Stats

The premises of a test were set using a command line interface to run a backtest on the stock symbol BB. This test used a breakout strategy, looking back 20 days over a period of 730 days, with a stop loss of 10% and a target profit of 30%. The results showed a return of 169.4115%, a drawdown of 11.1785%, a win rate of 35.4839%, and 31 trades over 501 bars. The buy and hold return was 230.3719%. This test did not include an independent holdout or cost calibration, so it is not trusted for trading. A separate test was run using vectorbt from January 2023 to December 2025 for training and 2026 for holdout. This test looked for close breaks prior to 20, 50, and 100 session highs, with volume greater than 1, 1.5, and 2 times the prior 20 mean, and close greater than the simple moving average of 200. The test entered trades following the open and exited following the open after the close was less than the simple moving average of 20. One share was bought per $1000 book, with 10 basis point fees and 10 basis point slippage on each side, and no option prices. The baseline test had a return of 0.268038%, a drawdown of 0.132377%, a win rate of 100%, and 3 trades. The holdout test had a return of 0.428765%, a drawdown of 0.151146%, a win rate of 100%, and 1 trade. A full 9 parameter grid holdout test had returns ranging from 0.18921% to 0.428765%, with 1 to 2 trades. The baseline was fixed before the results, and there was no best selection in the holdout. The last 252 high was 13.59 versus 7.99, and the current 52 week breakout is absent. A stock proxy cannot establish a call theta, crush, or net option edge, and one held out winner is not replication. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. The figures are percent of $1000 with one share exposure, not percent return on premium.

A test of a trading strategy for BlackBerry put options was conducted. The test was based on 13 events where the company issued important updates, and historical option prices were retrieved for 9 event windows. The test used the nearest out-of-the-money put option with a strike price within $0.30 of the stock's closing price, and the option was bought two sessions before the event and sold on the day after the event. The test was run on 7 pairs of data before 2026 and 2 pairs in 2026. The test used a $1 fee at entry and exit, and a $1 roundtrip cost. The results showed a -6.50% return and an 8.20% drawdown for the training data, and a -3.80% return and a 3.80% drawdown for the holdout data. The test also showed that increasing the friction cost to $0.05 and $0.10 decreased the returns. A control test using call options showed a -0.60% return and a 2.45% drawdown for the training data, and a -0.40% return for the holdout data. The test used data from https://runway.adityaperswal.workers.dev/data/option-bars and issuer observations 29472-84. The test found that a generic rule for buying cheap options did not work, and the historical test did not provide enough evidence to justify buying the options.

The automated trading fund manager made the following premises: the daily source of data is from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=PSIX,ETN,POWL,NVT,SPY&timeframe=1Day&feed=sip&adjustment=all. The data was used to train the model from 2016 to 2023, and to test it from 2024 to 2025, with 2026 being used for diagnostic purposes. PSIX started on December 26, 2024, and was not used for training, while NVT started on May 2018. The manager used a frozen L20 prior closing high and a Simple Moving Average of 50, which is a calculation of the average price of a stock over a certain period, to determine when to enter the market. The manager also used an arithmetic Average True Range of 20, which measures the volatility of a stock, to determine the stop loss, which is the price at which to sell a stock to limit losses. The target was set to solve a net 3R, which means a 3 times return on investment, with a 5 basis points fee and a 50 basis points liquidity cost, per side. The manager limited the number of sessions to 60 and used a vectorbt long only strategy, which means only buying stocks, with a fractional reinvestment of 10000. The manager also used a compounded trade return, which means calculating the return on investment over multiple trades, to reconcile the results to less than 1e-8. The manager counted boundary liquidations separately and used a cost stress of 100 basis points liquidity cost to hold the target fixed. The manager also used L10 and L40, which are calculations of the average price of a stock over a certain period, and SMA200, which is a calculation of the average price of a stock over a 200-day period, as sensitivities, but not as selected rules. The manager noted that PSIX SMA200 was unavailable at 2025 entries and that zero filtered trades is a warmup, not proof of protection. The results of the test are as follows: for the training period, the return was 26.578023309322376, the drawdown was 37.10277433475685, the number of trades was 29, the win rate was 31.03448275862069, the number of boundary liquidations was 1, and the return without the best trade was 3.3886674743498935. For the holdout period, the return was 87.17086632432587, the drawdown was 22.11321555380127, the number of trades was 11, the win rate was 54.54545454545454, the number of boundary liquidations was 0, and the return without the best trade was 33.423351254305175. For the recent period, the return was -0.6491850271975519, the drawdown was 26.604408244933385, the number of trades was 5, the win rate was 20, the number of boundary liquidations was 1, and the return without the best trade was -23.604175791528448. The manager concluded that the test only showed retrospective robustness, and that the results should not be used for primary bottleneck-event labels or actual 15-minute exits or +2R management. The manager also noted that deleting the best trade diagnoses concentration, but not an investable strategy, and that no allocation should be made solely based on this test.

