Notes

A news scan found the following data: the attribution results for EEM VM9110600 test are given. The adapter results show a beta of 0.3809806030909822, an annual intercept percentage of -3.5608608270545847, and an intercept HAC t of -0.854273444443563. Monthly results show a beta of 0.802969548618368, an annual intercept percentage of -0.11658285846390978, and an intercept HAC t of -0.04568083471415283. Constant results show a beta of 0.8592135509470493, an annual intercept percentage of -0.4103246131894796, and an intercept HAC t of -1.3570767130615387. Other results include a monthly minus constant mean percentage per period of -0.06393957342545054, a monthly minus constant confidence interval percentage per period of -0.5858323444733271 to 0.2789684510595115, a lag variance current variance correlation of 0.42869296578437954, a lag variance return correlation of 0.032974157692675296, a variance AR1 slope of 0.42821631854421616, a variance AR1 HAC t of 2.7433460526169378, and a return on lag variance HAC t of 0.18573220251596553. Regimes are also given, with two regimes: one with high variance false, 27 months, asset mean percentage of 2.5745674928308286, asset standard deviation percentage of 3.5301876417464753, managed mean percentage of 2.5066136470697624, and constant mean percentage of 2.167861259927719; and one with high variance true, 17 months, asset mean percentage of 0.03873472726572582, asset standard deviation percentage of 5.956204839121442, managed mean percentage of -0.6817767847511411, and constant mean percentage of 0.0217323731050349. This data is from a September 11 measurement, with a seed of 9110600 and 10000 three-month blocks. The half Kelly is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the following data: the raw trade has a probability of 0.4772727272727273, a win-loss ratio of 1.435017917488792, and a half Kelly value of 0.05650369952230899, which is a measure of binary risk. The mean percentage is 0.5090993323715772, the mean win percentage is 4.505008846107263, and the mean loss percentage is 3.139339788865353. The monthly fund has a probability of 0.4772727272727273, a win-loss ratio of 1.3581422098574514, and a half Kelly value of 0.046194339180715. The mean percentage is 0.34166721959791796, the mean win percentage is 3.6981503108129288, and the mean loss percentage is 2.722947776728831. The trade mean confidence interval percentage is between -0.7292211281405612 and 1.5989543310425587, and the fund month mean confidence interval percentage is between -0.73717099203627 and 1.2049415439300393. The reasons for trade exits are: gap stop with 13 occurrences, time with 13 occurrences, stop with 10 occurrences, target with 6 occurrences, and gap target with 2 occurrences. The target hit rate is 0.18181818181818182. The target Wilson 95 is between 0.09512692558258554 and 0.3196083675514233. There are 15 partials, and the R quantiles are -1.9748128318439022, -1.3789162907719033, -0.9814213425775453, 2.728958881761518, and 2.8208842731197765. There are 0 same day exits, and the mean calendar days is 17.181818181818183. This data is from a September 11 measurement, with a seed of 9110600 and 10000 three-month blocks. The half Kelly value is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

The attribution of the EEM VM9110600 train is as follows: the adapter has a beta of 0.2687020184583924, an annual intercept percentage of -7.606859151345016, and an intercept HAC t of -1.6362769340880003. The monthly attribution has a beta of 0.7318888727097785, an annual intercept percentage of -1.650106618058666, and an intercept HAC t of -0.6136294156296007. The constant attribution has a beta of 0.8554350049661257, an annual intercept percentage of 0.10674506448823226, and an intercept HAC t of 0.4213044923458753. The monthly minus constant mean percentage point is -0.17591050564893562, with a confidence interval of -0.7404203145348903 to 0.3259959928386928. The correlation between the current variance and the lag variance is 0.1542433953204125, and the correlation between the return and the lag variance is 0.20184925737980658. The variance AR1 slope is 0.15435268435242105, with a HAC t of 5.372166165079798. The return on lag variance HAC t is 4.872210379532583. There are two regimes: one with high variance being false, lasting 17 months, with an asset mean percentage of -1.035211586290992, an asset standard deviation percentage of 5.435151731780226, a managed mean percentage of -1.048418854167369, and a constant mean percentage of -0.8896543505578043; and one with high variance being true, lasting 31 months, with an asset mean percentage of 0.9963136417857116, an asset standard deviation percentage of 5.732716600594726, a managed mean percentage of 0.7021416250902367, and a constant mean percentage of 0.8874547770188275. This measurement was taken on September 11, with a seed of 9110600, using 10000 three-month blocks, and a half Kelly binary risk unit proxy, which is a measure of risk, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive, meaning they are used to describe the relationships between variables, with HAC standing for Heteroskedasticity and Autocorrelation Consistent. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the raw trade data for EEM VM9110600 with a probability of 0.3333333333333333, a win-loss ratio of 1.7492304177036748, and a half Kelly value of 0. The mean percentage change was -0.26116030967943354, the mean win percentage change was 5.465131218127857, and the mean loss percentage change was 3.124306073583079. The monthly fund data showed a probability of 0.3333333333333333, a win-loss ratio of 1.3891463529245547, and a half Kelly value of 0. The mean percentage change was -0.5325163734052663, the mean win percentage change was 3.6329971091938402, and the mean loss percentage change was 2.6152731147048196. The trade mean confidence interval percentage was between -1.873748895014355 and 1.1536148400141222, and the fund month mean confidence interval percentage was between -1.7455603955724142 and 0.47444355367614166. The reasons for trade exits were stop loss 17 times, time 15 times, gap stop 12 times, target 2 times, and gap target 2 times. The target hit rate was 0.08333333333333333, and the target Wilson 95 was between 0.03288322434995555 and 0.19553565161066291. There were 8 partial trades, and the R quantiles were -2.2081926997400654, -1.5713259086402114, -1.0480804406415611, 2.688024105329464, and 3.2016724274953896. One trade had a same-day exit, and the mean calendar days per trade was 17.8125. This data was measured on September 11, with a seed of 9110600 and 10,000 three-month blocks. Half Kelly refers to a binary risk unit proxy, not an allocation. Log wealth sizing is separate, and HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the following data: the EFA VM9110600 test attribution results are in. The attribution section shows the adapter's beta is 0.39156977501640333, annual intercept percentage is -5.399967028569129, and intercept HAC t is -1.5416131769722634. The monthly section has a beta of 0.8359278640502914, annual intercept percentage of -1.4286415336707956, and intercept HAC t of -0.8868785030466322. The constant section has a beta of 0.8823052164831265, annual intercept percentage of -0.38356354960796385, and intercept HAC t of -1.200189607408432. Other results include monthly minus constant mean pp of -0.14780877165965364, monthly minus constant CI pp of -0.4290058001709009 to 0.10462566288045382, lag var current var corr of 0.08087552025768092, and lag var return corr of 0.22066468939414555. The variance AR1 slope is 0.08119820041170756 and variance AR1 HAC t is 0.8935280289081604. The return on lag variance HAC t is 3.4065018485115344. There are two regimes: one with high variance false, lasting 5 months, with asset mean percentage of -0.2928613926366652, asset standard deviation percentage of 3.5997810871738687, managed mean percentage of -0.36135800936571316, and constant mean percentage of -0.26014392373505224; and one with high variance true, lasting 39 months, with asset mean percentage of 1.6723717376633271, asset standard deviation percentage of 3.623333065168414, managed mean percentage of 1.2863434265950557, and constant mean percentage of 1.4401258759507338. This data is from a September 11 measurement, using seed 9110600, with 10,000 three-month blocks, and a half Kelly binary risk unit proxy, which is a measure of risk, not an allocation method, and log wealth sizing is separate. HAC regressions are used for descriptive purposes, where HAC stands for Heteroskedasticity and Autocorrelation Consistent. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the raw trade data with the following statistics: the probability of a trade being a winner is 0.45454545454545453, the win-loss ratio is 1.4404806045125322, and the half Kelly, which is a measure of risk, is 0.0379419776097942. The mean percentage change is 0.2779066786959507, the mean percentage change for a win is 3.662258746157353, and the mean percentage change for a loss is 2.542386710855218. The monthly fund data shows similar statistics: the probability of a winner is 0.45454545454545453, the win-loss ratio is 1.3316334635130245, and the half Kelly is 0.022466164355794987. The mean percentage change is 0.14015935501623591, the mean percentage change for a win is 3.1193432220236303, and the mean percentage change for a loss is 2.3424938674899263. The trade mean confidence interval percentage is between -0.6362396287236463 and 0.972200192935251, and the fund month mean confidence interval percentage is between -0.718496017463252 and 0.8299946477401537. The reasons for trades are: time, which occurred 16 times, stop, which occurred 14 times, gap stop, which occurred 10 times, target, which occurred 3 times, and gap target, which occurred 1 time. The target hit rate is 0.09090909090909091, and the target Wilson 