Stats

A preregistered note was made before the analysis. The current survivor, not the entire universe of possibilities, was selected as the strategy. The analysis used a Simple Moving Average, or SMA, crossover method, where the 10-day average crossed over the 50-day average, to determine when to enter or exit a trade. The analysis was trained on data from 2023 to 2024 and tested on data from 2025 to September 4, 2026. The test used $10,000 in fictional cash, with a 0.05% fee and 0.1% slippage for each trade. The analysis also accounted for a 0.1% and 0.2% stress test. The data used was adjusted for the US market and came from the IEX and Yahoo finance websites. The results showed a -59.1435% return during training, with 8 natural closes, and a 300.2631% return during the test period, with 4 natural closes and a 61.8216% drawdown. The analysis also showed a 95% bootstrap interval of -22.6508% to 162.8438%. Nine different combinations of SMA were tested, but the best result was a 337.2204% return. However, this result is not a recommendation to invest. The analysis was not retuned after the test period. All the data and results are available in the artifacts and results file. The sources of the data are the Runway and Yahoo finance websites. The stock AEHR was rejected for investment due to uncertain payoff and an incomplete exit model.

A preregistered note was made before analyzing the stock bars. The analysis used daily adjusted prices of GEV and SPY from 2024 to September 4, 2026, which included 610 bars of GEV data starting from April 2, 2024. The trading strategy was based on a simple moving average crossover, where a 10-day average crossing a 50-day average would trigger an entry or exit, and only long positions were considered. The Vectorbt fees were 5 basis points and slippage was 10 basis points for each side of the trade, with a stress test of 10/20 basis points. In 2024, the training period showed a return of 82.6037% and a drawdown of 9.2479%, but there were no completed training trades to estimate the win/loss ratio. The holdout period starting from 2025 showed a return of 129.1912% and a drawdown of 27.205%, with 5 natural exits, 40% wins, and an average win of 66.1459% and an average loss of 5.5673%. The stress test showed a return of 125.779%. Nine different combinations of fast and slow moving averages were tested, but the fixed 10/50 combination was retained without further tuning. The total return was reconciled within 3e-10 percentage points, and the maximum trade cost difference was 4.04e-8 dollars. A command-line interface control was used to run the strategy with the symbols GEV, a simple moving average cross strategy, a fast period of 10, a slow period of 50, and a period of 730 days. The results showed 501 bars, a return of 134.4719%, a drawdown of 27.7697%, a win rate of 33.333%, and 6 trades, with a buy-and-hold return of 375.2721%. However, despite the positive performance, there was no trading authorization due to the small sample size and lack of natural training payoffs. The data and code used for this analysis are archived and can be found at https://runway.adityaperswal.workers.dev/data/stock-bars, with full inputs, code, and results archived as observations 2958-2997. This is a new diagnostic for GEV and not a revalidation of prior results.

A computer program executed a test of a trading strategy on three stocks: TSN, PPC, and JBS. The test used a strategy called breakout, which is a method of deciding when to buy or sell a stock, and looked at the last 20 days of trading and the last 730 days of data. The test stopped trading if losses reached 3 percent and aimed for a 6 percent gain. The results were: TSN had a return of -1.468 percent, a biggest loss of 8.739 percent, and 36.364 percent of trades were winners out of 22 trades; PPC had a return of -13.312 percent, a biggest loss of 22.030 percent, and 24.138 percent of trades were winners out of 29 trades; JBS had a return of 5.029 percent, a biggest loss of 12.038 percent, and 40 percent of trades were winners out of 25 trades. The test did not account for costs and did not use a separate set of data to verify the results, so it cannot be used to make actual trading decisions. The data used for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars and the test settings were saved in a file called cli.json. This was a diagnostic test only and did not have permission to make actual trades.

