Stats

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 started with $850.99 in an account and $153.18 in cryptocash, with a position venue cap of $212.75 and a fund cap of $153.18. The manager considered a hypothetical portfolio with $51.06 in major USD currencies and $38.29 in Ethereum, but did not place any orders. The manager used two premises to study risk: the first premise looked at a 48-hour book mean of -1.011511, a worst-case scenario of -15.532567 USD, and a P1 value of -13.055955. The second premise examined 20-day periods with 15-minute intervals, with a P1 value of -14.906163 and a worst-case scenario of -16.577630 USD, and 60-minute intervals, with a P1 value of -15.606363 and a worst-case scenario of -17.917748 USD. The manager found that a 3% equity breach occurred in 25.5297 out of 0 sample paths, but noted that zero sample breaches do not guarantee safety. The manager also probed a 20-day Ethereum scenario with a P1 value of -3.799714 and a worst-case scenario of -4.333579 USD, and found that a 30% gap in Bitcoin, Ethereum, and Solana prices could result in a loss of $45.95 USD, or 5.4% of the account on a full fund, and $11.49 USD, or 1.35% of the account on a quarter fund. This scenario exceeded historical bootstrap support. The manager concluded that correlated exposure should be capped and that half of the Kelly criterion should be derived from independently observed strategy returns. The manager also noted that an unconditional negative bootstrap mean alone supports a zero Kelly criterion, but cannot prove a conditional rule of zero. The current entry was absent, which set the actual size to 0, and a shortfall of $349.01 did not enter the formula. The data source was https://runway.adityaperswal.workers.dev/data/crypto-bars.

A simulation called C91206 uses a set of historical data, specifically 15-minute log returns from March 1 to September 12, to test trading strategies for Bitcoin, Ethereum, and Solana. This simulation uses 18,744 calendar rows of data, but is missing some data for Bitcoin, Ethereum, and Solana. The simulation creates 10,000 two-day paths and 2,000 twenty-day paths to test how these cryptocurrencies perform together, taking into account how their prices are related within and between days. The simulation also tests a stop-loss strategy, where a trade is stopped if a certain loss is reached, and a target price is set to meet a net return of 3R. The target price is calculated using a formula that takes into account the volatility of the asset and the desired return. The simulation uses point prices at 15-minute, 30-minute, and 60-minute intervals, rather than the highest or lowest price within a given time period. The results of the simulation show that for Ethereum, the mean return over a two-day period is -0.67%, and the target price is 0%. Over a twenty-day period, the mean return is -0.39%, and the target price is 3.35%. The simulation also found that the return for Ethereum over a twenty-day period is -9.92% when using a 1-minute interval, compared to -10.47% when using a 60-minute interval. The simulation is useful for testing the size of potential losses, but it does not provide a reason to override the timing of trades. The data used for the simulation is available at https://runway.adityaperswal.workers.dev/data/crypto-bars.

The automated trading fund manager performed an edge test on ten issuer-dated scheduled AMC earnings from March 2024 to June 2026. The test found that the median absolute change in stock price was 4.700378% and the signed mean change was -3.027709%. As of September 11, the stock price was $2.53, and the October 16 call option was priced at $0.21, while the put option was priced at $0.16. To break even, the stock price would need to increase by 9.090909% or decrease by 9.486166%. The manager also considered an old frozen position, which had a median return of 5.494505%. The manager did not find any documented expected option payoff. The source of the data was option_contracts and https://runway.adityaperswal.workers.dev/data/stock-bars. The issuer announced that it would release its third fiscal quarter 2026 financial results after markets close on September 14, 2026. The manager simulated various scenarios, including a NumPy seed of 91206 and 100,000 paths each side, and found that the hurdles exceeded the new median. The manager also found that the closes were not executable asks and the intrinsic value was not the actual option profit and loss. The manager ran three scenarios: a call option with a mean intrinsic value of $0.213531, a put option with a mean intrinsic value of $0.571515, and a scenario with full residual 32-day option value and IV spreads absent. The manager found that the call option had a maximum return of 1.588571 times the premium and the put option had a maximum return of 2.155093 times the premium. The manager also found that the worst-case scenario for the call option was a loss of 72.988% of the book and the worst-case scenario for the put option was a loss of 22.568% of the book. The manager approved a size of 0 for the trade and had a monthly shortfall of $349.01. The fund had $102.12 and the account had $850.99. The normal size for the trade was 3% of the fund, which was $25.5297, and the conviction size was 5% of the fund, which was $42.5495.

