Notes

A post-result endpoint audit found that the system was not authorized to execute an auction. Instead, it used the official daily close, keeping entries and splits, for a calculation on September 7. The results are shown in a table with columns including one-way break-even point, return, drawdown, win rate, sample, average win percentage, average loss percentage, mean trade percentage, quarter Kelly, annualized percentage, and cash reconcile absolute. The rows of the table show the following data: for a value of 0, the results were a return of -6.5081536976, a drawdown of 8.492818411, a win rate of 47.202797203, a sample of 572, an average win percentage of 0.17183768569, an average loss percentage of 0.18272385301, a mean trade percentage of -0.011208074098, a quarter Kelly of 0, an annualized percentage of -1.3507448627, and a cash reconcile absolute of 3.6379788071e-12. For a value of 2, the results were a return of -25.628394209, a drawdown of 25.924706887, a win rate of 36.713286713, a sample of 572, an average win percentage of 0.17468564449, an average loss percentage of 0.18223166994, a mean trade percentage of -0.051195592964, a quarter Kelly of 0, an annualized percentage of -5.8081525416, and a cash reconcile absolute of 7.7307049651e-11. For a value of 10, the results were a return of -70.218895005, a drawdown of 70.218895005, a win rate of 9.965034965, a sample of 572, an average win percentage of 0.31171491901, an average loss percentage of 0.26883814619, a mean trade percentage of -0.21098583025, a quarter Kelly of 0, an annualized percentage of -21.712698053, and a cash reconcile absolute of 9.6406438388e-11. https://runway.adityaperswal.workers.dev/data/stock-bars

A post-result endpoint audit found that the auction execution was not authorized. The system uses the official daily close instead of the final 30-minute bar close, and it keeps entries and splits. This calculation was done on September 7. The results are shown in a table with the following columns: one-way break-even point, return, drawdown, win rate, sample size, average win percentage, average loss percentage, mean trade percentage, quarter Kelly, annualized percentage, and cash reconcile absolute. The rows of the table show the following values: for a value of 0, the results are 3.614048323 for return, 8.0108632829 for drawdown, 48.714285714 for win rate, 700 for sample size, 0.17340308307 for average win percentage, 0.16099559947 for average loss percentage, 0.0055842295831 for mean trade percentage, 0.0027455252421 for quarter Kelly, 0.72003694843 for annualized percentage, and 3.6379788071e-11 for cash reconcile absolute. For a value of 2, the results are -21.690186964 for return, 24.218820056 for drawdown, 38.571428571 for win rate, 700 for sample size, 0.17355324502 for average win percentage, 0.16499158037 for average loss percentage, -0.034410004862 for mean trade percentage, 0 for quarter Kelly, -4.8208452351 for annualized percentage, and 9.5496943686e-11 for cash reconcile absolute. For a value of 10, the results are -74.449093243 for return, 74.461302606 for drawdown, 11 for win rate, 700 for sample size, 0.29467493862 for average win percentage, 0.25465316951 for average loss percentage, -0.19422707762 for mean trade percentage, 0 for quarter Kelly, -24.099313634 for annualized percentage, and 9.231371223e-11 for cash reconcile absolute. https://runway.adityaperswal.workers.dev/data/stock-bars

A post-result endpoint audit found that the system was not authorized to execute an auction. Instead, it used the official daily close, rather than the final 30-minute bar close, and kept the entries and splits from the September 7 calculation. The columns analyzed included one-way break-even point, return, drawdown, win rate, sample size, average win percentage, average loss percentage, mean trade percentage, quarter Kelly, annualized percentage, and cash reconciliation absolute value. The results were as follows: for a value of 0, the results were -7.2470431972 for return, 9.5499394267 for drawdown, 48.245614035 for win rate, 684 for sample size, 0.16202696375 for average win percentage, 0.17838920895 for average loss percentage, -0.010502095036 for mean trade percentage, 0 for quarter Kelly, -1.5087995429 for annualized percentage, and 2.0008883439e-11 for cash reconciliation absolute value. For a value of 2, the results were -29.44873707 for return, 29.46750534 for drawdown, 37.134502924 for win rate, 684 for sample size, 0.16404366993 for average win percentage, 0.17721437486 for average loss percentage, -0.050489896238 for mean trade percentage, 0 for quarter Kelly, -6.8066141922 for annualized percentage, and 9.0039975476e-11 for cash reconciliation absolute value. For a value of 10, the results were -76.383645336 for return, 76.383645336 for drawdown, 9.649122807 for win rate, 684 for sample size, 0.29069760949 for average win percentage, 0.26378385963 for average loss percentage, -0.21028126173 for mean trade percentage, 0 for quarter Kelly, -25.297399792 for annualized percentage, and 9.0949470177e-11 for cash reconciliation absolute value. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was made using a statistical method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for a set of data. This calculation looked at the return on investment for the TLT fund over the last half hour and the rest of the day. The results are based on a method called HAC5, which is a type of statistical test, and show a descriptive association, meaning they describe a relationship, but do not imply causation. The returns are given as decimal values. The results are as follows: for the TLT fund, with 170 data points, the intercept and first return had a beta of -0.000035467817224784146 and 0.002881191988335393, respectively, with HAC5 t-values of -0.43988466029270523 and 0.17845078982475207, and an R2 value of 0.0002058620007591161. Another calculation for the TLT fund, also with 170 data points, looked at the intercept, first return, and 12th return, and found beta values of -0.00003503362099835063, 0.002779649248119993, and -0.022034903656255284, with HAC5 t-values of -0.4382212061073656, 0.17218994894517625, and -0.3039588866198136, and an R2 value of 0.0008390464066386771. A third calculation for the TLT fund, with 170 data points, looked at the intercept and rest of day return, and found beta values of -0.00003448571699550978 and 0.008816427816437475, with HAC5 t-values of -0.42999529499548744 and 0.6633577356506899, and an R2 value of 0.003025284165777631. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was made using a method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for a set of data. This calculation looked at the return on investment for the TLT symbol, which is a type of bond, over the last half hour and the rest of the day. The results showed a descriptive association, which means it describes a relationship but does not imply cause and effect, between the returns. The returns are shown as decimal numbers. The calculation was done three times with different sets of data, and the results are as follows: for the first set, the intercept, or starting point, was 0.000248959387413663 and the beta, or measure of volatility, for the first return was 0.02359578057256328, with a HAC5 t-value, which is a measure of statistical significance, of 4.628045478418234 and 3.306330830166599, respectively, and an R2, or measure of how well the model fits the data, of 0.012466878925200042, and a correlation between the first return and the last return of 0.1116551786761366. For the second set, the intercept was 0.0002476156188481961, the beta for the first return was 0.02364430208445091, and the beta for the 12th return was 0.056441261545553505, with HAC5 t-values of 4.586398758528841, 3.298463752957348, and 1.1046236516478027, respectively, and an R2 of 0.014622877250393573, and the same correlation as before. For the third set, the intercept was 0.00025177431556341044, the beta for the return on the rest of the day was 0.02636233138984397, with HAC5 t-values of 4.682144620096881 and 4.479883138698519, respectively, and an R2 of 0.024079154323103924, and the same correlation as before. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: on September 7, a calculation was performed using ordinary least squares, or OLS, which is a method to find the best fit line, to analyze the last half hour return on the first return and/or the 12th return for the rest of the day. The results used HAC5, which stands for a statistical method to calculate standard errors, to show descriptive association, not causation. The returns are given as decimal numbers. The results for the TLT symbol, which represents a type of investment, are as follows: for the train split, which is a part of the data, with 1244 data points, the columns analyzed were intercept and r1, which refers to the first return, and the beta values, which measure the change, were 0.00004360245005053721 and -0.013059649928567042, with HAC5 t-values of 0.8423265376344887 and -0.5120607995475798, and an R2 value, which measures the goodness of fit, of 0.002468234035397643. The correlation between r1 and the last return was -0.04968132481524003. Another analysis with columns intercept, r1, and r12, which refers to the 12th return, had beta values of 0.00004125964744352307, -0.014882731457980625, and -0.05213426640036488, with HAC5 t-values of 0.7847925821060477, -0.6330631969103219, and -0.4372889290961637, and an R2 value of 0.004993636529019296. A third analysis with columns intercept and rod, which refers to the rest of the day, had beta values of 0.000032547142905647357 and 0.02740994507041745, with HAC5 t-values of 0.5514953420360819 and 1.1908025231207757, and an R2 value of 0.016747267682585942. https://runway.adityaperswal.workers.dev/data/stock-bars

