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A news scan found the report of a trading strategy for Elmos, ticker symbol ELG.DE. The strategy was tested on Yahoo dividend-adjusted prices with a 5 basis point fee and a 15 basis point liquidity gap, also known as a slip, plus a 45 basis point stress liquidity gap on each side. The strategy used a simple moving average, or SMA, which is a calculation of the average price of a stock over a certain period of time. It also used an average true range, or ATR, which is a measure of how much a stock's price moves on average. The test ran from 2016 to 2022, after a 200-day warm-up period, and was held out from 2023 to 2025, with a separate test in 2026 up to September 16. The results were: a 26.4273% loss in the training period, with a 42.6213% drawdown, or maximum loss, and a 25% win rate over 32 trades; a 44.4505% loss in the holdout period, with a 65.7873% drawdown and a 15.7895% win rate over 19 trades, and a stress test result of -50.4356%; and a 67.3586% gain in 2026, with a 20.1233% drawdown and a 75% win rate over 4 trades. Despite the positive result in 2026, the strategy did not replace the failed holdout period. The strategy had limits, including only selecting one company per day, using Euro prices that are not traded on the foreign exchange market, and having a fixed time period for the trade. The source of the data is https://query1.finance.yahoo.com/v8/finance/chart/ELG.DE?range=10y&interval=1d&events=div%2Csplits and scripts/tmp/supply92104/test.py, tests.json.

The analysis of the DSX stock used a signal based on a fixed high volume over the prior 20 sessions, with entry at the next open and exit at the 20th session close, and a 21-session separation. The analysis started with 1000 units of initial cash and an all-equity long stock position, with a 5 basis point fee and 25 basis points of slippage on each side. The training period was before 2026, with a known holdout period in 2026, excluding the episode that crossed January 1. The analysis was rerun independently from 2693 downloaded daily bars and reproduced the prior results: a train return of -42.50127%, a maximum drawdown of 44.05348%, a win rate of 40%, and 5 trades; and a known holdout return of -13.22070%, a drawdown of 25.07173%, a win rate of 33.3333%, and 3 trades. Using a volume of 1.25 times the original volume yielded the same results, while using 2 times the volume resulted in a train return of -47.55930%, a drawdown of 48.97496%, a win rate of 40%, and 5 trades; and a holdout return of -14.11950%, a drawdown of 19.22107%, a win rate of 0%, and 1 trade. A command-line interface node was run with the following parameters: /app/agent/dist/backtest.js --symbols DSX --strategy breakout --lookback 252 --days 3650 --stop-pct 10 --target-pct 30, and returned 1537 bars, 18 trades, a return of -11.480347%, a drawdown of 24.478377%, and a win rate of 22.22222%. The buy-and-hold return was 124.8175%. The conclusion is that the weak long stock continuation can motivate investigating puts, but does not prove put profit. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.

A diagnostic test, R92104, found that a chronological normalized put payoff failed for the 2026 holdout period. The test was based on two premises: first, an existing stock backtest, MG stock 2026 N3, with a return of 0.495888% and a drawdown of 0.308209%, resulted in a win rate of 66.667% on a notional value of $999.38 and cash of $29, without reaching the put edge. Second, a new vector backtest reproduced six legacy endpoint counterfactuals using a synthetic $100 funded wrapper, with an exit strategy based on a formula, and a $1 fee per side. The test did not use historical option prices, actual stops, or actual expiry dates, and only considered 28 calendar-day bookkeeping. The data was split into a pre-2026 training period and a 2026 testing period, using already seen history, not pristine out-of-sample data. The training period resulted in a return of 12.822727% and a drawdown of 1.401401%, with a win rate of 33.333%. The testing period resulted in a return of -4.2% and a drawdown of 4.104104%, with a win rate of 0%. An additional 1% wrapper slippage and dollar stress resulted in a training return of 12.0885% and a testing return of -4.764%, with a drawdown of 4.573146% and a win rate of 0%. The initial near-zero price representation was rejected due to excessive fees, and a corrected wrapper verified three closed trades. The target derived from the full sample was found to be leakage, indicating that this is a fragility audit, not a tradable backtest. The conclusion is that the merger put with stock long outcomes cannot be validated, as the target winner was absent in the test, and the regime-matched option history was still 0. No strategy adoption or order was made. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the scripts used were research/proxy_backtest.py and mg_audit.py.

