A news scan found the report that dividend adjustments made at 04UTC altered certain gaps but did not change the current worst-tail sizing. The report matched 1254 daily gaps from 2021-09-21 to 2026-09-18 for a selected exchange-traded fund, or ETF, which is a type of investment fund that is traded on a stock exchange. The worst raw gaps were -5.739837% for EEM, -5.357103% for QQQ, and -5.274746% for IWM, and the worst all gaps were -5.737169%, -5.357022%, and -5.272192%. The largest raw and all differences were 1.839892 percentage points, 0.353658 percentage points, and 0.512930 percentage points. The EEM had its largest gap in December 2020 and 2023, which coincided with a distribution of 0.748036 by the issuer, which is the company that created the investment fund. The IWM had a gap of -0.448057% on September 15, 2026, compared to -0.188048% when adjusted for splits, and the issuer made a distribution of 0.750436. The raw twelve-month returns were 25.854300%, 22.279661%, and 18.925028%, versus adjusted returns of 28.311639%, 22.869016%, and 20.131929%, and the top-three relative ordering remained unchanged. The conclusion is that dividends, which are payments made to investors, materially affect individual gaps and return levels but not the measured worst gaps in this case. The report notes that counterexample distributions could dominate a calmer future sample, so both series should be retained. The selected basket is descriptive, not an out-of-sample edge, meaning it is used to describe the current situation, not to predict future results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip adjustments raw,split,all, and the issuer originals are in the observations.
The automated trading fund manager made some observations based on data. They looked at the daily returns of some investments over a period of time and found that the performance of these investments is related. The correlations between QQQ and IWM, QQQ and EEM, and IWM and EEM over the past five years are 0.778152, 0.692313, and 0.649814 respectively. Over the last 60 days, these correlations are 0.711375, 0.833309, and 0.633538. IWM has fallen to 3.72733% of its simple moving average, EEM to 7.36752%, and QQQ to 8.27186%. The manager notes that annual momentum can be slow to react to a new reversal in the market. The correlation between these investments means they do not act like independent bets. A source of data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which is a website that provides stock market data.
A news scan found the headline '00UTC attack basket: high shared correlation and narrow IWM regime cushion'. This refers to a market analysis that looked at how closely the prices of certain stocks moved together from September 21, 2021, to September 18, 2026, using 1254 observations. The analysis found that the stocks QQQ and IWM had a correlation of 0.778152, QQQ and EEM had a correlation of 0.692313, and IWM and EEM had a correlation of 0.649814 over the five-year period. In the latest 60 observations, these correlations were 0.711375, 0.833309, and 0.633538, respectively. This means that even though QQQ, IWM, and EEM represent different geographic areas, they still share common risks. The analysis also found that IWM had the weakest protection against losses, falling to its simple moving average by only 3.72733%, compared to EEM's 7.36752% and QQQ's 8.27186%. Despite IWM having a 20.13193% annual return, its protection against losses is the weakest. The analysis did not suggest making any discretionary exits during the month, and the initial eligibility criteria were refreshed before entry. The data for this analysis came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. This analysis is based on a selected sample and descriptive inference, and it does not provide any live win probability or Kelly estimate since there is no actual trading data to base it on.
