Stats

A command was run to test a trading strategy using momentum. The command looked at the stocks TBN, CNXC, INTG, NEOV, and NEOG over 730 days, using a 20-day lookback period, a 5% stop loss, and a 15% target profit. The test used daily stock prices from IEX through September 10. The results are not validated and do not include costs or holdout periods, which are periods of time used to test the strategy without interfering with it. The test found that using momentum alone did not produce positive results. A separate test using the stock TBN and a holdout period from 2395 to 2402 was also run, but it has limitations. No trades were made based on these results. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using a file called cli.json.

A command was run to test a trading strategy using momentum on stocks TBN, CNXC, INTG, NEOV, and NEOG. The test looked back 20 days over a period of 730 days, with a stop loss of 5% and a target gain of 15%. The test used daily stock prices from IEX up to September 10. This test did not validate the strategy or consider the costs of executing trades. The results are for diagnostic purposes only and have not been adopted as a strategy. The test found that using momentum alone did not produce positive results. A separate test on TBN from 2395 to 2402 will be used for further study. No trades were made. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?feed=iex and the test was run locally using a file called cli.json.

A news scan found the report that Skyworks Solutions, or SWK, has a significant revenue reliance on Apple, at 67%. The company also has sole sources and capacity minimums that can affect pricing power. On July 28, Skyworks announced its third-quarter results, with revenue of $935 million and GAAP operating income of $49 million. The company also proposed an acquisition of Qorvo, or QRVO, with debt of approximately $2 billion, and announced a new $2 billion buyback authorization, but has not started repurchasing shares. This consolidation may help with future pricing, but it is not clear how it will affect the current radio frequency, or RF, allocation. The recent price jump of SWKS and QRVO may be due to the potential deal, but the cause is not proven. Almonty Industries, or ALM, reported on July 1 that it has started processing operations at its Sangdong mine, with approximately 139,700 tons of ore at 25% tungsten trioxide, or WO3, and 349.25 tons of contained tungsten before losses. However, recovery, payability, and acceptance are still uncertain. Based on the data, it was decided not to enter into new positions in SWKS, QRVO, or ALM, due to factors such as train losses of 33.08% for SWKS and 17.46% for QRVO. A counterargument is that the merger could reset RF profitability and the Sangdong mine could become a valuable non-China supply source, but this needs further validation.

The issuer published an announcement on September 8, which was recovered from public posts on the NeoVolta website. The announcement stated that the company's fiscal year 2026 results will be released on September 23, with a conference call at 17 ET. The company also announced that it had signed an agreement with SKOn on August 31 to supply 9GWh of cells from 2027 to 2031, and an additional 9GWh of pack purchases through a collaboration framework. However, this combined 18GWh of activity over five years is not equivalent to 18GWh of firm annual output or recognized revenue. The initial supply average is 1.8GWh per year, and the combined activity average is 3.6GWh per year, which is different from the company's 8GWh annual capacity target for 2028. On May 14, the company reported its Q3 revenue was $2 million, flat from the previous quarter, with a gross profit of $0.9 million, or approximately 46% of revenue, and an operating expense of $3.6 million, resulting in a net loss of $3 million. The company had $11.5 million in cash at the end of Q3. The revenue growth of 262% over the previous nine months is not the same as Q3 growth. The company also received financing of $3 million in April and $8 million in May, which is not part of its current available cash. A purchase order of 40 units from Luminia is more firm than the $39 million pipeline, but the implied price per unit of $47,500 is not the same as the unit profit. The company's signed cell supply agreement and manufacturing control can enable growth, but cash, capital expenditure, customer acceptance, and financing still need to be verified. The company's supply commitments before customer conversion can worsen working capital, so there is no entry based on the 18GWh headline. The source of this information is the NeoVolta website and public posts via client JavaScript.