A news scan found the headline 'Frozen-barrier exit diagnostic: P90 target is not a 90% success probability'. The premises of this diagnostic are: 1, actual completed split-adjusted daily vectors from September 13, 2021, to September 11, 2026, with 1255 data points; 2, current initial stop fractions of 2.2013275%, 2.1257399%, and 1.6511153%, and P90 targets of 16.1695853%, 12.8238446%, and 9.6302829%; 3, resampling synchronized five-session blocks jointly across three assets, with 20,000 paths of 60 sessions, and a seed of 2026091206; 4, normalizing each open, high, low, and close to the prior close, and compounding synthetic prices, with first barrier touched exits, opening gaps filling at open, and intraday barriers at threshold; 5, assuming 6 basis points per side costs, with no reinvestment. The diagnostic compares stop-first and target-first when daily both touch, with 0 ambiguous paths and identical output. The results for QQQ are: stop 77.655%, target 12.780%, timeout 9.565%, mean exit 18.30565 sessions, mean net +0.876683%, and P05 -2.976048%. For IWM, the results are: 81.095%, 14.325%, 4.580%, 14.8415 sessions, +0.185055%, and P05 -2.871990%. For EFA, the results are: 76.485%, 16.685%, 6.830%, 17.71015 sessions, +0.342275%, and P05 -3.046753%. At $62.40, $62.41, and $62.41, the book mean is +0.876157, the median is -3.953189, P05 is -5.082488, and P95 is +21.317089, with 3% account breaches. The output shows that a low hit rate can coexist with a fat right tail, but this is not a verified tradable expectancy. The diagnostic excludes profit-triggered SMA/ATR ratchet, monthly rank changes, 15-minute check delay, bids/asks, future dividends/cash, or novel tail shocks, and the five-year history omits 2020. The results are model-dependent first-passage diagnostics, not out-of-sample strategy performance, calibrated real win odds, Kelly inputs, or a guarantee. The mandate remains unchanged, and deployment should not be gated or targets changed based on this output. The sources are https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=split, live signal 2903, and gap stress preceding statistical analysis.

A 48-hour Ethereum block simulation was run with 20,000 paths, each with two resampled 24-hour blocks of 15-minute log returns, using 16,000 overlapping complete historical blocks from March 1 to September 12, 2004, UTC, resulting in 14,658 unique samples. This method preserves within-day dependence, breaks between days, omits missing blocks, and cannot invent unseen crashes. It is not conditioned on a pullback. The simulation started with a hypothetical completed spot price of 2513.175 at 03 UTC, with an entry price of 2515.688175 and a 10 basis point slip. The prior 20-day Average True Range (ATR), a measure of volatility, was 98.98817058. The stop loss was set at 2317.71183383 and the target at 3170.24364866, with a net gain of 3 times the risk after fees and slippage. The simulation checked every 15 minutes, with a quarter of the gross gain plus 2 times the risk if the stop loss was hit, and a full stop if the target was hit, with a 48-hour marked liquidation. The stop loss was hit first 3.9% of the time, the target was hit first 0.01% of the time, and a partial gain of 0.345% was achieved. The net 48-hour mean gain was -0.580697%, with a worst sample loss of -11.323239%. These 48-hour frequency figures cannot be used to price a 20-day thesis. However, using 965 overlapping daily windows from 2024 onward, the 20-day maximum high gain at the 90th percentile was 33.743953%, and the target gain of 26.0199% is within the measured range, although not guaranteed. The training data showed 7 wins and 6 losses out of 13 trades, with a win probability of 0.16101921 and a loss probability of 0.09851387, resulting in a half-Kelly bet of 1.299744. The capped risk notional was 153.18, with a quarter ceiling of 38.295 risks and a simulated worst-case loss of 3.224981 nominal USD, although the actual loss was 0 because the entry was fresh and the late policy was untested. The simulation used no leverage and no need-based sizing. The data sources were https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

