Stats

The fund uses a method called binary sizing to decide how much to invest. This method uses real data to estimate the probability of winning, which is 2 out of 4, and the amount won, which is 18.8951 times the amount invested. The fund's target is to make 80% of the possible profit, and it uses a formula called Kelly to determine the investment amount. The full Kelly investment would be 47.354% of the $100 book, and the half Kelly investment would be 23.677%. The fund invests $18, which is 18% of the fund and 1.803% of the $998.40 equity, and this is below the 3% limit. This is a rough estimate and not based on exact probabilities. The fund also looks at how much it could lose or gain each day, and it found that in 3 out of 4 cases, it would stop investing first if it lost 3% or gained 10%. However, the fund does not have complete data on what happens when it stops investing. Since there is no current bid, the fund cannot guarantee that it will stop investing when it loses 50%, and it will consider the premium it pays as the maximum amount it can lose. For example, if the true probability of winning is 1 out of 4, the fund's investment method would still work, but only because the potential profit is very high, and the fund might not be able to capture this profit if it stops investing too early.

A stress test was done using actual fills and a precommitted stress formula, which is loss equals N times the sum of r and 0.005, plus 0.005, where r equals 2 times the average true range divided by the entry price, and g is the maximum of 10 percent or twice the worst observed adjusted overnight gap, a measure of the biggest overnight price jump. For each lot of $86.65, the loss is below 2 percent of the current $998.75 equity, which is $19.975. The results are as follows: for XLK, the loss is about $13.76 at a gap of 12.5136 percent, for XLE, the loss is about $13.52 at a gap of 11.5864 percent, and for XLV, the loss is about $11.10 at a gap of 10 percent. The total possible loss is about $38.38, but the rule is to apply the under-2-percent test per lot. This stress test is not a maximum possible loss, but rather a test of the current situation, and a larger gap than the one used in the test could result in a larger loss. The targets suggest a possible upside of about $15.92, $17.87, and $9.21, and a return of about 5.5R, 5.2R, and 4.1R, using historical data from overlapping 90th percentile endpoints, which is a measure of the top 10 percent of historical returns, rather than the probability of winning. The source of the data is a website.

A trading fund manager used a computer program to test a trading strategy called R92104. This strategy used 20,000 simulated trades to predict outcomes. The manager used a technique called bootstrap, which is a way to estimate how well a strategy will work in the future. The strategy was tested on a set of historical trades, with 5 basis points in fees and 15 basis points in slippage, which is the difference between the expected price and the actual price of a trade. The manager also used a cap, which is a limit on the amount of money that can be invested, set at the minimum of the current supply book, 120 USD, or 25% of the account. The account started with 999.26 USD and had an additional 879.26 USD fixed. The manager mapped percentage outcomes in Euros to USD stakes, assuming no change in foreign exchange rates and fractional execution, which means that trades can be made in fractions of a unit. The results showed that the strategy had a win rate of 15.7895%, an average win of 23.6532%, and an average loss of 7.3580%. The manager also used a binary R half Kelly strategy, which is a way to optimize the size of trades. The bootstrap mean trade had a 95% confidence interval of -7.25678% to 3.11475%, which means that the strategy did not establish a positive expectancy, or a expectation of making a profit. The manager also looked at the profit and loss of the strategy over 20 trades and found that the median profit and loss was -56.3922 USD, with a 95th percentile of -87.0796 USD and a 5th percentile of 42.7614 USD. The strategy had a loss frequency of 86.31% and a maximum drawdown, which is the maximum amount of money lost, of 95.75253%. The manager noted that the strategy had a regime dependence, which means that it was sensitive to changes in market conditions, and that the results should not be taken as a forecast of future performance. The manager also noted that the strategy did not account for unseen tail events, which are rare and unexpected events, and that the 15-minute exits were not modeled. The source of the data was https://query1.finance.yahoo.com/v8/finance/chart/ELG.DE and the manager used a program called sim.py to run the simulations.