A news scan found the following text: Premises include retaining prior adverse gap for EEM at 125.794155019, QQQ at 109.666233203, and IWM at 138.906519177. The risk, or r, is calculated as 2 times the average true range, or ATR, divided by the current reference close. Stress is calculated as N times the risk plus 1 minus the risk times 2 times the gap plus 0.005, plus 0.50, where N is the floor of the minimum of 280 divided by 3, 250, and 30 minus 0.50 divided by the risk plus 1 minus the risk times 2 times the gap plus 0.005. The current equity is 1000. Results are as follows: for QQQ, the size is 93.33, the stop is 704.90264, the target is 838.105473380901, the probability of 90 percent, or p90, is 0.1616958533244174, the risk reward, or rr, is 7.049793645687354, and the stress is 23.108077650841185. For IWM, the size is 93.33, the stop is 277.666, the target is 320.5246054777956, the p90 is 0.12821050854556687, the rr is 5.661269113738809, and the stress is 28.421384363225883. For EEM, the size is 93.33, the stop is 65.1435, the target is 74.21425092953517, the p90 is 0.10717963493264468, the rr is 3.8082432703605487, and the stress is 26.413232637416957. The combined result is 77.942695, which includes a 1.50 fixed cost reserve, whereas the prior result of 76.442695 excluded that reserve. There is no market deterioration or size change inferred. All modeled individual losses are less than 30. Historical sampled extremes can be exceeded, so 3 percent is a sizing scenario, not a hard guarantee. A complete loss of 93.33 would exceed 3 percent. Prior results from 3644 EEM and 429 tail exceedances, and 3646 bootstrap retained, were not rerun as new evidence. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. There are no calibrated live win odds or Kelly from an empty execution ledger, so a fixed mandate of equal sizing prevails.
A news scan found the headline about a 06UTC held-out gap calibration for the EEM training tail undercovers later observations. The premises of this calibration are: 1) adjusted open/prior-close minus 1 gaps from the primary feed at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all; 2) fixed training from 2021 to 2024 with 1004 observations and untouched-by-this-fit holdout from 2025 to September 18, 2026, with 429 observations; and 3) the current basket was selected after the fact, so this is a measurement calibration, not an out-of-sample strategy return, which refers to a strategy's performance on new, unseen data. The results are as follows: for EEM, the training set had 1004 observations, the test set had 429 observations, the 1% training threshold was -0.023161498373743362, the minimum training value was -0.052591847005536096, the minimum test value was -0.05737169009480214, there were 15 test exceedances, and the test exceedance rate was 0.03496503496503497, with a 1% test threshold of -0.03355377029926494; for QQQ, the training set had 1004 observations, the test set had 429 observations, the 1% training threshold was -0.019642697452250054, the minimum training value was -0.05357022225224606, the minimum test value was -0.04140786749482395, there were 7 test exceedances, and the test exceedance rate was 0.016317016317016316, with a 1% test threshold of -0.027485668992367554; and for IWM, the training set had 1004 observations, the test set had 429 observations, the 1% training threshold was -0.0225591653094287, the minimum training value was -0.0527219225110348, the minimum test value was -0.04711779448621556, there were 5 test exceedances, and the test exceedance rate was 0.011655011655011656, with a 1% test threshold of -0.02526703165930107. The common-date thresholds were exceeded in two or more names on 7 days and in all three names on 2 days. The EEM lower 1% training threshold had a 3.4965% holdout exceedance, compared to 1.6317% for QQQ and 1.1655% for IWM, which suggests that the stationarity, or the idea that the data's statistical properties are constant over time, is unsupported. The historical worst equal basket overnight loss was 93.33, with a loss of 10.637725 USD. A counterexample is that the QQQ and IWM holdout minima were less negative than their train minima, but no universal worsening is claimed. The selected basket, adjusted-price proxy, serial dependence, and provider revisions limit the inference, and no fees or stop implementation were modeled. No new strategy is proposed, and the existing larger stress envelope will be retained.
The analysis is based on three premises: same-date all/split responses are paired with 1434 bars each, provider documents include all cash dividends, and the issuer independently lists two distributions for EEM. The source of the bars is https://runway.adityaperswal.workers.dev/data/stock-bars. A delta analysis was performed on several symbols, including SPY, TLT, EEM, EFA, GLD, IWM, and QQQ. The results show that the top three selections remain unchanged, but the return definitions are materially different. The annual delta percentage points (pp) range from 0.589355% for QQQ to 4.132789% for TLT. The analysis also found that IWM-SPY adjusted margin is 3.004713pp and SPY needs a 2.565342% extra cumulative gross-return correction relative to IWM to tie if other inputs are fixed. The EEM issuer's dividend reconstruction shows a difference of 0.002103pp between the ex-date-close reinvestment and adjusted methods. The largest all/split annual difference is 4.132789pp for TLT. The analysis concludes that the current selection is supported by observed robustness, but its soundness is bounded by common provider error.
