A sixteen-hour polling simulation was conducted from August 7 to September 5, using 15-minute venue bars for Bitcoin, Ethereum, and Solana, with 2880, 2823, and 2859 bars respectively, and excluding 57 Ethereum and 21 Solana bars with gaps. The simulation used 20,000 paths per asset, with a NumPy seed of 2026090608, and sampled 16 independent blocks of four contiguous 15-minute log returns. The results showed that the stop loss was overshot, but this does not necessarily mean a tradable win probability. The simulation had a stop loss of 0.795%, 1.360%, and 1.030% for the first poll, and a target of 1.165%, 2.940%, and 0.560%. However, neither the stop loss nor the target was reached in 98.040%, 95.700%, and 98.410% of cases. The median conditional stop overshoot was 0.124%, 0.188%, and 1.218% percentage points, with a 95th percentile of 0.612%, 1.187%, and 3.348% percentage points. It is noted that these results are Monte Carlo outputs and not calibrated live probabilities, strategy win rates, or validated loss limits. The simulation has several limitations, including a short 30-day rally sample, potential non-random missingness, and unverified 15-minute bar aggregation and outlier provenance. Additionally, the simulation did not account for fees, depth, or delay after the poll, and did not include a new out-of-sample backtest. The results suggest that the continuous range stop assumption does not bound polling loss, and that existing daily and hourly strategy rejections remain. The script and results are archived, and the source is the closest venue proxy, not the global market.
A news scan found the text describing a Monte Carlo simulation. The simulation used a Numpy seed of 9060600. It looked at how a joint portfolio of daily returns from six sectors changed over time, using 20,000 possible paths and 20 sessions, with the data split into training and holdout sets. The simulation put fractions of 0, 0.25, 0.5, and 1 of $278.67 into a strategy with equal parts, with the rest, $850.99, kept as cash in the account. The model did not require whole shares, specific trading venues, or fixed weights after considering historical drift. The baseline results showed a profit frequency of 51.51%, a median profit and loss of $0.2704, and a 5th percentile of -$10.5123 and a 95th percentile of $11.8105. There was a 23.875% chance of the account losing more than 1%. The simulation also looked at how sensitive the results were to 10 and 20 session blocks, with profit frequencies of 51.65% and 53.97%, and median profits of $0.2704 and $0.6030. These results are based on resampling frequencies and do not predict future probabilities or monthly funding. The simulation also looked at trend and low volatility scenarios, with profit frequencies of 37.10% and 34.95%, and negative median profits of -$1.4186 and -$0.8456. Another part of the simulation, called exit-odds bootstrap, drew whole entry-week groups 10,000 times, with results of 40.9699% for the target, 53.5117% for the stop, and 5.5184% for the time. The simulation used a quarter Kelly capital formula, which resulted in an allowable size of 0. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the fund trade source query is query_trades fund=supply IDs 3, 6.
A news scan found the headline about a pullback Monte Carlo analysis. This analysis is about small trade samples and five-day exit odds. It does not establish an edge. The analysis used a NumPy seed of 2026090606 and 20,000 circular two-trade block bootstrap means. The results showed that the 95% range for Bitcoin was between -2.881% and 1.818%, for Ethereum was between -1.355% and 2.364%, and for Solana was between -3.736% and 0.484%. The analysis also showed that the median end equity for 20 trades at 10% total equity notional was 837.91, 857.29, and 823.28. The loss frequency was 84.66%, 29.055%, and 98.815%. The analysis did not apply a 1% stop risk gate and is not a deployment or 20 trades per month forecast. The analysis sampled five 24-hour log return blocks from 2026 history, with 5,904 valid blocks each, and 20,000 times 120 hour paths. The last close ADR stop was 3.663%, 4.840%, and 6.386%, and the target was 5.495%, 7.261%, and 9.579%. The first hourly close crossing stop, target, and timeout for Bitcoin was 41.835%, 21.35%, and 36.815%, for Ethereum was 42.785%, 21.98%, and 35.235%, and for Solana was 34.465%, 16.845%, and 48.69%. The mean modeled net was -0.860%, -0.960%, and -0.901%. The analysis preserved intraday dependence but did not account for entry conditioning, multiday regimes, cross-asset joint tails, intrahour touches, or 15-minute venue polling. The exit odds are scenarios, not the rule's win probability. A strong counterargument is that the median positive for Ethereum 20 trades is due to selection survivorship, negative 35 trade training, and doubling costs negative holdout. The Kelly correction 360 is a risk fraction that divides by decimal L for notional. The ETH plug-in risk of 2.786% implies 105.34% notional before caps, not 2.786% notional. The unknown robust edge sets the actual zero before 212.7475 position, 235.86 fund, and 8.509 risk caps. All simulation figures were recorded on pages 6149-6151, and the code archive is 363-385. The source is https://runway.adityaperswal.workers.dev/data/crypto-bars.
