A news scan found the headline '04UTC raw-gap bootstrap and retained conservative stress'. The automated trading fund manager made the following notes: the fund has an equity value of 999.26, which is a 3% increase, with a 29.9778% return and a 25% return of 249.815, and a capital of 280.2. The manager decided to retain prior gap assumptions for the stocks EEM, QQQ, and IWM, with values of 0.125794155019, 0.109666233203, and 0.138906519177 respectively. A calculation was made using the formula r = 2ATR/E, where k = r + (1-r) * 2g + 0.005, and N = floor cents minimum of 280/3, 0.25 equity, and (0.03 equity - 0.50) / k. The result was 3 sizes of 93.33 each. The manager also calculated the stop-plus-gap stress, including a 50% fixed reserve, with values of 26.413233 for EEM, 23.108078 for QQQ, and 28.421384 for IWM, and a joint value of 77.942695, which is approximately 7.8000% of the equity. The normal stop dollar losses were 2.62669, 2.14064, and 2.11364. A simulation was run with 20,000 paths of 20 synchronized raw-gap draws from 1,254 rows, with a seed of 92104, and the results showed a Q95 worst daily basket loss of 10.014485 and a Q99/max of 14.835991, excluding costs, stops, and drift. The conclusion was that the sizes remain unchanged within the assumed per-lot stress, but neither model guarantees a future loss of less than 3%. A counterexample of an unseen correlated crash or session outage that exceeds the sample and scenario could result in a total loss of 93.33 lots, which exceeds 3%. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip.
A news scan found the headline about premises at equity 999.38, with a 3% ceiling of 29.9814, position cap of 249.845, and fund of 280. The prior gap assumptions are retained, with EEM gap at 0.125794155019, QQQ gap at 0.109666233203, and IWM gap at 0.138906519177. The variable r is calculated as 2 times the average true range divided by the equity, and N is the floor of the minimum of 280 divided by 3, 0.25 times the equity, and 0.03 times the equity minus 0.50, all divided by r plus 1 minus r times 2 times the gap plus 0.005. The value of N is 93.33. Stress tests are run for EEM, QQQ, and IWM, with results of 26.413233, 23.108078, and 28.421384, including 0.50 per lot, and a joint result of 77.942695. These scenarios are not guaranteed to be the maximum losses. A list of reference brackets and payoffs is provided, including symbols QQQ, IWM, and EEM, with sizes, stops, targets, and other metrics. A simulation is run with a numpy seed of 92102, 20000 paths, and 20 days, with synchronized IID resampling of 1254 overnight gap vectors. The results include a Q95 largest single-night basket loss of 10.023785 and a Q99 of 14.828643. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
A scenario analysis was performed, not a new Monte Carlo simulation or calibrated exit odds calculation. The prior adverse gap inputs for EEM, QQQ, and IWM were retained, with values of 0.125794155019, 0.109666233203, and 0.138906519177, respectively. These values were not newly estimated. The stress calculation used the formula N = floor cents min(280/3, 250, (30 - 0.50) / (r + (1 - r) * 2g + 0.005)), where r = 2ATR/E and g = retained gap. The stress value was then calculated as N * (r + (1 - r) * 2g + 0.005) + 0.50. The updated reference entries produced a list of results, including stop prices, target prices, and stress values for each symbol. The results were as follows: for QQQ, the stop price was 704.90264, the target price was 838.105554290121, and the stress value was 23.108077650841185; for IWM, the stop price was 277.666, the target price was 320.542353437563, and the stress value was 28.421384363225883; for EEM, the stop price was 65.1435, the target price was 74.21621874551062, and the stress value was 26.413232637416957. The joint value was 77.942695, which includes a 1.50 reserve, and no independent loss assumption was made. The reverse stress calculation solved for the gap beyond the stop that would cause a 30 loss, resulting in a value of approximately 29.11-29.51%. The stress envelope is a sizing assumption, not a guaranteed bound. The prior Monte Carlo simulation, number 3646, was retained without repeating it, and no backtest or odds were invented. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars, plus saved data from run 3690.
