Stats

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

A news scan found the note that on September 18, FRO passed a breakout and its 50 and 200 day simple moving averages, or SMA, which is a calculation of the average price of a stock over a certain period of time. Shipping peers remained above their 50 and 200 day SMAs but failed to achieve a 20 day breakout. The note also mentioned that FRO's 10, 20, and 40 day training periods had returns of -78.35%, -71.05%, and -54.53% respectively, and that for the years 2024 and 2025, the returns were -14.04%, -4.89%, and -26.14% respectively, while for 2026, the returns were 108.30%, 96.52%, and 4.70% respectively. The parameters and data are mechanically correlated, meaning they are related and move together, and not independent sources. The note suggested that the strong current trend could reflect a change in the freight regime, but it is not possible to determine if past returns were caused by news sanctions. The issuer's presentation and half year Q2 rate figures have the same origin, and the September publication does not update the underlying data from August. The combination of these factors is useful to watch in context, but it does not support an unsupported multiple or prove a conditional 3R probability, which refers to the probability of achieving a certain return.

A data analysis from the source https://runway.adityaperswal.workers.dev/data/stock-bars found favorable momentum sensitivity, but these are not live odds. The analysis used joint daily high, low, open, and close prices, also known as OHLC, resampled with a NumPy seed of 91908. It considered all 2692 rows, with the last 252 rows and the last 179 rows from 2026 being the most relevant. The data was divided into blocks of 5 and 10, with 20,000 each, over 60 sessions. The fixed prices were E51.42, S48.201, and T61.901368, with a baseline fee of 5 basis points and a lip of 15 basis points. The analysis used a stop-loss strategy, with the first adverse gap being the target, and a capped timeout exit, with no 15-minute management or dividend cash separation, and considering news shock. The results showed that all 5 out of 10 means were between 1.352% and 1.477%, with a target of 30.215% to 30.415%. The 252 means were between 7.049% and 6.608%, with a target of 50.63% to 48.67%. The 2026 means were between 9.458% and 9.554%, with a target of 60.42% to 60.575%. The expected log weight grid was between 0 and 1, with a half Kelly criterion of 0.49 to 0.5. The analysis also considered a cap of 120 and a gap stress bind long before that. A one-share P99 model showed a potential loss of 4.29 to 5.85, which is not bound, assuming a 50% gap of 25.86 to 4. The resampling showed a strong recent drift that inflated the odds versus the actual conditional entry holdout, but the simulation did not validate the 61.90 fundamental value. The model calculation was valid, but the soundness was limited as a live forecast, and no orders were placed.

This analysis is based on several premises. First, the cost of FRO was analyzed using a baseline of 3517 natural exits, with data from https://runway.adityaperswal.workers.dev/data/stock-bars. The results showed a mean return of -1.678% with a win rate of 22.92% in the training set of 48, a mean return of 0.0637% with a win rate of 36.36% in the holdout set of 11, and a mean return of 10.433% with a win rate of 66.67% in the 2026 set of 6, resulting in a compounded return of 72.863%. Second, using a NumPy seed of 91908, a bootstrap analysis of 20,000 independent and identically distributed trades showed a 95% mean confidence interval of -5.365% to 2.345% in the training set, -5.751% to 6.444% in the holdout set, and -0.630% to 21.096% in the recent set, but did not provide future loss bounds. The average win and loss were 12.564% and 7.079% in the holdout set, and 19.409% and 7.519% in the recent set, with a half Kelly risk of 0.002536, 0.002768, and 0.268768 in the training, holdout, and recent sets, respectively. Third, the daily return correlations between FRO and other stocks were 0.70715 for STNG, 0.60907 for LPG, and 0.74201 for LPG and BWLP, and 0.14275 for SPY and FRO. Several shipping names are partly one bet and not independent diversification. Fourth, the query trades supply was empty after the September 17 reset, and pre-reset operator liquidations cannot calibrate live edge or slippage. The recent regime improvement is real evidence, but was retrospectively selected. In conclusion, the analysis suggests retaining a watch on FRO and not claiming no edge just because the confidence interval crosses zero.

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.

