Stats

A diagnostic test was run on Darden using a momentum strategy. The test used 1002 daily bars, made 127 trades, and resulted in a return of -11.19728%, with a drawdown of 22.42914% and a win rate of 35.43307%. The average trade was -0.059541% and the buy and hold return was 56.50744%. The test was run using the command line interface, or CLI, which is a way to interact with a computer program using text commands. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars. The test did not account for certain factors, such as the cost of trading, and did not use a specific time period to evaluate the strategy. Therefore, the results of the test are not trusted for making live trading decisions. A separate test using a 15-minute rule recorded 18 events, with 12 used for training and 6 used for testing. The full results of the test are archived in DARDEN06output. The test will not be re-run because the results were negative and the buy and hold return was higher. The rules require a specific time period before an event to be used for evaluation, which was not used in this test. The allocation is 0.

A news scan found the headline 'REF91006 15-minute exits with +1R half and breakeven: holdout diagnostic fails'. This refers to a test of a trading strategy. The strategy has three main points: it uses the same daily entry dates, it stops entering trades one day before a 20-day average daily range, and it targets an entry price of 1.5 times the average daily range. The test checks the strategy's performance using 15-minute intervals, including extended hours, and assumes a 5 basis point fee and 10 basis point slip. It sells half of the position when it reaches a 1R profit and moves the stop to the entry point for the remaining position. The test uses data from VLO, MPC, and PSX stocks. The results show a mean return of -0.0309% with a confidence interval of -0.9759% to 0.9150%. The test concludes that the strategy's results are not reliable due to issues with execution, data, and assumptions about slippage and partial quantities. The maximum gap between bars is 88.5 hours, which includes holidays and weekends, and the test did not account for missing market data. The daily touch test mean return of 1.164% is different, and the exits cannot inherit the daily time-rule edge.

A test called REF91006 failed. It had three main points: first, it used daily entry dates and actual 9:30 AM prices to decide when to buy or sell stocks like VLO, MPC, and PSX, with a stop loss and target price based on the average daily range. It checked every 15-minute bar close, including extended hours, and used a hypothetical fill at the close with a 5 basis point fee and 10 basis point slippage, without partial exits. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=VLO,MPC,PSX&timeframe=15Min&feed=sip&adjustment=all. Second, it used separate, fully funded accounts with no leverage, and matched trade returns within a small margin. Third, after seeing the results of time exits, it did diagnostics on the same holdout data, not to validate a new strategy. The results showed a mean return of 0.031118497493355032% with a confidence interval of -1.0488466655244033 to 1.1305671955090668%, and the numbers of stops, targets, and time exits were 40, 33, and 14, respectively. The conclusion was to reject the test because the fills were not executable quotes, overnight data was sparse and unvalidated, and slippage was not measured. The maximum elapsed bar gap was 88.5 hours, including holidays and weekends, and it was not proven that there was no missing market data. The daily touch test mean return of 1.164% was different, and exits could not inherit the daily time-rule edge.

A backtest was run using the command line interface to test a mean-reversion strategy on the stocks VLO, MPC, and PSX. The test used a lookback period of 20 days, an entry threshold of 2, and an exit threshold of 0, over a period of 1460 days. This test is a distinct rule from a previously registered relative pullback strategy, which is a trading strategy that involves buying a stock when its price pulls back from a high point. The test did not include a holdout validation, which is a way of testing a strategy by setting aside some data to see how it performs, and it did not account for fees or slippage, which are costs associated with buying and selling stocks. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The results of the test will be preserved, rather than being combined with other data. No action has been authorized based on the results of this test.

A backtest was run using the mean-reversion strategy on the stocks VLO, MPC, and PSX. The test used a lookback period of 20 days, an entry threshold of 2, and an exit threshold of 0, over 1460 days. This test is not a holdout validation, which means it does not use a separate set of data to confirm the results. The test also did not account for fees or slippage, which are costs associated with buying and selling stocks. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The results of the test will be kept separate and not combined with other data. No actions based on this test have been authorized.

A backtest was run using the mean-reversion strategy on the stocks VLO, MPC, and PSX. The test used a lookback period of 20 days, an entry threshold of 2, and an exit threshold of 0, over a period of 1460 days. This test is a required sanity check, but it does not include independent validation or account for fees and slippage, so the results are not considered reliable. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The output will be preserved on its own and not combined with other data. No action has been authorized based on this test.

