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The next trading sessions are Friday, September 18, and Monday, September 21. On Friday, the Federal Reserve will release the G17 industrial production and capacity utilization report at 9:15 ET, followed by Bowman stress testing at 9:30 ET, and the H8 and H15 reports at 16:15 ET. On Monday, the CP13 and H10/H15 reports will be released at 16:15 ET. The Bureau of Labor Statistics will release August state employment data on Friday at 10 ET, but there are no releases scheduled for Monday. The Census Bureau has no scheduled releases for September 18 or 21. There are no new primary-confirmed earnings releases scheduled for these two sessions. The issuer of MLKN, MillerKnoll, has confirmed a conference call for September 22 at 8:30 ET. The FDA has a panel meeting scheduled for September 23 to discuss Galleri, but this is not an approval deadline. The fund's own orders have been resolved, and new orders have been set for 12:30 pre-open, 13:20, and Monday. The manager will monitor the flags, and there are no unattended orders or holdings that need to be protected. New data may change the research premise, but it will not automatically trigger a stock entry. Other fund monitors and the EEM trade 11 are not part of this fund's responsibilities.

The Panama Canal made two announcements. On September 4, ACP announced that it would retain the current 48-foot draft limit and postpone the planned October 1 cut. On September 16, ACP announced that it would permit consecutive Panamax dates across different booking periods. This has three consequences. First, it avoids a new draft cut and eases administrative access, but does not add new water or slots. Second, more predictable voyages may lower auction and wait costs, but also reduce inefficiency-supported freight rates. Third, margins depend on who owns the cargo, routing options, and cost pass-through. According to LPG's 10-K filing, wait times are usually unpaid. However, longer Cape voyages can still raise fleet utilization and net TCE. A 5-hour Neo outage on Monday does not establish scarcity rent by itself. ALM announced a take-or-pay agreement on September 17, which has three consequences. First, it creates a route to WBH for a minimum of 1720 tonnes with a 3-month upfront payment. Second, a customer-financed restart can bypass new mine lead time, but the margin floor is not disclosed. Third, additional recycled supply could reduce customer shortage and future producer premium. WBH and Sandvik benefit from supply security, while ALM benefits only after a successful restart and accepted profitable tonnes. Commodity scarcity is not one-way producer price power. There has been no independent valuation change since the 06 run. The binding constraint is measured by net cash and current execution, not fear of risk.

The fund manager of Dorian LPG made some notes about the company's financial situation. Here are the main points: The company's 10-K filing for May 27, 2026, shows that 99% of its revenue comes from Helios, and no single charterer makes up more than 10% of the pool. This means that the company's operational concentration is different from the risk of having a single buyer. Typically, the company does not get paid for waiting in the canal, and any increases in tolls may not be passed on to customers. On August 5, the company announced its financial results for the first quarter of fiscal year 2027, with a time charter equivalent of $75,926 per day, adjusted earnings per share of $2.52, and EBITDA of $165.43 million. A gain of $30.12 million is included in the EBITDA definition, so subtracting it gives $135.31 million. The company's earnings per share adjustment already excludes this gain, so it should not be subtracted twice. The sale of ships in July was a one-time event and not a recurring source of income. As of September 17, the stock price was $58.31, and annualizing the quarterly earnings per share implies a multiple of 5.78, but this may be misleading if peak rates do not continue. The company's order book is 35.7%, which is an estimate based on industry information provided by management. New capacity may erode future pricing power, but it does not affect the current available fleet. The fact that the company was able to capture rent in the past quarter proves that it is possible, but it does not mean that current canal tightening will increase net cash. There is a strong argument that rerouting ships to avoid fees can outweigh the costs, so the mechanism is accepted, but current net fixtures and usable valuation are required. The current fund price of $101.99 can buy one share, but a backtest using frozen LPG data does not validate entry, with a holdout of -25.66%, a drawdown of 40.44%, and a win rate of 20%. No target price is manufactured from peak earnings per share. The facts only go up to the filing period, and future time charter equivalent and multiple are unknown.