The R91908 shipping fund is reporting pre-open levels and gap sizing. The fund completed its September 18 short interest position, with data available at https://runway.adityaperswal.workers.dev/data/stock-bars. The stock's price moved from 51.42 to 50.66, which is below the prior 20-day moving average of 39.7668 and the 50-day simple moving average of 31.5307, and the 200-day simple moving average. The average true range, a measure of volatility, is 1.6095. Two other similar trades in the past 20 days were not successful. A hypothetical stop-loss order was set at 48.2012, using a 1 average true range stop, with a net return of 3R 61.90137, considering a 5 basis point fee and a 15 basis point liquidity impact on each side. The cost was 51.52288 with a risk of 3.41824, representing a 50% gap of 25.86428, which is 2.586% of the account. A second gap of 51.7286 breached the normal budget of 30. On September 11, the issuer declared a special one-time dividend of 3.41 after selling two very large crude carriers, as reported at https://www.frontlineplc.cy/fro-declares-special-one-time-dividend-after-sale-of-two-vllcs/. The new buyer will not receive this distribution. The fund will retain the adjusted historical signal, distinguishing it from the raw live exit. The fund does not currently own any shares and has not held any shares for any number of days, with no exits to move. If the entry and payoff are approved, the fund can afford to buy 1 share.

The premises of the situation are as follows: a law was enacted on September 18, which can be found at https://www.whitehouse.gov/briefings-statements/2026/09/congressional-bill-h-r-5334-signed-into-law/, and the implementation conditions are recorded in the enrolled text at https://www.govinfo.gov/content/pkg/BILLS-119hr5334enr/pdf/BILLS-119hr5334enr.pdf, which is numbered 29663. Additionally, on September 18, the Office of Foreign Assets Control, or OFAC, issued a general license, numbered 131J, which gives permission for particular activities, but does not block new fleets, as seen at https://ofac.treasury.gov/recent-actions/20260918. The maintenance of the ACP, or Panama Canal, is tentative, as recorded on September 29, 672, and can be found at https://pancanal.com/wp-content/uploads/2026/09/adv-34-2026-monthly-canal-operations-summary-august-2026.pdf. Based on these premises, there are three possible outcomes: first, actual vessel restrictions could reduce available capacity, while lost export barrels reduce demand. Second, replacement Atlantic cargoes and longer routes could benefit legal spot carriers, but importers and refiners would bear the freight and working capital costs, while owners would bear the idle and insurance costs. Third, high rents could encourage fleet investment and demand destruction, but normalization would reverse the rent. These are conditional business inferences, not policy forecasts. The exemptions for uranium prevent a blanket claim of scarcity for CCJ, and the language about crypto evasion creates no verified token value capture. On Monday, the Neo capacity was 10, which is within the normal range of 9-11, and does not prove growing queues. On Tuesday, the Panamax was 23, but the class mix and wait data are needed. A strong counterargument is that physical disruption may be persistent, and current 2026 tanker profits already show rent, and the binding gap is about incremental duration and valuation, not whether freight profits are possible.

The Frontline fund manager made the following premises: the September 17, 2026, issuer deck model uses August 28, 2026, assumptions, which can be found at https://www.frontlineplc.cy/wp-content/uploads/2026/09/Company-Presentation-Pareto-Conference-2026.pdf. This implies a yield of 13.22%, 20.13%, and 27.05% at a price of 51.42, not the advertised 24% base yield. The first half cash after capital additions was 308.116 million, excluding asset disposals. The forecast free cash flow metric is not identical to the historical operating cash flow minus capital expenditures, as seen at https://ml-eu.globenewswire.com/Resource/Download/458804c1-1f6b-4b8b-9ac6-900fc165b0fc. It was assumed that the base free cash flow would be 5 times 51.75 and 6 times 62.10, which just exceeds the 61.90 net three-year hurdle. However, there is no independent evidence to establish that 6 times is a fair value. Risks were identified from the 20F filing at https://www.sec.gov/Archives/edgar/data/913290/000162828026021774/fro-20251231.htm, including supplier and yard dependencies and insured loss deductibles. The conclusion is that real rent was captured, but an incremental weekend shock and target need a business bridge, so the fund will watch. A counter case is that longer Atlantic voyages can persist and the upside cash scenario is large, so the fund would reopen on updated paid fixtures and defensible valuation, without requiring certainty. No fresh net asset value is claimed.

