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A news scan found the headline 'R91106 raw-price 15-minute LPG audit: completed holdout +21.74%, stress training negative'. This is a report about an automated trading fund manager's notes. The manager uses a method called SMA50, which means Simple Moving Average over 50 days, to make decisions. The fund had a gain of 21.74% in a test period. The manager also used a stress test, which is a way to see how the fund would do in difficult conditions, and the result was negative. The fund's performance was measured over 15-minute periods, and the manager used a technique called vectorbt event replay to test the fund's decisions. The test period from 2021 to 2024 had a gain of 10.25%, and the test period from 2025 onwards had a gain of 12%. The manager also reported that the fund's performance was sensitive to changes in the market, and that the fund's rules for buying and selling stocks were not validated in real-time. The manager concluded that the current entry point for the fund is not valid, and that the fund should be watched but not invested in.

The R91106 trading strategy was tested with daily replication. The strategy is based on several premises: the stock's prior 20-day close and high, and its 50-day Simple Moving Average (SMA), which is a calculation of the average price of a stock over a certain period of time. The strategy also considers the next day's open price, the prior 20-day Average Daily Range (ADR), which is a measure of the average daily price range of a stock, and a target return of 3R, with a maximum of 20 sessions. The strategy stops trading at the first daily Open-High-Low-Close (OHLC) with gaps. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=1Day&feed=sip&adjustment=all. The strategy was trained on data from 2021 to 2024 and tested on data from 2025 to September 10, 2026. The training process used vectorbt, a backtesting library, with explicit long-only event replay, a fee of 5 basis points, and a slippage of 20 basis points on each side, and stress slippage of 50 basis points. The results were compared to manual compounding. The prior cold start, which started with 2021 data and needed a 50-bar warm-up period, resulted in a return of 48.98218086% with 28 trades. When the warm-up period was started in September 2020, the strategy had losses of -7.11%, -8.91%, and -9.62% on January 5, February 16, and March 15, respectively, but overall resulted in a return of 13.93883013% with 31 trades, a drawdown of 46.84%, and stress of -5.39983%. The same holdout period resulted in a return of 47.44972564%, a drawdown of 23.09653555%, and 15 trades, including one forced cutoff mark, and stress of 34.75874485%. The conclusion is that the earlier output was not arithmetically false, but the training depends strongly on the data boundary, and full-calendar training weakens the sizing. The validity of the results is explained by the materially changed sample, not improved alpha, which refers to the excess return of a stock or portfolio over a benchmark. The soundness of the results is already inspected, and the symbol and history are chronological validation, not untouched selection. It is recommended not to choose a cold start after seeing losses. The last trade on August 18, which was 17 sessions by September 10, is marked but not completed, and is excluded from subsequent exit simulations. There are no orders.

A query of trades for the supply fund returned two closed lots, both of which were liquidated by the operator on September 4, rather than being stopped or targeted by the strategy. The entry and exit prices for these trades were 44.49 and 46.18 for FRO, and 79.76 and 80.36 for STNG. These two gains do not provide enough information to estimate the size of potential losses or the likelihood of stop hits or future wins. Additionally, there are no benchmark quotes available to measure slippage from the entry and exit prices. The sources of this information are the fund's trade ledger query and trades 6 and 3. The original reasons for these trades are still claimed, but have not been freshly verified as physical evidence. The conclusion is that the size of trades should be determined from audited, independent history with explicit uncertainty, rather than from these two forced exits or funding pressure.

The R91106 scarcity chain refers to a situation where cargo displacement can lead to increased rents and unpaid costs. There are two premises to consider: first, according to Dorian's annual report, canal waiting times are generally unpaid and toll increases do not necessarily affect charter rates, and their Panamax chartered-in ships can use old locks. Second, Helios Pool's revenue distribution structure, as seen in their SEC filing, shows that no single charterer accounts for more than 10% of their revenue, reducing the risk of relying on a single customer. The consequences of this scarcity chain are threefold: firstly, cargo route constraints increase voyage demand while idling capacity; secondly, shipowners with usable capacity may collect higher freight, but other parties such as canal auctions, bunker suppliers, and chartered-in vessel owners also benefit; thirdly, relief, substitution, and new capacity can collapse extraordinary earnings before book assets are repriced. However, a strong counterexample exists where shortages can destroy trade volume rather than increase paid ton-miles. The strategy starts with a cash position of $278.67 and aims to achieve positive net asymmetric stock exposure, with the binding constraint being robust execution and payoff with the current signal, rather than the funding shortfall. Furthermore, the stocks FRO, STNG, and LPG should be tested for common return exposure before treating them as diversification opportunities.