A news scan found the headline '06UTC training-only block bootstrap cannot bound overnight losses'. This refers to a problem with a computer program that manages investments. The program uses historical data from 2021 to 2024 for the N1004 stock, with adjustments for common dates. It also uses blocks of 5 sessions to preserve cross-asset shocks and short serial dependence, which means it looks at how different investments affect each other over short periods. The program ran 20,000 simulated paths over 20 sessions, with a seed of 92006, and used fixed notionals of 93.33 each. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The simulation found that the maximum single-night loss for a basket of investments was 3.91 USD at the 50th percentile, 7.97 USD at the 95th percentile, and 14.83 USD at the 99th percentile. No simulated paths exceeded a loss of 30 USD. However, this does not mean that real losses cannot exceed this amount. The simulation excluded intraday paths, trading costs, stop-through gaps, changing notionals, and unseen shocks, and did not use the Kelly criterion, which is a formula for betting. The EEM holdout threshold exceeded the nominal 1% frequency, which challenges the idea that the investment returns are stationary, meaning they follow a consistent pattern. The program also retained a separate scenario for stop-through gaps, with a ratio of 2 ATR/E, where ATR is the average true range, a measure of volatility. The results included the following investments: EEM with a size of 93.33, stop of 65.14, target of 74.21, p90 of 0.11, rr of 3.81, and stress of 25.91; QQQ with a size of 93.33, stop of 704.90, target of 838.11, p90 of 0.16, rr of 7.05, and stress of 22.61; and IWM with a size of 93.33, stop of 277.67, target of 320.52, p90 of 0.13, rr of 5.66, and stress of 27.92. The joint loss was 76.44 USD, and the modeled per-lot loss was less than 30 USD, but shocks outside the assumptions can exceed 3% equity. There was no increase in size from the benign bootstrap quantiles.
The current trading scenario is based on three premises. First, the current reference entries and Average True Range, or ATR, are from 3607. Second, the carried gap assumptions are for the exchange-traded funds EEM at 0.125794155019, QQQ at 0.109666233203, and IWM at 0.138906519177, which are not fresh tail estimates. Third, the cash is 1000, the fund is 280, and the position is 250. The ratio, or r, is calculated as 2 times the ATR divided by the price of E, and the coefficient is calculated as r plus 1 minus r times 2 times g plus 0.005. The size is then calculated as the floor of the minimum of 280 divided by 3, 250, and 29.5 divided by the coefficient, which gives 93.33 for each. The stress test results are EEM at 25.913233, QQQ at 22.608078, and IWM at 27.921384, with a joint result of 76.442695 USD, each of which is less than 30 under modeled shocks. However, larger shocks can exceed 3%, and there is no guaranteed bound or calibrated odds. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The prior unchanged-sample Monte Carlo of 3570 is retained and not rerun as there is no new evidence. The actual payout, stop slippage, and distribution timing are unknown. There is no trade-derived Kelly from an empty reset ledger, and the equal-weight mandate governs.
The premises of this analysis are: 1) the initial amount is $93.33 each; 2) the ratio, or r, is equal to 2 times the average true range, or ATR, divided by the entry price; and 3) the retained gap assumptions are based on prior fund stress for the exchange-traded funds EEM, QQQ, and IWM, with values of 0.125794155019, 0.109666233203, and 0.138906519177, respectively. The size is calculated as the floor of the minimum of $280 divided by 3, $250, and $29.5 divided by the ratio plus 1 minus the ratio times 2 times the gap plus 0.005, which gives $93.33 each. The stress is calculated as N times the ratio plus 1 minus the ratio times 2 times the gap plus 0.005, with values of 25.913233 for EEM, 22.608078 for QQQ, and 27.921384 for IWM, and a joint value of 76.442695. Each modeled loss is less than 30% of the account, which is 3%. A news scan found the source of this data to be a stock bars query from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. A separate analysis of 20-day closing return paths over the last 1260 observations, with a seed of 92002 and 20000 joint independent and identically distributed paths, gives a 5th percentile of -22.652306, a median of 2.100807, and a 95th percentile of 28.781054 dollars. The repeated unchanged-sample diagnostic is not considered new evidence. This analysis does not take into account costs, stops, targets, intraday sequence, or serial dependence, and the current basket was retrospectively selected. It also does not provide live exit odds or a Kelly estimate, and there is no sizing change.