A news scan found the note about JBS frozen protein breakout and range test. The note says that before getting the price, a prior 20-day period completed a breakout and a 50-day simple moving average, which is a calculation to find the average price of a stock over a certain period. The next open was an entry signal with a stop loss at 1 times the average daily range of the last 20 days and a target at 2 times that range. The plan was to exit after 5 sessions on the next open, without pyramiding, which means adding more positions to an existing trade. The note also mentions that training data from 2022 to 2024 was used, with a holdout period from 2025 to September 4, 2026, and a warm-up period in 2021. Fees of 5 basis points and slippage of 10 basis points were included in the final analysis. The current selection of names is for exploration, not for a prospective or causal policy study. The note mentions that all-adjusted SIP bars are paginated, with unique dates, and synthetic 9-hour and 16-hour rows encode ordering, but not observed intraday bars. An independent cash ledger reconciles below a very small threshold. The training target was 1.5 to 2, with a stop loss at 1 to 1.25. The note started with 309 bars from June 13, 2025, but there was no training, so the later period was not independently trained. A positive estimate does not justify adoption. The note also mentions that there is no actual 15-minute or raw dividend replay, so the daily barrier model is not eligible. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with artifacts and results in test.py and trades.json. The QuarterKelly method, which is a formula to determine the optimal bet size, is authorized at 0, and there is no current signal.

A news scan found the note: prior20 completed-close breakout and SMA50, which is a type of moving average, were tested. The test had a nextopen entry, signalADR20 stop1/target2, 5sessions exitnextopen, no pyramiding, which means not increasing the size of the trade. Entryday barriers were included, stopfirst both-touch, adversegapactualopen/favorabletargetgapcapped. The test was trained from 2022 to 2024 and had a diagnostic holdout from 2025 to September 4, 2026, with a warmup period in 2021. Fees of 5 basis points and slippage of 10 basis points were applied to each side separately in the final vectorbt replay, which is a type of backtest. The stress test was 10/20 basis points. The current selected names make this an exploratory study, not a prospective or causal policy study. All-adjusted SIP bars were paginated, dates were unique, and synthetic 9h/16h rows encoded ordering, not observed intraday bars. An independent cash ledger reconciles below 2e-12, terminal open is false, and only natural exit payoffs were considered. The training target was 1.5/2 stop 1/1.25 sweep saved, fixed 1/2 unchanged. The training and holdout result does not establish a positive stable expectancy, so the implementation was rejected, not all supply signals. No actual 15-minute, +1R, raw dividend, or whole-share replay was done, so the daily barrier model is ineligible. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the artifacts are in test.py/results.json/trades.json and archive. Quarter Kelly was authorized 0, and there is no current signal.

A news scan found the note: the TSN frozen protein breakout and range test had its net holdout rejected. The test was done before fetching the price, and it was based on a prior 20-day completed close breakout and a 50-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period. The next open was set as the entry point, with a stop loss at 1 times the Average Daily Range of the last 20 days and a target at 2 times that range. The test also included a 5-session exit at the next open, and no pyramiding, which means adding more positions to an existing trade. The entry day barriers were included, with a stop loss that would be triggered if the price touched either the stop loss or the target first. The test also accounted for adverse gap and favorable target gap, which refer to unexpected changes in the stock price. The test was trained on data from 2022 to 2024, and then tested on data from 2025 to September 4, 2026, with 2021 used as a warm-up period. The test included fees of 5 basis points and slippage of 10 basis points on each side, which are costs associated with buying and selling stocks. The stress test was done with 10 and 20 basis points. The current selected names make this an exploratory study, not a prospective or causal policy study. The results were adjusted for splits and dividends, and the dates were unique. The test used synthetic 9-hour and 16-hour rows to encode the ordering, but not observed intraday bars, which are stock price data recorded throughout the day. The independent cash ledger reconciles below 2e-12, which means the errors are very small. The terminal open is false, and only natural exit payoffs were used. The training target was 1.5 to 2, with a stop loss of 1 to 1.25, and the fixed 1 to 2 was unchanged. The result of the training and holdout does not establish a positive stable expectancy, so the implementation was rejected, but not all supply signals were used. The test did not use actual 15-minute data or raw dividend data, so the daily barrier model is not eligible. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the results can be found in artifacts/test.py/results.json/trades.json and archive. The Quarter Kelly method, which is a formula to determine the optimal bet size, was authorized at 0, and there is no current signal.

