Stats

The automated trading fund manager made the following premises: the fund used daily stock prices from 2016 to 2022 for training, 2023 to 2025 for holdout, and 2026 up to September 18 for recent data. The fund started trading Alcoa stock on November 1, 2016. The fund used a fixed strategy based on the Relative Strength Index, or RSI, a measure of stock price movement, and the Simple Moving Average, or SMA, a measure of average stock price over time. The fund entered a trade when the RSI was less than 10 and the stock price was above the SMA, and it set a stop loss, a price at which to automatically sell a stock, and a target price, a price at which to automatically sell a stock for profit. The fund allowed same-day exits, or selling a stock on the same day it was bought, and it used a 5 basis point fee, a 15 basis point slip, and a 45 basis point stress slip, all measures of trading costs. The fund's training return was 176.6188%, its maximum drawdown, or loss, was 33.5615%, and it made 17 trades. The fund's holdout return was -22.9149%, its maximum drawdown was 44.5245%, and it made 7 trades. The fund's recent return was -17.1654%, and it made 4 trades. The fund also tested different variations of the RSI and trend gate, a measure of stock price trend, and it compared its results to a generic breakout strategy, a strategy based on stock price breaking out of a certain range. The fund concluded that it should preserve its positive results but it did not currently have a signal to trade, and it would not deploy its strategy live. The fund also noted that its historical targets did not validate its 15-minute checks, discretionary fundamental targets, or +2R management, a measure of risk management.

A news scan found the text: Premises: there is a rule to buy or sell stocks based on a vectorbt fixed 20 session next open rule, with a 30 USD stock allocation per 1000 book, and a fee of 10 basis points or a slip of 10 or 50 basis points on each side. This rule was trained through 2024 and tested from 2025 to September 18, 2026, with no holdout tuning. The results for HALO training were a gain of 0.49139%, a drawdown of 0.96270%, a win rate of 50%, and 14 trades. The holdout results were a loss of 0.468923%, a drawdown of 1.254913%, a win rate of 33.333%, and 3 trades. The results for TH training were a gain of 0.81828%, a drawdown of 0.71195%, a win rate of 50%, and 2 trades. The holdout results were a gain of 1.159881%, a drawdown of 0.469995%, a win rate of 50%, and 2 trades. The stress holdout results were a range of -0.539190% to 1.107141%. A CLI breakout of 252 days with a stop of 5 and a target of 15, independently reproduces HALO with a gain of 15.1221%, a drawdown of 9.515%, a win rate of 40.625%, and 32 trades. The results for TH were a loss of 7.9152%, a drawdown of 22.6158%, a win rate of 30.769%, and 26 trades, with a different entry and exit policy and no isolated holdout, for diagnostic purposes only. The conclusion is that this is a replication of a previously seen chronological holdout, not a fresh independent validation, and not an option profit and loss. The complete executable option history remains unavailable from the prior 5-path audit. Source https://runway.adityaperswal.workers.dev/data/stock-bars

This is a report on a test of a trading strategy. The test used daily stock prices from 2016 to 2026. The strategy had certain rules: it would buy a stock if its price was higher than its price 20 days ago and its 50-day average price, and it would sell if the price reached a certain target or stopped losing money after a certain time. The test also considered the costs of buying and selling, including a 5 basis point fee and a 15 basis point cost for buying or selling at a bad price. The test found that all 12 combinations of parameters lost money, so there is no reason to use this strategy just because of a shortage of a certain chemical, called acetyl. The test also found that having more cash can exist at the same time as failing to time the stock market correctly. A news scan found the script files used for the test, including test.py, tests.json, and trades.json.