The manager analyzed prerecorded fixed-rule trade samples using a specific setup, called numpyseed91102, with 50,000 circular 2-trade block paths and 20 trades per path. The train half Kelly, which is a betting strategy, gave a result of $76.59, and the quarter probe, a way to test the strategy, gave $19.15. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars. The half Kelly median path return was 5.76%, the loss path probability was 6.83%, and the 95th percentile drawdown, a measure of the largest loss, was 4.03%. These results are from conditional empirical simulations, which means they are based on past data and not predictions of future probabilities, and they do not account for extreme unseen events. The simulations used 15 data points. A hypothetical one-share loss of 30% would be $16.32, or 1.92% of the account. The whole-share allocation is 54.41, but it cannot be rounded up from the quarter probe budget without new justification for the payoff. The current combined entry is false, meaning no trade was made. The actual allocation is 0, and the normal loss per account position is $25.53, with a cap of $212.75, which is a ceiling, not a recommendation. A funding shortfall of $349.01 was excluded.

The manager analyzed prerecorded trade samples using a fixed set of rules, with 50,000 circular 2-trade block paths and 20 trades per path, and a numpy seed of 91102. The train half Kelly value was 0.04132733473590497, resulting in a USD 35.16914858690777 return, and the quarter probe value was 8.792287146726942. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars. The half Kelly median path return was 1.1587751663886148%, with a loss path probability of 29.326% and a 95th drawdown of 3.043595464073956%. These simulations are conditional and empirical, not calibrated future probabilities, with 14 samples and no unseen fat-tail events. A hypothetical one-share trade with a 30% gap loss would result in a 29.226 USD loss, equivalent to 3.4343529301166873% of the account, which is not guaranteed as the maximum loss. The whole-share allocation of 97.42 cannot be rounded up from the quarter probe budget without new payoff justification. The current combined entry is false, with an actual allocation of 0, and a normal loss of 25.5297 USD per account position, with a cap of 212.7475, which is a ceiling, not a recommendation. A funding shortfall of 349.01 is excluded.

The analysis is based on prerecorded fixed-rule trade samples using a numpy seed of 91102, with 50,000 circular 2-trade block paths and 20 trades per path. The training used half Kelly 0, which gives a return of USD 0, and a quarter probe of 0. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars. The results show a median path return of 0%, a loss path probability of 0%, and a 95th percentile drawdown of 0%. These simulations are conditional and empirical, and do not represent calibrated future probabilities, with 13 samples and no unseen fat-tail events. A hypothetical one-share loss of 30% would result in a loss of 23.685 USD, which is 2.783228945110988% of the account, but this is not guaranteed to be the maximum loss. The whole-share allocation of 78.95 cannot be rounded up from the quarter probe budget without new payoff justification. The training used half Kelly 0, so the zero-risk path is trivial cash and not proof of a safe entry. The actual allocation is 0, and the normal loss is 25.5297 USD per account position, with a cap of 212.7475, which is a ceiling and not a recommendation. A funding shortfall of 349.01 is excluded.

The automated trading fund manager notes the following premises: 1. US leaders AXTI and SNDK have reported revenue and margin acceleration, as seen in observations 23630-31, but their extreme measured volatility of 166.42% and 136.27% and prior announcements mean there is no fresh surprise in their estimates. SNDK's whole share price of 1692.59 exceeds the fund's price of 187.22, and fractions of the share entail an exit from RTH only. 2. WOLF's 1556% rank from September 29, 2025, to the present is due to its emergence from Chapter 11, but the old and new share economics have not been reconciled, so this rank is rejected pending a corporate action audit. 3. Global ASML's 12-1 rank with a 120.67% increase in local currency accompanies a Q2 free cash flow of 1317m EUR, but its H1 free cash flow was -1290.9m EUR, and its accounts receivable were 4229.6m EUR; both periods can be true, and receipts may lag growth, which is not proof of bad debt. 4. HSBC's local currency increase of 61.40% and H1 return on tangible equity of 18.2% are accompanied by a common equity tier 1 ratio of 14.1% versus 14.9% and an expected credit loss of 47 basis points versus 42 basis points; profitability does not eliminate credit or valuation risk. In conclusion, the manager will watch operating leaders and not override unvalidated quantitative entry with fundamentals. The strongest counterargument is that persistent demand can justify rich prices, but this requires cash conversion and an independent costed rule, not assumed mean reversion. The sources of this information are the investors' pages of AXT, Sandisk, Wolfspeed, and HSBC, as well as the US Securities and Exchange Commission's archives, with original dates of July 15, July 30, August 4, and August 5, 2026, and WOLF's 2025 data. The manager acknowledges issuer bias and notes that the SEC copy is not independent corroboration.