The automated trading fund manager analyzed the stock POWL. The manager used data from 2016 to 2023 to train the system and 2024 to 2025 to test it. The system also used data from 2026 for diagnostic purposes. The stocks PSIX, ETN, POWL, NVT, and SPY were analyzed. PSIX was only analyzed starting from December 26, 2024, and NVT was analyzed starting from May 2018. The system used a specific set of rules to decide when to buy and sell stocks. These rules included using the previous day's high and 50-day moving average to determine when to enter a trade. The system also used a stop-loss order, which is a type of order that sells a stock when it falls to a certain price, and a target price, which is the price at which the system aims to sell the stock. The system was tested with a fictional $10,000 investment and the results were compounded, meaning that the returns from each trade were added to the initial investment. The system was also tested with different levels of cost stress, which refers to the impact of trading costs on the system's performance. The results of the test were as follows: during the training period, the system had a return of -68.55% and a maximum drawdown of 73.17%. During the holdout period, the system had a return of 33.29% and a maximum drawdown of 51.38%. During the recent period, the system had a return of 81.45% and a maximum drawdown of 23.94%. The manager concluded that the test results only showed the system's performance in the past and did not necessarily indicate how it would perform in the future. The manager also noted that the test results should not be used to make investment decisions.

This is a report about an automated trading system. The system uses data from the internet to make decisions about buying and selling stocks. The data comes from a website and includes information about the stocks PSIX, ETN, POWL, NVT, and SPY. The system was trained using data from 2016 to 2023 and tested using data from 2024 to 2025. The system uses a set of rules to decide when to buy and sell stocks. These rules include using the average price of a stock over a certain period of time, called a Simple Moving Average or SMA, and a measure of how much the price of a stock is changing, called the Average True Range or ATR. The system also uses a concept called friction stress, which refers to the costs of buying and selling stocks, such as fees and the difference between the price at which a stock can be bought and sold. The results of the system's performance are shown in a table, which includes information about the return on investment, the maximum loss, and the number of trades made. The system performed well during the training period, but not as well during the testing period. The report concludes that the system's performance is robust, but it should not be used to make investment decisions without further testing and evaluation.

A news scan found the headline 'R91906 PSIX fixed net3R breakout: calendar holdout and friction stress'. This is a report from an automated trading fund manager. The manager used data from 2016 to 2023 to train and from 2024 to 2025 to test, with 2026 used for diagnosis. The data came from a daily source and included stocks like PSIX, ETN, POWL, NVT, and SPY. The manager started using PSIX on December 26, 2024, without training. NVT was added on May 2018. The manager used a rule to buy when the stock's price was higher than its previous high and a 50-day simple moving average, which is a calculation of the stock's average price over 50 days. The manager also used a stop-loss, which is a rule to sell when the stock's price falls by a certain amount, in this case, 2 times the average true range, which is a measure of the stock's price volatility. The target was to make a 3 times return, minus a 5 basis points fee and a 50 basis points slippage, which is the difference between the expected and actual price of a trade. The manager also used a rule to limit the number of trading sessions to 60. The results showed a return of 110.02% during the test period, with a maximum drawdown of 28.95%, which is the maximum loss from a peak to a trough. The manager also reported a return of -11.17% in the recent period, with a maximum drawdown of 44.56%. The manager concluded that the results were robust, but not investable, and that the test was not a primary bottleneck, which is a limitation or constraint.