A news scan found the report titled R91808 canal denominators, which states that there is no queue growth identifiable from monthly averages. The report includes the following points: 1. The DirectACP Sep10 AugustA34 report, available at https://pancanal.com/wp-content/uploads/2026/09/adv-34-2026-monthly-canal-operations-summary-august-2026.pdf, shows that the monthly total was 1029, with 33.19 transits per day and 33.1 arrivals per day. 2. A Python analysis found that the water-minus-transit time was 23.63 hours, which is 67.7077% of 34.90 hours, but this includes all non-transit time and not just congestion wait time. The rounded arrivals minus departures was -0.09 per day, which does not establish an actual queue change. 3. The report also found that 175 out of 178 Neo booked slots were used, which is 98.3146%, and 204 out of 227 auction slots were used, which is 89.8678%. However, Neo excludes auctions, so adding these percentages would double count and change the denominator. The physical capacity of 33.19 per day out of a sustainable combined capacity of 36-38 implies an utilization range of 87.34-92.19%. 4. The report concludes that prior constraints can raise freight, but the evidence weakens the inference of accelerating queue from headline booking fill. It also notes that concentrated Neo arrival waves can produce queues despite balanced monthly totals, and that daily class-specific arrivals, wait distribution, auction prices, and paid TCE are needed before calibrating stock edge.

The fund manager has decided not to place any orders. The current state is that there is $101.99 in cash, no open or queued positions, and an account balance of $849.92. The goal is to have profitable exposure to physical bottleneck rents with defensible asymmetric exits, excluding a monthly shortfall of $350.08. The decision is based on five premises: 1. Reviewing past notes and analyses, which showed that two prior wins were liquidated by the operator and cannot be used to calibrate the edge. 2. Analyzing costed vectorbt3310-21, which showed mixed results for different stocks, including TEN, PSIX, and ALM. 3. Evaluating the fundamental target and stop for TEN, which is $55.36 and $45.41, respectively, at a current price of $52.81. 4. Assessing the new ALM offtake, which improves the route to market but does not establish incremental net asset value. 5. Conducting a tail audit, which supersedes the previous assumed probability of 5%/-30% as a factual hard veto. The decision to not place any orders is based on the lack of a presently supported asymmetric setup. The validity of the decision is supported by the premises, but its soundness is limited by the source vintage, selected holdout, and unobserved disasters. The next steps include verifying certain numbers and preserving five jobs. No new fund will be created, as already-covered momentum is not a distinct discovered operation.

A study on the R91806 sequence risk compared empirical trades to explicit disaster replacement. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, which provided closed pre-2024 returns from a costed strategy. The study used 20,000 paths of 20 sequential trades, with portfolio rebalances to 12% of the account or a 3% quarter-fund probe. The study sampled actual trade outcomes with replacement and separately replaced an outcome with an assumed 30% loss at 0%, 1%, and 5%. The results showed that the full fund mean terminal account return was -3.93925% with 0% loss assumption, and the 5th percentile was -14.32465%. The probability of a loss greater than 10% was 20.74%. The study also found that a 5% loss assumption raised the probability of a 95th percentile drawdown to 19.07636%. The quarter fund mean return was -0.99976% with 0% loss assumption, and -1.84081% with 5% loss assumption. The study concluded that scaling exposure to drawdown shape and retaining uncertainty is important, rather than relying on assumed odds as a permanent cash veto. The study used various metrics, including P95 drawdown, mean return, and probability of loss, to analyze the risk of the R91806 sequence. The results were compared to the LPG full fund and quarter fund, which showed different mean returns and drawdown probabilities. The study also mentioned that the Half Kelly numerical estimates were capped research diagnostics, and that the 3% normal account-risk rule and gap stress should be preserved separately from expected value.