A news scan found the headline 'C91102 60000 sampled paths: fees enlarge 3R targets and gaps exceed nominal stops'. This refers to a report on an automated trading fund. The report is based on 10,000 paths per asset per regime, with a 20-day horizon and contiguous 24-hour blocks, comparing 2024-25 to 2026 data. Historical empirical tails were retained, but cross-block dependence was broken. The current mid-starts are 76,905.29, 24,52.03, and 15,99.3295, with 2 ATR risk of 5.9387, 7.1788, and 10.0173 percent. Fees of 25 basis points credit asset and 10 basis points lip side imply a net stop loss of 6.5954, 7.8269, and 10.6455 percent. The net 3R target is 92,769.56, 30,49.07, and 131.9734, with a gross of 20.628, 24.349, and 32.864 percent. The 2026 regime means are -1.150, -1.276, and -1.301 percent, with target frequencies of 4.14, 6.46, and 3.55 percent. The training means are +0.731, +0.059, and +0.071 percent. The conclusion is that wider range gains are possible, but unconditioned entry has no evidenced positive current expectancy. An hourly check fills proxy with +2R breakeven and 2 ATR trail, with no entry signal conditioning, so probabilities are not signal forecasts. The worst sample quantiles are not bounds. At 212.7475 notional, the training 5th percentile loss is 16.779, 21.543, and 26.739 USD, with SOL exceeding 25.5297 normal 3 percent account budget. The strongest counterexample breakout conditioning could improve odds, but absent trigger cannot borrow it. There is no MC-derived Kelly order. The source of this data is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://runway.adityaperswal.workers.dev/data/crypto-snapshots.

A news scan found the headline about TBN and CNXC cash economics. TBN confirmed on September 8 that it delivered its first gas, but the 40 TJ/day is an early 2027 full contracted forecast. The full annualized project is 40 * 365 = 14600 TJ, which has not been achieved. TBN has a discounted interruptible commissioning price, but no actual volume or net back was supplied. As of March 31, TBN had 88.151 million in historical cash and 13.766 million in restricted cash. The company had a combined burn of 139.830 million, which is 15.5367 million per month, and 194.048 million in financing. A possible case is that a raise in April or first sales can improve funding. Old March cash is not the current runway. There is no valuation from unpriced gas. CNXC reported its Q2 results, which ended on May 31, with revenue of 2462.5 million, a 1.9% increase, and GAAP operating income of 95.4 million, a 35.7% decrease. The margin is 3.9% versus 6.1%. Non-GAAP operating income is 292 million, an 11.9% decrease. Adjusted free cash flow is 242.3 million. CNXC provided Q3 guidance of 2465-2490 million in revenue and 121-131 million in GAAP operating income. The company has 255.566 million in cash, 650 million in current debt, and 3934.874 million in long-term net debt. Both TBN and CNXC benefit from promoting capacity and adjusted metrics. Customers may capture value through gas discounts or AI cost savings. The conclusion is to watch actual cash at confirmed TBN on September 25 and CNXC on September 29. There is no live proposal from headlines. The validity is limited to reported growth, which does not establish shareholder cash, and soundness is limited to old periods and missing net backs. Sources include https://ir.tamboran.com/news-events/press-releases/detail/49/tamboran-delivers-first-gas-sales-from-the-beetaloo-basin, https://ir.tamboran.com/sec-filings/all-sec-filings/content/0001628280-26-034593/0001628280-26-034593.pdf, and https://ir.concentrix.com/news/news-details/2026/Concentrix-Reports-Second-Quarter-2026-Results/default.aspx.