The company Dorian LPG Ltd. released its quarterly report on August 5, showing a time charter equivalent, or TCE, of 75,926 dollars per day, which is up from 39,726 dollars per day, and adjusted earnings of 107.2 million dollars. This increase in earnings is partly due to physical rerouting. The bunker benchmark, which is a measure of fuel prices, was 863 dollars per ton, compared to 511 dollars per ton, and charter hire was 22.6 million dollars, compared to 10.7 million dollars, indicating that ship owners are sharing the increased costs. The company's cash balance increased by 14.732 million dollars to 342.141 million dollars, and its current receivables from related parties increased by 93.411 million dollars to 190.449 million dollars. As of September 10, the stock closed at 54.41 dollars, and with 42.782681 million shares outstanding, the market capitalization was 2,327.8097 million dollars, and the price-to-book ratio was 1.8826. Annualizing the exceptional earnings per share of 2.52 dollars implies a price-to-earnings ratio of 5.40, but this is not a normalized value. According to the company's news release and stock price data, the conclusion is that while it is plausible that the company can capture more cash, a justified stock target needs to be based on durable financial performance and collections. The logic is valid, but exceptional earnings from one quarter cannot be used to establish a future run rate. It is possible that receivables may be converted to cash shortly, and earnings do not need to be paid before the stock price is discovered. Based on this analysis, no purchase is recommended.

This update changes operational references in document 2542, but does not change its investment decision. The final ledger check at 04:17 showed no open, pending, or closing orders for supply, with a fund cash balance of $278.67 per run context. Two prior forced-liquidation wins are not independent validation of stop and target points. Research was completed in about 20 minutes of the planned 44 minutes, with all journal and report work finished within the allowed time; no research thread took too long. Analyses of physical, fundamental, statistical, technical, consequence, backtest, and simulation data were recorded in documents 2487, 2510, 2511, 2513, 2514, 2521, 2522, 2531, 2532, 2537, 2538, and 2542. Lessons 754, 755, 756, and 760 were also recorded. The actual job, which started at 21:20, was run remotely and exited with a code of 0, but the saved output was cut off at 2000 characters; this was not enough to confirm that the output was visible. Job 212 was cancelled and replaced with a new script, number 214, which has 3412 characters and is limited to 90 seconds per run, with 20 runs per day. A local test of 1353 bytes includes all 10 task statuses and the end signal; the first remote run of script 214 is still pending, so it should not be claimed as completed. The update keeps the previous 8 IEX symbols, including UAMY, LRVT, EGA, and BWEN, and adds four new aluminum names, IAI, CSTM, KALU, and CENX, which are related to aluminum filings. Monitor 267 has been replaced by monitor 269, which retains both research branches and the new ID, at 06:00 UTC. Other active jobs, numbers 18, 36, 200, and 204, are retained; job 200 was verified to have ended at 03:30, while job 204 is still pending with a partial receipt of 2000 characters. Issues from documents 251 to 254 have been deferred rather than falsely resolved in this single deep dive. The source of the data is from the International Aluminium Organization, at https://international-aluminium.org/statistics/primary-aluminium-production/ and https://alvis.international-aluminium.org/api/publication/?publication=primary-aluminium-production. There are no separate fund cases or orders.

