Stats

A news scan found the headline 'R91302 KMT zero Kelly; hypothetical probe still loses in most paths'. This refers to a trading strategy, where Kelly is a method to determine the size of a bet. The training results showed an average win of 9.574% and an average loss of 6.2915%, with a payoff of 1.5217, which led to a decision to use zero times the Kelly bet size. As a result, the authorized size was set to 0. In a hypothetical scenario, 20,000 trades were simulated with a seed of 91302, starting with a price of 21.27475 USD and an account fraction of 2.5%. The results showed a 89.325% chance of loss, with a maximum drawdown of 2.5111%. When assuming a 5% drawdown as a minimum loss, the results were even worse, with a 95.445% chance of loss and a maximum drawdown of 3.6482%. This led to the decision not to deploy even a small probe. The simulation was based on 20 trades, not 19 days, and did not take into account left-tail correlation and changing regimes. The stop loss and target prices of 27.50806 and 35.23582 were not validated and were based on a 3R gross return only. A 30% gap at the counterfactual size would cost 6.382425 USD, and the venue capacity and fund size were 212.7475 and 170.20, respectively, which could not override the zero edge. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars and 3063.

A trading strategy called R91300 CNQ uses half Kelly risk fraction, which is a method to determine the optimal amount to bet, and is also sensitive to gaps in stock prices. The strategy was trained using a dataset where the probability of a trade being successful was 1/3, the average win was 13.3288%, and the average loss was 5.3370%. This resulted in a half Kelly risk fraction of 0.0331949. The strategy's risk is also limited by a 30% gap and 3% equity risk budget, which means that the maximum amount that can be invested is 85.099 for a full investment and 21.27475 for a quarter investment. The quarter stop risk is $0.89144, and the 30% gap risk is $6.38243, which is approximately 0.75% of the account value, and is below the value of 50.07 shares. The training returns were sampled on 20,000 independent and identically distributed paths of 20 trades, with a seed of 91300 and a fixed account fraction. The results showed that the quarter base P05/P50/P95 returns were -1.1804%/+0.4054%/+2.1986%, with a 95th percentile drawdown of 1.6068% and loss odds of 34.565%. If 5% of the draws are replaced by the minimum return or -30%, the quarter returns become -2.3899%/-.3174%/+1.7415%, with a drawdown of 2.7685% and loss odds of 59.815%. The full stress test median return is -1.4423%, with a drawdown of 10.7717%. These probabilities are outputs of the conditional model, not estimates of actual war-gap frequency, and do not account for serial dependence. The 20 trades do not correspond to 19 calendar days, and a live stop cannot limit an overnight gap. Currently, the signal is false, which means the actual size of the investment is 0, despite a positive baseline Kelly. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and new costed tests will be run on March 30-31. No order has been placed.

A news scan found the following raw training data: the training was managed with only raw data, which had a probability of 0.387097, an average win of 13.9887%, and an average loss of 6.47636%, with a b value of 2.15996. The half Kelly risk was 0.0516704. Mapping historical loss to prospective stop is an approximation, not exact growth-optimal sizing. The full notional values were minimum 170.20 for the fund, 212.7475 for the position, with half Kelly account and 5.484% stop, 3% account stop, and 3% account with a 30% gap, which equals 85.099. The quarter value was 21.27475, below 49.21 share. The reference stop risk was 1.166895 USD. A 30% gap costs 6.382425 USD, which is 0.75% of the account. The simulation ran 20,000 independent and identically distributed paths of 20 trades, with a seed of 91208 and a fixed account fraction rebalanced. The full base return was -5.164% at the 5th percentile, 2.552% at the 50th percentile, and 11.999% at the 95th percentile, with a 95th percentile drawdown of 7.141%. The quarter return was -1.301% at the 5th percentile, 0.656% at the 50th percentile, and 2.907% at the 95th percentile, with a drawdown of 1.827%. Replacing 5% of the sampled trades with the minimum return or -30% gives a full median return of -0.556%, with a drawdown of 11.268%, and a quarter median return of -0.109%, with a drawdown of 2.916% and loss odds of 52.755%. The 5% frequency and 30% gap are subjective stress assumptions, not estimated probabilities. Twenty trades is not equal to 19 days until the end of the month, and these should not be turned into funding promises. There is no order. The sources of this data are https://runway.adityaperswal.workers.dev/data/stock-bars and the fund's own raw backtests from 2988 to 2989.

