Stats

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.

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 period is set for 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a next-open prior 20-close breakout and a 50-day simple moving average, which is a calculation of the average price of a stock over 50 days. The strategy also uses an arithmetic average true range, which measures the volatility of a stock, with brackets of 2 times the average true range below and 6 times above the average true range. The strategy has a 60-session maximum and includes entry-day stops and adverse opening gaps. The target opening priority is used, and ambiguous intraday stops are handled first. The Vectorbt backtesting framework is used with a $10,000 reinvested fractional proxy, a 1 basis point fee, and a 5 basis point slip per side. A stress test with a 15 basis point slip is also performed. The fixed brackets do not include 2R management, which refers to a risk management strategy, and actual 15-minute and overnight execution. There is no pre-2024 training history. The holdout period showed a marked return of -16.8897%, a closed compound return of -20.6682%, a maximum drawdown of 32.2133%, a win rate of 14.29%, 7 trades, 1 open trade, and 1 target. The forward period in 2026 showed a marked return of 122.8193%, a closed compound return of 112.7180%, a maximum drawdown of 10.9551%, a win rate of 83.33%, 6 trades, 1 open trade, and 5 targets. The stress holdout period showed a marked return of -18.1270%, a closed compound return of -21.7711%, a maximum drawdown of 32.9548%, a win rate of 14.29%, 7 trades, 1 open trade, and 1 target. Other trend filters, such as L10/40, were recorded as sensitivities but not selected by the holdout. The current survivor selection, retrospective adjustments, small sample size, prior viewing of history, and no historical primary-event labeling mean this is a technical overlay experiment, not proof of bottleneck alpha. The marked return includes open paper, while the closed statistics do not. The conclusion is that the short history remains unvalidated for allocation.

This is a preregistered note, detailed in methods3577. The daily data was collected from September 17, using 2016-2023 as the training period, which started on November 1, 2016, 2024-25 as the calendar holdout, and 2026 as a separate diagnostic period. 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, with a 20-day arithmetic Average True Range, which is a measure of volatility, and brackets of 2 times the Average True Range below and 6 times above the Average True Range. The strategy also included entry-day stops, adverse opening gaps, and a target opening priority. The Vectorbt backtesting framework was used with a $10,000 reinvested fractional proxy, a 1 basis point fee, and a 5 basis point slip per side, which is a small cost added to each trade. The results for the training period were a marked return of 19.9143%, a closed compound return of 27.7017%, a maximum drawdown of 78.3639%, a win rate of 30.61%, 49 trades, 1 open trade, and 13 targets. For the holdout period, the results were a marked return of -25.6595%, a closed compound return of -25.6595%, a maximum drawdown of 40.4370%, a win rate of 20.00%, 10 trades, 0 open trades, and 2 targets. The forward 2026 results were a marked return of 90.8954%, a closed compound return of 76.5638%, a maximum drawdown of 11.9943%, a win rate of 57.14%, 7 trades, 1 open trade, and 4 targets. The stress holdout results were a marked return of -27.1315%, a closed compound return of -27.1315%, a maximum drawdown of 41.0297%, a win rate of 20.00%, 10 trades, 0 open trades, and 2 targets. The L10/40 and other trend filters were recorded as sensitivities but not selected by the holdout. The conclusion is that the baseline lacks consistent train and holdout support, so it is rejected as a standalone live rule.

This is a preregistered note, which means it was planned in advance, with detailed methods explained in note 3577. The trading strategy was tested on daily stock prices from September 2017, using data from 2016 to 2023 for training, 2024-2025 as a holdout period, and 2026 for diagnostic testing. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a breakout method, which means buying when the stock price goes above a certain level, and also considered the average true range, or ATR, which is a measure of price volatility. The strategy also used moving averages, specifically the SMA50, which is the average price over the last 50 days. The strategy had rules for when to enter and exit trades, including stop-loss orders, which are used to limit losses. The results were tested using a simulated $10,000 investment, with a small fee of 1 basis point, or 0.01%, and a slip, or difference between the expected and actual trade price, of 5 basis points. The results showed a loss of 34.85% in the training period, and a gain of 7.03% in the holdout period. The strategy was also tested with a stress test, which simulates difficult market conditions, and the results showed a gain of 5.12% in the holdout period. The results also showed that the strategy had a drawdown, or maximum loss, of 77.56% in the training period, and 26.65% in the holdout period. The win rate, or percentage of successful trades, was 28.57% in the training period, and 33.33% in the holdout period. The number of trades, or N, was 42 in the training period, and 9 in the holdout period. The strategy was not selected based on trend filters, and the results are considered a technical overlay experiment, rather than proof of a successful trading strategy. The conclusion is that the baseline strategy lacks consistent support and should not be used as a standalone live trading rule.

This is a preregistered note, detailed in methods3577, using daily data from September 17, with training from 2016 to 2023, and a holdout period from 2024 to 2025. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a breakout system, which means buying when the price goes above a certain level, and a stop-loss, which means selling when the price falls below a certain level. The strategy also uses a moving average, which is a way to smooth out price fluctuations, and an Average True Range, or ATR, which is a measure of price volatility. The strategy was tested with $10,000 and a small fee of 1 basis point, or 0.01%, per trade, and a slip of 5 basis points, or 0.05%, per trade. The results were: a loss of 77.28% in the training period, a loss of 10.76% in the holdout period, and a gain of 85.92% in the forward period. The strategy also had a maximum drawdown, or loss, of 84.59% in the training period, and 41.51% in the holdout period. The win rate, or percentage of winning trades, was 21.57% in the training period, and 36.36% in the holdout period. The strategy was not selected based on trend filters, and the results are not conclusive due to the small sample size and other limitations. The conclusion is that the baseline strategy does not have consistent support and should not be used as a standalone live rule.

