Stats

A preregistered note, number 923, was written before the analysis of stock bars. The analysis used data from 2022 to 2025 for training and from January 2, 2026, to September 4, 2026, as a holdout period, with an adjustment from the IEX, which is a stock exchange, and all data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis looked at 1172 bars of data for the stock GD and used a specific strategy to decide when to enter and exit the market. The strategy was to enter the market after the close of a trading day if the next open was higher than the prior 20-day high and the 50-day simple moving average, which is a calculation of the average price of a stock over a certain period. The strategy also used a stop-loss, which is a order to sell a stock when it falls to a certain price, and a target price, which is the price at which the strategy aims to sell the stock. The analysis used a daily gap-stop, which is a stop-loss that is triggered when the stock price gaps up or down, and allowed for same-day entry and exit. The analysis also included a fee of 5 basis points, which is a unit of measurement for interest rates and fees, and 10 basis points of slippage, which is the difference between the expected price of a trade and the actual price, on each side of the trade. The analysis used a stress test, which is a simulation of a worst-case scenario, with 25 basis points of slippage. The analysis was done using a cash-only strategy, which means that the strategy did not use leverage, or borrowed money, and used a hypothetical capital of $10,000, which was reinvested after each trade. The analysis was not a proposal for a fund size. The analysis found that the strategy did not perform well in the training period, with a return of -15.0392% and a maximum drawdown, which is the maximum loss of value, of 23.34594%. The strategy also did not perform well in the holdout period, with a return of -7.4434% and a maximum drawdown of 9.51235%. The analysis also found that the strategy had 7 trades, with 1 win and 6 losses, and that the average win was 2.29118% and the average loss was 1.65053%. The analysis used a bootstrap, which is a statistical method, to simulate the performance of the strategy and found that the mean return was -1.08743% with a 95% confidence interval of -1.99661% to 0.175079%. The analysis also found that the strategy did not have an edge, which means that it did not perform better than a random strategy. The analysis used a Quarter Kelly criterion, which is a method for allocating capital, and found that the optimal allocation was 0, which means that the strategy did not have a positive expected return. The analysis also included an implementation audit, which is a review of the implementation of the strategy, and found that the strategy had been implemented correctly. The analysis also found that the daily closing mark was not the worst-case scenario and that the end window was forced liquidation, which means that the strategy was closed at the end of the period. The analysis also found that all 7 trades in the holdout period were natural closes, which means that they were closed at the end of the period. The analysis was a price-rule test, which means that it tested a specific strategy, and not a backtest of Bombardier political events. The analysis also noted that the exits were not validated and that the sample could not be used to infer the odds of new events.

This is a preregistered note, number 879, which analyzed 1002 daily stock price bars for the period from September 8, 2022, to September 4, 2026, using data from https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis used a method called vectorbt, which executes trades at the next open price after the previous close price breaks out above the high price of the previous 20 days and the 50-day Simple Moving Average, or SMA, which is a calculation of the average price over a certain period. The trade is exited when the condition is lost or when a 5% stop loss or 8% target profit is reached, with a fee of 0.1% and a slip of 0.1% on each side. The training period ended on December 31, 2025, and the holdout period was in 2026. The full measurements are available in a companion observation. The training period showed a return of 363.2611% over 73 trades, but the holdout period showed a loss of 26.4354% over 9 trades, with a stress test showing a loss of 28.0341%. There is currently no signal to trade. All tested neighbors, which are similar trading rules, were rejected, and there was no retuning, or adjustment, of the rule. A diagnostic test was run without a filter or explicit costs, which showed the following results for the symbol ATI: a return of 32.14784989699413%, a buy and hold return of 576.3242375601926%, a maximum drawdown of 15.093386654757857%, a win rate of 39%, and an average trade return of 0.3234454560696176%. For the symbol IREN, the results were: a return of 57.103550067208594%, a buy and hold return of 868.9804772234273%, a maximum drawdown of 36.103355338559105%, a win rate of 42.04545454545455%, and an average trade return of 0.6986307433360928%. The tickers were selected after the fact, and the holdout dates were not a prospective, or future, population. The daily stops used in the analysis cannot validate the results of 15-minute exits, entry day ordering, partial profit-taking, or gap bounds. The rejection of the rule implementation does not mean that every future opportunity will be rejected.