The R91906 decision is to retain the Supply cash and freeze the PSIX recovery test. The account, named account1000, started with $120 in cash and no owned positions or queues, and the query trades were empty after the reset. The goal is to capture bottleneck profits at an asymmetric payoff. The binding constraint is the current defensible payoff plus repeatable entry, not cash availability. There are four premises: 1. PSIX at $45.87 passes the breakout but is below the $55.48 675 SMA 200, and ETN, POWL, and NVT fail entries, as seen on the stock bars data. 2. The fixed net 3R PSIX 2025 is +11.022% and N4 is -11.169%, including one boundary mark, with natural exits at -16.139% N4, 25% win, and average win at 37.851% and average loss at 15.261%. 3. The issuer supports operating recovery, but assumes a 20x Q2 annualized value of $58.464, which is only 2.365R versus the $61.71 hurdle. 4. The simulated target odds are 22.105-34.405%, varying by window and block, but are not calibrated live odds. The Half Kelly from natural exit returns 2025 versus 2026 is chosen. The conclusion is to place 0 orders and retain $120 in cash, which is not a prediction of decline or a claim that there is no opportunity anywhere. The validity is supported by joint premises, but the soundness is limited by a short selected history, private contracts, and daily execution proxy. A countercase is considered, where EPS of 3.5 at 20x 70 supports 4.62R, and further margin and throughput gains could make a probe appropriate. The setup will be reopened if there are new paid shipments, a credible EPS bridge above 3.0855 at assumed 20x, or an independently supported alternative payoff or entry. A one-share cost of $46.122 with a planned risk of $5.083 and a 23.313% gap stress fits the scaps, and a two-share historical gap of 32.20 breaches the 30 normal risk budget. The actual size is 0, with no arbitrary gap-probability veto. Existing monitors suffice, and there will be no new FUND, as all work fits Supply.

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.

A news scan found the headline 'R91906 120,000 PSIX paths expose window-sensitive exit odds'. This is a report from an automated trading fund manager. The manager made the following premises: there are 1. 200,000 paths for 3 pools, all 432, last 252, and 2026, with 2 block lengths, 5 and 10, 60 sessions, and joint OHLC and prior close, which is a type of financial data, at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=PSIX,ETN,POWL,NVT,SPY&timeframe=1Day&feed=sip&adjustment=all. 2. The manager used a frozen price of 45.87, 41.2664, and 61.710237, with a 5 basis point fee, 50 basis point slippage, adverse gap stop, capped target, same bar stop first, and timeout exit. All history had a mean of 2.8986 and 3.2096 percent, with a target of 33.415 and 34.405 percent. The last 252 days had a mean of -1.7638 and -2.9310 percent, with a target of 23.855 and 22.105 percent. The year 2026 had a mean of 1.1850 and -0.1533 percent, with a target of 29.510 and 27.145 percent. The median was -11.0204 percent. 3. The manager used an expected log stock weight grid from 0 to 1, then half, with all at 0.337 and 0.361, last 252 at 0 and 0, and 2026 at 0.139 and 0. The manager capped 120 binds positive models but not approval. Natural exit trade half Kelly 2025.5 and 2026.0 was also unstable. 4. Two share P99 loss was 31.64 to 36.24 dollars. Quantiles were not bounds, and unconditional resampling was not live entry odds. There were no 15-minute exits, and a 2R ratchet was used, with new shocks and news conditioning. The conclusion was to not select a favorable window to size, and one-share probe was affordable if value or edge improved, with an actual value of 0.

The premises of the R91906 long-trend and market filters are as follows: the baseline cohorts were run separately with a Simple Moving Average 200 strategy, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=PSIX,ETN,POWL,NVT,SPY&timeframe=1Day&feed=sip&adjustment=all. The PSIX 2025 four entries lack a 200-bar warmup, so the comparison is unknown and not below trend. Zero SMA 200 filtered trades cannot prove protection. In 2026, the baseline was above SMA 200 N2 mean 11.32%, and below N3 mean -8.21568%, including one boundary liquidation. However, the filter rerun N3 is not directly comparable because it changes the entry sequence. All 5 baseline 2026 entries had SPY above SMA 200, yet the total was -11.169%, which means the market trend does not rescue the issue-risk. The L10, L20, and L40 returns for 2026 were -6.52%, -11.17%, and +6.42%, respectively. Selecting 40 now is considered overfit. The RSI 71 and SMA 50 breakout derive from the same price history, and are not independent sources. The conclusion is to retain the frozen rule, and prospective outcomes are needed for conditional odds.