A news scan found the text describing the historical performance of a trading strategy for SFIX put options. The strategy was tested on 10 events from 2019 to 2026, using option bars from a specific data source. The rule for selecting options was to choose the nearest out-of-the-money put with a strike price in a $0.50 grid, with an exact-day trade and a monthly expiry at least 7 days past the event. The strategy was tested using a vectorbt portfolio with $1000, one contract, and cash sharing, with no concurrent events. The baseline spread and slippage were set at $0.02 per share and $0.50 per side, with a floor of $0.0001 per share for distressed sales. The results showed a gain of 5.05% for the first 5 events before 2026, with a maximum drawdown of 4.195166% and a win rate of 40%. The held-out events in 2026 showed a loss of 1.85%, with a drawdown of 1.85% and a win rate of 0%. The pooled results showed a gain of 3.20%, with a drawdown of 5.882353% and a win rate of 28.5714%. A sweep of $0.05 per side showed a gain of 2.90% for the training events and a loss of 2.20% for the held-out events. A sweep of $0.10 per side showed a gain of 5.45% for the training events and a loss of 3.20% for the held-out events. The results are based on actual historical trade marks and are not executable bid-ask history or actual validation. The conclusion is that the broad buy-cheap-puts earnings rule is not established, and the results should not be used to claim net option expectancy.

A news scan found the headline 'R91904 required CLI diagnostic and frozen evidence audit'. This refers to an automated trading fund manager's notes. The manager used a trading strategy called CLI breakout with specific settings: 20-day breakout, 1460 days, 15% stop, and 45% target. This strategy had returns of 19.0078%, a maximum drawdown of 36.4922%, and a win rate of 26.1905% over 42 trades, with a buy-and-hold return of 282.0051% over 1003 bars. The strategy had a signal exit and execution and cost semantics, but no independent holdout, which means it did not have a net stock edge. The manager retrieved prior analyses from 2016 to 2023, which had returns of -83.8205%, a maximum drawdown of 84.2734%, and a win rate of 23.9130% over 46 trades. The holdout period from 2024 to 2025 had returns of +19.1551%, a maximum drawdown of 51.3588%, and a win rate of 35.7143% over 14 trades. The manager also considered doubleslipage, which added 3.5876%. The same strategy was applied with a 20-day close breakout, SMA50, next open, 2ATR stop, and 6ATR target over 60 sessions, with a fixed long-only 5bp fee and 50bp slip. The manager decided not to rerun the unchanged sweep or pretend the known holdout was fresh. New tests will focus on fundamental cash conversion, not reselecting a historical winning parameter. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. There are no live supply trades to calibrate execution after reset.

A trading strategy using a fixed length of 20 days and a simple moving average of 50 days, with a stop loss of 2 times the average true range and a target of 6 times the average true range, was tested on Forgent stock from February 5 to September 18, 2026. The average true range is a measure of volatility, or how much the stock price moves. From February 5 to June 30, the strategy returned 19.165689%, with a maximum drawdown of 19.078171%, and 66.667% of trades were winners, with 3 trades in total. From July 1 to September 18, the strategy returned -0.749195%, with a maximum drawdown of 0.749195%, and 0% of trades were winners, with 1 trade in total. This one trade was forced to close at the end of the testing period and is not a natural finish to the trade. Nearby settings of 15 and 20 days had the same result, with no trades, and settings of 30 days had no trades either. This means that the results are not reliable and cannot be used to claim that the IPO price pattern has a validated expected return. The current stock price is 39.44, which is higher than the previous 20-day high of 38.06, and the 50-day simple moving average is 35.4472. The stop loss is set at 35.03481, which is 2 times the average true range, and the target is set at 53.60498, which is 3 times the average true range, with a 30 basis point fee on each side. The estimated earnings per share for fiscal year 2027 is 1.33, which implies a stock price of 40.30 at the target threshold, assuming a price-to-earnings ratio of 30-40. However, this range is not approved as a target. The current stock price alone, along with new guidance, is not enough to assign a probability of success or a bet size. It is recommended to watch the company's cash conversion and prospective policy, with a position size of 0. The data used for this analysis can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all and the company's press releases can be found at https://ir.forgentpower.com/news-events/press-releases/detail/107/forgent-reports-record-fourth-quarter-and-full-year-2026-results-exceeds-high-end-of-guidance-and-enters-fiscal-2027-with-all-time-high-backlog.