A preregistered note, number 879, analyzed 1002 daily stock bars for the period of September 8, 2022, to September 4, 2026, from the source https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis used a trading rule based on the Simple Moving Average, or SMA, which is a calculation of the average price of a stock over a certain period. The rule executed a trade at the next open price if the prior close price broke out above the high price of the prior 20 closes and the SMA50, which is the average price over 50 days. The trade was exited if the next open price lost the breakout condition or reached a 5% stop loss or an 8% target profit, with a fee of 0.1% and a slip of 0.1% on each side. The training period ended on December 31, 2025, and the holdout period was in 2026. The training period resulted in a loss of 19.7981% over 73 trades, while the holdout period had a gain of 27.0245% over 21 trades, with a mean 95% bootstrap interval of -0.481668% to 3.058334%. However, the training failure and uncertainty, which includes zero, led to the rejection of the deployment despite the attractive holdout results. A diagnostic test without explicit costs was run for the ticker symbol ATI, which is a stock symbol, and resulted in a return percentage of 32.14784989699413% over 1001 bars, with a buy and hold percentage of 576.3242375601926%, a maximum drawdown percentage of 15.093386654757857%, a win rate of 39, and an average trade percentage of 0.3234454560696176. Another diagnostic test was run for the ticker symbol IREN, which is also a stock symbol, and resulted in a return percentage of 57.103550067208594% over 1001 bars, with a buy and hold percentage of 868.9804772234273%, a maximum drawdown percentage of 36.103355338559105%, a win rate of 42.04545454545455, and an average trade percentage of 0.6986307433360928. The tickers were selected retrospectively, and the holdout dates were not unseen prospective population. The daily stops used in the analysis cannot validate 15-minute exits, entry day ordering, partial profit-taking, or gap bounds. The rejection is of the rule implementation, not every future opportunity.

A news scan found the headline about a fixed breakout for the stock ticker BWEN. The data used for this analysis came from a website and included 1002 adjusted bars of stock data from September 8, 2022, to September 4, 2026. A command line interface was run with the following parameters: symbols BWEN, strategy breakout, lookback 20, days 1460, stop percentage 5, and target percentage 8. The results showed a gross return of -0.581118%, a drawdown of 25.928124%, and 32.653061% of trades were winners. This analysis was done for diagnostic purposes only and did not take into account costs. Another analysis was done using vectorbt version 1.1.0, which is a backtesting framework, with a fixed 20-day close breakout strategy, and the results were trained through December 31, 2025, with a return of 25.191988% and a drawdown of 24.545056%. The holdout period from January 1, 2026, to September 4, 2026, showed a return of -41.566941% and a drawdown of 48.55146%. The average win was 8.465371% and the average loss was 8.003927%. When stress testing with a slippage of 0.3% on each side, the return was -42.332839%. Other diagnostic lookbacks of 10 and 40 days also failed the holdout period with returns of -39.277357% and -45.131608% respectively. The stress testing for these lookbacks showed returns of -44.307145% and -45.525128%. No winning parameters were chosen. The baseline was specified before the independent test, but the ticker was selected with current fundamentals. This was not a prospective universe holdout. The daily open, high, low, and close data and the stop priority do not reproduce the 15-minute venue, entry day order, or the partial take profit. The limitations of this analysis cannot justify upgrading a severe rejected result. This is a reject rule and not all possible BWEN strategies. The reproduction of the local research and test results are preserved in the file test_bwen.py and the record observation 9303 or the nearest result id carries all metrics.

A note, numbered 593, requested sensitivity information about exiting American Depositary Receipts, or ADRs. The main rules for holding and entering the market will remain the same, with 26 main-rule holdout entries, and the next eligibility will also stay the same. Early exits will not allow for additional entries. At the next market open, a stop will be set at one prior average daily high-low range below the entry point, and a target will be set at two ranges above. If the stop is not touched, the position will be exited at the close of the fifth session. The model will account for an opening gap at its opening price and a same-bar conflict stop-first. None of the observed group had ambiguous touches. This is a sensitivity test for a matched-entry holdout, not a separate strategy with its own validation set. The data source is from the Securities Information Processor, or SIP, all-adjusted, from 2025 to September 4, 2026, and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The Vectorbt long-only model allows for same-day entry and exit, with synthetic open and exit accounting steps. The pre-exit mark is set at the actual daily open, and the close is set after the exit. This is an accounting proxy, not based on observed intraday timestamps. The entry and exit counts, as well as the compounded net equity returns, are consistent. The fees are 10 basis points for each side, plus 10 basis points for slippage, with a baseline of 20 basis points for stress. The exact baseline results are: slippage of 0.001, return percentage of -21.602592602959675, drawdown percentage of 22.82503128442278, 26 trades, win percentage of 38.46153846153847, average win percentage of 5.814500996130267, average loss percentage of 4.889033270116533, and mean percentage of -0.7722893215600712. The stress results are: slippage of 0.002, return percentage of -25.575087102594818, drawdown percentage of 25.575087102594818, 26 trades, win percentage of 38.46153846153847, average win percentage of 5.603082989308801, average loss percentage of 5.079065551359626, and mean percentage of -0.97054688187177. The returns use both entry and exit fees in the calculation. The drawdown is sampled from open to close and does not include unknown excursions between samples. The conclusion is that the required stop-target economics have a negative expectation and compounded return. The positive five-session-close backtest cannot be applied to other situations. The long-only Kelly optimum is zero for the observed negative-mean exit distribution, and no proposal is made.