The automated trading fund manager made a final decision on R91104. The decision is based on several premises. Firstly, primary physical evidence supports a remaining bottleneck in aluminum production, with a 0.3753% increase in daily production and a 3.7212% increase in monthly production, according to the International Aluminium Institute. Additionally, the GCC production decreased by 43.98% year-over-year. The EGA's smelter restoration is expected to be completed in the first quarter, but it does not currently provide delivered metal. The sources for this information are the International Aluminium Institute and EGA's website. Secondly, cash capture varies among companies, with CENX having a formula that shares metal-price upside and CSTM and KALU being able to pass input costs through and earn conversion and recycling rent. However, cash needs and competitor restarts remain a concern. CSTM has a non-cash lag of $129 million, which is 29.38% of its Q2 EBITDA. The source for this information is the SEC website. Thirdly, all four stocks on September 10 failed the prior 20 high and SMA50 entry filter. The source for this information is a data feed. Fourthly, frozen new 3R downstream tests showed that CSTM's train was -38.61% and holdout was +31.78% N15, while KALU's train was -14.05% and holdout was +47.76% N17. Both mean CIs cross zero, and stress weakens training. The conclusion is that there are no orders and no exits changed, with a supply cash of $278.67 retained. The validity of this decision is based on the missing required current entry and supported payoff, which implies no trade under this rule. The soundness of this decision is established by the sources, which provide dated facts. The goal remains to achieve paid asymmetric exposure, with the binding constraint being investable net payoff, not account funding gap. The collector preserves 73 plus new IAI and aluminum coverage, and the monitor has both outcomes, with no automatic purchase. Gaps were recorded, but there are no new funds outside the mandate.

A command was run using a node to test a trading strategy on the stocks CSTM and KALU. The strategy used is called a breakout strategy, which means it looks for a stock to break out of a certain price range. The test looked back at 20 days of data and ran for 1500 days. It used a stop loss of 8 percent and a target gain of 24 percent. The data for the test came from a website called https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex. This test used its own signals to decide when to buy or sell, and it looked at the full history of the stocks. The results of this test cannot be used to replace other tests that use frozen data. This test was not a live test and did not use a holdout sample, which is a separate set of data used to verify the results. The test was run again to save the full results, but it did not produce a new independent result. The conclusion is that the test was run as a diagnostic, but it does not provide any information about how the stocks will perform in the future.

A command was run using a node to test a trading strategy on the stocks CSTM and KALU. The strategy used was called breakout, which means it looks for when a stock price breaks out of a certain range. The test looked back 20 days and ran for 1500 days. It used a stop loss of 8 percent and a target gain of 24 percent. The data for the test came from a website called https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex, which provides stock market data. The test used its own signals to decide when to buy or sell, and it used the full history of the stocks. This means the results cannot be used to replace other tests that use frozen data. The test was not a real-world test, but rather a diagnostic test to check the system. The test was run again to save the full results, but it was not a new independent test. The conclusion is that the diagnostic test was completed, but it does not provide any information about how the trading strategy would work in real life.

A news scan found the report from EGA on August 12, which stated that as of Monday, August 10, there were 227 cells. Another report from EGA on August 26 stated that there were 315 cells out of 1262, which is roughly a one week stabilization. The implied interval speed is 88 cells divided by 16 days, which equals 5.5 cells per day. With 947 remaining cells, it would take approximately 172.18 days to reach the target, plus 7 days for stabilization, which would be February 21, 2027. This rough estimate is consistent with, but not independent confirmation of, the issuer's Q1 target. The assumptions made include that all 315 existing cells are stable, have identical capacity, and that the cast output is a proxy of 1.6 million tonnes per year. A simulation using NumPy with 50,000 paths and a fixed speed per path of 3 to 5.5 to 8 cells per day was run. The completion days after August 26 were estimated to be 138.35 days at the 5th percentile, 178.92 days at the 50th percentile, and 256.53 days at the 95th percentile. The model estimated that there is an 81.662% chance of completion before March 31, but this is not a real-world forecast odds. The integrated missing cast output proxy through March 31 was estimated to be 237,423 tonnes at the 5th percentile, 304,145 tonnes at the 50th percentile, and 421,743 tonnes at the 95th percentile. However, the precision of delivered tonnes is lower due to factors such as heterogeneous cells, hot-metal reuse, alloy content, existing stocks, and exports. In conclusion, scenarios should be used to bound questions for the next operational update, but should not be used to derive a stock target or trade probability. New measured output and net shipped volumes are more important than this simulation.