A diagnostic strategy review found that the CLI cross-check differs from the costed bracket tests. The CLI was run with a breakout strategy, looking back 20 periods over 1460 days, with a 10% stop percentage and a 30% target percentage, using data from https://runway.adityaperswal.workers.dev/data/stock-bars, which included all 1001 bars. The results showed the following returns, drawdowns, wins, and numbers for the stocks: LFUS had a return of -17.3486, a drawdown of 27.8021, a win of 32.3944, and 71; ATI had a return of 61.7478, a drawdown of 17.9160, a win of 37.2340, and 94; VSH had a return of 74.9844, a drawdown of 20.8508, a win of 45.1613, and 62; UMC had a return of 31.8682, a drawdown of 26.4650, a win of 40.2778, and 72; PXS had a return of -45.1063, a drawdown of 57.1767, a win of 23.8095, and 63; SEI had a return of -16.3227, a drawdown of 33.4083, a win of 30, and 60. The CLI uses the same close and signal exits and does not have an explicit costed chronological holdout, which is not equivalent to the frozen next-open 2ATR/net 3R60 session vector bt policy. The contradiction in SEI results is due to different exit policies and windows, not because one calculation is wrong. The primary adoption decision uses recorded held-apart costed tests, not the CLI's recent pooled return. There is no order from the CLI.

The R92102 SEI trading strategy has a frozen breakout holdout and cost sensitivity. The strategy is based on several premises, including a rule that freezes the same as rule 4039 before extra results. This means that the strategy will close a position if the current close is greater than the prior 20-day close high and the 50-day Simple Moving Average, which is a calculation of the average price of a stock over a certain period of time. The strategy also uses a stop-loss order, which is an order to sell a stock when it falls to a certain price, set at 2 times the prior Average True Range, which is a measure of the volatility of a stock. The strategy has a net target cost of 3 times the base cost and a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, with the SEI starting in 2017 and its predecessor business before that. The holdout period is from 2023 to 2025, and there is a separate period in 2026. The strategy uses a long-only approach, which means it only buys stocks and does not sell them short, with a fee of 5 basis points and a liquidity cost of 15 basis points on each side, and a stress test of 45 basis points. The strategy exits positions on the same day they are entered, with a gap stop open, which means that if the stock price gaps up or down significantly, the position will be closed. The strategy also uses a window end mark, which is a way of reconciling net trade cash. The strategy has been tested for sensitivity to different parameters, with 18 different combinations of parameters tested, but no attempt has been made to choose the best combination. The base holdout for the SEI has a drawdown of 24.07288852433759% and a win rate of 35%, with 20 trades and a stress test result of 10.042651723169296%. In 2026, the result was -5.903726863718896% with 4 trades. The current breakout is true, but the power-business acquisitions have changed the regime, which refers to the underlying market conditions. The positive mixed-business holdout is not an independent power-rent event test, which means that it is not a reliable indicator of future performance. The strategy is subject to selected-current-universe bias, which means that the stocks selected for the strategy may not be representative of the broader market, and daily proxy versus 15-minute exit limits remain, which refers to the way that the strategy uses daily data to make decisions about when to exit positions. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the target is the payoff hurdle, which is the minimum return required for the strategy to be considered successful, rather than valuation, which refers to the intrinsic value of the stocks. There is no order to buy or sell stocks based on this information.

The automated trading fund manager made some notes about the stock PXS. The manager uses a set of rules to decide when to buy or sell stocks. These rules include looking at the stock's closing price compared to its high price over the last 20 days and its average price over the last 50 days. The manager also looks at how much the stock's price has changed over the last two days and sets a target price based on this. The manager tested these rules using data from 2016 to 2022 and will use data from 2023 to 2025 to see how well the rules work. The manager will also look at how sensitive the rules are to changes in costs. The manager found that the rules did not work well for PXS, with a loss of 52.17631191970964% and a win of 20%. The manager also found that the rules were very sensitive to changes in costs, with a loss of 56.292478422587614% when costs were high. The manager decided not to use these rules to buy PXS because the return on investment was negative. The manager gets stock price data from https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the following trading notes: Premises for the trade are based on note4041, which freezes the same rule as 4039 before extra results. The trade rule is to close when the price is higher than the prior 20-day close high and the 50-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period of time. The following open, 2 prior Average True Range stop, which is a measure of price volatility, net 3R base cost target, and 60 session maximum are also considered. The training data is from 2016 to 2022, with SEI starting in 2017 and its predecessor business. The holdout period is from 2023 to 2025, and 2026 is separate. The Vectorbt long-only strategy has a fee of 5 basis points and a liquidity cost of 15 basis points on each side, with a stress test of 45 basis points. The entry day exits, gap stop open, stop first if both, and window end mark are all reconciled to net trade cash. The sensitivity analysis of 10, 20, and 40 percent was observed in all 18 cells, but the best option was not chosen. The UMC base holdout had a return of -9.41 percent, a maximum drawdown of 38.02 percent, a win rate of 35.29 percent, and 17 trades, with a stress test result of -18.20 percent and a 2026 return of 60.08 percent with 10 trades. The current breakout is true, but the negative held-apart return rejects this entry rule. The selected current universe bias and daily proxy versus 15-minute exit limits remain. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the target is a payoff hurdle, not a valuation. No order was given.