A note, numbered 593, asked about the sensitivity of exits for American Depositary Receipts (ADRs). To study this, the main rules for 22 entries were kept, and the next eligibility was preserved. Early exits did not allow for additional entries. At the next open, a stop was set one prior 20 average daily high-low range below the entry, and a target was set two ranges above. If the stop or target was not touched, the position was exited at the close of the fifth session. A model was used to simulate an opening gap at its opening price and a stop-first conflict in the same bar. None of the observed group had ambiguous touches. This study was a matched-entry holdout sensitivity, not a separate strategy with its own validation set. The data source was the Securities Information Processor (SIP) all-adjusted from 2025 to September 4, 2026, found at https://runway.adityaperswal.workers.dev/data/stock-bars. The Vectorbt long-only model used synthetic open and exit accounting steps, allowing for same-day entry and exit. A pre-exit mark was made at the actual daily open, and the position was closed after exit. This was an accounting proxy, not based on observed intraday timestamps. The entry and exit counts and compounded net equity returns were reconciled. Fees were 10 basis points (0.1%) each side, plus 10 basis points slippage as a baseline, and 20 basis points stress. The exact baseline results were: slippage of 0.1%, return percentage of -3.94%, drawdown percentage of 23.25%, 22 trades, win percentage of 40.91%, average win percentage of 5.16%, average loss percentage of 3.70%, and mean percentage of -0.08%. The stress results were: slippage of 0.2%, return percentage of -8.08%, drawdown percentage of 24.47%, 22 trades, win percentage of 40.91%, average win percentage of 4.95%, average loss percentage of 3.90%, and mean percentage of -0.28%. Returns included both entry and exit fees in the calculation. The drawdown was sampled from open to close and did not include unknown excursions between samples. The study concluded that the required stop-target economics had a negative expectation and compounded return. The positive five-session-close backtest could not be applied to other situations. The long-only Kelly optimum was zero for the observed negative-mean exit distribution, so no proposal was made.

A preregistered note, number 593, was written before prices were known. The note said to buy CENX, a stock symbol, when its price closed above its 50-day Simple Moving Average, which is a way to calculate the average price of a stock over a certain period. The plan was to enter the trade at the next session's open and exit at the fifth session's close, without overlapping trades or crossing boundaries. This strategy was trained on data from 2020 to 2024 and tested on data from 2025 to September 4, 2026. The data used was adjusted for dividends and included 1678 unique bars per symbol from January 2, 2020, to September 4, 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. The test included fees of 10 basis points and slippage of 10 basis points on each side. The results showed a positive return, but the edge, or advantage, was uncertain. The train results were: symbol CENX, lookback 20, trend true, return percentage 118.11, drawdown percentage 40.22, win percentage 52.31, average win percentage 9.30, average loss percentage 6.62, and mean percentage 1.71. The test results were: symbol CENX, lookback 20, trend true, return percentage 4.59, drawdown percentage 25.85, win percentage 53.85, average win percentage 5.80, average loss percentage 5.84, and mean percentage 0.43. A test with double slippage showed a return percentage of -0.71. The results also showed a four-trade circular block mean CI of 10000 resamples with a seed of 90607. The conclusion was that the positive historical return alone does not establish a net edge, and there is currently no entry signal. This is a generic technical rule, not a historical disruption-event backtest. Separate ADR exits require their own payoffs, and this win rate cannot be applied to a stop-target trade. There is also a selection bias from the two surviving research names, regime changes, and only 22 out of 26 test trades, which limits generalization.

A preregistered note, number 593, was written before prices were known, with a strategy to buy and sell aluminum based on certain conditions. The conditions were: the previous close was a breakout and the close was higher than the 50-day Simple Moving Average, which is a way to smooth out price changes over time. The plan was to enter the market at the next session's open and exit at the fifth session's close, with no overlapping trades and no trades that cross boundaries. This strategy was tested using data from 2020 to 2024 and then applied to data from 2025 to September 4, 2026. The data used was adjusted for things like dividends and volume, and was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The results showed a positive return, but it is uncertain if this strategy has an edge, meaning it is unclear if it will continue to perform well in the future. The results were: Train = {symbol: AA, lookback: 20, trend: true, return percentage: 187.05, drawdown percentage: 32.26, win percentage: 61.90, average win percentage: 6.82, average loss percentage: 5.83, mean percentage: 2.00, buy and hold percentage: 78.67} and Test = {symbol: AA, lookback: 20, trend: true, return percentage: 16.16, drawdown percentage: 18.62, win percentage: 50, average win percentage: 5.97, average loss percentage: 4.23, mean percentage: 0.87, buy and hold percentage: 33.29}. A test with double slippage, which is a way to account for the cost of buying and selling, showed: {symbol: AA, lookback: 20, trend: true, return percentage: 11.16, drawdown percentage: 19.75, win percentage: 50, average win percentage: 5.76, average loss percentage: 4.42, mean percentage: 0.67, buy and hold percentage: 33.29}. The results also showed that the strategy did not have a clear edge, and that there were limitations to the test, including selection bias and regime changes. The strategy was not approved for use, and it was noted that separate exits for ADRs, which are a type of investment, would require their own payoffs.

A diagnostic test was run using the command line interface to analyze the performance of a trading strategy on aluminum stocks. The test used the symbols AA and CENX, a breakout strategy, and looked back at 20 days of data over a period of 1460 days, with a 5% stop loss and a 10% target profit. The data came from the IEX daily stock bars source, which provided 1002 bars per symbol, covering the period from 2022 to September 4, 2026. The results showed that for AA, there were 71 trades, a return of -29.87397%, a maximum drawdown of 43.12439%, and a win rate of 33.80282%, with a mean return of -0.41949% and a buy and hold return of 2.24064%. For CENX, there were 84 trades, a return of 14.87024%, a maximum drawdown of 27.47976%, and a win rate of 35.71429%, with a mean return of 0.27465% and a buy and hold return of 511.35770%. The test did not include an independent holdout set, and the results are only for diagnostic purposes and cannot be used to determine the size of a trade. A separate study using a frozen SIP fee-inclusive vectorbt from 2020 to 2024 for training and 2025 and later for holdout will provide the main results, and its data and settings should not be mixed with this diagnostic test.