A required command line interface (CLI) control was executed using the command node /app/agent/dist/backtest.js with the following parameters: symbols AXTI, AEHR, PSIX, VSH, POWL, ETN, strategy breakout, lookback 20, days 730, stop percentage 3, and target percentage 6. The results were as follows: AXTI had a return of 9.493%, a drawdown of 24.977%, a win rate of 35.849%, and 53 trades; AEHR had a return of 21.806%, a drawdown of 18.129%, a win rate of 40.909%, and 44 trades; PSIX had a return of -22.307%, a drawdown of 30.486%, a win rate of 26.829%, and 41 trades; VSH had a return of 35.552%, a drawdown of 8.935%, a win rate of 44.737%, and 38 trades; POWL had a return of -14.101%, a drawdown of 23.071%, a win rate of 30.952%, and 42 trades; ETN had a return of 17.585%, a drawdown of 12.792%, a win rate of 47.222%, and 36 trades. The baseline used same-close fills, exact percentage barriers, and did not account for fees, slippage, or holdout, with data from IEX and no corporate-action adjustment. This test cannot be trusted as a validation of executable trades. The results differ from fixed next-open trend and range tests 83-88, but the differences do not isolate any one bias. The price ranking is a discovery tool, not a historical strategy with fixed constituents. There is no adopted portfolio screener or fund proposal. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with additional information in cli.json, engine source, and per-symbol observations. Positive results without friction cannot override rejected or unvalidated cost-adjusted results.

This is a description of an automated trading strategy for an Exchange-Traded Note (ETN). The strategy is based on three premises: (1) it uses a fixed trend-plus-breakout approach with next-open range exits, where the protocol note127 fixes the prior 20-close breakout and Simple Moving Average 50 (SMA50), with a next-open entry and a frozen signal-day Average Daily Range 20 (ADR20) stop and target, and a timeout after 5 sessions at the next open, with a long-only position. (2) The strategy uses vectorbt to represent two events per daily bar, which are the open and the later barrier or close, including entry-day exits, with synthetic timestamps that do not claim intraday observations. Adverse gaps fill the open, while favorable target gaps conservatively target, with dual touches stopping at the first touch. Fees are 5 basis points and slippage is 10 basis points on each side, with stress doubling both. Independent cash and share values reconcile with a maximum error of 3.637978807091713e-12. (3) The strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, onwards, using all-adjusted IEX data, which is paginated. The strategy rejects training and later-period net returns that are negative. A training sweep of targets and stops resulted in the following returns: (1.5, 1) = -12.752220910464256, (1.5, 1.25) = -6.8086762427363094, (2, 1) = -10.708581864755178, and (2, 1.25) = -12.052126108536413. The strategy notes that all contemporary selected names have survivorship and selection bias, and no pristine prospective evidence is claimed. Terminal open marks are excluded from the payoff sample, with currentholdoutopen0 and trainingopen0. An initial entry-day omission was detected and corrected before journal metrics, and discarded output was not used. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, which means the strategy is not live eligible, regardless of favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in a measured-dataset archive. The current signal is false.

The POWL trading strategy is based on three premises. First, it uses a fixed trend-plus-breakout approach with next-open range exits, where the prior 20-day close breakout and 50-day simple moving average (SMA50) are considered, and the next open is used as the entry point, with a frozen signal-day average daily range (ADR20) stop and target. Second, the strategy uses a vectorbt approach with two event rows per daily bar, representing open and later barrier or close, and synthetic timestamps that do not claim intraday observations. Adverse gaps are filled at the open, and favorable target gaps are targeted conservatively, with dual touches stopping at the first touch. Fees of 5 basis points and slippage of 10 basis points are applied to each side, and stress tests double both. Independent cash and share values are reconciled with a maximum error of 3.637978807091713e-12. Third, the strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, onwards, using all-adjusted IEX data. A watchlist of positive selected later periods shows that all four training variants lose, and the mean interval crosses zero. The training sweep targets 1.5 and 2, with stops at 1 and 1.25, resulting in losses of -6.741325252297993, -18.69843938469283, -3.852527187305641, and -21.788176286351028, respectively. The strategy has survivorship and selection bias, and no pristine prospective evidence is claimed. Terminal open marks are excluded from the payoff sample, and the current holdout open is 0, with the training open also at 0. An initial entry-day omission was detected and corrected before journal metrics, and discarded output was not used. The strategy does not account for cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, making the strategy not live eligible, regardless of favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in the measured-dataset archive. The current signal is false.

A trading strategy for VSH uses a fixed trend-plus-breakout approach with next-open range exits. The strategy has three main premises: (1) it uses a protocol that notes the prior 20-day close breakout and the 50-day simple moving average, with next-open entry and a frozen signal-day average daily range stop and target, and a timeout after five sessions at the next open, only for long positions. (2) The strategy uses vectorbt to represent open and close barriers, including entry-day exits, with synthetic timestamps that do not claim intraday observations. Adverse gaps fill the open, and favorable target gaps conservatively target, with dual touches stopping at the first touch. Fees are 5 basis points and slippage is 10 basis points on each side, with stress doubling both. Independent cash and share values reconcile with a maximum error of 3.637978807091713e-12. (3) The strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, using all-adjusted IEX data. However, all four training variants lost, and double costs turned the holdout negative. The training sweep targeted stops of 1.5 and 2, with results of -13.635595520075913, -19.17348293114387, -3.52923149049484, and -8.73000753401997 for the respective stop and target combinations. The strategy has survivorship and selection bias, and no prospective evidence is claimed. The current signal is false, and the strategy is not eligible for live trading, despite favorable historical results. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test and full trades are stored in the measured-dataset archive.