95, which is a statistical measure, is between 0.035921611478836546 and 0.21159519397917487. There were 10 partials, and the R quantiles, which are statistical measures, are -2.0839677234199643, -1.5882957122054246, -1.0694235457736627, 2.662150179001067, and 2.789047169518773. There were 0 same day exits, and the mean calendar days is 17.886363636363637. This data is from a September 11 measurement, with a seed of 9110600, and 10000 three-month blocks. The half Kelly is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the following data: a train attribution report for EFA VM9110600. The report includes attribution details such as adapter, monthly, and constant data. The adapter data has a beta of 0.19489176178336554, an annual intercept percentage of -6.559083927401948, and an intercept HAC t of -1.8902218681508647. The monthly data has a beta of 0.5305203602049549, an annual intercept percentage of -1.44228544262843, and an intercept HAC t of -0.5121811740759884. The constant data has a beta of 0.8786102986475177, an annual intercept percentage of 0.23672240153173205, and an intercept HAC t of 0.7889029397670969. Other details include a monthly minus constant mean percentage per period of -0.3429394285123677, a monthly minus constant confidence interval per period of -0.8994980631674195 to 0.29261873762153917, a lag variance current variance correlation of 0.15957186378462454, and a lag variance return correlation of 0.16505400796479158. The variance AR1 slope is 0.15962794603447494 and the variance AR1 HAC t is 4.21299998761816. The return on lag variance HAC t is 5.821297519333921. The report also includes regime details, such as high variance, months, asset mean percentage, asset standard deviation percentage, managed mean percentage, and constant mean percentage. The report is based on a September 11 measurement, with a seed of 9110600 and 10,000 three-month blocks. The half Kelly is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A raw trade analysis shows the following results: the probability of a trade is 0.3958333333333333, the win-loss ratio is 1.3522593084813814, and the half Kelly, a measure of risk, is 0. The mean percentage change is -0.1926965098488086, the mean win percentage is 3.7820848328052525, and the mean loss percentage is 2.7968635964152626. For the monthly fund, the probability is 0.3958333333333333, the win-loss ratio is 0.9950112176946655, and the half Kelly is 0. The mean percentage change is -0.41831224196250916, the mean win percentage is 1.9791223119056855, and the mean loss percentage is 1.9890452255313262. The trade mean confidence interval percentage is between -1.2850815599380776 and 0.8218933427426802, and the fund month mean confidence interval percentage is between -1.1313987275638229 and 0.16416226109843754. The reasons for trades include time, stop, gap stop, and gap target, with 17, 17, 13, and 1 occurrences respectively. The target hit rate is 0.020833333333333332, and the target Wilson 95, a statistical measure, is between 0.003687015979399688 and 0.10899469137531158. There are 7 partials, and the R quantiles, a measure of distribution, are -2.6092980940474484, -1.6179430447683532, -1.0656732239049251, 1.9273516469378293, and 3.949226326987943. There are 2 same day exits, and the mean calendar days is 16.9375. This analysis is based on a September 11 measurement, with a seed of 9110600 and 10000 three-month blocks. The half Kelly is used as a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the following data: this is a test attribution for SPY with VM9110600. The attribution includes adapter, monthly, and constant data. The adapter data has a beta of 0.3658281530533049, an annual intercept percentage of 0.21389520013303392, and an intercept HAC t of 0.05950769478601343. The monthly data has a beta of 0.726197044773899, an annual intercept percentage of 0.1048559656535997, and an intercept HAC t of 0.04371354969810008. The constant data has a beta of 0.8622292759649755, an annual intercept percentage of -0.19278101213828217, and an intercept HAC t of -1.12765749917698. The monthly minus constant mean percentage per period is -0.20680714512999465, with a confidence interval of -0.535729602196267 to 0.0894571323059104. The lag variance current variance correlation is 0.12589747359306347, and the lag variance return correlation is 0.21632605611244757. The variance AR1 slope is 0.12595639463941583, with a HAC t of 2.0241208286219083. The return on lag variance HAC t is 6.073305062442768. There are two regimes: one with high variance set to false, lasting 4 months, and one with high variance set to true, lasting 40 months. The first regime has an asset mean percentage of 1.0060544854697928, an asset standard deviation percentage of 1.8212084671977171, a managed mean percentage of 1.0060544854697957, and a constant mean percentage of 0.8684800633728895. The second regime has an asset mean percentage of 1.8370843072604277, an asset standard