A diagnostic test was run using a command line interface, or CLI, with the following parameters: node /app/agent/dist/backtest.js --symbols UAMY --strategy breakout --lookback 20 --days 1460 --stop-pct 5 --target-pct 10. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test resulted in 1002 bars, 68 trades, a return of 65.4721%, a maximum drawdown of 34.1623%, and a win rate of 39.7059%. The average return per trade was 0.9560%, and a buy-and-hold strategy would have returned 1185.9267%. This test is not the same as a previously registered strategy and does not have a separate model to validate its results. Another test, called vectorbt train/holdout test 543, was used to make a decision and the results of the CLI test were rejected. The good performance of the CLI test should not be confused with a proven advantage in the stock market.

A preregistered note, number 552, was written before a custom test. The stock UAMY was selected after today's filings, so the historical holdout is not pristine or prospective. The data used is from https://runway.adityaperswal.workers.dev/data/stock-bars, which includes SIP-adjusted daily data from January 3, 2022, to September 4, 2026, with 1173 bars and no pagination. The strategy used a fixed close price greater than the prior 20-day close high and a 50-day Simple Moving Average, or SMA, which is a calculation of the average price of a stock over a certain period of time. The next open price was also considered, as well as a frozen prior 20-day Average Daily Range, or ADR, with a stop loss at 1 ADR and a target at 2 ADR, with the fifth session close being the key. The strategy also included a stop loss on the first ambiguous daily bar and a stop gap with a worse open. Fees were 5 basis points, or bp, plus slippage of 10 bp on each side, with a stress test of 20 bp. The vectorbt long-only strategy with two marks per day was used, and all 35 holdout trades were long, with 35 exits, and the compounded entry-fee-inclusive Profit and Loss, or PNL, matched the portfolio with an accuracy of 1e-8. The daily Open, High, Low, and Close, or OHLC, data was used, but not 15-minute trigger or intraday liquidity validation. The Drawdown, or DD, was sampled twice a day and may understate intraday DD. The training period from 2022 to 2024 had 39 trades, with a return of -41.6594% and a DD of 57.9309%, with 30.7692% wins. The holdout period from 2025 to September 4, 2026, had 35 trades, with a return of -7.11606% and a DD of 58.2452%, with 40% wins, an average win of 12.96375%, and an average loss of 8.05117%. The mean return was 0.35480%, and the 95% mean interval was [-3.18592, 4.12585]%. The stress return was -13.39560%, with a DD of 58.8669% and 37.1429% wins, with an average win of 13.72262% and an average loss of 7.86336%. The primary net quarter Kelly point was 0.6842% of capital, and the stress was 0.2811%, but the uncertainty-adjusted approved value was 0 because the mean interval crossed zero, the training failed, and the drawdown was large and the current entry was false. Neighboring sessions were tested for robustness only, with train returns of -54.2387% and -38.8928%, and holdout returns of +17.8054% and +36.7124%, with 41 and 25 sessions, respectively, but the mean intervals crossed zero, so the best holdout was not switched to. This rejects the fixed stock rule, but does not prove negative fundamentals. The numeric edge cannot be imported from two operator-directed tanker wins. The internal default both-direction version was discarded, and the later raw trade return denominator was corrected before analysis. Earlier observation averages were superseded by corrected observations, but the portfolio outcomes were unchanged. There is no trade or paper profit claim.

This is an evidence review only, using previous figures from FRO36/59, not a new backtest or sample. On September 6, a readback was done on a frozen strategy that uses a next-open 20 close breakout, with a 1 ADR stop and 2 ADR target, over 5 sessions, using vectorbt for long-only trades with 5 basis points fees and 10 basis points slippage, trained from 2022 to 2024 and held out from 2025 to September 4, 2026. The results showed a 20.24% return and 24.42% drawdown, with 41 trades, a 41.46% win rate, and an average win of 5.46% and average loss of 2.96%. The mean confidence interval was between -0.874% and 1.959%, which includes zero. A total of 53 out of 417 trades were already executed and audited. Previous tests using a six-sector baseline showed a 3.545% return with 26.398% double cost, while a trend and low volatility strategy showed a 10.294% loss over 221 trades. New notices did not repair controls or identify a new stock rule. There is no new strategy, redundant retest, or selected winner optimization. Studies 437 to 439 are not stock backtests. The historical holdout data is not pristine prospective data, with a size of 0. Prior tanker edge cannot be transferred to Maersk, Hapag, or ZIM. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, with query research supply numbers 36, 53, 176, 409, 410, 417, and 420. Code archive note 390 remains a journal, even though the prior temporary workspace is absent.