A trading strategy for PSIX uses a fixed trend-plus-breakout approach with next-open range exits. The strategy has three main premises: (1) it uses a protocol that combines a prior 20-day close breakout and a 50-day simple moving average, with a next-open entry and a frozen signal-day average daily range stop and target, and a timeout after five sessions at the next open, only for long positions. A simple moving average is a calculation of the average price of a stock over a certain period. (2) The strategy uses a vectorbt approach, which is a backtesting framework, with two event rows per daily bar representing open and later barrier or close, including entry-day exits, and synthetic timestamps that do not claim intraday observations. Adverse gaps fill open, and favorable target gaps conservatively target, with dual touches stopping at the first touch. The strategy also includes fees of 5 basis points and slippage of 10 basis points on each side, with stress tests that double both. (3) The strategy was trained on data from 2022 to 2024 and diagnosed on data from later periods, up to September 4, 2025, using all-adjusted IEX data, which is a type of stock exchange data. The training sweep targets stops of 1, 1.25, 1.5, and 2, with results of 0 for each combination. The strategy has several limitations, including survivorship and selection bias, and does not claim to have pristine prospective evidence. Terminal open marks are excluded from the payoff sample, and the current holdout open is 0, as is the training open. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, so the strategy is not eligible for live trading, regardless of favorable historical results. A news scan found the source of the data to be https://runway.adityaperswal.workers.dev/data/stock-bars, with an executable test.py and full trades stored in a measured-dataset archive. The current signal is false.

The automated trading fund manager has a strategy for the stock AEHR that is based on three premises. First, the strategy uses a fixed trend-plus-breakout approach, which means it looks at the stock's prior 20-day closing price and its 50-day simple moving average, or SMA50, to decide when to enter a trade at the next open. The strategy also uses a stop-loss order, which is set at one times the average daily range, or ADR20, and a target price, which is set at two times the ADR20. If the trade is not successful after five sessions, it is closed at the next open. This strategy only considers long positions, meaning it only buys the stock. Second, the strategy uses a system called vectorbt, which represents each day as two events: the open and the close. This system does not claim to have intraday observations, meaning it does not look at the stock's price during the day, only at the open and close. The strategy also takes into account gaps in the stock's price, both adverse and favorable, and uses a dual touch approach to set stop-loss orders. The strategy includes fees of 5 basis points and slippage of 10 basis points on each side of the trade, and these fees and slippage are doubled in stressful market conditions. The cash and share values are reconciled to ensure accuracy, with a maximum error of 3.637978807091713e-12. Third, the strategy was trained on data from 2022 to 2024 and was tested on data from 2025 up to September 4. The strategy uses adjusted data from the IEX exchange and is paginated, meaning it is divided into pages or sections. However, the strategy was rejected because its net returns were negative during both the training and later periods. The strategy was tested with different target and stop-loss prices, and the results were: (1.5, 1) -38.643851939335995, (1.5, 1.25) -57.67302561057384, (2, 1) -32.86874570103676, and (2, 1.25) -46.090402422060095. The strategy notes that all contemporary selected names have survivorship and selection bias, meaning they have been chosen because they have performed well in the past, and there is no pristine prospective evidence to support the strategy. The strategy also notes that terminal open marks are excluded from the payoff sample, and the current holdout open is 0, while the training open is 1. An initial entry-day omission was detected and corrected before journal metrics were calculated, and discarded output was not used. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, which means the strategy may not be eligible for live trading, regardless of its favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in a measured-dataset archive. The current signal for the strategy is false, meaning it is not recommending a trade at this time.

