A news scan found the headline 'R92006 daily extremes raise modeled stop crossings'. This refers to a trading model called R92006. Premises of the model include: it looks at the EEM and XLE stocks from September 2021 to December 2025, with 1076 data points; it uses paired 5-day blocks, with 10,000 paths, a seed of 200906, and 60 sessions; and it has static start prices of 67.37 and 64.63, stop prices of 65.48 and 62.10, and target prices of 74.59 and 77.97. The data comes from https://runway.adityaperswal.workers.dev/data/stock-bars. The model compares the same paths using close-only prices versus daily low and high prices, with stop-first on ambiguous days. For EEM, the close-only stop was 66.89%, the target was 17.57%, and neither was 15.54%; for OHLC, these numbers were 71.52%, 17.46%, and 11.02%. For XLE, the close-only stop was 59.86%, the target was 15.28%, and neither was 24.86%; for OHLC, these numbers were 67.88%, 15.12%, and 17.00%. There was no ambiguity in the sample when using both barriers. The unstopped combined terminal values were -17.24573, 3.05739, and 23.70781 dollars at the 5th, 50th, and 95th percentiles. Daily extremes increased modeled stops by 4.63 and 8.02 percentage points; however, this does not prove that these stops can be executed in 15 minutes or during extended hours. The model omits profit ratchet, actual quotes and spreads, gaps through stop fills, and slippage; it also notes that model frequencies are not live odds or backtest profits. The model uses a fixed one-share size and does not use the Kelly criterion from static barrier frequencies. There is no retest of the accepted momentum edge.
A question was asked about how much static-bracket exit estimates change with sample and serial dependence. The analysis was based on three premises: the current fixed basket and brackets from 3645.2, historical synchronous OHLC/prior close ratios that preserve same-day cross-sector dependence, and resampling consecutive blocks of 1, 5, and 20 days separately from 1152 pre-2026 and 179 recent 2026 sessions. Each case had 10,000 paths, 60 sessions, a seed of 92006, and 86.66 each, with 6 basis points per side assumed friction. No fitted parameter was selected. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars. The method used was to stop first for ambiguous same-day dual touch, gap stop at worse opening price, target at fixed target, and remaining mark at 60-day close. The analysis used static current brackets, stop first on same-day dual touch, opening gap stop at worse open, and no ratchet, monthly rerank, or 15-minute checks. The results showed that pre-2026 target frequencies for XLK were 12.96-17.29%, XLE were 11.17-15.56%, and XLV were 13.94-16.61%, while 2026 frequencies for XLK were 20.60-26.17%, XLE were 32.97-39.15%, and XLV were 20.50-23.43%. The basket loss frequency was 48.39-50.20% versus 19.76-22.72%, and the modeled mean dollar PnL was 2.796-3.655 versus 9.291-9.632. The conclusion was that sample choice materially dominates implied success odds, so no empirical Kelly input or sizing/target override was used. Ratchets, monthly replacement, and 15-minute monitoring were omitted deliberately to isolate model sensitivity. This was not an execution backtest, not a validated premium test, and not live probabilities. The analysis noted that counterexample crisis correlations and gaps outside the observed sample could defeat the benign recent results.
A news scan found the headline '06UTC training-only block bootstrap cannot bound overnight losses'. This refers to a problem with a computer program that manages investments. The program uses historical data from 2021 to 2024 for the N1004 stock, with adjustments for common dates. It also uses blocks of 5 sessions to preserve cross-asset shocks and short serial dependence, which means it looks at how different investments affect each other over short periods. The program ran 20,000 simulated paths over 20 sessions, with a seed of 92006, and used fixed notionals of 93.33 each. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The simulation found that the maximum single-night loss for a basket of investments was 3.91 USD at the 50th percentile, 7.97 USD at the 95th percentile, and 14.83 USD at the 99th percentile. No simulated paths exceeded a loss of 30 USD. However, this does not mean that real losses cannot exceed this amount. The simulation excluded intraday paths, trading costs, stop-through gaps, changing notionals, and unseen shocks, and did not use the Kelly criterion, which is a formula for betting. The EEM holdout threshold exceeded the nominal 1% frequency, which challenges the idea that the investment returns are stationary, meaning they follow a consistent pattern. The program also retained a separate scenario for stop-through gaps, with a ratio of 2 ATR/E, where ATR is the average true range, a measure of volatility. The results included the following investments: EEM with a size of 93.33, stop of 65.14, target of 74.21, p90 of 0.11, rr of 3.81, and stress of 25.91; QQQ with a size of 93.33, stop of 704.90, target of 838.11, p90 of 0.16, rr of 7.05, and stress of 22.61; and IWM with a size of 93.33, stop of 277.67, target of 320.52, p90 of 0.13, rr of 5.66, and stress of 27.92. The joint loss was 76.44 USD, and the modeled per-lot loss was less than 30 USD, but shocks outside the assumptions can exceed 3% equity. There was no increase in size from the benign bootstrap quantiles.