A news scan found the headline 'R92102 costed breakout holdouts: DSX long proxy -13.22%, NEO -6.57%, MTW +9.89%'. This refers to a trading strategy, which is a set of rules for buying and selling stocks. The strategy is based on two premises: the stock's closing price must be higher than its highest price in the last 252 days, and the trading volume must be 1.5 times higher than the average volume in the last 20 days. If these conditions are met, the strategy enters a trade at the next opening price and exits at the closing price 20 sessions later, with a 21-session separation between trades. A piece of software called Python vectorbt was used to test this strategy on a proxy of 1000 stocks, with fees of 5 basis points and slippage of 25 basis points on each side of the trade. The test was run on data before 2026, and the results were compared to a holdout period in 2026. The results for three stocks were: DSX with a train return of -42.5013% and a holdout return of -13.2207%, NEO with a train return of 3.04995% and a holdout return of -6.57084%, and MTW with a train return of -0.08943% and a holdout return of 9.89410%. The strategy's performance was also tested with different volume sensitivities, and the results were: DSX holdouts -13.22%, -13.22%, and -14.12%, NEO all -6.57%, and MTW 18.87%, 9.89%, and 13.81%. The results suggest that the stocks are weak in terms of direction, which supports investigating put options, but this is not the same as the profit and loss of the options. A command-line interface was used to run diagnostics on the strategy, and the results were: DSX -11.4803% with a drawdown of 24.4784% and a win rate of 22.22%, NEO -12.5995% with a drawdown of 21.8478% and a win rate of 28.57%, and MTW 14.1120% with a drawdown of 6.31448% and a win rate of 33.33%. However, the command-line interface returned only 1537-1545 bars, not the full 10 years, and the exit logic was different, so the results are not interchangeable. An attempt was made to study the options for DSX, but only 3 daily trade bars were returned, and no complete entry and exit path was available, so the return, drawdown, and win rate of the options are unavailable. The current survivor universe and incomplete financing classification limit the generalization of the results. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/option-bars, and the node /app/agent/dist/backtest.js was used to run the backtest.

A news scan found the headline 'R92102 chronological costed stock controls and CLI diagnostics do not equal option returns'. The premises of a study were as follows: a vectorbt model with a fixed cost of $29 notional and $999.38 cash, and fees and lippage of 10 basis points each side. The earliest 6 training data and latest 3 test data were frozen before the grid was read. The training results showed a return of -0.150077%, a drawdown of 0.218039%, and a win rate of 16.667% out of 6. The test results showed a return of -0.269959%, a drawdown of 0.269959%, and a win rate of 0% out of 3. A 5-test result showed a return of -0.398827%. With 50 basis points lippage, the T3 test showed a return of -0.338660% and the T5 test showed a return of -0.467012%. A split-only breakout control with the next open to 20 session close, with the same costs, was also tested. The training data was from before 2026 and the test data was from 2026. The results for MG, DSX, and TRMD were as follows: MG test return was 0.495888%, drawdown was 0.308209%, and win rate was 66.667% out of 3; DSX return was -0.331909%, drawdown was 0.792241%, and win rate was 33.333% out of 3; TRMD return was 0.040213%, drawdown was 0.651913%, and win rate was 33.333% out of 3. With 50 basis points lippage, the results were: MG return was 0.424120%, DSX return was -0.400362%, and TRMD return was -0.029731%. A 10/20 horizon grid was recorded but none were chosen from the holdout. A 20-day breakout with a 1500-day period, 5% stop, and 15% target, with a different policy, was also tested. The results were as follows: COST return was 3.0477%, drawdown was 9.0719%, and win rate was 36.1446% out of 83; TRMD return was 65.5168%, drawdown was 18.4602%, and win rate was 38.6667% out of 75; HZO return was -46.7140%, drawdown was 47.4222%, and win rate was 24.1935% out of 62. There was no isolated CLI holdout and only diagnostics were used. The conclusion was that the underlying controls were completed but could not convert long stock loss into put edge or stock gain into option gain. The actual historical executable option paths were not established. The current winners were selected retrospectively and the short test of 3 and previously visible history were not pristine out-of-sample. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the research was done using research/validate.py and candidate_validation.py with no options order.

