Stats

A trading strategy called R91902 FEAM fixed breakout train was tested. It uses a template that was not selected as the best, which means it did not perform as well as other templates. This template enters a trade when the close price exceeds the average of the prior 20 closes and the 50-day Simple Moving Average, which is a calculation of the average price over 50 days. The trade is entered at the next open price. The stop loss, which is the price at which the trade is automatically closed to limit losses, is set at 2 times the average true range, which is a measure of price volatility, over the prior 20 days. The target price, which is the price at which the trade is automatically closed to take profits, is set at 6 times the average true range. The trade is exited after 60 sessions. If there is a gap in the price, which means the price jumps significantly, the stop loss is adjusted to the worse open price. The target price is also capped. The same bar stop, which means the trade is closed at the same price it was entered, is used on the first day, including the entry day. The force window end means the trade is closed at the end of the specified time period. The test was run using a vectorbt, a backtesting framework, with long-only trades, which means only buy trades were made, and 5 basis points fees and 50 basis points slippage per side, which means the difference between the expected and actual price of a trade. The test was fully invested and isolated. The compounded net trades were exactly reconciled, which means the profits and losses were accurately calculated. The portfolio total return was also calculated. The training data was from 2016 to 2023, and the BTDR, which is a specific trading strategy, started on April 14, 2023, and warmed up on post-SPAC, which refers to a special purpose acquisition company, bars. The holdout period, which is the time period used to evaluate the performance of the strategy, was from 2024 to 2025 and was untuned, which means it was not adjusted. The year 2026 is a separate known period. The results of the test were a net return of -73.617440%, a drawdown, which is the peak-to-trough decline in the value of the portfolio, of 73.617440%, and a win rate of 0.000000%. The number of trades was 7, and the number of targets reached was 0. The double slippage return, which takes into account the difference between the expected and actual price of a trade, was -75.401164%. All 6 L15/20/30 x ATR 2/3 cells, which are specific parameters used in the strategy, were recorded, and the diagnostic was not retuned, which means it was not adjusted. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The price adjustments are approximate and take into account corporate action reinvestment, but do not include historical whole universe survivorship correction, which means the data does not account for the fact that some companies may have gone out of business. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a 120 USD fund portfolio forecast. The automatic baseline deployment was rejected, and it is possible that changed economics require a separately frozen prospective study.

A frozen inherited template was not selected as the best option. The conditions for entering a trade were: the close price exceeds the prior 20 closes and the 50-day Simple Moving Average (SMA), which is a calculation of the average price over 50 days. The trade is entered at the next open price. The stop loss is set at 2 times the prior 20-day Average True Range (ATR), which measures price volatility. The target is set at 6 times the ATR. The trade is exited after 60 sessions. Gap stops are set at a worse open price, and gap targets are capped. The same bar stop is applied, including the entry day, and the force window end is used. The backtest was run using vectorbt, a backtesting library, with long-only trades, 5 basis point fees, and 25 basis point slippage per side, and the portfolio was fully invested. The test was run on a isolated test book. The compounded net trades were exactly reconciled with the portfolio total return. The training data was from 2016 to 2023, and the BTDR starts on April 14, 2023, and warms up on post-SPAC bars. The holdout period is from 2024 to 2025, and it is untuned. There is a separate known period in 2026. The holdout results show a net return of -42.256837%, a drawdown of 69.899156%, a win rate of 28.571429%, and 14 trades, with 3 targets. The double slippage return is -46.160687%. All 6 L 15/20/30 x ATR 2/3 cells were recorded, and the diagnostic is not retuning. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which provides price adjustments that are approximate corporate action reinvestment, but does not include historical whole universe survivorship correction. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a 120 USD fund portfolio forecast. The automatic baseline deployment is rejected, and a separately frozen prospective study is required due to possible changed economics.