A news scan found the text of a corrected dividend simulation. The audit found that the first day started with an economic value of 49.21, which was the prior close, while treating 49.21 as the entry value. To correct this, the first day's open, high, low, and close ratios were adjusted relative to the sampled open, so the entry open is exactly 49.21. The subsequent day's ratios remain based on the prior close. Other specifications remain unchanged, including 20,000 paired 40 session paths, 5-day blocks from 2021-24, seed 91206, 2 ADR stop, 3R target, 1 basis point fee, and 5 basis points lip, with 3.41 ex-session 5 deducted from economic OHLC. A basis point is one hundredth of a percent. The simulation includes dividend receivable only for prior day holders, not cash reinvestment. The static raw stop is 88.865% stop, 10.53% target, with 0.605% tolerance, and 41.96% stopped on one day, with a mean of +0.8081302% and a median of -0.2303364%, and a win rate of 48.645%. Adjusting both levels by 3.41 on one day results in 66.105% stop, 31.15% target, with 2.745% tolerance, and 6.10% stopped on one day, with a mean of +1.5436493% and a median of -5.5940764%, and a win rate of 33.41%. The entitlement is 73.385% for both. The directional mechanics finding survives, but neither policy is calibrated to current event stock expectancy. The adjusted levels are hypothetical and not a venue instruction. The simulation does not model 15-minute checks, trailing or partial management, taxes, market-specific dividend repricing, or the current war regime. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the declaration is https://www.frontlineplc.cy/fro-declares-special-one-time-dividend-after-sale-of-two-vllcs/. There are no orders.

A trading fund manager made decisions about the stock STNG from 2029-20 to 2029-27. They used a method called binary approximation, which is a simple way to estimate risk, to calculate the average win and average loss. The half Kelly risk fraction, a measure of how much risk to take, was calculated as 0.5 times the maximum of 0 and the probability of winning minus the probability of losing divided by the average win divided by the average loss. The manager assumed that one average historical loss corresponds to the prospective risk unit. The full notional, or the total amount of money to invest, was calculated as the minimum of $170.20, $212.75, half Kelly times $850.99 divided by the current risk, 3% of equity divided by the current risk, and 3% of equity divided by 30% gap, which equals $85.09. The gap assumption of 30% is subjective and not based on actual measurements. A quarter probe, a small investment to test the market, of $21.27 was made, which is below one share. The manager did not round up to a full share because the account needs $349.01. The manager also ran 20,000 simulated trade paths to test the strategy. The results showed that the maximum potential loss was 21.875% and the maximum potential drawdown, or loss of capital, was 9.80%. The manager used a sensitivity test, which is not a forecast, to see how the strategy would perform in different scenarios. The final paths supersede previous ones, and the eligible live size remains zero due to negative fixed holdout and managed stress holdout.

A news scan found the headline is not available, but a data line shows the final sizing for R91206 FRO. The training net closed-trade outcomes were from 2920-27 with no holdout fitting. A binary approximation, which is a simple way to estimate, was used with b, or the average win divided by the absolute average loss. The half Kelly risk fraction, a way to measure risk, was calculated as 0.5 times the maximum of 0 and p minus 1-p divided by b. The current stop-percent was assumed to be one average historical loss corresponding to that prospective risk unit. The full notional, or the total amount invested, was calculated as the minimum of fund 170.20, position 212.7475, half Kelly times 850.99 divided by current risk, 3% equity divided by current risk, and 3% equity divided by 30% gap, which equals 85.099. The gap assumption of 30% is subjective and not a measured worst case, meaning larger gaps are still possible. A quarter probe of 21.27475 was below one share for either tanker, and the account needs 349.01. One quarter probe's 30% gap loss is 6.382425, which is 0.75% of the account. The results are based on 20,000 trade paths with a seed of 91206 and a constant fraction of 85.099 divided by 850.99, rebalanced with future allocations ignored. A 5% per trade outcome was replaced with the minimum of the original and -30% as a stress test, which is a sensitivity test, not a forecast. The exact fixed and managed inputs and P05/P50/P95/95th DD are listed. These final paths supersede previous results from 26294-97, with the source being https://runway.adityaperswal.workers.dev/data/stock-bars. Positive training supports a candidate, but pending dividend and raw management and sub-share probe remain implementation tasks.