A trading fund manager made some simulations to decide how much to invest in the stock market. The manager looked at the performance of the SPY stock, which is a way to invest in the US stock market as a whole, over 1,420 nights. The manager found that the probability of making a profit was 37.1127%, the average profit was 0.3810%, and the average loss was 0.4890%. However, the average return was -0.1661% after considering the costs of buying and selling, which is known as a 10bp per side cost, meaning a 0.1% cost for each transaction. This means that the manager should not invest in this stock, according to a formula called the quarter-Kelly allocation, which helps decide how much to invest based on the potential risks and rewards. The manager also did some other simulations, including one with 10,000 samples of five trades, and another with 100,000 paths of 25 trades, to see how the investment would perform over time. The results showed that the investment would likely lose money, with a median profit and loss of -$8.51, and only 6.961% of the simulations resulting in a profit. The manager also used some statistical models, including a Gaussian and a Student-t model, to simulate the performance of the investment, and found that the average return was around -0.1752%. The manager used some data from a website and some scripts to run the simulations, and the results were stored in some files called simulations.json, barriers.json, and scripts.
A news scan found the headline 'HAIN empirical event-path exit simulation stays negative in the recent cohort'. The details are as follows: a simulation was run using a Numpy seed of 60606. The simulation resampled 50,000 whole 10-session OHLC windows from 13 training events and 50,000 from 6 holdout events, normalizing each entry to open at 1. The current barriers were set at -9.93029% and +16.25249% on September 4. These barriers are used for diagnosis, not as a historical rule. The simulation used a fee of 5 basis points and a slip of 10 basis points on each side. The training results showed a mean return of +3.318961%, a win rate of 61.48%, and a target rate of 7.866%. The holdout results showed a mean return of -5.847043%, a win rate of 16.676%, and a target rate of 16.676%. The time exit was set at T-1 close. A separate simulation of 12 trades with independent and identically distributed compounding showed median ending values of 49.14, 34.69212, 23.70674, and 9.94125 USD, with a loss probability of 0, 98.78, 99.042, and 99.354%. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=HAIN,SPY,XLP&timeframe=1Day&feed=iex&adjustment=all.
A news scan found the headline 'XLF base-hit exit simulations reject both baseline and favorable-model candidates'. This refers to a simulation of trades for the XLF stock. The simulation was based on three premises: first, that the trade exits at the bid price after fees; second, that the target price must fit within a measured range and have a good reward-to-risk ratio; and third, that the precision of the Monte Carlo model is conditional, not based on empirical evidence. The simulation first chose the largest baseline net among 145 affordable contracts with at least 4 sessions, which was the September 18 K62 call with a bid of $0.01 and an ask of $0.02. However, the simulation found that the trade would not be profitable after fees. Another contract, the September 18 K60.5 call, was also simulated, but it too was found not to be profitable. The simulation used a model with 100,000 paths and 52 steps, and it found that the mean loss was -$1.462946 and the win percentage was 10.301%. The simulation also found that the mean loss would increase by $1.10 if the fee was increased from $0.20 to $1.30. The source of the data is https://runway.adityaperswal.workers.dev/data/option-chain/XLF?feed=indicative and adjusted stock bars.
A study examined how changes in helium prices affect distributor profits. It was based on three premises: first, contract shares disclose volume, not realized prices; second, the baseline unit price is 90% contract and 10% spot, with helium revenue at 100 and cost at 65, and all costs are variable; third, certain assumptions about contract repricing, spot price changes, volume retention, and cost inflation were made, but these are not estimates of likelihood. The study used Python to run 192 grid combinations and 100,000 numpy seed draws from September 6, 2026. If contracts and volume remain unchanged, doubling the spot price would result in revenue of 110, and cost inflation of 0, 10, 20, or 40% would result in profits of 45, 38.5, 32, and 19, respectively, compared to a base profit of 35. A 15.3846% unit cost inflation would erase the profit increase. Assuming an 8% helium revenue share for the APD group, this would translate to only a 0.8% group sales uplift before considering costs, mix, and volume. The study found that the 5, 50, and 95 quantiles for profit change are -61.17, -21.31, and +22.20%, respectively. Additionally, 22.199% of draws exceed the base profit, and 6.924% of draws have a profit increase but a margin percentage decrease. These results are based on model geometry and do not represent a 22.199% chance of winning or a forecast. The study notes that fixed costs, working capital, capex, correlations, and repricing clauses could worsen or improve outcomes, and that the Kelly criterion cannot be assigned based on assumed uniform distributions. The valid inference is that the delivered price-cost spread and volume are needed, not just the spot price. The source contract anchor is https://investors.airproducts.com/static-files/1c55c69d-3196-4a46-b646-fd85adeec0f2, and the original model note is 295, observations 5437-5438. The study concludes that no equity-price simulation is justified without an empirical payoff distribution.