The edge test analyzed 11 issuer-dated morning earnings from January 2024 to July 2026, looking at US ADR daily prices before and after the release. The median absolute change was 5.864627%, with a range of -8.474903% to 11.444479%. For example, on September 11, the price was 11.13, and on October 30, the call option with a strike price of 13 had a price of 0.45, which would need a 21.293801% increase to be profitable, considering a roundtrip cost of 0.05 per share. However, none of the options met the required criteria, with 0 out of 11 being profitable, and the premium cap was exceeded in some cases. The test also found that there was no documented option payoff that was three times the expected payoff. The expiry date of October 30 was eight days after the issuer's date, and the default exit was on October 21. The sources used for this analysis include Nokia's financial calendar and option contracts data. The test results showed that the intrinsic mapping was not the actual option profit and loss. The test was run with a numpy seed of 91204 and 100,000 resamples. The results showed that all 11 intrinsic values were zero, and a hypothetical scenario with 27 dollar premiums plus 5 dollar costs each would result in a loss of 100%. The test also found that the full premium was at risk, with a normal account size of 25.5297 and a conviction account size of 42.5495. The model used was a half Kelly model with a negative expected net, and the approved size was 0. The test also considered a countercase where an unobserved large jump or residual value may pay, but this was not enough to waive the mandate of a 40% or 3x gate.

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 stress test was performed on an account with $850.99 and a fund of $144.67. The test used fixed notionals of $48.22, $48.22, and $48.23, and stop distances of 2.2013275%, 2.1257399%, and 1.6511153%. An adverse gap was applied multiplicatively beyond the stop. The test assumed an extra 50 basis points of liquidation friction, which is not a measured spread. The loss was calculated using the formula Loss_i = N_i * [r_i + (1 - r_i) * g_i + 0.005]. The total stop loss was $2.882845. The five-year double-worst loss was $6.35524, $6.24496, and $5.30789 per lot. The crisis-inclusive double-worst loss was $10.22234, $10.04622, and $12.06440, which is 1.18-1.42% of the equity, but less than 3%. The joint sum was $32.33296, which is 3.79945% of the equity. If 20% beyond stops and 50 basis points are lost, the loss would be $31.963626, which is 3.75605% of the equity. If 10% is lost, the loss would be $17.784910, which is 2.08991% of the equity. This means that per-position compliance in these stress scenarios does not imply a book-level 3% limit. A Monte Carlo simulation was run with 50,000 paths and a random number generator seed of 20260912. The simulation used 60 independently sampled historical joint gap vectors and preserved contemporaneous cross-ETF dependence. The maximum single-night adverse stress loss per path was recorded. The five-year results showed a P95 of $8.481268 and a P99 of $10.328320. The expanded 2016-2026 results showed a P95 of $13.431428 and a P99 of $17.969580. No sampled paths exceeded $25.5297 because the bootstrap cannot generate unseen worse gaps. The model does not include price drift, serial clustering, compounding, dynamic stop lifecycle, historical entries, targets, liquidation quotes, or win probabilities. It is neither a strategy backtest nor a profit forecast. Daily tail dates were checked against opening 15-minute prices. Six symbol/date checks showed that delay can worsen or improve fills. The 50 basis points is not a guaranteed delay allowance. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=split and the same endpoint with timeframe=15Min. The results suggest adopting scenario-based sizing evidence and rejecting guaranteed-loss and calibrated-exit-odds claims.

A study was done on the EEM stock to see how it reacts to big price jumps. The study used 20,000 fake paths, each with 60 overnight price jumps, to see what could happen. It found that 42.745% of these paths had at least one price jump below a certain level, which is 2.815448% below the current entry point. The median worst price jump was 2.556054%, and the worst 5% of price jumps were 5.568038% or worse. This study assumes that the stock is held for all 60 nights and does not take into account things like stop-loss orders, target prices, or dividends. It also does not try to predict what will happen in the future. The study found that a 1.91% stop loss should not be considered guaranteed. The allocation of one share remains the same. This study does not change or add to the current understanding of the stock's momentum.