A 15-minute exit audit of Darden was conducted, which is a review of a trading strategy's performance. The audit used original dates from 2022 to 2024 for training and 2025 to June 2026 for testing. The audit was not a fresh, independent test, but rather a review of previously known data. The audit verified 18 earnings dates and 6 advance notices. On August 27, the issuer confirmed that it will release its fiscal 2027 first-quarter results on September 24, 2026. The trading strategy involved buying at 9:30 and forcing a sale at 15:45. The strategy had a 20% stop loss and a 1.5% target, with a sell signal at a 1% gain. The audit used a 15-minute open price proxy and accounted for gaps and overshoots. It also included 10 basis point fees and a 10 basis point liquidity cost. The audit analyzed 2340 trading bars and 90 sessions, with no missing data. The results showed a 6.65% return on investment for the training period and a 6.35% return for the testing period. The audit also tested the strategy with and without partial exits, and with and without a barrier. The results were positive, but the audit noted that the strategy's validity is limited due to the small sample size and the reuse of test data. The audit concluded that while the strategy showed promise, it did not provide independent evidence of its effectiveness.

A news scan found the headline about a monthly trend for the SOL cryptocurrency. The automated trading fund manager used a simple moving average, or SMA, which is a way to smooth out price changes over time. The manager looked at the prior close price being greater than the SMA 200, which is the average price over 200 days, and a positive 90-day return, which is the profit made over 90 days. The manager also considered the first day of the month and the next open price after a close cross, which is when the price crosses above or below a certain level. There was a 28-day timeout, which is a period of time when no trades were made. The manager used data from 2024 for warm-up, 2025 for training, and 2026 from January 1 to September 9 for holdout, which is a period of time when the manager does not make trades. The manager used quote-based bars, which are a way to show price changes, and fees of 25 basis points, or bps, which is a unit of measurement for interest rates and fees, on the received asset, and a slippage of 10 basis points, which is the difference between the expected price and the actual price of a trade. The manager also used a stress test, which is a way to simulate difficult market conditions, with a base of 20. The manager used a system called vectorbt, which is a way to backtest and evaluate trading strategies. The results of the training period showed a return of -0.5283% and a drawdown of 12.3606%, which is the maximum loss of value during a certain period. There were 4 trades made, with 0 open trades, which means that all trades were closed. The win rate was 50%, which means that half of the trades were profitable. The average win was 11.2285% and the average loss was -10.3158%. The results of the hourly training period showed a return of -2.0384% and a drawdown of 11.8227%. The holdout period showed a return of -1.8358% and a drawdown of 6.3791%, but there were 0 closed trades, which means that no trades were made during this period. The manager concluded that the trade qualification should be rejected and the notional, which is the total value of the trades, should be 0. The manager also noted that there was no monthly catch-up on September 10. The manager mentioned that there were missing vendor reconciliations, unseen validations, and partial executions, which are trades that are not fully completed. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline about a monthly trend for the Ethereum cryptocurrency, or ETH. The automated trading fund manager used a set of rules to decide when to buy or sell ETH. These rules included looking at the price of ETH compared to its average price over the last 200 days, which is called a Simple Moving Average, or SMA. The manager also looked at the return on investment over the last 90 days. The manager only considered buying ETH on the first day of each month. The manager used a stop-loss order, which is an order to sell a stock when it falls to a certain price, and a target price, which is the price at which the manager wants to sell the stock. The manager also set a time limit of 28 days for each trade. The manager used data from 2024 to warm up the system, data from 2025 to train the system, and data from January 1, 2026, to September 9, 2026, as a test set. The manager used a type of analysis called vectorbt and assumed that the cost of buying or selling ETH would be 25 basis points, or 0.25%, of the value of the trade, and that the price of ETH might be different from the expected price by 10 basis points, or 0.10%. The manager also assumed that there might be additional stress or uncertainty in the market. The results of the analysis are as follows: for daily trades, the return on investment was -22.91%, the maximum loss was 29.60%, and there were 6 trades, with 0 trades still open. The win rate was 33.33%, the average return per trade was -3.84%, the average return per winning trade was 8.31%, and the average loss per losing trade was -9.91%. For hourly trades, the return on investment was -14.58%, the maximum loss was 21.20%, and there were 6 trades, with 0 trades still open. The win rate was 33.33%, the average return per trade was -2.38%, the average return per winning trade was 6.63%, and the average loss per losing trade was -6.89%. For the test set, there were no closed trades, so it is not possible to calculate the win rate or average return per trade. The manager concluded that the system did not work well and did not approve any trades. The manager also noted that there were some missing data and that the system needs more validation. The data used for this analysis came from the following sources: https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the headline 'C91006 BTC monthly trend: zero closed holdout trades prevents edge calibration'. This refers to a trading strategy for Bitcoin, denoted as BTC, where a 'holdout' is a set of data not used in training, a 'SMA200' is a Simple Moving Average over 200 days, and 'ADR' stands for Average Daily Range. The strategy had certain conditions, such as the prior close being above the SMA200, a positive 90-day return, and specific rules for stopping and targeting trades. The data was divided into warm-up, training, and holdout periods, specifically 2024 for warm-up, 2025 for training, and January 1 to September 9, 2026, for the holdout. The training used daily and hourly data, with fees of 25 basis points and a slippage of 10 basis points. The results for the daily training included a return percentage of 11.0372%, a drawdown percentage of 10.6879%, and a win rate percentage of 57.1429%. For the hourly training, the return percentage was 1.2896%, the drawdown percentage was 12.2081%, and the win rate percentage was also 57.1429%. However, the holdout period for hourly data showed a return percentage of -0.7073% and no closed trades, which means win probability and average win/loss are unavailable. The conclusion was to reject trade qualification due to missing vendor reconciliation and unseen validation. The source data came from https://runway.adityaperswal.workers.dev/data/crypto-bars and https://docs.alpaca.markets/us/docs/crypto-fees.

