Notes

A query on September 20 found that the HALO261016C00130000 contract is active and tradable, with an American standard of 100 shares and a multiplier of 100. The contract has an unknown open interest and metadata showing a close price of 0.35 as of September 15. On September 18, the indicative bid was 0 and the ask was 0.23, with a size of 0 and 15, and the latest trade was 0.33 on September 15. The system should not use the 0.35 USD metadata close price as the current price, or claim that the 0.23 USD ask price is a valid quote. The nearest contract, C125, has a metadata price of 0.53, which requires a chain reprice. The chosen contract, C130, has a current gross price of 7.968 times the 80th percentile, with a 50% stop at 1.15 times the 80th percentile and a target of 1.83 times the 80th percentile, based on hypothetical numbers. option_contracts

A news scan found the following data: this is a list of hypothetical option path sensitivity results for the ticker symbol TH. The results are as follows: for a call option, denoted by 'C' which means the buyer has the right to buy, with a strike price of 27, the buyer paid 0.18. The target price is 3.58. The iv_fit, which is a measure of volatility, is 0.6851261646420067, and the rv60, which is a measure of historical volatility, is 0.5840230132559768. Three different scale scenarios were tested: 0.75, 1, and 1.5. Each scenario had 30000 simulations, denoted by 'N'. The mean, or average, results were -3.5416888084229163, 7.279610427344152, and 28.750056236349923 respectively. The median, which is the middle value, results were -12.67875280774188, -13.012514945100495, and -13.165595817448121 respectively. The P05, which is the fifth percentile, results were -17.44907720862383, -18.473143180641515, and -18.850922253277254 respectively. The win, stop, target, and full loss percentages were also calculated. These results were generated using a NumPy seed of 92002, with 252 daily returns, and an IID bootstrap, which is a method of resampling, with 20 steps and 25 calendar days. The hypothetical entry date was September 21, with a constant BSM IV fit to ask, which is a method of calculating volatility, and an interest rate of 4%. The stop loss was set at 50%, and the targets were capped at the stated price, with an exit friction of 0.01 and a 1 USD fee. The model does not account for fills, which are executed trades. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: this is a list of hypothetical option trading results for the stock HALO. The option is a put, which is a type of option that gives the buyer the right to sell a stock at a certain price, known as the strike price. The results are based on a model that uses a constant volatility estimate, which is a measure of how much the stock price is expected to fluctuate, and a risk-free interest rate of 4%. The model also assumes that the option will be stopped out, or sold, if it loses 50% of its value, and that the target price is capped at 1.93. The results show three different scenarios, each with a different scale factor, which is a measure of the size of the trade. The scenarios have the following results: for a scale factor of 0.75, the mean return is -12.89%, the median return is -13.11%, and the probability of winning is 0.34%; for a scale factor of 1, the mean return is -10.21%, the median return is -13.57%, and the probability of winning is 2.05%; for a scale factor of 1.5, the mean return is -0.84%, the median return is -14.19%, and the probability of winning is 7.40%. The model uses a bootstrap method, which is a statistical technique for estimating the uncertainty of a result, and assumes that the daily returns are independent and identically distributed, which means that they are random and follow the same pattern. The results are based on 25 calendar days and 20 steps, and assume an exit friction of 0.01 and a fee of 1 USD. The model does not assume that all trades will be filled, which means that it takes into account the possibility that some trades may not be executed. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: this is a list of hypothetical option paths for the stock HALO. The option paths have the following characteristics: stock symbol is HALO, strike price is 130, premium paid is 0.23, call option, target price is 1.83, implied volatility fit is 0.33414218773462845, realized volatility over 60 days is 0.45299795674716264, and the number of simulations is 30000. The list includes three scenarios with different scale factors: 0.75, 1, and 1.5. For each scenario, the mean, median, and 5th percentile returns are calculated, as well as the win rate, stop rate, target rate, and full loss rate. The scenarios are based on a NumPy seed of 92002, 252 daily returns, and an IID bootstrap with 20 steps and 25 calendar days. The hypothetical entry date is September 21, with a constant BSM IV fit to ask, an interest rate of 4%, a stop loss at 50%, and targets capped at the stated price, with an exit friction of 0.01 and a fee of 1 USD. The model does not account for fills. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: this is a list of hypothetical trading results for the stock ticker TH. Each result is based on a different scale factor. The results are as follows: for a scale of 0.75, the mean return was -7.886605283660142, the median return was -12.449135461811936, the 5th percentile return was -16.39025156937178, the win rate was 0.11056666666666666, the stop rate was 0.8893333333333333, the target rate was 0.11043333333333333, and the full loss rate was 0.019066666666666666. For a scale of 1, the mean return was -4.707827127715289, the median return was -12.536057013921312, the 5th percentile return was -17.007302142015455, the win rate was 0.1848, the stop rate was 0.8151333333333334, the target rate was 0.1846, and the full loss rate was 0.030133333333333335. For a scale of 1.5, the mean return was 0.022086684885183967, the median return was -12.293801554098174, the 5th percentile return was -17.515256510848108, the win rate was 0.29006666666666664, the stop rate was 0.7099333333333333, the target rate was 0.2900333333333333, and the full loss rate was 0.035333333333333335. These results were generated using a NumPy seed of 92002, 252 daily return IID bootstrap, and 20 steps over 25 calendar days, with a hypothetical entry date of September 21, a constant BSM IV fit to ask, an interest rate of 4%, a stop loss of 50%, targets capped at the stated price, an exit friction of 0.01, and a fee of 1 USD. The model does not fill orders. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data lines for HALO option path sensitivity: the option has a strike price of 95, a paid price of 0.18, and is a put option, which is a type of option that gives the buyer the right to sell a stock at a certain price. The data lines show different scenarios with varying scales: 0.75, 1, and 1.5. For each scenario, the data includes the mean, median, and 5th percentile of potential losses, as well as the probability of winning, stopping, and reaching the target. The data is based on a hypothetical entry on September 21, using a constant Black-Scholes Merton model, which is a mathematical model used to estimate the value of options, with an interest rate of 4% and a stop loss of 50%. The target prices are capped, and there is an exit friction of 0.01 and a fee of 1 USD. The model does not account for fills, which refers to the execution of a trade. The scenarios were generated using a NumPy seed of 92002, with 25 calendar days and 20 steps, and an IID bootstrap, which is a statistical method used to estimate the variability of a dataset. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data line: this is a list of hypothetical trading results for the stock HALO, which is a ticker symbol, a code used to identify a stock on an exchange. The results are based on a model that uses a constant volatility, or how much the price of a stock changes, estimate, and a specific set of rules to decide when to buy or sell. The rules include a stop loss, which is a price at which to automatically sell a stock to limit losses, of 50% and a target price, which is a price at which to automatically sell a stock to lock in profits, capped at the stated price. The model also includes a small fee, of 