A research simulation was conducted with 20,000 paths per asset over 48 hours, sampling two 24-hour blocks from 4,634 overlapping complete blocks from March 1 to September 12 at 01 UTC. This historical resampling preserves within-day clustering, but destroys longer dependence and cannot invent unseen crashes. It is not based on 20,000 independent historical trades. The simulation used close-only hourly exits, which approximate a 1-hour delay, not actual 15-minute fills. For ETH spot, the price was 2,514.15, with a prior-day Average True Range (ATR) of 95.3601, resulting in a stop at 2,323.4298 and a net 3R target at 3,157.0172, with 25 basis points credited for fees and 10 basis points for slippage on each side. The net stop loss was 8.2311%, with a stop at 3.09% and a target at 0.04%. The 48-hour net 1st percentile was -9.0551%, with the worst sample at -12.3155%. For BTC, the stop was at 3.365% and the target at 0.01%, while for SOL, it was 1.10% and 0%, respectively. The sizing uses old frozen ETH trade odds, with an average win of 0.05848427 and an average loss of 0.03072019. The formula f = (p*w - q*l) / (w*l) = 16.001716 was used, with half at 8.000858. Prohibited leverage resulted in a clip at a minimum of 153.18 fund, 212.7475 venue, and 25.5297 normal risk budget, divided by 0.0823108663 to 153.18. A quarter-ceiling of 38.295 would risk 3.1521 nominal USD or 4.7161 at the simulated worst, about 0.55% of the account. This is a model ceiling, not a current recommendation, as the actual value is 0 since there is no fresh entry and policy uncertainty. The full fund nominal risk is 12.6084 USD, but unseen market gaps may exceed the model's predictions. The premises of the simulation are that a small reused sample does not justify a raw Kelly, and limiting dollars is preferred over assuming precision. The sources used were https://runway.adityaperswal.workers.dev/data/crypto-bars, frozen research 2765, and https://docs.alpaca.markets/us/docs/crypto-fees.

The automated trading fund manager made some calculations based on certain premises. The premises are: the probability of a successful trade, or p, is 0.5294118, the win amount, or W, is 0.01325795, and the loss amount, or L, is 0.01995905. Using a formula called the half Kelly criterion, which is a way to determine the optimal amount to bet, the result was 0, meaning no trade was made. This decision was not based on the amount of money available to trade, or venue position cap. The manager also ran a simulation, called a bootstrap, 50,000 times to see what might happen if the trade was made. The simulation used 20 past trade results and assumed the trade would be rebalanced every quarter, with a 1.4436127% change in the account per trade. The results showed that 71.9% of the time, the account would end up with less money than it started with. The simulation also showed that the account could end up with as little as 848.742887 or as much as 852.097942, with a median of 850.389251, compared to the starting amount of 850.99. These results are based on 20 quarterly events, which is equivalent to about 5 years, not 20 days or 1 month. The simulation included costs of 1 basis point fee plus 5 basis points per trade, but did not include other costs such as subscription fees or interest on cash. The simulation also assumed that each trade is independent, which means it does not take into account changes in the market or correlations between trades. The manager also tested what would happen if the fund lost 50% of its value, which would be a loss of 24.57, or 2.887225% of the account, and a total fund loss of 5.77445%. The manager did not assign a probability to this event. The manager also looked at the performance of a stock called CAG, which had a mean return of -0.6443% and did not meet the target return of 3 times the risk, or 3R. The manager did not make any trades and the approved amount to trade was 0. The manager used data from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=CAG&feed=sip&adjustment=all and 23 other URLs, as well as local research and simulation files, but did not use the actual fund record because it does not follow a normal distribution.