A news scan found the following report: The current signal map for R91806 is based on data from September 17, which was obtained from https://runway.adityaperswal.workers.dev/data/stock-bars. The current close, prior 20-day close high, and 50-day Simple Moving Average (SMA) for several stocks are as follows: TEN $52.81/$52.31/$41.49, FRO $50.66/$50.32/$39.42, STNG $87.85/$85.73/$78.89, LPG $58.31/$57.86/$47.50, BWLP $26.14/$25.84/$22.04, PSIX $45.55/$43.15/$35.78, and FTK $30.60/$28.13/$27.25. These seven stocks passed the breakout and SMA50 test. However, ALM, AA, CENX, and MU failed. The positive screen is not an independent edge, meaning it does not provide a unique advantage. The results of the training and holdout experiments for these stocks are mixed, with some showing positive results and others showing negative results. For example, TEN, FRO, and STNG have negative training results, while AA and CENX have positive training results but inactive signals. The report also notes that the old defended price for TEN was $45.41/$55.36 at $52.81, which yields a net return of 0.314527R under a 1bp fee and 15bps lip on each side. The report concludes that there are no current orders and that hypothetical levels should be preserved separately from committed exits. The fund cap is $101.99, which allows for the purchase of one share of TEN, one share of LPG, one share of STNG, up to two shares of PSIX, or up to seven shares of ALM. However, affordability is not an edge, meaning it does not provide a unique advantage. The report also notes that FRO dividends require raw-price and deliverable reconciliation before live stops, and that adjusted closes are not quotes. No fresh snapshots were claimed in this run, and the current SIP for PSIX differs from the IEX collector by feed/sample, so an internally consistent SIP should be used for research.

A news scan found the report on R91806 CENX. The report is about a model with 20,000 paths and 60 sessions. It used historical data to calculate open, high, low, and close ratios. The model also considered fees and stops, including opening gaps and intraday ambiguity stops. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The report found that for the period before 2024, the stop was triggered first 67.480% of the time, the target was reached first 30.295% of the time, and the trade timed out 2.225% of the time. The mean net result was 2.2449%, the median was -10.9632%, and the 1st percentile was -14.7113%. For the last 252 days, the stop was triggered first 60.875% of the time, the target was reached first 34.025% of the time, and the trade timed out 5.100% of the time. The mean net result was 4.3584%, the median was -10.9632%, and the 1st percentile was -15.8823%. The report also calculated the half-Kelly fraction, which is a measure of the optimal bet size, using a grid search and closed trade distributions. The results showed a constrained half-Kelly stock weight of 0.5000 and a cap-adjusted research size of $101.99. The report noted that recent-window drift and selected survivor bias can create attractive resampled odds without an executable edge. The decision was to not use favorable bootstrap odds to override failed holdout, absent current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account, which is $849.92, and a quarter fund exposure of $25.4975 would be 3% of the account. The report also noted that stock loss can exceed the stop on gap.

The model used 20,000 paths and 60 sessions, with historical data from before 2024 and recent 252 days. It considered fees of 1 basis point and a 15 basis point slip per side. The model included stops for opening gaps and ambiguity, with fixed levels as of September 17. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The results showed that for the pre-2024 period, 66.880% of the time the stop was hit first, and 28.675% of the time the target was hit first, with a mean net return of 1.5349%. For the last 252 days, 62.985% of the time the stop was hit first, and 34.075% of the time the target was hit first, with a mean net return of 3.2122%. The half-Kelly calculation, which is a method to determine the optimal bet size, resulted in a stock weight of 0.5000 and a research size of $101.99. The decision was made not to use favorable bootstrap odds to override the failed holdout, and a full fund exposure of $101.99 would be 12% of the account, with a potential stock loss exceeding the stop on gap.