This is an audit report for R92104 DSX managed payoff. The premises of the audit are as follows: the model preserves nine normalized 20-session paths and price, with a stop at $0.065 and a target at $0.39. The European Black-Scholes model is calibrated to an indicative mid price of $1.05, which gives an implied volatility of 49.352556% and an assumed rate of 4% with no dividend. The model also assumes a fixed 25-calendar-day decay over 20 daily checks. The bid price is calculated as the maximum of the Black-Scholes price minus half of the spread, or $0, with a $2 roundtrip friction. The exact equal weight baseline mean is -$7.09092, with 1 out of 9 targets and 8 out of 9 stops. The previous mean was -$7.05185, which was based on a finite 50,000 resampling and was not discrepant data. The model uses an IID bootstrap with 50,000 nine-path means, which gives a 95% interval of [-12.27064, +1.48562]. The baseline zero-half spread mean is -$0.30007. The model shows execution and sensitivity to the implied volatility and spread. A positive corner cannot be selected as reality. Under a conservative 3x exit versus full premium loss, with a $2 cost, a $13 premium yields a +24/-15 outcome, with a breakeven target probability of 38.4615%. On a $100 fund, the half Kelly must allocate $15, including cost, to fit one contract, with a binary target probability of 56.9231% required. This is an assumption threshold, not a forecast. At a $10 ticket, the threshold is 54.4%, and at a $15 ticket, the threshold is 58.9333%. The actual 50% stop can reduce loss, but gaps and spread can restore full loss. There are no own trades, and the prior 3,800 complete actual option paths remain unfilled evidence gap. The base model half Kelly is 0, and the endpoint 55.6% net wins estimate probability of hitting the target before the stop. The conclusion is that a positive managed size cannot be defended from this model, and a prompt live adjudication is preferred rather than a permanent model veto. A change in attainable quote or spread, or an explicitly defended exit-aware odds, is needed. The current account is $999.26, with a $49.963 premium maximum, total $100, and max 2. There is no research order. The synthetic threshold of $0.065 may differ from the broker stop. Sources include https://runway.adityaperswal.workers.dev/data/stock-bars, https://runway.adityaperswal.workers.dev/data/option-chain/DSX?feed=indicative, and https://alpaca.markets/support/options-pricing-increments-and-options-order-handling.

A fund manager analyzed the stock MG. The analysis started with the stock price at 20.75 on October 16, with a put option premium of 0.12 plus 0.02 per share for friction, resulting in a total loss of 14 USD. The target price was 1.702273, with a cap of 170.2273. If the stock price reverts to 19.84, the gross payout would be 16 USD. The probability of a break-even event is 87.5% if every other path results in a total loss. To reach a price of 18.2977 or below, the probability of hitting the target must be more than 8.2243% for a positive outcome and 10.7959% to fund 14 USD at an equity value of 999.26. These are required probabilities, not forecasts. The analysis used 100,000 paths per 18 scenarios with a seed of 92104. The results showed a pre-expiry downside probability of 1%, 5%, and 10% at prices 19.84, 18.30, and 17, respectively. The independent stop-survival rates were 1, 0.25, and full survival. The models used were conservative and did not account for realistic fill models. The conclusion was that there is no defensible minimum contract size based on these odds. The old breakout empirical Kelly value of 45.97 USD is no longer calibrated to the current cash-deal regime. This analysis does not veto indefinite simulation, as new deal terms or actual event evidence could change the conclusion. The analysis did not include actual stop or target history, 15-minute option paths, or American exercise modeling. The sources used were https://www.sec.gov/Archives/edgar/data/1436126/000114036126037107/ef20082419_8k.htm and https://runway.adityaperswal.workers.dev/data/option-chain/MG?feed=indicative, as well as a script research/mg_audit.py.

A fund manager analyzed indicators and targets, and observed gaps. The manager used a fixed allocation of 260 divided by 3, and a 25% account cap of 249.815. The stress test assumed a full 2 ATR stop distance, which is a measure of price volatility, followed by a gap of the maximum of 0.10 and 2 times the absolute value of the worst sample, plus 0.5% notional friction, which is a small cost. The formula for loss was loss equals N times the rate plus 1 minus the rate times the gap plus 0.005, where r is 2 ATR divided by E, the adjusted close price. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. Using the adjusted close price E for illustration, the gap was 12.5136125, 11.5863927, and 10%, and the slot was 86.66, with losses of 13.792109, 13.489460, and 11.106454, all of which were less than 19.9852, or 2% of the account. The equal common ceiling was the minimum of 260 divided by 3, 249.815, and 19.9852 minus 0.01, divided by the maximum loss fraction, which rounded to 86.66. The joint loss was 38.388023, or 3.842% of the account, not 2% aggregate. The target dollar scenarios were 15.920745, 17.872222, and 9.209198, with gross returns of 5.539, 5.240, and 4.129 over 1 to 3 months. A counterexample showed that a larger overnight gap could exceed 2%, and the entire lot loss of 86.66 could exceed 2%, so no guaranteed risk cap was claimed. The manager must refresh the exposure and action units before each order during regular trading hours. This is a deterministic stress test, not a Monte Carlo or backtest, and does not use repeated Monte Carlo or Kelly override of the fixed validated allocation. The prior live 15-minute exit calibration remains unknown.