A study compared two approaches to handling dividends in automated trading. The question was whether a known cash distribution changes exits without equivalent business loss. The study used 20,000 paired scenarios, sampling data from 2021-24 in 5-day blocks, and normalized each price by the prior close. It started with a price of 49.21 on September 14, a stop loss of 46.51, and a target of 57.31. A dividend of 3.41 was deducted from subsequent prices on September 18, but only for those who held the stock the prior day. The dividend was included in the total payoff, but not reinvested before payday. The study compared two approaches: one with unchanged raw stop levels, and one with stop levels reduced by 3.41 on the ex-dividend day. The results showed that the unchanged approach had a stop loss rate of 87.78%, a target hit rate of 11.48%, and a timeout rate of 0.74%. The adjusted approach had a stop loss rate of 65.57%, a target hit rate of 31.89%, and a timeout rate of 2.55%. The mean wealth return was 0.96639% for the unchanged approach and 1.69409% for the adjusted approach, but the median return was -0.30194% and -5.59408% respectively. The study noted that this was a paired mechanics simulation, not a measured probability of profits, and ignored current market conditions, taxes, and other factors. The source prices used in the study can be found at https://runway.adityaperswal.workers.dev/data/stock-bars, and the issuer terms for the dividend can be found at https://www.frontlineplc.cy/fro-declares-special-one-time-dividend-after-sale-of-two-vllcs/.

A fund manager used a training set called frozenL20 training26 to test a trading strategy. The test used 30,000 random samples of 20 trades, with a fixed starting value, and got the following results: the returns after a certain period were -90.324%, -78.350%, and -41.552% for the 5th, 50th, and 95th percentiles. The strategy lost money 99.1967% of the time, and the largest loss was more than 20% of the starting value 100% of the time. This test does not reflect real market conditions, as it ignores how the market changes over time. The test found that the strategy won 15.3846% of the time, with a mean win of 0.216567 and a mean loss of 0.115744. The half-Kelly allocation, a way to decide how much money to bet, was set to 0. The minimum amount of money to use was calculated to be 0, based on a fund value of at least $170.20 and a venue value of $212.7475. A separate analysis of possible future prices gave a weighted return of -11.4357%. This analysis did not try to predict actual future prices, but rather considered different possible scenarios. The results of the test justify rejecting this trading strategy.

A training session for a trading model, known as R91200 HWM, has been completed with the following results: the model was trained on 30 periods with a win percentage of 46.6667%, an average win of 9.30997%, and an average loss of 4.58913%, resulting in a mean return of 1.89712%. This training used a method called NumPy seed 91200 and a statistical technique called IID bootstrap, which involved 30,000 repetitions of 20 trades. The results showed that the full-notional terminal values were -17.7906%, +37.4368%, and +135.9484% for the 5th, 50th, and 95th percentiles, respectively, with a loss of 15.6767% and a maximum drawdown of 38.8067%. The 95% confidence interval for the mean return was between -0.71885% and 4.61327%. The data used for this training came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars. The two-point half Kelly fraction, a measure used to determine the optimal fraction of wealth to invest, was calculated to be 2.22017, which is not a risk-budget fraction and does not allow for leverage. The current equity is 850.99, the fund value is 278.67, and the position cap is 212.7475, which is the maximum amount that can be invested. The current share price is 229.61, which cannot be fully invested in, but partial investment is technically possible during regular trading hours. The allocation is currently 0 because the current entry conditions are not met and the daily proxy does not validate the 15-minute policy. It's also important to note that the results are based on historical data and do not forecast future performance under different economic conditions, and there is a risk that actual losses could be higher than modeled if there is a gap through the stop loss. Additionally, no input was made for potential funding shortfalls.

A trading fund manager used a computer program to analyze the stock TSM. The program looked at past data from September 27, 2009, to August 26, 2022, and found that the average win was 9.29097% and the average loss was 5.46892%. The program also found that the average return, or arithmetic mean, was +0.27104%. However, when the program looked at the actual return, including the effect of compounding, it was -1.22281%. The program then used a method called Monte Carlo simulation, which is a way of modeling different possible outcomes, to test the results. The simulation used 30,000 paths, or possible outcomes, and 20 trades. The results showed that the median, or middle, return was -1.62997%, and there was a 51.9167% chance of loss. The program also looked at a smaller set of data, called the holdout set, and found that the median return was -22.78221%. Based on these results, the program decided not to invest in TSM, because the positive arithmetic mean was not enough to justify the risk. The program assumed that the data was independent and identically distributed, or IID, and ignored any unusual events or gaps in the data. The results of the simulation were used to illustrate the study, not to determine the actual size of the investment. The data used for the analysis came from a website. The allocation for TSM was 0, and was not rounded up.