The automated trading fund manager made some premises for the R92004 CE costed chronological breakout and fixed-bracket test. The premises are as follows: the test uses daily bars from 2016 to September 18, 2026, with 2693 bars per name, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The frozen baseline for the test is a prior 20-day close breakout and a 50-day simple moving average, which is a calculation to determine the trend of a stock, with the next session's open, a stop loss of 2 times the 20-day average true range, which is a measure of volatility, and a target that solves for a net 3R at 20 basis points per side, with a time limit of 60 sessions. The test also considers sensitivity to losses of 40 and 60 percent. The test was trained on data from 2017 to 2022, and then held apart from 2023 to 2025, with recent data from 2026. The test already researched historical windows for CE, so it is not a completely new and untouched test. The fees for the test are 5 basis points plus 15 basis points of slippage per side, with a stress test of 50 basis points of slippage and a frozen target. The test uses a long-only strategy, which means it only buys stocks, and it allows for same-day exits. The test also reconciled the portfolio return to the compounded profit and loss divided by the entry value plus entry fees. The conclusion of the test is that all 12 held-apart parameter and cost cells lose, and there is no basis to adopt this entry solely from acetyl scarcity, which refers to a lack of availability of a certain stock. The test also found that larger cash sensitivity can coexist with failed conditional stock timing.

A diagnostic test was run on a trading strategy using the command line interface, or CLI, with the following settings: the symbols used were CE and EMN, the strategy was a breakout strategy, the lookback period was 20 days, the test lasted for 1460 days, the stop loss was set at 8 percent, and the target gain was set at 24 percent. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test found that the built-in signal decay exits were different from the custom fixed bracket rule. The CLI did not have a specific holdout period and its friction, or the costs of trading, were not separately determined. This test was only for diagnostic purposes and its results were not adopted. The full results of the test can be found in the file /tmp/supply92004/cli.json. The test did not find any evidence that the strategy could consistently predict stock shortages.

A diagnostic test was run using a computer program called node to analyze a trading strategy. The test used the backtest.js program to look at the stocks with the tickers CE and EMN, using a strategy called breakout, which is a type of trading strategy that involves buying a stock when its price breaks out of a certain range. The test looked back at 20 days of data and used 1460 days of data to make decisions. It also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 8 percent, and a target gain, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 24 percent. The data for the test came from a website called https://runway.adityaperswal.workers.dev/data/stock-bars. The test found that the built-in signal decay exits, which are rules to sell a stock when its signal gets weaker, were different from the custom fixed bracket rule, which is a type of rule to buy and sell stocks. The test also found that the command line interface, or CLI, did not have a clear rule for holding onto stocks over time and that the friction, or costs, of trading were not established independently. This test was only for diagnostic purposes and the results were not adopted. The full results of the test can be found in a file called /tmp/supply92004/cli.json. The test did not find any evidence that the trading strategy could consistently make a profit by predicting shortages.

A test of stock controls was conducted using a set of premises. The first premise involved a breakout strategy, which was tested on a $1000 account with a $30 stock allocation per entry, a 10 basis point fee, and 10 basis point slippage per side. The strategy was trained on data from 2016 to 2024 and tested on data from 2025 to September 18, 2026. The results showed a return of 4.9139% with a drawdown of 9.6270% and a win rate of 50% for the HALO train, and a return of -4.68923% with a drawdown of 12.54913% and a win rate of 33.333% for the holdout. The TH train showed a return of 8.1828% with a drawdown of 7.1195% and a win rate of 50%, and the holdout showed a return of 1.159881% with a drawdown of 4.69995% and a win rate of 50%. A stress test with 50 basis point slippage showed returns of -5.3919% and 1.10714% for the holdouts. A different exit and signal policy was also tested, showing a return of 15.1221% with a drawdown of 9.51497% and a win rate of 40.625% for the HALO, and a return of -7.91524% with a drawdown of 22.61577% and a win rate of 30.769% for the TH. Five historical calls were also examined, but none of them resulted in complete and affordable executable paths. The conclusion is that the stock control is reproducible, but it does not validate option returns or 15-minute stops. The holdout has been examined in earlier runs and is not a new independent out-of-sample test. The data used for this test can be found at https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-bars, and the scripts used are research/study.py, models.py, and history.py. No trade is authorized by this stock proxy.

A diagnostic test was run using the command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the stocks CE, IMOS, and BTDR. The strategy used was a breakout strategy, which means it looks for stocks that are breaking out of a certain price range, and it looked back at 20 days of data and ran the test for 1460 days. The test also used a stop loss, which is a rule to sell a stock if it loses a certain percentage of its value, in this case 8 percent, and a target profit, which is a rule to sell a stock if it gains a certain percentage of its value, in this case 24 percent. The test got its data from a website, https://runway.adityaperswal.workers.dev/data/stock-bars, and used 1002 bars of data, where a bar is a piece of data that shows the price of a stock at a certain point in time. This test also exits if the signal, or the reason to buy or sell a stock, gets weaker. This test is different from a custom test that uses a different rule to buy and sell stocks. The test did not hold out any data to test its accuracy and did not account for costs, so it is only for diagnostic purposes and its results should not be used to make actual investment decisions. The stock BTDR includes data from before it started operating, and a custom test that only looks at data from when the company was operating gives different results. The full results of the test are in a file called cli_test.json.

