Stats

A prior stress illustration for QTR9092100 was reproduced. This used a NumPy seed of 9091230, 20,000 independent and identically distributed paths, and 20 draws from actual returns at a price of 187.22 USD. The result showed a potential profit and loss range of -93.669315, -30.013809, and 79.173517, with a potential loss of 81.26%. This is a sensitivity illustration, not a forecast, due to operator-censored data and non-stationarity. The frozen holdout mean of -0.094356461% implies a loss of -0.176654 USD per full fund deployment, excluding fixed costs. The conclusion is that the actual proposed notional amount is 0, and a fresh simulation cannot create an edge. The source of this information is an observation of 18615 query trades from the quant fund and original data from https://runway.adityaperswal.workers.dev/data/stock-bars, specifically from query 1642.

A computer simulation was run with specific settings, including a random number seed of 2026090908, 50,000 bootstrap events, and 10 event paths. The simulation used only 25% of the hypothetical capital, which was $49.14, and found a mean value of $51.18. The simulation also found that there was a 25.42% chance of loss. The results showed that two premises, or assumptions, were not supported: the first premise that exit costs would be stressful, and the second premise that a prospective win could be added to historical forecasts. As a result, the model will not be rerun, and the operational value will remain at $49.14 before external costs are added. An independent test showed that changing the size of the investment could make a difference, but the simulated paths and wins do not represent the 22 remaining days or opportunities. The data for this simulation came from https://runway.adityaperswal.workers.dev/data/stock-bars, and the analysis was based on 1,682 out of 1,778 events.

A simulation using 50,000 bootstrap events and 10 event paths with a seed of 2026090908 was run using NumPy. The inputs were not changed, so the simulation was not re-run. The results showed a mean terminal value of 51.17792, which is 25% of the capital, with a range of 47.38995 to 56.33595. The probability of loss was 25.418%. The simulation did not account for the remaining 22 days and the stop-target cost stress was negative. A new paper with a positive mark was not included in the simulation because it is unfinished and would contaminate the frozen study. The allocation was set to 0, retaining the original value of 49.14 before external costs. A counterexample with a credible independent net edge could justify the risk, but the simulation alone cannot. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, with original analyses from 1682 and 1685, and 1754.

A test used 20,000 paths to simulate possible outcomes, using past returns as a guide, with a starting value of $187.22. The results showed potential profit or loss ranges, with a loss of 81.26% possible. The same test was run again with the same inputs, which means it did not provide an independent check. The way the test was set up limits its ability to predict when to stop or exit, and it cannot estimate the odds of this happening within 15 minutes. The test also suggested that on average, the fund would lose $0.176654 per full deployment. The conclusion was to use zero risk, based on a method called the Kelly criterion. The test used data from a fund called quant, and was part of analysis number 1642, which was observed and simulated.

A prior stress illustration for QTR9091630 has been reproduced without any new predictive sample. The first part of the analysis used a NumPy seed of 9091230 to generate 20,000 paths with 20 independent and identically distributed draws from actual closed return fractions, which reproduced 1740 out of 1706 cases, with a hypothetical profit and loss of 187.22 USD and a 2.5, 50, and 97.5 percentile range of -93.669315, -30.013809, and 79.173517, respectively, resulting in an 81.26% loss. The second part of the analysis found that the assumption of independence and stationarity is not supported due to censored operator liquidations, and resampling cannot create a clinical stock edge. The third part of the analysis calculated a holdout mean of -0.09435646%, which implies a loss of -0.176654 USD per 187.22 USD deployment, and found that gross repeatability has not been established. This analysis does not provide a forecast or stop-exit odds, and the allocation is 0 because the quarter Kelly is 0. The risk cap is 8.5099 and the fund is 187.22 USD, which are hard constraints if a future edge passes. The observation number is 18550 and the simulation observation is from the source query_trades fund=quant, with results saved in quant1630/stats.json.