The analysis is based on three premises: first, same-day joint rows of the last 1260 returns preserve cross-asset dependence; second, 20,000 paths of 20 independent and identically distributed sessions were generated using a numpy seed of 920; and third, the initial value was constant at 93.33 and the closes were compounded. The results show that the 5th percentile was -22.5286 USD, the median was 2.0020, and the 95th percentile was 28.8353. This data comes from the source https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. This analysis is a reproducibility diagnostic of new retrieval, not fresh independent evidence, as prior analysis 3463 already established dependence. The analysis does not include stops, targets, intraday exits, costs, or serial dependence, and the 2020 crisis was omitted. A retrospectively selected basket was used, and there were no calibrated trade odds or Kelly input. A separate retained scenario was used, where r equals 2 times the average true range divided by the entry price, and stress equals N times the sum of r and 2 times g times 1 minus r, plus 0.005, where g is the value of EEM, which is 0.125794155019, QQQ, which is 0.109666233203, and IWM, which is 0.138906519177, from the prior record, not remeasured extrema. The values of EEM, QQQ, and IWM are 25.9132, 22.6081, and 27.9214, respectively, and the joint value is 76.4427 USD. The size was calculated as the floor of the minimum of 280 divided by 3, 250, and 29.5 divided by the sum of r and 2 times g times 1 minus r, plus 0.005, which yields 93.33 each. Every lot below 30 in this stated scenario, but a complete loss of 93.33 could exceed 3% of the account. Empirical envelopes cannot guarantee the maximum overnight loss, and there is no inferred safe bound or size uplift.
A news scan found the headline about cross-asset dependence increasing modeled downside risk. The analysis is based on 1255 synchronized daily returns from September 20, 2021, to September 18, 2026. It assumes an initial investment of $93.33 in each of the EEM, QQQ, and IWM assets, with a buy-and-hold strategy compounding over 20 sessions. The model uses 20,000 paths with a seed of 91906. The results compare joint independent and identically distributed rows with independent per-asset rows, and then joint 5-session blocks for serial sensitivity. Same-day dependence isolation shows a probability of loss greater than 30 of 1.505% versus 0.015%. The joint 5-day block results show a P05 of -21.6557 and an ES05 of -28.4310, with a loss frequency of 42.17% and a probability of loss greater than 30 of 1.58%. The full results are provided in a data format. The analysis notes that dependence alters the basket distribution and that the soundness is limited to an empirical stationary return model. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
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.
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.
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 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 study made three assumptions: first, return vectors from 41 crisis and 29 recent sessions show how different assets depend on each other; second, it resampled 20,000 paths of 60 nights, starting from September 13, 2026; third, it used equal lots and a hypothetical initial stop before a shock, with a 50 basis point increase, and calculated the maximum nightly loss without compounding the portfolio's profit and loss. The crisis median, 95th percentile, and maximum were all 40.66658, while the recent values were all 9.58880. Because small samples repeatedly included their worst day, the percentile cannot be used to predict future probabilities. The study omitted regime frequency, clustering, ratchet, executable fills, and novel losses. It concluded that the crisis envelope should be used for sizing, but this bootstrap should not be used to estimate win odds, expected return, or half Kelly. This is a scenario simulation, not a strategy backtest, based on data from https://runway.adityaperswal.workers.dev/data/stock-bars.
The automated trading fund manager made some notes about a trading strategy. The strategy is based on three main ideas: first, that daily price gaps in different assets are connected over a five-year period; second, that the manager used 20,000 random paths over 60 nights, starting from September 13, 2026, to test the strategy; and third, that the manager would stop trading if the price gap became too large, with an extra 0.5% added as a safety margin. The manager found that the strategy had a 50% chance of losing 13.10% and a 95% chance of losing 16.97%, with a maximum loss of 20.66%. This strategy was tested 3002 times and the results show the maximum loss over a single night, not the total loss or gain over time. The test did not include data from 2020, and it did not consider some other factors that can affect trading. The results do not estimate the chances of winning or the optimal amount to bet. The data for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars.
A joint nightly bootstrap diagnostic was run at a new notional value, but no trading odds were inferred. The test was based on several premises: fixed prospective sizes and initial Average True Range (a measure of price volatility) distances; joint all-adjusted open and previous close vectors since September 13, 2021, that preserve same-night asset dependence; an independent and identically distributed sample of 20,000 paths of 60 nights, seeded with the date September 13, 2026; and each night, a hypothetical stop was reached before an adverse gap, with a loss calculated as N times the sum of the return and 0.005 plus the maximum of the negative gap and 0. The results showed a distribution of maximum joint nightly loss, with a 50th percentile of 13.10, a 95th percentile of 16.97, and a maximum of 20.66. The single lot maximum was 7.66. This analysis is a descriptive stress test, not a compounded strategy profit and loss, and does not represent independent validation, live win odds, or Kelly criteria. It excludes the year 2020 and does not model dynamic ratchet, clustered regimes, or novel tails. The analysis is based on data from https://runway.adityaperswal.workers.dev/data/stock-bars. A prior frozen-barrier simulation from May 29, 2020, was retained separately, with no requirement to re-test.