A news scan found the following information about a merger. The premises are as follows: the stock price of TXNM on September 4 was 58, with an offer of 61.25, resulting in a gross spread of 3.25 USD or 5.60345%. This calculation uses different break points, such as 40, 45, 50, and 55, and different time periods, such as 180, 270, and 365 days, with hurdle rates of 0, 4, and 6% and either no dividends or assumed continued dividends of 4.225 USD per quarter. At a break point of 50 and 270 days with a 4% annual alternative return, the required success probability is 87.1697% without dividends or 76.1309% with assumed dividends of 1.25014 USD. The cost of a win is 7.59741% and the cost of a loss is 11.89916% in the dividend case. A Bernoulli simulation with 100,000 paths per scenario and a seed of 90605 was run, and the results showed that the Kelly criterion, a measure of optimal bet size, is widely different for assumed success rates of 60, 75, and 90%. A wrapper simulation with 19 quarterly payoffs, 10000 paths, and a seed of 90604 showed a mean return of -0.32820% for MRGR and -0.26958% for MNA, with 95% confidence intervals of [-0.50152, -0.17937] and [-0.59914, 0.05320] respectively. The quarter Kelly was clipped to 0 for both. A hypothetical 25% fund allocation for 4 quarters showed terminal returns of -0.61930%, -0.31185%, and -0.03280% for MRGR and -0.88974%, -0.21603%, and 0.32215% for MNA at the 5, 50, and 95 percentiles respectively. These results cannot bound unseen crashes. The operator liquidations showed a mean return of -0.86097% and a Kelly criterion of 0, which are not natural strategy outcomes. The sources of this information are https://runway.adityaperswal.workers.dev/data/stock-bars, query_trades fundquant, https://www.txnmenergy.com/investors/acquisition.aspx, and https://www.prnewswire.com/news-releases/txnm-energy-board-declares-quarterly-common-stock-dividend-302699014.html.
A simulation of 100,000 paths was run with a seed of 60604 and 10 sessions. The simulation sampled two 5-day blocks from the latest 252 daily SRRK close returns, including the tails. A diagnostic exit was triggered at the first sampled close crossing +2 Average Daily Range (6.8113%) or -1 Average True Range (-3.6738%), or otherwise on day 10. Fees of 5 basis points and a slip of 10 basis points were applied to each side. This simulation does not account for continuous fills or 15-minute venue execution. An independent adverse jump of -30% was injected at a uniform day with an assumed window probability of 0%, 1%, 5%, or 10%. The results showed mean returns of 1.4974%, 1.3593%, 0.7600%, and 0.0374%, with win rates of 47.536%, 47.288%, 46.265%, and 45.027%. The 5th percentiles were -7.4857%, -7.5778%, -9.5225%, and -14.2296%. At a 10% assumed hazard, 4.603% of paths lost more than $8.5099 when hypothetically deploying the full $49.14. Even with zero-added hazard, there was an overshoot because checks were made at daily close. These results are conditional stress outputs, not forecast win probabilities or approved sizing. A 30% jump alone on $49.14 would lose $14.742, which is more than 1% of $8.5099. The full-loss cap of $8.5099 also fails whole-share affordability. The empirical momentum edge was -303, and the quarter Kelly override was negative, overriding the positive bootstrapped path mean. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SRRK&timeframe=1Day&feed=iex&adjustment=all. The facts end at bar returns, and all hazard/jump parameters are explicit assumptions.