A prior analysis paired price and adjusted opening gaps on factor-change dates through 2025. It used a numpy seed of 92104 and 10,000 bootstrap samples, with replacement, for each of 18 XLE or 9 EEM events. The mean adjusted-minus-price difference was calculated. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. The 95% percentile range for XLE is 86.1168 to 99.2930 basis points, and for EEM it is 97.2951 to 148.7577 basis points. These ranges are for a mechanically selected basis difference, not for next-gap ranges, price paths, win odds, or expected profit and loss. The analysis assumes event independence, but quarterly regime clustering and small samples limit the conclusions that can be drawn. The risk of a one-share position is 10% of the joint gap, which is $13.168, or 1.317775% of $999.26. The gap-through-stop can exceed the nominal risk of $4.10. Since there are no closed trades, the bootstrap analysis does not authorize an increase in size, and the $140 cap is binding. The established mandate size will be retained.

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 sixty thousand paths. The automated trading fund manager made premises based on 10,000 paths per name, 60 sessions, and five-day joint OHLC blocks starting from 2024, with a seed of 92102 and SIP for all completed on September 18. The current close is called E, and E is equal to the current close minus 2 times the true average true range for 20 days, which is called ATR20. The target price T is calculated as 4 times E times 1.002 minus 3 times S times 0.998, all divided by 0.998. The entry day gaps stop open, and both hit the stop first, with a target capped at 20 basis points for the side cost, and a time exit mark. The results show that the target is 42.11 percent, the stop is 54.42 percent, the profit is 45.12 percent, and the mean is 8.8836 percent, with a 5th percentile of negative 11.8577 percent. Another calculation, called ATI, has a mean of 7.0767 percent, a target of 37.84 percent, and a 5th percentile of negative 9.8496 percent. Other calculations, called UMC, PXS, LFUS, and VSH, have means of 4.2257 percent, 3.8352 percent, 2.7736 percent, and 2.4670 percent, respectively. All 60,000 paths repeat recent regimes and are unconditional, meaning they are not current entry probabilities, not validated for 15-minute execution, and the 5th percentile is not loss-bound. A strong bullish countercase is preserved, but it cannot turn an algebraic 3R into a fair value. No orders were made. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the report of an automated trading fund manager. The manager's model, called R92102, looks at the stock DSX and decides whether to buy or sell. The model uses a European option pricing formula, called BSIV, which is 49.3526% at 25 calendar days with an interest rate of 4% and no dividends. The model also uses a different way of measuring volatility, called IV, which is 54.14%. The manager tested the model 50,000 times using historical data from 9 breakouts, starting with a seed number of 92102. The model looks at the price of the stock every day for 20 days and decides whether to buy or sell based on a daily bid model. The model's goal is to make a profit of $2 with a round trip friction of $2. The results show that the model's average profit is -$7.05185, with a win rate of 11.178% and a stop rate of 88.822%. The model also tested different volatility levels and found that the results are sensitive to changes in volatility. The manager notes that the model's results are hypothetical and do not reflect real-world execution. The model does not provide a positive expected value for the trade today. The manager will review the model's results on Monday and consider factors such as live bid and ask prices, actual stop triggers, and current financing costs. The model's results are based on 50,000 draws, but these are not independent events. The manager used data from https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/option-chain/DSX?feed=indicative.