A test of Conagra Brands, or CAG, showed that from July 2024 to July 2026, the median absolute change in the stock price on mornings when the company announced its earnings was 2.064410%. On September 11, the stock price was $14.60, and on October 9, the price of a call option, or a contract to buy the stock at a certain price, was $15.5. To buy this option would require a return of 8.904110% or 7.876712%, including a cost of 0.05%. The test also looked at a put option, or a contract to sell the stock at a certain price, which would require a return of -4.041096% or -5.136986%. A news scan found the headline that Conagra Brands would release its fiscal 2027 first-quarter earnings on September 30, 2026. Another company, Lamb Weston, or LW, would release its earnings on October 6, 2026. The test also showed that the median change in the stock price of LW from July 2024 to July 2026 was 13.153864%. The test used a method called intrinsic value, which means the value of the stock based on its actual worth, not its market price. The results of the test were not good, with a mean, or average, loss of 1.498726 USD per share, which is 2.724957 times the current premium, or price, of the option. The test also showed that buying a put option on CAG would not be a good idea, with a 40% chance of losing money and no expected payoff. The test used historical data from option contracts and stock prices, but the results are not executable, meaning they cannot be used to make actual trades. The test also used a method called resampling, which means creating new sets of data from existing data, to simulate different scenarios. The results of the test were that the mean terminal value, or the value of the stock at the end of the test, was 107.7455, with 88.596% of the scenarios losing money, and 47.218% of the scenarios losing all three times. The test also showed that each ticket, or option contract, would require a return of more than 5% to be profitable, even with an old price. The test was deliberately not current, and did not include residual time, changing volatility, spreads, correlated returns, and 15-minute exits. The result was that the size of the trade was approved to be 0, meaning that buying a put option on CAG would not be a good idea.

A news scan found the text describing a risk analysis of the EEM investment. The analysis used historical data from a source that adjusts for certain changes, and it used a specific method called numpy with a seed of 20260912 to create 20,000 different paths of 60 daily returns. The starting point was 67.84, with a stop at 65.93472 and a target at 75.111066. The analysis found that 56.865% of the time, the stop was hit first, 38.82% of the time the target was hit first, and 4.315% of the time neither was hit. The average return was 2.749172%, with 5% of the paths returning -2.808491% or less, and 95% of the paths returning 10.717963% or less. This analysis is simplified and does not take into account certain factors, such as costs and overnight drops, so it should not be used to make actual investment decisions. The analysis assumes a prior trend in equity and a fixed investment size of 1 share. It also notes that a sudden overnight drop or a crash could affect the results. The conclusion is that this analysis is only an illustration of potential outcomes and should not be used to adjust investment targets or sizes.

A news scan found the text: Numpyseed91200; 100,000 independent and identically distributed paths of 10 trades were resampled from 16 SCHL training event returns of the preregistered T5/1ADRstop/3ADRtarget daily proxy. The same underlying 22 issuer date cohort and explicit IEX source https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex&adjustment=all were used. The quarter fund deployment mean terminal was -0.258428%, the loss frequency was 55.485%, and the 5th and 95th percentiles were -4.54516% and +4.46660% respectively. The 95th percentile drawdown was 5.10136%. The full fund mean was -1.14268%, the loss frequency was 57.209%, and the 5th and 95th percentiles were -17.35808% and +18.26072% respectively. The 95th percentile drawdown was 19.18431%. These are model frequencies conditional on 16 events, not estimates from 100,000 independent observations, and omit unseen FDA gaps or regime changes. The premises are: historical training edge is negative; repeated trades compound that loss; sizing only changes amplitude. The conclusion is allocation 0, training half Kelly 0; it is not possible to manufacture positive expectancy by quarter probe. A strong countercase is that an unseen new regime could improve payoff, but it needs prospective evidence, not a shortfall of 349.01. The hard limit is a fund of 49.14; an assumed 50% whole fund gap costs 24.57, which is 2.88723% of the account, and the total loss is 5.77445%. These deterministic stress losses carry no assigned odds. There is no traded simulation and no live stop.