The R92102 VSH costed breakout strategy is based on certain premises. It uses a fixed note4039 that considers the completed close price in relation to the prior 20-day close high and the 50-day Simple Moving Average (SMA), which is a calculation to find the average price of a stock over a certain period. The strategy enters at the next open, sets a stop loss at 2 times the prior 20-day Average True Range (ATR), which measures price volatility, and targets a net 3R return at the base 20 basis points per side, with a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, held apart from 2023 to 2025, and separately tested through September 18, 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The Vectorbt long-only strategy targets a portfolio return of 1000, with a 5 basis point fee and 15 basis points of slippage per side, and a stress test of 45 basis points of slippage. The strategy includes entry-day stops, gap stops at the open, and both-hit stops, with the upside target capped and a forced window exit. The portfolio returns are reconciled to compounded net trade cash within 2.5e-15. The strategy was tested with 10, 20, and 40 lookbacks for sensitivity, without choosing the best one. The base held-out return was -61.1856%, with a drawdown of 63.1110% and a win rate of 5.5556% out of 18 trades. The stress test result was -65.1592%, and the 2026 result was 155.5103% out of 7 trades. The strategy preserves positive evidence where present. Currently, there is no entry signal for all three, and no order is placed. The selection uses current names, adjusted Open, High, Low, and Close (OHLC) prices, and daily exits as proxies, without 15-minute execution or a full corporate action audit. The backtest target is the required payoff, not the fair value, and does not borrow recent winner performance as training. All 18 cells were separately observed.

The R92102 ATI costed breakout strategy is based on certain premises. It uses a fixed note4039 that considers the completed close price in relation to the prior 20-day close high and the 50-day Simple Moving Average (SMA), which is a calculation to find the average price of a stock over a certain period. The strategy enters at the next open, with a stop loss set at 2 times the prior 20-day Average True Range (ATR), which measures the volatility of a stock. The target is a net 3 times the risk, with a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, held apart from 2023 to 2025, and separate data was used from 2026 through September 18. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a long-only approach, with a target portfolio size of $1000, and includes fees of 5 basis points and slippage of 15 basis points per side, as well as a stress test of 45 basis points slippage. The strategy includes entry-day stops, gap stops at open, and both-hit stops, with the upside target capped and a forced window exit. The portfolio returns are compounded and reconciled within a net trade cash difference of 2.5e-15. The strategy was tested with 10, 20, and 40-day lookbacks for sensitivity, without choosing the best one. The base held-out return was 105.2551%, with a drawdown of 34.2886%, a win rate of 42.1053%, and 19 trades. The stress test result was 83.1403%, and the 2026 result was 63.6936%, with 4 trades. The strategy preserves positive evidence where present. Currently, there is no entry signal for all three, and no order is placed. The selection uses current names, adjusted Open, High, Low, and Close (OHLC) prices, and daily exits as proxies, without 15-minute execution or full corporate action audit. The backtest target is the required payoff, not the fair value, and does not borrow recent winner performance as training. All 18 cells are separately observed.

The R92102 LFUS costed breakout strategy is based on certain premises. It uses a fixed note4039 that considers the completed close price in relation to the prior 20 close high and the 50-day Simple Moving Average (SMA), which is a calculation of the average price of a stock over a certain period. The strategy enters at the next open, with a stop loss set at 2 times the prior 20-day Average True Range (ATR), which measures price volatility. The target is a net 3 times the risk at a base of 20 basis points per side, with a maximum of 60 sessions. The strategy was trained on data from 2016 to 2022, held apart from 2023 to 2025, and separate data was used for 2026 up to September 18. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a long-only target percent portfolio of $1000, with a 5 basis point fee and 15 basis points of slippage per side, and a stress test of 45 basis points of slippage. The strategy includes entry-day stops, gap stops at open, and both-hit stop-first, with an upside target capped and a forced window exit. The portfolio returns are reconciled with compounded net trade cash within 2.5e-15. The strategy has a 10, 20, and 40 lookback sensitivity, with no choose-best approach. The base held-out return is 3.8910%, with a drawdown of 26.9686%, a win rate of 30.7692%, and 13 trades. The stress test return is -3.9046%, and the 2026 return is -2.4526%, with 7 trades. The strategy preserves positive evidence where present. Currently, there is no entry signal for all three, and no order is placed. The selection uses current names, adjusted Open, High, Low, and Close (OHLC) prices, and daily exits are proxies, with no 15-minute execution or full corporate action audit. The backtest target is the required payoff, not the fair value, and does not borrow recent winner performance as training. All 18 cells are separately observed.

