.95% of the results were negative, with a worst-case loss of -15.174916 and a fifth-worst loss of -9.811266. This is not a model that decides when to stop or target a trade, and it does not take into account real-life trading conditions like fees or sudden changes in the market. The diagnostic was updated because the amount of capital and the weights of the assets changed. The current selection of assets was chosen with hindsight, and this diagnostic does not include a backtest of actual trading or a fresh validation. The data source is a website. The model may not be able to handle large and unexpected changes in the market, which could affect the results.

The premises for resetting capital are as follows: 1) the stock bars data was completed on September 18, with current supplied equity of $1000 and capital of $360, and an empty own ledger. 2) The existing model uses a risk calculation of 2 times the average true range divided by the equity, and a gap stress calculation of the maximum of 0.10 and 2 times the absolute value of the worst negative gap. The loss fraction is calculated as the risk plus 1 minus the risk times the gap stress plus 0.005. The equal number of units, N, is calculated as the floor of the minimum of one-third of the capital, 0.25 times the equity, and 0.02 times the equity minus 0.01, divided by the maximum fraction, which equals 120. The stressed losses for XLK, XLE, and XLV are 19.104343, 18.675601, and 15.377727, respectively, each less than 20, with a total of 53.157671. Note that this is not a guaranteed bound, and the loss-to-zero is 120 each. Historical gaps omit unseen tails. 3) The frozen method and seed 91900 were reused to translate the same distribution to the reset notional, without testing a new edge. This involved generating 10,000 joint circular 5-day bootstrap paths from 252 returns over 60 sessions, with hypothetical close-only fixed brackets, a 50 basis point exit deduction, and survivors closing at 60. There were no intraday ratchet or entry costs. The results include recent drift and zero drift data, with the zero-drift median being negative. The frequencies are model diagnostics, not calibrated live odds or Kelly inputs. The equal size remains unchanged by forecast, and there is no funding shortfall. The P90 is a return quantile, not a target-hit probability.

A trading fund manager analyzed data using a numpy seed of 91900, generating 20,000 paths with 20 trades sampled from 36 historical trades from 2018 to 2024. Each trade was 12% of the account's notional value. The results showed terminal returns of -1.594%, 7.302%, and 17.559% for the 5th percentile, median, and 95th percentile, respectively, with a terminal loss of 9.2% and a maximum drawdown of 5.576% at the 95th percentile. These results are based on past data and do not reflect current stock target odds or loss bounds. A separate analysis measured the worst daily opening gap since 2018 at -16.0305%, and a stress test with a 2ATR stop and 50 basis point allowance resulted in a notional loss scenario of 38.3726%. The fund's stress budget is 3% of the account, which is 25.4976, and the notional value is 66.4474 below the fund's cap of 101.99. A quarter probe of 16.6118 may be applied if a later entry qualifies. The point estimate of half Kelly, or average payoff, is 0.11196, but this is constrained by the normal 3% equity risk and 12% notional cap. The current signal is false, and the net target is not approved, resulting in an actual size of 0. The data source is https://runway.adityperswal.workers.dev/data/stock-bars.

A news scan found the following text: Premises are that the technical analysis of 3353 fresh adjusted SIP is available at https://runway.adityaperswal.workers.dev/data/stock-bars, with an equity of 849.92 and a capital of 339.97. The stress definition is set to r = 2ATR/E, where g is the maximum of 0.10 and 2 times the absolute value of the worst historical negative gap, and the fraction is r plus 1 minus r times g plus 0.005. The values for XLK, XLE, and XLV are 0.125185405, 0.115854679, and 0.1 respectively. The total value is 320.10 and the residual value is 19.87. The loss values are 16.986945, 16.605722, and 13.673363, which are all less than 16.9984 per lot. The sum of these values is 47.266029. The model does not guarantee that the loss will not exceed 2% or that unobserved gaps will not exceed historical values. Equal weights are used to obey the tightest constraint, with no correlation discount. A new completed bar and bear attack justify drift sensitivity, but not strategy validation. A joint circular 5-day bootstrap of recent 252 daily returns was performed, with 10,000 paths and a seed of 91900, over 60 sessions. Both recent drift and subtract each asset sample mean were used. The output is ordered by XLK, XLE, and XLV, with values for recent drift, zero drift, and other metrics. The conclusion is that the model median flips sign if recent drift vanishes, but target frequencies cannot be converted to actual expectancy or Kelly. The mandate sizing and targets are retained, rather than selecting favorable simulations. Historical worst gaps and finite resampling omit unseen tail states, and genuine live win odds are unavailable. There is no repeat edge backtest per explicit mandate.