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 news scan found the text of an experiment with the title C91200. The experiment is based on three premises. Premise 1 uses 90 days of hourly log returns, with a seed of 91200 and 20,000 paths per asset. Each path concatenates two sampled 24-hour blocks, resulting in 2,136 overlapping historical blocks per asset, rather than 60,000 independent market observations. Premise 2 sets illustrative stops at 5.7564%, 7.5801%, and 9.7048% below the prices of BTC, ETH, and SOL, and targets at 17.2692%, 22.7403%, and 29.1145% above. Premise 3 analyzes the frequencies of first-stop and first-target events, which are 1.125%, 0.990%, and 0.320% for first-stop, and 0.030%, 0.225%, and 0% for first-target. The conclusion is that the measured ranges cannot justify expecting a net 3R payoff in two days, so a multiweek thesis horizon should be retained if entry later validates. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, with all measured path figures in observations 25,189, 25,191, and 25,193.

The fund manager is analyzing the risk of a $50 all-in ticket, which is stressing the small fund. The manager is looking at intrinsic-only scenarios, but notes that these are not forecasts of option outcomes. The manager is testing the edge of the fund's risk using various calculations, including median absolute percentages and required returns. The manager is also looking at the earnings dates of various companies, including BB, HITI, and GIS, and the corresponding option contracts. The manager notes that the closes are not executable prices and that the intrinsic proxy excludes residual time. The manager is using various tools, including NumPy and historical data, to stress test the fund's dependence on various events. The results of the stress test show a high probability of loss and a significant median drawdown. The manager is also analyzing the fund's allocation and notes that a $50 all-in ticket would account for 27.98% of the fund, while three $150 tickets would account for 83.94% of the fund. The manager concludes that the intrinsic-only expected net return is negative and that the actual Kelly criterion is unidentified without option outcomes.

A simulation was run to test the risk of a trade involving the stock AVAV. The simulation used 10,000 samples of five-session blocks of actual log returns from 2016 to 2020, and validated the results using data from 2021 to 2025. The simulation assumed a fixed starting point, with one share purchased at $147.065, and a fee of 1 basis point and a slip of 15 basis points for each trade. The target return was set at 3 times the risk, with daily close sampling and full exits. The results showed a historical validation profit and loss range of -$14.1958 to $19.3857, with a median profit of $0.1541. The simulation also found that daily sampled losses exceeded 1 average daily range in 21.87% of resamples and 3 average daily ranges in 0.48% of resamples. The conclusion is that a nominal 3 to 1 return does not guarantee a realized 3 to 1 return or a bounded stop loss. The simulation used a cash-constrained half-Kelly point of 0.29433 and a binary half-risk proxy of 0.02791, but these are not directly applicable to live trading without considering conditional net edge. The simulation made assumptions that historical blocks are representative, costs are adequate, and daily close fills are possible, but omitted factors like quote latency and unseen regimes could worsen outcomes. The simulation is based on data from https://runway.adityaperswal.workers.dev/data/stock-bars and the research/risk_study.py script.

A one-session range stress test for PRE91108 showed negative results and was unable to price FDA tails. The test involved analyzing daily high, low, and close ratios for 71 names, translating them to a September 10 reference price. A diagnostic test with a 1ADR stop and 3ADR target was used, with a 30 basis point assumption on each side. The mean net results were -0.7844% for HITI, -0.9210% for HAIN, -0.9050% for PLAY, and -0.6544% for TLX, with target frequencies of 0/0/1.378/0%. This was an unconditional range analysis, not a catalyst backtest or executable model. Entry and overnight gaps were omitted, so the TLX result cannot estimate FDA loss odds. A separate test assumed a 50% gap on a full $49.14, resulting in a cost of $24.57, or 2.8872% of account equity, with a full loss of 5.7745%. The conclusion is that the model does not support inventing a last-day 3R trade. The approved allocation is 0, and no Kelly odds were inferred from this uncalibrated simulation. Running more Monte Carlo paths did not add to the 71 empirical observations. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all, with recorded vectors 25095-25098 and pre91108/study.py.