A diagnostic test was run using a computer program called backtest.js on the stocks GLW, OCC, BTDR, and FEAM, using a strategy called breakout, which means buying when a stock's price breaks out of a certain range. The test looked back 20 days and used 1500 days of data, with a stop loss of 10% and a target gain of 30%. The test used 1030 bars of data, which is a unit of time in trading, and had its own signal to exit the trade and a price basis, but did not account for costs or hold out any data for verification, so it is only for diagnostic purposes. The test also recorded the results in a chronological and costed manner, and compared the raw data to the corporate-adjusted data, which may differ, especially for FEAM due to stock splits. The results should not be borrowed as a validated edge without further verification. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run on a trading strategy using the command line interface, or CLI, to analyze the stocks GLW, OCC, BTDR, and FEAM. The test, which was not a trade authorization, used a breakout strategy with a 20-day lookback period and 1500 days of data. The test also included a 10% stop loss and a 30% target price. The results showed that the CLI has its own signal exit and price basis, but this test did not include reserved holdout or verified cost semantics, so it was only used for diagnostic purposes. Separate tests were also run using vectorbt to record chronological and costed results and to control decision-making. It's worth noting that the raw CLI results may differ from the corporate-adjusted results, especially for stocks like FEAM that have undergone splits. The results from this test should not be borrowed as a headline-positive CLI return or used as a net validated edge. The data for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run using a computer program called node with a script called backtest.js on the stocks GLW, OCC, BTDR, and FEAM. The test used a strategy called breakout, which means buying or selling when a stock's price breaks out of a certain range, and looked back at 20 days of data over 1500 days. The test also used a stop loss, which is a rule to sell a stock when it loses 10% of its value, and a target price, which is a goal to sell a stock when it gains 30% of its value. The test did not reserve any data to check its results and did not consider the costs of buying and selling stocks. The results of the test should not be used to make actual trades. The data for the test came from a website at https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test was run on the stock symbol GLW, along with OCC, BTDR, and FEAM, using a breakout strategy. The test, which used 20 days of lookback data and spanned 1500 days, had a 10% stop loss and a 30% target profit. The test was done using a command line interface, or CLI, which has its own rules for exiting a trade and calculating prices. The results of this test should not be used to authorize a trade. The test also compared the performance of the stocks using different methods of calculating costs and returns. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars.

A trading strategy was tested using a frozen template that was not the best choice. The strategy 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 to smooth out price fluctuations. It enters the trade at the next open price, with a stop loss set at 2 times the average true range of the prior 20 days, a target of 6 times the average true range, and an exit after 60 sessions. The strategy also includes gap stops at a worse open price, capped gap targets, and a same-bar stop including the entry day. The test was run with 5 basis points fees and 10 basis points slippage per side, and the portfolio was fully invested. The results showed a net return of 27.199576%, a maximum drawdown of 56.992109%, a win rate of 44.444444%, and 18 trades, with 6 targets. The test also recorded all 6 L 15/20/30 x ATR 2/3 cells. The data used for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, which provides stock bar data with price adjustments for corporate actions. The test did not account for historical whole universe survivorship bias, which refers to the tendency for successful companies to be overrepresented in historical data. The results are based on a daily fixed bracket and do not validate 15-minute monitoring or discretionary management. The whole position returns are not a forecast for a $120 USD fund portfolio. The strategy will be promoted to watch for current qualified entry and valuation, but a strong holdout alone does not address all regime uncertainties.

A trading strategy called R91902 MU fixed breakout train has been set up with specific rules. The rules are: if the closing price is higher than the average of the last 50 closing prices and the previous 20 closing prices, then buy at the next opening price. The stop loss is set at 2 times the average true range of the last 20 days, and the target is set at 6 times the average true range. The position will be closed after 60 sessions. If there is a gap in the price, the stop loss will be adjusted to the worse opening price. The strategy also includes a 5 basis point fee and 10 basis point slippage per trade. The strategy was tested on data from 2016 to 2023, and the results show a net return of 7.793984%, a maximum drawdown of 68.718323%, and a win rate of 32.692308%. The strategy made 52 trades and hit 14 targets. The data used for testing comes from the website https://runway.adityaperswal.workers.dev/data/stock-bars, which provides price adjustments for corporate actions. The strategy does not account for historical whole universe survivorship bias. The results do not validate the strategy's performance in real-time monitoring or discretionary management. The strategy will be promoted to watch for current qualified entry and valuation, but a strong holdout alone does not eliminate all regime uncertainties.