This analysis is based on three premises: the current notionals are 90.04, 103.20, and 83.56; the last 756 completed common daily returns ended on September 18; and a computer program called numpy was used with a seed of 919 to generate 10,000 paired contiguous 5-day blocks, which helps to understand how different assets are related. The results show that the worst possible loss over a 5-day period could be -33.635197, and 39.46% of the outcomes are negative, with a median return of 1.469630. This analysis is a stress diagnostic, not a prediction of actual losses or gains, and it does not take into account things like fees or stop-loss orders. The results should not be used to decide how much to invest because they are based on past data and do not account for unexpected events. The analysis is based on data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A study on the R91806 sequence risk compared empirical trades to explicit disaster replacement. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, which provided closed pre-2024 returns from a costed strategy. The study used 20,000 paths of 20 sequential trades, with portfolio rebalances to 12% of the account or a 3% quarter-fund probe. The study sampled actual trade outcomes with replacement and separately replaced an outcome with an assumed 30% loss at 0%, 1%, and 5%. The results showed that the full fund mean terminal account return was -3.93925% with 0% loss assumption, and the 5th percentile was -14.32465%. The probability of a loss greater than 10% was 20.74%. The study also found that a 5% loss assumption raised the probability of a 95th percentile drawdown to 19.07636%. The quarter fund mean return was -0.99976% with 0% loss assumption, and -1.84081% with 5% loss assumption. The study concluded that scaling exposure to drawdown shape and retaining uncertainty is important, rather than relying on assumed odds as a permanent cash veto. The study used various metrics, including P95 drawdown, mean return, and probability of loss, to analyze the risk of the R91806 sequence. The results were compared to the LPG full fund and quarter fund, which showed different mean returns and drawdown probabilities. The study also mentioned that the Half Kelly numerical estimates were capped research diagnostics, and that the 3% normal account-risk rule and gap stress should be preserved separately from expected value.

A news scan found the report on R91806 CENX. The report is about a model with 20,000 paths and 60 sessions. It used historical data to calculate open, high, low, and close ratios. The model also considered fees and stops, including opening gaps and intraday ambiguity stops. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The report found that for the period before 2024, the stop was triggered first 67.480% of the time, the target was reached first 30.295% of the time, and the trade timed out 2.225% of the time. The mean net result was 2.2449%, the median was -10.9632%, and the 1st percentile was -14.7113%. For the last 252 days, the stop was triggered first 60.875% of the time, the target was reached first 34.025% of the time, and the trade timed out 5.100% of the time. The mean net result was 4.3584%, the median was -10.9632%, and the 1st percentile was -15.8823%. The report also calculated the half-Kelly fraction, which is a measure of the optimal bet size, using a grid search and closed trade distributions. The results showed a constrained half-Kelly stock weight of 0.5000 and a cap-adjusted research size of $101.99. The report noted that recent-window drift and selected survivor bias can create attractive resampled odds without an executable edge. The decision was to not use favorable bootstrap odds to override failed holdout, absent current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account, which is $849.92, and a quarter fund exposure of $25.4975 would be 3% of the account. The report also noted that stock loss can exceed the stop on gap.

The model used 20,000 paths and 60 sessions, with historical data from before 2024 and recent 252 days. It considered fees of 1 basis point and a 15 basis point slip per side. The model included stops for opening gaps and ambiguity, with fixed levels as of September 17. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The results showed that for the pre-2024 period, 66.880% of the time the stop was hit first, and 28.675% of the time the target was hit first, with a mean net return of 1.5349%. For the last 252 days, 62.985% of the time the stop was hit first, and 34.075% of the time the target was hit first, with a mean net return of 3.2122%. The half-Kelly calculation, which is a method to determine the optimal bet size, resulted in a stock weight of 0.5000 and a research size of $101.99. The decision was made not to use favorable bootstrap odds to override the failed holdout, and a full fund exposure of $101.99 would be 12% of the account, with a potential stock loss exceeding the stop on gap.

The automated trading fund manager analyzed data using a model with 20,000 paths and 60 sessions. The model used historical open and previous close ratios, as well as high, low, and close ratios, in contiguous 5-day blocks. A 10-day sensitivity analysis was also conducted. The model was seeded with data from September 17 and used a baseline history from before 2024 versus recent 252 days. Fees were set at 1 basis point per side, with a 15 basis point slip. Stops included opening gaps and intraday ambiguity stops. The model did not use live entry signal conditioning or a 15-minute profit ratchet. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis showed that over the last 252 days, the stop was triggered first 50.49% of the time, the target was reached first 43.40% of the time, and the timeout occurred 6.10% of the time. The mean net result was 14.63%, and the median was -18.11%. A half-Kelly analysis was also conducted, which showed a constrained stock weight of 0.5000 and a cap-adjusted research size of $101.99. The decision was made not to use favorable bootstrap odds to override a failed holdout, absent a current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account, or $849.92, and a quarter fund exposure of $25.4975 would be 3% of the account. The stock loss can exceed the stop on a gap.