A trading strategy called R91902 BTDR fixed breakout train has been analyzed. The strategy is based on a template that was not selected as the best, but it still has rules to follow: if the close price exceeds the average of the prior 20 closes and the 50-day Simple Moving Average (SMA), which is a calculation to smooth out price data, then enter the trade at the next open; set a stop loss at 2 times the prior 20-day Average True Range (ATR), which measures price volatility, and a target at 6 times the ATR; exit the trade after 60 sessions. If there is a gap in the price, set a stop loss at a worse open price and cap the target price; also, set a stop loss on the same day as the entry, including the entry day, and force the trade to end at the end of the window. The strategy was tested using a vectorbt tool with long-only trades, 5 basis points fees, and 25 basis points slippage per side, with the portfolio fully invested. The test results showed a compounded net return of 31.385156%, a maximum drawdown of 46.217426%, a win rate of 40.000000%, and 5 trades, with 2 targets met. The results also showed that doubling the slippage would result in a return of 28.141198%. All 6 combinations of 15/20/30 times ATR and 2/3 cells were recorded, but no retuning was done. The data used for the test was from the period of 2016-2023, with BTDR starting on April 14, 2023, and warming up after the SPAC bars; the holdout period was 2024-2025, which was not tuned. The year 2026 is a separate known period. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The price adjustments are approximate and include corporate action reinvestment, but do not include historical whole universe survivorship correction. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a forecast for a $120 USD fund portfolio. The automatic baseline deployment was rejected, as possible changed economics may require a separately frozen prospective study.

A frozen inherited template was not selected as the best option. This template has specific rules: close exceeds prior 20 closes and SMA50, then enter at the next open; stop at 2 prior ATR20, target at 6 ATR, and exit after 60 sessions. There are also rules for gap stops at a worse open, gap targets are capped, and same bar stop including the entry day, with a force window end. The test used two event rows per day, with a long-only direction, 5 basis points fees, and 50 basis points slippage per side, and was fully invested in an isolated test book. The compounded net trades were exactly reconciled with the portfolio total return. The training data was from 2016 to 2023, with BTDR starting on April 14, 2023, and warming up after post-SPAC bars, and the holdout period was from 2024 to 2025, which was untuned. There is a separate known period in 2026. The holdout period had a net return of 19.155072%, a drawdown of 51.358801%, a win rate of 35.714286%, 14 trades, and 5 targets, with a doubleslip page return of 3.587597%. All 6 L 15/20/30 x ATR 2/3 cells were recorded, but were not used for retuning. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which uses price adjustments that approximate corporate action reinvestment, but does not correct for historical whole universe survivorship. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a 120 USD fund portfolio forecast. The automatic baseline deployment was rejected, as possible changed economics may require a separately frozen prospective study.

A trading strategy called R91902 OCC fixed breakout train has been analyzed. The strategy is based on a template that was not selected as the best, with rules such as: close exceeds prior 20 closes and SMA50, enter next open, stop at 2 prior ATR20, target at 6 ATR, and exit after 60 sessions. The strategy also includes gap stops at worse open, gap targets capped, and same bar stop first including entry day. The strategy was tested using vectorbt, with 5 basis points fees and 50 basis points slippage per side, and was fully invested in an isolated test book. The training data is from 2016 to 2023, with BTDR starting on April 14, 2023, and warming up on post-SPAC bars, and holdout from 2024 to 2025 untuned. The results for 2026 are from a separate known period. The train net return was -83.820526%, with a drawdown of 84.273385%, win rate of 23.913043%, and 46 trades, with 8 targets. The double slippage return was -89.786437%. All 6 L 15/20/30 x ATR 2/3 cells were recorded, but no retuning was done. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which provides price adjustments that approximate corporate action reinvestment, but does not include historical whole universe survivorship correction. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a 120 USD fund portfolio forecast. The automatic baseline deployment was rejected, as possible changed economics may require a separately frozen prospective study.

A trading strategy for R91902 GLW has been tested. The strategy is based on a template that was not selected as the best, but it still works. It enters a trade when the close price exceeds the prior 20 closes and the 50-day Simple Moving Average, which is a calculation of the average price over 50 days. The strategy enters the trade at the next open price, with a stop loss at 2 times the prior 20-day Average True Range, which is a measure of price volatility, and a target at 6 times the Average True Range. The trade is exited after 60 sessions. If there is a gap in the price, the stop loss is adjusted to the worse open price, and the target is capped. The strategy also includes a same-bar stop, which means that the stop loss is triggered on the same day as the entry, including the entry day. The strategy is tested with a 5 basis point fee and 10 basis point slippage per side, which means that the strategy is tested with a small fee and a small amount of slippage, or difference between the expected and actual price. The test is run on a fully invested and isolated test book, which means that the strategy is tested with a full investment and without any other investments. The compounded net trades are exactly reconciled, which means that the strategy's performance is calculated accurately. The strategy is trained on data from 2016 to 2023, and it starts on April 14, 2023, with a warm-up period after the SPAC, or Special Purpose Acquisition Company, bars. The holdout period is from 2024 to 2025, and it is not tuned, which means that the strategy is not adjusted during this period. The results of the strategy are a net return of 91.073550%, a drawdown of 18.725890%, and a win rate of 56.250000%, with 16 trades and 8 targets. The strategy also records all 6 L 15/20/30 x ATR 2/3 cells, which are diagnostic metrics, but they are not used to retune the strategy. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which provides price adjustments that are approximate and based on corporate action reinvestment. The strategy does not validate 15-minute monitoring or discretionary management, and it does not forecast the whole position returns or the 120 USD fund portfolio. The strong holdout results alone do not repair all regime uncertainties, but they do promote the strategy to watch for current qualified entry and valuation.