deviation percentage of 3.7771545737167447, a managed mean percentage of 1.3266982254123052, and a constant mean percentage of 1.5679435272649902. This data is from a September 11 measurement, with a seed of 9110600 and 10000 three-month blocks. The half Kelly is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the raw trade data for the SPY test payoff on September 11, with the following results: the probability of success, or p, was 0.5227272727272727, the win-loss ratio was 1.6341679041739288, and the half-Kelly, a measure of risk, was 0.11533435563498673. The mean percentage change was 0.8496575984270752, the mean win percentage was 3.6834540486621963, and the mean loss percentage was 2.254024228020915. For the monthly fund, the probability of success was also 0.5227272727272727, the win-loss ratio was 1.5951202810316893, and the half-Kelly was 0.11175964318889042. The mean percentage change was 0.6789390601847992, the mean win percentage was 3.0374965453195437, and the mean loss percentage was 1.9042429473437303. The trade mean confidence interval percentage was between -0.20030333073272774 and 1.7850536727445152, and the fund month mean confidence interval percentage was between -0.20727291773851736 and 1.5200662730649657. The reasons for trade exits were time, with 23 occurrences, stop, with 16 occurrences, target, with 3 occurrences, and gap stop, with 2 occurrences. The target hit rate was 0.06818181818181818, and the target Wilson 95 percentage was between 0.023458970172840127 and 0.1822532133661719. There were 14 partial trades, and the R quantiles were -1.2272098014072719, -1.1146441017493367, 0.26140675161251115, 2.6522598623705926, and 2.752057449324053. The number of same-day exits was 3, and the mean calendar days was 20.272727272727273. This data is from a measurement on September 11, using a seed of 9110600 and 10000 three-month blocks. The half-Kelly is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the following data: The attribution for SPY VM9110600 train is given by an adapter with a beta of 0.17050328275811139, an annual intercept percentage of -0.9049935830372202, and an intercept HAC t of -0.27774951927366953. The monthly attribution has a beta of 0.41057788526456046, an annual intercept percentage of 1.2854794683225874, and an intercept HAC t of 0.37442046576822824. The constant attribution has a beta of 0.8593065649726743, an annual intercept percentage of 0.30374580930156947, and an intercept HAC t of 0.8977934151115974. The monthly minus constant mean percentage per period is -0.44335082305631435, with a confidence interval of -1.0634149130325823 to 0.2518089155975867. The correlation between lag variance and current variance is 0.23115616612963902, and the correlation between lag variance and return is 0.31209167315985015. The variance AR1 slope is 0.23087387245115004, with a HAC t of 4.81189563086068, and the return on lag variance HAC t is 10.151682889533964. There are two regimes: one with high variance being false, lasting 8 months, with an asset mean percentage of -1.6364667777597763, an asset standard deviation percentage of 3.2061977953124305, a managed mean percentage of -1.7021095306461467, and a constant mean percentage of -1.413434960764072; and one with high variance being true, lasting 40 months, with an asset mean percentage of 1.7488727381460831, an asset standard deviation percentage of 5.85482275010421, a managed mean percentage of 1.0709399822341228, and a constant mean percentage of 1.545226055925285. This data is from a September 11 measurement, with a seed of 9110600, using 10,000 three-month blocks, and a half Kelly binary risk unit proxy, which is a measure of risk, not an allocation, with log wealth sizing separate, and HAC regressions being descriptive, where HAC stands for Heteroskedasticity and Autocorrelation Consistent. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A news scan found the raw trade data for SPY VM9110600, which includes the following information: the probability of a trade being successful is 0.4583333333333333, the win-loss ratio is 1.6422006430731866, and the half Kelly, a measure of binary risk, is 0.0642456903683622. The mean percentage change is 0.6203464233967758, the mean win percentage is 4.82792246328063, and the mean loss percentage is 2.939910225735716. The monthly fund data shows a probability of 0.4583333333333333, a win-loss ratio of 1.3731991650539566, and a half Kelly of 0.031938660543686215. The mean percentage change is 0.13666895407288643, the mean win percentage is 2.139553627897892, and the mean loss percentage is 1.5580796160867338. The trade mean confidence interval percentage is between -0.5921222249902173 and 1.8089380745043324, and the fund month mean confidence interval percentage is between -0.3950856121951117 and 0.6412495512369847. The reasons for trades include time, with 22 occurrences, stop, with 20 occurrences, and gap stop, with 6 occurrences. The target hit rate is 0, and the target Wilson 95, a statistical measure, is between 0 and 0.0741026511527422. There were 6 partials, and the R quantiles, a measure of distribution, are -2.153333569062762, -1.1589679507095927, -0.85697015822385, 2.16908579154269, and 2.6504742480578076. There were 3 same-day exits, and the mean calendar days is 18.895833333333332. This data is from a September 11 measurement, with a seed of 9110600, and 10000 three-month blocks. The half Kelly is a binary risk unit proxy, not an allocation, and log wealth sizing is separate. HAC regressions are descriptive. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