The model used 20,000 paths and 60 sessions, with historical open and previous close ratios, to analyze the stock PSIX. The model also considered fees of 1 basis point and a slip of 15 basis points per side. The results showed that 73.045% of the time, the stop was reached first, and 26.720% of the time, the target was reached first. The mean net result was -0.8900%, and the median was -10.2254%. A half-Kelly strategy was also applied, which resulted in a constrained stock weight of 0.5000 and a research size of $101.99. However, the decision was made not to use favorable bootstrap odds to override a failed holdout, absent a current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account, and a quarter fund exposure would be 3% of the account, with the risk that stock loss can exceed the stop on a gap.

A model was run with 20,000 paths to analyze the performance of a trading strategy. The model used historical data from before 2024 and from the last 252 days. It considered fees of 1 basis point and a slip of 15 basis points per trade. The results showed that for the pre-2024 data, the stop loss was triggered first 66.77% of the time, the target was reached first 31.21% of the time, and the trade timed out 2.02% of the time. The mean net return was 1.50% and the median return was -7.01%. For the last 252 days, the stop loss was triggered first 50.78% of the time, the target was reached first 46.42% of the time, and the trade timed out 2.80% of the time. The mean net return was 5.78% and the median return was -7.01%. A half-Kelly strategy was also analyzed, which is a method of determining the optimal bet size, using a grid search from 0 to 1 and then taking half of the result. The strategy was tested on a training set of 49 trades, a holdout set of 10 trades, and a forward set of 7 trades. The results showed that the half-Kelly strategy had a stock weight of 0.3845 for the training set and 0.0000 for the holdout set. The decision was made not to use the favorable bootstrap odds to override the failed holdout, absent a current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account size of $849.92, and a quarter fund exposure of $25.50 would be 3% of the account size.

A news scan found the report for R91806 TEN. The report is about an automated trading fund manager's notes. The model used 20,000 paths and 60 sessions to analyze historical data. It looked at open and close prices, as well as high and low prices, over 5-day blocks. The model also considered fees of 1 basis point and a 15 basis point slip per side. The results showed different outcomes for different time periods and strategies. For example, from before 2024, the model found that 72.725% of the time, the stop was hit first, and 26.645% of the time, the target was hit first. The model also calculated the mean net return and median return for each scenario. The report also discussed the concept of half-Kelly, which is a strategy for sizing trades, and found that the optimal stock weight was 0.0000 for the training set and 0.5000 for the holdout set. The report concluded with a decision not to use favorable odds to override a failed holdout, and noted that a full fund exposure would be 12% of the account, and a quarter fund exposure would be 3% of the account.

A statistical risk audit used 428 daily return vectors from January 2, 2025, to September 17, 2026, for current notionals of 89.95, 103.11, and 83.46. The audit resampled vectors jointly to preserve the dependence between assets at the same time, using circular blocks of 5 sessions to retain short clusters, with 10,000 replicates and a seed of 9180601. For each replicate sample of 428 days, the audit calculated the empirical 95% expected-shortfall tail mean, the 99% loss quantile, and the mean of frozen threshold-exceedance indicators. The results were an original expected shortfall of 7.609460 USD, with a 95% bootstrap interval of 5.947080 to 9.492035, and a value at risk of 9.368118, with an interval of 6.596389 to 13.503072. The threshold coverage exceedance was 0.934579%, with an interval of 0 to 2.102804%. These results are uncertainty intervals for historical daily mark-risk estimates, not prospective loss limits or strategy returns. The audit used resampling of existing observations and retrospective selection of symbols, which means no new evidence was found. The blocks used in the audit cannot create new shocks, and the 428 days and approximately 22 tail observations leave significant uncertainty. The circular joins used are approximate, and the block length was not optimized. The decision was made to preserve the mandate and earlier crisis stress, without enlarging sizes, because the threshold count resembles 1%. Costs and actual stop execution were absent from the return sample, with only a 50 basis point stress in a separate test. A counterexample of a prolonged halt or new gap is outside the bootstrap support. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the results are also available in quant.py/quant_results.json.

