Stats

A trading analysis was completed with a seed value of 91106 in a mathematical system called NumPy. The analysis used 50,000 simulated trade paths, each with 20 completed trades, based on 31 training outcomes from a 15-minute trading policy in the year 2638. The results showed that 48.387% of the trades were wins, with an average win of 8.87505% and an average loss of 6.86037%. A risk management strategy called binary-payoff half-Kelly was applied, which resulted in a 4.24528% return. A probe, or test, of the trading system showed a potential return of 1.06132% of the account value, which was $9.03173. However, when stress and fees were taken into account, the potential return was reduced to $1.87672. The analysis also showed that there was a 38.58% chance of losing money and a 0.76248% potential drawdown, or loss, of the account value. The results of the analysis were uncertain and did not provide a reliable forecast. Based on the analysis, the decision was made not to allocate funds to the trade, due to the potential for execution gaps and the fragility of the training data. The need for funding was also excluded.

A news scan found the headline 'CNXC91106 block Monte Carlo sizes the rejected edge at zero'. This refers to a simulation, called a Monte Carlo, which is a method to model random events. The simulation used the following premises: the trained net returns were only considered, with a probability of winning of 46.67% and a probability of losing of 36.0765%. The invested capital was calculated using the Kelly criterion, which is a formula to determine the optimal amount to invest, and was found to be 0. The simulation also considered historical daily exit frequencies, which are the rates at which trades were stopped or targeted. The simulation used 20,000 paths, 10 trades, and a circular 2-event block, with a seed of 91106. The results showed a median return of -5.44436% and a loss of 69.38%. The simulation also considered a scenario where the fund was rebalanced to 54.945055% of its original value, which resulted in a median return of -2.87017% and a loss of 68.41%. The simulation assumed a 1% probability of a -30% shock per trade, which resulted in a median return of -3.68634%. The conclusion of the simulation was to approve an allocation of 0, meaning that the Monte Carlo simulation could not find a way to make the investment profitable. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline that BlackBerry will announce fiscal second-quarter 2027 results on September 12. The company, referred to as the issuer, will release its earnings on Thursday, September 24, 2026, at 8:00 a.m. ET, which is 8 days after the October 2 option expiry. The current option price is $0.40, which requires an 11.405836% increase in the stock price to reach the target, covering 3 out of 12, or 25%, of the possible scenarios. Alternatively, the option price could decrease by 11.405836% to reach the target, covering 2 out of 12, or 16.67%, of the possible scenarios. The stock's historical earnings show a median absolute change of 9.987505% from the pre-earnings close to the first post-earnings close. The current 40% gate, or threshold, does not pass, and the documented 3x expected payout does not occur. The sources of this information include stock bars data and historical issuer quotes. To analyze this, a counterfactual European Black-Scholes model, which is a type of financial model, was used with a dividend of 0 and 22 calendar days to October 2. The implied volatility, or the expected volatility of the stock, was solved from a single September 10 mark and stock price, which was not observed. The results show that the current option price of $0.40 implies an expected volatility of 79.2860%, while a price of $0.15 implies an expected volatility of 43.4344%. A baseline of 30%, 50%, or 80% was assumed, and the event variance, which is a measure of the uncertainty of the stock's price, was preserved before the announcement and removed at the first post-close. The analysis also rescaled each historical 10-interval stock path from $7.54 and found that the target daily close was greater than or equal to 4 times the premium plus $0.05, which gives a 3 times return net of assumed cost against premium risk. The results show that the current option price of $0.40 with a baseline of 30% or 50% does not meet the target, while a cheaper option price of $0.15 with a baseline of 30% meets the target in 2 out of 12 scenarios before the exit and 5 out of 12 scenarios after the exit. However, these are path-scenario frequencies, not executable exit odds or historical option backtest results. The assumed implied volatility decay can flip the mean, and the calibration to the mark is not validation. There is no documented 3x expected payout and no hold-through override from this model.