The automated trading fund manager analyzed the Acetyl candidate with the following premises: the daily feed-price returns are based on empirical data from https://runway.adityaperswal.workers.dev/data/stock-bars. The manager used fixed current hypothetical 2 ATR/net 3 R barriers from technical analysis, which is a method of evaluating securities by analyzing statistical patterns and trends in their price movements and other market-related data. The analysis included 60 session paths, 5 session moving blocks, seed 92004, 20000 per cell, and history from 2017 onward or 2025 onward. The manager also considered the following: CE long history stop at 57.52%, target at 17.20%, and timeout at 25.28%, with a mean net profit per share of $1.505 USD; recent results were 69.85%, 24.55%, and 5.60%, with a mean of -$0.2645. For EMN, the long history was 61.65%, 20.93%, and 17.42%, with a mean of $0.2290; recent results were 70.215%, 19.28%, and 10.505%, with a mean of -$0.8359. The manager allowed for full close overshoot and 20 basis points per side cost, but did not consider intraday extrema or 15-minute execution, event probabilities, or conditional breakout or fundamental drift model. The analysis found that positive historical bootstrap means did not provide a trade edge. The manager also considered the following: CE 2 share P1 PNL was -21.85% long and -18.93% recent; EMN 2 share was -21.25% and -28.54%, but 2 EMN exceeded the 120 cap, so the diagnostic was not proposed size. The manager concluded that the odds change with the regime, and recommended using conditional trade half Kelly 0 from 3639, not unconditional path odds, for the current allocation of 0. The manager also recommended preserving positive counter evidence and not treating all cash as proof that CE has no upside. The scripts stats.py/stats.json contain reproducible assumptions.
The automated trading fund manager made certain assumptions to model option sizing. The premises of the model include using a NumPy seed of 92004, 20,000 paths, and four sampled 5-session blocks from the last 252 log returns. The model also assumes 20 steps or 25 calendar days to October 16, with an unchanged spot price. A Black-Scholes model, or BSM, with constant implied volatility, or IV, was fit to the data for HALO and TH. The model asks for 33.414 and 68.513 percent, with a rate of 4 percent, and targets of 1.83 and 3.58, with a 50 percent premium stop. The model assumes a roundtrip fee of 1 USD. If there is no exit markdown, the means for HALO and TH are 4.90 and 4.50 USD, with losses of 87.97 and 93.23 percent. The half Kelly criterion, a measure of optimal bet size, is 22.95 and 11.50 USD of 1000 equity. A 20 percent mark discount changes the means to -1.19 and 0.57, and the half Kelly criterion to 0 and 1.50 USD. The model also found that constant bid ratio of less than or equal to 0.5 triggers an immediate loss. The actual venue mark is unknown, and the indicative ratios are not actual bid asks. The model also considered conditional cheaper fills, where HALO and TH have means of 9.05 and 8.16, and half Kelly criteria of 39.35 and 21.80, with costs of 16 and 14. The conclusion is that there is no calibrated positive sizing at observed indicative asks, and fresh pricing and honest odds can change it. The model's sensitivity is only considered, not a new mandatory gate or proof of cash forever. The historical terminal Kelly is optimistic relative to the exit policy. The source of the data is from stock bars and option chains for HALO and TH.
A study of total-return paths for the R92004 fund found that they slightly overstate the price-target crossing. The study was based on three premises: first, it used daily stock bars from the source https://runway.adityaperswal.workers.dev/data/stock-bars, which were split and normalized; second, it looked at 1255 returns that ended on September 18, paired into 5-session blocks, with a seed of 200904 and 10,000 paths each, over 60 sessions; and third, it used common sampled blocks that started at 67.37 and 64.63, with daily close barriers of 65.48 and 74.59, and 62.10 and 77.97. The results showed that the total-return proxy had exit frequencies of 0.6264 for EEM and 0.5419 for XLE, where the stop was reached first, and 0.2315 for EEM and 0.178 for XLE, where the target was reached first. The price-only proxy had similar exit frequencies, with 0.6462 for EEM and 0.5616 for XLE, where the stop was reached first, and 0.2176 for EEM and 0.1642 for XLE, where the target was reached first. The unstopped terminal values were also calculated, with the 5th percentile at -13.69 for the total-return proxy and -14.49 for the price-only proxy, the 50th percentile at 5.58 for the total-return proxy and 4.67 for the price-only proxy, and the 95th percentile at 25.08 for the total-return proxy and 24.21 for the price-only proxy. The conclusion was that the total-return proxy target frequencies exceeded the price-only target frequencies by 1.39 percentage points for EEM and 1.38 percentage points for XLE. However, neither proxy accurately represented the actual execution odds, as they omitted factors such as live gaps, slippage, 15-minute checks, profit ratchets, costs, and regime change. This was not a strategy backtest or a calibrated Kelly input, and it was recommended to keep one share and not select a favorable convention to justify doubling.