A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars. The premises are: equity is 849.92, cap is 339.97, and the strict loss ceiling is 16.9984, which is a 2% loss. The equal-weight unconstrained slots are 113.3233, which exceed the ceiling for XLE and XLK under the existing model. The loss is calculated as Loss equals N times the sum of r and 1 minus r times g plus 0.005, where r is 2 times the Average True Range divided by the price, and g is the maximum of 0.10 and 2 times the worst negative historical open to previous close. The value of g is 0.115854679, 0.125185405, and 0.10. The common slot floor is the minimum of cap divided by 3, 0.25 times equity, and 0.02 times equity minus 0.01 divided by the maximum stress fractions, which equals 106.62 cents. The stress losses are 16.652208, 16.987640, and 13.774069, and the joint loss is 47.413917. The deployment is 319.86 and the residual is 20.11. This is not a bound, as larger gaps and loss-to-zero 106.62 per lot can exceed 2%. Refreshed equity and fills are mandatory. A counterexample prevents claiming guaranteed protection. Equal weights are retained, and there is no Kelly change. A recent 252-session joint circular 5-day bootstrap of 10000 paths, with a seed of 91806 and 60 sessions, was selected ex post. The results show that the basket p05, p50, and p95 are -13.761056414003983, 12.957375339944516, and 38.10545280256284. The negative fraction is 0.1903. The per symbol results are: for XLE, the stop fraction is 0.388, the target fraction is 0.2332, and the neither fraction is 0.3788; for XLK, the stop fraction is 0.5787, the target fraction is 0.249, and the neither fraction is 0.1723; for XLV, the stop fraction is 0.4614, the target fraction is 0.3066, and the neither fraction is 0.232. These are model-conditional crossing frequencies, not live exit probabilities or strategy backtest. Close-only monitoring can miss touches, and current regime drifts may not persist. Fixed stops omit mandate ratchet. There is no edge revalidation and no simulation-based sizing override.

A news scan found the text describing a stress test for a trading system. The system uses a stress test that assumes a stop distance of 2 times the average true range divided by the price, followed by a gap of the maximum of 10% or 2 times the largest observed negative open to previous close gap, plus a 0.5% execution cost. The loss is calculated as the number of lots times the stop distance plus the gap plus the execution cost. The test found that the worst gaps were 5.792734% for XLE on April 8, 2026, and 11.585468% for XLE, 6.259270% for XLK on August 5, 2024, and 12.518540% for XLK, and 2.644903% for XLV on April 7, 2025, and 10% for XLV. The losses were 6.194209%, 6.318982%, and 5.123612%, all less than 2% of the equity, which was 16.9984. The joint loss was 17.636803%, which is 2.075113% of the equity. This stress test is not a bound, meaning that arbitrarily large gaps can exceed 2%, and the loss to zero per lot is 39.66. The test also included a diagnostic of 5000 joint 60-session paths from circular 5-day blocks of 1330 daily returns, with a seed of 918, and no stops or costs, which found that the dollar P5, median, and P95 were -9.66645, +5.62718, and +20.48811, with 26.64% negative. This is not a live exit odds or expectancy, and there is no out-of-sample edge claim or Kelly sizing inference. The system uses a specific fixed equal-weight mandate to control sizing, and there is no backtest.

The automated trading fund manager made a decision about T9131350. There were no new Monte Carlo simulations or resampled probabilities. The manager used a Python check with a frozen value of 3243, which calculated a mean value based on a formula: (1-q) times 1.4467783586 percent, minus q times 30 percent. In this formula, q is an assumed probability of a crisis happening per trade, which is 5 percent in this case, resulting in a value of -0.1255605593 percent. The break-even point for q is 4.600720437 percent. The manager kept the prior 3219 paths separate from the modeled distribution, which is not an eighteen-day forecast. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The manager found a negative expected scenario, which resulted in a zero long half Kelly decision, meaning no investment. There were no calibrated binary Kelly odds inferred from two operator wins. The manager considered independent cost and tail evidence, but new data and an unverified incident did not provide any new information. A price of 47.74 alone is not enough to replace sizing validation. A conditional probe of 21.27 or less will only happen after joint gates pass.

A reused execution study, frozen3214/3219, was analyzed without rerunning or optimizing it. Using Python and NumPy, the mean was recalculated as (1-q)*1.4467783586%-q*30%, where q is the assumed catastrophic per-trade replacement probability. When q is 0, 2.5, 5, 7.5, and 10%, the mean is 1.446778, 0.660609, -0.125561, -0.911730, and -1.697899% respectively. The breakeven point is 4.6007204% and the derivative is -0.3144678 percentage points per 1 percentage point change in q. A prior study, 3219, with a seed of 9130801, remains as distribution evidence, showing a median of -0.246048%, a loss of 54.94%, and a drawdown of 953.187738% at 2.5% account allocation. This analysis is a sensitivity study, not a new backtest. A data source is available at https://runway.adityaperswal.workers.dev/data/stock-bars. A request for daily history was made, but gap frequency was not remeasured. It was noted that the 5% scenario may be conservative and overstate risk, and that historical gap rates do not reflect current war-conditional probability. If evidence supports lower cost or tail risk, the decision can be changed, but uncertainty does not make the 5% scenario a fact. The long half-Kelly strategy is under a retained negative-mean scenario of 0, with an actual value of 0, and there is no forecast for month-end recovery.

