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This is a report on a trading strategy called R92002 IMOS long-only breakout. The strategy is based on certain premises. Firstly, it uses daily high, low, open, and close prices from September 18, 2016, available at https://runway.adityaperswal.workers.dev/data/stock-bars. Secondly, it freezes the close price of the last 20 days if it is higher than the maximum price of the prior 20 days and the 50-day simple moving average, which is a calculation to smooth out price data over a period, and then it stops the trade at two times the average true range, which is a measure of price volatility, over 20 days, and targets six times the average true range over 20 days, with a time exit after 60 sessions, considering opening gaps, which are differences between the current day's price and the previous day's price. The strategy also honors stop losses, which are orders to sell when a stock falls to a certain price, first when the daily price path is ambiguous. Buying and selling costs are 5 basis points fee plus 15 basis points liquidity cost each side, with a stress test of 5 plus 50 basis points. Brackets, which are price ranges, are frozen when stressing costs. The strategy does not model partial or trailing management, which are methods to adjust the size of a trade, and it uses daily bar data as a proxy, not 15-minute execution data from a specific venue. The strategy uses vectorbt, a backtesting library, to generate orders with a long-only direction, on two marks per day, and independently compounds trade returns, which means it calculates the returns of each trade separately. The strategy was trained from 2016 to 2022 and resulted in a loss of 8.9717%, with 45 trades, and from 2023 to September 18, 2026, it resulted in a loss of 12.0162%, with a maximum drawdown of 38.5439%, a win rate of 29.1667%, and 24 trades, with a stress test result of -25.6227%, and a subset result of -2.5298% with 8 trades. The conclusion is that the strategy has a baseline long-horizon compounded loss, and it is recommended to preserve any positive recent regime, but not to infer a durable edge, which is a lasting advantage. There is no adoption or order from this test, and the historical 3R bracket alone is not a defended current valuation.

A news scan found the following text: Premises for the R92002 CE long-only breakout strategy are as follows: 1. The strategy uses daily high, low, open, and close prices from September 18, 2016, available at https://runway.adityaperswal.workers.dev/data/stock-bars. 2. The strategy freezes the close price above the prior 20-day maximum and the 50-day simple moving average, which is a calculation of the average price over a 50-day period, and then uses the next open price. It stops at 2 times the average true range, a measure of price volatility, over 20 days, targets 6 times the average true range over 20 days, and exits after 60 sessions. The strategy also honors opening gaps, which are large price movements at the start of trading, and stops first when the daily price path is unclear. Buying and selling costs include a 5 basis point fee and a 15 basis point liquidity cost, with an additional stress cost of 5 and 50 basis points. Brackets are frozen when stressing costs. The strategy does not use a 2R partial or trailing management model, uses daily bar data as a proxy, and does not account for 15-minute execution. 3. The strategy uses vectorbt, a backtesting library, to simulate trades with a long-only direction, on two marks per day, with independently compounded trade returns that reconcile exactly. The strategy discarded an initial default-direction shorting bug before records and did not select the best cell. The strategy was trained from 2016 to 2022 and resulted in a -21.9553% return, with 35 trades. From 2023 to September 18, 2026, the strategy resulted in a -25.1199% return, with a drawdown of 38.5325%, a win rate of 17.6471%, and 17 trades. The stress test resulted in a -33.5209% return, and the 2026 subset resulted in a -6.0643% return with 4 trades. The conclusion is that the strategy has a baseline long-horizon compounded loss, and any positive recent regime should be preserved, but no durable edge should be inferred. There is no adoption or order from this test, and the historical 3R bracket alone is not a defended current valuation.

A command was run to test a trading strategy on the stock POWL. The strategy was to buy when the stock price breaks out, looking back 20 days, over 1460 days, with a 10% stop loss and a 30% target profit. The test used old prices before April 6 and new prices after the stock split, which was announced by the company. The split changed the stock price from 584.73 on March 25 to 183.96. The test results showed a return of -4.989%, a drawdown of 33.077%, a win rate of 38.46%, and 78 trades. However, these results are only for diagnostic purposes and do not account for all costs or the effect of the stock split. The results should not be compared to another system called vectorbt, as the strategies and costs are different. Based on the test, no trades were justified.

The automated trading fund manager made the following premises: the data source is https://runway.adityaperswal.workers.dev/data/stock-bars with SIP adjustment from 2016 to 2026, verified on April 6 against https://www.sec.gov/Archives/edgar/data/80420/000008042026000103/ex991-powlq3xfy2026earning.htm. The manager used a fixed baseline of close price greater than the prior 20-day high and 50-day Simple Moving Average, with the next open price as the target, and a stop loss at 2 times the prior 20-day Average True Range. The manager also set a target net of 3 times the risk, with fees of 5 basis points and a slippage of 15 basis points per side, and a time boundary of 60 sessions for liquidation. The manager used a long-only vector and backtested the strategy from 2016 to 2023, which resulted in a return of -44.5846%, a drawdown of 64.3839%, and a win rate of 26.53% over 49 trades. The manager also backtested the strategy from 2024 to 2025, which resulted in a return of 33.4085%, a drawdown of 49.6634%, and a win rate of 37.5% over 16 trades. In 2026, the strategy resulted in a return of 76.3395%, a drawdown of 25.9815%, and a win rate of 60% over 5 trades. The holdout stress test resulted in a return of 19.273%, and the recent return was 70.274%. The manager performed a parameter sweep of 18 cells, but did not choose a winner. The manager concluded that the recent regimes deserve future tests, but the current price of 183.96 does not meet the breakout condition of 197.67 and the 50-day Simple Moving Average of 203.20, so no trade was made.