The current trading scenario is based on three premises. First, the current reference entries and Average True Range, or ATR, are from 3607. Second, the carried gap assumptions are for the exchange-traded funds EEM at 0.125794155019, QQQ at 0.109666233203, and IWM at 0.138906519177, which are not fresh tail estimates. Third, the cash is 1000, the fund is 280, and the position is 250. The ratio, or r, is calculated as 2 times the ATR divided by the price of E, and the coefficient is calculated as r plus 1 minus r times 2 times g plus 0.005. The size is then calculated as the floor of the minimum of 280 divided by 3, 250, and 29.5 divided by the coefficient, which gives 93.33 for each. The stress test results are EEM at 25.913233, QQQ at 22.608078, and IWM at 27.921384, with a joint result of 76.442695 USD, each of which is less than 30 under modeled shocks. However, larger shocks can exceed 3%, and there is no guaranteed bound or calibrated odds. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The prior unchanged-sample Monte Carlo of 3570 is retained and not rerun as there is no new evidence. The actual payout, stop slippage, and distribution timing are unknown. There is no trade-derived Kelly from an empty reset ledger, and the equal-weight mandate governs.
A test called R92002 was done to see how sensitive some trading rules are. The test used daily stock prices from a website and made 20,000 fake paths for each stock over 5-day blocks. The test looked at all the stock's history and the last 252 and 60 sessions. The test used fixed price ranges for four stocks: CE with a low of 45.41, a middle of 42.027, and a high of 56.2870, IMOS with a low of 61.30, a middle of 56.99918, and a high of 75.1852, BTDR with a low of 12.97, a middle of 11.11183, and a high of 18.75245, and AMTX with a low of 2.16, a middle of 1.90454, and a high of 2.96101. The test tried to make a profit of 3% after fees and slippage. The test found that the average return for all the stocks was CE 0.682%, IMOS 1.156%, BTDR 4.464%, and AMTX 0.208%. The test also found that the probability of hitting the target was CE 18.835%, IMOS 23.395%, BTDR 32.685%, and AMTX 25.695%. The test stopped if both targets were crossed, and the stop loss was between 61.945% and 73.9%. The test found that even if there are some big losses, the trading rules cannot be calibrated to make a profit. The test used a method called Half Kelly to manage the money, but it was limited to between 0.03 and 0.5. The test found that buying one share of IMOS with a 50% gap would cost 30.834, which is more than the normal risk budget. A news scan found the note was about a 160,000 path test, not 60,000.
A news scan found the following text: Premises for this analysis are based on a hypothetical scenario where the spot price on September 21 remains unchanged, with 20 daily steps to October 16, which is 25 calendar days, and 30,000 paths per scenario with a seed of 92002. The analysis uses a method called Bootstrap to estimate daily returns, which are scaled at 0.75, 1, and 1.5, and a constant volatility, known as BSMIV, calibrated to the ask price and a 4% interest rate. The results show that for certain stocks, including HALO and THC, the paid premiums and implied volatility percentages are as follows: HALO C130 paid 0.23 with a fit IV of 33.414%, HALO P95 paid 0.18 with a fit IV of 38.114%, and THC 27 paid 0.18 with a fit IV of 68.513%. The analysis also shows that the RV60 for HALO is 45.30 and for THC is 58.40%. The options stop at 50% and the modeled targets are filled no better than the target, with an exit friction of 0.01 per share plus a 1 USD roundtrip fee. The mean premium targets for HALO and THC are -8.90, -3.58, and 1.78 USD, and -7.89, -4.71, and 0.02, respectively. The P80 targets, which are independently derived, show mean values of -8.27, 1.42, and 16.26 for HALO, and -3.54, 7.28, and 28.75 for THC. The baseline target hits are 10.657% and 4.557%, and the stops are 89.257% and 91.873%. The analysis concludes that the whole premium should be kept at 23 or 18, which is less than 30 each, with a potential total of 41, which is less than 100, and a maximum of 4. The worst-case scenario is 41 plus costs, despite half premium stops. The terminal intrinsic Kelly is 49.7 and 235.25 USD, which is unreliable, and a hard cap of 30 binds. The prospective single contracts are minimum granularity, with a quarter of 30 and a probe of 7.50, which is unavailable. No orders were closed, and the capital and actual quote should be refreshed in the entry run. The source of the data is /data/stock-bars and /data/option-chain/HALO and THC, with a feed of indicative, from 30184 to 89.