1 USD, and a small amount of friction, or cost, of 0.01, when buying or selling. The results are as follows: for a scale, which is a measure of how much to buy or sell, of 0.75, the mean, or average, result was a loss of 8.90, the median, or middle, result was a loss of 15.75, and the probability of winning, or making a profit, was 12.74%. For a scale of 1, the mean result was a loss of 3.58, the median result was a loss of 15.83, and the probability of winning was 22.20%. For a scale of 1.5, the mean result was a gain of 1.78, the median result was a loss of 15.46, and the probability of winning was 31.74%. These results are based on 30,000 simulations, or hypothetical trades, and use a random number generator with a seed, or starting point, of 92002. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: the current values for TH are mom20 at 0.20192853091321616, dist50 at 0.21966662061979103, rv20 at 0.6891776758119668, and vr at 2.4195459251783733. The feature correlations are as follows: mom20 is correlated with mom20 at 1, dist50 at 0.8977127300149603, rv20 at 0.7105159936786607, and vr at 0.13502632778177204. Dist50 is correlated with mom20 at 0.8977127300149603, dist50 at 1, rv20 at 0.6668649570007068, and vr at 0.131037955488919. Rv20 is correlated with mom20 at 0.7105159936786607, dist50 at 0.6668649570007068, rv20 at 1, and vr at 0.18904551127485889. Vr is correlated with mom20 at 0.13502632778177204, dist50 at 0.131037955488919, rv20 at 0.18904551127485889, and vr at 1. The regressions are: for mom20, correlation is 1, coefficients are -5.530600393189074 and 16.616599375393537, and mean absolute error is 1.022149963179038. For dist50, correlation is -1, coefficients are 2.3667489028279944 and -4.610094218446491, and mean absolute error is 0.39992887413086303. For rv20, correlation is 1, coefficients are -1.140219872191624 and 1.1380365523318086, and mean absolute error is 0.5189002872732229. For vr, correlation is 1, coefficients are -0.4589668764592188 and 0.10109351163937304, and mean absolute error is 0.3051035897415185. The train size is 2, the test size is 2, and the null mean absolute error is 0.2884166750038654. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following information about HALO. The current values are: mom20 at 0.059649453448925804, dist50 at 0.17141395758741873, rv20 at 0.21406831671968268, and vr at 6.930643987473309. The feature correlations are as follows: mom20 is correlated with dist50 at 0.8850712730067118, rv20 at 0.5249851201273023, and vr at 0.105254746945394. Dist50 is correlated with mom20 at 0.8850712730067118, rv20 at 0.5071235840790429, and vr at 0.13199728261642732. Rv20 is correlated with mom20 at 0.5249851201273023, dist50 at 0.5071235840790429, and vr at -0.05656539178985633. Vr is correlated with mom20 at 0.105254746945394, dist50 at 0.13199728261642732, and rv20 at -0.05656539178985633. The chronological regressions are: for mom20, correlation is 0.40795209603685034, coefficients are -0.06365496346197759 and 0.5326273593405505, and mean absolute error is 0.2188841179140324. For dist50, correlation is 0.03323438800609336, coefficients are 0.004664532631087741 and 0.07204560149543948, and mean absolute error is 0.2078025596490262. For rv20, correlation is -0.008359939159144401, coefficients are 0.019453654318813747 and -0.00811489459415411, and mean absolute error is 0.20677990276481142. For vr, correlation is 0.07582688061273701, coefficients are 0.00795220055119001 and 0.0015628420922032004, and mean absolute error is 0.2089745022994224. The training period is 14 and the testing period is 3, with a null mean absolute error of 0.20722040709997258. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following results for the NEO R92002 vectorbt nextopen holdout sweep 6: the results are given in a list of dictionaries, where each dictionary contains the hold period, slip, era, return, drawdown, win rate, and number of trades. The list contains six dictionaries with the following values: the first dictionary has a hold period of 20, slip of 0.005, era of train, return of -0.0030015152704535807, drawdown of 0.930979575429236, win rate of 55.55555555555556, and 9 trades. The second dictionary has a hold period of 20, slip of 0.005, era of test, return of -0.21517686695278898, drawdown of 0.4493239237520208, win rate of 0, and 1 trade. The third dictionary has a hold period of 30, slip of 0.001, era of train, return of -0.5775911393926147, drawdown of 1.3442498999831187, win rate of 50, and 8 trades. The fourth dictionary has a hold period of 30, slip of 0.001, era of test, return of 0.5555327575107296, drawdown of 0.432407415035696, win rate of 100, and 1 trade. The fifth dictionary has a hold period of 30, slip of 0.005, era of train, return of -0.7676628465421949, drawdown of 1.4137088429440614, win rate of 50, and 8 trades. The sixth dictionary has a hold period of 30, slip of 0.005, era of test, return of 0.5292603540772536, drawdown of 0.4324592339343458, win rate of 100, and 1 trade. The stock exposure is 30 USD per 1000 book, fees are 10 basis points, and slip is 10 or 50 basis points. The training period is from 2016 to 2024 and the testing period is from 2025 to September 18, 2026. The signal is based on a 252-day high and volume of 1.5. This is a proxy underlying, not an option strategy, and the results are for the profit and loss, or PnL. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following results for the NEO R92002 vectorbt nextopen holdout sweep 0: the results are presented in a list of six items, each representing a different combination of parameters. The parameters include hold, which refers to the number of days to hold a position, slip, which refers to the difference between the expected and actual price of a trade, era, which refers to the time period, either train for training or test for testing, return, which refers to the profit or loss, dd, which refers to the maximum drawdown or largest loss, win, which refers to the percentage of winning trades, and n, which refers to the number of trades. The results are as follows: a hold of 10 days and a slip of 0.001 resulted in a return of 0.17328643730892493 and a drawdown of 0.5732024201515862 during the train era, with a win rate of 63.63636363636363 and 11 trades, and a return of -0.16575371156273633 and a drawdown of 0.4119511869139503 during the test era, with a win rate of 50 and 2 trades. A hold of 10 days and a slip of 0.005 resulted in a return of -0.09193593080842746 and a drawdown of 0.7525064907292878 during the train era, with a win rate of 63.63636363636363 and 11 trades, and a return of -0.21318612913405702 and a drawdown of 0.447337857238872 during the test era, with a win rate of 0 and 2 trades. A hold of 20 days and a slip of 0.001 resulted in a return of 0.21428893089931408 and a drawdown of 0.8562301969964436 during the train era, with a win rate of 55.55555555555556 and 9 trades, and a return of -0.1918971759656646 and a drawdown of 0.43803017990386994 during the test era, with a win rate of 0 and 1 trade. The stock exposure was 30 USD per 1000 book, with fees of 10 basis points and a slip of either 10 or 50 basis points. The train era was from 2016 to 2024 and the test era was from 2025 to September 18, 2026. The signal was based on a 252-day high and a volume of 1.5. This is a proxy underlying, not an option strategy, and the results are for the profit and loss, or PnL. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data line: {"median":0.0463738668333854,"p80":0.10664122137404586,"medianCI":[0.030630435266119527,0.10416666666666674],"p80CI":[0.04426383244763985,0.12495612495612485],"legs":[{"occ":"NEO261016P00016000","K":16,"paid":0.18,"hurdle":0.20542440984429933,"p80pay":0,"multiple":0,"netwin":0,"meanpay":0,"halfKelly":0,"mapped_payoffs":[0,0,0,0,0,0,0,0,0,0]}]} which appears to be a set of statistical results, including median and p80 values, with confidence intervals, for a financial instrument, specifically the NEO R92002, based on 20000 resamples, noting that the results are for empirical terminal intrinsic value, not actual option profit or loss, and that the half Kelly calculation is applied to mapped payoffs after accounting for 0.02 friction, without calibrated odds. https://runway.adityaperswal.workers.dev/data/option-chain/NEO?feed=indicative