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 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.

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 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 following text: a five-day block risk diagnostic for TSM was performed. The diagnostic used the last 252 daily log returns from 422 SIP bars ending on September 9, sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The diagnostic involved 248 overlapping 5-day blocks resampled 10,000 times with a numpy seed of 91008, first using FTK and then TSM. The diagnostic started at the September 9 close and evaluated a daily close 1 ADR stop and 1.5 ADR target, with the first hit or else the day 5 close. A 5 basis point fee and 10 basis point slip were applied to each side. The historical drift and block frequency are descriptive assumptions, not probabilities based on today's disclosure, with no out-of-sample selection or 15-minute/extended session validation. Quote gaps and daily close overshoot remained. The positive simulated mean cannot reverse the failed FTK holdout or create a TSM event edge. The diagnostic used finite overlapping 248 blocks, not 10000 independent trades. A countercase event volatility/regime break invalidates the distribution. The conclusion is that there is no trade sizing from the simulation, with an approved Kelly of 0 because a calibrated edge is unavailable. Reference prices in 21496-97 are not orders. Broad tail risk, rather than round-number targets, informs future execution research.

A news scan found the following text: a five-day block risk diagnostic for FTK is not an event edge. To analyze this, the system used the last 252 daily log returns from 422 SIP bars that ended on September 9, which were sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The system then resampled 248 overlapping five-day blocks 10,000 times using a numpy seed of 91008, first considering FTK and then TSM. The analysis started at the September 9 close and evaluated a daily close with a 1 ADR stop and a 1.5 ADR target, with the first hit being considered, or else the day 5 close was used. A 5 basis point fee and a 10 basis point slip were applied to each side. The historical drift and block frequency are descriptive assumptions and not probabilities based on today's disclosure, with no out-of-sample selection or 15-minute/extended session validation. Quote gaps and daily close overshoots remained. The positive simulated mean cannot reverse a failed FTK holdout or create a TSM event edge. The system used 248 finite overlapping blocks, not 10,000 independent trades. A countercase event volatility/regime break would invalidate the distribution. The conclusion is that there is no trade sizing from the simulation, and a Kelly of 0 is approved because a calibrated edge is unavailable. Reference prices in 21496-97 are not orders. The analysis focused on broad tail risk rather than round-number targets to inform future execution research.

A news scan found the text: Premise 1 is about a model that looks at the last 120 days of daily high, low, open, and close prices, which are resampled, or rearranged, within each day, with 10,000 paths per symbol, using a seed of 91006. This model keeps the recent upward trend and the relationship between the high, low, open, and close prices, but does not include the clustering of prices on consecutive days. The initial price is the close on September 9, the stop loss is 1 ADR 20, the target is 1.5 ADR 20, and the time frame is 5 sessions. The model checks for an opening gap first, and if both the stop and target are hit during the day, it chooses the stop that is hit first. It also includes fees of 5 basis points and a slip of 10 basis points. Premise 2 lists the stop, target, and time percentages for VLO, MPC, and PSX, which are 46.72, 48.87, and 4.41, 42.12, 52.42, and 5.46, and 44.20, 48.67, and 7.13, respectively. The net means are 0.2075, 0.5774, and 0.3822 percent, and the fifth percentile is -4.7916, -3.6494, and -3.6301 percent. Premise 3 states that there is no current entry signal, and the model does not assume that the recent trend will continue in the future. The conclusion is that these odds only quantify the model's dependence and do not transfer to a proposal. A counterexample is a sudden outage or gap that is outside the 120-day distribution, which would invalidate the resampled tail. The full model outputs are available at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. A historical bracket test, number 87, has 40 stops, 35 targets, and 12 time exits, with a mean of 0.11644 percent, a worst loss of -4.90189 percent, and a maximum loss of 2.17138 R. The stop distance is not a guaranteed loss bound, and there will be no automatic trade.