A news scan found the headline about an R92100 attack on endpoint rejection. The system re-fetched historical option contracts, which are agreements to buy or sell something at a set price, and found that 8 of them had both endpoint trade closes, but the ones from January and April 2024 and April 2025 were still missing. The system looked at the data from 11 sessions before a reaction and the cost, and then observed the daily high, low, open, and close prices, which are called OHLC for short, and stopped the process if it lost 50% or reached a target three times the initial amount. The system also stopped if there was a big gap at the open. The cost was 0, 0.02, 0.05, and 0.075 on each side. The results showed that there was no threefold target in 8 paths. At a cost of 0.02, all 8 paths lost money, with an average loss of 31.193% in the training data and 43.4783% in the data from 2026. The hypothetical results, which are not real returns, showed a loss of 90.7889% in the training data and 68.4783% in the holdout data, which is a separate set of data used to test the system. There was only one complete and affordable training path at a cost of 0.02, which was in September 2025. The system also looked at data from a vectorbt pre-event stock proxy, which is a way to estimate stock prices before an event, with a fee of 1 basis point and a slip of 5 or 15 basis points, and found that all long training sessions were negative, and all short holdout sessions in 2026 were also negative. The system also tested shorting, which is selling a stock with the expectation of buying it back at a lower price, and found that it was not profitable. The system used data from https://runway.adityaperswal.workers.dev/data/option-bars and https://runway.adityaperswal.workers.dev/data/stock-bars.

A test of a stock control system was done using a set of rules, including a fixed time period from 3 days before a stock's release to the day it is released, a stock value of 30 USD per 1000 units, and fees and slippage of 10 basis points on each side. The test used 8 events from the earliest available data to September 4, 2025, to June 2026, without using any data from after that date. The results of the test showed a return of 0.3378% with a drawdown of 16.8532% and a win rate of 50% for the 8 events used for training, but a return of -23.7380% with a drawdown of 25.5680% and a win rate of 0% for the 4 events used for testing. A sensitivity test was also done, which showed a return of -20.3367% with a drawdown of 21.9014% and a win rate of 25%. Another test with 50 basis points of slippage showed a return of -33.2621% for one set of conditions and -29.8745% for another. A different set of rules, including a breakout of 20 days and a stop loss of 5, returned -12.719744% with a drawdown of 13.191835% and a win rate of 29.1667% over 24 events. The test concluded that there was no validation of the stock run-up and that stock losses did not support the use of long puts or an option holding policy. The data used for the test was from a previously visible historical window and not from new, untouched data. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and the test was based on 12 observations from individual issuers, using the research/backtest_nke.py script.

This report is based on a set of premises, including the earnings dates of the frozen13 issuer. The model takes the underlying close three sessions before a reaction and exits at the last close before the reaction. This models the close from Monday to Wednesday for a Thursday morning event, excluding 9:50 execution and option profit and loss. The model uses vectorbt, starts with $1000, and includes a fee of 1 basis point per side, with a baseline slip of 5 basis points and stress of 15 basis points. The model was trained from 2023 to 2025 and held apart from 2026. The results show that the long baseline train return was -8.6877%, with a drawdown of 15.0405% and 36.36% wins, while the holdout return was -2.7203%, with a drawdown of 2.7203% and 0 wins. The long stress train return was -10.6747%, with a holdout return of -3.1086%. The short diagnostic baseline train return was +5.0551%, with a drawdown of 7.5176% and 63.64% wins, while the holdout return was +2.2679%, with a drawdown of 1.8368% and 2 wins. The short stress train return was +2.7823%, with a holdout return of +1.8680%. However, shorting itself is outside the mandate and positive stock returns do not cover put spread and theta. A CLI command was used to reproduce the full sample, resulting in a return of +114.1606%, with a drawdown of 30.9747% and 34.21% wins, across 685 bars. The CLI aggregate lacks explicit holdout and cost settings and is a separate diagnostic that cannot override failed event gates. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with dates ranging from 30869 to 30894. The data selection and history are already known to prior managers, and the chronological holdout is not new prospective evidence. Actual historical option quotes and 15-minute exits remain unvalidated.