This report is based on two premises. First, it uses paired adjusted daily returns from a website through September 18, with confirmed split repair. Second, it uses a numpy seed of 200902, with 10,000 paths each, 60 sessions, and paired contiguous 5-day blocks sampled uniformly within five-year and recent 252 windows. The initial prices are 67.37 and 64.63, with the first daily close crossing 65.48 and 74.59, and 62.10 and 77.97. The results are as follows: for the five-year window, there were 1,255 samples, with terminal unstopped losses of -13.92, 5.83, and 25.29 at the 5th, 50th, and 95th percentiles, respectively. For the EEM ticker, 62.41% of the time, the stop was hit first, 23.7% of the time the target was hit first, and 13.89% of the time neither was hit. For the XLE ticker, 53.93% of the time the stop was hit first, 18.6% of the time the target was hit first, and 27.47% of the time neither was hit. For the recent 252-window, there were 252 samples, with terminal unstopped losses of -3.22, 11.30, and 26.16 at the 5th, 50th, and 95th percentiles, respectively. For the EEM ticker, 58.02% of the time the stop was hit first, 35.65% of the time the target was hit first, and 6.33% of the time neither was hit. For the XLE ticker, 39.86% of the time the stop was hit first, 23.82% of the time the target was hit first, and 36.32% of the time neither was hit. The recent window shows a terminal 5% loss of -3.22, compared to -13.92 for the five-year window, which exposes sample dependence. The endpoint returns are unstopped dollar changes, not actual strategy profits and losses. The frequencies do not include intraday paths, execution gaps, fees, profit ratchets, and changing regimes, and are not calibrated live probabilities or Kelly inputs. This report is about risk sensitivity, not revalidation or backtest of the accepted momentum premium. The strategy keeps one share per slot, with no size changes from the favorable recent window.
The premises of this analysis are: 1) the initial amount is $93.33 each; 2) the ratio, or r, is equal to 2 times the average true range, or ATR, divided by the entry price; and 3) the retained gap assumptions are based on prior fund stress for the exchange-traded funds EEM, QQQ, and IWM, with values of 0.125794155019, 0.109666233203, and 0.138906519177, respectively. The size is calculated as the floor of the minimum of $280 divided by 3, $250, and $29.5 divided by the ratio plus 1 minus the ratio times 2 times the gap plus 0.005, which gives $93.33 each. The stress is calculated as N times the ratio plus 1 minus the ratio times 2 times the gap plus 0.005, with values of 25.913233 for EEM, 22.608078 for QQQ, and 27.921384 for IWM, and a joint value of 76.442695. Each modeled loss is less than 30% of the account, which is 3%. A news scan found the source of this data to be a stock bars query from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. A separate analysis of 20-day closing return paths over the last 1260 observations, with a seed of 92002 and 20000 joint independent and identically distributed paths, gives a 5th percentile of -22.652306, a median of 2.100807, and a 95th percentile of 28.781054 dollars. The repeated unchanged-sample diagnostic is not considered new evidence. This analysis does not take into account costs, stops, targets, intraday sequence, or serial dependence, and the current basket was retrospectively selected. It also does not provide live exit odds or a Kelly estimate, and there is no sizing change.
A test was run with 92,000 paths, using a NumPy seed of 92,000, and a joint daily OHLC, or open, high, low, close, prior close block of 5 bootstrap samples, with 20,000 paths each for all history and the last 252 days for FRO and POWL stocks, using data from https://runway.adityaperswal.workers.dev/data/stock-bars. The research looked at price brackets for FRO of 51.42 to 48.20 to 61.90, and for POWL of 183.96 to 167.96 to 234.92, with a 20 basis point, or 0.20%, stop on each side, and a target capped at the opening gap, with a 60-session close for liquidation. The mean net returns were 1.40% for FRO for all periods, and 6.81% for the last 252 days, with target returns of 30.39% and 49.70%, and 2.95% for POWL for all periods, and 4.60% for the last 252 days, with target returns of 28.89% and 37.03%. The stop probabilities were 67.98% and 47.90% for FRO, and 61.68% and 61.13% for POWL, showing that positive expectancy can coexist with frequent losses. The test used a Half Kelly portfolio-weight maximization over a grid of 0 to 1, with a constrained upper grid, and found that the optimal portfolio size was 0.5, but this was not an optimal unconstrained size estimate. This test ignored current entry absence, changing valuation, war shutdown, new gaps, 15-minute venue checks, and ex-dividend accounting, and did not infer live odds. A 50% gap in FRO would cost about 25.86 for one share, and 51.73 for two shares, which is more than 30% of a normal account budget. For POWL, one share would require more than 120 in capital, and a 50% loss would be 60, or 30 before costs, and the simulation loss quantiles were not risk bounds. No order was placed.