A command was run on a node using the file backtest.js, which is located in the /app/agent/dist directory, with the following parameters: the symbols FRO, STNG, and LPG, the breakout strategy, a lookback period of 20 days, 1460 days of data, a stop loss percentage of 10, and a target percentage of 30. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The results for each symbol were: FRO had a return of 2.652%, a maximum drawdown of 29.209%, a win percentage of 32.530%, and 83 trades; STNG had a return of 25.885%, a maximum drawdown of 12.084%, a win percentage of 37.5%, and 72 trades; LPG had a return of 94.803%, a maximum drawdown of 11.303%, a win percentage of 38.554%, and 83 trades. The test used a fixed percentage engine, with no fees, gap execution, or held out window, and was separate from a costed vectorbt test run from 2017 to 2020, with no borrowing, and resulted in a positive diagnostic for LPG as a live edge.

A news scan found the report of an automated trading fund manager. The manager used data from https://runway.adityaperswal.workers.dev/data/stock-bars SIP/all to September 18. The manager used a Python system called vectorbt long only, which means it only buys stocks and never sells short, or bets against a stock. This system looks at the stock's close price and prior 20-day high close, and also at the Simple Moving Average, or SMA, over 50 days. The system sets a stop loss, which is a price at which to automatically sell a stock, at 2 times the prior 20-day Average True Range, or ATR. The system also sets targets to solve for a net 3R baseline with a 5 basis point fee and a 15 basis point slippage on each side. A basis point is one hundredth of a percent. The system has a 60-session time-out and a window end liquidation count shown. The system allows two slots per bar for entry and exit. The stress test has a 50 basis point slippage hold that brackets the baseline. The system was trained from 2016 to 2023, held apart from 2024 to 2025, and recently used in 2026. The system already researched the holdout, which is not pristine out-of-sample, or OOS, and selected a survivor universe. The prices were adjusted to approximate cash inclusive returns, not raw live bracket execution. There is no 15-minute or +2R trailing manager policy. The compounded trades were reconciled against vectorbt with a precision of less than or equal to 1e-7. Six L10, L20, and L40 cost cells per window recorded observations, but no optimal cell was chosen. The BWLP shortlisting history has no training data. The baseline and stress packets are: for BWLP with a lookback of 20 days in the holdout period with no stress, the return was -17.679001357267065 percent, the drawdown was 33.24910136625081 percent, there were 8 trades, the win rate was 25 percent, the mean return was -2.0295482049527234 percent, and there was 1 boundary. For BWLP with a lookback of 20 days in the holdout period with stress, the return was -22.162324569214025 percent, the drawdown was 35.76946703088314 percent, there were 8 trades, the win rate was 25 percent, the mean return was -2.7129545707853753 percent, and there was 1 boundary. For BWLP with a lookback of 20 days in the recent period with no stress, the return was 82.88717331315368 percent, the drawdown was 19.32264223475223 percent, there were 7 trades, the win rate was 71.42857142857143 percent, the mean return was 9.661804144751141 percent, and there was 1 boundary. For BWLP with a lookback of 20 days in the recent period with stress, the return was 74.1416164512579 percent, the drawdown was 19.885418243897092 percent, there were 7 trades, the win rate was 71.42857142857143 percent, the mean return was 8.896843142039867 percent, and there was 1 boundary. There will be no trade solely from this study, and the new net 3R targets mean this study complements rather than supersedes older fixed gross bracket studies.

A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars SIP/all to Sep 18. This is a study of a trading strategy using Python and a library called vectorbt, which is a backtesting framework, meaning it tests how well a strategy would have worked in the past. The strategy is long-only, meaning it only buys, not sells, and it uses a set of rules to decide when to buy and sell. The rules are: close price is higher than the prior 20-day high close and the 50-day simple moving average, next open price, and prior 20-day average true range times 2 as a stop loss, which is a price level at which to automatically sell. The strategy also has a target, which is a price level at which to sell for a profit, and it solves for a net 3R baseline with a 5 basis point fee and 15 basis points slippage on each side, meaning the strategy takes into account the costs of buying and selling. A basis point is a unit of measurement, equal to 0.01%. The study used data from 2016 to 2023 for training, 2024 to 2025 as a holdout, and 2026 as a recent regime. The results are shown in a list of packets, which are sets of data, for the symbol LPG, with different lookback periods, periods, stress levels, returns, drawdowns, number of trades, win rates, mean returns, and boundaries. The study found that the strategy had a return of -22.96% in the training period with no stress, and -45.33% with stress. In the holdout period, the return was -17.34% with no stress, and -22.38% with stress. In the recent period, the return was 68.66% with no stress, and 61.73% with stress. The study concludes that this strategy complements rather than supersedes older fixed gross bracket studies, meaning it can be used in addition to other strategies, but it does not replace them.