This is a report on a trading strategy test. The test used daily stock data from 2015, but the actual data started in 2016 for LPG and on April 29, 2024, for BWLP. The rules for the test were set before the results were known: the stock's closing price had to be higher than its closing price 20 days before, and higher than its 50-day simple moving average, which is a calculation of the average price over 50 days. The test entered a trade at the next open price, with a stop loss at 2 times the true average range of the stock's price over 20 days, and a target at 6 times that range. The test also had a time limit of 60 sessions. The test was hypothetical, with a starting capital of $1000, and fees of 5 basis points plus 15 basis points of slippage on each side of the trade, which means the difference between the expected price and the actual price of the trade. The test also accounted for stress, with an additional 45 basis points of slippage. The test looked back at 10, 20, and 40 days to check the sensitivity of the results, but did not choose the best result after seeing the outcome. The test trained on data from 2016 to 2022, held out data from 2023 to 2025, and tested on recent data from 2026 through September 18. BWLP had no training history before the holdout period. The results showed a base holdout return of -18.7795%, a maximum drawdown of 33.2491%, and a win rate of 25.0000%, with 8 trades and 1 target hit. The stressed return was -22.5860%, and the recent return was 114.3538%, with 7 trades. All the results are available in a file called results.json. The test reused historical windows, which is not the same as a prospective out-of-sample test, and did not account for 15-minute execution or corporate actions. The gross return was below the net return after costs, and was not a fair value. There was no order to buy or sell. The positive recent regime was preserved as counter-evidence, and the current entry signal was false.

A news scan found the following text: Premises: SIP/all adjusted daily data https://runway.adityaperswal.workers.dev/data/stock-bars, requested 2015 but actual LPG 2016 on/BWLP 2024-04-29 on. Rule fixed before results: close > prior 20 close maximum and > SMA50, which means a Simple Moving Average of 50 days; next open enter; stop 2 prior true ATR20, target 6 ATR, force 60 sessions; entry day stop/target allowed, gaps through stop at open, both hit stop first, upside gaps capped at target. Vectorbt long only target percent capital 1000 is hypothetical fully invested test book, not actual supply allocation. Fees 5 bp plus 15 bps lippage each side; stress 45 bps lip. 10/20/40 lookbacks are sensitivity, not choose best after seeing outcome. LPG train 2016–22, held apart 2023–25, recent 2026 through Sep 18; BWLP no training history before holdout, so transfer diagnostic not standalone OOS validation. All 18 cells reconcile portfolio return to compounded net trade returns within 1e-8. Base holdout return 22.9143%, DD 41.2651%, win 35.2941%, N 17, targets 5; stress 10.9951%; recent 86.0430%, N 8. All cells in observations and /tmp/supply92008/results.json. Reused historical windows not pristine prospective OOS; no 15 min execution or corporate action certification, adjusted prices/fractional capital proxy. Gross 3R bracket is below 3R net after costs and not fair value; no order. Positive recent regime preserved as counter evidence, current entry false.

This is a report on a stock control test. The test was based on certain premises: it used a fixed vector, the stock price at the opening on the third day before an event and the closing price on the day of the event, with a notional value of 30 USD per stock and a test book of 1000. The test also included fees of 10 basis points and lippage of 10 and 50 basis points on each side. The dates for the issuer AMC were from 30843 to 53. The training period was from December 2023 to June 2025, with 7 events, and the test period was from September 2025 to June 2026, with 4 events. No parameters were fitted. The base train return was -0.06727%, the drawdown was 0.44617%, and the win rate was 57.14%. The test return was +0.207454%, the drawdown was 0.186279%, and the win rate was 50%. A lippage test with 50 basis points resulted in a return of +0.110431% and a drawdown of 0.221864%. The training return was -0.235339%. A CLI breakout test with a stop of 5 and a target of 15 resulted in returns of -3.7511%, a drawdown of 22.033%, and a win rate of 29.412% over 34 events. This test did not validate an option strategy or replace a premium cap, and no actual trade was made. The December 2023 transition financials were retained as disclosed, with no tuning to select a winner. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the issuer schedule URLs are 30843-53. The script research/study.py and CLI results were saved locally, and all statistics are in the journal. A news scan found the source to be a data line.