The current price of GLD is 396.36. The Average True Range, or a measure of price volatility, over the past 20 days up to September 10 is 7.92. The annualized percentage return over the past 20 days is 28.26%. The return over the past 63 days is 5.81%. The price is currently below the Simple Moving Average, or average price, over the past 200 days, which is 415.81. As of September, the weight, or importance, given to this data is 0.20. The total number of whole shares held at the close of trading is 0. This analysis was measured on September 11. The analysis uses a frozen stress primary model with a seed value of 9110600 and 10,000 three-month blocks of data. It also uses daily and monthly statistical models. The half Kelly criterion is used as a binary risk unit proxy, which means it is used to measure risk, but not to determine the location of positions. The empirical log wealth sizing is recorded separately. The whole share figures are based on the entire fund value of 187.22 at the close of trading on September 10, not on the quote or order price. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

The current price of TLT is 80.78. The Average True Range over 20 days up to September 10 is 0.7003750000000011. The annual percentage return over 20 days is 10.903027184382665. The return over 63 days is -3.730187105231786 percent. The price is below the Simple Moving Average over 200 days, which is 84.52709999999999. The weight for September is 1. The number of whole shares at the close is 2. This data was measured on September 11. The analysis uses a frozen stress primary model with a seed of 9110600 and 10,000 three-month blocks of confidence intervals. The daily and monthly descriptive statistics are calculated using ordinary least squares with heteroskedasticity and autocorrelation consistent standard errors. The half Kelly criterion is used as a binary risk unit proxy, not as a positional location. Empirical log wealth sizing is recorded separately. The whole share figures are based on the entire 187.22 fund at the September 10 close, not on a quote or order. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

The current analysis of EEM, specifically the VM9110600, shows the following indicators: the close price is 67, the ATR20 through September 10 is 0.9323899999999987, the RV20 annual percentage is 19.12744178908615, and the return over 63 days is 4.150474117829939 percent. The price is above the Simple Moving Average of 200 days, which is 61.681549999999994. The weight for September is 0.9781921385095332. The whole shares at close are 2. This analysis was measured on September 11. The methodology includes frozen stress primary, seed 9110600, and 10,000 three-month blocks with confidence intervals. Daily and monthly statistical models are used for descriptive purposes. The half Kelly measure is a binary risk-unit proxy, not a positional location, and empirical log wealth sizing is recorded separately. Whole-share figures are based on the entire 187.22 fund at the September 10 close, not on quotes or orders. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

The current analysis of EFA as of September 10 shows a close price of 105.66. The Average True Range over 20 days, or ATR20, is 0.8319, which is a measure of price volatility. The realized volatility over 20 days, or RV20, is 9.8477 percent per year. The return over 63 days is 5.6706 percent. The price is above the Simple Moving Average over 200 days, or SMA200, which is 100.58775. The weight for September is 1. The whole shares at close is 1. This analysis was measured on September 11. The analysis uses a frozen stress primary model with a seed of 9110600 and 10,000 three-month blocks of data, which is a type of statistical simulation. The daily and monthly calculations use a type of statistical method called ordinary least squares, or OLS, with a HAC5 and HAC3 correction, which helps to account for errors in the data. The half Kelly method is used as a binary risk unit proxy, which means it is used to estimate risk, but not to determine the location of positions. The empirical log wealth sizing is recorded separately. The whole share figures are based on the entire 187.22 fund at the September 10 close, not on individual quotes or orders. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