A required command line interface run was performed using the command node /app/agent/dist/backtest.js with the parameters --symbols SMH, XLI, XLB, XLE, XLP, IYT, SPY, --strategy sma-cross, --fast10, --slow50, and --days2440. The code in /app/agent/dist/backtest/bars.js was reviewed, which has a request limit of 10000, no loop for the next page token, uses the feediex source, and does not include an adjustment parameter. The combined response from this setup provided 10000 bars but truncated the data for XLP to 792 bars, whereas the main fetch retrieved 1536 bars. To fix this issue, XLP was requested separately, resulting in 1536 bars, with a return of -8.831933%, a drawdown of 17.011767%, a win rate of 31.818182%, and 22 trades. In contrast, the truncated XLP data showed a return of -5.80394%, a drawdown of 12.85960%, a win rate of 30%, and 10 trades. The results for the other symbols over the full period were SMH with a return of 94.7988%, XLI with 44.7503%, XLB with -61.0437%, XLE with -51.3255%, IYT with -72.0330%, and SPY with 68.8189%. These results are based on raw price simple moving average controls and do not account for frozen range-exit protocol, chronological holdouts, or corporate-action-safe equity edge estimates. The vectorbt controls, which use all adjusted, pagination per symbol, next open, fees, and slippage, as well as separate windows, still failed to meet the entry criteria. The conclusion is to run the command line interface as required but to inspect the coverage and defaults independently before trusting the results. The shared command line interface infrastructure should not be modified during this research run. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the implementation is in /app/agent/dist/backtest/bars.js.

A trading strategy that combines trend following with low volatility did not improve short-term exit results. The strategy was based on a set of rules, including a preregistered gate that checked if the prior close was above the 50-day simple moving average, which is a calculation of the average price of a stock over 50 days, and if the average true range, a measure of volatility, over 14 days was below the median of the last 126 sessions. The strategy also included a stop-loss order, which is an order to sell a stock when it falls to a certain price, set at 1 times the average true range, and a target price set at 1.5 times the average true range, with a 5 basis point fee, which is a 0.05% fee, and a 10 basis point slip, which is a 0.1% difference between the expected and actual price. The strategy was tested on data from 2021 to 2023 and resulted in a return of -20.1377%. From 2024 to September 4, 2026, the strategy resulted in a return of -10.2942% with a maximum drawdown, which is the maximum loss from a peak to a trough, of 11.4872%. Out of 221 closed trades over 84 weeks, 36.6516% were winners, with an average win of 1.98073% and an average loss of -1.60064%, resulting in a mean return of -0.288016%. A bootstrap analysis, which is a statistical method to estimate the variability of a result, found that the 95% confidence interval for the mean return was between -0.578047% and -0.025141%. The return after accounting for costs was -20.8938%, which is lower than the return of a fixed strategy with the same cohort, which is a group of stocks, of -0.586704%. Only 1 out of 54 variations of the strategy, which included different stop-loss and target prices and sectors, resulted in a positive return, but this result is not statistically significant because the variations are not independent trials. The roundtrip cost, which is the cost of buying and selling a stock, was higher than the baseline cost, with a median of 0.228694R versus 0.196076R. The strategy's reduction in portfolio drawdown, which is the maximum loss from a peak to a trough, was partly due to the fact that the strategy was not invested for as long, and it did not result in a positive expected payoff. Therefore, the strategy was rejected for live use. The results were based on data from https://runway.adityaperswal.workers.dev/data/stock-bars, and all outputs were recorded without any retuning of old strategies or labels after the outcomes were known. A news scan found the source of the data.

A trend filter, which is a tool used to determine the direction of market trends, failed to perform well in two different time periods. The filter used a baseline strategy that included a stop loss of 1 average true range (ATR), a target of 1.5 ATR, and a time horizon of 10 sessions, with a fee of 5 basis points and slippage of 10 basis points on each side. An additional condition required the stock's prior close to be above its 50-day simple moving average (SMA). The filter was trained on data from 2021 to 2023 and tested on data from January 2024 to September 4, 2026. During the test period, the filter resulted in a return of -16.7431% and a maximum drawdown of 18.2796%, with 413 closed trades and 121 weeks with an open position. The filter had a win rate of 38.7409%, a mean return of -0.250908%, an average win of 2.21739%, and an average loss of -1.81189%. The results were analyzed using a bootstrap interval, which is a statistical method used to estimate the uncertainty of a result, and the results showed that the filter's performance was not significantly better than random. The filter was also tested with different stop loss and target levels, but none of the combinations resulted in a significant improvement. Based on these results, the trend filter was rejected as a standalone strategy and as an improvement to a future disruption cohort. The data used to train and test the filter came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, and the results were verified using the vectorbt and independent replay tools on September 6 at 06UTC.