A stress test on BB whole-contract shows that cheap premiums still expose the small fund to clustered losses. The test looked at BB Sep10 S7.54 and Oct2C8 close at 0.40, which requires a 11.405836% increase, covering 3 out of 12 periods, or 25%. It also looked at Oct2P7 close at 0.32, which requires an 11.405836% decrease, covering 2 out of 12 periods, or 16.67%. The test used 12 consecutive issuer-dated earnings from Sep2023 to Jun2026, with a median absolute change of 9.987505%. The current 40% gate and documented 3x expected payout did not pass. The issuer announced that it will release its fiscal second-quarter 2027 results on September 12. The test used data from various sources, including stock bars and historical issuer quotes. It also used a simulation with 30,000 paths and 20 sequential counterfactual one-contract tickets. The simulation showed that the fund would lose money 97.58667% of the time and would not be able to complete 20 tickets 96.64333% of the time. The test also looked at hypothetical scenarios with different premium prices and found that the fund would still be at risk of losses. The log utility optimization of intrinsic net payoffs gave a current half Kelly of 0 and a hypothetical cheap half Kelly of 8.09373%. The test also found that the book concentration would be 33.57395% despite the account cap.

A news scan found the following text: 1. The mean payoff of ETH rolling 480 hour holdout 6 trades is 2.874945%, with a 95% confidence interval of -1.587870% to 8.390857%. The best trade had a payoff of 15.2404%. If the best trade is excluded, the mean payoff is 1.735528% compounded. The mean payoff of calendar hour 7 trades is 1.206384%, with a 95% confidence interval of -2.735977% to 6.438506%. If the best trade is excluded, the mean payoff is -6.869318%. The mean payoff of daily 5 trades is -3.521877%, with a 95% confidence interval of -9.559165% to 3.335655%. These are reused trades, not new independent proof. 2. A cash-constrained log growth grid with a step of 0.001 gives daily 0, calendar hour 1, and rolling hour 1, with half wealth fractions of 0, 0.5, and 0.5. The fund started with 235.86 and ended with 117.93, with a full ETH allocation of 29.4825 and a quarter probe of before eligibility. The fixed 50% notional bootstrap terminal loss is 13.39% rolling and 34.195% calendar, with rolling 5th and 95th terminal returns of -3.128142% and 24.133260%. The model uncertainty is large and not forecast. 3. Separate unconditional path stress draws of 10,000 paths per asset, with 20 complete 24 hour blocks from the last 365 days, were analyzed. The number of blocks for BTC, ETH, and SOL are 363, 362, and 361. The current 2 ATR stop is 5.9418%, 7.1973%, and 10.0209%, with a net 3R price up of 20.6377%, 24.4043%, and 32.8751%. The target first is 4.25%, 8.12%, and 3.94%, with a stop first of 59.81%, 64.69%, and 55.19%. The mean static net is -1.8309%, -2.0018%, and -2.4120%. The ETH current net stop is 7.845261%, which implies a half notional risk of 9.251916 USD per quarter, or 2.312979 USD, which is 0.271799% of the account. However, the actual size is 0 due to the absence of a signal. The validity of the results is based on seeded 91104 empirical bootstrap and conditional payoff arithmetic. The soundness of the results is limited by small N, IID trades, block stationarity, unseen jumps, and close-mark execution limit forecast, and cannot use unconditional negative paths to disprove conditional edge. The conclusion is to keep hypothetical size illustration distinct from order and act only on actual validated signal. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the report from EGA on August 12, which stated that as of Monday, August 10, there were 227 cells. Another report from EGA on August 26 stated that there were 315 cells out of 1262, which is roughly a one week stabilization. The implied interval speed is 88 cells divided by 16 days, which equals 5.5 cells per day. With 947 remaining cells, it would take approximately 172.18 days to reach the target, plus 7 days for stabilization, which would be February 21, 2027. This rough estimate is consistent with, but not independent confirmation of, the issuer's Q1 target. The assumptions made include that all 315 existing cells are stable, have identical capacity, and that the cast output is a proxy of 1.6 million tonnes per year. A simulation using NumPy with 50,000 paths and a fixed speed per path of 3 to 5.5 to 8 cells per day was run. The completion days after August 26 were estimated to be 138.35 days at the 5th percentile, 178.92 days at the 50th percentile, and 256.53 days at the 95th percentile. The model estimated that there is an 81.662% chance of completion before March 31, but this is not a real-world forecast odds. The integrated missing cast output proxy through March 31 was estimated to be 237,423 tonnes at the 5th percentile, 304,145 tonnes at the 50th percentile, and 421,743 tonnes at the 95th percentile. However, the precision of delivered tonnes is lower due to factors such as heterogeneous cells, hot-metal reuse, alloy content, existing stocks, and exports. In conclusion, scenarios should be used to bound questions for the next operational update, but should not be used to derive a stock target or trade probability. New measured output and net shipped volumes are more important than this simulation.