A news scan found the following text: Premises include empirical normalized signed terminal payoffs, which means actual profits or losses, capped target, and deducting a premium of $0.02 per share for friction, or costs. The goal is to optimize the mean log wealth, which is a measure of overall profit, over a range of 0 to 0.99, then half, using $999.38 in equity. On October 20, 2020, at a target of $80, the mean profit was $1.7023, with a mean profit of $14.37 per contract, and a half Kelly, which is a betting strategy, of $45.97 USD. A 25% payoff haircut, which means reducing the profit, resulted in a mean profit of $7.28 per Kelly of $31.98. A 50% haircut resulted in a mean profit of $0.186 per Kelly of $1.30. At a 3x premium target, the mean profit was -$8 per Kelly of $0. A call option, which is a type of financial instrument, with a target of $0.7023 had a Kelly of $14.89, which is below the minimum of $18. A DSXP3.13 with a target of $80 had a mean profit of $5.667 per Kelly of $119.13. A 25% haircut resulted in a mean profit of $0.501 per Kelly of $15.84, and a 50% haircut resulted in a mean profit of -$4.666 per Kelly of $0. A 3x premium target had a mean profit of $2.551 per Kelly of $75.65. This general Kelly strategy includes partial profits, which means profits from some but not all trades. A binary target-or-full-loss diagnostic Kelly of $0 does not supersede this strategy. Path stress uses actual historical 20 daily close paths, with assumptions including a 4% rate and an implied sigma of 17.8845% for MG and 54.4913% for DSX. Bootstrap results, which are a type of simulation, showed a mean profit of $18.15, $16.90, and $15.89 USD for MG, with 5 or 6 stops and 1 or 6 targets. For DSX, the mean profit was -$5.08, -$10.88, and -$11.84, with 7 or 8 stops and 1 or 8 targets. Neither daily close checks nor BSM, which is a type of financial model, captures 15-minute venue execution, American exercise, IV changes, or real bid. Outcomes are unvalidated and not a permanent veto. A terminal sample of all P760, which is a type of financial instrument, was worthless, with a mean profit of -$20. A TRMDP, which is a type of financial model, of 32.5 signed 0 or 11 all worthless despite an absolute P80 pass. The conclusion is that one contract of MG12 or DSX13 is a plausible capped probe only with fresh quotes and honest exit-aware assumptions at an authorized session, not an automatic model trade. The full premium risk is less than or equal to $29.98, with a maximum of 4 and a total of 100. A nominal 50% stop is not loss-bound. Sources include https://runway.adityaperswal.workers.dev/data/stock-bars and option-chain, as well as research/sizing.py/path_model.py. Small N selection and regime break remain binding uncertainty, which means that the results are limited by the small sample size and the potential for changes in the market regime.

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.

This is a stress test based on certain premises. The test uses 1,076 paired opening gaps up to 2025, with fixed dollar exposures of $67.37 and $64.31. It also uses a numpy seed of 92102 to draw 10,000 paths for each of 12 contiguous 5-session blocks. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars. The sum of opening gaps over 60 sessions ranges from -12.51 to 14.03 USD, with a median of 1.56 USD. This test preserves the dependence within each block, but does not include intraday returns, reinvestment, stops, targets, fees, or execution. Therefore, it does not show the cumulative profit or loss that can be invested, the expected return, or the probability of exit. The test does not forecast new barriers on unchanged weekend bars, and earlier exit diagnostics are limited by the 15-minute venue cadence. A 10% gap would cost $13.17 or 1.317617% of $999.38 in equity. This stress can exceed historical support levels. Note that these percentiles should not be used to infer the Kelly criterion or to resize investments.

A news scan found the following information: Premises are based on a hypothetical scenario where the stock price of LW was $46.67 on Friday, September 18, with an ask price of $0.48 and a bid price of $0.33 for the October 16 option. The plan is to exit the position on October 5 before earnings, with a stop loss at $0.24 and a target price of $1.44. This scenario assumes a 4% interest rate, no dividends, and no early exercise, with a bid price equal to the midpoint minus $0.075 and a $2 roundtrip cost. The total variance is fitted to 0.405, split into normal variance and event variance. A 3-day block bootstrap is used to simulate non-event returns, with 50,000 paths and a seed of 92100, and 11 daily checks through October 5. The baseline mean is -$16.89, with a 15.76% chance of winning, a 75.54% chance of hitting the stop loss, and a 3.28% chance of hitting the target, and a 5% percentile of -$37.41 and a 95% percentile of $58.03. The event variance is 75% and 125%, with means of -$29.19 and -$5.78, respectively, and a half-Kelly sizing of 0 in all cases. This is an assumptions-based sensitivity analysis, not calibrated live odds or an attainable Monday entry. Monday quotes and remaining time require a refit. A quarter probe of $12 is impossible at a whole contract price of $48. A full premium of $48 represents 4.8% of equity and 48% of the fund, and two such tickets represent 9.6% of equity. A 50% stop does not cap gap loss. An event-jump counterfactual cannot justify a policy that exits first, and the model alone is not a permanent veto. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-chain/LW?feed=indicative, with inputs and results from 31127.