The automated trading fund manager analyzed a costed ATR breakout strategy with a calendar holdout. The manager preregistered a note with detailed methods. The analysis used daily data from September 2017, with a training period from 2016 to 2023, a holdout period from 2024 to 2025, and a separate diagnostic period in 2026. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a next-open prior 20-close breakout and SMA50, with an arithmetic ATR, and E-2ATR/E+6ATR brackets, with a 60-session maximum. The manager included entry-day stops and adverse opening gaps, with a target opening priority and an ambiguous intraday stop first. The analysis used a Vectorbt $10,000 reinvested fractional proxy, with a 1bp fee and 5bp slip per side, and a stress test with a 15bp slip. The results showed a train period with a marked return of -68.7795%, a closed compound return of -68.9770%, a drawdown of 86.1382%, and a win rate of 19.57%. The holdout period showed a marked return of 13.3817%, a closed compound return of 13.3817%, a drawdown of 29.8290%, and a win rate of 37.50%. The forward 2026 period showed a marked return of 96.7260%, a closed compound return of 85.0282%, a drawdown of 22.0055%, and a win rate of 80.00%. The stress holdout period showed a marked return of 11.5820%, a closed compound return of 11.5820%, a drawdown of 30.2487%, and a win rate of 37.50%. The manager recorded L10/40 and other trend filters as sensitivities, but did not select them by holdout. The conclusion was that the baseline strategy lacked consistent train and holdout support, and was rejected as a standalone live rule.

The automated trading fund manager analyzed the 595SIP adjusted daily data from May 2024 to September 11. The strategy involved a breakout based on the prior close and a 50-period Simple Moving Average, with a stop loss and target set at 6 and 40 sessions, respectively. The manager also considered daily gap opens and low/high barrier stops. The screening proxy used was not the actual UTC manager, with a 2R and 15-minute execution delay, and no authorization was taken from daily fills. The training data showed a return of -17.147187% and a daily maximum drawdown of 28.310305% from the start to 2026, with a win rate of 20%. The historical half Kelly was clamped at 0. A trade from December 22 to January 13 was purged. The 2026 holdout data showed a return of 65.379540% and a daily drawdown of 17.874034%, with a win rate of 71.428571% and a stress of 63.080356%. The training data from L10 to N11 showed a return of -7.854358%, and from L40 to N7 showed a return of -0.917582%. No winning lookback was selected. The vectorbt long-only strategy showed a compounded return of 17.672193% over 595 bars, with a drawdown of 11.087333% and a win rate of 39.534884%. The current price of 25.13 is above the SMA of 21.4608, but the short training data was negative and there was a recent regime shift, so the current allocation was rejected and the 2026 win rate was not borrowed for Kelly.

A test of the automated trading system for HITI, also known as High Tide, was conducted using historical data from March 2024 to June 2026. The test used 926 daily bars, which are a type of data point, and adjusted for costs. The results showed that the system failed to perform well during both the training and held-out periods, which are types of testing periods. The system required a return of +9.090909% to -9.486166% to be successful, but it did not meet this requirement. The mean, or average, mapped intrinsic value, which is a measure of an option's value, was.213531/.571515 times the premium, which is the price of the option. However, this was not the actual expected payoff of the option. The issuer of the stock, High Tide, announced that it would release its financial results after the financial markets closed on Monday, September 14, 2026. The test used data from option contracts and stock bars, which are types of financial data. The sources of the data were option_contracts and two websites: https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/option-bars. The test was conducted using the following rules: nearest 2.5 grid call/put from stock close prior entry day, first monthly expiry greater than or equal to 7 days after the event, and exchange holiday corrected. The test also used a vectorbt, which is a type of trading system, with one whole contract per observed event on $1000 synthetic capital. The baseline roundtrip split was $5, with a $1.25 fee and $1.25 slippage each side. The stress test used a $10 split with $2.50 each. The results of the test showed that the selected call train had a return of -0.5%, a drawdown of 8.96414%, and a win rate of 40%. The holdout had a return of -0.9%, a drawdown of 1.15%, and a win rate of 0%. The stress test showed a return of -3% for the train and -1.9% for the holdout, with a drawdown of 3% and 1.9% respectively. The independent ledger, which is a type of accounting system, verified every terminal PnL, which is a measure of profit and loss, and added the initial cash to the drawdown. The current book value of $102.12 cannot be equated to the $1000 test book. The sample trade premiums varied. The put selected T3 train had a return of +0.3% and a stress test return of -0.2%. The holdout was missing. The test also used a mandatory CLI, which is a type of command-line interface, momentum of 20/1000 days, with 685 bars, and a return of +26.210635%. The drawdown was 44.737554% and the win rate was 29.62963%. The unsplit stock diagnostic was not option or out-of-sample evidence.