The automated trading fund manager analyzed data using a model with 20,000 paths and 60 sessions. The model used historical open and previous close ratios, as well as high, low, and close ratios, in contiguous 5-day blocks. A 10-day sensitivity analysis was also conducted. The model was seeded with data from September 17 and used a baseline history from before 2024 versus recent 252 days. Fees were set at 1 basis point per side, with a 15 basis point slip. Stops included opening gaps and intraday ambiguity stops. The model did not use live entry signal conditioning or a 15-minute profit ratchet. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The analysis showed that over the last 252 days, the stop was triggered first 50.49% of the time, the target was reached first 43.40% of the time, and the timeout occurred 6.10% of the time. The mean net result was 14.63%, and the median was -18.11%. A half-Kelly analysis was also conducted, which showed a constrained stock weight of 0.5000 and a cap-adjusted research size of $101.99. The decision was made not to use favorable bootstrap odds to override a failed holdout, absent a current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account, or $849.92, and a quarter fund exposure of $25.4975 would be 3% of the account. The stock loss can exceed the stop on a gap.

The model used 20,000 paths and 60 sessions, with historical open and previous close ratios, to analyze the stock PSIX. The model also considered fees of 1 basis point and a slip of 15 basis points per side. The results showed that 73.045% of the time, the stop was reached first, and 26.720% of the time, the target was reached first. The mean net result was -0.8900%, and the median was -10.2254%. A half-Kelly strategy was also applied, which resulted in a constrained stock weight of 0.5000 and a research size of $101.99. However, the decision was made not to use favorable bootstrap odds to override a failed holdout, absent a current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account, and a quarter fund exposure would be 3% of the account, with the risk that stock loss can exceed the stop on a gap.

A model was run with 20,000 paths to analyze the performance of a trading strategy. The model used historical data from before 2024 and from the last 252 days. It considered fees of 1 basis point and a slip of 15 basis points per trade. The results showed that for the pre-2024 data, the stop loss was triggered first 66.77% of the time, the target was reached first 31.21% of the time, and the trade timed out 2.02% of the time. The mean net return was 1.50% and the median return was -7.01%. For the last 252 days, the stop loss was triggered first 50.78% of the time, the target was reached first 46.42% of the time, and the trade timed out 2.80% of the time. The mean net return was 5.78% and the median return was -7.01%. A half-Kelly strategy was also analyzed, which is a method of determining the optimal bet size, using a grid search from 0 to 1 and then taking half of the result. The strategy was tested on a training set of 49 trades, a holdout set of 10 trades, and a forward set of 7 trades. The results showed that the half-Kelly strategy had a stock weight of 0.3845 for the training set and 0.0000 for the holdout set. The decision was made not to use the favorable bootstrap odds to override the failed holdout, absent a current signal or undetermined cash capture. A full fund exposure of $101.99 would be 12% of the account size of $849.92, and a quarter fund exposure of $25.50 would be 3% of the account size.

A news scan found the report for R91806 TEN. The report is about an automated trading fund manager's notes. The model used 20,000 paths and 60 sessions to analyze historical data. It looked at open and close prices, as well as high and low prices, over 5-day blocks. The model also considered fees of 1 basis point and a 15 basis point slip per side. The results showed different outcomes for different time periods and strategies. For example, from before 2024, the model found that 72.725% of the time, the stop was hit first, and 26.645% of the time, the target was hit first. The model also calculated the mean net return and median return for each scenario. The report also discussed the concept of half-Kelly, which is a strategy for sizing trades, and found that the optimal stock weight was 0.0000 for the training set and 0.5000 for the holdout set. The report concluded with a decision not to use favorable odds to override a failed holdout, and noted that a full fund exposure would be 12% of the account, and a quarter fund exposure would be 3% of the account.