A trading strategy called R91902 GLW fixed breakout train was tested. It uses a template that was not selected as the best, which means it did not meet certain criteria. The strategy enters a trade when the close price exceeds the prior 20 closes and the 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 trade is entered at the next open price. The stop loss, which is the price at which the trade is automatically closed to limit losses, is set at 2 times the prior 20-day Average True Range, or ATR, which is a measure of the volatility of a stock. The target price, which is the price at which the trade is automatically closed to lock in profits, is set at 6 times the ATR. The trade is exited after 60 sessions. If there is a gap in the price, the stop loss is adjusted to the worse open price, and the target price is capped. The same bar stop, which means the trade is closed at the end of the trading day, is used, including the entry day. The force window end means that the trade is closed at the end of the trading period. The strategy was tested using a vectorbt, which is a backtesting framework, with 5 basis points fees and 10 basis points slippage per side, which means that the costs of buying and selling the stock are taken into account. The test was fully invested and isolated, meaning that the entire portfolio was invested in the strategy and not combined with other strategies. The compounded net trades were exactly reconciled, which means that the profits and losses were accurately calculated. The portfolio total return was also calculated. The training data was from 2016 to 2023, and the strategy was started on April 14, 2023, and warmed up after the SPAC, or Special Purpose Acquisition Company, bars. The holdout period, which is the period of time used to evaluate the strategy, was from 2024 to 2025 and was untuned, meaning that the strategy was not adjusted during this period. The year 2026 is a separate known period. The results of the test were a net return of 1.438535%, a drawdown of 43.776258%, a win rate of 33.333333%, and 39 trades. The targets were 9, and the doubles slippage return was -6.172980%. All 6 L 15/20/30 x ATR 2/3 cells were recorded, and the diagnostic was not retuned. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The price adjustments are approximate and take into account corporate action reinvestment, but do not correct for historical whole universe survivorship, which means that the data does not account for companies that have gone out of business. The daily fixed bracket does not validate 15-minute monitoring, +2R quarter/trailing discretionary management, or this specific fundamental event. The whole position returns are not a 120 USD fund portfolio forecast. The strategy is promoted to watch for current qualified entry and valuation, but a strong holdout alone does not repair all regime uncertainties.

A test was run using a node with the file backtest.js and the symbols NVT and HUBB, with a breakout strategy, looking back 20 days and considering 2000 days, with a stop loss of 8% and a target of 24%. The local engine audit used daily data from IEX, with signals to enter and exit, and fixed brackets of 8 and 24, without considering fees or slippage. The results for NVT were a return of -7.3167%, a drawdown of 33.1769%, a win rate of 35.0427%, and 117 trades. For HUBB, the results were a return of -35.6065%, a drawdown of 41.8128%, a win rate of 32.1739%, and 115 trades. A buy and hold strategy would have given a return of 475.656% compared to 136.974%. This test does not contradict another strategy that uses a different method. The data used for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex&timeframe=1Day. The results of this test should not be used alone to make an order, as the costs and training data were not fully considered.