On September 7, a calculation was done using a method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for some data. This calculation looked at the return of the EFA stock over the last half hour and the first return and/or the 12th return for the rest of the day. The HAC5 method was used, which is a way to correct for problems with the data, and it showed a descriptive association, not a cause-and-effect relationship. The returns were given as decimal numbers. The results were as follows: for the first calculation, the intercept was 0.00015170849876177867 and the beta for the first return was 0.03658996323586186, with a HAC5 t-value of 1.1529085227867857 and 2.884724129409552, an R2 value of 0.04861774586731127, and a correlation between the first return and the last return of 0.22049432162146704. For the second calculation, the intercept was 0.00014556836680757729, the beta for the first return was 0.03840888674023462, and the beta for the 12th return was 0.13304567227240102, with HAC5 t-values of 1.1250293504118587, 3.0899130124802117, and 1.6004161865311206, an R2 value of 0.07568781471580954, and the same correlation. For the third calculation, the intercept was 0.0001564316243796275 and the beta for the rest of the day was 0.025416704846470205, with HAC5 t-values of 1.2269028120568617 and 1.8060868682511708, an R2 value of 0.0284113043429689, and the same correlation. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was made using Ordinary Least Squares, or OLS, which is a method to find the best fit line. The calculation looked at the return on the first return and/or the 12th return for the rest of the day, for the last half hour. This used HAC5, which is a statistical method to calculate standard errors, to find a descriptive association, not a cause-and-effect relationship. The returns are given as decimal numbers. The results for the symbol EFA, which is a stock market ticker, in the holdout set, which is a part of the data not used for training, are as follows: for the first model, the intercept was 0.00006689610017491031 and the beta for the first return was -0.001546302068832937, with a HAC5 t-value of 1.503591315437261 and -0.174465164833615, and an R2 value of 0.000057569478913999106. For the second model, the intercept was 0.00006647647062517855, the beta for the first return was -0.0011561300853593882, and the beta for the 12th return was 0.08983520315772228, with HAC5 t-values of 1.4936961304155791, -0.13156560478161847, and 1.6709349852506306, and an R2 value of 0.006664019267157539. For the third model, the intercept was 0.00006422681531507994 and the beta for the rest of the day was 0.006965383680357814, with HAC5 t-values of 1.4300355424297628 and 1.0343336252494053, and an R2 value of 0.001827527257958761. The correlation between the first return and the last return was -0.007587455364873097 for all models. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was performed using Ordinary Least Squares, or OLS, which is a method to find the best fit line. The calculation looked at the return on the first return and/or the 12th return for the rest of the day, with the last half hour return. This used HAC5, which is a statistical method to calculate standard errors, to find descriptive association, not causation. The returns were in decimal form. The results were as follows: for the symbol EFA, which is a stock ticker, in the train split, with 1247 data points, the columns included the intercept, which is a starting point, and r1, which is the first return, with beta values of 0.00006564872991631818 and 0.024224035523706743, and HAC5 t values of 1.1669704765137179 and 1.4991421711960247, with an R2 value of 0.012101133557348542, and a correlation of 0.11000515241273141. Another calculation for EFA in the train split with 1247 data points included the columns intercept, r1, and r12, which is the 12th return, with beta values of 0.00006390020005458628, 0.02379356088473252, and 0.054938540214414966, and HAC5 t values of 1.1388856980901652, 1.515158307983134, and 0.5561713662900177, with an R2 value of 0.013414668404556296, and a correlation of 0.11000515241273141. A third calculation for EFA in the train split with 1247 data points included the columns intercept and rod, which is the rest of the day return, with beta values of 0.00006189093248617362 and 0.022569496216873777, and HAC5 t values of 1.1003262009401087 and 1.8439993952355738, with an R2 value of 0.014810817198015358, and a correlation of 0.11000515241273141. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was done using a statistical method called Ordinary Least Squares, or OLS for short, which is a way to find the best fit line for a set of data. This calculation looked at the return on investment for the first half hour and the 12th return for the rest of the day for the ticker symbol EEM. The results showed a descriptive association, which means it describes a relationship but does not imply cause and effect, and used a method called HAC5, which is a way to correct for errors in the data. The returns were given as decimal numbers. The results were as follows: for the first set of data, the intercept was 0.0002861148274201484 and the beta, which is a measure of volatility, was 0.03676187782773016, with a HAC5 t-value of 1.5641244738118045 and 3.985971552599923, and an R2 value, which measures how well the model fits the data, of 0.060628292786570115. For the second set of data, the intercept was 0.00027039094117163625, the beta for the first return was 0.03582097377699645, and the beta for the 12th return was 0.16915325101536732, with HAC5 t-values of 1.4846553231311133, 3.9378278067207018, and 1.4498881961158447, and an R2 value of 0.10155580156310651. For the third set of data, the intercept was 0.0002767828597448005 and the beta for the rest of the day was 0.024845801007632114, with HAC5 t-values of 1.5090513405288424 and 2.572825417830551, and an R2 value of 0.035618870441375905. All three sets of data had a correlation coefficient, which measures the strength of the relationship, of 0.24622813159054335 for the first return and the last return. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was made using a statistical method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for a set of data. This calculation looked at the return on investment for the EEM stock, which is a type of investment fund, over the last half hour and the first return and/or the 12th return for the rest of the day. The results showed a descriptive association, which means a relationship between the variables, but not causation, which means one variable does not directly cause the other. The returns were measured in decimal form. The results were as follows: for the first model, the intercept was 0.000027520262844514123 and the beta for the first return was -0.016425440843982796, with a HAC5 t-value of 0.6524754473441092 and -2.647112954973954, respectively, and an R2 value of 0.011241342209811322. For the second model, the intercept was 0.000028772240271428708, the beta for the first return was -0.016650617463735168, and the beta for the 12th return was 0.0987313213264456, with HAC5 t-values of 0.684652363094179, -2.787482416580704, and 1.8305478867135405, respectively, and an R2 value of 0.019659104293779373. For the third model, the intercept was 0.000029302502439510615 and the beta for the rest of the day was -0.010275817625934634, with a HAC5 t-value of 0.6893282160347943 and -1.8918356444343836, respectively, and an R2 value of 0.006154723187208444. The correlation between the first return and the last return was -0.1060251961083373 for all models. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was done using a method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for a set of data. This calculation looked at the return on investment for the first half hour and the 12th return for the rest of the day for the ticker symbol EEM. The results are based on a statistical method called HAC5, which is used to understand how things are related, but not to prove that one thing causes another. The returns are given as decimal numbers. The calculation found the following results for the train set: for the first model, the intercept was -0.000003719475741544989 and the beta for the first return was 0.024879848934631934, with an R2 of 0.016324647057750385 and a correlation between the first return and the last return of 0.12776794221458834. For the second model, the intercept was -0.0000037626375105604467, the beta for the first return was 0.025036835884500095, and the beta for the 12th return was -0.027746219313515622, with an R2 of 0.016769720994585957 and the same correlation. For the third model, the intercept was -0.000007865558264136548 and the beta for the rest of the day was 0.02293508205197672, with an R2 of 0.019333044242150987 and the same correlation. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was done using a statistical method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for a set of data. This calculation looked at the return on the SPY stock, which is a type of investment fund, over the last half hour and the rest of the day. The calculation also used a method called HAC5, which is a way to account for variability in the data. The results show the relationship between the returns, but do not imply cause and effect. The returns are shown as decimal numbers. The results are as follows: for the SPY stock, with 170 data points, the intercept, or starting point, and the first return, had a beta, or measure of change, of 0.00003808084305584512 and 0.014347746370045647, with a HAC5 t-value, which is a measure of statistical significance, of 0.26803135899371444 and 0.7432753321333443, and an R2, which is a measure of how well the model fits the data, of 0.0029154516431361133. Another calculation for the SPY stock, also with 170 data points, looked at the intercept, the first return, and the 12th return, and found a beta of 0.000048481192446250334, 0.014296370570782741, and 0.16631125933884194, with a HAC5 t-value of 0.3581974435416352, 0.7559567718035617, and 1.4547490415999824, and an R2 of 0.036419022559405545. A third calculation for the SPY stock, again with 170 data points, looked at the intercept and the rest of the day return, and found a beta of 0.00004692422136098132 and -0.0007509498546889387, with a HAC5 t-value of 0.3326586141235977 and -0.05226310036364114, and an R2 of 0.000014530645656707364. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was done using a statistical method called Ordinary Least Squares, or OLS, which is a way to find the best fit line for a set of data. This calculation looked at the return on the SPY stock, which is a type of investment fund, for the last half hour of the day and the first return and/or the 12th return for the rest of the day. The results are based on a method called HAC5, which is a way to calculate the standard error of the estimates, and are meant to show descriptive association, not causation, meaning they show how things are related, but not why. The returns are given as decimal numbers. The results are as follows: for the SPY stock, with a holdout sample size of 1245, the intercept and first return had a beta of -0.00006729389716341411 and -0.028105691953958897, respectively, and the HAC5 t-statistic was -0.8909465843894513 and -1.834869227583961, respectively, with an R2 of 0.005556990525373173, and the correlation between the first return and the last return was -0.07454522469865617. Another model with the same sample size and stock had a beta of -0.00006476604877979231, -0.0292262233320651, and 0.15329976633523298 for the intercept, first return, and 12th return, respectively, with an HAC5 t-statistic of -0.8516001675972809, -1.8963084233396355, and 2.621378055210914, respectively, and an R2 of 0.020447536297419022. A third model had a beta of -0.00008002213694805797 and 0.00021320619319778705 for the intercept and rest of day return, respectively, with an HAC5 t-statistic of -1.0629354094239696 and 0.020639414279396733, respectively, and an R2 of 6.940989014658783e-7. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following data: On September 7, a calculation was performed using a method called Ordinary Least Squares, or OLS, which is a statistical technique, to analyze the return on the SPY stock, which is a type of investment fund. The analysis looked at the relationship between the first half hour return and the rest of the day return, and also between the 12th return and the rest of the day return. The results are based on a type of statistical analysis called HAC5, which is used to understand how things are related, but does not imply cause and effect. The returns are given as decimal numbers. The results include three different models, which are sets of data used to make predictions. The first model has 1247 data points and includes two columns of data, the intercept and r1, which is a type of return. The beta values, which measure the relationship between the variables, are -0.000033508040212143604 and 0.02043805135151162. The HAC5 t-values, which are used to determine the significance of the results, are -0.3456095698171475 and 0.5486734061328367. The R2 value, which measures how well the model fits the data, is 0.0019336165159477003. The correlation between r1 and the last return is 0.04397290661245747. The second model also has 1247 data points and includes three columns of data, the intercept, r1, and r12, which is another type of return. The beta values are -0.00003298000458315649, 0.020917773476934593, and -0.02012096380120869. The HAC5 t-values are -0.3404841895984766, 0.5771174076549123, and -0.13479911669226102. The R2 value is 0.0020661626602445082. The correlation between r1 and the last return is 0.04397290661245747. The third model has 1247 data points and includes two columns of data, the intercept and rod, which is another type of return. The beta values are -0.0000342357465678499 and 0.015960183350506335. The HAC5 t-values are -0.3611846308855028 and 0.6214827498356522. The R2 value is 0.002360208293866073. The correlation between r1 and the last return is 0.04397290661245747. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following statistics for TLT, calculated on September 7, 2026, using a source of SIP adjusted 30-minute and daily history from 2016 to September 4, 2026. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows use a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes, which refers to extreme price movements within a single bar or period of time. The results include a mean trade confidence interval of -0.1729% to -0.1406%, a quarter Kelly value of 0, an actual size of 0, and 24-calendar day profit and loss percentages of -4.11%, -2.31%, and -0.06% at the 5th, 50th, and 95th percentiles, respectively. The 24-calendar day loss percentage is 95.24%, the 24-calendar day profit greater than or equal to 349 percentage is 0, and the 24-calendar day 95th percentile closed wealth drawdown percentage is 0.48%. The gross mean basis points are 4.30, the approximate break-even one-way basis points are 2.15, and the training range median is 0.0024. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following statistics for TLT, calculated on September 7, 2026, using adjusted 30-minute and daily history from 2016 to September 4, 2026. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows with a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes. The results include a 95% confidence interval for the mean trade of -0.1681146097236288 to -0.13828876646560911, and a 24-calendar-day profit and loss range of -4.800172668508367 to -0.25427636878305293. The statistics also show a 96.225% loss percentage, and a 0.5715208861203802% maximum drawdown percentage. The gross mean basis points are 4.625616330249153, and the approximate break-even one-way basis points are 2.3128081651245767. https://runway.adityaperswal.workers.dev/data/stock-bars