The source https://runway.adityaperswal.workers.dev/data/stock-bars SIP/all Jan2016-Sep17 shows that TEN has 2691 daily gaps and none are less than or equal to -30%. The worst gap was -9.5963% on April 29, 2020, and the worst pre-2024 close return was -17.58995%. This does not mean there is no risk of a future disaster. Using a simple model, the daily upper limit is 11.1262%, which is 6.46126% over 60 sessions. A 5% probability per trade cannot be directly compared to a daily frequency, and crisis regimes can happen in groups, which undermines the simple model. So, the earlier 5%/-30% remains a stress case, not a calibrated probability, and the absence of such an event does not refute it. PSIX had an actual adjusted gap of -34.18735% on May 12, 2026, and -29.99754% on November 7, 2025. LPG had a worst pre-2024 gap of -22.27513%. The AA feed on November 1, 2016, showed an apparent -74.35646% gap, which was due to a corporate separation, as confirmed by https://investors.alcoa.com/resources/investor-faqs/default.aspx. If we start the current AA history from November 1, 2016, the clean worst gap is approximately -13.2465%. The correlation between TEN and other stocks is: TEN/FRO is 0.819413, TEN/STNG is 0.708182, and TEN/LPG is 0.600194. This means that the holdings would share a shipping factor, not independent bets. The conclusion is to retire the uncalibrated tail veto while keeping explicit gap sizing and regime uncertainty. The sample of closed trades cannot estimate the edge. The hard account risk and fund 101.99 cap remain, and there is no forced entry from removing an invalid inference.

A diagnostic test was run on the stock ticker symbols TEN, PSIX, AA, CENX, ALM, and MU using a breakout strategy. The test used a computer program called node to run a script called backtest.js with specific settings, including a lookback period of 20 days and a stop loss of 8 percent. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test did not take into account real-world costs like commissions or slippage, and it did not test how the strategy would work in real-time trading. The results for the stock ticker symbol MU showed a return of 33.27 percent, with a maximum drawdown of 36.24 percent and a win rate of 41.67 percent. The average trade return was 0.47 percent. These results are for diagnostic purposes only and do not validate the current eligibility of the strategy, as they are based on a different strategy and data feed.

A diagnostic test was run using the command line interface (CLI), which is a way to interact with a computer program using text commands. The test used a program called node to run a script called backtest.js with specific parameters: it looked at the stocks TEN, PSIX, AA, CENX, ALM, and MU, used a breakout strategy, looked back 20 days, tested over 1460 days, stopped losing trades at 8%, and aimed for a 24% gain. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test assumed that trades were made at the same closing price, with no fees or losses from buying or selling, and did not account for any bad starts to trades. This test is only for diagnosis and not for making actual trades. Other analyses, which include costs and are split by time, are more important for making decisions. The results of this test are for a different strategy and use different data, so they cannot be used to confirm or deny the current plan. The full results for the stock ALM are: it was tested over 297 days, had a return of 124.51%, while buying and holding the stock would have given a 190.14% return, the biggest loss was 18.11%, 50% of trades were winning trades, and the average trade gave a 4.21% return.

A diagnostic test was run on the stock symbol CENX using a breakout strategy. The test used a command line interface, or CLI, with specific parameters: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data for the test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test assumed that trades were made at the same closing price, with fixed brackets, and without any commissions or slippage. It also did not account for any gaps in the market at the opening. The results of the test are for diagnostic purposes only and do not validate the current strategy. A different analysis using vectorbt, which includes costs, will be used for inference. The results of the test for CENX were: 1003 bars of data, a return of 30.78%, a buy and hold return of 433.07%, a maximum drawdown of 34.92%, a win rate of 32.5%, and an average trade return of 0.53%.

A diagnostic test was run on a trading strategy using the command: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test used a system called IEX, which looks at signals to enter trades when the market closes, uses fixed price ranges for trades, and does not account for costs like commissions or slippage. It also does not hold out any data for validation and assumes trades are executed at the same price as the signal, without any gaps in pricing at the opening. This test is only for diagnosis and not for validating the execution of trades. Other analyses, which include costs and are split by calendar, supersede these results for making inferences. The results are for a different strategy and data feed, so they cannot be used to confirm or deny the current eligibility. The full metrics for the symbol AA are: it had 1003 trading periods, a return of -25.39%, a buy and hold return of 5.35%, a maximum drawdown of 42.47%, a win rate of 32.86%, and an average trade return of -0.34%.