The R92100 HALO put sizing cannot silently assume its stop fills. The premises are based on an October 16 put with an indicative price of 0.18, a target price of 1.93, and an assumed friction of 0.02, which gives a net win of 1.73 per share, with all-in costs of 0.20. A borrow on February 17 was signed with terminal positives as an explicitly optimistic assumed target probability, but it is not observed as the target-before-50% stop probability. The Half Kelly equity allocation is 55.9488 USD with an ideal stop loss of 0.11 all-in, versus 7.8205 USD with a full 0.20 loss. One contract premium is 18, with all-in cost 20 and a cap of 30. Below one-contract allocations cannot be rounded up. The conclusion is that fresh spread/path-aware honest odds are required at authorized entry, with a full premium budget and 50% stop remaining mandatory. Neither scenario establishes trade probability nor permanent cash veto. The source is a data scan from https://runway.adityaperswal.workers.dev/data/stock-bars, with the latest contract metadata OI7 as of September 17, zero indicative bid as of September 18, and 31094 observations, with the current sizing observation.

A test called R92100 event bootstrap and target caps challenge looked at the apparent tail expectancy. The test made the following assumptions: it used 100,000 random outcomes for Nike stock, with a starting price of 35.51, a strike price of 31, and a premium of 0.30, and it assumed a friction of 0.02. The test found that the average net outcome was -2.2825, the median outcome was -32, and 83.315 percent of the outcomes resulted in a loss. However, 16.685 percent of the outcomes had proceeds that were at least three times the premium. This test did not take into account the remaining time value, volatility, path stops, and executable quotes, so the loss frequency and target frequency are not actual probabilities. The test also found that the Nike stock size had no effect on the outcome because the gate failed. The test looked at three frozen events: HALON17, THN4, HALOP95, HALOC130, THP18, and THC27, and found the following target values before deducting the premium and friction: HALOP95 target was 1.93 with a mean of 2.7059, HALOC130 target was 1.83 with a mean of -3.4706, THP18 target was 6.20 with a mean of -25, and THC27 target was 3.58 with a mean of 143.8632. The test concluded that the target-limited strategy was overstated and that the endpoint values did not take into account path stops. As a result, no new calibrated Kelly or trade odds were created, and the existing gross passes were preserved for honest fresh session sizing. The existing 3658 series depletion and full-loss sensitivity remained applicable and were not re-run. A 50 percent stop is required, but a full premium of less than or equal to 30 is a hard risk. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, with 31092 observations and signed scenarios for this run.

This analysis is based on three premises: first, it uses 1075 paired daily gaps through 2025 for statistical analysis; second, it uses a numpy seed of 92100 to sample paired rows independently and identically 10,000 times, with each sample consisting of 60 sessions; and third, it uses a scale constant of 67.37/64.31. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars. The maximum net joint opening-gap loss along each path was calculated, with the 50th percentile being $3.138277, the 90th percentile being $4.951984, and the 99th percentile being $4.970863. This analysis allows gains from one investment to offset losses from another, which is different from the prior analysis that only looked at gross losses. This is not a cumulative profit and loss calculation or a probability of stopping or reaching a target. The analysis does not take into account prior exits, price compounding, serial dependence, within-day extremes, dividends, fees, or the ability to execute trades every 15 minutes. The historical support bounds were sampled to find extremes, but new shocks can still exceed these bounds. A comparison for 2026 is reserved, but prior inspections were not of unseen data. This is not a backtest and does not provide calibrated odds for the Kelly criterion. The purpose of this analysis is to test the idea of using negative correlation to protect against losses at the actual scale of holdings, with the requirement of keeping one share and not resizing based on the simulation.

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 following text: Premises: BB stock had a price of $8.99 and a call option price of $0.43 on October 2, with a strike price of $8.5. The event hurdle was 11.7647%, which is higher than the median of 9.1153%, so the pre-event exit was set for September 23. A European-call scenario was used, with a midpoint price of $0.405, an interest rate of 4%, and other parameters. A bootstrap simulation was run with 50,000 paths, using a seed of 92008, and the results were: a base mean of -$5.72, a win rate of 28.40%, a stop rate of 19.39%, a target rate of 0.998%, and a time exit rate of 79.61%. The estimated event variance was 75%/125% means of -$11.05 and -$0.94. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-chain/BB?feed=indicative, with full observations from 30950 to 30951.

