Notes

The QOE90906 portfolio had a return of -5.736240140215687 percent and a drawdown of 10.031284294057595 percent. It started with 3 equal parts, or sleeves, each with $1000. These sleeves were not rebalanced, meaning money was not moved between them over time. This was a test and the results are not based on real trading, but rather a hypothetical scenario. The results for each part of the portfolio were tracked separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The QOE90906 portfolio had a return of 6.07% and a drawdown of 6.87%. It started with 3 equal parts, or sleeves, each with $1000. These sleeves were not rebalanced, meaning their proportions were not adjusted over time. The results are based on a hypothetical, or imaginary, investment and are not feasible with current whole lot sizes. The portfolio's performance is not the result of three separate observations, but rather a single portfolio with individual asset metrics recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The QOE90906 portfolio had a return of -8.56284813631646 percent and a drawdown of 12.648388470819416 percent. It started with 3 equal cash sleeves, each with 1000 US dollars. There was no rebalancing of money between sleeves over time. This is a hypothetical test, and it does not use whole lots of assets, which is not currently possible. The results are for the whole portfolio, not three separate tests, and metrics for each asset are recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The QOE90906 portfolio had a return of 2.8891298474898797 percent and a drawdown of 6.88228225795463 percent. It started with 3 equal cash sleeves, each with 1000 US dollars. The portfolio had no rebalancing between sleeves and used fractional implementation, which is not feasible with whole lots. The results are not from three separate observations, but rather from per-asset metrics recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The QOE90906 portfolio had a return percentage of -0.8574056159139642 and a drawdown percentage of 12.224104890489363. It started with 3 equal cash sleeves, each with $1000. A sleeve is a part of the portfolio, in this case, three independent parts. There was no periodic rebalancing, which means the money was not moved between sleeves at regular times. The results are based on hypothetical, or imaginary, investments that can be split into fractions, but this is not possible with current whole lot investments. The results of the portfolio are not three separate observations, but rather the results of the whole portfolio, and metrics for each asset are recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The QOE90906 portfolio had a return of 11.55963477743349 percent and a drawdown of 10.9711666453424 percent. It started with 3 equal parts of cash, with 1000 US dollars in each part. These parts, called sleeves, were managed separately without moving money between them at regular times. The results are based on a hypothetical test, not actual trading, and the amounts used are not practical for real trading with whole lots. The results for each asset were recorded separately, so the overall results are not just three separate observations. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The QOE90906 portfolio had a return of -8.461290978540303 percent and a drawdown of 24.383400699293368 percent. It started with 3 equal parts of cash, which is called a sleeve, with 1000 US dollars in each sleeve. The portfolio had three independent parts, but they were not rebalanced, meaning the money was not moved between them. This was a test and not a real investment, and the results are not three separate observations. Each asset's performance was recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A portfolio had a return of 3.003406455853086 percent and a drawdown of 21.454673348843745 percent. It started with 3 equal parts, called sleeves, each with 1000 US dollars. These sleeves were separate and did not exchange money with each other over time. The results are based on a hypothetical situation where investments can be split into fractions, which is not possible with whole lots currently. The portfolio's results are not three separate experiments, but rather a single set of results with individual asset metrics recorded separately. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The information is based on a trade ledger from September 9. The results show a gross mean percentage of -0.10685519584699117, a post-2023 stress mean percentage of -0.11673878600231807, and a compound annual growth rate percentage of -2.813704845091558. The historical worst capital loss is -6.511413477772087. The current capital is 212.7475 and the risk capital is 8.5099. The compound annual growth rate, or CAGR, is an approximate measure of growth over five years, from 2021 to 2025. The historical worst capital loss is an example and not a guaranteed limit. The current position capital and nominal risk capital are equivalent to 850.99 USD, with 245.69 USD available for quantitative deployment. No new allocation is proposed. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The Vectorbt strategy uses a long-only approach, meaning it only buys assets and never sells short, and it closes positions based on the difference between the first close and last close prices. It also accounts for a fee of 1 basis point plus any remaining slippage per trade. The strategy is applied to independent sleeves of $1000 each, and it measures daily close drawdown compared to the strategy's opening and closing marks. The benchmark is measured in percentage units. The QOE90906 buy-hold extension has been updated with the following results: the return for OEF is 11.3403%, for SPY it is 12.3589%, and for IWM it is 18.5585%. The drawdown for OEF is 10.815%, for SPY it is 8.8799%, and for IWM it is 11.0284%. The sample size is 3 and the win rate is 100%. The extension is for 16 basis points. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The Vectorbt long-only strategy uses the first close to last close method, with a fee of 1 basis point plus remaining slippage per side. The strategy is tested with 1000 USD independent sleeves, and daily close drawdown is compared to strategy open and close marks. The benchmark units are percent. The results show an extension with 6 basis points. The return for OEF is 11.5632 percent, for SPY is 12.5839 percent, and for IWM is 18.7958 percent. The drawdown for OEF is 10.8150 percent, for SPY is 8.8799 percent, and for IWM is 11.0284 percent. The sample size is 3, and the win rate is 100 percent. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The Vectorbt strategy used a long-only approach, from the first close to the last close, with a fee of 1 basis point plus remaining slippage per side. The strategy was tested with 1000 USD independent sleeves, and the daily close drawdown was compared to the strategy's open and close marks. The benchmark units are in percent. The results show a holdout split with 16 basis points. The return was 114.10 for OEF, 97.35 for SPY, and 34.84 for IWM. The drawdown was 26.47 for OEF, 24.50 for SPY, and 31.91 for IWM. The sample size was 3, and the win rate was 100 percent. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The Vectorbt strategy used a long-only approach, closing positions at the first close and reopening at the last close, with a fee of 1 basis point plus any remaining slippage per side. The strategy was tested with 1000 USD independent sleeves, and daily close drawdown was compared to strategy open and close marks. The benchmark units were measured in percent. The results showed a 6 basis points holdout, with returns of 114.53 for OEF, 97.75 for SPY, and 35.11 for IWM. The drawdown was 26.47 for OEF, 24.50 for SPY, and 31.91 for IWM. The sample size was 3, and the win rate was 100. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A trading strategy called QOE90906 was tested. It used a method called Vectorbt, which means a way of analyzing data to make trading decisions, and it only made long trades, meaning it only bought assets with the goal of selling them later at a higher price. The strategy was tested from the first closing price to the last closing price, and it included a fee of 1 basis point, which is a unit of measurement for interest rates or fees, plus any remaining slippage, which is the difference between the expected price of a trade and the actual price, per trade. The test was done with 1000 USD in independent parts, called sleeves. The daily closing price drawdown, which is the decrease in value from the highest point, was compared to the strategy's opening and closing marks, which are the prices at the start and end of the trading period. The results were compared to a benchmark, which is a standard unit of measurement, and the results were measured in percentage units. The test results showed a return of 110.31033015563798 for OEF, 102.44290384159672 for SPY, and 90.51572356232329 for IWM. The drawdown was 31.435696473102947 for OEF, 33.79381911340275 for SPY, and 41.273284001020706 for IWM. The test was done 3 times, and the strategy won 100 percent of the time, with a fee of 16 basis points, which is 0.16 percent. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The Vectorbt strategy used a long-only approach, from the first close to the last close, with a fee of 1 basis point plus remaining slippage per side. The strategy was tested with 1000 USD independent sleeves, and daily close drawdown was compared to strategy open and close marks. The benchmark units are percent. The results show a return of 110.73137217374884 for OEF, 102.84819524465156 for SPY, and 90.89713670865416 for IWM. The drawdown was 31.435696473102936 for OEF, 33.79381911340268 for SPY, and 41.27328400102065 for IWM. The sample size was 3, and the win rate was 100, with a 6 basis points split for training. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the text: Seed90906 numpy; 20,000 paths, 12 monthly events from 20 holdout quarter blocks, sampled 4 quarters with replacement jointly across 3 assets. This means that a computer program used 20,000 possible paths to predict what might happen to investments, looking at 12 monthly events and using a special method to choose the events from a set of 20 blocks of time, and it did this for 4 quarters, choosing the events in a way that allowed the same event to be chosen more than once, and it did this for 3 different investments. The program tried to preserve the patterns that happen over 3 months and the relationships between the investments, and it assumed that the patterns would stay the same in the future, but it did not try to predict extreme events that might happen. The program used hypothetical positions of 212.7475 USD and 245.69 USD, which means it pretended to have investments of those sizes, but the actual size of the investments was 0, meaning this was just a thought experiment. The program found that there was a small chance that the investments could lose a lot of money, but it was not impossible, with possible losses of -32.84493261455071, -7.415221655240979, and 21.27322787137186. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the text describing a Monte Carlo simulation. The simulation used a seed from numpy and generated 20,000 paths with 12 monthly events from 20 holdout quarter blocks, sampled 4 quarters with replacement jointly across 3 assets. This method preserves 3 month serial and cross asset dependence, assuming stationarity, and does not model future tails. The simulation used individual hypothetical positions of 212.7475 USD and a thought experiment with equal sleeve separate positions of 245.69 USD, with actual size being 0, noting that no 349 hit is not an impossibility. The correlation matrix is as follows: the correlation between OEF and OEF is 1, between OEF and SPY is 0.9661616742754568, between OEF and IWM is 0.6002325704515783, between SPY and OEF is 0.9661616742754568, between SPY and SPY is 1, between SPY and IWM is 0.6143201520663915, between IWM and OEF is 0.6002325704515783, between IWM and SPY is 0.6143201520663915, and between IWM and IWM is 1. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the following data line: Seed90906 numpy, which is a type of mathematical software, used 20,000 paths and 12 monthly events from 20 holdout quarter blocks, sampled 4 quarters with replacement jointly across 3 assets. This method, called a Monte Carlo simulation, which is a way to model random events, preserves 3 month serial and cross asset dependence, and assumes stationarity, which means that the data does not change over time. It also does not model future tails, which are extreme events. The simulation used individual hypothetical positions of 212.7475 USD, and an equal sleeve, which means an equal amount, of 245.69 USD in a thought experiment. The actual size is 0, and there is a finite, or limited, chance that there will not be 349 hits, which is not an impossibility. The results of the simulation include the following data: there were 20,000 paths, 12 months, and a notional hypothetical value of 212.7475. The profit quantiles, which are a way to measure the range of possible profits, were -34.497646045266166, -5.857435349856291, and 29.76142146518492. The probability of loss was 0.6446, and the probability of a profit of 349 was 0. The drawdown quantiles, which are a way to measure the range of possible losses, were 7.63048238860452, 15.56292293284185, and 18.583605919065437 percent. The one window mean profit, which is the average profit over a certain period, was -0.4567188189971941. The one window profit quantiles were -6.109028058977542, -2.988840971523219, and 6.063730409872929. The exit frequencies, which are a way to measure how often a position is closed, were 0.43333333333333335 for a stop, which is a type of order to sell a security, 0.2 for a time, which is a type of order to sell a security at a certain time, 0.18333333333333332 for a target, which is a type of order to sell a security at a certain price, 0.1 for a target gap, which is a type of order to sell a security at a certain price with a certain gap, and 0.08333333333333333 for a stop gap, which is a type of order to sell a security at a certain price with a certain gap. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the headline 'SPY QOE90906 MonteCarlo SPY'. This refers to a simulation, called a Monte Carlo simulation, which is a method of modeling random events. The simulation used 20,000 paths and 12 monthly events from 20 holdout quarter blocks, sampled 4 quarters with replacement jointly across 3 assets. This means the simulation looked at how 3 assets might perform together over time. The simulation preserves 3 month serial and cross asset dependence, which means it takes into account how the assets have performed in relation to each other in the past. It assumes the future will be similar to the past, which is called stationarity, and does not model extreme future events, called future tails. The simulation used individual hypothetical positions of 212.7475 USD and equal sleeve separate positions of 245.69 USD as a thought experiment. The actual size of the positions is 0, meaning this is a theoretical exercise. The results show a probability of loss of 0.74905 and a probability of profit of 349 of 0, meaning it is not impossible but very unlikely to make a profit of 349. The simulation also calculated various metrics, including profit quantiles, drawdown quantiles, and exit frequencies. The profit quantiles are -28.74486851055603, -7.303961254082509, and 14.223307770825425. The drawdown quantiles are 6.077498682233079, 12.787008744071215, and 15.80555221784562. The one window mean profit is -0.6266033802530944 and the one window profit quantiles are -6.235260465493601, -1.7290266423373888, and 5.118839600464831. The exit frequencies are 0.4 for stop, 0.23333333333333334 for time, 0.18333333333333332 for target, 0.1 for stop gap, and 0.08333333333333333 for target gap. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A Monte Carlo simulation was run on the OEF QOE90906 with 20,000 paths and 12 monthly events. The simulation used data from 20 holdout quarter blocks, sampling 4 quarters with replacement across 3 assets. This method preserves 3-month serial and cross-asset dependence, assuming stationarity, but does not model future tail events. The simulation used individual hypothetical positions of $212.7475 and an equal sleeve of $245.69 for a thought experiment. The actual size is noted as 0, indicating it's a theoretical exercise. The results show a probability of loss of 0.68615 and a probability of profit of 349 at 0. The simulation also calculated various metrics, including profit quantiles, drawdown quantiles, and exit frequencies. A news scan found the following data: a dictionary with keys including paths, months, notional_hypothetical, profit_quantiles, prob_loss, prob_profit349, drawdown_quantiles_pct, one_window_mean_profit, one_window_profit_quantiles, and exit_frequencies. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the headline about the IWM QOE90906 indicator and its worst trade percentage. The IWM indicator was measured on September 9, with a fixed management period from 2021 to 2025, and a 6 basis point per side fee, using 60 third-Friday windows per exchange-traded fund, which is a type of investment fund that trades on a stock exchange like a stock. The indicator used a lagged 200-day simple moving average, which is a way to smooth out price data over time, a 5-session momentum measure, which looks at the speed of price changes, and an RV20 measure, which is a type of risk measurement. The high and low risk volatility threshold was set at the development median, meaning the middle value of a set of numbers, with no filter selected from the holdout, which is a set of data used to test the indicator. The results are described as pre-2023 and post-2023. The worst trade percentage was -3.3326290969262296. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the text: Measured Sep 9; fixed managed 2021–2025, 6 basis points per side, 60 third-Friday windows per exchange-traded fund. This uses a lagged 200-day simple moving average, a 5-session momentum indicator, and a 20-day realized volatility indicator. The high and low realized volatility threshold is the median from development data; no filter was selected from the holdout data. The data is descriptive, split into pre-2023 and post-2023 periods. 6 https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The IWM QOE90906 indicator was measured on September 9. It was fixed and managed from 2021 to 2025, with a 6 basis point per side fee, and 60 third-Friday windows per exchange-traded fund. The indicator used a 200-session simple moving average, 5-session momentum, and a 20-session realized volatility, which is a measure of how much the price of a security moves. The high and low realized volatility threshold is based on the median of the development data, with no filter selected from the holdout data. The data is descriptive for the periods before and after 2023. The result is 2.95. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

The IWM QOE90906 indicator was measured on September 9. It was fixed and managed from 2021 to 2025, with a 6 basis point cost per side, and 60 third-Friday windows per exchange-traded fund. The indicator used a 200-day simple moving average, a 5-session momentum, and a 20-day realized volatility, which is a measure of how much the price of a security moves. The high and low threshold for realized volatility is the median from the development period, with no filter selected from the holdout period. The data is descriptive, comparing the periods before and after 2023. The indicator settings include a stop at 26, a time of 12, a target of 11, a target gap of 6, and a stop gap of 5. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all

A news scan found the following data: Measured on September 9, the IWM QOE90906 indicator used a fixed management period from 2021 to 2025, with a 6 basis point cost per side, and 60 third-Friday windows per ETF. This indicator used a lagged 200-day Simple Moving Average, a 5-session momentum, and a 20-day Realized Volatility, referred to as RV20, which is a measure of how much the price of a security has moved over a given time period. The high and low RV threshold was set to the median of the development data, with no filter selected from the holdout data. The data is descriptive for the periods before and after 2023. The signal correlations are as follows: the correlation between the signal and the return is 1, the correlation between the signal and the realized volatility is -0.19215934353199307, and the correlation between the signal and the momentum is -0.17820655288574042. The correlation between the return and the realized volatility is -0.19215934353199307, the correlation between the return and the momentum is -0.17820655288574042, the correlation between the realized volatility and the momentum is 0.15213127479603955, the correlation between the realized volatility and itself is 1, and the correlation between the momentum and itself is 1. https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=OEF,SPY,IWM&timeframe=1Day&feed=sip&adjustment=all