A simulation called Reuse1734/1720/1682 used a random number generator, called NumPyseed, set to 2026090908, to create 50,000 paths, which are like possible outcomes, for 10 selected time periods, considering only the returns from specific events. The simulation used a correction, called Kelly unit correction, to manage risk, with a starting capital of 1685 units. The results showed a hypothetical 25% increase in capital, with a mean, or average, terminal value of 51.17792, starting from 49.14. The results also showed that there was a 25.418% probability of loss. These 10 events are not opportunities that will happen again this month, and the costs and risks are too high. A paper about this, called LOVE, is not finished yet, and was last updated at 16:14, so there is no new information about how the results are distributed. The simulation did not add any new data about the population, and did not validate the results. The adopted allocation, or investment strategy, is to do nothing, which would result in a terminal value of 49.14 before external costs. The hypothetical trade odds should not be advertised. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the analyses 1734/1682/1685 were reused.

A re-run of the 1706 NumPy seed 9091230 was performed with actual three closed return fractions of -0.0539236448052, 0.0793994130743, and -0.0513049904703, using 20,000 paths and 20 independent and identically distributed draws, resulting in a compound hypothetical value of 187.22 USD. The profit and loss percentages were 2.5/50/97.5 percent, which were -93.669315, -30.013809, and 79.173517. The loss was 81.26 percent, which reproduces the old result and is not a new test. This result is based on independent and identically distributed draws, stationarity, and execution, which are unsupported by the N3 operator liquidations. Resampling cannot generate missing independent information, so the 81.26 percent loss should not be interpreted as a clinical trade loss probability. The observed quarter Kelly was 0, proposed USD was 0, and there were no assumed stop or target or Monte Carlo exit odds without a qualified rule. The hard constraints of the fund are 187.22 USD, account cap is 212.7475, and risk is 8.5099, which further limit any future positive edge. The source is a query of trades from the quant fund with IDs 9/7/1, with 18,528 observations, and prior 1706. This illustration is valid conditional on explicit unsupported assumptions and cannot justify risk.

A reused simulation, not re-run, recorded 1720/1682 Monte Carlo paths with a 1685 Kelly unit correction. This means a computer program tried 1682 different paths 1720 times, using a special formula called the Kelly criterion to make decisions. The simulation used a random number generator with a seed of 2026090908 and created 50,000 different paths of 10 selected time-only event returns. The results showed a hypothetical mean terminal value of 51.17792, starting from 49.14. The 5, 50, and 95 percentiles were 47.38995, 50.79397, and 56.33595, respectively, with a loss probability of 25.418%. These results are based on 10 events, not 10 sessions or remaining month opportunities. The sample size is 10, concentrated and required costs stressed exits negative. A new paper about LOVE, released on September 9, is not yet finished, so it cannot be used to update the empirical distribution. The strongest argument against cashing out is the favorable conditional mean, but the simulation does not add any new data about the population or validate the exit strategy. As a result, the allocation was set to 0, with a terminal cash value of 49.14 before external costs. No live profit odds are claimed. The sources of this information include a data feed from https://runway.adityaperswal.workers.dev/data/stock-bars, which provides stock bars for symbols LOVE and SPY, and prior analyses from 1682, 1685, and 1720.

A trading fund manager reused a simulation called Monte Carlo 1682 with corrected Kelly units. This was not a new simulation or a live forecast. The manager used a past simulation, called Read 1682, which involved 50,000 resamples of 10 empirical time-only returns over 10 hypothetical events. The simulation started with $49.14 and used 25% of the capital. The mean terminal value was $51.17, with 5%, 50%, and 95% quantiles of $47.39, $50.79, and $56.33, respectively. The probability of loss was 25.42%. If all capital was used, the mean would be $57.64, with a loss of 26.78%. The manager noted that these events are not the same as 10-day or month-end opportunities. There are three key points: these events are dominated by one broad market day, mandatory exit costs are stressful and negative, and the paths do not add population information. The manager also mentioned that analysis 1685 and lesson 514 supersede the cash-Kelly interpretation in paragraph 3 of simulation 1682. The manager used a formula to calculate capital and concluded that the favorable conditional mean is an explicit counterargument to using cash, but it is unsupported for the actual window. The manager adopted a fraction of 0. The data came from a source that included stock bars for symbols LOVE and SPY with a 15-minute timeframe. The manager decided not to generate another 50,000 paths from the same unstable input.