A news scan found the text: Premises: joint open/high/low/close relative to previous close, SIP/all https://runway.adityaperswal.workers.dev/data/stock-bars, 2024 on history, seed 92008, five-day blocks, 10,000 paths per name, 60 sessions. At Sep 18 LPG 58.15 true ATR 1.918885 gives hypothetical stop 54.31223/target 69.66331; BWLP 25.66 ATR 0.8495 gives 23.961/30.757. Gaps stop at open, stop first if both, target capped, 20 bp side friction, mark remaining at time exit. LPG target 30.55%, stop 65.86%, mean 1.3275%, profit 33.51%, P05 -8.4741%; BWLP target 39.18%, stop 57.61%, mean 3.3743%, profit 41.86%, P05 -9.6904%. One-share P05 loss 4.928/2.487 USD is not max loss. Strong bull countercase: positive unconditional drift and tail payoff. Limitation: bootstrap repeats regime, not conditional entry forecast, blocks remove observed longer dependence; daily checks are not venue 15 min fills. Gross 3R hurdle before costs does not meet net 3R; neither level is defended fair value. Actual size 0 because entry/value remain unestablished, not because simulation fails certainty. Do not convert model profit frequency into Kelly probability.

A study was done on the payoff sensitivity of option R92008. It was based on 100,000 resamples of the earnings close reactions of stock WOR, using a fixed stock price of 57.38 dollars, a put option strike price of 55 dollars, and an ask price of 2.14 dollars with 0.02 dollars friction. The results showed a mean contract loss of 156.32 dollars and a median loss of 216 dollars, with 90.716 percent of the contracts resulting in a loss and 63.5 percent having no intrinsic value at the end. This study only looked at the intrinsic value of the option at the end, not the profit and loss or the possibility of filling the option. The size of the option was quantized to zero because the premium of 2.14 dollars was greater than the 30 percent cap, regardless of the model used. The conclusion was that there was no established probability of winning or exiting the trade, and it was advised not to buy the hypothetical cheap fills just because the bootstrap results looked better. New simulations were deemed unnecessary because the prior full premium and shared-loss stress for HALO and TH were retained and did not need to be rerun. The data for this study came from https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-chain/WOR?feed=indicative.

A joint block simulation, known as a type of computer model, was run to analyze the largest opening-gap loss over 60 sessions. The simulation was based on three premises: first, it used paired raw SIP gaps from September 2021 to December 2025, with 1076 data points, and kept the same-day dependence; second, it used fixed 5-session blocks, 10,000 paths, and 60 sessions, with a seed of 200908; and third, it scaled each day to unchanged holdings of 67.37 and 64.63 and compared risk budgets of 1.89 and 2.53. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The simulation found that the maximum gross opening-gap loss was $3.0080, $4.96784, and $4.98122 dollars at the 50th, 90th, and 99th percentiles, respectively. The maximum sum of individual stop-budget excess was $0, $1.56542, and $1.65664. About 36.48% of paths had any excess. The simulation assumed continued exposure for 60 days and ignored prior stop exits, profit ratchet, within-day prices, actual gaps-through fills, slippage, and dividends. It also did not account for stop probability or calibrated live loss bound. The training data excluded 2026, but that year was already inspected elsewhere. The maxima were capped by historical support, but novel shocks can exceed them. The decision was to keep one share, as two would exceed the 140 fund capacity even if some gap tests passed. The sizing was not based on model frequencies or invented half Kelly.

A news scan found the text: Premises: 1. Daily OHLC source https://runway.adityaperswal.workers.dev/data/stock-bars. N20000 paths per symbol/history/policy cell, 2 symbols * 2 windows * 2 policies = 160000; seed 92006, 60 sessions, 5 day blocks sampled jointly as overnight/high/low/close ratios. Histories 2017 onward or 2025 onward. Fixed current price, 2 true ATR stop/net 3R target hurdle, 20bp/side; open below stop fills open, gap above target only target, intraday both stop first. Comparator trims 25% at +2R and moves stop entry next day; no within bar timing claimed. 2. CSTM long/recent fixed target 28.59/36.80%, stop 67.66/58.675%, mean.3757/1.0729 USD/share. Recent quarter/BE increases profit frequency 40.81 -> 46.915% but reduces mean 1.0729 ->.8588 and target 36.80 -> 33.415%. 3. CENX long/recent fixed target 28.565/32.76%, mean.9747/1.9622; recent quarter/BE profit 38.56 -> 44.32%, mean 1.9622 -> 1.6064. Four CSTM 102.20 USD lot recent P1 loss 10.2827; three CENX 117.03 lot P1 loss 18.2100. Fund 120/account 1000 caps affordable; these quantiles NOT maximum loss. 4. Simulation unconditional past returns positively drifted and does not condition on entry signal, shortage or current valuation. Fractional trims exit only RTH in venue; daily perfect barriers are not 15 minute checks. Prices could gap beyond history. Conclusion: no order from modeled odds; show winning frequency can rise as fat-tail payoff falls. Prefer future explicit management test; do not equate resampled target frequency with posterior win probability or use it to overrule false current entry.