A news scan found the text about automated trading fund manager notes. The manager uses training payoffs, not favorable holdout, from fixed tests 2521–2522, with data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. For CSTM, the training payoff is 40%, with a mean win of 8.4956% and a mean loss of 7.6217%, resulting in a clamped half Kelly of 0. For KALU, the training payoff is 41.9355%, with a mean win of 10.7182% and a mean loss of 7.6758%, giving a rough half Kelly of 0.176252% of equity. This is a binary-payoff approximation and a sizing ceiling, not proof of optimal allocation. The manager notes that stress costs make KALU training mean negative and half Kelly 0. Both CSTM and KALU currently fail entry, so the actual allocation is 0. The manager also notes that whole-share KALU 158.42 and stock minimum 1 exceed the hypothetical probe of $0.375, and therefore should not be rounded up. The manager ran simulations using NumPy with 50,000 paths of 20 independent and identically distributed training roundtrips, with a seed of 91104. The results show that allocating 25% per trade yields a median account return of -6.56% for CSTM, with a loss probability of 73.34% and a 95th percentile drawdown of 22.50%. For KALU, the median account return is -0.98%, with a loss probability of 53.40% and a 95th percentile drawdown of 19.89%. The manager notes that these are conditional empirical simulations, not calibrated future loss bounds, and omit unseen gap and persistent regime risk. The historical training target-hit frequencies are 2/30 for CSTM and 2/31 for KALU, versus 15/30 and 18/31 for stops. The conclusion is that the venue maximum is not a position recommendation, and no funded probe is justified by these distributions. A funding shortfall does not change the odds, target, or size.

A news scan found the text: Premises: 1. Sample primary 5 stress holdout daily cash portfolio, not best neighbor, as recorded. 2. Seed 9110400 draws 10,000 paths from overlapping 21-session blocks, 12 blocks = 252 sessions. This method preserves dependence within each block but misses new regimes, long memory, and unseen fat-tail gaps; it does not forecast. 3. Applying the full $187.22 USD fund gives 21 session P5/median/P95 PnL - $13.13308 / - $1.058883 / + $14.38771 USD, with a 55.84% loss probability; zero empirical paths gain $349.01 USD, which is not proof of impossibility. The 252 session return P5/median/P95 is -25.72362 / -3.98388 / +24.46864%, with a drawdown median/P95 of 17.16606 / 30.95414%. 4. The half-Kelly formula, which is a betting strategy, is 0.5 * (p - (1-p) / (avg win / avg loss)), clipped at 0. The primary stress p is 44.3137%, and the win/loss ratio is 1.22364, resulting in a negative unclipped Kelly, which is set to 0. The training stress is also 0. The baseline plugin of 3.146% does not override the cost failure. The account normal 3% risk is $25.5297 USD, with a position ceiling of $212.7475 and a fund of $187.22; all additional caps are in place, and there is no entitlement to maximum size. A full fund gap loss would be 22% of the account, so the fund cap alone is not a gap budget. The conclusion is to size at zero; funding need does not determine barriers. A counterargument that resampling misses future bonanza is accepted, but there is no independent positive edge that justifies paying for it with this rule. The source is https://runway.adityaperswal.workers.dev/data/stock-bars and research/inference.py/json.

A test was done on September 11 to see how the value of BlackBerry stock would change. The stock price was $7.54 on September 11 and the price of a call option to buy the stock at $8 on October 2 was $0.40. This option would be profitable if the stock price increased by 11.405836% to cover the cost of the option for 3 out of 12 months, which is 25% of the time. The price of a put option to sell the stock at $7 on October 2 was $0.32, which would be profitable if the stock price decreased by 11.405836% to cover the cost of the option for 2 out of 12 months, which is 16.67% of the time. The test looked at 12 quarterly releases of news from September 2023 to June 2026 and found that the median absolute change in stock price was 9.987505%. The test also found that neither the 40% gate nor the documented 3x expected option payout was met. The option expires on October 2, which is 8 days after a major event on September 24. The test used a European Black-Scholes model to value the options, which is a way of calculating the value of an option, and assumed that the interest rate and dividend were 0. The test also assumed different levels of volatility, which is a measure of how much the stock price is expected to change, and found that the mean gross payout for the call option was 0.7999 to 1.4841 times the premium, and for the put option was 0.2878 to 0.8956 times the premium. However, no scenario gave a 3x mean or a 4x single-event gross target. The test used 20,000 paths and a maximum of 20 whole-contract tickets, and found that the median final cash was $106.6357 and the fund drawdown was 46.2201% for the call option, and $16.6977 and 90.6565% for the put option. The strongest countercase is persistent high post-event volatility and pre-event rally, but this is not measured with historical option executions. A cheap-threshold scenario is recorded separately, but this is not permission to substitute today's larger premium.