A news scan found the text about automated trading fund manager notes. The manager uses training payoffs, not favorable holdout, from fixed tests 2521–2522, with data from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. For CSTM, the training payoff is 40%, with a mean win of 8.4956% and a mean loss of 7.6217%, resulting in a clamped half Kelly of 0. For KALU, the training payoff is 41.9355%, with a mean win of 10.7182% and a mean loss of 7.6758%, giving a rough half Kelly of 0.176252% of equity. This is a binary-payoff approximation and a sizing ceiling, not proof of optimal allocation. The manager notes that stress costs make KALU training mean negative and half Kelly 0. Both CSTM and KALU currently fail entry, so the actual allocation is 0. The manager also notes that whole-share KALU 158.42 and stock minimum 1 exceed the hypothetical probe of $0.375, and therefore should not be rounded up. The manager ran simulations using NumPy with 50,000 paths of 20 independent and identically distributed training roundtrips, with a seed of 91104. The results show that allocating 25% per trade yields a median account return of -6.56% for CSTM, with a loss probability of 73.34% and a 95th percentile drawdown of 22.50%. For KALU, the median account return is -0.98%, with a loss probability of 53.40% and a 95th percentile drawdown of 19.89%. The manager notes that these are conditional empirical simulations, not calibrated future loss bounds, and omit unseen gap and persistent regime risk. The historical training target-hit frequencies are 2/30 for CSTM and 2/31 for KALU, versus 15/30 and 18/31 for stops. The conclusion is that the venue maximum is not a position recommendation, and no funded probe is justified by these distributions. A funding shortfall does not change the odds, target, or size.

A preregistered note, number 2178, outlines a trading strategy for KALU, which involves closing above the prior 20-day close high and SMA50, with a next session open entry, stop at 2 times the prior 20-day average daily range, and a target of 6 times the average daily range, with a 3R ratio, and exiting on the 20th session close. The strategy was trained on data from 2020 to 2024 and tested on a holdout period from 2025 to September 10, 2026. The data source is from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. The results show a return of -14.05% during the training period, with a drawdown of 55.15%, and a return of 47.76% during the holdout period, with a drawdown of 28.95%. The stress test return was 42.82%. A four-trade circular block bootstrap test with 10,000 samples showed that the mean confidence interval crosses zero. The conclusion is that the positive holdout results alone cannot fix the negative compounded training results or support retrospective sizing, and therefore, it is a research watch only, with no current signal and no defensible new allocation.

A preregistered note, number 2178, outlined a trading strategy for the CSTM stock, which is a type of aluminum-chain benchmark. The strategy was to buy the stock if its price closed above the highest price of the previous 20 days and the 50-day moving average, and then sell it when it reached a target price or after 20 days. The strategy was tested using data from 2020 to 2024, and then its performance was evaluated using data from 2025 to September 10, 2026. The results showed that the strategy had a return of -38.61% during the training period and 31.78% during the holdout period. The holdout period is a time when the strategy is tested on new data to see how well it performs. The stress test return was 27.89%. The results also showed that the strategy had a high drawdown, which is the largest decline in value from a peak to a trough, of 47.12% during the training period and 19.56% during the holdout period. A drawdown is a measure of risk. The strategy was also tested using a bootstrap method, which is a statistical technique, and the results showed that the mean confidence interval crossed zero. The conclusion is that the strategy is only for research purposes and does not currently have a signal to buy or sell the stock, and the positive results during the holdout period are not enough to make up for the negative results during the training period.

A news scan found the primary supply indicator for R91104. The indicator is based on data from the International Aluminium Institute's public webpage chart API. The latest data is from July 2026. In June, the total production was 5939 kilotonnes over 30 days, which is 197.966667 kilotonnes per day. In July, the total production was 6160 kilotonnes over 31 days, which is 198.709677 kilotonnes per day. This means the monthly total increased by 3.721165% and the daily rate increased by 0.375321%. The daily rate is a more accurate measure because it removes the effect of different calendar lengths. The data also shows that the daily rate in the GCC region increased by 2.734923% from June to July. However, compared to July 2025, the GCC production decreased by 43.977055%. The primary definition of production excludes alloy additions and recycling. The conclusion is that the recovery is gradual and there is no evidence of a 3.72% increase in daily capacity. Other factors, such as producer inventories and export routes, may improve deliverability even if global output barely changes. A joint indicator that includes normalized production rate, deliverable inventory, and issuer net cash is required. The London Metal Exchange official stock summary did not provide current data, and secondary claims of 244525 tonnes were not verified as current deliverable inventory. This is a physical indicator, not a tested stock signal.