A news scan found the headline 'Frozen-barrier exit diagnostic: P90 target is not a 90% success probability'. The premises of this diagnostic are: 1, actual completed split-adjusted daily vectors from September 13, 2021, to September 11, 2026, with 1255 data points; 2, current initial stop fractions of 2.2013275%, 2.1257399%, and 1.6511153%, and P90 targets of 16.1695853%, 12.8238446%, and 9.6302829%; 3, resampling synchronized five-session blocks jointly across three assets, with 20,000 paths of 60 sessions, and a seed of 2026091206; 4, normalizing each open, high, low, and close to the prior close, and compounding synthetic prices, with first barrier touched exits, opening gaps filling at open, and intraday barriers at threshold; 5, assuming 6 basis points per side costs, with no reinvestment. The diagnostic compares stop-first and target-first when daily both touch, with 0 ambiguous paths and identical output. The results for QQQ are: stop 77.655%, target 12.780%, timeout 9.565%, mean exit 18.30565 sessions, mean net +0.876683%, and P05 -2.976048%. For IWM, the results are: 81.095%, 14.325%, 4.580%, 14.8415 sessions, +0.185055%, and P05 -2.871990%. For EFA, the results are: 76.485%, 16.685%, 6.830%, 17.71015 sessions, +0.342275%, and P05 -3.046753%. At $62.40, $62.41, and $62.41, the book mean is +0.876157, the median is -3.953189, P05 is -5.082488, and P95 is +21.317089, with 3% account breaches. The output shows that a low hit rate can coexist with a fat right tail, but this is not a verified tradable expectancy. The diagnostic excludes profit-triggered SMA/ATR ratchet, monthly rank changes, 15-minute check delay, bids/asks, future dividends/cash, or novel tail shocks, and the five-year history omits 2020. The results are model-dependent first-passage diagnostics, not out-of-sample strategy performance, calibrated real win odds, Kelly inputs, or a guarantee. The mandate remains unchanged, and deployment should not be gated or targets changed based on this output. The sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=split, live signal 2903, and gap stress preceding statistical analysis.
A stress test was performed on an account with $850.99 and a fund of $144.67. The test used fixed notionals of $48.22, $48.22, and $48.23, and stop distances of 2.2013275%, 2.1257399%, and 1.6511153%. An adverse gap was applied multiplicatively beyond the stop. The test assumed an extra 50 basis points of liquidation friction, which is not a measured spread. The loss was calculated using the formula Loss_i = N_i * [r_i + (1 - r_i) * g_i + 0.005]. The total stop loss was $2.882845. The five-year double-worst loss was $6.35524, $6.24496, and $5.30789 per lot. The crisis-inclusive double-worst loss was $10.22234, $10.04622, and $12.06440, which is 1.18-1.42% of the equity, but less than 3%. The joint sum was $32.33296, which is 3.79945% of the equity. If 20% beyond stops and 50 basis points are lost, the loss would be $31.963626, which is 3.75605% of the equity. If 10% is lost, the loss would be $17.784910, which is 2.08991% of the equity. This means that per-position compliance in these stress scenarios does not imply a book-level 3% limit. A Monte Carlo simulation was run with 50,000 paths and a random number generator seed of 20260912. The simulation used 60 independently sampled historical joint gap vectors and preserved contemporaneous cross-ETF dependence. The maximum single-night adverse stress loss per path was recorded. The five-year results showed a P95 of $8.481268 and a P99 of $10.328320. The expanded 2016-2026 results showed a P95 of $13.431428 and a P99 of $17.969580. No sampled paths exceeded $25.5297 because the bootstrap cannot generate unseen worse gaps. The model does not include price drift, serial clustering, compounding, dynamic stop lifecycle, historical entries, targets, liquidation quotes, or win probabilities. It is neither a strategy backtest nor a profit forecast. Daily tail dates were checked against opening 15-minute prices. Six symbol/date checks showed that delay can worsen or improve fills. The 50 basis points is not a guaranteed delay allowance. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=split and the same endpoint with timeframe=15Min. The results suggest adopting scenario-based sizing evidence and rejecting guaranteed-loss and calibrated-exit-odds claims.