The premises for the analysis are as follows: a data scan found the stock bars for symbols PSIX, ETN, POWL, NVT, and SPY on September 18, with a one-day timeframe and all adjustments. PSIX's price of 45.87 is greater than its prior 20-day close of 45.55 and its 50-day simple moving average of 36.0078, but less than its 200-day simple moving average of 55.48675. The average true range for PSIX is 2.3018, its simple relative strength index is 1471.0399, and its 20-day annualized volatility is 79.4489%. ETN's price of 424.77 is less than its trigger price of 425.37, POWL's price of 183.96 is less than its trigger price of 197.67 and its 50-day simple moving average of 203.2032, and NVT's price of 157.88 is less than its trigger price of 162.57. All three of these stocks failed to meet the entry criteria. There is a difference in PSIX's price between the IEX feed of 45.82 and the SIP feed of 45.87, but this is not a gain. A hypothetical trade with an entry price of 45.87, a stop loss of 41.2664, and a target price of 61.710237 would have a net risk of 5.082862 and a potential gain of 15.248585 per share, with a 5 basis point fee and a 50 basis point slippage per side. One cost would be 46.1224, and two costs would be 92.2448, which would fit a 120 fund. However, two historical gaps of 34.187% and 32.2002% are greater than the normal budget of 30%, and one gap of 16.1001%. Assuming a 50% gap, one would be 23.3135 and two would be 46.627, but these are not probabilities or bounds. There are no orders, holdings, or orders from the previous day being held.

The analysis of PSIX is based on two premises. First, the daily adjusted returns of PSIX and other stocks, including ETN, POWL, NVT, and SPY, from December 27, 2024, to September 18, 2026, are available at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=PSIX,ETN,POWL,NVT,SPY&timeframe=1Day&feed=sip&adjustment=all. Second, the total return of PSIX is 55.5443%, and if the best and worst 5 days are removed, the returns become -40.3676% and 628.9994%, respectively. The best 5 days account for 217.025% of the net log gain. This symmetric deletion is a descriptive stress test, not a feasible trading strategy or a reason to avoid investing in PSIX due to its fat-tail nature, which refers to a distribution with extreme values. The mean daily return is 0.32275%, the median daily return is -0.34725%, and the 1st and 99th percentiles are -17.5354% and 19.0215%, respectively. An ordinary least squares regression, or OLS, which is a method to estimate the relationship between variables, was performed with the SPY index and an equal weight of ETN, POWL, and NVT. The results show an intercept of 0.065405% per day, a market beta of 1.37135, and a power beta of 0.83742, with an R-squared of 0.27974. However, the presence of correlated factors and selected survivors, which are stocks that have survived a certain criteria, precludes causal alpha, or the ability to generate excess returns. The natural exit in 2025 had a mean return of 23.6247%, with a 95% confidence interval of -2.3439 to 49.5933, and in 2026, the mean return was -1.9832%, with a confidence interval of -15.9065 to 24.5858. These wide intervals indicate that the bootstrap method, which is a statistical technique to estimate the distribution of a parameter, cannot recover missing crises. In conclusion, tail dependence is expected, but the regimes and few trades cannot calibrate this entry, meaning that the analysis cannot accurately predict the behavior of PSIX due to its complex nature.

The R91906 generator faces scarcity rents that are divided among suppliers, customers, and expansion. There are two premises: first, the FY25 Item 1A report from PSIX, available at https://www.sec.gov/Archives/edgar/data/1137091/000162828026013207/psix-20251231.htm, limits quick substitution of sophisticated inputs and warns of competitive and contractual cost pass-through limits. Second, the August 6 Q2 report, available at https://www.sec.gov/Archives/edgar/data/1137091/000162828026054328/psix-20260630.htm, states that Supplier A has less than 10% purchases, but has $290.108 million in 2026-29 SWIEC minimums and selected engine 1.75% MOR fees. The broad collaboration expiry does not cancel these contracts. The first order effect is that power demand can raise production, while input and ramp costs absorb margin. The second order effect is that vendor and customer terms divide cash rent, and tariff cash is not automatically shareholder cash. The third order effect is that expanded capacity and substitution can improve delivery while eroding scarcity prices. This analysis uses chains of conditional inference, not forecasts. The bear case is that shipment timing and supplier terms make backlog unreliable for cash. The bull case is that Q2 sequential margin and positive ex-refund free cash flow show progress. There are binding unknowns, including customer pricing, cancel rights, throughput, and mix. Purchase share is not a switching cost, and there is no asserted new outage.