The current state of the aluminum market is characterized by a supply fund that is flat, impaired physical production in the Gulf, and some competitors restarting their operations. The goal is to own paid scarcity rents at a favorable stock payoff. The main constraint is the net realized margin after considering energy, input metal, working capital, and recovery, rather than the global metal headline. The first issue is that interrupted primary production and export routes are constraining deliverable metal. For example, EGA's sales decreased by 31.61% in the first half of 2026, while their cast production decreased by 29.15%, and their operating cash flow decreased by 56.87% despite an increase in EBITDA of 10.85%, according to their report. This shows that operational and cash channels differ. The second issue is that companies like CENX, which has exposure to the US regional premium, can retain value, while others like LME-linked Iceland power and alumina, and Sebree market power, share value with suppliers. Fabricators like KALU and CSTM can pass metal costs through, benefit from scrap spreads, and scarce qualified rolling capacity, but they have to fund higher inventories, as seen in their reports. End customers face higher invoice prices or delays, but allocation and pass-through timing are not measured here. The third issue is that scrap suppliers can renegotiate, customer substitution can lower demand, and restored primary and rolling capacity can compress premiums. For instance, Novelis reports that Oswego restarted in June and Bay Minette is commissioning, yet their operating cash flow was -455m and adjusted free cash flow was -1.1bn in the June quarter. However, insurance timing gave them an 18m net EBITDA benefit from the outage. These are conditional consequences, not probabilities. A strong counterargument to pessimism is that CSTM's EBITDA rose by 87.88% and generated 90m quarterly free cash flow. On the other hand, a strong counterargument to bullishness is that restarts and cash needs can reverse that rent. In conclusion, it is recommended to keep separate primary-metal and conversion-spread watches within supply, and no unrelated fund is warranted.

A news scan found the following information about four aluminum-chain stocks. The stocks are AA, CENX, CSTM, and KALU. Here are the reasons why they failed the frozen current entry filter: 1. On September 10, the adjusted closing prices were AA $48.30, CENX $44.36, CSTM $25.99, and KALU $158.42, which were obtained from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=sip&adjustment=all. 2. Each stock's price is below its prior 20-day high, which were $51.85, $48.17, $29.92, and $189.49, and also below its 50-day Simple Moving Average, which is a calculation of the average price over 50 days, and were $48.5086, $45.6681, $28.4026, and $166.195. 3. The Average Daily Range over 20 days, which is a measure of price volatility, is 1.972495, 2.333405, 0.97824, and 7.1416, and the 20-day returns are -5.70%, -7.91%, -11.90%, and -16.40%. The annualized RV20, which is a measure of risk, is 45.88%, 42.93%, 39.99%, and 60.78%. These measurements are based on adjusted daily prices and are not executable after-hours quotes or stop fills. The conclusion is that there will be no breakout entry tonight, even if operating margins are favorable. This screen does not test for value or reversal entries and cannot authorize one. Stops and targets require an actual future entry and independently justified payoff, and no anticipatory limit is queued. The source observations contain every symbol's numbers.

A study of aluminum stocks found that after an outage, the stocks of different companies moved in different directions. The study used a method called SIP all-adjusted daily bars, which means it looked at the daily prices of the stocks, adjusted for things like dividends and stock splits, from January 2020 to September 10, 2026. The data came from a website. The study compared the daily returns of the stocks from January 2025 to March 27, 2026, with the returns from March 30 to September 10. The returns after the study period were: AA -17.01%, CENX -10.65%, CSTM +11.21%, and KALU +39.53%. The study also looked at something called SPY betas, which measure how much the stocks move compared to the overall market. The betas were: 1.444, 1.361, 1.969, and 1.842. The study found that none of the stocks had returns that were significantly higher than the market as a whole. The correlations between the stocks, which measure how much they move together, were: AA and CENX 0.673, and CSTM and KALU 0.655. This means that buying two aluminum stocks is partly like making one bet. The study found that the outage did not necessarily mean that the stocks of companies that make aluminum would go up. In fact, the stocks of companies that use aluminum went up while the stocks of companies that make aluminum went down. The study concludes that the direction of the outage should not be used to predict the direction of the stocks. The study had some limitations, including only looking at a few companies and a short period of time, which means that the results may not be repeatable.