This is a stress illustration, not a trade forecast. It is based on two premises. First, it uses actual closed trade percentage outcomes from query_trades with quant IDs from September 7, 2001. It uniformly samples 20 trades with replacement in 20,000 NumPy paths, with a seed of 9091230, and a compound hypothetical full amount of 187.22 USD. Second, the result shows profit and loss percentiles of -93.6693, -30.0138, and 79.1735, with a loss of 81.26%. The illustration cannot price clinical drift or justify risk due to non-stationarity and unknown fees. The quarter Kelly criterion is applied to the original mean payoffs of 0 and proposed notional of 0. Today's fund cap is 187.22 USD, which differs from the previous cash note of 245.69 USD and account position cap of 212.7475 USD. There is no additional risk to the fund's 349.01 USD shortfall. The source is a query_trades fund with quant, observation 18398, and is reproducible using numpy.default_rng(9091230).choice and other calculations.

A trading simulation, called a Monte Carlo, was run on the stock LOVE with certain conditions. The simulation used a random number generator with a seed of 2026090908 and ran 50,000 paths over 10 events. The results showed different outcomes based on the fraction of the portfolio invested. The results were as follows: a fraction of 0 had a mean terminal value of 49.14, a fraction of 0.25 had a mean terminal value of 51.18, and a fraction of 1 had a mean terminal value of 57.64. The probability of loss for each fraction was 0, 0.25418, and 0.26782, respectively. The simulation had some limitations, such as not accounting for unexpected jumps in price. A calculation called the quarterKelly formula was used to determine the optimal bet size, which was approximately 2.16 USD, less than the price of one share. The simulation was run using data from the website https://runway.adityaperswal.workers.dev, which provided stock prices for LOVE and SPY with a 15-minute timeframe.

The automated trading fund manager made decisions based on certain premises. First, the manager looked at past trades of EEM, which had a probability of 0.4563492, an average cash win of 0.490802%, and an average cash loss of 0.585548%. The quarter Kelly, a measure of optimal bet size, was calculated as 0.25 times the probability of win minus the probability of loss times the loss to win ratio, which equals 0. Second, the manager used a random number generator, called NumPy seed 90908, to simulate 20,000 paths of 20 sessions. These simulations were based on 1,255 past daily cash returns of EEM, including days when no trades were made. The initial investment was $212.7475, which is not the actual fund allocation. The results of these simulations showed that the potential profit and loss after 20 sessions could be between -$7.09363 and $4.76108, with a 60.355% chance of loss and a 95th percentile drawdown of 3.07343%. The manager also looked at stress test results, which showed a potential profit and loss between -$9.91765 and $3.31380, with a 78.31% chance of loss and a 95th percentile drawdown of 4.13089%. None of the simulated paths reached a profit of $349.01. The expected profit and loss of one trade was -$0.200741. The manager also noted that there are approximately 50.4 trading opportunities per year, but these are not guaranteed. The manager excluded certain trading strategies, such as PDT, whole lots, and 15-minute polling, which may have worsened the outcome. The conclusion was to keep the allocation at zero, despite a funding gap. The manager also looked at daily bar outcomes, such as stop 30/252, target 13/252, and time 209/252, which are not the same as execution frequencies. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the figures 18316-18319 were referenced. The reproducible code is archived.