The protocol notes, numbered 355 and 356, were written before the trading bars. The system was tested on six sectors, each with its own separate cash account, and the SPY was compared separately. The system bought stocks on Monday mornings using a formula based on the stock's price movement over the past 14 days, known as the average true range or ATR, which is a measure of volatility. The stop loss, which is a price level at which to automatically sell a stock, was set at one ATR, and the target price, which is the price at which to sell a stock for a profit, was set at 1.5 ATR. The system sold stocks after 10 sessions, which is a period of trading, and did not buy and sell the same stock at the same time. The system paid a fee of 5 basis points, which is a unit of measurement for interest rates and fees, plus a slippage of 10 basis points on each trade, which is the difference between the expected price and the actual price. If the stop loss and target price were both reached, the system sold at the stop loss first. The system was trained on data from February 22, 2021, to December 2023, because the data begins on July 27, 2020, and the indicators, which are mathematical calculations used to predict stock prices, needed time to warm up. The system was tested on data from January 2024 to September 4, 2026, starting with no money made or lost. The training return was -17.4442%, and the test return was +3.5450%, with a maximum loss of 9.5597% and 598 trades closed naturally in 126 weeks of buying and selling. One trade in the XLP stock was not included in the calculations of winning trades. The system won 43.9799% of the time, with an average win of +2.4392% and an average loss of -1.8573%, and the average return was +0.032293%. The system was also tested with a double-cost rule, which means that the fees were doubled, and the return was -26.3984%, with 603 trades. The system did not have any trades that were ambiguous, meaning that the stop loss and target price were not both reached at the same time. The accounting method used was normalized research accounting, which is a way of calculating returns that takes into account the effects of fees and slippage, and not raw cash dividends or quotes, which are the actual prices of stocks. The system was tested using a method called vectorbt longonly and independent cash replay, which is a way of simulating trades, and the maximum error was 9.83e-11 USD, which is a very small amount. The system was also tested on two stocks, SMH and XLE, which had returns of +40.593% and +42.982%, respectively, but these returns are not statistically significant, meaning that they could have occurred by chance. This system is an execution control, which means that it is a way of testing trading strategies, and not a backtest, which is a way of testing a strategy on historical data. The system should not be used in live trading, because it has not been tested on a completed sample of prospective incidents. The data used to test the system came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, and every result and uncertainty was recorded on September 6 at 06 UTC, which is a time zone, and the exact inputs were archived.

A mandated command line interface, or CLI, was executed to run a simple moving average, or SMA, with parameters 10 and 50, over 1344 days on six US stock names before any proposal. The results showed the following performance metrics for each stock: a news scan found the data line with the following information: AEHRAEHR had a return percentage of 40.0021602846025, a drawdown percentage of 74.36484400892152, a win rate of 25, a sample size of 12, and a buy and hold return of 368.3410263372251. COHU had a return percentage of 44.44058175393799, a drawdown percentage of 40.8719973086827, a win rate of 35.714285714285715, a sample size of 14, and a buy and hold return of 59.754369390647135. AXTI had a return percentage of 992.4277589978756, a drawdown percentage of 74.57548265618689, a win rate of 18.181818181818183, a sample size of 11, and a buy and hold return of 1308.4571428571428. MP had a return percentage of 58.778171621743255, a drawdown percentage of 52.36997291189489, a win rate of 40, a sample size of 10, and a buy and hold return of 125.53807947019865. MU had a return percentage of 334.5504196438671, a drawdown percentage of 49.117752620514274, a win rate of 42.857142857142854, a sample size of 14, and a buy and hold return of 1915.0883462378401. NX had a return percentage of -5.087252629102723, a drawdown percentage of 52.15464592547335, a win rate of 50, a sample size of 10, and a buy and hold return of -4.556856187290981. These results are from control runs, which are different from holdout tests and were not from the same model used in a previous note. The native control's signal timing and corporate action handling also differ, so the results should not be combined with holdout returns. The governing tests use data from 2023 to 2024 for training and 2025 to September 4 for holdout, with a 5 basis point fee and 10 basis point slippage on each side, and a 10 and 20 stress test, with a 3 by 3 sweep during training only, and no parameter was chosen from the holdout. The canonical CLI JSON and independent audit are in the 227 part NX archive, with a decoded SHA of fb6f0085bae7adadf109f9f22ae7aae7da5c635b87657ecc0aa3b743304ea3bd. There is zero authorization from the full-period performance. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline about Quanex Building Products announcing its third-quarter 2026 results. The same protocol that was used before to analyze data found that Quanex was the top liquid mover in the US market, based on past data. From 2023 to 2024, the training data showed a return of 10.5272%. However, from September 4, 2025, to the present, the holdout data showed a loss of 36.1835%, with a maximum drawdown of 53.2616%. The system had 4 natural closes that covered all losses, plus 1 open mark. A fee of 5 basis points and a 10 basis point slip on each side were applied. The next open will use a simple moving average with a 10/50/flat window start. The stress test showed a loss of 37.0392% with a 10/20 basis point change. The training data showed a winner with a 5/50 strategy, but the holdout data showed a loss of 9.8913%, which means the system failed and cannot be retuned. The closed payoff mean was -15.5929%, with a 95% confidence interval of -19.9132% to -11.2727% based on 10,000 bootstrap simulations. A simulation with a 10% allocation showed a median loss of 14.5736%. An ordinary least squares regression with 420 daily SPY data points showed a beta of 1.55985 and a correlation of 0.46099, with an annual intercept of -15.5606%. The native command-line interface control was also archived, and an independent cash reconciliation using vectorbt showed a maximum error of 1.82e-12. The current crossover signal is false, and the top 1-day price jump of 21.178% is a discovery, not an entry signal.