The current analysis of SPY as of September 10 shows a close price of 757.83. The Average True Range over 20 days, known as ATR20, is 5.37, and the annual percentage return over 20 days, or RV20_ann_pct, is 8.54%. The return over 63 days, or return63_pct, is 4.73%. The stock is currently above its Simple Moving Average over 200 days, or SMA200, which is 711.49. The weight for September is 1. The whole shares at close are 0. This analysis was measured on September 11 and uses a frozen stress primary model with a seed of 9110600 and 10,000 three-month blocks of data. The analysis includes daily Ordinary Least Squares with a 5-day lag, known as HAC5, and monthly HAC3, which are descriptive statistics. The half_Kelly measure is used as a binary risk-unit proxy, not to determine positional location. Empirical log wealth sizing is recorded separately. Whole-share figures are based on the entire fund value of 187.22 at the September 10 close, not on quote or order data. https://runway.adityaperswal.workers.dev/data/stock-bars ; research/inference.py

A data scan found the following information about the GLD fund: it has 2687 data points from January 4, 2016, to September 10, 2026. The data was calibrated over 36 months. The constant weight is 0.8917 and the last close price was 396.36. The last Average True Range over 20 days, which is a measure of price volatility, was 7.84. The weight for September is 0.2001 and the volatility percentage for August was 26.66. The average daily volume over 20 days in US dollars was 4,973,342,987.13. This data was retrieved on September 11, but the request was for data starting from 2010, however the actual start date was 2016. The cash yield is 0, and the calibration months are 36. The last Average True Range over 20 days is lagged through September 9 for the September 10 bar, and the next signal will be recorded separately through September 10. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following information about TLT: it has 2687 data points from January 4, 2016, to September 10, 2026. The calibration period is 36 months. The constant weight is 0.8995558166540826 and the last close price is 80.78. The last Average True Range over 20 days, or ATR20, which is a measure of price volatility, is 0.6681250000000013. The weight for September is 1 and the August volatility percentage is 10.372170146127225. The average daily volume over 20 days in US dollars, or ADV20_USD, is 2463810528.2904997. This data was retrieved on September 11, but the actual data starts from 2016, not 2010 as requested. The cash yield is 0 and the last ATR20 is lagged through September 9 for the September 10 bar. The next signal ATR is recorded through September 10 separately. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following information about EEM: it has 2687 data points from January 4, 2016, to September 10, 2026. The data was calibrated over 36 months. The constant weight is 0.8604532246408725 and the last close price is 67. The last Average True Range over 20 days, or ATR20, which is a measure of price volatility, is 0.918389999999998. The weight for September is 0.9781921385095332 and the volatility percentage for August is 18.960002901777134. The average daily volume over 20 days in US dollars, or ADV20_USD, is 1103679135.292. This data was retrieved on September 11, but the request was for data starting from 2010, however the actual start date was 2016. The yield is 0, which means there is no return when an investment is made with cash. The 36 calibration months were used to adjust the data. The last ATR20 value is from September 9 and will be used for the September 10 data point, and the next signal will use the ATR20 value through September 10, which will be recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars

A data scan found the following information about EFA: it has 2687 data points from January 4, 2016, to September 10, 2026. The calibration period is 36 months. The constant weight is 0.8847 and the last close price is 105.66. The last Average True Range over 20 days, or ATR20, which is a measure of price volatility, is 0.8146. The weight for September is 1 and the volatility percentage for August is 8.704. The average daily volume over 20 days in US dollars, or ADV20_USD, is 1,000,717,243.89. This data was retrieved on September 11, but the actual data starts from 2016, not 2010 as requested. The cash yield is 0, and the last ATR20 value is from September 9, used for the September 10 bar, and the next signal will use the ATR through September 10, recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the data line: {"bars":2687,"start":"2016-01-04 00:00:00","end":"2026-09-10 00:00:00","calibration_months":36,"c":0.00006542470079181939,"constant_weight":0.8642180358388333,"last_close":757.83,"last_ATR20":5.2983599999999855,"September_weight":1,"August_vol_pct":10.053171904935972,"ADV20_USD":28341302788.4875}; this is a data set for the SPY