A training process for BFRI911 used 20,000 random scenarios, called trade paths, to test investment strategies. These scenarios were based on 134 past returns from a training dataset, with all capital reinvested. The results showed a median return of -25.03%, a loss of 85.595%, and a drawdown of more than 20% in 86.415% of the scenarios. The 5th percentile return was -43.61%. The training data had a win rate of 17.9104%, a loss rate of 5.3252%, and a percentage of trades that were wins of 11.3814%. Using a formula called the half Kelly criterion, which is a method for determining the optimal amount of capital to allocate to an investment, the result was 0, meaning no capital should be allocated. This training process ignored the potential effects of economic dependence and regime changes, as well as gaps in FDA data, and did not attempt to forecast price paths or estimate the probability of FDA approval. The results did not provide a basis for estimating the odds of winning FDA approval. The full fund had a loss of 5.7745%, and the quarter fund had a total loss of 1.4436%. However, the affordability of an investment alone cannot make up for a negative expected return. A 3% account risk and a 25% position cap were not necessary in this case. The current allocation is 0. The data used for this training can be found at https://runway.adityaperswal.workers.dev/data/stock-bars, and the trades can be queried at runway:query_trades?fund=catalyst.

An edge test was conducted on several stocks, including PLAY, VRA, SFIX, and TCOM, with specific option contracts. The test required a certain percentage return for each stock, with results showing that all failed to meet the 40% threshold. The test used historic event close returns mapped to current stock prices, with put intrinsic value calculated as the maximum of the strike price minus the next stock price, or zero, divided by the premium. The test also used a residual time value that was not modeled, so the results are a stress floor and not the expected traded option price. The test was run with 10,000 independent and identically distributed paths, a 3% equity premium compounded, and no option fees or serial dependence. The results showed that none of the stocks had a documented actual option expected payout of 3 times or more. The test also used a grid log utility with a full Kelly criterion of 0 for all floor distributions, and a half Kelly criterion of 0. The results showed that SFIX had one scenario with a gross return of 5.1653 times, but the mean return was 0.95357 times, which was insufficient. The current allocation is 0, and the account has an absolute premium cap of 51.0594, a normal cap of 25.5297, and a conviction cap of 42.5495. The results also showed that three premiums of 51.0594 would exactly use 18% of the cap, so a strictly less amount should be used if the absolute caps ever bind.

The fund manager made certain assumptions: they used a sample of daily portfolio data from the last 252 days, divided into three parts, with 3000 units of capital. They started with a seed value of 9110200 and generated 10000 possible paths, re-sampling every 21 days, for a total of 12 blocks, which is equal to 252 trading sessions. This method keeps the relationships within each block, but does not account for new situations, long-term patterns, and future gaps. The fund's performance over 21 sessions showed a loss of 8.5383 USD, a median gain of 1.0334 USD, and a high gain of 11.9949 USD, with a 42.98% chance of loss and zero empirical gain of at least 349.01 USD. The annual return was between -10.6848% and 29.3271%, with a median of 7.1039%, and the drawdown was between 9.4809% and 17.6986%, with a median of 7.1039%. This is not a forecast, and the possibility of zero gain is not impossible. The primary training data showed negative results, so the manager kept half of the Kelly criterion, which is a formula used to determine the optimal amount to bet. A 6.14% risk plugin was considered, but it would be capped at 3% normal risk, or 25.5297 USD, due to the fund's deployment and position ceiling. A total loss of the fund would be 22% of the account, so the fund cap alone is not enough to cover the risk. The strongest argument against this approach is that it does not capture extreme positive outcomes, but this does not justify changing the target to meet the funding gap of 349.01 USD. The rate of return was 7.1887% per year over 7.67 years, compared to a total return of 70.3186%, which is not a monthly return of 70%. The sources used were frozen2406 and https://runway.adityaperswal.workers.dev/data/stock-bars, as well as the research/inference.py/json file.