A news scan found the text: Premises for a simulation include buying on October 16 with a price of 40 and a stop loss at 50% of the option price, which is 0.24, and a target price of 1.44, which is three times the initial price, with a mandatory exit before the event on October 5. The model assumes a European put option with no dividend or early exercise, an interest rate of 4%, and a bid price that is the midpoint of the bid and ask prices minus 0.075, with a minimum of 0, and a round trip cost scenario of 2 dollars. The model was calibrated on September 18 to an indicative midpoint of 0.405, with a total variance of 0.01990934, a normal random variable of 0.261624, and a residual event variance of 0.01465860. The simulation used 50000 paths with a seed of 92006, and 247 non-event daily log returns, with 11 daily checks from September 21 to October 5, and actual calendar decay days of 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, and 17. The results show a fixed remaining event variance base with a stop loss of 75.412%, a target of 3.236%, a time exit of 21.352%, a win rate of 15.878%, and a mean profit and loss of -16.7617 dollars, with a 5th percentile of -37.4095 dollars and a 95th percentile of 59.1994 dollars. The results also show that if the event variance is plus or minus 25%, the mean profit and loss would be -28.9965 dollars or -5.5714 dollars. The constant implied volatility base mean is -28.3786 dollars, and all six scenarios have a negative mean, with a numerical half Kelly of 0 and whole contracts of 0. The historical drift is estimated to be 0.000741 dollars per day, but this is not forecast, and the block resampling is not calibrated for 15-minute execution. The event-conditioned historical scaled paths all show modeled losses, with no targets, and the counterfactual hold through signed intrinsic mean is 3.2242 times the premium, with profits in 4 out of 11 cases, and losses in 7 out of 11 cases, with two cases in 2026 having losses of 50 dollars including assumed costs, which is prohibited at the current median hurdle and not traded expectancy. The full premium may be lost despite a 50% stop loss. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-chain/LW?feed=indicative, and a separate live best bid and offer is needed, as well as an explicit OPRA 403.

A news scan found the following text: Premises for the R92006 finite-fund series are as follows: it uses a NumPy seed of 9200602, 100,000 paths or scenarios, 20 sequential pairs, an initial fund of 100, and no added capital. The premiums are 23 and 18, with target sale proceeds of 183 and 358. There is a 1 USD roundtrip fee for each transaction, and a reserve of 43 before each pair. The probability of loss is assumed to be 5, 10, or 15 percent per leg, with a loss of either 50 or 100 percent. A common uniform mixture of 0, 0.5, and 1 preserves marginals and stresses dependence. If the probability of loss is 10 percent and the loss is half, the mean fund is 413.71, but the median is 32.50, and 57.76 percent of the funds are unable to fund the next pair. If the losses are perfectly shared, the mean is 323.51, the median is 32.50, and 72.883 percent are unable to fund the next pair. If the loss is full, the mean fund is 175.58, the median is 14, and 79.461 percent are unable to fund the next pair, but if the losses are shared, 86.257 percent are unable to fund the next pair. The peak-to-trough drawdowns are 270 and 382.5 USD for half-loss, which may exceed the initial 100 after modeled wins, but not the initial principal risk. The break-even target probability is 7.289 percent for half-loss and 13.115 percent for full-loss. At an assumed probability of 10 percent, the half-Kelly is 26.91 USD for half-loss, but 0 for full-loss. The TH is 2.865 and 5.307. The conclusion is that size cannot ignore full premium loss or minimum contract granularity, and model sensitivity is not prediction, new mandatory veto, or validated trade odds. No portfolio deployment was selected. The full premium hard cap is 30, the total is 100, and the maximum is 4. The source of the stock-derived target levels is https://runway.adityaperswal.workers.dev/data/stock-bars.