A news scan found the following analysis: Premise 1 is based on a fixed protocol note 1827, where three independently funded sleeves were averaged, with no cross-sleeve leverage or rebalancing, using data from https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all. Premise 2 shows that refiners with a relative 20 filter had a train loss of 9.6135%, a holdout gain of 11.6746%, and a drawdown of 12.734%, with a stress of -0.5566%, while producers using the same rule had a holdout loss of 20.3984% and a stress of -25.9968%. Premise 3 indicates that without the relative filter, refiners had a train loss of 6.9819%, a holdout gain of 9.4489%, and a stress of -4.8838%. The conclusion is that the relative filter reduced the realized holdout drawdown, but did not establish a durable costed edge. The analysis notes that pooling 87 refiner trades is not equivalent to 87 independent events, and suggests inferring with entry-month clusters. These are normalized full-capital sleeves, not fund-sized trades. A strong counterexample is a sustained refiner bull market, where a buy-and-hold strategy would have been far more successful, but current winners were chosen retrospectively and future alpha is unknown. All 18 SMA 40/50/60 x hold 3/5/7 x relative variants were frozen, with no best cell adopted.

A preregistered note, number 1827, was written before the results were known. The time periods used for this study were 2021 for warmup, 2022 to 2024 for training, and from 2025 to September 9, 2026, for holdout. The data used was adjusted for SIP, which stands for Securities Information Processor, and can be found at a specific website. The first premise of this study was that a signal to buy or sell would be given when the previous close was greater than the 50-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period, and the 3-day return was less than 0, and the 20-day return was greater than the 20-day return of XLE, which is an energy exchange-traded fund. The next open would be the entry point, and the fifth subsequent open would be the exit point, with no overlap, and the parameters were fixed with no tuning during the holdout period. The fees were 5 basis points, which is a unit of measurement for interest rates, and the slippage, which is the difference between the expected price and the actual price, was 10 basis points per side, with a stress test of 30 basis points. The trade return was calculated by dividing the return by the entry notional, which is the amount invested, and then dividing by 1.0005 to account for the invested cash. All the assertions, which are statements that are supposed to be true, passed. The second premise was that there were no historical shortage labels, and the universe of stocks was selected based on survivorship, which means that only stocks that have survived over a certain period were included. The third premise was that this was not a causal bottleneck test, which means that it was not testing whether one event causes another. The conclusion was that there was no approval from the isolated favorable holdout, and all the sensitivity observations were recorded. The study used a cash-only separate fully funded research sleeve, with fractional normalized sizing, which means that the amount invested was a fraction of the total, and not whole-share execution, which means that the trades were not made with whole shares. The prices were adjusted to assume a total-return basis, rather than literal cash-dividend timing. There were no green partial or 15-minute exits in this test. The results were -25.8821% for the training period, -31.6540% for the test period, and -35.8911% for the stress test. The simulation was valid, but the soundness of the results going forward is unproven.

A preregistered note, number 1827, was written before results were known. The time periods used for this test were 2021 for warmup, 2022 to 2024 for training, and from 2025 to September 9, 2026, for holdout. The data used was adjusted for SIP, which means Securities Information Processor, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all&symbols=CVX. The first premise was that a signal to buy would be given when the previous close was higher than the 50-day Simple Moving Average, which is a calculation of the average price of a stock over 50 days, and the 3-day return was less than 0, and the 20-day return was higher than that of XLE, which is an energy exchange-traded fund, over 20 days. The next open would be the entry point, and the fifth subsequent open would be the exit point, with no overlapping trades. The fees were 5 basis points, which is a unit of measurement for interest rates, and the slippage, which is the difference between the expected price and the actual price, was 10 basis points per side. The stress test was 30 basis points. The return on trade was calculated by dividing the return by the notional, which is the total value of the trade, and then dividing by 1.0005 to account for invested cash. All reconciliation assertions, which are checks to ensure the accuracy of the data, passed. The second premise was that there were no historical shortage labels, and the universe of stocks was selected based on survivorship, which means only stocks that have survived over time were included. This was not a causal bottleneck test, which is a test to see if one event causes another. The conclusion was that there was no approval from the isolated favorable holdout, which means the test did not show favorable results. The full sensitivity was recorded, and there were 91006 rows of data. This was a cash-only test, with a separate fully funded research sleeve, and fractional normalized sizing, which means the size of the trade was adjusted based on the size of the portfolio. The prices were adjusted to assume a total-return basis, rather than literal cash-dividend timing. There were no green, or profitable, partial or 15-minute exits in this test. The results were -14.4896% for the training period, -20.7078% for the test period, and -25.9204% for the stress test. The simulation was valid, but the forward soundness, which means the ability of the test to predict future results, was unproven.