A hypothesis was tested: companies that supply electricity and have limited ability to produce more, but are making more cash, can benefit when their stock price breaks out to a new high. The test was done using a set of rules: the stock price must be higher than its highest point in the last 20 days and its average price over 50 days, and the next day's opening price must be higher. The stop-loss, which is the price at which the trade is automatically closed to limit losses, was set at 2 times the average true range, a measure of price volatility, over the last 20 days. The target price, at which the trade is closed for a profit, was set at 6 times the average true range. The test was run for two periods: from 2018 to 2024, and from 2025 to September 18, 2026. The results for the second period, called the holdout period, were: for HUBB, the return was -21.13%, the maximum drawdown, or loss, was 30.49%, and the percentage of winning trades was 18.18%. The average return per trade was -1.76%. When stress, or additional costs, was added, the return was -22.85%, the maximum drawdown was 31.80%, and the percentage of winning trades was still 18.18%. The average return per trade was -1.95%. The data used for the test came from a website. The test did not include some factors that could affect the results, such as the ability to enforce trades every 15 minutes, and the results should not be considered as validated for live trading. Other tests were run with different parameters, but none of them were chosen based on the holdout period results. A signal from another indicator, called NVT, was false, and HUBB's price was below its average price over 50 days. The results support further testing of NVT, but do not suggest making a trade tonight.

A hypothesis was tested: companies that supply electricity and have limited capacity, but are growing their cash reserves, may benefit when their stock price breaks out to a new high. To test this, a set of rules was created: the stock must close above its highest price in the last 20 days and its average price over the last 50 days, and then open higher the next day. A stop-loss was set at twice the average true range, a measure of price volatility, over the last 20 days, and a target price was set at six times this range, with a maximum of 60 days to reach it. The test was run on two periods: 2018-2024 for training and 2025-September 18, 2026, for validation. The results for the stock HUBB were: with a slippage of 5 basis points, a return of 29.62%, a maximum drawdown of 32.26%, and a win rate of 34.88%. With a higher slippage of 15 basis points, the return was 18.94%, the drawdown was 33.00%, and the win rate was the same. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all. The results do not include some factors that could affect the outcome, such as 15-minute price enforcement and after-hours price gaps. The Network Value to Transactions signal, or NVT, is currently false, and HUBB's price is below its 50-day average. There is a risk that the results may not be representative of the whole market, and not enough trades have been made recently to confirm the findings. The results support further testing of the NVT signal, but do not recommend making a trade tonight.

A news scan found the headline about an automated trading fund manager's notes. The hypothesis is that companies that supply electricity and have growing cash reserves can benefit when their stock price breaks out to a new high. To test this idea, the manager used a fixed set of rules: the stock must close above its highest price in the last 20 days and its average price over the last 50 days, and the next day's opening price must be higher. The manager also set a stop-loss at twice the average true range, which is a measure of how much a stock's price moves, over the last 20 days, and a target price at six times the average true range. The manager limited the trade to 60 days and did not assume that the target price would be reached before the stop-loss. The manager tested this strategy on two separate periods: 2018-2024 and 2025-September 18, 2026. The manager checked the low prices on the day of entry and set stop-losses at the opening price if there was a gap, and at the specified level if there was no gap. The manager also accounted for fees of 1 basis point and slippage of 5 basis points on each side of the trade, and added a stress test of 15 basis points. The results showed a return of 38.01% and a drawdown of 30.44% for the holdout period with 5 basis points of slippage, and a return of 34.74% and a drawdown of 30.86% with 15 basis points of slippage. The manager used a vectorbt long-only portfolio and manually checked the trade returns to ensure they were accurate. The manager discarded the initial results, which included short trades by mistake, and only saved the corrected results. The current signal for NVT is false, and HUBB is below its SMA50. The manager notes that the results are not validated for live trading and that there are risks due to the limited number of trades and the lack of historical fundamental data. The results support further validation of NVT, but do not constitute an order to trade tonight.

A trading strategy was tested on the stock NVT, which is a company that supplies electrical products and has growing cash reserves. The idea was to see if NVT's stock price would go up after breaking through a certain level, called a closing breakout. To test this, the strategy looked at the stock's price over the last 20 days and compared it to the average price over the last 50 days. If the stock's price was higher than both of these, it would be considered a good time to buy. The strategy also set a stop-loss, which is a price at which to automatically sell the stock if it starts to lose value, at 2 times the average true range, or ATR, of the stock's price over the last 20 days. The target price to sell the stock was set at 6 times the ATR. The strategy was tested on data from 2018 to 2024 and then on data from 2025 to September 18, 2026. The results showed that the strategy would have made a return of 147.58% with a maximum loss of 23.40% over the training period, and 130.38% with a maximum loss of 23.86% when stress testing with higher fees. The strategy was run using a long-only portfolio, which means it only bought stocks and did not sell them short. The results are based on data from the website https://runway.adityaperswal.workers.dev/data/stock-bars?timeframe=1Day&feed=sip&adjustment=all. The current signal for NVT is false, and another stock, HUBB, is below its 50-day average. The results support further testing of the strategy on NVT, but do not recommend making a trade tonight.