The manager analyzed prerecorded fixed-rule trade samples using a specific setup, called numpyseed91102, with 50,000 circular 2-trade block paths and 20 trades per path. The train half Kelly, which is a betting strategy, gave a result of $76.59, and the quarter probe, a way to test the strategy, gave $19.15. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The half Kelly median path return was 5.76%, the loss path probability was 6.83%, and the 95th percentile drawdown, a measure of the largest loss, was 4.03%. These results are from conditional empirical simulations, which means they are based on past data and not predictions of future probabilities, and they do not account for extreme unseen events. The simulations used 15 data points. A hypothetical one-share loss of 30% would be $16.32, or 1.92% of the account. The whole-share allocation is 54.41, but it cannot be rounded up from the quarter probe budget without new justification for the payoff. The current combined entry is false, meaning no trade was made. The actual allocation is 0, and the normal loss per account position is $25.53, with a cap of $212.75, which is a ceiling, not a recommendation. A funding shortfall of $349.01 was excluded.

The manager analyzed prerecorded trade samples using a fixed set of rules, with 50,000 circular 2-trade block paths and 20 trades per path, and a numpy seed of 91102. The train half Kelly value was 0.04132733473590497, resulting in a USD 35.16914858690777 return, and the quarter probe value was 8.792287146726942. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The half Kelly median path return was 1.1587751663886148%, with a loss path probability of 29.326% and a 95th drawdown of 3.043595464073956%. These simulations are conditional and empirical, not calibrated future probabilities, with 14 samples and no unseen fat-tail events. A hypothetical one-share trade with a 30% gap loss would result in a 29.226 USD loss, equivalent to 3.4343529301166873% of the account, which is not guaranteed as the maximum loss. The whole-share allocation of 97.42 cannot be rounded up from the quarter probe budget without new payoff justification. The current combined entry is false, with an actual allocation of 0, and a normal loss of 25.5297 USD per account position, with a cap of 212.7475, which is a ceiling, not a recommendation. A funding shortfall of 349.01 is excluded.

The analysis is based on prerecorded fixed-rule trade samples using a numpy seed of 91102, with 50,000 circular 2-trade block paths and 20 trades per path. The training used half Kelly 0, which gives a return of USD 0, and a quarter probe of 0. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The results show a median path return of 0%, a loss path probability of 0%, and a 95th percentile drawdown of 0%. These simulations are conditional and empirical, and do not represent calibrated future probabilities, with 13 samples and no unseen fat-tail events. A hypothetical one-share loss of 30% would result in a loss of 23.685 USD, which is 2.783228945110988% of the account, but this is not guaranteed to be the maximum loss. The whole-share allocation of 78.95 cannot be rounded up from the quarter probe budget without new payoff justification. The training used half Kelly 0, so the zero-risk path is trivial cash and not proof of a safe entry. The actual allocation is 0, and the normal loss is 25.5297 USD per account position, with a cap of 212.7475, which is a ceiling and not a recommendation. A funding shortfall of 349.01 is excluded.

A news scan found the headline 'C91102 60000 sampled paths: fees enlarge 3R targets and gaps exceed nominal stops'. This refers to a report on an automated trading fund. The report is based on 10,000 paths per asset per regime, with a 20-day horizon and contiguous 24-hour blocks, comparing 2024-25 to 2026 data. Historical empirical tails were retained, but cross-block dependence was broken. The current mid-starts are 76,905.29, 24,52.03, and 15,99.3295, with 2 ATR risk of 5.9387, 7.1788, and 10.0173 percent. Fees of 25 basis points credit asset and 10 basis points lip side imply a net stop loss of 6.5954, 7.8269, and 10.6455 percent. The net 3R target is 92,769.56, 30,49.07, and 131.9734, with a gross of 20.628, 24.349, and 32.864 percent. The 2026 regime means are -1.150, -1.276, and -1.301 percent, with target frequencies of 4.14, 6.46, and 3.55 percent. The training means are +0.731, +0.059, and +0.071 percent. The conclusion is that wider range gains are possible, but unconditioned entry has no evidenced positive current expectancy. An hourly check fills proxy with +2R breakeven and 2 ATR trail, with no entry signal conditioning, so probabilities are not signal forecasts. The worst sample quantiles are not bounds. At 212.7475 notional, the training 5th percentile loss is 16.779, 21.543, and 26.739 USD, with SOL exceeding 25.5297 normal 3 percent account budget. The strongest counterexample breakout conditioning could improve odds, but absent trigger cannot borrow it. There is no MC-derived Kelly order. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://runway.adityaperswal.workers.dev/data/crypto-snapshots.