A news scan found the headline about a cheap put option for NFLX. The premises of this evaluation are to consider planned bid exits, not just the terminal fair value, and to use physical paths that are independent of the market's implied volatility, or IV, which is a measure of how much the market thinks a stock's price will move. The results are conditional until they are validated against the actual option history. The specific option being considered is the NFLX Sep 11 K72 put, which had a bid of $0.02 and an ask of $0.03 on Friday, with 4 remaining trading sessions. The mid-calibrated session IV is 32.94478%, and the rate is 4%, with q, or the dividend yield, being 0. The initial terminal 2-session geometric Brownian motion, or GBM, which is a mathematical model for stock price movement, with fixed IV and half spread, and fees of $0.20 per contract, gives a blend physical volatility of 38.03027%, with a win rate of 16.93%, an average win of $18.2765, an average loss of $2.9420, and a mean of +$0.6503. The training wins or volatility is 35.93251%, with a mean of -$0.04315, and the low volatility is 30.15233%, with a mean of -$1.54882. The positive unbounded tail mean is not a base-hit validation, which means it is not a reliable measure of success. The explicit exit diagnostic is a 1.5 average true range, or ATR, 14 decline, which is equal to 3.4575 underlying points over 2 sessions, giving a Black-Scholes model, or BSM, bid of $0.091519, with a round target down of $0.09, and a stop of $0.01 below the initial bid of $0.02. The target net profit is $5.80, versus a full premium plus assumed fees of $3.20, which gives a risk-reward ratio, or RR, of 1.8125. This is not an order. The evaluation used NumPy seed 2026090605, with 100,000 paths, and 52 equal 15-minute-like grid steps over 2/252 years, with common normal draws for all volatility scenarios. The remaining time, or T, decreases from 4/252 to 2/252, and the modeled bid is the maximum of the BSM minus $0.005, or 0. The first bid is less than or equal to $0.01 stop, or greater than or equal to $0.09 target, which exits at the modeled observed bid, while others exit at the end. The blend win rate is 16.382%, with an average win of $7.399572, an average loss of $2.460156, and a mean of -$0.844935. The stop rate is 83.514%, the target rate is 16.252%, and the time rate is 0.234%. The wins or win rate is 15.161%, with a mean of -$0.972319, and the low win rate is 11.074%, with a mean of -$1.383048. The qKelly, which is a measure of the optimal fraction of wealth to invest, is 0 for all scenarios. This evaluation does not match historical option liquidity or actual closure variance, as it uses a continuous variance grid that compresses overnight, with IV and spread fixed, and no overnight jump or stochastic IV or latency or American path model. Favorable overshoot fills are assumed, but not promised. Stops do not bound full premium risk, and one contract with $3 plus fees is affordable, but the edge is zero. The sources of this data are https://runway.adityaperswal.workers.dev/data/option-chain/NFLX?feed=indicative and https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=NFLX&timeframe=1Day&adjustment=all&feed=iex. The conclusion is to reject this option and not remove exits to accommodate an optimistic mean.
A news scan found the headline 'Three to four exits cannot support Kelly deployment'. The text that follows is an explanation of this headline. Premises include fixed control with hourly payoffs for Bitcoin, Ethereum, and Solana. For example, Bitcoin had a win rate of 21.3586% and a loss rate of 10.2452%. Ethereum had a win rate of 32.5817% and a loss rate of 18.3817%. Solana had a win rate of 7.9297% and a loss rate of 14.4321%. The plug-in quarter Kelly for Bitcoin had an equity of 0.33874%, but the naive notional value of $2.88 was below the $10 minimum for cryptocurrencies. The uncertainty gate was independently set to zero. Using a NumPy seed of 20260906, a 20,000 iteration bootstrap mean was calculated, resulting in 95% ranges of -14.1482% to 21.3586% for Bitcoin, -28.8701% to 32.5817% for Ethereum, and -25.2149% to 4.4192% for Solana. However, these intervals are not calibrated confidence intervals due to the small sample size. Conditional scenarios with 10% equity exposure resulted in end medians of 850.99, 852.80, 825.07, and 711.47, with loss frequencies of 48.685%, 62.845%, and 99.885%. The empirical median for Bitcoin was positive, but it could not cancel out the negative full allocation compound return or wide uncertainty. The IID assumption omits regime and serial dependence, and the forced terminal winner is not a natural exit. The 20 trades are not a monthly forecast. As a result, there is no calibrated positive edge, and the actual size is 0 before the position cap of 212.7475, fund cap of 235.86, and planned risk of 8.509. The conclusion is to preserve cash and never round up the Kelly to the venue minimum. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars, which was recorded on hours 306-308.
A trading fund manager made some notes about buying and selling stocks. The manager started with three premises: first, the manager buys at the ask price and sells at the bid price; second, high physical volatility does not guarantee that the implied volatility will be repriced; and third, simulated means are not validated to have a win or loss edge. The manager looked at hypothetical snapshots from September 4, including a SNAP put option with a strike price of $4.5 and a bid price of $0.01 and an ask price of $0.03, and a MARA put option with a strike price of $8.5 and a bid price of $0.01 and an ask price of $0.02. The manager used the Black-Scholes model, or BSM, which is a mathematical model used to estimate the value of a stock option, with a risk-free rate of 4%, a dividend yield of 0%, and an implied volatility of 48.93596% for SNAP and 78.62248% for MARA. The manager did not carry over the implied volatility across different time periods without recalibrating it. The manager used a random number generator with a seed of 2026090602 and made 100,000 draws per contract and scenario, assuming a zero expected stock return, 5 sessions, and a physical volatility of 0.8 minutes, which is a measure of the rate of change of the stock price. The manager also used a fixed variance blend forecast, which is a way of predicting the volatility of a stock. The terminal bid price was the maximum of the BSM price minus half of the observed spread, or $0, and the fees were $0.20 per 100 share contract. The results showed that the SNAP forecast with no implied volatility recovery had a win rate of 21.095%, an average win of $6.03493, an average loss of $2.73711, and a mean of -$0.88665. The MARA forecast with no implied volatility recovery had a win rate of 17.303%, an average win of $13.29695, an average loss of $2.05319, and a mean of $0.60284. The manager used the Quarter Kelly formula, which is a way of determining the optimal fraction of a portfolio to invest in a stock, and found that the conditional allocation was 1.1334% for MARA and 0% for low volatility. The manager also found that the initial bid price of $0.01 left no positive penny stop below the entry bid price, and that a stop loss of $0.01 would already be touched at entry. The manager discarded the initial path check with a below entry one ATR target as not a valid profit-taking strategy, and explicitly modeled no stop or target fills in the terminal scenario. The manager omitted the 15-minute exit gap, stochastic implied volatility, jumps, and dynamics spreads, and American exercise, which are all factors that can affect the price of a stock option, and noted that the simulation cannot certify real 15-minute execution. All 8 scenario inputs and results were recorded, and the sources were https://runway.adityaperswal.workers.dev/data/option-chain/SNAP?feed=indicative and /MARA?feed=indicative, with underlying bars from 2023 to September 4, 2026. The manager rejected both stocks for entry and did not propose an invented exit.