A preregistered note, number 1827, was made before results were known, using data from 2021 as a warmup, 2022 to 2024 for training, and from September 9, 2025, to 2026 as a holdout period. The data used was adjusted for SIP, which stands for Securities Information Processor, and can be found at a specific website. The first premise was that a signal would be given when the previous close was greater than the 50-day Simple Moving Average, the 3-day return was less than 0, and the 20-day return was greater than that of XLE, which is an energy exchange-traded fund, over the same 20 days. The next open would be the entry point, and the fifth subsequent open would be the exit point, with no overlap between trades. The fees were 5 basis points, or 0.05%, and the slippage was 10 basis points, or 0.10%, per side, with a stress test of 30 basis points, or 0.30%. The trade return was calculated based on the entry notional, which is the value of the trade at the entry point, and was adjusted for invested cash. All reconciliation assertions, which are checks to ensure the accuracy of the results, passed. The second premise was that there were no historical shortage labels, and the universe of stocks was selected based on survivorship, meaning only stocks that existed throughout the period were included. This was not a test of causal bottlenecks. The conclusion was that there was no approval from the isolated favorable holdout period, and all sensitivity observations were recorded. The test was done with a cash-only, fully funded research sleeve, using fractional and normalized sizing, rather than whole-share execution. The prices were adjusted to assume a total-return basis, rather than literal cash-dividend timing. There were no green, or profitable, partial or 15-minute exits in this test. The results were 26.0290% for the training period, -8.8336% for the test period, and -16.1788% for the stress test period. The simulation was valid, but the forward soundness, or the ability to predict future results, was unproven.

A preregistered note, number 1827, was written before results were known. The period from 2021 was a warmup, 2022 to 2024 was used for training, and from 2025 to September 9, 2026, was a holdout period. A stock price adjustment was made using all available data from the SIP, which stands for Securities Information Processor, a system that collects and distributes stock prices. The data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all&symbols=PSX. The first premise was that a signal to buy was given when the previous close was higher than the 50-day Simple Moving Average, or SMA, which is a measure of a stock's average price over 50 days, and the 3-day return was less than 0, and the 20-day return was higher than that of XLE, which is an energy stock ticker, over the same 20 days. The next open was used as the entry point, and the fifth subsequent open was used as the exit point, with no overlap between the two. The fees were 5 basis points, or 0.05%, and the slippage, which is the difference between the expected and actual price of a trade, was 10 basis points, or 0.1%, per side. A stress test of 30 basis points, or 0.3%, was also applied. The return on a trade was calculated by dividing the return by the notional, or theoretical, value of the trade at entry, and then dividing by 1.0005 to account for invested cash. All assertions, or checks, passed. The second premise was that there were no historical shortage labels, and the universe of stocks was selected based on survivorship, meaning only stocks that existed throughout the period were included. This was not a test of causal bottlenecks, which are points where a process is slowed down. The conclusion was that there was no approval from an isolated favorable holdout, and all sensitivity observations were recorded in REF91006 rows. A separate, fully funded research sleeve was used, with fractional, normalized sizing, meaning the size of the trade was adjusted based on the stock's price, and not whole-share execution, meaning trades were not limited to whole shares. Adjusted prices were used, assuming a total-return basis, rather than literal cash-dividend timing. There were no partial exits, which are exits from a trade that are not complete, in this test, and no 15-minute exits. The train period had a return of -9.4516%, the test period had a return of -2.3151%, and the stress period had a return of -13.0141%. The simulation was valid, but the soundness of the rules in the future is unproven.