A news scan found the source https://runway.adityaperswal.workers.dev/data/stock-bars SIP/all to Sep 18. This is a Python study using vectorbt for long-only trades from orders. The study uses a simple moving average, or SMA, which is a way to smooth out price data, with a 50-period SMA and a 20-period average true range, or ATR, which measures price volatility. It also uses a 20-period close and high price comparison to determine trades. The study has a 2 times ATR stop loss, which is a price level at which to automatically close a trade to limit losses, and a net 3R target, which is a profit goal. The study has a 5 basis point fee, which is a small cost, and a 15 basis point slippage, which is a small price movement. The study uses a same-bar stop, which is a stop loss set at the same price as the current bar, and an adverse opening gap, which is a large price movement at the start of trading. The study also has a 60-session timeout, which is a time limit for trades, and a window end liquidation count, which is a way to measure the end of a trading period. The study allows two slots per bar for entry and exit. The stress test uses a 50 basis point slippage hold, which is a way to test the study's performance. The study was trained from 2016 to 2023, held apart from 2024 to 2025, and recently tested in 2026. The study used adjusted prices, which are approximate cash-inclusive returns, not raw live bracket execution. The study did not use a 15-minute or 2R trailing manager policy. The study reconciled compounded trades against vectorbt with a precision of less than or equal to 1e-7. The study recorded six L10, L20, and L40 cost cells per window, but did not choose an optimal cell. The study found the following baseline and stress packets for the symbol STNG: a train period with a return of -14.1339%, a drawdown of 76.47%, 40 trades, a win rate of 32.5%, and a mean of 0.859; a train period with stress and a return of -35.104%, a drawdown of 79.18%, 40 trades, a win rate of 32.5%, and a mean of 0.155; a holdout period with a return of 3.917%, a drawdown of 27.26%, 9 trades, a win rate of 33.33%, and a mean of 1.032; a holdout period with stress and a return of -2.428%, a drawdown of 28.77%, 9 trades, a win rate of 33.33%, and a mean of 0.328; a recent period with a return of -7.209%, a drawdown of 26.79%, 7 trades, a win rate of 28.57%, and a mean of -0.479; and a recent period with stress and a return of -11.646%, a drawdown of 29.06%, 7 trades, a win rate of 28.57%, and a mean of -1.174. This study does not solely determine trades, but rather complements older fixed gross bracket studies with new net 3R targets.

A news scan found the headline 'R91908 FRO fixed costed breakout holdout and recent regime'. The source is https://runway.adityaperswal.workers.dev/data/stock-bars SIP/all to Sep 18. This uses a Python system called vectorbt, which is a type of automated trading system, with specific rules for buying and selling. The rules are: buy when the close price is higher than the prior 20-day high and the 50-day moving average, and sell at the next open or when the price moves 2 times the 20-day average true range, with a target of 3 times the risk. The system also has rules for stopping trades, such as when there is an adverse opening gap or after 60 sessions. The system was trained on data from 2016 to 2023, and tested on data from 2024 to 2025 and 2026. The results are shown in a list of packets, which are sets of data, for the stock FRO, with different periods and stress levels. The stress level refers to the amount of risk or uncertainty in the system. The results include returns, drawdowns, and win rates for each period. The system was reconciled, or checked, against the vectorbt system to ensure accuracy. The results show that the system had different returns and drawdowns for each period, with the recent period having the highest return. The study does not solely determine trades, but rather complements older studies.

A command was run on a node using the file backtest.js with the symbols PSIX, ETN, POWL, and NVT, and the breakout strategy. The command looked back 20 days over a period of 1460 days, with a stop loss of 10 percent and a target gain of 30 percent. The data for this command came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, which provided stock bars for the symbols PSIX, ETN, POWL, NVT, and SPY, with a timeframe of one day, a feed of sip, and all adjustments. An inspection of the engine used for this command, found in the file engine.js, showed that it used same-close signals and entries, fixed percentage brackets, and did not account for fees, holdout, or adverse gap execution. This was a diagnostic test, not a trade authorization, and the results of the costed next open vector, labeled as bt3479-3482, are from a different strategy. The results for each symbol were: PSIX had a return of 24.8316 percent, a drawdown of 18.7723 percent, a win rate of 41.9355 percent, and 31 trades; ETN had a return of 15.4513 percent, a drawdown of 16.1143 percent, a win rate of 41.7582 percent, and 91 trades; POWL had a return of -4.9892 percent, a drawdown of 33.0766 percent, a win rate of 38.4615 percent, and 78 trades; NVT had a return of -14.7825 percent, a drawdown of 27.9100 percent, a win rate of 31.9588 percent, and 97 trades. The full records and parameters are available, and the results from the command line interface will not be chosen over the costed test results.

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 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.