A simulation of the R92000 exit policy was conducted. The premises of the simulation were as follows: the stock price was $46.67, the option price was $37.5, and the paid premium was $0.23, with an indicative implied volatility of 55.94%. The simulation used 50,000 paths per scenario, with a seed of 92000, and 20 business steps from September 21 to October 16, with an event step on October 6. A zero-drift Geometric Brownian Motion, or a random process used to model stock prices, with ordinary volatility of 26%, 40%, and 60% was used. The Black-Scholes Model, a mathematical model used to estimate the value of a call option or a put option, was used to calculate the put option price, with a floor of the intrinsic value, or the difference between the stock price and the strike price, and a 4% interest rate assumption. The initial model price was $23.647, versus an ask price of $0.23. The daily stop loss was $0.115, the target was $0.69, and the exit price was $0.01 per share, with a friction of $1 USD and a roundtrip fee. The hypothetical hold-through means were $3.27, $8.81, and $7.67 USD, with targets of 4.83%, 11.78%, and 22.62%, but the current hurdle of 20.14% exceeded the median of 10.02%, so this hold policy is outside the mandate. A mandate-consistent forced exit on October 5, day 11, resulted in means of -$13.81, -$9.02, and $0.88 USD, with targets of 0.556%, 7.006%, and 20.698%. These uncalibrated daily marks omit American and dividend details, and 15-minute quote and liquidity risk. A full-premium loss remains possible despite a half-premium stop. A separate empirical terminal intrinsic, or the value of the option at expiration, after a friction of $0.02, resulted in 2 wins out of 11, with a mean intrinsic value of $0.650308, and a grid half Kelly, a betting strategy, of 6.704% equity. However, this cannot override a failed gate, and actual size is 0, with a future one premium of less than or equal to 30, and a total of 100, with a maximum of 4. A crucial counterexample is that a higher price of a nearer strike may pass the median when cheap enough, so it is better to reprice rather than claim model profits at an impermissible hold. The sources of the data are https://runway.adityaperswal.workers.dev/data/option-chain/LW?feed=indicative, stock bars, and issuer October 6 date 29824.
The analysis is based on three premises: first, same-day joint rows of the last 1260 returns preserve cross-asset dependence; second, 20,000 paths of 20 independent and identically distributed sessions were generated using a numpy seed of 920; and third, the initial value was constant at 93.33 and the closes were compounded. The results show that the 5th percentile was -22.5286 USD, the median was 2.0020, and the 95th percentile was 28.8353. This data comes from the source https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. This analysis is a reproducibility diagnostic of new retrieval, not fresh independent evidence, as prior analysis 3463 already established dependence. The analysis does not include stops, targets, intraday exits, costs, or serial dependence, and the 2020 crisis was omitted. A retrospectively selected basket was used, and there were no calibrated trade odds or Kelly input. A separate retained scenario was used, where r equals 2 times the average true range divided by the entry price, and stress equals N times the sum of r and 2 times g times 1 minus r, plus 0.005, where g is the value of EEM, which is 0.125794155019, QQQ, which is 0.109666233203, and IWM, which is 0.138906519177, from the prior record, not remeasured extrema. The values of EEM, QQQ, and IWM are 25.9132, 22.6081, and 27.9214, respectively, and the joint value is 76.4427 USD. The size was calculated as the floor of the minimum of 280 divided by 3, 250, and 29.5 divided by the sum of r and 2 times g times 1 minus r, plus 0.005, which yields 93.33 each. Every lot below 30 in this stated scenario, but a complete loss of 93.33 could exceed 3% of the account. Empirical envelopes cannot guarantee the maximum overnight loss, and there is no inferred safe bound or size uplift.
This is a report on a simulation of an automated trading strategy. The simulation was based on the following premises: it used paired daily returns from the last 5 years, which were obtained from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The simulation used a random number generator with a seed of 200900, generated 10,000 paths, and simulated 60 trading sessions in blocks of 5 days, sampled uniformly. The starting prices were 67.37 and 64.63, and the simulation used a fixed initial stop and target, with the first close crossing being the key factor. The simulation did not take into account intraday paths, costs, gaps at stop execution, or profit ratchet. The results of the simulation were as follows: the terminal dollar quantiles were -13.946045912979042, 5.786306726172633, and 25.56167991248244. For the EEM stock, the probability of hitting the stop first was 0.6359, the probability of hitting the target first was 0.224, and the probability of neither was 0.1401. For the XLE stock, the probability of hitting the stop first was 0.5348, the probability of hitting the target first was 0.189, and the probability of neither was 0.2762. Note that these results are for the terminal dollar quantiles of the simulation, not the strategy's profit and loss. The frequencies are not calibrated to live odds and cannot be used to supply the Kelly criterion. This simulation is a counterexample to a non-stationary rate or commodity regime. The model sensitivity is recorded, but the strategy is not revalidated and the positions are not resized.