A trading strategy for FCEL was tested. The strategy is based on a template that was not selected as the best. It enters a trade when 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 a certain period. 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 used, which means that if the price gaps up or down, the stop loss is adjusted. The same bar stop is used, including the entry day, and the force window end is used, which means that the trade is closed at the end of the trading period. The test was run using vectorbt, a backtesting library, with long-only trades, 5 basis point fees, and 25 basis point slippage per side. The portfolio was fully invested and isolated. The compounded net trades were exactly reconciled, and the total return was calculated. The training data was from 2016 to 2023, and the backtest started on April 14, 2023. The holdout period was from 2024 to 2025, and the strategy was not tuned. The separate known period was 2026. The results of the holdout period were a net return of -68.896891%, a drawdown of 79.356427%, a win rate of 21.428571%, and 14 trades. The targets were 3, and the doubles slippage return was -70.999683%. All 6 L 15/20/30 x ATR 2/3 cells were recorded, but the diagnostic was not retuned. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which provides stock bar data with price adjustments for corporate actions. 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 a separately frozen prospective study is required due to possible changed economics.

A trading strategy for FCEL was tested. The strategy is based on a template that was not selected as the best. It enters a trade when the close price exceeds the average of the prior 20 closes and the 50-day moving average. The strategy uses a stop loss of 2 times the average true range of the prior 20 days and a target of 6 times the average true range. The trade is exited after 60 sessions. The strategy also includes gap stops and targets, and a same-bar stop. The test was run using vectorbt, with 5 basis point fees and 25 basis point slippage per side. The test was fully invested and isolated. The results showed a net return of -42.280418%, a drawdown of 77.890289%, and a win rate of 29.787234%. The strategy was trained on data from 2016 to 2023 and tested on data from 2024 to 2025. The results also showed that doubling the slippage would result in a return of -54.368738%. The strategy was tested on 6 different parameters, but no retuning was done. The data used was from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which includes price adjustments for corporate actions, but does not account for survivorship bias. The strategy does not validate 15-minute monitoring or discretionary management. The results are not a forecast for a $120 USD fund portfolio. The automatic baseline deployment was rejected, as the economics may have changed and require a separate study.

A trading fund manager used a frozen template that was not the best choice. The template said to buy when the closing price was higher than the average of the last 20 closes and the 50-day Simple Moving Average (SMA), which is a calculation to smooth out price changes. The manager would enter the trade at the next opening price, with a stop loss at 2 times the average true range (ATR) of the last 20 days, which measures price volatility, and a target at 6 times the ATR. The trade would exit after 60 sessions. If there was a gap in the price, the stop loss would be adjusted to the worse opening price. The manager also capped the target price and stopped the trade on the same day if necessary, including the entry day. The manager forced the trade to end at a certain time. The manager used a vectorbt tool to analyze the trades, looking at long positions only, with 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 manager was fully invested and isolated the test. The net trades were exactly reconciled with the portfolio's total return. The manager trained the model using data from 2016 to 2023, and the model started on April 14, 2023, after a special purpose acquisition company (SPAC) event. The holdout period, which is a separate period used to test the model, was from 2024 to 2025, and was not adjusted. There was also a separate period in 2026. The results of the holdout period were a net return of -48.092675%, a maximum drawdown of 63.370939%, a win rate of 22.222222%, and 9 trades, with 2 targets met. The return with double slippage was -52.560526%. All 6 combinations of ATR and cells were recorded, but the diagnostic was not used to adjust the model. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all, which is a data source that provides stock prices with adjustments for corporate actions, such as mergers and acquisitions. The price adjustments were approximate and did not account for the historical performance of the entire universe of stocks, which is known as survivorship bias. The daily fixed bracket did not validate 15-minute monitoring or discretionary management, which is a type of management that involves human judgment. The whole position returns were not forecast for a $120 USD fund portfolio. The manager rejected automatic baseline deployment, which means using a standard model without adjustments, because the economics may have changed and require a separate study.

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.