A diagnostic test was run on a trading strategy called breakout using a computer program. The test used two stocks, LPG and BWLP, and looked at their performance over 1460 days. The test used a 20-day lookback period, which means it considered the stock's performance over the previous 20 days when making decisions. The test also used a 10% stop loss, meaning it would sell a stock if its price fell 10% below its purchase price, and a 30% target gain, meaning it would sell a stock if its price rose 30% above its purchase price. The data for the test came from a website at https://runway.adityaperswal.workers.dev/data/stock-bars. The results of the test were: for LPG, 1002 trades were made, with a return of 93.998%, a drawdown of 11.3034%, and a win rate of 38.5542%. For BWLP, 600 trades were made, with a return of 23.1213%, a drawdown of 11.0873%, and a win rate of 40.9091%. The test used a system that sells stocks based on signals and executes trades at the same closing price, which is different from another system called frozen2ATR/6ATR60daystudy. The test did not include any costs or holdout period, which is a period of time where the system does not trade. The results of the test are for diagnostic purposes only and are not enough to validate the trading strategy. The latest results for LPG are slightly different from the previous test, which had a return of 94.8027%. This difference is due to the moving window of data used in the test, which can change over time. No trades were made based on this test.

A news scan found the headline is not available, but a data line was found with the following information: Ran backtestCLI SFIX/BB breakout20 days1000 stop10%target30%, LWbreakout252 days1600. The results of the CLI aggregate are as follows: SFIX return was -7.1357, drawdown was 38.6560, win was 38.0952, and the number of trades was 42; BB return was 114.1606, drawdown was 30.9747, win was 34.2105, and the number of trades was 38; LW return was -1.9840, drawdown was 2.9359, and the number of trades was 4. The CLI has no explicit cost or holdout flags, it is for diagnostic purposes only. Separately, vectorbt was run from 2022 to 2025 for training and 2026 was held apart, with the following conditions: close price is greater than the prior high 20, 63, or 252 days, plus volume is greater than 1.5 times the prior 20 days; entry is at the next open, exit is at the next open after the close is less than the SMA 50, and force window end. Fees are 1 basis point per side, and slip is 5 basis points for the baseline and 15 basis points for stress. The results for LW are as follows: train 20 to 40 was -40.7232%, train 63 was -21.6027%, and train 252 was 4.1088% with 2 trades; the selected 252 holdout had 0 trades and 0 return, and cannot be validated. SFIX had negative returns for all train periods; the 20 holdout period had a return of -14.2149%, a drawdown of 22.2933, and 1 trade. BB had negative returns for all train periods; the 20 holdout period had a return of 127.4192%, a drawdown of 30.7207, and 1 trade, but this must not be cherry-picked after losing training. Stress test results for the 20 holdout period are as follows: LW had a return of 2.3640, SFIX had a return of -14.3863, and BB had a return of 126.9649%; a total of 36 cells were recorded with observations. The rules differ from CLI intentionally, and this is not claiming replication. There is no current LW or SFIX 52 week breakout. These stock diagnostics do not price options, justify shorts, or override earnings entry rule. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars. No test establishes a new tradable strategy, and known historical holdout is not independent prospective discovery.

This is a pre-event option audit for R92006. The audit had certain premises: it looked at regular sessions before BMO earnings, with an exit before the close of the prior session, and a target relative strike of 40/46.67, rounded to the nearest 2.5. The first standard monthly expiry had to be at least 7 days after the event. The audit requested 11 OCC paths via option bars, but only 9 were available, and only 8 endpoints were available. The audit found that 8 endpoint pairs all lost money after adding 0.05 to the entry and subtracting 0.05 from the exit. Only 2 training tickets fit the $50 criteria, but neither had complete 11 daily bars. The historical open interest and bid asks were not verified. The training results showed a mean loss of 35.5830%, a compounded loss of 93.2563%, and a drawdown of 93.2563%, with 0% win rate. The 2026 holdout results showed a mean loss of 49.9256%, a compounded loss of 75.4464%, and a drawdown of 75.4464%, with 0% win rate. A vectorbt short underlying direction proxy, which is not allowed in actual trades, showed a 7.7635% gain in training and a 12.2338% loss in holdout. The audit concluded that event tail profit cannot be borrowed into mandatory pre-event exit. The data source is https://runway.adityaperswal.workers.dev/data/option-bars and /data/stock-bars, with dates 30507-30517. This is a known historical chronological holdout, not a prospective independent test.