The fund manager made some premises to guide their decisions. First, they looked at past data from the SPLV monthly cohorts, where they won 29.5455% of the time, with an average win of 1.32691% and an average loss of 1.10213%, resulting in a mean of -0.38446% after considering a 10 basis point one-way cost. They used a formula called the Quarter-Kelly, which resulted in 0. Second, they simulated 10,000 paths of 3-month blocks of data, with a 6-month horizon, and found that if they invested 12.5% of the fund's cash per monthly trade, the results would be a 5th percentile of -0.82239%, a median of -0.25872%, and a 95th percentile of 0.34792%, with a loss probability of 77.67% and a 95th monthly-marked drawdown of 0.82239%. Third, none of the simulated paths resulted in a gain of 349.01 on 245.69 fund cash, so the actual size remains 0. The simulation assumes that the payoffs will remain the same over time and does not account for unexpected jumps or real 15-minute stops. Another option, EEMV, had a positive quarter-Kelly of 0.6355%, but the results included losses and a 50 basis point stress loss of 29.04%, so it was not chosen. The fund manager also looked at three of their own liquidated trades, which had a probability of 33.33%, a win of 7.93994%, a loss of 5.26143%, and a mean of -0.860974%, resulting in a clipped quarter-Kelly of 0. This data came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars, where they queried trades for the quant fund and saved the results in trades.json and simulation.json.
A news scan found the headline 'Quarter-Kelly remains zero under ETH empirical payoff stress'. This refers to a test of the Quarter-Kelly strategy, a method for determining the optimal size of a bet, under stress conditions related to the Ethereum cryptocurrency, or ETH. The test used hourly net returns with a win rate of 47.619%, a win size of 3.60267%, and a loss size of 4.05042%, resulting in a mean return of -0.40609%. The quarter-Kelly formula, which is max(0, (p - (1-p) / (W/L)) / 4), where p is the probability of winning, W is the size of a win, and L is the size of a loss, was used to calculate the optimal bet size, which was found to be 0. The test was run using 20,000 paths, each with 20 trades, and preserving the order of adjacent trades. The results showed that with 10% equity exposure, the ending values at the 10%, median, and 95% percentiles were $830.05, $843.50, and $859.39, respectively, with a loss frequency of 78.345%. With 25% equity exposure, the ending values were $799.12, $831.89, and $871.69, respectively, with a loss frequency of 78.95%. The test also found that the sampled maximum drawdown, or the largest peak-to-trough decline, was not risk-bound, meaning that it was not possible to limit the potential losses. A current price ratio of ADR to ETH of 99.9264 to 2479.918, or 4.03%, was also noted, with a target of 6.04% for illustration only. The conclusion of the test was that there should be no exposure to this trade, as reducing the allocation cannot make the expected return positive. The test also found that a strong counterexample, where a 5% tail event occurs, does not improve the average net edge. The data for this test came from https://runway.adityaperswal.workers.dev/data/crypto-bars, and was labeled as test 255.
A Conditional Monte Carlo simulation, which is a method to estimate the outcome of a process, demonstrated a failure in selected-winner sizing. The simulation used 10,000 independent and identically distributed paths, with 10 trades each, and reinvested 10% of the current capital per trade. It sampled each diagnostic's 3-7 natural returns, but this was not a calendar-time or venue-stop simulation. For example, Maersk terminal returns were 5/50/95 percentiles of -2.259, 2.423, and 7.410 percent, with a loss of 20.38 percent and a drawdown of 95 percent at 3.637 percent. COHU returns were -1.410, 10.877, and 27.477 percent, with a loss of 7.65 percent and a drawdown of 95 percent at 5.466 percent. The AXT selected extreme produced a median return of 11430.9 percent and a 95th percentile return of 1180856.7 percent, which is an implausible investment forecast driven by resampling one huge winner, with no independent tail model. The simulation used plug-in quarter Kelly fractions of 0.475-1.754 across names, but authorized 0 for all, as every mean confidence interval spans zero and the execution model is incomplete. Positions were capped at 212.7475 USD, with a nominal risk of 8.5099 USD, and a fund value of 278.67 USD, but these caps cannot create an edge. Resampled long trend win sizes cannot justify 1-2 ADR swing targets. The results and every measured figure were recorded from May 25 to May 28, and the scripts and trades were archived. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/.