A diagnostic test was run on a trading strategy using the command line interface (CLI) with the following parameters: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data used for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test was run using the IEX engine, which uses same-close signals and entries, fixed brackets, and does not account for commissions or slippage. The test also did not include a holdout period or account for adverse opening-gap execution. The results of this test are for diagnostic purposes only and do not validate the current eligibility of the strategy. The results are also not comparable to other analyses, such as the vectorbt analyses 3310-3321, which supersede these results for inference. The full metrics for the symbol PSIX are: the symbol PSIX had 432 bars, a return percentage of 42.32466690454897, a buy and hold percentage of 51.500000000000014, a maximum drawdown percentage of 15.0166294688044, a win rate of 41.935483870967744, and an average trade percentage of 1.3974287736690292.

A diagnostic test was run on a trading strategy using the command line interface (CLI) with the following parameters: node /app/agent/dist/backtest.js --symbols TEN,PSIX,AA,CENX,ALM,MU --strategy breakout --lookback 20 --days 1460 --stop-pct 8 --target-pct 24. The data used for this test came from https://runway.adityaperswal.workers.dev/data/stock-bars. The test was run using the IEX engine, which uses same-close signals and entries, fixed brackets, and does not account for commissions or slippage. The test also did not include a holdout period or account for adverse opening-gap execution. The results of this test are for diagnostic purposes only and do not validate the current eligibility of the strategy. The results should not be compared to other analyses, such as the vectorbt analyses 3310-3321, which are more comprehensive and include costs. The full metrics for the symbol TEN are: it had 1001 bars of data, a return percentage of 23.779307240557834, a buy and hold percentage of 178.24367088607596, a maximum drawdown percentage of 19.727161495833574, a win rate of 37.333333333333336, and an average trade percentage of 0.39570689620069754.

The automated trading fund manager analyzed the daily stock market data from the source https://runway.adityaperswal.workers.dev/data/stock-bars. The manager used a statistical method called NumPy OLS to predict the stock returns over the next 20 sessions. This method used current breakout and moving average data, including SMA50 and SMA200, as well as 20-day momentum. The analysis was trained on data up to 2023 and did not include data from 2024 and 2025. The manager also used a technique called Bartlett HAC20 to address overlapping data. The results showed that for certain stocks, including TEN, FRO, STNG, LPG, AA, CENX, FTK, and MU, the breakout days were above the SMA50. However, these results were not independent and were strongly overlapping. The manager also analyzed the signal coefficients for these stocks, which measure the relationship between the stock prices and the predicted returns. The results showed that the signal coefficients for most of the stocks had confidence intervals that included zero, meaning that the results were not statistically significant. Additionally, six out of eight of the out-of-sample R2 values were negative, indicating that the model did not perform well on new data. The manager concluded that there was no statistically reliable extra independent signal from the confluence of these stocks and decided to retain the original hypotheses and collect more primary economic data.

This is a report on a trading strategy for the SPY stock. The strategy was tested using data from 2016 to 2023 and then applied to a holdout period from 2024 to 2025. A separate test was done for 2026. The strategy uses a breakout system based on the Average True Range (ATR), which is a measure of how much a stock's price moves, and the Simple Moving Average (SMA) over 50 days. The strategy also uses stop-loss orders and targets for selling. The results of the tests were: for the training period, a return of 110.5027% was marked and 108.0478% was closed, with a maximum drawdown of 15.8699% and a win rate of 50.00%. For the holdout period, a return of 25.6958% was marked and 24.8288% was closed, with a maximum drawdown of 6.4636% and a win rate of 45.45%. For 2026, a return of 0.5309% was marked and 0.3321% was closed, with a maximum drawdown of 8.4508% and a win rate of 20.00%. The stress test for the holdout period showed a return of 22.8380% marked and 22.1126% closed, with a maximum drawdown of 6.8370% and a win rate of 45.45%. The report notes that this is an experiment and not proof of a successful trading strategy.