The premises of the aluminum market are based on several factors. Firstly, Emirates Global Aluminium (EGA) is expected to restart 315 out of 1262 cells by August 26, with full output forecasted for Q1 2027, according to their website. The company's H1 issuer reports also mention distinct shipping constraints, but this does not necessarily mean a recovery in export deliveries. Secondly, Century Aluminum's (CENX) 2025 power terms imply a cost of 15.33 MWh/t at capacity, and a hypothetical $500/t premium would be offset by a $32.6158/MWh power cost at Sebree, before considering alumina, hedges, and other costs. A 450kt US full capacity with a $500/t premium would generate $225m/y gross, but if only Sebree power costs increase by $25/MWh, the cost would be $84.315m/y, resulting in $140.685m/y or $35.171m/quarter before other offsets. Neither the $500 premium nor the $25 power move is forecasted. The LME indexed Iceland power and alumina dampen metal beta, and Glencore's 54% sales and limited input suppliers add counterparty risk. Thirdly, CENX's August 6 report shows a Q3 EBITDA midpoint of $335, which is only 2.478% above Q2, and H1 CFO of $236, with capex of $134.4, resulting in $101.6m. The company's equity proxy is $4391.34m, with an EV of $4527.94m, and a 3.463x annualized Q2 EBITDA, but a 4.627% annualized H1 FCF yield. However, this is not considered fair value as it misses NCI, leases, pensions, current dilution, tax refund timing, cycle, and growth capex. Fourthly, downstream companies are not automatic losers, as seen with CSTM's Q2 ex-lag of $310m versus $165m, with a 29.385% non-cash lag, and H1 FCF of $95, which needs to be above $205 in H2 to meet the guide of $300. KALU's metal pass-through and conversion also show a positive trend, with $437m versus $374.2. In conclusion, a positive operating rent exists, but the latest cash and contract evidence is necessary. The logical existence of scarcity does not necessarily mean positive stock expectancy, as energy prices can rise, metal prices can reverse, or rolling capacity can restart. The facts stop at the cited periods, and sensitivity and annualization are assumptions. A strong counterargument is that a cheap CENX multiple cannot normalize earnings by itself.

The final decision for R91102 is to preserve $278.67 in cash. This decision was made after conducting wider screen and fixed payoff tests. The fund has $850.99 in the account, with $278.67 in cash and a position capacity of $212.75. Two past trades, referred to as N2, cannot calibrate losses of $2465. A directory-wide US screen of 2389 covered 12584 out of 13176 snapshots, with 1565 qualifying fresh names, but 2394 foreign equities showed no signal, and KAP was invalid. Additionally, 9 crypto pairs had no physical rent edge identified. The current listings screen is not a backtested factor alpha. Fundamentals of four companies, VIST, SWKS, LEU, and ALM, were examined, with VIST paying export cash but having working capital and oil assumptions, SWKS having 67% of its revenue from Apple, LEU having cost growth greater than price, and ALM having unknown recovery cash. Sources for this information include earnings releases and SEC filings from these companies. Frozen backtests from 2417 to 2422 were also conducted, with results including a 29.4389% loss for VIST, a 5.2136% loss for SWKS, and a 17.4599% loss for QRVO. Simulations from 2426 to 2428 were also run, but could not justify rounding quarter probe 19.15 LPG and 8.79 CCJ up to 54.41 and 97.42 whole shares. The conclusion is to make no orders or exit changes, and to choose cash because validated payoff, current entry, and execution were not jointly met. This decision is based on logical conditional validity, but the primary periods and small selected samples limit the soundness of any positive forecast. A strong counterargument is the positive VIST holdout of 22.78 and current breakout, but it is insufficient to rewrite failed training after seeing it. The next monitor will be on 04 UTC, with job 204 creating a new shortlist and preserving tankers, and 200 existing physical panels. No separate bookcase is justified, and all credible work fits the supply. There is a funding shortfall of $349.01, but it is not input.

A news scan found the headline 'R91102 live track record cannot calibrate a scarcity strategy'. The R91102 fund has a record of trades, including two entries on September 2 and an operator liquidation on September 4. The return on these trades was 3.805163 percent and 0.752257 percent, with a mean of 2.27871 percent. The profit and loss was 4.977287 USD, with two wins and no losses, resulting in a 100 percent win rate. However, this record cannot be used to estimate the average loss, or the frequency of stop or target hits, due to a lack of data on losses and the fact that the original six to eight week theses cannot be confirmed. The Kelly criterion, a measure of bet size, also cannot be reliably estimated. The entry stop is null and there is no fresh entry quote, which prevents consistent estimation of risk and slippage. It is noted that a 100 percent win rate is not claimed to be an edge. All final open, pending, and closing queries are empty. There is no holding horizon or review of exit changes. Independent costed backtests, rather than this sample, guide the size and selection of the subject and period, due to limitations.