A simulation of a trading book over ten cycles kept its initial investment size at zero. The simulation was based on certain premises, including that the actual payoffs from exiting trades after 15 minutes were all negative, with a quarter of the Kelly criterion being 0.2. The simulation only looked at how changes in certain variables affected the outcome, using an initial book value of 235.86 and an initial investment of 50, which was 21.199% of the total, rebalanced over ten cycles, with 10,000 random paths generated for the simulation, using a seed of 90309. The median, or middle, value of the terminal prices was 232.708 USD for Bitcoin, 232.959 USD for Ethereum, and 234.934 USD for Solana, with the fifth percentile being 224.267, 220.746, and 221.932 USD respectively, and the 95th percentile being 241.655, 246.676, and 249.107 USD respectively, and the frequency of losses being 72.8, 63.86, and 53.83%. The simulation did not try to predict what would happen over the next 22 days or what the best time to enter the market would be, and it did not take into account things like correlated market regimes, unobserved liquidity, gaps, and cost uncertainty. The simulation used a stop-loss order, which is a type of order that sells a security when it falls to a certain price, with a maximum daily overshoot of 2.2121, 1.8739, and 1.4293 times the average true range, and a maximum hourly overshoot of 0.6485, 0.4115, and 0.3794 times the average true range, and a maximum 15-minute overshoot of 0.4898, 0.2225, and 0.2099 times the average true range, which means that even with a 15-minute stop-loss order, the simulation could still lose 1.49 times the average true range before fees and slippage. The conclusion of the simulation was that the planned loss limit was 8.5099 USD, which is not a guarantee, and the selected allocation was zero. One argument against this conclusion is that the 95th percentile of the simulation was positive, which shows that there is a possible upside, but it does not mean that the expected payoff is positive. The data for the simulation came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A simulation was run to test the uncertainty of a trading strategy. The simulation was based on two premises. First, a test of the strategy, called Frozen backtest456, showed that it had 17 wins out of 41 attempts, with an average win of 5.46270406% and an average loss of 2.96357664%. This information came from a website. Second, a statistical model called Jeffreys prior was used to estimate the probability of the strategy working. The model was updated with the test results to get a new estimate of the probability. The simulation then tested 20 possible outcomes for the strategy, using a fixed average payoff. The simulation was run 20,000 times with a random number seed of 2026090908. The results showed that if 5%, 10%, or 25% of the investment was used, the median return after 20 trades would be 0.403450%, 0.799880%, or 1.946348% respectively. The worst 5% of results showed a loss of 1.617944%, 3.212056%, or 7.854540% respectively. The strategy lost money 38.17% of the time. The simulation also estimated that the optimal investment amount, known as the quarter Kelly, would be 2.426669%. However, this was not approved for use. One argument for using the strategy was that it had a positive average return and low estimated losses. But this argument is not valid because the simulation did not take into account all the possible risks and changes that can happen in real markets. The simulation was only meant to illustrate how the strategy might work, not to prove that it actually works. The results should not be used to change the investment strategy without further testing.

The automated trading fund manager analyzed the OEF fund with a managed holdout probability of 0.3833333, an average win of 1.45051385%, and an average loss of 1.26921956%. The quarter Kelly sizing, which is a risk management strategy, was calculated as 0. The manager used a Monte Carlo simulation with 20,000 paths across 12 monthly windows, retaining contemporaneous OEF, SPY, and IWM vectors. The simulation showed that a hypothetical $212.7475 USD OEF exposure would have a year profit with 2.5/50/97.5 percentiles of [-27.782671, -5.728007, +17.417338] USD, a loss frequency of 68.615%, and a 95th maximum drawdown of 12.47927%. The mean profit for one expiration window was -0.482203 USD. None of the 20,000 paths reached $349.01 USD. The manager also observed 60 event exit counts, with 51.667% stop, 31.667% target, and 16.667% time. These frequencies are based on historical data and may not reflect live exit odds. The data source for this analysis is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all and https://www.sciencedirect.com/science/article/pii/S0378426613003051.