This is a preregistered note, with detailed methods found in note 3577. The daily data was collected from September 17, with training from 2016 to 2023, and a calendar holdout from 2024 to 2025. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used a next-open prior 20-close breakout and a 50-day simple moving average, which is a calculation to determine the average price of a stock over a certain period. The strategy also used an average true range, which is a measure of volatility, with brackets of 2 times the average true range below and 6 times above the average true range. The maximum holding period was 60 sessions. The strategy included entry-day stops, which are orders to sell a stock when it reaches a certain price, and adverse opening gaps, which occur when a stock opens at a lower price than the previous day's close. The target was to open at a priority price, and the stop-loss order was set to be ambiguous during the day. The simulation used a $10,000 investment, with a 1 basis point fee and a 5 basis point slippage per trade, and a stress test with a 15 basis point slippage. The results showed a return of 14.8198% during the training period, with a closed compound return of 15.8252%, a maximum drawdown of 69.0629%, and a win rate of 33.33%. The holdout period showed a return of 32.4452%, with a closed compound return of 28.6361%, a maximum drawdown of 56.5251%, and a win rate of 41.18%. The forward period in 2026 showed a return of 57.9976%, with a closed compound return of 54.7930%, a maximum drawdown of 36.9163%, and a win rate of 50.00%. The stress test during the holdout period showed a return of 27.8900%, with a closed compound return of 24.3360%, a maximum drawdown of 57.0437%, and a win rate of 41.18%. The results also included trend filters, such as the L10/40, which were recorded as sensitivities but not selected by the holdout. The conclusion is to retain this strategy as a research candidate, as historical positivity alone does not establish current signal, valuation, or executable payoff.

This is a preregistered note, detailed in methods3577. The daily data used is from September 17, with training from 2016 to 2023, a calendar holdout from 2024 to 2025, and a separate diagnostic for 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy used is a next-open prior20-close breakout and a 50-day Simple Moving Average, with a 20-day arithmetic Average True Range, which is a measure of volatility. The strategy also includes entry-day stops and adverse opening gaps, with a target opening priority. The results are as follows: in the training period, the strategy had a marked return of -89.0755%, a closed compound return of -89.0755%, a maximum drawdown of 94.3601%, and a win rate of 16.67%. In the holdout period, the strategy had a marked return of -27.3629%, a closed compound return of -30.9049%, a maximum drawdown of 45.5460%, and a win rate of 26.32%. The forward 2026 results show a marked return of -29.2466%, a closed compound return of -29.2466%, a maximum drawdown of 32.3656%, and a win rate of 16.67%. The stress holdout results show a marked return of -30.1412%, a closed compound return of -33.4812%, a maximum drawdown of 46.7841%, and a win rate of 26.32%. The conclusion is that the baseline strategy lacks consistent support between the training and holdout periods, and is therefore rejected as a standalone live rule.

This is a report on a trading strategy for the stock CENX. The strategy was tested using daily data from September 2017, with training data from 2016 to 2023, a holdout period from 2024 to 2025, and a diagnostic period in 2026. The data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The strategy uses a breakout system, where a trade is entered when the stock price breaks out of a certain range, and includes stop-loss orders and target prices. The strategy was tested using a $10,000 investment, with a 1 basis point fee and a 5 basis point slippage per trade. The results show that the strategy performed well in the training period, with a marked return of 106.4773% and a closed compound return of 118.1736%. However, the performance was lower in the holdout period, with a marked return of 55.3394% and a closed compound return of 29.6065%. The strategy also performed poorly in the 2026 diagnostic period, with a marked return of -28.4114%. The report concludes that the strategy should be retained as a research candidate, but notes that historical performance does not guarantee future results.