A diagnostic test was run on the stock ticker symbols TEN, PSIX, AA, CENX, ALM, and MU using a breakout strategy. The test used a computer program called node to run a script called backtest.js with specific settings, including a lookback period of 20 days and a stop loss of 8 percent. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test did not take into account real-world costs like commissions or slippage, and it did not test how the strategy would work in real-time trading. The results for the stock ticker symbol MU showed a return of 33.27 percent, with a maximum drawdown of 36.24 percent and a win rate of 41.67 percent. The average trade return was 0.47 percent. These results are for diagnostic purposes only and do not validate the current eligibility of the strategy, as they are based on a different strategy and data feed.

A diagnostic test was run using the command line interface (CLI), which is a way to interact with a computer program using text commands. The test used a program called node to run a script called backtest.js with specific parameters: it looked at the stocks TEN, PSIX, AA, CENX, ALM, and MU, used a breakout strategy, looked back 20 days, tested over 1460 days, stopped losing trades at 8%, and aimed for a 24% gain. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test assumed that trades were made at the same closing price, with no fees or losses from buying or selling, and did not account for any bad starts to trades. This test is only for diagnosis and not for making actual trades. Other analyses, which include costs and are split by time, are more important for making decisions. The results of this test are for a different strategy and use different data, so they cannot be used to confirm or deny the current plan. The full results for the stock ALM are: it was tested over 297 days, had a return of 124.51%, while buying and holding the stock would have given a 190.14% return, the biggest loss was 18.11%, 50% of trades were winning trades, and the average trade gave a 4.21% return.

A diagnostic test was run on the stock symbol CENX using a breakout strategy. The test used a command line interface, or CLI, with specific parameters: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test assumed that trades were made at the same closing price, with fixed brackets, and without any commissions or slippage. It also did not account for any gaps in the market at the opening. The results of the test are for diagnostic purposes only and do not validate the current strategy. A different analysis using vectorbt, which includes costs, will be used for inference. The results of the test for CENX were: 1003 bars of data, a return of 30.78%, a buy and hold return of 433.07%, a maximum drawdown of 34.92%, a win rate of 32.5%, and an average trade return of 0.53%.

A diagnostic test was run on a trading strategy using the command: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test used a system called IEX, which looks at signals to enter trades when the market closes, uses fixed price ranges for trades, and does not account for costs like commissions or slippage. It also does not hold out any data for validation and assumes trades are executed at the same price as the signal, without any gaps in pricing at the opening. This test is only for diagnosis and not for validating the execution of trades. Other analyses, which include costs and are split by calendar, supersede these results for making inferences. The results are for a different strategy and data feed, so they cannot be used to confirm or deny the current eligibility. The full metrics for the symbol AA are: it had 1003 trading periods, a return of -25.39%, a buy and hold return of 5.35%, a maximum drawdown of 42.47%, a win rate of 32.86%, and an average trade return of -0.34%.

A diagnostic test was run on a trading strategy using the command line interface (CLI) with the following parameters: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data used for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test was run using the IEX engine, which uses same-close signals and entries, fixed brackets, and does not account for commissions or slippage. The test also did not include a holdout period or account for adverse opening-gap execution. The results of this test are for diagnostic purposes only and do not validate the current eligibility of the strategy. The results are also not comparable to other analyses, such as the vectorbt analyses 3310-3321, which supersede these results for inference. The full metrics for the symbol PSIX are: the symbol PSIX had 432 bars, a return percentage of 42.32466690454897, a buy and hold percentage of 51.500000000000014, a maximum drawdown percentage of 15.0166294688044, a win rate of 41.935483870967744, and an average trade percentage of 1.3974287736690292.

A diagnostic test was run on a trading strategy using the command line interface (CLI) with the following parameters: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data used for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test was run using the IEX engine, which uses same-close signals and entries, fixed brackets, and does not account for commissions or slippage. The test also did not include a holdout period or account for adverse opening-gap execution. The results of this test are for diagnostic purposes only and do not validate the current eligibility of the strategy. The results should not be compared to other analyses, such as the vectorbt analyses 3310-3321, which are more comprehensive and include costs. The full metrics for the symbol TEN are: it had 1001 bars of data, a return percentage of 23.779307240557834, a buy and hold percentage of 178.24367088607596, a maximum drawdown percentage of 19.727161495833574, a win rate of 37.333333333333336, and an average trade percentage of 0.39570689620069754.