The analysis is based on three premises: the current basket of stocks was selected looking back, the daily returns of the stocks are from the last 1260 days up to September 18, and a subset of 599 days where the equally weighted basket of stocks had a return of less than 0. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The correlation between the stocks QQQ and IWM is 0.778166, between QQQ and EEM is 0.692624, and between IWM and EEM is 0.650545 when looking at all the data. However, when only looking at the days when the stocks did poorly, the correlations are 0.615061, 0.475723, and 0.396780 respectively. These lower correlations are based on a specific set of conditions and do not necessarily mean that the stocks will help reduce losses during a crisis. In fact, if many investors sell their stocks at the same time, it can cause big losses even if the historical correlation between the stocks is not very high. This analysis is just a description of the data and does not provide any independent investment strategy or expected returns.
The audit is based on three premises: the current selected basket of 3536, the last 1260 aligned close returns through September 18, and the use of Pearson correlation, which describes normal conditions, not crisis conditions. The correlations between QQQ and IWM are 0.778166, between QQQ and EEM are 0.692624, and between IWM and EEM are 0.650545. The worst single-day loss for the basket was -15.1493 USD, with a 93.33 percent equal loss. This data comes from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. This shows that even when stocks are diversified, their correlations can increase during a crisis, and past losses do not predict future losses. The winners were chosen after the fact, so this is not a test of a real-world strategy. This does not mean the rankings or sizes should be changed. A gap scenario of 76.4427 is considered separately.
A news scan found the headline 'Weekend gap tails versus ordinary overnights: joint risk is real, mean penalty uncertain'. This refers to a study of stock market gaps, which are differences between the closing price of a stock on one day and its opening price the next day, a concept known as a gap. The study looked at gaps in the prices of certain stocks, specifically EEM, QQQ, and IWM, over a period of time from September 2021 to September 2026. It found that weekend gaps were worse than ordinary overnight gaps, with a joint negative return of 31.1538% for weekends compared to 28.2653% for ordinary nights. The study also found that the correlations between the gaps in these stocks were high, with a correlation of 0.716731 between EEM and QQQ, 0.653395 between EEM and IWM, and 0.826848 between QQQ and IWM. The largest joint gap occurred on August 5, 2024, with EEM falling 5.25918%, QQQ falling 5.35702%, and IWM falling 5.27219%. The study concluded that while weekend gaps were worse, there was no established mean penalty or future bound, meaning that it is unclear whether these gaps will result in long-term losses. The study also noted that the worst individual gap for EEM occurred on an ordinary day, March 3, 2026, with a loss of 5.73717%. The data for this study came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
The fund manager made some assumptions to analyze a basket of investments. They chose the top 3 investments, which are EEM, QQQ, and IWM, and gave each the same weight of 93.33%. They looked at the daily returns of these investments over 252 days and found that they are highly correlated, meaning they tend to move together, with correlation values of 0.831269, 0.690365, and 0.739377. On 74 out of 252 days, all three investments had negative returns. The worst loss over a day was 13.874044% and the 99th percentile loss was 8.254056%. This analysis is based on past performance and does not guarantee future results. The fact that these investments are from different parts of the world does not mean they are independent of each other, as they can still be affected by the same global events. The data for this analysis came from the website https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The fund manager has not yet measured the performance of this strategy in real-time or determined its win rate or optimal betting size, also known as Kelly calibration, as there are no live trades recorded in their ledger.