A news scan found the note about the stock 5803.T. This note was written before looking at the past performance. The strategy used was to pick the current best performer, not to look at all possible options. The plan was to buy when the 10-day average price crossed over the 50-day average price, and to sell when it crossed back. Each time period started with no investment, and there was a practice period to get started. The training period was from 2023 to 2024, and the test period was from 2025 to September 4, 2026. The test used a fictional $10,000 and accounted for a 0.05% fee and 0.1% slippage for each trade. The stress test added an extra 0.1% or 0.2% to the costs. The US stock prices were adjusted for dividends and splits, and foreign prices were adjusted for dividends and splits in their local currency, but did not account for exchange rates. The test did not use stop-loss orders that would sell a stock after it fell by a certain amount, and did not require buying whole shares. The training period had a return of 345.6336%, with 5 natural closes, which means the position was closed at the end of the day. The test period had a return of 132.3479%, with a maximum drawdown of 57.3359%, and 4 natural closes, with 1 position still open. The confidence interval, which is a measure of how sure we are about the results, was between -21.514976837353068% and 134.70798314842915%. Nine different combinations of short-term and long-term averages were tested, but only the results of the best one, which used a 10-day and 60-day average, are reported, with a return of 234.5618%. The results are available in a file called artifacts/results.json, along with the code used to run the test. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/5803.T. The strategy was rejected for use with real money because the potential profit was uncertain and the plan for when to sell was not complete.

A preregistered note was made before analyzing the history of LYC.AX. The analysis used a selected current survivor strategy, not an unbiased universe strategy. It looked at the Simple Moving Average, or SMA, which is a way to smooth out price data, with a 10 and 50 day crossover, meaning the short-term average crossed over the long-term average. The analysis used a completed-close SMA10/50 crossover, with entry and exit points determined by the next session's open and reverse cross. Each window started flat, with a historical warmup period. The data was split into a training period from 2023 to 2024 and a holdout period from 2025 to September 4, 2026. The analysis used a vectorbt all-in fractional diagnostic with $10,000 in cash, a 5 basis point fee, and 10 basis point slippage on each side, with a stress test of 10 and 20 basis points. The data used was all-adjusted from the US IEX and Yahoo, with no foreign exchange or venue 15-minute stops, and a whole-share model was not used. The training period showed a return of -31.2388% with 7 natural closes, while the holdout period showed a return of 101.1909% with a maximum drawdown of 45.1439% and 4 natural closes, with no open marked positions. The mean 95% bootstrap interval was between -1.9762500013106816% and 52.820585043496656%. Nine training-only combinations were archived, with the best combination showing a subsequent return of 96.8950%. However, this analysis was not used to authorize any trades. No retuning was done after the holdout period. All figures, code, and trades are available in the artifacts and results file. The sources used were https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/LYC.AX. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.

A preregistered note, number 246, was written before looking at the history of the stock. The strategy used was to select the current survivor, not an unbiased universe strategy. A Simple Moving Average, or SMA, crossover was completed, which means the short-term average price crossed over the long-term average price, and the next session's open was used as an entry or exit point. The analysis started from a flat position at the beginning of each window and used historical data for warmup. The training period was from 2023 to 2024, and the holdout period was from 2025 to September 4, 2026. The analysis used a vectorbt all-in fractional diagnostic with $10,000 in cash, a 5 basis point fee, and 10 basis point slippage on each side, with a stress test of 10/20 basis points. The data used was from the US IEX, which is an all-adjusted stock exchange, and from Yahoo, which is an overseas stock exchange that provides adjusted Open, High, Low, and Close, or OHLC, data in local currency. The analysis did not account for foreign exchange rates, venue 15-minute stops, or whole-share models. The training period showed a return of 6.9090%, with 6 natural closes, and the holdout period showed a return of 53.9612%, with a maximum drawdown of 26.7121%, 5 natural closes, and 1 open marked position. The mean 95% bootstrap interval was between -5.772772362317384% and 11.050257229369842%. Nine combinations of training-only data were archived, and the best combination showed a subsequent return of 14.3513%. However, this analysis is not authorization for investment. The figures, code, and trades are available in the artifacts and results file. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/MAERSK-B.CO. The stock, MAERSK-B.CO, was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.

A preregistered note was made before analyzing the history of the stock MU. The analysis used a selected current survivor strategy, not an unbiased universe strategy. It looked at the Simple Moving Average, or SMA, which is a way to smooth out price data, with a 10 and 50 day average crossover, meaning the short-term average crossed over the long-term average, to decide when to enter or exit a trade, with the next session's open price as the entry or exit point. The analysis started with a flat position at the beginning of each window and used historical data to warm up. The data was split into a training period from 2023 to 2024 and a holdout period from 2025 to September 4, 2026. The analysis used a diagnostic tool called vectorbt, which is a way to backtest trading strategies, with a fictional $10,000 in cash, a 5 basis point fee, and 10 basis point slippage, which is the difference between the expected price and the actual price, on each side of the trade, and stress tested with an additional 10 and 20 basis points. The analysis used adjusted prices from the US IEX exchange and Yahoo finance for overseas prices, without considering foreign exchange rates or venue-specific 15-minute stops or whole-share models. The training period showed an 11.5689% return, with 8 natural closes, which means the position was closed at the end of the trading day, and the holdout period showed a 294.8017% return, with a maximum drawdown of 37.3481%, 7 natural closes, and 0 open marked positions. The mean 95% bootstrap interval, which is a way to estimate the uncertainty of the results, was between -7.5558% and 81.9222%. Nine combinations of SMA parameters were tested during the training period, but not during the holdout period, and the best combination showed a 229.0861% return. However, this result is not authorization to deploy capital. The analysis was based on data from https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/MU. The strategy was rejected for capital deployment due to uncertain payoff and incomplete executable exit model.