A descriptive model, not a forecast of a specific event, was used to analyze TBN with 50,000 paths and 130 quarter-hour steps over 5 sessions, starting with a seed of 91102 and assuming zero daily log drift, with a sigma of 0.02346009 divided by the square root of 26. The starting price was 39.33, the stop price was 37.887045, and the target price was 43.658865. The first observed crossing of the stop price was at the path price or target limit, otherwise the final path price was used, with a 30 basis point buffer on each side. The net mean was -0.4890%, the target was 4.352%, the stop was 44.7%, the 5th percentile was -4.791%, and the 95th percentile was 9.083%. This model, a Geometric Brownian Motion, does not account for jumps, weekend news, parameter uncertainty, correlation, or real venue fills, and cannot override a positive but weak event history. The training data used a two-point half Kelly strategy with a win rate of 2.5595% and a loss rate of 2.6799%, resulting in an invested capital fraction of 12.292. However, this is not a percentage and does not permit margin. The assumption that the sample loss captures the tail is indefensible, and the holdout data showed no loss, making the loss severity unknown and the Kelly criterion undefined. The approved allocation is 0 pending prospective evidence and current date window. The whole-share price of 39.33 represents 80.04% of the fund and 4.622% of the account's maximum loss. A 25% fraction probe of 9.83 would lose at most 1.155% of the account but requires regular trading hour fractional exits. No convex long option was selected or premium edge established. The stop loss budget must consider shock, with a 45.86% RARE-like drop on one 39.33 share costing 18.04, or 2.119% of the account, and a 100% loss of 39.33 representing 4.622% of the account. These are severity scenarios, not estimated probabilities. The sources of this data include https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TBN&feed=sip&adjustment=all and runway:query_trades?fund=catalyst, with analyses 2399/2400.

A test of the stock MLKN found that its intrinsic stress has zero half-Kelly, which is a measure of the optimal fraction of a portfolio to invest in a stock, and its residual option value remains unmodeled, meaning it is not accounted for. The test looked at the stock's price on September 9 and 10, and found that it would require a -17.211538% move to reach the option's strike price of 17.5. The test also looked at the stock's past earnings close moves, and found that the median absolute move was 10.745876%, with only 12.5% of the moves being covered by the option, which is a low coverage rate. The test also found that the option's premium, or price, was 0.28, and that the stock's price would need to be at or below 16.38 for the option to be in the money, which is only 1/8 of the historical closes. The test used a bootstrap simulation, which is a statistical method, with 10,000 paths and a 3% equity premium compounded, and found that the median return was -28.12351%, and the 5th percentile return was -45.62057%, with a profit odds of 9.38%. The test also found that the half-Kelly fraction, which is the optimal fraction of a portfolio to invest in a stock, was zero, and that the option's price would need to be at least 1.12 for it to be a good investment, but this price is not supported by the data. The test used data from the option contracts and stock bars from the website https://runway.adityaperswal.workers.dev/data/stock-bars, and the issuer's dates 23055/23066–73.