A news scan found the following data: a list of events for NEO R92002, which is a type of investment fund. The list includes three events: on 2020-10-14, the price was 42.82 and the return was 0.0207846800560485, on 2023-05-10, the price was 19.65 and the return was -0.11653944020356233, and on 2026-06-26, the price was 13.98 and the return was -0.06008583690987124. A signal to buy or sell is triggered when the price closes above the highest price in the last 252 days, and the volume is at least 1.5 times the average volume of the last 20 days, and the dollar volume is at least 2 million. The entry point is the next session's open, and the exit point is the signal plus 20 session closes, with a 21 session separation. The history of this fund goes from 2016 to September 2018 and 2026. The dates are measured from the bar-signal dates, not from events related to the issuer, which means the company that issued the investment. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found a list of events for NEO R92002. The events are: a breakout on 2017-08-31 with a price of 10.08 and a return of 0.10416666666666674, a breakout on 2018-06-01 with a price of 12.6 and a return of 0.040476190476190554, a breakout on 2018-10-26 with a price of 15.89 and a return of 0.036500943989930645, a breakout on 2019-03-29 with a price of 21.1 and a return of -0.009952606635071137, a breakout on 2019-05-02 with a price of 22.85 and a return of -0.04989059080962799, a breakout on 2019-08-01 with a price of 25.9 and a return of -0.042857142857142816, and a breakout on 2019-12-18 with a price of 28.49 and a return of 0.12495612495612485. The signal to buy is when the price closes above the highest price in the last 252 days, and the volume is at least 1.5 times the average volume of the last 20 days, and the dollar volume is at least 2 million. The entry point is the next session's open, and the exit point is the close of the session after the signal, with a 21-session separation. This data is from 2016 to September 2018, and 2026 history, and the dates are measured from the signal, not from the issuer's events. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following results for the TH R92002 vectorbt nextopen holdout sweep 6: the results are given in a list of six items, each representing a different combination of parameters. The parameters include hold, which is the number of days to hold a position, slip, which is the difference between the expected and actual price of a trade, era, which is either train, meaning training data, or test, meaning testing data, return, which is the profit made, dd, which is the maximum drawdown, or largest loss, win, which is the percentage of winning trades, and n, which is the number of trades. The results are as follows: a hold of 20 days and a slip of 0.005 in the train era gave a return of 0.7669074360153679, a drawdown of 0.712034558306196, a win rate of 50, and 2 trades, a hold of 20 days and a slip of 0.005 in the test era gave a return of 1.1071406800994108, a drawdown of 0.4994154860902378, a win rate of 50, and 2 trades, a hold of 30 days and a slip of 0.001 in the train era gave a return of 0.9782585970996145, a drawdown of 0.7891485226721895, a win rate of 50, and 2 trades, a hold of 30 days and a slip of 0.001 in the test era gave a return of 1.686445288846989, a drawdown of 0.8345756951795957, a win rate of 100, and 2 trades, a hold of 30 days and a slip of 0.005 in the train era gave a return of 0.9262455496637798, a drawdown of 0.7892431248694987, a win rate of 50, and 2 trades, a hold of 30 days and a slip of 0.005 in the test era gave a return of 1.631596659061817, a drawdown of 0.8636961507573426, a win rate of 100, and 2 trades. The stock exposure was 30 USD per 1000 book, fees were 10 basis points, and slip was 10 or 50 basis points. The training data was from 2016 to 2024 and the testing data was from 2025 to September 18, 2026. The signal was 252 high and volume 1.5. This is a proxy underlying, not an option strategy, and the results are for profit and loss. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following results for the TH R92002 vectorbt nextopen holdout sweep 0: a list of trading performance metrics, including return, drawdown, and win rate, for different combinations of hold period and slip, which refers to the difference between expected and actual trade prices. The results are divided into training and testing periods, with the training period spanning from 2016 to 2024 and the testing period from 2025 to September 18, 2026. The stock exposure is 30 USD per 1000 book, with fees of 10 basis points, and slip of either 10 or 50 basis points. The signal is based on 252-day high and volume of 1.5. The results are as follows: [{ https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: the median payoff is 0.2739 and the 80th percentile payoff is 0.4432. The median confidence interval is between 0.0907 and 0.4861, and the 80th percentile confidence interval is between 0.1332 and 0.4861. There are two legs: the first is TH261016C00027000 with a strike price of 27, a paid price of 0.18, a hurdle of 0.2827, an 80th percentile payoff of 3.5811, a multiple of 19.895, a net win of 0.5, a mean payoff of 1.8664, and a half Kelly of 0.2353. The second leg is TH261016P00018000 with a strike price of 18, a paid price of 0.23, a hurdle of 0.1614, an 80th percentile payoff of 6.2011, a multiple of 26.961, a net win of 0, a mean payoff of 0, and a half Kelly of 0. The data includes 20000 resamples, and notes that the empirical terminal intrinsic is not the actual option profit and loss, and the half Kelly is calculated on mapped payoffs after a 0.02 friction, and is not calibrated odds. https://runway.adityaperswal.workers.dev/data/option-chain/TH?feed=indicative

A news scan found the following data: the TH R92002 fund had breakout events on specific dates. These events occurred on April 20, 2022, with a price of 6.98 and a return of -0.13323782234957027, on July 11, 2022, with a price of 9.89 and a return of 0.41456016177957533, on March 12, 2026, with a price of 9.73 and a return of 0.48612538540596106, and on May 11, 2026, with a price of 18.08 and a return of -0.09070796460176977. The signal to buy or sell is triggered when the price closes above the highest price in the last 252 days, and the volume is at least 1.5 times the average volume of the last 20 days, and the dollar volume is at least 2 million. The entry point is the next session's open, and the exit point is the signal plus 20 session closes, with a 21 session separation. This data is based on the history from 2016 to September 2018 and 2026. The dates are measured from the signal dates, not from any events related to the issuer. https://runway.adityaperswal.workers.dev/data/stock-bars

[{"hold":20,"slip":0.005,"era":"train","return":0.1527476401722403,"dd":1.0209283538865077,"win":50,"n":14},{"hold":20,"slip":0.005,"era":"test","return":-0.5391896211080849,"dd":1.288128452459869,"win":33.33333333333333,"n":3},{"hold":30,"slip":0.001,"era":"train","return":0.716623967069188,"dd":1.1056657021271676,"win":61.53846153846154,"n":13},{"hold":30,"slip":0.001,"era":"test","return":-0.5149427241087985,"dd":1.2866344090780912,"win":33.33333333333333,"n":3},{"hold":30,"slip":0.005,"era":"train","return":0.401129977211054,"dd":1.1789863802713185,"win":61.53846153846154,"n":13},{"hold":30,"slip":0.005,"era":"test","return":-0.5850250355237783,"dd":1.3197225694331571,"win":33.33333333333333,"n":3}] 30USDstockexposure/1000book,fees10bp,slip10or50bp,train2016–24/test2025–Sep18,2026; signal252highandvolume1.5. Proxyunderlying,notoptionstrategyPnL. https://runway.adityaperswal.workers.dev/data/stock-bars

[{"hold":10,"slip":0.001,"era":"train","return":0.6264065353629661,"dd":0.847449927054067,"win":68.75,"n":16},{"hold":10,"slip":0.001,"era":"test","return":-0.5322562086462703,"dd":1.0565154126462306,"win":33.33333333333333,"n":3},{"hold":10,"slip":0.005,"era":"train","return":0.2391296323184708,"dd":0.8973451389252429,"win":50,"n":16},{"hold":10,"slip":0.005,"era":"test","return":-0.6022691967998384,"dd":1.1003812167998506,"win":33.33333333333333,"n":3},{"hold":20,"slip":0.001,"era":"train","return":0.49138783169051975,"dd":0.9626956734599124,"win":50,"n":14},{"hold":20,"slip":0.001,"era":"test","return":-0.4689230467205803,"dd":1.2549132805792729,"win":33.33333333333333,"n":3}] 30USDstockexposure/1000book,fees10bp,slip10or50bp,train2016–24/test2025–Sep18,2026; signal252highandvolume1.5. Proxyunderlying,notoptionstrategyPnL. https://runway.adityaperswal.workers.dev/data/stock-bars