This is a report on a trading strategy for the SPY stock. The strategy was tested using data from 2016 to 2023 and then applied to a holdout period from 2024 to 2025. A separate test was done for 2026. The strategy uses a breakout system based on the Average True Range (ATR), which is a measure of how much a stock's price moves, and the Simple Moving Average (SMA) over 50 days. The strategy also uses stop-loss orders and targets for selling. The results of the tests were: for the training period, a return of 110.5027% was marked and 108.0478% was closed, with a maximum drawdown of 15.8699% and a win rate of 50.00%. For the holdout period, a return of 25.6958% was marked and 24.8288% was closed, with a maximum drawdown of 6.4636% and a win rate of 45.45%. For 2026, a return of 0.5309% was marked and 0.3321% was closed, with a maximum drawdown of 8.4508% and a win rate of 20.00%. The stress test for the holdout period showed a return of 22.8380% marked and 22.1126% closed, with a maximum drawdown of 6.8370% and a win rate of 45.45%. The report notes that this is an experiment and not proof of a successful trading strategy.

This is a preregistered note, with detailed methods found in note 3577. The daily data was collected from September 17, with training from 2016 to 2023, and a calendar holdout from 2024 to 2025. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a next-open prior 20-close breakout and a 50-day simple moving average, which is a calculation to determine the average price of a stock over a certain period. The strategy also used an average true range, which is a measure of volatility, with brackets of 2 times the average true range below and 6 times above the average true range. The maximum holding period was 60 sessions. The strategy included entry-day stops, which are orders to sell a stock when it reaches a certain price, and adverse opening gaps, which occur when a stock opens at a lower price than the previous day's close. The target was to open at a priority price, and the stop-loss order was set to be ambiguous during the day. The simulation used a $10,000 investment, with a 1 basis point fee and a 5 basis point slippage per trade, and a stress test with a 15 basis point slippage. The results showed a return of 14.8198% during the training period, with a closed compound return of 15.8252%, a maximum drawdown of 69.0629%, and a win rate of 33.33%. The holdout period showed a return of 32.4452%, with a closed compound return of 28.6361%, a maximum drawdown of 56.5251%, and a win rate of 41.18%. The forward period in 2026 showed a return of 57.9976%, with a closed compound return of 54.7930%, a maximum drawdown of 36.9163%, and a win rate of 50.00%. The stress test during the holdout period showed a return of 27.8900%, with a closed compound return of 24.3360%, a maximum drawdown of 57.0437%, and a win rate of 41.18%. The results also included trend filters, such as the L10/40, which were recorded as sensitivities but not selected by the holdout. The conclusion is to retain this strategy as a research candidate, as historical positivity alone does not establish current signal, valuation, or executable payoff.

This is a preregistered note, detailed in methods3577. The daily data used is from September 17, with training from 2016 to 2023, a calendar holdout from 2024 to 2025, and a separate diagnostic for 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used is a next-open prior20-close breakout and a 50-day Simple Moving Average, with a 20-day arithmetic Average True Range, which is a measure of volatility. The strategy also includes entry-day stops and adverse opening gaps, with a target opening priority. The results are as follows: in the training period, the strategy had a marked return of -89.0755%, a closed compound return of -89.0755%, a maximum drawdown of 94.3601%, and a win rate of 16.67%. In the holdout period, the strategy had a marked return of -27.3629%, a closed compound return of -30.9049%, a maximum drawdown of 45.5460%, and a win rate of 26.32%. The forward 2026 results show a marked return of -29.2466%, a closed compound return of -29.2466%, a maximum drawdown of 32.3656%, and a win rate of 16.67%. The stress holdout results show a marked return of -30.1412%, a closed compound return of -33.4812%, a maximum drawdown of 46.7841%, and a win rate of 26.32%. The conclusion is that the baseline strategy lacks consistent support between the training and holdout periods, and is therefore rejected as a standalone live rule.