fund, which is a type of investment fund that tracks a stock market index, in this case the S&P 500. The data covers 2687 bars, or periods of time, from January 4, 2016, to September 10, 2026. The calibration period, which is the time used to adjust the model, is 36 months. The last closing price was 757.83 and the last average true range, or ATR20, which measures price volatility, was 5.2983599999999855. The data also includes a constant weight of 0.8642180358388333, a September weight of 1, and an August volatility percentage of 10.053171904935972. The average daily volume, or ADV20, in US dollars was 28341302788.4875. A note says SIP, or a type of investment plan, was retrieved on September 11, and the data was requested for 2010 but actually starts in 2016, with a cash yield of 0. The last ATR20 value is lagged, or delayed, through September 9 for the September 10 bar, and the next signal ATR is recorded separately through September 10. https://runway.adityaperswal.workers.dev/data/stock-bars

A test of the GLD adapter model from January 3, 2023, to August 31, 2026, showed a return of 37.26 percent, with a maximum drawdown of 9.46 percent. The compound annual growth rate, or CAGR, which is a measure of an investment's annual growth rate, was 9.08 percent. The Sharpe ratio, which is a measure of risk-adjusted return, was 1.19. The annualized volatility, or ann_vol, which is a measure of the investment's risk, was 7.56 percent. There were 918 trading sessions and 44 months in the test period. The win rate, which is the percentage of winning trades, was 52.27 percent. A total of 44 trades were made, with 52.27 percent of months being positive. The average trade return was 1.16 percent, with an average win of 4.65 percent and an average loss of 2.66 percent. The mean exposure, which is the average amount of capital invested, was 0.33. The test also showed a vectorbt max error, which is a measure of the model's accuracy, of 3.64e-12. A total of 104 orders were made. The test started on January 3, 2023, and ended on August 31, 2026. This was a measurement taken on September 11, using a frozen 2230 SIP, with a fee of 1 basis point, and fractional normalization, with daily proxy. https://runway.adityaperswal.workers.dev/data/stock-bars

A trading model called an adapter for the symbol GLD had the following results: it made 8.594624456479094 percent return, with a drawdown of 12.863200967894684 percent, and a compound annual growth rate, or CAGR, of 2.0826844150378054 percent. The Sharpe ratio, which is a measure of risk, was 0.2938675447944952, and the annual volatility was 8.14649785275333 percent. Over 1008 sessions and 48 months, the model had a win rate of 47.91666666666667 percent, with 48 trades, and 47.91666666666667 percent of months being positive. The mean trade was 0.39022782352469954 percent, with an average win of 3.394105559086327 percent and an average loss of 2.373339693191998 percent. The model had a mean exposure of 0.3765074530558196, and a vectorbt max error of 3.637978807091713e-12, with 108 orders. The trading period was from 2019-01-02 to 2022-12-30. This was measured on Sep 11, with a frozen value of 2230, using a system called SIP, with a fee of 1 basis point, or 1/100th of a percent, and fractional normalization, with daily proxy data. https://runway.adityaperswal.workers.dev/data/stock-bars

A test of the GLD adapter model, which is a type of computer program that helps make investment decisions, found the following results: the symbol being traded was GLD, the model being used was an adapter, and the sample being tested was a test sample. The test had a slippage of 5 basis points, which is a measure of the difference between the expected price of a trade and the actual price, and a c multiplier of 1, which is a factor that affects the model's decisions. The return on investment was 43.29%, the drawdown, which is the largest decline in value, was 8.48%, and the compound annual growth rate, or CAGR, was 10.38%. The Sharpe ratio, which is a measure of risk-adjusted return, was 1.36, and the annual volatility, which is a measure of how much the value of the investment changes, was 7.48%. The test ran for 918 sessions, or periods of trading, over 44 months, and had a win rate of 52.27%. There were 44 trades made, and 52.27% of months had a positive return. The average return per trade was 1.33%, the average win was 4.80%, and the average loss was 2.47%. The mean exposure, which is the average amount of money invested, was 0.33. The test also found a maximum error of 3.64e-12, which is a very small number, and there were 104 orders, or trades, made. The test started on January 3, 2023, and ended on August 31, 2026. This test was measured on September 11, and used a frozen dataset from 2230, with a system of SIP, or systematic investment plan, and a fee of 1 basis point, with fractional and normalized daily proxy data. https://runway.adityaperswal.workers.dev/data/stock-bars