A simulation was run with certain premises. The first premise was to create paths of 20 days per asset from 253 supplied daily returns, using observations from days 23079 to 23081, with a circular 3-day block, and using numpy with 10000 seeds. The second premise was to use the latest 2 ATR stop and 6 ATR target, with daily close being the first stop and first target, resulting in odds of 47.50% and 7.27% for BTC, 51.91% and 10.29% for ETH, and 42.68% and 4.11% for SOL, with the remainder not meeting these conditions. The third premise was that these scenarios only consider closing prices and do not account for intraday thresholds, crisis gaps, costs, and breakout conditioning. The conclusion is that there is no inferred live win probability or Kelly from this simulation. The 5th percentile account loss at 25% allocation was -4.29%, -5.73%, and -6.02%, indicating that venue capacity alone cannot substitute for risk control. The validity of the simulation is conditional on resampling arithmetic, but its soundness fails as a calibrated trading forecast if regime changes or aggregation errors matter. A counterexample shows that trend-conditioned returns can be much better, tested separately. The stop distances were 5.967%, 7.224%, and 10.091%, and the nominal targets were 17.902%, 21.671%, and 30.273%, but no actual stop or target was set because there was no holding or entry. Fees also reduce the nominal 3R to less than 3R net, and a live policy requires new validation. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the text describing N911 LPG bootstrap sizing scenarios. The text mentions premises and costed holdout N15 cash returns, with independent manual entry fee-inclusive compounding matching a vectorbt portfolio, which is a type of investment portfolio analysis. The text also mentions a numpy seed, which is a way to generate random numbers, and 50000 iid bootstrap paths, which is a statistical method, of 20 trades from the same 15 returns. The training half Kelly, a betting strategy, is 9.00039% equity, with a win rate of 10.8027% and a loss rate of 6.91355%. The quarter probe, a way to test a strategy, is 2.25010%. The holdout half Kelly, another test, is 13.0303% and is diagnostic, not a selected allocation. The 9% allocation simulation median is +5.66527%, with a loss probability of 9.146%, and a 95th drawdown, a measure of loss, of 4.58894%. The quarter probe median is +1.40506%, with a 95th drawdown of 1.16244%. A one-share simulation, where one share costs 54.41, has a 6.39373% equity simulation 95th drawdown of 3.27679%. These paths do not account for serial dependence, future structural breaks, and fat-tail events, which are extreme events, absent from N15, and are not a calibrated 20-trade forecast. A fixed hypothetical 15% gap costs 8.1615 USD per share, which is 0.9591% equity, and is not the maximum possible loss. The quarter probe budget of 19.15 cannot buy a whole share of 54.41. The current signal is false and execution is unvalidated, resulting in an actual size of 0. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the methods are in local research/lpg_audit.py, with measurements recorded. There is no funding target input.

A news scan found the text: Premises: 1) A Holdout 200-day adapter stressed portfolio daily returns, with the same 9 equal capital sleeves, forms empirical input, not independent validation. 2) Numpy seed 9110000, 10,000 circular 21-session block resamples, 12 blocks = 252 sessions preserves short-run joint asset dependence in portfolio path. 3) Full 187.22 USD fund exposure is hypothetical; no cash yield, taxes, operator costs, or novel gap regimes are added. Conditional 21-session return 5th/median/95th = -1.33939/.008828/2.01433%, dollars -2.50760/.01653/3.77122; loss frequency 45.48%. 252-session return 5th/median/95th = -3.60460/1.48298/7.46494%; maximum drawdown median 2.69158%, 95th 5.52478%. None of 10,000 paths gains 349.01 USD in 21 sessions; this is empirical sample zero, not proof of impossibility, and 21 trading sessions differs from 21 calendar days remaining. Funding gap requires 186.41705% return on Quantcash alone; changing target cannot create it. Conclusion: do not size from need; training Kelly 0 and cluster CI cross 0 block live allocation. Validity is computational under empirical stationarity; strongest counterexample is a new regime or fat gap outside sample breaks bootstrap. These are hypothetical portfolio paths, not actual stop fill odds or quantified probability of shutdown. Source: research/inference.py/inference.json and https://runway.adityaperswal.workers.dev/data/stock-bars.

A preregistered analysis of EPM, which is a stock ticker, was conducted. The analysis involved two sessions with 3R sensitivity, meaning it measured the effect of a 3% change in the stock price. The results showed that after costs, the sensitivity was negative. The analysis used 60 daily log returns, with two future sessions divided into 52 quarter-hour increments, and 50,000 paths with a seed of 2026091100. The daily volatility was 2.771389%. The starting price was 3.84, and the stop loss was set at 1 old ADR lower, while the target was set at 3 old ADR higher. The stop loss was filled at the observed crossing price, and the target was filled at the limit price. The time was set at the 52nd step, with 30 basis points per side fees and slippage assumption. The results showed a net mean of -0.529246% for the zero log drift target, and -0.587239% for the sample log drift. The empirical two-day IID terminal bootstrap showed a p05 of 3.62895, a median of 3.84074, and a p95 of 4.06270. Only 0.72% of the terminal values were above the target, and it was not the first passage. The gross 3R was 2.31196R, but after costs, the net was only 2.31196R. The conclusion was approved 0, meaning the model did not show a positive expected return. The analysis notes that a countercase, such as a catalyst jump or fat tails, could invalidate the geometric Brownian motion model used. The model also omits gap, weekend news, dependence, and parameter uncertainty, and is not an event odds, backtest, 15-minute execution validation, or paper retune. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EPM&feed=sip&adjustment=all, and the note number is 2087.