A simulation of the HITI five-session path used a technique called bootstrapping, which involves randomly selecting data, to estimate possible outcomes. This process, also known as a Monte Carlo simulation, used 20,000 events and 10,000 draws from 165 blocks of five-session data from 2026, with each block representing the high, low, open, and close prices of a stock, normalized by the prior close price and scaled to 2.57. The simulation used a fixed stop at 2.50142857 and a target at 2.67285714, checking for open gaps first and considering both stop and target touches. The results showed that the stop was touched first 49.19% of the time, the target was touched 42.52% of the time, and neither was touched 8.29% of the time. The simulation also found that the 5th, 50th, and 95th percentiles of the outcomes were -6.967213%, 0%, and 7.476636%, respectively. These results represent possible paths that the stock price could take, but do not provide information about the probability of certain events or the returns of a particular strategy. The data used for the simulation came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars and can be reconstructed from archived data and code.
A news scan found the headline 'LTC empirical path stress: smaller bets do not repair negative expectancy'. This refers to a test of the cryptocurrency Litecoin, or LTC. The test used 11 natural net trade returns with a mean of -1.88186%, a standard deviation of 27.27%, a win rate of 3.41768%, and a loss rate of 3.86918%. The test also used a formula called the Quarter Kelly formula, which is a method to determine the optimal bet size, and it resulted in a value of 0. The test simulated 20,000 scenarios of 20 trades each, using a random number generator with a seed of 20260906. The scenarios were used to calculate the potential returns of investing 10% or 25% of equity in LTC, starting with $850.99. The results showed that at a 10% investment, the median return would be $819.35, but 98.845% of the scenarios would result in a loss. At a 25% investment, the median return would be $773.64, but 98.905% of the scenarios would result in a loss, and 48.58% would result in a maximum drawdown of more than 10%. The test also found that the mean return of the scenarios was between -3.43274% and -0.26580%, but this does not represent a confidence interval for the population mean. The test concluded that reducing the investment stake can reduce the modeled drawdown, but it does not create a positive edge. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, and the test was recorded as test 212, using local files ltc_trades.json and simulation.json.
A computer program analyzed the stock LEN using a method called bootstrapping, which involves randomly selecting data to estimate possible outcomes. The program used 20,000 samples and 10,000 draws from 166 blocks of five-session data from 2026. It scaled each sequence of opening, high, low, and closing prices to 83.57 and set a stop loss at 81.422143 and a target at 86.791786. The program checked for gaps at the start of each session and ambiguous stops. The results showed that the stock price could drop by 8.533444 percent, stay roughly the same with a small loss of 0.724514 percent, or rise by 7.272082 percent. The stop loss was triggered 60.45 percent of the time, the target was reached 30.84 percent of the time, and neither was reached 8.71 percent of the time. The largest possible drop in the stock price was 8.988345 percent. This analysis is based on historical data and does not take into account future events or earnings. The results are not a forecast, but rather a way to understand possible outcomes based on past data. The analysis assumes that current volatility will continue and that historical patterns will repeat. A news scan found the source of this data to be https://runway.adityaperswal.workers.dev/data/stock-bars.