A news scan found the headline is not available, but a preregistered note, number 246, was written before the history. The strategy used was a current survivor, not an unbiased universe strategy. This strategy completed a close simple moving average, or SMA, 10/50 crossover, which means it looked at the average prices of a stock over 10 and 50 days to make decisions. The strategy entered or reversed positions at the next session's open and exited at the reverse cross, starting each window flat, with a historical warmup period. The strategy was trained on data from 2023 to 2024 and held out from 2025 to September 4, 2026. The diagnostic used a vectorbt all-in fractional approach, which is a way of testing a strategy, with a cash amount of $10,000, a fee of 5 basis points, and slippage of 10 basis points on each side, and stress of 10/20 basis points. The data used was from the US IEX, which is a stock exchange, and was all-adjusted, and from Yahoo, which is a financial website, and was adjusted for overseas stocks, with no foreign exchange, or FX, and 15-minute stops, but not a whole-share model. The training period showed a return of -20.2269%, with 5 natural closes, and the holdout period showed a return of 100.8691%, with a drawdown, or DD, of 52.6487%, and 6 natural closes, and 1 open marked position. The mean 95% bootstrap interval, which is a way of measuring the uncertainty of a result, was between -9.281859484395701% and 83.34189392058218%. Nine combinations of training-only data were archived, and the best subsequent return was 62.0380% for the 10/40 combination, but this was not authorization for deployment. The strategy was not retuned after the holdout period. All figures, code, and trades are available in the artifacts and results file, and the sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/MP, which is the ticker symbol for the stock. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.

A preregistered note was made before looking at the history. The current survivor, not the entire universe of possibilities, was chosen as the strategy. A simple moving average, or SMA, crossover was used to decide when to enter or exit a trade, with the SMA10 and SMA50 being the two averages used, which are averages of the stock prices over 10 and 50 days respectively. The trade was entered at the next session's open and exited at the reverse crossover, with each window starting from a flat position and a historical warmup period. The training period was from 2023 to 2024, and the holdout period was from 2025 to September 4, 2026. The diagnostic used a vectorbt all-in fractional approach, with a cash amount of $10,000, a fee of 5 basis points, and slippage of 10 basis points on each side, and stress tests of 10 and 20 basis points. The US stock prices were adjusted for dividends, splits, and other events, and overseas prices were adjusted for similar events, but no currency exchange rates were used, and there were 15-minute stops, but no whole-share model was used. The training period resulted in a return of -70.4170%, with 8 natural closes, and the holdout period resulted in a return of 3806.8958%, with a maximum drawdown of 51.0432%, 3 natural closes, and 0 open marked positions. The mean 95% bootstrap interval was between -26.874767394405925% and 3755.0995828171845%. Nine combinations of SMA parameters were tried, but only the training-best combination of 5/60 resulted in a subsequent return of 2247.0847%, but this was not used for authorization. No further tuning was done after the holdout period. All figures, code, and trades are available in the artifacts and results file. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/AXTI. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.

A preregistered note was made before looking at the history of the stock COHU. The strategy used was to select the current survivor, not the entire universe of possibilities, without bias. The strategy was based on a Simple Moving Average, or SMA, crossover, which is a way to determine when to buy or sell a stock. The SMA10 and SMA50 crossover was used to decide when to enter or exit a trade, with the next session's open price used to enter or reverse the trade. Each window started flat, with a historical warmup period. The training period was from 2023 to 2024, and the holdout period was from 2025 to September 4, 2026. The diagnostic used a vectorbt all-in fractional approach, which means it considered all possible trades, with a cash amount of $10,000, a fee of 5 basis points, and slippage of 10 basis points on each side. The stress test used 10 and 20 basis points. The data used was from the US IEX, adjusted for all factors, and from Yahoo, adjusted for overseas markets, in local currency, without considering foreign exchange rates. The training period resulted in a return of -18.3796%, with 7 natural closes, while the holdout period resulted in a return of 63.6120%, with a maximum drawdown of 34.2930%, 6 natural closes, and 0 open marked positions. The mean 95% bootstrap interval was between -6.253898200108404% and 33.81850909884469%. Nine combinations of SMA were tested, but only the 10/40 combination resulted in a subsequent return of 57.7266%. However, this was not considered authorization for deployment. No further adjustments were made after the holdout period. All figures, code, and trades are available in the artifacts and results file. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/COHU. The strategy was rejected for capital deployment due to uncertain payoff and an incomplete executable exit model.