{"median":0.10526315789473673,"p80":0.17236693065385034,"medianCI":[0.029842647856755278,0.14196108550947262],"p80CI":[0.10615901455767078,0.2333023122628678],"legs":[{"occ":"HALO261016C00130000","K":130,"paid":0.23,"hurdle":0.15811471765228988,"p80pay":1.8326613520254682,"multiple":7.96809283489334,"netwin":0.11764705882352941,"meanpay":1.72257999341889,"halfKelly":0.0497,"mapped_payoffs":[0,0,0,0,0,0,0,0,20.89539322145444,0,0,0,0,0,0,0,8.388466666666687]},{"occ":"HALO261016P00095000","K":95,"paid":0.18,"hurdle":0.1567807914628724,"p80pay":1.9326613520254625,"multiple":10.73700751125257,"netwin":0.11764705882352941,"meanpay":0.6807229816238982,"halfKelly":0.039150000000000004,"mapped_payoffs":[0,0,0,0,0,0,0,0,0,2.7874456736467863,0,0,0,0,8.784845013959483,0,0]}]} 20000IIDresamples; empiricalterminalintrinsic notactualoptionPnL; halfKelly onmappedpayoffs after.02friction, notcalibratedodds. https://runway.adityaperswal.workers.dev/data/option-chain/HALO?feed=indicative

[{"i":2349,"date":"2025-05-07","E":69.845,"ret":-0.2333023122628678},{"i":2442,"date":"2025-09-19","E":78.12,"ret":-0.14196108550947262},{"i":2662,"date":"2026-08-06","E":90,"ret":0.2306666666666668}] Signal close above prior252intraday highs, volume>=1.5prior20mean,dollarvol>=2m. Entry nextsessionopen, exit signal+20sessionclose;21session separation; 2016–Sep18,2026 history. Dates are measured bar-signal dates, not issuer events. https://runway.adityaperswal.workers.dev/data/stock-bars

[{"i":1130,"date":"2020-06-30","E":27.37,"ret":-0.025940811107051598},{"i":1208,"date":"2020-10-20","E":30.39,"ret":0.34188877920368554},{"i":1294,"date":"2021-02-24","E":50.62,"ret":-0.17996839193994474},{"i":1638,"date":"2022-07-07","E":50.73,"ret":-0.10526315789473673},{"i":1727,"date":"2022-11-10","E":54.37,"ret":0.047084789405922534},{"i":2120,"date":"2024-06-06","E":51.08,"ret":0.028778386844166004},{"i":2167,"date":"2024-08-14","E":58.09,"ret":0.042175933895679085}] Signal close above prior252intraday highs, volume>=1.5prior20mean,dollarvol>=2m. Entry nextsessionopen, exit signal+20sessionclose;21session separation; 2016–Sep18,2026 history. Dates are measured bar-signal dates, not issuer events. https://runway.adityaperswal.workers.dev/data/stock-bars

[{"i":302,"date":"2017-03-16","E":14.42,"ret":-0.11165048543689315},{"i":428,"date":"2017-09-14","E":16.03,"ret":0.10917030567685582},{"i":467,"date":"2017-11-08","E":19.2,"ret":-0.011979166666666652},{"i":488,"date":"2017-12-08","E":19.7,"ret":-0.05279187817258879},{"i":974,"date":"2019-11-14","E":18.43,"ret":0.029842647856755278},{"i":1014,"date":"2020-01-14","E":19.95,"ret":0.0025062656641603454},{"i":1083,"date":"2020-04-23","E":22.09,"ret":0.1063829787234043}] Signal close above prior252intraday highs, volume>=1.5prior20mean,dollarvol>=2m. Entry nextsessionopen, exit signal+20sessionclose;21session separation; 2016–Sep18,2026 history. Dates are measured bar-signal dates, not issuer events. https://runway.adityaperswal.workers.dev/data/stock-bars

Sep18completed SIP/all, retrievedSep20. Column order:s,n,t,S,priorH,priorC,highpass,closepass,vr,dvol20,sma50,sma200,atr20,mom20,pass. Rows:[["WFC",285,"2026-09-18T04:00:00Z",86.12,96.18,94.83,false,false,1.888456,1157749344.484,86.9966,84.39835,2.016405,0.028913,false],["ZBH",285,"2026-09-18T04:00:00Z",95.36,106.03,103.15,false,false,2.319744,165919241.277,96.0902,91.15565,2.74871,-0.036476,false],["YUMC",285,"2026-09-18T04:00:00Z",41.12,57.33,56.9,false,false,0.979934,66582057.0245,45.0486,47.01895,0.97399,-0.153561,false],["ZGN",285,"2026-09-18T04:00:00Z",12.29,15.95,15.25,false,false,1.442762,14871800.242,13.5916,11.9853,0.438795,-0.098313,false],["YOU",285,"2026-09-18T04:00:00Z",38.1,68.83,61.97,false,false,2.08714,77519322.252,48.1906,46.3102,1.54425,-0.108772,false]] https://runway.adityaperswal.workers.dev/data/stock-bars