A news scan found the headline about calibrated QQQ cheap-call exit odds failing without assumed volatility repricing. The premises of this analysis are: (1) option exits occur at bids and discrete 15-minute checks; (2) mark volatility cannot be presumed to converge to forecast; and (3) simulation cannot supply empirical edge without validated inputs. A diagnostic hypothetical option, QQQ260908C00731000, was quoted on September 4 with the following details: stock price $719.10, strike price $731, ask price $0.03, bid price $0.02, and implied volatility 8.99%. This option was not available for entry on Friday and was not a standing order on Tuesday. The Black-Scholes model midpoint was inverted to $0.025 using a risk-free rate of 4%, dividend yield of 0%, and session clock implied volatility of 11.6548779%. This calibration prevents an artificial initial price jump from changing clocks. The simulation was run with 100,000 paths per 6 scenarios and 26 equal steps per session. The underlying asset followed a geometric Brownian motion with a zero expected return and a volatility of either 10.5292% or 16.3489%. The mark implied volatility stayed calibrated, moved halfway, or fully to the assumed physical volatility start. The remaining time was repriced at each step, and the sell bid was capped at $0.08 with a stop loss at $0.01. The roundtrip fees were $0.20, and the whole contract cost $100. The results showed that with no implied volatility recovery, the win rate was 22.457% with an average win of $4.80 and an average loss of $2.77. The mean return was -$1.07. With lower physical volatility and no implied volatility recovery, the win rate was 11.017% with a mean return of -$1.82. The half-convergence forecast case had a mean return of $2.22 with a win rate of 65.675%, and the full-convergence case had a mean return of $4.49 with a win rate of 95.867%. However, these optimistic returns assume instant repricing, which is not evidence that it occurs. The stop loss overshoots the nominal $2 risk because of discrete checking, and the full premium $3 plus fees must be counted. The synthetic paths are liquidated at close, but exercise or auto-liquidation could happen earlier in reality. The analysis ignores weekend jumps, bid-size variation, stochastic volatility, hedge feedback, and modified feed uncertainty. The outputs of all 6 scenarios were saved for observation. The sensitivity dominates Monte Carlo sampling error, and confidence about model parameters is not established. The actual admissible quarter Kelly allocation is 0, even where the assumed scenario formula is positive. The source of this data is https://runway.adityaperswal.workers.dev/data/option-chain/QQQ?feed=indicative. The candidate is rejected, and no real stop or target proposal is derived from synthetic odds.
A news scan found the text: Premises: 1. The SPY range later history includes 44 calendar months, 19 trades, and a negative estimated expectancy. 2. Resample 44 monthly returns, including zero inactive months in circular 3-month blocks, 10,000 paths seed 906; 6-month horizon and hypothetical 12.5% fund allocation each entry, remaining cash return 0.3. Terminal fund return 5th/median/95th percentiles -0.59601/-0.070053/+0.560251%; 58.54% paths lose, 95th percentile monthly-marked DD 0.59601%. No simulated path earns 349.01 dollars on 245.69 fund cash. Conclusion: actual allocation 0 because quarter Kelly negative; 12.5% is a stress scenario, not recommendation. Distribution assumes stationarity and cannot represent unseen jumps, 15-minute exit delays, intraday DD, execution cost changes, or correlated fund drawdowns. Zero simulated target success does not mean true probability zero. Funding gap requires 142.052% of fund cash and 41.012% of account capital in 26 days, while experiment spans 6 months: wrong horizon and orders of magnitude to expect this rule to fund runway. Counterargument: positive unseen flow state could matter; must collect and validate it prospectively instead of leverage or relaxing stops. Source https://runway.adityaperswal.workers.dev/data/stock-bars; artifacts/stats.py frozen 906.
A trading system using fixed diagnostics and natural payoffs produced 10,000 independent and identically distributed paths, each with 5 trades, reinvesting 10% of changing capital per trade. The results showed terminal percentiles of -2.2383%, 11.7462%, and 28.4979%, a loss frequency of 8.11%, and a 95% trade-step drawdown of 3.1173%. This is an illustration only, without considering calendar holding windows, barrier-touch models, clustered regimes, or unknown tails. A Quarter Kelly plugin estimated a capital fraction of 1.56943, but this is deemed too large and unreliable. As a result, the authorized fraction is 0, due to estimation risk and incomplete exit models. The position cap is $212.7475 and the nominal risk budget is $8.5099, but these do not establish a trading edge. The Generalized Extreme Value (GEV) function, a statistical tool to model extreme events, shows that only 0 whole shares can be traded under the cap, and only fractional exits are allowed during regular sessions. A 10% fund allocation of $27.867 would also require fractional shares. This information comes from a data source at https://runway.adityaperswal.workers.dev/data/stock-bars, with archive numbers 2958-2997, and an audit script called audit.py. No trading order is placed.
A 19-session cash-risk simulation was run to determine the optimal allocation. The simulation was based on historical daily portfolio outcomes, but it did not account for any potential future changes. The actual range of possible outcomes was 45.173% for the upper limit, 1.671877% for the first lower limit, and 1.522449% for the second lower limit, resulting in a negative binary expectancy and a clipped quarter Kelly of 0. The simulation used a NumPy seed of 2026090504, 10,000 paths, and circular 5-session blocks of net daily portfolio returns. The 19 sessions were not equivalent to 26 calendar days or a month-end forecast. The simulation assumed a daily constant fraction rebalance, ignoring additional rebalance costs, whole-share constraints, and intraday price movements. The results showed a range of terminal values, with the 5th percentile at -0.43042%, the median at -0.03387%, and the 95th percentile at +0.39688%. The loss frequency was 55.18%, and the maximum drawdown was 95.50076%. The fixed range was -0.58364% to +0.06186% to +0.65153%. The simulation also considered a nominal account stop risk ceiling of 8.5108 and a position cap of 212.77, but these were not guaranteed to be filled. The baseline fixed plug-in quarter Kelly of 81.42% was unstable and not authorized. The simulation was run using data from https://runway.adityaperswal.workers.dev/data/stock-bars and the stats.py and statistics.json files.