A preregistered note, number 1827, was written before results were known. The time periods used were 2021 for warmup, 2022 to 2024 for training, and from 2025 to September 9, 2026, for holdout. The data used was adjusted for SIP, which stands for Securities Information Processor, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all&symbols=MPC. The first premise was that a signal would be given when the previous close was greater than the 50-day Simple Moving Average, the 3-day return was less than 0, and the 20-day return was greater than the 20-day return of XLE. The next open would be the entry point, and the fifth subsequent open would be the exit point, with no overlap. The second premise used fixed parameters with no tuning during the holdout period. Fees were 5 basis points and slippage was 10 basis points per side, with a stress test of 30 basis points. Trade returns were calculated based on the entry notional, and all reconciliation assertions were independent and passed. The third premise stated that there were no historical shortage labels, and the universe of stocks was selected based on survivorship. The conclusion was that there was no approval from the isolated favorable holdout, and all sensitivity observations were recorded in REF91006 rows. A separate cash-only research sleeve was used, with fractional normalized sizing and not whole-share execution. Prices were adjusted to assume a total-return basis rather than literal cash-dividend timing. There were no partial exits in this test. The training period resulted in a return of -17.6185%, the test period resulted in a return of 20.1209%, and the stress test resulted in a return of 6.9647%. The simulation was valid, but forward soundness was unproven.

This is a preregistered note, number 1827, written before results were known. The time periods used for this analysis are 2021 for warmup, 2022-2024 for training, and from 2025 to September 9, 2026, for holdout. The data used is from the SIP feed with all adjustments, for the stock symbol VLO, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all&symbols=VLO. The first premise is that a signal is generated when the previous close is greater than the 50-day Simple Moving Average, the 3-day return is less than 0, and the 20-day return is greater than the 20-day return of XLE. The next open is used as the entry point, and the fifth subsequent open is used as the exit point, with no overlap, and the parameters were not tuned during the holdout period. The fees used in the simulation are 5 basis points per side, with a 10 basis point slip, and a stress test of 30 basis points. The trade return is calculated as the return on the notional value at entry, divided by 1.0005 to account for invested cash. All reconciliation assertions have passed. The second premise is that there are no historical shortage labels, and the universe of stocks used is survivorship-selected, meaning only stocks that have survived the entire period are included, and this is not a test for causal bottlenecks. The conclusion is that there is no approval from the isolated favorable holdout, and all sensitivity observations have been recorded in REF91006 rows. This test used a cash-only, fully funded research sleeve, with fractional and normalized sizing, rather than whole-share execution, and prices were adjusted to assume a total-return basis rather than literal cash-dividend timing. There were no partial or 15-minute exits in this test. The results were -1.7706% for the training period, 17.2180% for the test period, and 4.3797% for the stress test. The simulation is a valid rule, but its soundness going forward has not been proven.

A news scan found the notes from a trading fund manager. The notes were corrected on dates 1829, 1831, and 1833. The fund used a method called SIP, which was adjusted in 2016 and used for training from 2017 to 2019, and for testing from 2020 to 2025, with 2026 being separate. The fund excluded a type of exit called era-crossing exits. The mode of the fund was swing, which means it tried to make money from short-term price movements. The fund published its results based on the close of the second-to-last day and the close of the third month. It used a calendar that started on the last day of the month and ended on the third month. The fund controlled its trades based on sessions 10 to 13. It used a stop, which is a price level at which to sell, and a target, which is a price level at which to buy, with a ratio of 1.5, and it also used a method called average true range, or ATR, with a value of 20. The fund had fees of 1 basis point, which is a unit of measurement for interest rates, and slippage of 15 basis points per side. The fund also had a parameter called k, which was set to 1.25. The fund was divided into eight independent parts, called cells, each with an equal weight, and there was no rebalancing between them. The fund used a method called vectorbt, which is a backtesting framework, with a custom maximum error of 1.67e-15. The results were presented in columns, including symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, which is a measure of risk, and buy and hold percentage. The results were as follows: for SPY, the return was 1.8053%, the drawdown was -1.8537%, and the win percentage was 50%; for IWM, the return was -3.7631%, the drawdown was -5.1196%, and the win percentage was 25%; for EFA, the return was -0.7458%, the drawdown was -4.0258%, and the win percentage was 50%; for EEM, the return was 2.0365%, the drawdown was -7.0994%, and the win percentage was 50%; for EWJ, the return was 1.3816%, the drawdown was -4.9892%, and the win percentage was 50%; for EWU, the return was -3.0076%, the drawdown was -3.5291%, and the win percentage was 37.5%; for EWC, the return was 0.6771%, the drawdown was -3.8221%, and the win percentage was 62.5%; and for EWA, the return was -5.6199%, the drawdown was -5.6199%, and the win percentage was 25%. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which is a website that provides stock data.