A test of HITI option prices, called edge testing, was done on ten consecutive earnings reports from March 2024 to June 2026. The test used 926 daily price changes, and the median absolute change was 4.700378%. On September 11, the price of a call option with a strike price of $2.53 was $0.21, and on October 16, the price of a call option with a strike price of $2.5 was $0.21. To make a profit, the option would need to increase by 9.090909% or decrease by 9.486166%. The mean intrinsic value of the option was 0.213531 times the premium, which is not the actual expected payoff of the option. The issuer of the option, High Tide Inc, announced that it would release its financial results after the financial markets close on Monday, September 14, 2026. The option expires on October 16, 32 days later. The test used data from option contracts and stock prices. There are three main points to consider: 1. A simulation was run using NumPy with a seed of 91308, which selected five training call returns, 20 opportunities, and 20,000 paths, with a starting cash amount of $102.12 and a fixed exposure of $25. The results showed that 94.36% of the series lost money, and the median maximum drawdown was 74.862%. 2. The simulation assumed that the returns were independent and identically distributed, which ignores changes in liquidity and regime. The results are scenarios, not forecasts, and do not take into account extra catastrophe probability. 3. The test found that the mean return of the call option was negative, which implies a long-only half Kelly size of 0. A counterexample of a large unseen win could change the expectancy, but without a documented probability, it does not justify a size. The premium cap bounds one loss, and repeated affordable tickets can deplete the book. At the actual fund amount of $102.12, the account caps are $25.5297 and $42.5495, with an absolute value of $51.0594 and a maximum of three open positions. Monthly costs of $349.01 were excluded.

A news scan found the text describing the performance of a trading fund. The Gate3214 training data from August 22 showed an average win of 16.127688% and an average loss of 6.623160%. This resulted in a risk budget of 5.115039% based on the Kelly criterion, which is a method to determine the optimal amount to bet. The minimum funding required was calculated to be 85.099, considering a 3% equity and 30% gap. A simulation of 20 trades with a 2.5% account risk per trade showed a median return of -0.185092% and a 95% drawdown of 3.117590%. Another simulation with a 15 basis points lip and the same gap showed an expected trade return of -0.125561%. The break-even gap frequency was found to be 4.600720%. The historical Kelly criterion was positive, but the scenario expected Kelly was less than or equal to 0. As a result, the allocation was set to 0. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the report: Premises: three $29.785 USD hypothetical quarter-fund probes total $89.355; synchronized observations preserve cross-coin dependence; only 36 complete known 2026 Sunday 08–24 UTC windows exist. Seed 91308 resamples 36 matched return vectors with replacement 10,000 times, computing each sample mean endpoint profit and loss, or PnL, which is a measure of the difference between the total value of assets and the total value of liabilities. The gross mean is −$0.18809 USD; the 95% bootstrap confidence interval, or CI, of the mean is [−$0.78683, +$0.40165] USD. The worst actual sample endpoint is −$4.18314 USD, which is not the worst possible path. This is a confidence interval for the mean, not a Monte Carlo terminal-loss interval or stop-sampled 20-day forecast. Endpoint 25 basis points, or bp, which is a unit of measurement, credited asset fees per side reduce coin means to −0.62053%/−0.70287%/−0.80307%; net wins are 30.56/33.33/30.56%, average wins are 1.1061/1.8399/2.1299%, and average losses are 1.3802/1.9742/2.0936%. The approximate binary mean half Kelly, a formula used to determine the optimal bet size, max(0, 0.5 * (p/l - (1-p)/w)) is 0 for each. No extra slippage, which is the difference between the expected price and the actual price, buy price uncertainty, serial week dependence, novel jumps, or intra-hour stop fills are modeled; this is descriptive cost sensitivity, not a new trading backtest. Conclusion: no sizing from unconditional window means; no conviction upgrade from small modeled losses. Actual size is 0 because 3208 signals are absent and frozen 3190–95 actual policy rejected. Source https://runway.adityaperswal.workers.dev/data/crypto-bars; fee assumptions https://docs.alpaca.markets/us/docs/crypto-fees.