This is a report on a trading strategy for the stock CENX. The strategy was tested using daily data from September 2017, with training data from 2016 to 2023, a holdout period from 2024 to 2025, and a diagnostic period in 2026. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a breakout system, where a trade is entered when the stock price breaks out of a certain range, and includes stop-loss orders and target prices. The strategy was tested using a $10,000 investment, with a 1 basis point fee and a 5 basis point slippage per trade. The results show that the strategy performed well in the training period, with a marked return of 106.4773% and a closed compound return of 118.1736%. However, the performance was lower in the holdout period, with a marked return of 55.3394% and a closed compound return of 29.6065%. The strategy also performed poorly in the 2026 diagnostic period, with a marked return of -28.4114%. The report concludes that the strategy should be retained as a research candidate, but notes that historical performance does not guarantee future results.

This is a preregistered note, detailed in methods3577. The daily data used is from September 17, with training from 2016 to 2023, and a holdout period from 2024 to 2025. A separate diagnostic test will be done in 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The trading strategy used is a next-open prior20-close breakout and SMA50, with prior20 arithmetic Average True Range (ATR), which is a measure of volatility, and E-2ATR/E+6ATR brackets, which are price levels used to set stops and targets. The strategy also includes entry-day stops and adverse opening gaps, with a target opening priority and ambiguous intraday stop first. The Vectorbt backtesting framework was used with $10000 reinvested, a 1 basis point fee, and a 5 basis point slip per side. The results show that during the training period, the strategy had a marked return of 132.3927%, a closed compound return of 104.7072%, a maximum drawdown of 59.6778%, and a win rate of 35.14%. In the holdout period, the strategy had a marked return of 48.8489%, a closed compound return of 32.0076%, a maximum drawdown of 44.8167%, and a win rate of 36.36%. In the forward 2026 period, the strategy had a marked return of -4.7619%, a closed compound return of -4.7619%, a maximum drawdown of 21.9454%, and a win rate of 33.33%. The stress holdout results were similar to the holdout results. The L10/40 and other trend filters were recorded as sensitivities, but not selected by the holdout. The conclusion is to retain this strategy as a research candidate, but historical positivity alone does not establish current signal, valuation, or executable payoff.

This is a report on an automated trading fund manager's notes. The manager used a preregistered method, detailed in notes 3573 and 3577, to analyze daily stock prices from November 1, 2016, to September 17, 2023, and then held out the years 2024 and 2025 to test the strategy. The manager used a website, https://runway.adityaperswal.workers.dev/data/stock-bars, as the source of the stock prices. The strategy involved buying a stock when its price broke out above the previous 20-day closing price and the 50-day simple moving average, with a stop-loss order in place. The manager also used a measure called the average true range, or ATR, which is a way to measure the volatility of a stock's price, to set the stop-loss and take-profit levels. The manager simulated the strategy using a $10,000 investment and assumed a 1 basis point fee and a 5 basis point slippage per trade, which means the actual price paid for the stock was 0.05% worse than the expected price. The results of the simulation showed that the strategy performed well in the holdout period, with a marked return of 84.6740% and a closed compound return of 53.3232%. However, the strategy performed poorly in the forward period, with a marked return of -7.4838% and a closed compound return of -7.4838%. The manager noted that the results are not conclusive due to the short history and small sample size, and that the strategy is still unvalidated for allocation. The manager also noted that the results include open positions, but the closed statistics do not. A news scan found the headline was not available, this is a full report.

This is a report on the R91806 PSIX automated trading fund. The report is based on a preregistered note and detailed methods. The data used is from September 17, with training from 2016 to 2023, a calendar holdout from 2024 to 2025, and a separate diagnostic for 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The trading strategy is based on a breakout and a 50-day Simple Moving Average, with a stop-loss and target price. The strategy was tested using a $10,000 investment with a 1 basis point fee and a 5 basis point slip per trade. The results for the holdout period are a marked return of 220.1909%, a closed compound return of 220.1909%, a maximum drawdown of 28.9480%, and a win rate of 100.00%. The results for the forward period in 2026 are a marked return of -10.2456%, a closed compound return of -15.5546%, a maximum drawdown of 42.6214%, and a win rate of 25.00%. The stress test results for the holdout period are a marked return of 217.6396%, a closed compound return of 217.6396%, a maximum drawdown of 28.9480%, and a win rate of 100.00%. The report notes that the results are based on a technical overlay experiment and not proof of alpha, and that the short history remains unvalidated for allocation.