The company PSIX reported its Q2 earnings as of June 30, with a gross margin of 27.119%, compared to 22.882% in Q1. Sales decreased by 21% year-over-year, and costs from the Wisconsin ramp persist. The operating cash flow for the first half of the year was $75.724 million, with capital expenditures of $2.736 million, resulting in $72.988 million. After removing a $22.687 million customer refund liability, the cash flow was $50.301 million. Additionally, removing $12.594 million in accounts receivable and $3.992 million in inventory release resulted in $33.715 million in cash-quality sensitivity, which is not a normalized forecast. The company also made an acquisition of $11.911 million. The Q2 annualized earnings per share was $2.9232, compared to $2.09485 for the first half of the year. Neither of these numbers matches the forecast. With a stock price of $45.87 and a market capitalization of $1057.3035 million, the Q2 annualized price-to-earnings ratio is 15.692. The 2ATR stop is $41.2664, and the cost of the 3R is $61.7102, assuming a 20x Q2 value of $58.4639, which yields 2.36484R. To reach the target, the earnings per share would need to be $3.08551 at 20x. A bull case scenario would require earnings per share of $3.5, which would yield 4.62R, but this is unconfirmed. In conclusion, the recovery can justify an upside, but assuming 20x is not fair value, and it should not be raised to force the 3R. One possible counterexample is that further throughput or mix could exceed $3.09 in earnings per share. The information comes from one corporate source group, and the annual and quarterly reports are not independent corroboration.

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

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

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

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

The account started with $1000 and $120 in Supply cash, with no holdings or queued orders. The goal is to buy stocks that will benefit from bottlenecks and have a high potential payoff. The investment constraint is to find a stock with a defensible price and a good entry point, rather than just looking at available capital. There are four premises to consider: (1) the company's operating benefit and cash flow are real, but its liability financing exceeds its operating cash flow, making its annualized margins and cash uncertain. This can be seen in its Q3 report and previous report. (2) The standard terms offer protection against repricing, but the actual realization and allocation are unknown, as stated in the company's terms. (3) A growth scenario with $110 million in revenue and a 38% gross margin could yield $19.815, but this is not a guarantee. (4) The current stock price of $14.82 is below the breakout price of $14.92, and the prior cost does not establish a scarcity edge, as shown in the stock bars data. The conclusion is to keep the $120 in Supply cash for a qualified payoff, as the current size is zero and there are no orders or exits. This decision supports rejecting the current setup, but does not guarantee the stock will fall. A countercase scenario where the company improves its allocation and releases working capital could make the bull scenario happen quickly. The subjective business prior for positive post-investment cash in the next two quarters has moved from 40% to 55% after reviewing the terms and Q3 report. The current modeled risk stakes range from 5.15% to 6.049%. A one-share probe at $14.82 would cost $14.82, with a planned net risk of $2.299 and a net gain of $6.898. A three-share probe with a 50% gap would stress the budget, and there is no hard assertion that gaps cannot exceed 50%. The existing job monitors operating filings until the next run, and the monitor covers put and refinancing preparation. The source saturation has added no new primary cash economics, and the private contract and SKU price and volume gaps remain. This decision belongs in the Supply category.

A simulation called R91904 was run to understand potential risks. It used 20,000 paths and 60 sessions, looking at five-day blocks of stock prices and ratios. The simulation used data from 2016 to 2023 and the latest 252 sessions. The target price was $14.82, with a stop loss at $12.67 and a target at $21.92, with a 55 basis point gap on each side. The simulation was based on data from https://runway.adityaperswal.workers.dev/data/stock-bars. The results showed a mean net return of +0.2761% and +5.5309%, with a median return of -15.4293% for both. The mean win rate was 26.8199% and 45.7187%, with mean loss rates of 14.7034% and 15.8906%. The profit rate was 36.075% and 34.77%. The simulation used a binary half-Kelly risk-stake approximation, which suggested a risk of 5.148% and 6.0489% of the bankroll. However, this is not a direct position and has not been validated. The simulation also showed that using recent data nearly triples the target frequency, but also raises the stop frequency. The model has limitations, including not accounting for monitoring delays, future jumps, and conditional fundamental information. The simulated maximum loss is not a bound. For example, buying four shares at $59.28 would imply a planned net stop risk of $9.197 and an assumed 50% gap loss of $29.64, plus costs. This would be below 3% of the account, approximately. If the entry and payoff are qualified later, the half-Kelly risk-stake should be recomputed with current evidence and cap.