A diagnostic test was run using the command line interface, which is a way to interact with a computer program using text commands. The test used a node, which is a computer program, to run a backtest, which is a simulation of a trading strategy, on the stocks CE, IMOS, and BTDR. The strategy used was a breakout strategy, which means buying a stock when its price breaks out of a certain range, and it looked back at 20 days of data and ran for 1460 days. The test also used a stop loss, which is a rule to sell a stock when it loses a certain percentage of its value, in this case 8%, and a target profit, which is a rule to sell a stock when it gains a certain percentage of its value, in this case 24%. The test got its data from a website and used 1002 bars of data, which are like buckets of time. This test also exits when a signal, which is like a hint to buy or sell, gets weaker. This test is different from a custom test that only buys and holds stocks for a long time. The test did not set aside any data to check its results and did not consider any costs, it was just a diagnostic test and its results were not used to make any decisions. The stock BTDR includes data from before it started operating, but a custom test that only looks at data from when it started operating gives a more accurate result. The full results of the test are in a file called cli_test.json.

A diagnostic test was run using the command line interface, which is a way to interact with a computer program using text commands. The test used a node, a type of computer program, to run a backtest, which is a simulation of a trading strategy, on the stocks CE, IMOS, and BTDR. The strategy used was a breakout strategy, which means it looks for stocks that are breaking out of a certain price range, and it looked back at 20 days of data. The test ran for 1460 days and used a stop loss, which is a rule to sell a stock when it falls by 8%, and a target profit, which is a rule to sell a stock when it rises by 24%. The test got its data from a website, https://runway.adityaperswal.workers.dev/data/stock-bars, and used 1002 bars of data, which are like rows of data. This test also stopped when the signal, or the reason to buy or sell, got weaker. This test is different from a custom rule that only buys and holds stocks for a long time. The test did not separate the data into training and testing sets, and it did not account for costs. It was just a diagnostic test and not a real trading strategy. The stock BTDR includes data from before it started operating, and a custom test, number 3591, has more accurate results. The full results of the test are in a file called cli_test.json, located in a temporary directory.

The automated trading fund manager made certain premises for the R92002 costed breakout controls and sparse historical-option audit. These premises include a fixed 252 intraday high plus 1.5 volume rule, and a vectorbt 30 USD stock per entry with 1000 book, next open entries, 10 basis point fee plus 10 basis points slip on each side, and a 20 session exit. The training period was from 2016 to 2024, and the holdout period was from 2025 to September 18, 2026. The results for HALO train were a return of 0.49139%, a drawdown of 0.96270%, a win rate of 50%, and 14 trades. For the holdout period, the results were a return of -0.468923%, a drawdown of 1.254913%, a win rate of 33.333%, and 3 trades. The results for TH train were a return of 0.81828%, a drawdown of 0.71195%, a win rate of 50%, and 2 trades. For the holdout period, the results were a return of 1.159881%, a drawdown of 0.469995%, a win rate of 50%, and 2 trades. The results for NEO holdout were a return of -0.191897%, a drawdown of 0.43803%, a win rate of 0%, and 1 trade. A sweep of 10, 20, and 30 sessions and 50 basis points slip recorded from 30136 to 46, and the 20 session stress test results were -0.53919% for HALO and 1.10714% for TH. There was no best holdout selection. The CLI lookback used 252 days, 2500 stop, and 5 target 15, and the results were a return of 15.1221% and a drawdown of 9.515 for HALO, a return of -7.9152% and a drawdown of 22.616 for TH, and a return of -6.0274% and a drawdown of 15.971 for NEO. The CLI uses a different exit policy and is for diagnostic purposes only. Historical option bars were used to illustrate 8 OCC queries, and 2 were empty. The queries included HALO May 8, 2025, P60 open 0.80 oversized, and August 7, 2026, C100 open 3.93/P70.60 oversized. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/option-bars. No trade was made during this research run.