The automated trading fund manager made some premises. They chose three equity ETFs. The returns of these ETFs are aligned on common days. The sizes of the ETFs are hypothetical, not based on historical data. Over 252 sessions, the Pearson correlation between EEM and QQQ was 0.831269, between EEM and IWM was 0.690365, and between QQQ and IWM was 0.739377. All three had negative returns on 74 out of 252 days. The current static basket has a daily loss of 8.159653 at the 1% quantile and a worst loss of 13.786708. On Friday, the hypothetical close-to-close profit and loss was +0.424213, which is not the missed executable intraday profit. IWM had a return of -0.465964% on Friday, -4.307993% over 20 days, and is 3.871641% above its Simple Moving Average, or SMA, which is a calculation to find the average price of a stock over a certain period. QQQ had a return of +0.631870% on Friday, +1.479752% over 20 days, and is 9.017802% above its SMA. EEM had a return of +0.179345% on Friday, +0.615431% over 20 days, and is 7.953498% above its SMA. This is a counterexample, meaning it is an exception to the general rule, and correlations may rise in a crisis and daily closes may conceal stop overshoot, which is when a stock price moves past a stop-loss order. The selection of these ETFs is based on hindsight, which means looking back at past data, and does not allow for strategy win-rate or Kelly inference, which is a way to measure the performance of a strategy. There are no closed trades, so the expectancy, or expected return, and slippage, or the difference between the expected and actual price of a trade, are unmeasurable. The specific mandate validation has already been done, and there will be no repeat backtest, which is a test of a strategy using historical data, or discretionary short-term signal veto, which is when a trader overrides a signal to buy or sell. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which is a website that provides stock market data.
A news scan found the text of a chronological risk-model audit. The audit is based on three premises: the current sizes of EEM, QQQ, and IWM, which are 89.95, 103.11, and 83.46, determine the fixed dollar daily return exposure; the adjusted close returns from 2016 to September 2017 and 2026 are aligned; and the model decay is 0.94, which was fixed before this computation. The standardized loss at the 99th percentile was estimated from 2016 to 2024 after 252 warm-up observations. The test period is from January 2, 2025, to September 17, 2026, which is 428 days, and the training period is 2263 days. The prior-day exponentially weighted moving average, or EWMA, squared return forecasts the next-day scale. The test data updates volatility only after realization, and the threshold multiplier is 3.015193607. There were four exceedances out of 428 days, which is 0.934579%. The latest next-day threshold is 8.343642 USD. This audit is a chronological risk calibration, not an independent out-of-sample, or OOS, strategy validation. The symbols and weights were chosen with hindsight, and prior runs already saw historical data. No selection, rebalancing, exits, or fees were modeled. No tuning sweep or gate was imposed. The pair correlations for EEM-QQQ, EEM-IWM, and QQQ-IWM are 0.719423, 0.689657, and 0.756580 for the training period, and 0.772195, 0.682891, and 0.802395 for the test period. The last 60 days had correlations of 0.832760, 0.634451, and 0.711276. The downside 189 days conditional correlations are 0.578614, 0.393634, and 0.642452, subject to selection conditioning. The test actual joint daily standard deviation is 3.530578, versus 2.239607 for independence. Ignoring dependence understates dispersion. The historical test worst dollar daily loss was 15.128603 on April 4, 2025, and the empirical value at risk, or VaR, at 99% is 9.368118, and the expected shortfall, or ES, at 95% is 7.609460. The dollar returns assume daily constant exposure for a risk lens, not buy-and-hold net asset value, or NAV. The valid conclusion is to retain dependence-aware stress sizing, not per-lot loss interpreted as book loss. The soundness limits one provider, revised history, daily closes miss exit delays and tails, and there is no confidence that 99% of future losses will stay below the threshold. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the results are reproducible with quant.py and quant_results.json.
A trading fund manager made some notes about buying and selling stocks. The manager looked at the history of the stocks from 2016 to September 2017 and from 2020 to February 18, 2020, to April 15, 2020. The manager used a special formula to decide how much to buy or sell, which included a 50 basis point friction assumption, meaning a 0.5% cost. The formula also included a variable called g, which is the maximum of the daily adverse open gap and the adverse first two 15-minute lows relative to the prior close. The manager chose to buy and sell the following stocks: QQQ, IWM, and EEM. For each stock, the manager calculated the stress, stop, target, and gain. The stress is a measure of how much the stock price might drop, the stop is the price at which the manager would sell the stock to limit losses, the target is the price at which the manager hopes to sell the stock for a profit, and the gain is the potential profit. The manager also calculated the normal size of the trade and the size with a 3% equity budget. The manager found that the combined stress of the three stocks was 74.99695, which is 8.823% of the equity, and the normal stop was 6.88904, with a target gain of 37.02158 before costs. The manager used data from the website https://runway.adityaperswal.workers.dev/data/stock-bars to make these calculations. The manager noted that this is just a sizing analysis and not a backtest of the strategy or a calibrated live odds/Kelly analysis.