A trading strategy for AXTI uses a fixed trend-plus-breakout approach with next-open range exits. The strategy has three premises: (1) it uses a protocol note127 with a prior 20-day close breakout and a 50-day simple moving average, with a next-open entry and a frozen signal-day average daily range stop and target, and a timeout after five sessions at the next open, only for long positions. (2) The strategy uses vectorbt with two event rows per daily bar to represent open and later barrier or close, including entry-day exits, and synthetic timestamps do not claim intraday observations. Adverse gaps fill the open, and favorable target gaps conservatively target, with dual touches stopping at the first touch. Fees are 5 basis points and slippage is 10 basis points on each side, with stress tests doubling both. Independent cash and share values reconcile with a maximum error of 3.637978807091713e-12. (3) The strategy was trained from 2022 to 2024 and diagnosed from September 4, 2025, onwards, using all-adjusted IEX data, paginated. The strategy rejects training and later-period net returns that are negative. A training sweep of targets and stops resulted in the following net returns: (1.5, 1) -31.442725836626114, (1.5, 1.25) -35.08498901772668, (2, 1) -34.23281713352532, and (2, 1.25) -38.59042367158891. The strategy notes that all contemporary selected names have survivorship and selection bias, and no pristine prospective evidence is claimed. Terminal open marks are excluded from the payoff sample, and the current holdout open is 0, with the training open also 0. An initial entry-day omission was detected and corrected before journal metrics, and discarded output was not used. The strategy does not use cash dividends, 15-minute monitoring, partial fills, or whole-share execution models. Daily target touches may not execute under venue timing, so the strategy is not live eligible, regardless of favorable historical results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the executable test.py and full trades are stored in a measured-dataset archive. The current signal is false.

A news scan found the output of a command line interface audit. The audit executed a node using the file backtest.js with the symbols FRO and STNG, a breakout strategy, a lookback of 20 days, 730 days of data, a stop percentage of 3, and a target percentage of 6. The results for FRO were a return of 3.57694%, a drawdown of 18.75681%, a win rate of 33.3333%, and 51 trades. The results for STNG were a return of 16.44747%, a drawdown of 11.37013%, a win rate of 41.1765%, and 34 trades. Each symbol had 502 bars of data. The mean trade return was 0.11895% and the buy and hold return was 107.32255%. An inspection of the source code found that the strategy uses same-signal-close entry and exact stop and target fills, with no fees or slippage. The data source uses IEX and does not account for next page tokens or adjustments. The audit rejected the command line interface as a valid executable edge. A corrected implementation with 36 and 37 backtests used explicit next-open fills and costs, with separate training and holdout data. The results differed in terms of rules, periods, accounting, and costs, and could not be attributed to timing alone. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the artifacts are stored in cli.json. The library runtime used versions 1.1.0 of vectorbt, 2.4.6 of numpy, and 3.0.5 of pandas. No trades were executed based on the command line interface.

A trading fund manager made notes about a strategy for buying and selling STNG stocks. The strategy had three main parts: first, the manager would buy stocks when their price at the start of a trading day was higher than the average price of the previous 20 days; second, the manager would use a computer program to automatically place orders to buy stocks, with a 0.05% fee and a 0.1% slippage per trade; and third, the manager would sell stocks after 5 trading days or when the price reached a certain level, with no additional buying or selling of the same stock. The manager tested this strategy using historical stock prices from 2022 to 2024 and found that it did not work well, with losses ranging from 33.74% to 36.69%. The manager also tested the strategy using more recent stock prices, from January 1, 2025, to September 4, 2026, and found that it did not perform consistently well. The manager used a website to retrieve stock price data on September 5 and found 1172 data points, but one date was missing. The manager notes that this strategy has limitations, such as not accounting for dividends or stock splits, and that the results may not be accurate due to assumptions about trading costs. The manager concludes that this strategy is not valid and does not authorize new trades.

A trading fund manager made some notes about a strategy called FRO. The strategy has three main parts: first, it looks at the previous 20 days to decide when to buy or sell; second, it uses a computer program to make trades, taking into account fees and other costs; and third, it was tested using historical data from 2022 to 2024 and then checked against data from 2025 to 2026. The strategy aims to make a return of 1.5% to 2%, but the results were not consistent and were affected by costs and uncertainty. The manager used data from a website called IEX and wrote code to test the strategy, which is stored in files called artifacts, test, and results. The manager notes that the strategy has some limitations, such as not taking into account dividends and not being able to enforce exact stop-loss orders. The results of the test were not strong enough to support using the strategy, and the manager does not recommend making trades based on it. A news scan found the source of the data at https://runway.adityaperswal.workers.dev/data/stock-bars, which was retrieved on September 5.