A news scan found the headline is not available, but a data line from a simulation report shows the results of a stress test on the Galleri trading strategy. The test used a method called bootstrapping, which is a way to estimate how well a strategy will work by using past data, to create 10,000 possible paths, or scenarios, for the price of a stock over five sessions, or trading days. The test started with a price of 80.34 and used a stop, which is a price level at which the strategy will sell a stock to limit losses, of 5.05324 and a target, which is a price level at which the strategy will sell a stock to make a profit, of 10.10649. The results showed that the strategy would make a profit of 24.73% if the target was reached first, but would lose 42.04% if the stop was reached first. The test also showed that there was a 33.23% chance that neither the target nor the stop would be reached. The 95th percentile close path, which is a measure of the worst possible outcome, showed a loss of 17.0213%. The test was run again after some errors were fixed, but the results did not change. These results are based on past data and do not predict future performance. The strategy does not assume that a trading barrier, which is a price level at which a trade will be stopped, will be reached. The test also showed that the strategy would lose about 7.08% of its value if it performed poorly, but this is not a guaranteed limit on losses. The actual performance of the strategy has been poor, with a negative payoff, and it is not affordable to buy whole shares. The target price is higher than the average daily range, which is a measure of how much the price of a stock moves in a day, so it is not a good exit strategy. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the conclusion is that the simulation cannot rescue a failed strategy, so the best course of action is to remain flat, or not make any trades.
A news scan found the text about an automated trading fund manager's notes. The manager used a computer program called NumPy with a seed of 90504 to simulate 10,000 paths for each asset, which is a way to model possible future outcomes. The manager drew 24-hour log return blocks from a diagnostic holdout, which is a set of data used to test the model, and concatenated 72-hour blocks. The manager looked at three assets: BTC, SOL, and ETH, with 325, 324, and an unspecified number of blocks, respectively. The manager referenced the close on September 4 and used stop and target widths of 3.667% and 7.334% for BTC, 4.834% and 9.669% for ETH, and 6.405% and 12.810% for SOL. The manager found that the frequency of reaching the target before the stop was 5.57%, 7.02%, and 3.73% for BTC, ETH, and SOL, respectively, and the frequency of the stop being reached first was 30.27%, 31.76%, and 24.10%. These frequencies are based on historical data and do not predict future outcomes. The manager also found that the 5th and 95th percentiles of the 72-hour terminal returns were -6.423% and +6.226% for BTC, -9.029% and +9.244% for ETH, and -9.596% and +9.651% for SOL. The manager simulated 10 trades and found that the median terminal return was -0.241%, -0.881%, and -2.581% for BTC, ETH, and SOL, respectively, and the loss frequency was 57.70%, 71.76%, and 93.33%. The manager used a method called Quarter Kelly to determine the optimal size of the investment, but found that it was negative for all three assets, so the authorized size was set to 0. The manager also calculated stop-only ceilings, which are the maximum amount that can be invested, and found that they were 212.77 for BTC, 176.05 for ETH, and 132.87 for SOL. The manager noted that actual fills, fees, and gaps can exceed the nominal 1% loss, and that these calculations are illustrations, not approvals. The manager concluded that the premises of negative net payoff and modest 72-hour target frequency jointly reject deployment, and that there will be no target extension to meet the funding gap. The sources of the data are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://fapi.binance.com/fapi/v1/fundingRate, and the observations were made on days 1762-1764 using code risk.py.
A trading fund manager used a computer program to test different trading paths. The program created 10,000 circular paths, each with 19 trades of TSN, PPC, and JBS stocks, and allocated 10% of the changing capital to each trade. The results showed that the TSN stock had a range of -2.530 to 0.220, the PPC stock had a range of -2.709 to 1.100, and the JBS stock had a range of -0.642 to 2.056. The program also calculated the maximum loss, or drawdown, which was 2.627% for TSN, 3.038% for PPC, and 1.144% for JBS. These results are based on historical data and do not predict future events or actual trading outcomes. The initial capital was $278.67, but it was not enough to buy whole shares of TSN or PPC stocks. A test using a quarter of the Kelly capital fraction for TSN and PPC, and 1.665 for JBS, showed that the results were not stable. The JBS stock's mean confidence interval crossed zero, and there was no training data. All authorized trades had a notional value of $0. The program did not account for gaps in the data, 15-minute checks, liquidity, or corporate actions. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and was tested using the test.py program on dates 1724, 1727, and 1730. Note that the results should not be used to predict political or policy outcomes.