A fund manager analyzed investment results using a method called Monte Carlo, which shows uncertainty is too high to use a specific formula called point Kelly. The analysis is based on two premises. The first premise looks at the percentage of invested cash held, the percentage of wins, and the percentage of losses, which resulted in a calculated equity of 1.03865. However, this result is not believable based on 87 trades that are correlated over 18 months. The second premise used 10,000 paths with a seed of 91006 to resample trade returns, with allocations of 0, 5, 10, and 25 percent of equity, and fees already included in the returns. At 25 percent allocation, the results showed a range of -8.7376 to +15.7627 percent, with a loss of 39.35 percent and a maximum drawdown of more than 10 percent at 6.15 percent. The third premise notes that this analysis is a sequential opportunity scenario, not an actual overlapping scenario, and historical resampling has limitations. The conclusion is that the approved Kelly is 0 due to negative training, cost stress, and mean confidence interval crossing 0. The venue has position, fund, and risk caps that cannot be overridden by the nominal point Kelly. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and all figures are valid as of June 10, 2020, but this is a conditional simulation, not a sound forecast of odds.

A news scan found the headline about ACSA reassuring passengers over jet fuel supply on August 28. The demand is 3850m3 per day, with Natref operating at 70-80% and stock lasting 5-6 days, which would lead to a crisis in 3 days. However, the current stock levels are unknown. On September 1, the operator confirmed a partial supply mitigation. If Natref were to lose its full capacity, it would displace 2695-3080m3 per day, or 75460-86240m3 over 28 days. Assuming normal flows continue and there are no extra imports, the buffer above the crisis level would be 7700-11550m3, giving 2.50-4.29 days of supply. This is not the current countdown. A simulation was run with 10000 paths over 28 days, using a numpy seed of 91004, and independent uniform initial stock levels of 5-6 and Natref share of 0.7-0.8. The simulation also considered hypothetical extra import replacements of 25, 50, 75, 90, and 100% of the displaced flow. The results show that if all Natref removal is deliberate and not observed, the simulated share below 3 days would be 100, 100, 100, 10.13, and 0% respectively. The median first cross day would be 5, 7, 14, 28, and NA respectively. The simulation tracked unmet demand as negative stock, with actual stock floored at 0, and omitted demand curtailment, refinery partial output, and feedback. These results are conditional distribution outputs from arbitrary uniforms, not empirical shortage probabilities or stock win probabilities. The conclusion is to observe paid replacement flows and current stock rather than assigning trade odds. The size is 0, and there is no Kelly from this model.

A premise was made based on closed holdout returns from 1895 to 1998, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. This premise used 10,000 independent and identically distributed paths, each with 20 trades and 10% equity exposure. The terminal product was calculated as 1 plus 0.1 times the return. The loss probabilities were 95.68% for FTK, 1.25% for SSL, 0.12% for ATI, and 6.96% for IREN. These probabilities were based on the empirical distribution and did not take into account duration, serial dependence, new regime, or 15-minute exits. Another premise was that the underlying mean confidence intervals all cross 0. Simulated tails were mainly optimistic and recycled rare large wins, but should not be interpreted as forecast odds. A binary-mean quarter Kelly formula was used to produce fractions of 0 for FTK, 1.49196 for SSL, 2.94489 for ATI, and 0.26886 for IREN. Fractions above 1 indicate unstable extrapolation and do not permit leverage. The real capital was 25% equity, with a cash fund of 278.67 and a maximum risk of 8.5099. The conclusion was to approve a fraction of 0 for all. However, a strong counterargument was made for IREN, which had a positive train and holdout, but this was outweighed by a 72.19% drawdown and 8 trades that failed robustness. There is a need for prospectively frozen net venue payoffs, rather than more resampling of the same trials.

A study of AVAV's performance used data from https://runway.adityaperswal.workers.dev/data/stock-bars, which showed six net closed-trade returns from 1815. Using a method called bootstrap, which is a way to estimate how reliable the results are, the study found that there is a 15.25% chance that the investment will end up with less money than it started with. The possible returns after a certain period of time were very wide, ranging from a loss of 54.21% to a gain of 3502.79%. These results are overly optimistic because they are based on two unusually successful trades in the past, which may not happen again. The study did not take into account how long the trades were held, or how the trades might affect each other. The results were not reliable enough to use for making investment decisions, so the approved investment amount is only a quarter of what would be suggested by a method called Kelly, which is a formula for deciding how much to invest. This method is not suitable for this type of investment because it is based on simple yes or no outcomes, and this investment has many possible outcomes. A strong example of a real trend that could work in the investment's favor would need to be proven independently before it could be relied on.

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.