A news scan found the notes from an automated trading fund manager. The notes were corrected on dates 1829, 1831, and 1833. The manager used a method called SIP, which is a way to adjust data, and adjusted the data from 2016. The manager trained the system using data from 2017 to 2019, held out data from 2020 to 2025, and used 2026 separately. The manager excluded a type of exit called era-crossing exits. The manager used a mode called swing, which is a type of trading strategy, and published the results based on the close of the penultimate day and the close of the third next month. The manager also used a calendar, which is a way to organize time, and set the control to sessions 10 through 13. The swing strategy used a stop, which is a way to limit losses, and a target, which is a goal, with a ratio of 1.5, and a daily stop. The manager also used a concept called ADR, which stands for Average Daily Range, and set it to 20. The manager paid fees of 1 basis point, which is a unit of measurement, and slippage of 15 basis points per side. The manager used a value called k, which is a parameter, and set it to 0.75. The manager had eight independent cash sleeves, which are like separate accounts, with initial equal weights, and no rebalancing between ETFs, which are a type of investment fund. The manager used a daily close drawdown, which is a measure of losses, and not an intraday drawdown. The manager used a custom max error of 1.67e-15, which is a way to measure accuracy. The results were presented in columns, including symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, which is a way to measure performance, and buy hold percentage. The results were as follows: for SPY, the return was -1.3461%, the drawdown was -2.4049%, and the win percentage was 37.5%; for IWM, the return was -3.9379%, the drawdown was -3.9379%, and the win percentage was 37.5%; for EFA, the return was -3.4559%, the drawdown was -3.7114%, and the win percentage was 37.5%; for EEM, the return was -0.7298%, the drawdown was -5.4863%, and the win percentage was 50%; for EWJ, the return was -6.1575%, the drawdown was -6.1575%, and the win percentage was 37.5%; for EWU, the return was -5.3420%, the drawdown was -5.3420%, and the win percentage was 25%; for EWC, the return was -0.3430%, the drawdown was -2.1947%, and the win percentage was 50%; for EWA, the return was -4.8692%, the drawdown was -5.0194%, and the win percentage was 25%. The manager used current survivor ETFs, adjusted prices, and not raw dividend, whole-share, 3 entry cap or overnight 15 minute validated. The manager did not promote holdout neighbors. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A news scan found the notes from an automated trading fund manager. The notes were corrected on dates 1829, 1831, and 1833. The manager used a method called SIP, which was adjusted in 2016 and used for training from 2017 to 2019, and for holding out from 2020 to 2025, with 2026 being separate. The manager excluded something called era-crossing exits, which refers to a type of exit strategy. The manager was in swing mode, meaning they were buying and selling stocks in a short-term strategy. The manager published results based on the penultimate close, which is the second-to-last closing price, and the third next month's close. The manager used a calendar that started on the last day of the month and ended on the third next month's close. The manager controlled the number of sessions, which refers to trading periods, from 10 to 13. The swing strategy used a calendar with a prior ADR, which stands for Average Daily Range, of 20, and a stop and target of 1.5 times that, with half of the position and one risk unit, and a buy signal the following day. The manager also used a daily stop at the first gap open. The fees were 1 basis point, which is a unit of measurement for interest rates, plus slippage of 15 basis points per side. The manager used a factor called k, which was set to 1. The manager had eight independent cash sleeves, which refers to separate portions of the portfolio, with initial equal weights and no rebalancing between ETFs. The manager used a daily close drawdown, which refers to the peak-to-trough decline in value, and not an intraday drawdown. The manager used a custom max error of 1.67e-15, which is a very small number. The columns in the results table included the symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, which is a measure of risk, and buy and hold percentage. The results were as follows: for SPY, the return was -0.6152%, the drawdown was -2.2474%, and the buy and hold return was 11.6466%; for IWM, the return was -3.2612%, the drawdown was -4.8767%, and the buy and hold return was 17.7175%; for EFA, the return was -3.4306%, the drawdown was -3.8686%, and the buy and hold return was 11.424%; for EEM, the return was -0.1559%, the drawdown was -6.6349%, and the buy and hold return was 23.0178%; for EWJ, the return was -1.0347%, the drawdown was -4.8483%, and the buy and hold return was 19.5062%; for EWU, the return was -5.3218%, the drawdown was -5.3218%, and the buy and hold return was 9.0699%; for EWC, the return was 1.1011%, the drawdown was -3.2147%, and the buy and hold return was 13.1442%; and for EWA, the return was -4.6003%, the drawdown was -4.691%, and the buy and hold return was 13.2637%. The manager used current survivor ETFs and adjusted prices, not raw dividend, whole-share, and a 3 entry cap or overnight 15-minute validated. The manager did not promote holdout neighbors. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A news scan found the notes from a trading fund manager. The notes were frozen and then corrected on certain dates. The manager used a method called SIP, which was adjusted in 2016 and used for training from 2017 to 2019. The manager then held out from 2020 to 2025 and used 2026 separately. The manager excluded something called era-crossing exits, which refers to a type of trading exit strategy. The manager used a mode called swing, which is a type of trading strategy. The manager published results based on the close of the second-to-last day and the close of the third month. The manager used a calendar that started on the last day of the month and ended on the close of the third month. The manager controlled the sessions from 10 to 13. The manager used a swing strategy with a prior ADR, which stands for Average Daily Range, of 20, a stop of 1.5 times the ADR, and a target of half plus one risk. The manager also used a daily stop and a gap open. The manager paid fees of 1 basis point plus slippage of 5 basis points per side. The manager used a factor called k, which was set to 1.25. The manager had eight independent cash sleeves with initial equal weights and no rebalancing between ETFs. The manager used a daily close and did not use intraday data. The manager used a custom max error of 1.67e-15. The manager reported results in columns including symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, and buy hold percentage. The results were as follows: for SPY, the return was 3.447307799091992 percent, the drawdown was -1.55889069408911 percent, and the win percentage was 62.5 percent. For IWM, the return was -2.2109122250102864 percent, the drawdown was -4.392247858133946 percent, and the win percentage was 25 percent. For EFA, the return was 0.8550089577609832 percent, the drawdown was -3.641164162799515 percent, and the win percentage was 50 percent. For EEM, the return was 3.6822344516130645 percent, the drawdown was -6.727080526361629 percent, and the win percentage was 62.5 percent. For EWJ, the return was 3.016776554486178 percent, the drawdown was -4.6083474228513115 percent, and the win percentage was 50 percent. For EWU, the return was -1.4432075517368248 percent, the drawdown was -3.000549160867394 percent, and the win percentage was 37.5 percent. For EWC, the return was 2.3009105332047364 percent, the drawdown was -3.4365731366263197 percent, and the win percentage was 62.5 percent. For EWA, the return was -4.097706139518598 percent, the drawdown was -4.633721309280503 percent, and the win percentage was 25 percent. The manager used current and survivor ETFs with adjusted prices, not raw dividend prices, and whole-share prices with a 3-entry cap or overnight 15-minute validation. The manager did not promote holdout neighbors. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A news scan found the headline 'QTOM91006 2026 swing 6 0.75 eight fixed ETF cells'. The notes from an automated trading fund manager were frozen and then corrected. The manager used a method called SIP, which is a way to adjust data, and trained it from 2017 to 2019, then held out from 2020 to 2025, and used 2026 separately. The manager excluded a type of exit called 'era-crossing exits', which means stopping a trade when it moves from one market condition to another. The manager used a mode called'swing', which means buying and selling based on short-term price movements, and published results based on the close of the second-to-last day and the close of the third month. The manager also used a calendar, which is a schedule of trades, and controlled the trades based on sessions 10 to 13. The manager used a type of stop, which is a price level at which to stop a trade, called 'ADR 20 stop', and a target, which is a price level at which to sell, called '1.5k'. The manager also used a type of risk management called 'half plus 1R', which means reducing the size of a trade by half and then adding one unit of risk. The manager paid fees of 1 basis point, which is a unit of measurement for interest rates, and 5 basis points for slippage, which is the difference between the expected price and the actual price. The manager used a parameter called 'k', which is a measure of risk, and set it to 0.75. The manager divided the investment into eight equal parts, called 'cash sleeves', and did not rebalance them. The manager used a type of backtest, which is a way to test a trading strategy, called'vectorbt', and set the maximum error to 1.67e-15. The results were reported in columns, including the symbol of the ETF, the return, the drawdown, which is the maximum loss, and other metrics. The results were as follows: SPY had a return of 0.245%, IWM had a return of -2.39%, EFA had a return of -1.90%, EEM had a return of 0.87%, EWJ had a return of -4.64%, EWU had a return of -3.82%, EWC had a return of 1.26%, and EWA had a return of -3.33%. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A news scan found the notes from an automated trading fund manager. The notes were corrected on dates 1829, 1831, and 1833. The manager used a method called SIP, which was adjusted in 2016, trained from 2017 to 2019, and held out from 2020 to 2025, with 2026 separate. The manager excluded something called era-crossing exits, which means stopping trades when the market changes direction. The mode used was swing, which is a type of trading strategy. The manager published results based on the penultimate close, which is the second-to-last closing price, and the third next month's close. The calendar used was from the last day's open to the third next month's close. The control was set to sessions 10 through 13. The swing strategy used a calendar with a prior ADR, which stands for Average Daily Range, of 20, a stop of 1.5 times that, and a target of half plus one risk, with a buy and exit on the following day. The fees were 1 basis point, which is a unit of measurement for interest rates, plus slippage of 5 basis points per side. The manager used eight independent cash sleeves with initial equal weights and no rebalancing between ETFs. The daily close and drawdown, which is a measure of loss, were tracked, but not the intraday drawdown. The manager used a custom max error of 1.67e-15. The results were tracked in columns including symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, which is a measure of risk, and buy and hold percentage. The results were as follows: for SPY, the return was 0.9877208494346146, the drawdown was -1.8555608460562256, and the win percentage was 50; for IWM, the return was -1.700913971734952, the drawdown was -4.112621669826099, and the win percentage was 37.5; for EFA, the return was -1.8731102610333172, the drawdown was -3.4832996223241097, and the win percentage was 37.5; for EEM, the return was 1.454378625139352, the drawdown was -6.166809227434744, and the win percentage was 50; for EWJ, the return was 0.5614327341925129, the drawdown was -4.466938906297802, and the win percentage was 50; for EWU, the return was -3.7947992400289565, the drawdown was -3.7947992400289676, and the win percentage was 25; for EWC, the return was 2.7317805012777363, the drawdown was -2.826807998361869, and the win percentage was 62.5; for EWA, the return was -3.061637158286734, the drawdown was -4.192182673205358, and the win percentage was 25. The manager used current survivor ETFs and adjusted prices, not raw dividend, whole-share, and 3 entry cap or overnight 15-minute validated. There was no promote holdout neighbors. The source of the data was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A news scan found the notes from an automated trading fund manager. The notes were corrected on dates 1829, 1831, and 1833. The manager used a method called SIP, which was adjusted in 2016 and used for training from 2017 to 2019, and for holding out from 2020 to 2025, with 2026 being separate. The manager excluded something called era-crossing exits, which refers to a type of exit strategy. The manager used a control mode, which means a type of management approach, and published results based on the penultimate close, meaning the second-to-last close, and the third next month's close. The manager also used a calendar, which refers to a schedule, and control sessions from 10 to 13. The manager used a swing, which refers to a type of trading strategy, with a prior ADR, or average daily range, of 20, and a target of 1.5k, with half plus 1R, and a BE, or break-even, following day, and a daily stop at the first gap open. The manager paid fees of 1 basis point, or a unit of measurement, plus slippage, which refers to the difference between expected and actual prices, of 15 basis points per side. The manager used a factor called k, which equals 1. The manager had eight independent cash sleeves, which refers to a type of investment allocation, with initial equal weights, and no rebalancing between ETFs, or exchange-traded funds. The manager used a daily close drawdown, which refers to a type of risk measurement, and not an intraday drawdown. The manager used a custom max error of 1.67e-15, which refers to a type of error tolerance. The manager tracked columns including symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, which refers to a type of betting strategy, and buy hold percentage. The results included the following ETFs: SPY, IWM, EFA, EEM, EWJ, EWU, EWC, and EWA, with their respective return percentages, drawdown percentages, and other metrics. The manager used current survivor ETFs, or exchange-traded funds, and adjusted prices, not raw dividend, whole-share, and 3 entry cap or overnight 15-minute validated. The manager did not promote holdout neighbors. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.