A news scan found the following text: Premises are based on 11 close-event reactions and a stock price of $15.13, with a strike price of $14 and an indicative ask of $0.23. The model uses 50,000 paths, a seed of 91908, and 20 business days from September 21 to October 16, with an event step on September 30. The model uses a geometric Brownian motion with 22.5, 35, and 50 percent annual volatility. A Black-Scholes model put is used with a floored intrinsic value, pre and post IV scenario volatility, and an assumed risk-free rate of 4 percent. The initial model value is $0.168158, which differs from the ask price of $0.23. The model has a daily stop loss of $0.115, a target of $0.69, and an expiry date, with an exit price of $0.01 per share plus a $1 round trip friction. The mean profit and loss is -$12.3303, -$6.1652, and +$0.1245 USD, with a target percentage of 5.052, 13.464, and 21.866 percent, and a stop percentage of 93.924, 85.970, and 78.006 percent. The fifth percentile is -$23.2405, -$20.4268, and -$21.2576 USD. The model does not account for American exercise, dividend boundary, and actual 15-minute liquidity. The conclusion is to size zero for a failed mandate, even in a high-volatility scenario. The source is https://runway.adityaperswal.workers.dev/data/stock-bars and option-chain/CAG?feed=indicative.
A data analysis from the source https://runway.adityaperswal.workers.dev/data/stock-bars found favorable momentum sensitivity, but these are not live odds. The analysis used joint daily high, low, open, and close prices, also known as OHLC, resampled with a NumPy seed of 91908. It considered all 2692 rows, with the last 252 rows and the last 179 rows from 2026 being the most relevant. The data was divided into blocks of 5 and 10, with 20,000 each, over 60 sessions. The fixed prices were E51.42, S48.201, and T61.901368, with a baseline fee of 5 basis points and a lip of 15 basis points. The analysis used a stop-loss strategy, with the first adverse gap being the target, and a capped timeout exit, with no 15-minute management or dividend cash separation, and considering news shock. The results showed that all 5 out of 10 means were between 1.352% and 1.477%, with a target of 30.215% to 30.415%. The 252 means were between 7.049% and 6.608%, with a target of 50.63% to 48.67%. The 2026 means were between 9.458% and 9.554%, with a target of 60.42% to 60.575%. The expected log weight grid was between 0 and 1, with a half Kelly criterion of 0.49 to 0.5. The analysis also considered a cap of 120 and a gap stress bind long before that. A one-share P99 model showed a potential loss of 4.29 to 5.85, which is not bound, assuming a 50% gap of 25.86 to 4. The resampling showed a strong recent drift that inflated the odds versus the actual conditional entry holdout, but the simulation did not validate the 61.90 fundamental value. The model calculation was valid, but the soundness was limited as a live forecast, and no orders were placed.
This analysis is based on certain premises. The data comes from a source at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which provides paired adjusted close returns for the last 1255 observations. The analysis uses a numpy seed of 190908, generates 10,000 paths, and spans 60 sessions with 12 independent uniform 5-day block starts, preserving cross-asset pairing and compounding from queued limits. The initial brackets are fixed and horizontal, with the first close crossing recorded, and it does not account for intraday prices, gaps, costs, ratchets, or live execution. The results show the following: the seed used was 190908, the number of paths generated was 10,000, the number of days was 60, the block size was 5 days, and the sample size was 1255 observations. The terminal dollar quantiles were -13.725602935665727 at the 5th percentile, 6.065331103171697 at the median, and 25.972824647228244 at the 95th percentile. For EEM, 62.26% of the time the stop was hit first, 23.73% of the time the target was hit first, and 14.01% of the time neither was hit. For XLE, 53.83% of the time the stop was hit first, 18.41% of the time the target was hit first, and 27.76% of the time neither was hit. Note that these terminal dollar quantiles are based on mark-to-market values without stops, not the strategy's profit and loss. Also, changes in market conditions or overnight gaps can affect the validity of these results. The frequencies of crossing are not meant to predict actual odds, and no new strategy or sizing is implied. The analysis assumes one share is maintained throughout.
A news scan found the headline 'R91906 120,000 PSIX paths expose window-sensitive exit odds'. This is a report from an automated trading fund manager. The manager made the following premises: there are 1. 200,000 paths for 3 pools, all 432, last 252, and 2026, with 2 block lengths, 5 and 10, 60 sessions, and joint OHLC and prior close, which is a type of financial data, at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=PSIX,ETN,POWL,NVT,SPY&timeframe=1Day&feed=sip&adjustment=all. 2. The manager used a frozen price of 45.87, 41.2664, and 61.710237, with a 5 basis point fee, 50 basis point slippage, adverse gap stop, capped target, same bar stop first, and timeout exit. All history had a mean of 2.8986 and 3.2096 percent, with a target of 33.415 and 34.405 percent. The last 252 days had a mean of -1.7638 and -2.9310 percent, with a target of 23.855 and 22.105 percent. The year 2026 had a mean of 1.1850 and -0.1533 percent, with a target of 29.510 and 27.145 percent. The median was -11.0204 percent. 3. The manager used an expected log stock weight grid from 0 to 1, then half, with all at 0.337 and 0.361, last 252 at 0 and 0, and 2026 at 0.139 and 0. The manager capped 120 binds positive models but not approval. Natural exit trade half Kelly 2025.5 and 2026.0 was also unstable. 4. Two share P99 loss was 31.64 to 36.24 dollars. Quantiles were not bounds, and unconditional resampling was not live entry odds. There were no 15-minute exits, and a 2R ratchet was used, with new shocks and news conditioning. The conclusion was to not select a favorable window to size, and one-share probe was affordable if value or edge improved, with an actual value of 0.