A news scan found the headline about restaurant sizing using a Monte Carlo method. This method is a way of using random samples to make predictions. The results are based on 50,000 random paths of 10 events, with initial cash of 49.14 and allocation fractions of 0, 0.25, 0.5, and 1. A full record of the results shows that with a zero allocation, the terminal cash is always 49.14, with no loss probability and no drawdown. With a 0.25 allocation, the mean terminal cash is 46.62, with a loss probability of 0.9435 and a 95th percentile drawdown of 11.13%. With a 0.5 allocation, the mean terminal cash is 44.22, with a loss probability of 0.9443 and a 95th percentile drawdown of 21.28%. With a full allocation, the mean terminal cash is 39.75, with a loss probability of 0.9467 and a 95th percentile drawdown of 38.87%. The results assume that the events are independent and stationary, with no calendar frequency forecast, no cross position overlap, and no unprecedented gap. The estimated pooled p value is 0.0211, with a win rate of 0.0390. The binary quarter Kelly clipped is 0, and the TXRH point Kelly is 0.0684, which is selected as positive with a confidence interval crossing zero. The operational size is 0, but the strongest counterargument is that regimes may change, and independent positive net actual window evidence would be needed. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with 56 primary dated event rows and no shutdown shortfall in parameters. Daily ADR exit odds are separately diagnostic, not Monte Carlo inference of approval hazards.

A trading fund manager made some simulations to test how well a trading strategy works. The manager used real data from March 1 for Bitcoin, Ethereum, and Solana. The strategy was to buy and sell at certain times based on the prices of these cryptocurrencies. The manager found that the strategy did not work well, with losses of 42.24% for Bitcoin, 11.31% for Ethereum, and 21.93% for Solana. The manager also found that the strategy was sensitive to changes in the market and that more data is needed to make good decisions. The manager used a technique called resampling to test the strategy, which involves repeating the simulation many times to see how well it works. The results were not good, with high losses and low gains. The manager concluded that more data is needed to make good decisions and that the strategy should not be used without more testing. The manager used data from two sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A study on HUBB sizing used a Monte Carlo method, which is a way of using random samples to make predictions. The study looked at trade cash returns from May 2026, using data from WCC, HUBB, and SPY stocks. It used 20,000 simulated paths with 20 trades each, replacing 5, 10, and 25 percent of the account exposure at random. The results showed a loss probability of 49.5 percent, a median return of 1.09 to 5.53 percent, and a 95th percentile drawdown of 0.26 to 1.31 percent. Another simulation, called a nested bootstrap, used a different random seed and found a loss probability of 7.58 percent and a 95th percentile drawdown of 0.76 percent. This study also calculated the quarter Kelly, which is a measure of the optimal bet size, and found it to be 0.11, but this was not approved due to other factors. A daily barrier diagnostic, which checks for stop and target prices, found that the target was hit first in 2 cases, the stop was hit first in 1 case, and there was 1 ambiguous case. The study also looked at the maximum favorable excursion, which is the maximum profit that could have been made, and found it to be between 1.5 and 6.7 percent. A news scan found the headline was not available, but the data was from a source at https://runway.adityaperswal.workers.dev/data/stock-bars.

A study on WCC sizing used a Monte Carlo simulation to analyze parameter uncertainty and its effect on loss odds. The simulation was based on observed trade cash returns from 2026, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The simulation created 20,000 paths with 20 trades each, using independent and identically distributed replacement at 5%, 10%, and 25% account exposure, with a seed of 2026090906. The results showed loss probability percentages of 32.23%, 32.74%, and 33.23% for 5%, 10%, and 25% exposure, respectively. A nested bootstrap simulation with a seed of 2026090907 was also run, resulting in a loss probability percentage of 39.50% for 10% exposure. The raw quarter Kelly was 0.0321, and the indicative USD was 27.33, but the trade was not approved due to failed confidence interval, training, entry, and venue gates. A daily barrier diagnostic was also run, with results showing that the target was first reached in 4 sessions, and the stop was first reached in 4 sessions. The maximum favorable excursion percentages were also calculated, ranging from 0.60% to 12.98%. No stop or target order was inferred from the average range.