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 news scan found the following text: Premise 1 is based on 552 hourly bars per asset from August 19, 08 UTC, to September 11, 07 UTC, with 547 overlapping 5-hour return blocks. The seed 91108 draws 20,000 blocks per asset, totaling 60,000 draws, but only 547 historical blocks each, preserving within-block dependence and not introducing new independent evidence. Premise 2 freezes the current 480-hour thresholds. The required gains for BTC, ETH, and SOL are 6.5140%, 4.03979%, and 10.82051%, respectively, with hourly-close fixed-threshold touches of 0%, 3.375%, and 0% in the bootstrap. The ETH terminal 5-hour mean is 0.263294%, with the 1st and 99th percentiles being -3.221144% and +8.031913%, and the worst observed 5-hour return being -3.733345%. Premise 3 samples include one recent market regime, are not conditioned on CPI events or breakout conditions, and exclude intrahour excursions and new unseen shocks. The conclusion is to not anticipate entry, as the rolling production threshold can change while the scenario keeps it fixed. The descriptive bootstrap supports monitoring relevance only, not order size, CPI probability, or costed policy return. A prior 90k 20-day study, numbered 2670, remains the risk reference and was not rerun. The source is https://runway.adityaperswal.workers.dev/data/crypto-bars, with local study.py and study.json, and observations from 25017 onward.

A fund manager's notes were analyzed. The manager used a method called resampling to test a portfolio based on the SPY instrument, which is a type of stock market index fund, with 10,000 simulated paths and 21 overlapping blocks of sessions, using a seed number 9110600 for reproducibility. The full simulation showed a potential loss of 7.14 USD and a potential gain of 9.15 USD, with a 41.26% chance of loss. No paths reached a gain of 349.01 USD, but this does not prove it is impossible. The return over 252 sessions could be as low as -7.38% or as high as 26.10%. The blocks used preserve the dependence of 21 sessions and cannot generate unobserved extreme events, which are called tail gaps. These are empirical scenarios, not forecasts. A unit check was done using a binary method, which gave a risk-unit proxy of 3.19% in training and 11.18% in testing. The manager used a method called Kelly criterion to find the optimal investment amount, which gave an unconstrained notional value of 2.99531 and 9.38231. Halving and capping the cash gave a value of 1.0, while halving the cash-constrained optimum gave 0.5. Neither of these estimates includes uncertainty or gaps beyond historical data. The live fraction is 0 because the mean confidence intervals cross zero, and the incremental alpha, which is a measure of investment return, is unproven. The monthly entry is not due, and the 15-minute policy is unvalidated. The fund cap, account cap, and normal stop risk are ceilings, not position recommendations. The funding gap is excluded from targets. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the code is in research/inference.py and sizing.py.

A news scan found the headline is not available, but a data line was found. The data line describes a trading strategy called C91106. The strategy was tested on the ETH asset with a unique 20-day hourly path. The test ran from September 11, 2025, to September 11, 2026. The strategy used a static and trailing share path with 180,000 possible outcomes. The strategy aimed for a 3R target return, which means three times the risk, with a 25 basis point fee and a 10 basis point lip, or slippage, which is the difference between expected and actual trade prices. The strategy also used a 2R cost-benefit ratio and a peak-2 ATR trail, which is a type of stop-loss order that uses the average true range, or ATR, to set the stop price. The first sampled close exit was used, with a 20-day timeout. The results showed a mean net return of -2.00844%, -2.08325%, and -2.38655% for the static 1, 24, and 72-hour targets. The trailing mean returns were -1.87692%, -1.97279%, and -2.25903%. A separate test was run on 20,000 independent and identically distributed, or IID, bootstrap samples of ETH trades. The results showed a mean return of 2.874945% with a 95% confidence interval of -1.587870 to 8.390857. The win rate was 66.667% with an average win of 5.848427 and an average loss of 3.072019. The best compound return was 1.735528%. A cash-only log growth grid maximizer was used to determine the optimal allocation. The results showed that a 50% allocation would result in a terminal loss of 13.14% with a 5th percentile of -3.12814 and a 95th percentile of 23.84138. The same test was run on the BTC asset with similar results, but the SOL asset showed negative results and was not deployed. The conclusion is to retain cash now and retain a conditional watch. The results do not disprove the conditional breakout edge, and the empirical bootstrap does not give the probability of a true edge or unseen crisis coverage. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars, with observations from 24796 to 24819.