This is a report on a trading strategy test. The test used daily stock prices from September 18, 2016, available at https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy had two main rules: it would buy when the closing price was higher than the maximum price of the last 20 days and the 50-day average, and it would sell when the price reached a target or stopped losing money after 60 sessions. The test also considered costs such as fees and slippage, which are costs associated with buying and selling, and stress, which refers to the impact of unexpected events on the strategy. The test results showed a return of 59.0565% from 2016 to 2022 and 7.8856% from 2023 to September 18, 2026, with a maximum loss of 69.1088% and a win rate of 33.3333%. However, when stress was applied, the return became negative at -6.8632%. A news scan found the conclusion that the strategy has a positive baseline but is sensitive to costs, and stress can make it negative. The test was done on historical data and does not represent a current investment recommendation.

This is a report on a trading strategy called R92002 BTDR long-only breakout. The strategy is based on certain premises: it uses daily high, low, open, and close prices from September 18, 2016, available at https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy has rules such as a stop loss set at 2 times the average true range over 20 days, and a target set at 6 times the average true range over 20 days, with a time exit after 60 sessions. The strategy also takes into account opening gaps and daily path ambiguity. There are costs associated with buying and selling, including a 5 basis point fee and a 15 basis point liquidity cost on each side, with an additional stress test of 5 and 50 basis points. The strategy uses a vectorbt model with long-only direction and independently compounded trade returns. The results show a sensitivity test with L15, L20, and L30, and no best-cell selection. The training data is from 2016 to 2022, but it is not available for BTDR, which only started operating on April 14, 2023. The test results from 2023 to September 18, 2026, show a return of -14.1235%, a maximum drawdown of 75.7896%, a win rate of 33.3333%, and 24 trades, with a stress test result of -27.4041%. The 2026 subset shows a return of 7.1773% with 5 trades. The conclusion is that the strategy shows a baseline long-horizon compounded loss, and any positive recent regime should be preserved, but it does not infer a durable edge. This test does not recommend adoption or ordering, and the historical 3R bracket alone is not a defended current valuation.

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 news scan found the text: Premises: 11-event calendar3554, Oct2024 usesOct2 with timing sensitivity retained. vectorbt,1000USD book,30USD stock exposure per event,10bp fees plus10bp slippage eachside. Enter close1/3/5sessions before reactionclose; exit reactionclose. Fixed T1 long train2024–25 return-1.90786%,DD2.00696%,win44.444%,N9;2026holdout return-.267331%,DD.285865%,win50%,N2. T1 short diagnostic train+1.69907%,holdout+.220306%,DD.036535%,win50%,N2; shorts unavailable under mandate. T3/T5 long holdouts-.101134/-.082821%; short+.053443/+.035056%, no bestholdoutselection. Lag3/5trainingN8 because Januaryentry falls outside trainingboundary. CLI breakout20,730days,5%stop/15%target returns25.933855%,DD7.50904%,win37.931%,29trades, buyhold-28.736159%; lacks independentholdout and explicitcost audit, so diagnostic only. Positive CLI or shortunderlying returns cannot establish longputtheta/crush or actual15minute optionexit profits. No live optionstrategy passed; no trade rests on proxy. Source https://runway.adityaperswal.workers.dev/data/stock-bars.

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 study was done on Conagra stock controls and breakout using a diagnostic tool called CLI. The study used 11 events from 2023, with $1000 initial investment and $30 stock exposure per event. The study was divided into training from 2024 to 2025 and holdout in 2026. The results showed that the long position had a return of -0.348071% during training, with a drawdown of 0.499122% and 33.33% win rate. In the holdout period, the return was -0.074364% with a drawdown of 0.074364% and 0% win rate. The short position, which is only for diagnostic purposes, had a return of 0.133033% during training and 0.026566% in the holdout period. The study also used a CLI tool with parameters such as 20-day lookback, 730 days, 5% stop percentage, and 15% target percentage, and found a return of -7.29374% with a drawdown of 8.05669% and 29.4118% win rate. The study concludes that neither the breakout strategy nor the 2-event stock control validates purchasing the Conagra stock option. The study notes that the real option bid/ask history and 15-minute exit backtest are still missing.

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.