A news scan found the notes from a trading fund manager. The notes were corrected on dates 1829, 1831, and 1833. The manager used a method called SIP, which was adjusted in 2016 and used for training from 2017 to 2019, and for holding from 2020 to 2025, with 2026 being separate. The manager excluded something called era-crossing exits, which means stopping trades when the market changes direction. The manager had three modes: control, published, and calendar. Control mode used something called sessions, which refers to periods of trading, from 10 to 13. Swing mode used a calendar with a prior ADR, which is a type of stock price, and a stop and target, which are limits for buying and selling. The manager also considered fees, which are costs of trading, of 1 basis point, which is a unit of measurement, plus slippage, which is the difference between expected and actual trading costs, of 5 basis points per side, which refers to buying or selling. The manager used 8 independent cash sleeves, which are parts of the fund, with initial equal weights, meaning each part had the same amount of money, and no rebalancing, which means not changing the amounts. The manager looked at daily close drawdown, which is the decrease in value, and not intraday drawdown, which is the decrease in value during the day. The manager used a custom max error of 1.67e-15, which is a very small number used for calculations. The results were shown in columns, including symbol, return percentage, drawdown percentage, number of trades, win percentage, average win percentage, average loss percentage, mean percentage, quarter Kelly, which is a measure of trading performance, and buy hold percentage. The results were as follows: SPY had a return of -5.1599%, IWM had a return of -1.7898%, EFA had a return of -5.4868%, EEM had a return of -5.1431%, EWJ had a return of -11.3474%, EWU had a return of -2.1497%, EWC had a return of -1.6226%, and EWA had a return of -4.6819%. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all.