A simulation was run to analyze a Thursday gap in stock prices. The simulation used 13 observed overnight gaps from August 29 to September 4, with a stock price of $7.99. The simulation took into account 10 trading steps from Monday, September 21, to October 2, and an event on Thursday, September 24, related to the company BlackBerry, which can be found at https://investors.blackberry.com/. The simulation used a corrected step 4, which replaced the initial step 3 outputs of 29595-97. The simulation used 50,000 paths and a scenario seed of 91906, with a stock price that had no expected drift, and an annual volatility of 40%, 60%, and 80%. A gap was injected on day 4, with an interest rate assumption of 4%. The simulation used a put option model with a floored intrinsic value, and pre-event and post-event volatility assumptions. The simulation did not use American exercise boundary calibration. The exit strategy was to model the daily mark, which was less than or equal to 0.5 paid or greater than or equal to the maximum of 3 paid and the 80th percentile of the intrinsic value. The actual execution used a 15-minute quote and liquidity, and a gap stop, but this was not validated. The simulation assumed a roundtrip cost of $1 and an exit slippage of $0.01 per share. The target price for the K7 put option was $0.20, with a target of $0.60, and the means were -$13.6358, -$10.8047, and -$6.7817. The stop percentages were 95.850%, 92.566%, and 87.452%, and the target percentages were 3.234%, 6.130%, and 11.384%. For the K7.5 put option, the hypothetical target price was $0.20, with a target of $0.60, and the means were -$7.1241, $1.7658, and $10.9070. The stop percentages were 83%, 71.85%, and 60.444%, and the target percentages were 13.416%, 25.566%, and 37.804%. The current ask price for the K7.5 option is $0.46, which costs more than the $0.30 cap. The means were -$30.4092, -$23.0016, and -$15.1475. The $0.20 price is not quoted and is not attainable. The model often shows a full premium plus fee, despite a half-premium stop. A separate analysis used a signed close intrinsic payoff log utility grid, with a half Kelly criterion of 0, for the K7 put option with a price of $0.20, and the K7.5 put option with prices of $0.20, $0.23, and $0.46, after a friction of $0.02. The intrinsic value excludes the remaining time value, so it is not proof of a true option expected value being negative. The current live allocation is 0, with a hard one premium less than or equal to $30, and a total less than or equal to $100, with a maximum of 4. A quarter probe cannot rescue an invalid quote or a failed mandate. The risk is the full premium, not the model stop. The data source is https://runway.adityaperswal.workers.dev/data/option-chain/BB?feed=indicative and stock bars.
A study was done on the uncertainty of a historical estimator using a Monte Carlo method. The data came from a source at https://runway.adityaperswal.workers.dev/data/stock-bars, which provided stock prices from September 1, 2020, to September 18, 2026. The study used 1255 rolling 60-session gains and a numpy seed of 190906 to generate 3000 replicates for each block length of 20, 60, and 120. The results showed percentile ranges of 2.5-97.5 in percent for two stocks: EEM with block lengths 20 [8.4246, 14.6764], 60 [7.7705, 15.2419], and 120 [7.2695, 14.6641], and XLE with block lengths 20 [16.7909, 24.9857], 60 [14.2856, 26.7455], and 120 [13.1971, 27.1555]. The medians for EEM were 10.7581, 10.8267, and 10.6242, and for XLE were 20.5145, 20.4653, and 20.5145. These results are uncertainty bands for the historical estimator and do not predict future returns or portfolio gains. The study did not consider fees or fills because it did not claim to model execution. The conclusion was that the sizing and accepted queues remained unchanged, and no empirical Kelly criterion was inferred. The planned risk of 4.42 is not a loss bound. This study is different from a prior gap-path diagnostic and is not a repeated strategy backtest. A news scan found the headline was not available, this is a raw script output.