A news scan found the text: a fund manager's notes about a trading model. The manager tested a model called NEWNumPyscenario with 50,000 paths and 10 events, using a seed from September 10, 2026, and a two-point independent and identically distributed law with a win probability of 4/6, a win rate of 2.643209564%, and a loss rate of 3.262590862%. These rates are based on stressed averages from 1928, without resampling the original six returns. The model started with a value of 49.14 and used a raw half-Kelly bet size of 0.1276117927. The results showed a mean value of 49.564657, a 5th percentile value of 48.575049, a median value of 49.686265, and a 95th percentile value of 50.441163, with a potential loss of 21.336%. The manager identified two issues with the model: it collapses the distribution and ignores parameter uncertainty, binary gaps, and dependence, and it failed training and had different 3R exits, which prohibits using this probability for sizing. The manager concluded that the model is not approved for use, with a raw notional value of 6.270843 being insufficient for authorization. The manager also noted that a countercase with fat tails could pay much more, but the model has removed them, so the result of 0 out of 50,000 recoveries with a value of 349.01 is only a bounded scenario result, not market odds. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EPM&feed=sip&adjustment=all and runway:query_research?fund=catalyst&analysisKind=backtest&symbol=EPM.

A news scan found the headline about High Tide Inc. announcing its third fiscal quarter 2026 financial results. The HITI Oct16 K2.5 call/put closed at.31/.18 on Sep9. The underlying stock price was 2.555 through Sep10 19:20UTC. The issuer-dated AMC earnings had a directional break-even point of +9.98043% and -9.19765%. The coverage was 1/9, which is 11.111% each, less than 40%. The median absolute close was 5.494505% and the median absolute gap was 2.34375%. The thresholds exceeded the close median by 4.485925 and 3.703146 percentage points, so there was no hold-through exception. A recent put was.16 and still 1/9 at 8.414873%. The intrinsic-only mean payoff was.166696/.471908 times the premium, which is not the option expected value. The premises of the analysis were to scale each of the 9 historical next-session stock close returns to 2.555, compute the call or put value, divide by the reference premium, and subtract 1. This is the intrinsic liquidation floor with time value discarded, not the actual 30+ day remaining option price or expiry return. The analysis used NumPy seed 910 and 10,000 independent and identically distributed bootstrap paths of 20 tickets, with a 3% current equity premium each. The search for fractions 0-6% in 0.01 percentage point steps maximizing mean log growth resulted in 0 for both. The intrinsic sample mean returns were -83.3304% for the call and -52.8092% for the put. The median series returns were -40.0481% and -27.7979%, with a 5th percentile of -44.3359% and -35.8266%. The profit odds were 0% and 0.01%. The analysis notes that residual time value could overturn these figures, so these are rejection stress scenarios, not proof of fair option value. The empirical mandated coverage of 11.1% and missing option value evidence independently reject the option, so the size is 0. One actual call was $31, which is 3.643% of equity, and one actual put was $18, which is 2.115% of equity. The full premium absolute ceiling was $51.0594, the normal 3% was $25.5297, and the high conviction 5% was $42.5495. The primary event is the announcement of High Tide Inc.'s third fiscal quarter 2026 financial results, and the data and nine primary history dates are in the statistical analysis 2301.

A review of the existing EPM simulation, which is based on a Monte Carlo method, a statistical technique used to model random events, found that it cannot price the required 3R strategy. The simulation used 50,000 paths and 10 events with resampling of 6 stressed returns, with an initial value of 49.14 and an old quarter Kelly value of 0.063805896. The mean value was 49.352116, with a 5th percentile of 48.845875, a median of 49.352178, and a 95th percentile of 49.856086, resulting in a loss of 24.838%. The simulation is based on two premises: first, that the returns are independent and identically distributed, which means they do not model binary gaps or dependence between events, and second, that the new half Kelly and 3R rule change allocations and payoff law, but cannot transfer odds. The conclusion is that the simulation cannot be used, with a count of 0. There is a possibility of upside, but it is unmeasured rather than zero, and there is no promise of simulated month-end profit. The sources of the data are a stock bars feed and a research query.