A fund manager made some notes about a trade called RARE. The trade actually lost 45.8600619% of its value. The manager assumed that a normal event would earn 1.5% net. The manager also assumed that there were independent hazards of 0%, 1%, 5%, and 10%. These assumptions are not based on real data, but rather used for simulation. The manager used a computer program called NumPy with a seed of 20260909 to simulate 50,000 paths of 10 events, reinvesting $49.14 each time. If the assumed hazard is 5%, the expected return per event is -0.8680031%. The manager found that the terminal value of the investment could be $16.2255, $57.0290, or $57.0290, with a 40.296% chance of loss. The manager also found that the maximum drawdown could be 69.8027%. Across 10 events, the expected number of shocks is 0.5, but the median profit hides the potential for severe losses. If the hazard is 1%, the loss frequency is 9.472%, and if the hazard is 10%, the loss frequency is 64.868%. The break-even point is 3.16723%. This is a sensitivity threshold, not a measured likelihood. One argument against this is that earlier exits could reduce the hazard and the ordinary payoff may differ. The manager used data from a source called query_trades and a news release from Ultragenyx to make these observations, which are numbered 17355-58. The manager also calculated a risk budget of 8.5099 and a notional value of 18.5562, which is a scenario cap. The operational allocation is 0 until the actual-window costed edge and risk are calibrated.

A test was done to see if changing the rules for buying and selling would improve results. This test, which is called a counterfactual and not a proposal for changing the size of trades, looked at what would happen if the rules for trading with USDC were applied to trades made in 2026. The test used 10,000 different paths, each with 10 cycles, and started with $235.86, with $78.62 in each major trade. The test also took into account fees and the difference between the price at which a trade was made and the price at which it was executed, which is called slippage. The average result of these trades was a loss of 3.21270%. The test also looked at how the prices of different cryptocurrencies, including BTC, ETH, and SOL, were related to each other. The results showed that the prices of these cryptocurrencies were highly correlated, with correlation coefficients of 0.91259 for BTC and ETH, 0.85103 for BTC and SOL, and 0.88099 for ETH and SOL. The test also found that the ending values of the trades were $125.56 at the 5th percentile, $168.44 at the 50th percentile, and $222.01 at the 95th percentile, and that the frequency of losses was 98.28%. The median maximum drawdown was 30.43% and the 95th percentile maximum drawdown was 47.46%. The test used only 8 empirical vectors and did not take into account the fact that the results may be different over time, which is called serial dependence, or the fact that the market may change, which is called regime change, or the risk of extreme events, which is called tail risk. The test also did not use stops or targets, and trades were executed every 15 minutes. The idea that trading in three different cryptocurrencies would reduce risk is not strong, and the results of the test show that the outcomes of the trades are still highly dependent on each other. The Quarter Kelly criterion, which is a measure of the optimal fraction of wealth to invest, was zero because the individual means were negative, so no dollars were approved for investment. The simulation shows what would happen if the rules for overriding the gate, which is a mechanism for controlling risk, were not followed, and it does not take into account the probability of future losses. The sources used for this test include https://stablecoins.llama.fi/stablecoin/2 and https://runway.adityaperswal.workers.dev/data/crypto-bars, and the files research/audit_sim.py and json.

A fund manager analyzed data and came to several conclusions. First, they looked at past results from September 2026, where LIN had a 37.5% win rate, with an average win of 2.67676% and an average loss of 2.69251%, and APD had a 42.857% win rate, with an average win of 2.14510% and an average loss of 2.20104%. Both of these had negative expected outcomes. They also used a computer program to simulate 20,000 trades with 10% account exposure and found that LIN had an 86.365% loss rate and APD had a 69.025% loss rate. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars. However, these simulations do not account for certain factors like event clustering and 15-minute exits. The fund's past record only has two winning trades and no losses, which is not enough to estimate replacement probability or slippage. Based on this analysis, the conclusion is to have a zero allocation, not 10%. The fund's risk capacity and position capacity are 8.5099 and 212.7475, respectively, which are maximum limits, not targets.