A trading fund manager made some assumptions about how to model exit odds. The manager used two main premises: first, they used stock prices from September 18, which were normalized, meaning adjusted to a standard scale, and these prices were used to simulate how the model would work. Second, they created 10,000 paths, or possible outcomes, for each regime, or market condition, using a circular 5-day block of time, and they preserved the relationships between different parts of the market within each block. The manager also divided each stock's price by its previous close, and then combined these ratios over time. They used a fixed stop, which is a price level at which a trade is automatically stopped, and a target, which is a price level at which a trade is automatically closed, based on the stock's average true range, or volatility, over the past two days, and the 90th percentile of its price over the past five years, which were both 86.66. The manager did not use a trailing stop, which is a stop that moves as the stock price moves, or a simple moving average, which is a way of smoothing out price movements, to adjust the stop or target. This study was meant to test how sensitive the model is to different assumptions, not to test how well the model works in practice. The manager tested the model at the close of each trading day, and also tested it at the high and low prices of each day. If a trade went beyond the stop or target, it was filled at the open of the next day. The manager assumed that costs were 6 basis points, or 0.06%, for each trade, and also tested a stress scenario with costs of 20 basis points. The manager did not include cash interest in the model. The results showed that the mean, or average, return for the full sample of trades was 6.0981 USD for the close model, and 4.5986 USD for the high-low model, and the median return was 4.6280 USD for the close model and 2.6125 USD for the high-low model. The manager also found that the stop was hit 69.40% of the time for the full sample, and the target was hit 18.58% of the time. For the recent sample, the mean return was 12.8759 USD for the close model, and 10.4871 USD for the high-low model, and the stop was hit 66.53% of the time. The manager noted that the model assumes that there is enough liquidity, or ability to buy and sell, at the barrier, and that prices are continuously observed, unlike in some venues where prices are only checked every 15 minutes. The manager also noted that trailing rules, which are rules that adjust the stop or target based on the stock's price movement, were omitted, and that the model does not make any claims about the direction of the stock's price movement. The manager concluded that the model's results should not be taken as a guarantee of future performance, and that the model's sensitivity to different assumptions explains the uncertainty of the results.
A news scan found the headline about cross-asset dependence increasing modeled downside risk. The analysis is based on 1255 synchronized daily returns from September 20, 2021, to September 18, 2026. It assumes an initial investment of $93.33 in each of the EEM, QQQ, and IWM assets, with a buy-and-hold strategy compounding over 20 sessions. The model uses 20,000 paths with a seed of 91906. The results compare joint independent and identically distributed rows with independent per-asset rows, and then joint 5-session blocks for serial sensitivity. Same-day dependence isolation shows a probability of loss greater than 30 of 1.505% versus 0.015%. The joint 5-day block results show a P05 of -21.6557 and an ES05 of -28.4310, with a loss frequency of 42.17% and a probability of loss greater than 30 of 1.58%. The full results are provided in a data format. The analysis notes that dependence alters the basket distribution and that the soundness is limited to an empirical stationary return model. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.
A news scan found the headline 'R91904 SFIX gap-bootstrap option paths: cheap limit odds remain regime-sensitive'. This refers to a type of financial analysis. The analysis is based on certain premises, including historical overnight gaps from observations 29367 to 76, and a specific stock price on September 18 and a put option price on October 16. The analysis uses 30,000 paths, or possible scenarios, and a volatility scenario seed of 91904. It also assumes a risk-free interest rate of 4 percent and zero stock drift, meaning the stock's price does not change over time. The analysis uses a type of option pricing model called BSM, which stands for Black-Scholes-Merton, a mathematical model used to estimate the value of a stock option. The model approximates the value of an American option, which can be exercised at any time, and uses a type of statistical analysis called NumPy. The results show that at a premium of 0.13, the mean dollar profit and loss is -5.2523, -2.5318, and 1.1347, and the stop percentage is 80.4167, 77.89, and 73.8533 percent. The analysis also shows that at a premium of 0.28, all results are negative, and at a premium of 0.11, the mean results are -3.2232, -0.5282, and 3.1574. The analysis includes fees of 1 USD round trip and 0.01 USD per share exit slippage, which refers to the difference between the expected price of a stock and the actual price. The data sources for the analysis are https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/option-chain/SFIX?feed=indicative.
A simulation called R91904 was run to understand potential risks. It used 20,000 paths and 60 sessions, looking at five-day blocks of stock prices and ratios. The simulation used data from 2016 to 2023 and the latest 252 sessions. The target price was $14.82, with a stop loss at $12.67 and a target at $21.92, with a 55 basis point gap on each side. The simulation was based on data from https://runway.adityaperswal.workers.dev/data/stock-bars. The results showed a mean net return of +0.2761% and +5.5309%, with a median return of -15.4293% for both. The mean win rate was 26.8199% and 45.7187%, with mean loss rates of 14.7034% and 15.8906%. The profit rate was 36.075% and 34.77%. The simulation used a binary half-Kelly risk-stake approximation, which suggested a risk of 5.148% and 6.0489% of the bankroll. However, this is not a direct position and has not been validated. The simulation also showed that using recent data nearly triples the target frequency, but also raises the stop frequency. The model has limitations, including not accounting for monitoring delays, future jumps, and conditional fundamental information. The simulated maximum loss is not a bound. For example, buying four shares at $59.28 would imply a planned net stop risk of $9.197 and an assumed 50% gap loss of $29.64, plus costs. This would be below 3% of the account, approximately. If the entry and payoff are qualified later, the half-Kelly risk-stake should be recomputed with current evidence and cap.