A backtest was run using the command line interface, which is a computer program that uses text commands, to test a trading strategy on the stocks AA, CENX, CSTM, and KALU. The strategy used was called RSI, which stands for Relative Strength Index, a way to measure stock price movements. The test used 2000 days of data from the website https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex. The test assumed that the trading system would make decisions based on the stock prices at the close of each day, and it did not account for any costs or losses from buying and selling stocks. The results of the test were: CSTM had a 77.1203% return, with a 25.2182% drawdown, which is a measure of the biggest loss, and a 68.2927% win rate, with 41 trades; AA had a 7.7708% return, with a 31.5583% drawdown, and 39 trades; CENX had a -37.2666% return, with a 63.7342% drawdown, and 35 trades; and KALU had a 64.8039% return, with a 23.1352% drawdown, and 43 trades. The test found that the strategy could not be validated, or proven to work, because it was using a new and untested method. The test was replaced with a new analysis that took into account the costs of buying and selling stocks, and this new analysis met the requirements of the command line interface, but it is not evidence that the strategy will work in real trading. The differences between the test and real trading are too big to say whether the strategy will make more money than just buying and holding the stocks.

A trading fund manager analyzed the stock KALU using a trend-filtered pullback strategy. The analysis was based on three premises: first, the manager used daily stock prices from 2016 to 2026, with the data split into training, holdout, and recent periods. Second, the manager used a fixed strategy based on the Relative Strength Index (RSI), a measure of stock price movement, and the Simple Moving Average (SMA), a measure of stock price trend. The manager entered trades when the RSI was below 10 and the stock price was above the SMA, and exited trades when the stock price reached a certain target or stop loss. Third, the manager tested the strategy using a simulation tool called vectorbt, which applied a 5 basis point fee and a 15 basis point slip, and reported a stress test result of 45 basis points slip. The results showed a training return of 24.4367%, a holdout return of 5.4265%, and a recent return of 4.9991%. The manager also tested the sensitivity of the strategy to different RSI parameters and recorded 168 observations. The conclusion was to preserve positive results but not to deploy the strategy live, as the historical targets did not validate certain checks and management strategies.

This is a report about a trading strategy called R92006 CSTM. The strategy is based on two main ideas: first, it uses daily stock prices from 2016 to 2026, with some years used for training and others for testing. The prices are adjusted for dividends and splits. Second, it uses a fixed set of rules to decide when to buy or sell stocks, including a measure called the Relative Strength Index, or RSI, which is a way to measure how quickly a stock's price is changing, and a moving average, which is a way to smooth out price changes over time. The strategy also uses a stop-loss, which is a rule to sell a stock if it falls below a certain price, and a target price, which is a price at which to sell a stock for a profit. The strategy was tested on a set of stocks and the results were: a return of 165.7370% during the training period, a drawdown of 36.0061%, and 25 trades. During the testing period, the return was 74.8097%, the drawdown was 18.5252%, and there were 50% winning trades out of 12 trades. The strategy was also stressed with a 45 basis point slip, which means that the strategy was tested with an additional cost of 0.45% per trade, and the return was 62.6657%. Recently, the strategy had a return of -2.0641% with 4 trades. The report concludes that the strategy should be preserved where it is working, but there is no current signal to buy or sell among the 4 stocks being watched, and the strategy should not be deployed live. The report also notes that the strategy is not validated by historical data and should not be used for discretionary trading or for managing risk.

This is a report about an automated trading fund manager's notes. The manager used a system with the following premises: they looked at daily stock prices from 2016 to 2022 for training, 2023 to 2025 for testing, and 2026 up to September 18 for recent data. They started using this system on November 1, 2016. The system uses a fixed rule based on the Relative Strength Index, or RSI, which measures how fast a stock's price is changing, and a Simple Moving Average, or SMA, which is the average price of a stock over a certain time period. The system buys a stock when the RSI is less than 10 and the closing price is above the SMA, and sells when it hits a stop loss or a target price. The system also has a time limit of 60 sessions. The manager tested this system with a 5 basis point fee and a 15 basis point slip, which is the difference between the expected price and the actual price, and a stress test with a 45 basis point slip. The results were: an 85.6062% return during training, a 45.8463% drawdown, which is the biggest drop in value, and 23 trades; a 91.3941% return during testing, a 55.9653% drawdown, 46.154% wins, and 13 trades; and a 77.0326% return during the stress test. Recently, the system had a 2.7092% return with 3 trades. The manager also tested the system with different parameters and compared it to a generic breakout system. The conclusion is to keep the positive results but not to deploy the system live because there is no current signal among the 4 stocks being considered.