A study analyzed helium utilization using observations from 17195 to 17220 and 17359. It referenced SEC filings from Aspiotopes. The study used assumed prior probabilities, including a triangular joint utilization rate of 0.25 to 1, with a most likely value of 0.70, and a helium price range of 300 to 900 USD per million cubic feet, with 85% of the price uncontracted and 15% contracted at a lower price of 600. The study also considered liquefied natural gas prices and annual operating costs. Using a computer program with 100,000 random draws, the study calculated potential revenue and operating results. The results showed a wide range of possible revenue and operating results, with only 11.452% of the scenarios resulting in a positive operating result. The study concluded that the frequency of different scenarios cannot be used to predict company profit or stock performance. It also noted that higher utilization rates and lower costs could significantly change the results, but the current model is not reliable for making precise predictions.

A baseline test was done with a fixed management strategy, where the probability of success, or p, was 0.4307228916, the average win was 1.45484631%, and the average loss was 1.32374598%. The quarter Kelly, a measure of optimal bet size, was calculated as 0. Stress was also 0. A simulation was run using Numpy with a seed of 90904, generating 83 monthly four-asset vectors, with 20000 paths over 12 months, and compounding independently at first, with equal initial weights. The correlations between asset returns within a month were between 0.52669 and 0.80692. A hypothetical portfolio with $245.69, the full research portfolio, had an annual profit with 2.5, 50, and 97.5 percentiles of -24.87985, -3.89916, and 19.27193 dollars, respectively, and a loss frequency of 63.785%. None of the 20000 paths reached $349.01, a finite empirical result, not an impossibility. The mean profit over one calendar window was -0.311885 dollars, with 2.5, 50, and 97.5 percentiles of -4.68193, -1.08460, and 6.39000 dollars. Fixed costs were excluded, and no forecast cash interest was considered, assuming stationarity and independent months. This experiment does not meet current whole-lot constraints and is not an allocation. At most, one window remains in September after September 9, and it is not possible to turn monthly frequency into daily income. There were 34 stopgap exits out of 332, with the worst trade being -6.75243%, showing that a nominal stop is not a guaranteed risk bound. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the code used was finish_stats.py and frozenstudy.py.

A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars. This source used a frozen costed ten-session next-open rule in sessions 1312-1322. The numpy seed was 2026090902. The analysis included 20,000 bootstrap means and 20,000 independently and identically resampled 20-trade paths per symbol at 10% equity exposure, using 2026 completed trade returns. The same random number generator advances were used across names. The results showed that SEI had 6 trades with a 100% loss-path probability, a median account return of -18.6672%, and a 95th percentile drawdown of 23.2374%. LEU had 6 trades with a loss of 91.285%, a median return of -9.4346%, and a 95th percentile drawdown of 21.6087%. HUT had 6 trades with a loss of 18.22%, a median return of 5.2125%, and a 95th percentile drawdown of 7.2227%. OESX had 4 trades with a loss of 99.95%, a median return of -15.8911%, and a 95th percentile drawdown of 22.7591%. These estimates are conditional empirical stress estimates, not stock forecast probabilities or 15-minute stop/target odds. The independently and identically distributed assumption ignores regimes and small sample sizes, which can badly miss tails. The QuarterKelly formula, which is a method to determine the optimal bet size, was used to calculate the allocation for each symbol. The results were: HUT had a point value of 0.04950, ALM had a point value of 0.123063, and CCJ had a point value of 0.09631. However, these symbols do not qualify for allocation because their mean confidence intervals include 0, the samples are small, the universe was selected after current moves, and actual stop execution is untested. The mean confidence intervals for HUT and ALM were -7.22352% to 12.92386% and -7.82064% to 16.67254%, respectively. The premises of this analysis are that the payoff distribution must estimate future setup and exits before sizing, but these samples do not establish either. A counterexample of a genuine new structural event may improve future payoffs, but that would need prospective independent evidence. The actual sizing was enforced at 0, not point Kelly, and the 1% equity risk was 8.5099, with a position cap of 212.7475, which would also bind any future qualified order.