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 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.
The premises are as follows: the equity is 850.99 and the fund is 289.34, which is higher than the previous value of 221.26. The sizes are 96.44, 96.45, and 96.45. The retained split-adjusted worst gaps from prior to September 11, 2016, are 9.457211%, 9.301937%, and 11.623512%, all of which occurred on March 16, 2020. The loss is calculated using the formula loss = N * (r + (1 - r) * 2g + 0.005), where r is 2 times the average true range divided by the close. A news scan found the following data: the symbol QQQ has a size of 96.44, a normal value of 2.1229602366830784, a gain of 15.593948094606814, an rr of 7.3453792610693736, a stress of 20.44468315542464, a breach gap of 0.2430583044256341, and an sma distance pct of 8.437307785361249. The symbol IWM has a size of 96.45, a normal value of 2.0502761345148643, a gain of 12.368598158700099, an rr of 6.032649919922496, a stress of 20.094531818797652, a breach gap of 0.243614842541859, and an sma distance pct of 5.756535927075612. The symbol EFA has a size of 96.45, a normal value of 1.5925006841611973, a gain of 9.288407846549198, an rr of 5.832592688298649, a stress of 24.12629631591408, a breach gap of 0.24726510275948646, and an sma distance pct of 5.986340973057924. The joint stress is 64.6655, which is 7.5989% of the equity, and the ordinary stress is 5.7657. The gross all-target is 37.251 over 1-3 months, which is not the expectation. The new 252 daily return correlations are as follows: QQQ has a correlation of 1 with itself, 0.7375146484818859 with IWM, and 0.7068026873334806 with EFA. IWM has a correlation of 1 with itself, 0.7375146484818859 with QQQ, and 0.7494056074136088 with EFA. EFA has a correlation of 1 with itself, 0.7068026873334806 with QQQ, and 0.7494056074136088 with IWM. A larger cap increase of 30.77% does not increase the evidence. The per lot stressed losses are below 25.5297, but the EFA margin is only 1.4034. A gap beyond the stop is needed for a 3% loss, which would be between 24.31 and 24.73. A full loss of 96.45 exceeds 3%, so there is no hard guarantee. The hard fund and 25% position caps pass. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars, which retained the prior risk history and has a new adjusted correlation window.
The fund has increased its capital by 18.18%. The current equity is $850.99 with a capital of $221.26, which replaces the previous capital of $187.22. The fund is divided into three equal parts of $73.75, $73.75, and $73.76. The worst split-adjusted gaps from 2016 to September 11 are retained for the stocks QQQ, IWM, and EFA, with percentages of 9.457211%, 9.301937%, and 11.623512% respectively, all as of March 16, 2020. A scenario is used to calculate the stress, where L equals N times the current rate plus 0.005, and the current rate is the current average true range divided by the close price. The assets in the fund are QQQ, IWM, and EFA, with sizes of $73.75, $73.75, and $73.76 respectively. The joint stress is 5.81% of the equity, and the gap beyond the stop needed to lose 3% of the account is $32.63, $32.69, and $33.01. The correlations between the assets range from 0.779 to 0.817, which means they are not independent. The gross target is $28.49, and the ordinary stop is $4.41. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. A prior simulation from 2905 is retained, but it is not a live win odds or Kelly, and no new backtest is required due to the fixed mandate.