These statistics for TLT were calculated on September 7, 2026, using adjusted historical data from 2016 to September 4, 2026. The results are based on 20,000 circular 20-session block draws for mean confidence interval, and 24-calendar-day historical windows with a hypothetical 25% account allocation, excluding fixed costs and extreme price movements within a trading session, known as intrabar extremes. This analysis does not provide forward probabilities, which are predictions of future outcomes. The statistics include a mean trade confidence interval of -0.17334109292049046 to -0.14299331859973335 percent, and a 24-calendar-day historical window with a 5th percentile profit or loss of -4.551086971763766 percent, a 50th percentile of -2.5482532603531354 percent, and a 95th percentile of -0.2635976089085261 percent. The analysis also shows a 96.34 percent loss rate over 24 calendar days, and a 0 percent chance of profit greater than or equal to 349 percent over the same period. The gross mean basis points, a measure of return, is 4.162266587971378, and the approximate break-even point, the point at which the trade neither makes nor loses money, is 2.081133293985689 basis points one way. The results are also broken down into four buckets: range high with 446 occurrences and a mean trade percentage of -0.15067437507511128, range low with 126 occurrences and a mean trade percentage of -0.1851136375019941, r1 r12 agree with 263 occurrences and a mean trade percentage of -0.15834480070363313, and r1 r12 disagree with 309 occurrences and a mean trade percentage of -0.15818901949415978. The training range median, a measure of the middle value of a set of data, is 0.0024143179618283496. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the headline 'EFA QIM90708 statistics pdt'. Calculated on September 7, 2026, this data is based on a source that adjusts for SIP, using 30-minute and daily history from 2016 to September 4, 2026. The future outcomes are unknown. The statistics were calculated using 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows used a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes, which refers to extreme price movements within a single bar or period of time. The results are as follows: the mean trade confidence interval at 95% is between -0.20404990350856747 and -0.1760966801969343, the quarter Kelly is 0, and the actual size is 0. The 24-calendar-day profit and loss statistics show a 5th percentile of -4.738713440380593, a 50th percentile of -2.871308013350472, and a 95th percentile of -0.8741709899459238. The 24-calendar-day loss percentage is 99.455, and the 24-calendar-day profit greater than or equal to 349 percentage is 0. The 24-calendar-day 95th percentile closed wealth drawdown percentage is 0.5568471357337401. The gross mean basis point is 0.9560875359419461, and the approximate break-even one-way basis point is 0.47804376797097303. The buckets are empty, and the training range median is 0.0023747841105353. https://runway.adityaperswal.workers.dev/data/stock-bars

These statistics were calculated on September 7, 2026, using adjusted historical data from 2016 to September 4, 2026. The source is a 30-minute and daily history from the SIP. The future outcomes are unknown. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval. A 24-calendar-day historical window was used with a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes. This does not include forward probabilities. The symbol is EFA and the rule is rod. The number of paths is 20,000 and the block days is 20. The mean trade confidence interval at 95% is between -0.1995826954672361 and -0.1766430093881681. The quarter Kelly is 0 and the actual size is 0. The 24-calendar-day profit and loss percentages at 5%, 50%, and 95% are -5.667457212872705, -3.357957427076485, and -1.3934756038320302. The 24-calendar-day loss percentage is 100 and the 24-calendar-day profit greater than or equal to 349 percentage is 0. The 24-calendar-day 95% closed wealth drawdown percentage is 0.6680873272140508. The gross mean basis points is 1.1499794356090014 and the approximate break-even one-way basis points is 0.5749897178045007. The training range median is 0.0023747841105353. https://runway.adityaperswal.workers.dev/data/stock-bars

These statistics for EFA were calculated on September 7, 2026, using adjusted historical data from 2016 to September 4, 2026. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows with a hypothetical 25% account allocation, excluding fixed costs and extreme price movements within a trading period, which is called an intrabar extreme. The results include a 95% confidence interval for the mean trade of -0.202469804454124% to -0.178243507792137%. The quarter Kelly, which is a measure of the optimal bet size, is 0, and the actual size is also 0. The 24-calendar-day profit and loss percentages at the 5th, 50th, and 95th percentiles are -5.637430728285449%, -3.427102602114587%, and -1.1809598898056493%, respectively. The 24-calendar-day loss percentage is 99.855%, and the percentage of days with a profit greater than or equal to 3.49% is 0%. The gross mean basis point, which is a unit of measurement for investment returns, is 0.9462161240253759. The approximate break-even one-way basis point is 0.47310806201268796. The results are also divided into buckets, which are groups of trades with similar characteristics. The buckets include range high with 409 trades and a mean trade percentage of -0.18693449544769383%, range low with 230 trades and a mean trade percentage of -0.19644281162601646%, r1 r12 agree with 310 trades and a mean trade percentage of -0.1912247988004951%, and r1 r12 disagree with 329 trades and a mean trade percentage of -0.18953911150132852%. The training range median, which is the middle value of a set of trade returns, is 0.0023747841105353. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following statistics for EEM, calculated on September 7, 2026, using a source of SIP adjusted 30-minute and daily history from 2016 to September 4, 2026. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval, which is a measure of the range of possible outcomes. The results also use 24-calendar-day historical windows with a hypothetical 25% account allocation, excluding fixed costs and extreme price movements within a single time period, known as intrabar extremes. The statistics do not predict future outcomes. The mean trade confidence interval is between -0.2138% and -0.1908%. The quarter Kelly, a measure of optimal bet size, is 0, and the actual size is also 0. The 24-calendar-day profit and loss percentages are -4.666%, -2.919%, and -1.297% for the 5th, 50th, and 95th percentiles, respectively. The 24-calendar-day loss percentage is 99.5%, and the 24-calendar-day profit greater than or equal to 349% is 0%. The 24-calendar-day 95% closed wealth drawdown percentage is 0.549%. The gross mean basis points, a measure of profit, is -0.246%, and the approximate break-even one-way basis points is -0.123%. The training range median is 0.0038%. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following statistics for EEM, calculated on September 7, 2026. The source is SIP adjusted 30-minute and daily history from 2016 to September 4, 2026. The future outcomes are unknown. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows use a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes, which are extreme price movements within a single bar or time period, and do not represent forward probabilities, which are probabilities of future events. The symbol is EEM, and the rule is rod. The results include a 95% mean trade confidence interval of -0.21739248718119247 to -0.19706596768589008, a quarter Kelly value of 0, an actual size of 0, and 24-calendar-day profit and loss percentages of -5.799398260572502, -3.5215964720603097, and -1.7232017548203509. The 24-calendar-day loss percentage is 100, the 24-calendar-day profit greater than or equal to 349 percentage is 0, and the 24-calendar-day 95% closed wealth drawdown percentage is 0.6814884147372524. The gross mean basis points value is -0.7336512810797877, and the approximate break-even one-way basis points value is -0.36682564053989386. The training range median is 0.0038294613224406. https://runway.adityaperswal.workers.dev/data/stock-bars

These statistics for EEM were calculated on September 7, 2026, using adjusted historical data from 2016 to September 4, 2026. The source notes that future outcomes are unknown. The calculations are based on 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows with a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes. The results do not represent forward probabilities. The statistics include a mean trade confidence interval of -0.2161849690428567 to -0.1936092256664569 percent, and a 24-calendar-day historical window with a 5th percentile profit/loss of -5.637478157052442, a 50th percentile of -3.4834502343514373, and a 95th percentile of -1.4567871277831728. The 24-calendar-day loss percentage is 99.54499999999999, and the 24-calendar-day profit greater than or equal to 349 percentage is 0. The 24-calendar-day 95th percentile closed wealth drawdown percentage is 0.662778189657387. The gross mean basis points are -0.5246049308647782, and the approximate break-even one-way basis points are -0.2623024654323891. The results are also broken down into buckets, including range high, range low, r1_r12 agree, and r1_r12 disagree, with corresponding mean trade percentages. The training range median is 0.0038294613224406. https://runway.adityaperswal.workers.dev/data/stock-bars

A news scan found the following statistics for SPY, calculated on September 7, 2026. The source is SIP adjusted 30-minute and daily history from 2016 to September 4, 2026. The future outcomes are unknown. The statistics are based on 20,000 circular 20-session block draws for the mean confidence interval, and 24-calendar-day historical windows use a hypothetical 25% account allocation, excluding fixed costs and intrabar extremes, which are extreme price movements within a single bar or time period on a chart. The results do not provide forward probabilities, which are probabilities of future events. The statistics include a mean trade confidence interval of -0.2445% to -0.1987%, a quarter Kelly value of 0, an actual size of 0, and 24-calendar-day profit and loss percentages. The 24-calendar-day loss percentage is 96.985%, and the 24-calendar-day profit greater than or equal to 349% is 0%. The gross mean basis points are -2.1694, and the approximate break-even one-way basis points are -1.0847. Basis points are units of measurement for interest rates and investment returns, equal to one hundredth of a percent. https://runway.adityaperswal.workers.dev/data/stock-bars