The valuation sensitivity of R91102 Vista depends on oil and cash assumptions. On September 10, the stock closed at 78.95. The company's Q2 earnings release reported 108.909959 million weighted average shares, EBITDA of 805.2 million, net debt of 3056.8 million, and ex-deal free cash flow of 491 million, which includes a 274.2 million working capital release. Using the weighted shares as a proxy, the equity is 8598.44 million and the enterprise value is 11655.24 million, with an annualized Q2 EBITDA of 3220.8 million, resulting in a multiple of 3.619x. This is not verified against the current diluted market capitalization or forward multiple. The annualized ex-deal free cash flow, minus the working capital release, is 867.2 million, which gives a proxy yield of 10.086%. If the oil price decreases by 15 USD/bbl, the company would lose 741.46 million in gross annual revenue, and assuming 60% of this loss flows to cash after costs and taxes, the cash loss would be 444.88 million, resulting in a yield of 4.912%. The conclusion is that the valuation can look attractive, but oil, capacity, and cash assumptions dominate, and do not support a target price of 95.68. The company has a hard limit on its pipeline and trucking capacity, and customer concentration can interrupt payment. The facts end at the issuer and price level, and all annualization, share proxy, constant volumes, and 60% flow-through are explicit assumptions. A current capitalization and normalized forecast are required before a fundamental price target can be set.

A required command was given to a node to run a backtest using the file backtest.js with the symbols VIST, SWKS, CCJ, and LEU, and a breakout strategy. The test looked back 20 periods and ran for 1500 days, with a stop loss of 10 percent and a target gain of 30 percent. The data came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The command line interface used the IEX data source without any adjustments, and it used 1030 bars of data. The signal exits were different from a custom fixed 20 session test. This test was not held out, not adopted, and not interchangeable with SIP chronological tests from 2017 to 2022. The costs were recorded for a 15 minute exit, but did not include venue mapping. The numbers recorded were for diagnostic purposes only, and no order was given.

A required command was given to a node at the address /app/agent/dist/backtest.js to run a test on the stocks VIST, SWKS, CCJ, and LEU using a breakout strategy. This test looked back 20 periods and ran for 1500 days, with a stop loss of 10 percent and a target gain of 30 percent. The data for this test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test used the IEX data source without any explicit adjustments, and it used 1030 bars of data. The signal exits from this test were different from a custom fixed 20 session test. This test was not held out, not adopted, and not interchangeable with SIP chronological tests from 2017 to 2022. The costs were recorded for a 15 minute exit, but this was not a test of venue mapping. The numbers from this test were recorded for diagnostic purposes only, and no orders were placed.

A required command was given to a node to run a backtest using the file backtest.js with the symbols VIST, SWKS, CCJ, and LEU, and a breakout strategy. The command included parameters such as a 20-day lookback period, 1500 days of data, a 10% stop loss, and a 30% target profit. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The command line interface, or CLI, used the IEX data source without any explicit adjustments, resulting in 1030 bars of data. The signal exits differed from a custom fixed 20-session 3R strategy. This test was not held out, not adopted, and not interchangeable with SIP chronological tests from 2017 to 2022. The costs were recorded with a 15-minute exit, but did not include venue mapping. The numbers recorded were for diagnostic purposes only and did not result in any orders.

A required command was given to a node to run a backtest on the stocks VIST, SWKS, CCJ, and LEU using a breakout strategy. This strategy, which is a type of trading approach, looks at the prices of these stocks over the past 20 periods and 1500 days. The command also included rules to stop trading if the stock price falls by 10 percent and to take profits if it rises by 30 percent. The data for this test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars. The test used a service called IEX without making any adjustments, and it looked at 1030 periods of stock prices. The results showed that the signals to buy or sell the stocks were different from a custom strategy that uses a fixed 20-session rule. This test was not set aside, not adopted, and cannot be used with other tests that look at data in chronological order from 2017 to 2022. The costs were calculated based on a 15-minute exit, but this does not match the actual trading costs. The numbers from this test are only for diagnostic purposes and do not represent actual trades.