The fund allocation has increased by 29.41% from 144.67 to 187.22, with equity remaining unchanged at 850.99. A proposed allocation of 62.40/62.41/62.41 is being considered. The crisis-inclusive period from 2016 to September 11, 2026, has been analyzed, with 2687 split-adjusted daily opens and prior closes. A loss scenario has been modeled, including a 2ATR stop, multiplicative adverse gap, and assumed 50bp friction. The formula used is L=N*[r+(1-r)*g+.005], where r is 2ATR/close. The worst gaps were found to be QQQ 9.457211%, IWM 9.301937%, and EFA 11.623512%, all on March 16, 2020. Gap correlations were QQQ/IWM 0.811520, QQQ/EFA 0.778810, and IWM/EFA 0.816941. Using DOUBLE, each worst gap gives 13.228414/13.002589/15.611427 dollars, which is 1.554474/1.527937/1.834502% equity each below the 25.5297 risk threshold. The joint total is 41.842430, which is 4.916912% equity. A common 20% extra beyond stop+50bp loses 41.364711, which is 4.860775% equity, and a common 10% loses 23.015787. Ordinary stops sum to 3.730764, and all initial targets yield 24.103442 gross, which is not the expected payoff. To reach a 3% per lot requires 39.071750/39.112107/39.405948% extra gap beyond stop under this equation. A total loss of 62.41 lot is 7.333811% equity, so there is no guaranteed 3% bound. An unconditional per-lot total-loss cap would need to be <=25.52 before fees. The conclusion is that the current equal lots pass explicit severe historical scenarios, but diversification does not imply a 3% bound. This is stress sizing, not a trade-edge backtest or probability forecast, and full-loss possibility and pre-2016/sample omissions remain. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=split&start=2016-01-01 and user venue/account context on September 12, 06:01 UTC.
A question is raised about whether a fixed allocation of roughly $48.22 is still acceptable when partial exits are delayed until regular trading hours. This is based on several premises: the current equity is $850.99, with 3% being $25.53; losses can increase rapidly when stop-loss orders are triggered; the selected exchange-traded funds (ETFs), which are a type of investment fund that trades on a stock exchange like a stock, share risks associated with the overall stock market; and historical data may not fully capture potential future shocks. Data from a source, which is a website providing financial information, shows the daily performance of three ETFs, QQQ, IWM, and EFA, over a one-day period with adjustments made for stock splits. The data vendor uses a specific method, known as SIP daily open/prior close-1, split-only adjustments, to preserve price gaps that occur when a stock goes ex-dividend, meaning it no longer includes the right to the next dividend payment. Over a five-year period from September 2021 to September 2026, the worst losses were -5.3571% for QQQ, -5.2747% for IWM, and -4.5015% for EFA. The worst losses for QQQ and IWM occurred on August 5, 2024, and for EFA on March 3, 2026. Analyzing Monday data, the worst losses were -5.3571%, -5.2747%, and -3.5974%. The correlations between the daily gaps in prices were.8245,.7278, and.7597, and the total return correlations were.7784,.7349, and.7545. The worst joint opening loss at the proposed weights was $6.8617 on August 5, 2024. Expanding the data to January 2016 to September 2026, the worst losses for all three ETFs occurred on March 16, 2020, with QQQ losing -9.457211%, IWM losing -9.301937%, and EFA losing -11.623512%. The joint loss was $14.651681, with correlations of.81152,.77881, and.81694. This expanded data changes which ETF had the largest loss in the tail of the distribution, indicating that relying only on the five-year data may be overly optimistic. A chronological check of the tail risks from 2016 to 2024 and from 2025 to 2026 shows different cutoffs and breaches. This analysis was not frozen in time and does not claim to have a pristine holdout sample. The selection of today's ETFs is conditioned on their past performance and survival, and the historical data lacks extremes like those seen in 2008. No inference is made about the probability of future compliance with these risk bounds. The fact that all three ETFs had their worst loss on the same date is a counterexample to the assumption of independence, and the earlier 2020 observations exceeded the five-year maximum for EFA, showing that historical maximums may not be limits. The conclusion is to retain fixed ranks, use risk estimates that include crisis scenarios, and explicitly disclose the potential for common shocks, without re-testing momentum.