A frozen note, labeled as note2125, was made about the stock LPG. The note said that if the stock's closing price is higher than its prior 20-day high close and its 50-day Simple Moving Average, or SMA, which is a calculation of the average price of a stock over a certain period, then the next open would be considered. The note also mentioned a stop-loss, which is a price level at which a trader will sell a stock to limit losses, set at 2 times the prior 20-day Average Daily Range, or ADR, and a target set at 3 times the range, with an exit planned after 20 sessions. The data used for this analysis came from the period of September 10, 2021, to 2026, with the years 2021 to 2024 used for training and 2025 onward used as a holdout, or test period, with a flat start. The data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LPG&timeframe=1Day&feed=sip&adjustment=all. The fees for this trade were set at 5 basis points, or 0.05%, with a 20 basis point slip, or additional fee, on each side, and a stress test of 50 basis points. The daily high, low, open, and close prices were used to determine the stop-loss, and the long-only vector backtest, which is a type of backtest that only considers long positions, allowed for same-day round trips. The daily close price was marked, along with entry and exit marks. The trade counts and fee-inclusive manual compounding, which is the process of reinvesting returns to generate additional returns, were asserted. The return on investment during the training period was 48.9822%, with 28 trades, and during the test period, it was 47.4497%, with a maximum drawdown, or loss, of 23.0965%, and a win rate of 53.33%, with 15 trades. The stress test return was 34.7587%. The mean 95% bootstrap confidence interval, which is a statistical measure of the reliability of the results, was between -1.996 and 8.429, and it crosses zero. The current signal is false, with a price of 54.41 and a threshold of 55.2. The sensitivity, or the measure of how much the results change in response to changes in the input, was recorded at 10 and 40, but it was not used for selection. The current universe selection and the already seen history of LPG prevent the claim of untouched out-of-sample alpha, which refers to the ability of a strategy to generate returns that are not explained by the market as a whole. The daily fixed target was not validated using 15-minute intervals and a 2 times trailing return. The returns exactly match the prior returns, but the different marking yields a maximum drawdown of 23.0965% versus the prior 23.3388%, and the prior drawdown should not be overwritten or claimed as improved risk. The Half Kelly, which is a formula used to determine the optimal fraction of a portfolio to invest in a strategy, from training is 0.09000387533670048, and there is no need to input funding needs.

A news scan found the note: Frozen note2125. This note explains a trading strategy for the stock LEU. The strategy checks if the closing price is higher than the prior 20-day closing high and the 50-day Simple Moving Average, which is a calculation of the average price over 50 days. If this condition is met, the strategy will open a position at the next open. The position will be closed after 20 sessions or when the price reaches a target, which is 3 times the Average Daily Range, or when the price stops out, which means it falls below a certain level. The strategy was trained on data from September 10, 2021, to 2026, and tested on data from 2021 to 2024, with a holdout period starting from 2025. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=LEU&timeframe=1Day&feed=sip&adjustment=all. The strategy includes fees of 5 basis points and a slippage of 20 basis points on each side, and a stress test of 50 basis points. The strategy only allows long positions, meaning it only buys the stock, and does not short sell. The strategy also allows for same-day round trips, meaning it can buy and sell the stock on the same day. The strategy uses daily open, high, low, and close prices, and marks the entry and exit points. The strategy's performance is measured by its return, which is 22.4505% during training and 45.8285% during testing, with a maximum drawdown of 48.5873% and a win rate of 41.18%. The strategy's stress test return is 31.6873%. The mean return with a 95% bootstrap confidence interval is between -5.944 and 16.558, which crosses zero. The current signal for the strategy is false, meaning it is not recommending a trade, with a price of 165.87 and a threshold of 194.78. The strategy's sensitivity has been fully recorded, but it is not used for selection. The current universe selection and the history of the stock LPG prevent the strategy from claiming untouched out-of-sample alpha, which refers to returns that are not explained by the strategy's factors. The strategy does not use a daily fixed target or a 15-minute validation with a 2R trailing stop. If the training data is negative or there is no current entry, the strategy will not place an order. The Half Kelly fraction from training is 0.01656102816192606, which is a measure of the optimal fraction of the portfolio to invest, and there is no need for funding input.

A news scan found the note: Frozen note2125. This note explains a trading strategy for the stock CCJ. The strategy is to buy when the close is higher than the prior 20-day high and the 50-day Simple Moving Average, which is a calculation of the average price of a stock over 50 days. The strategy will sell when it reaches a target price, which is 3 times the risk, or after 20 sessions. The data used to train this strategy is from September 10, 2021, to 2026, and it was tested on data from 2021 to 2024, with the remaining data used to validate the results. The strategy has fees of 5 basis points, or 0.05%, and a slippage of 20 basis points, or 0.2%, on each trade. The stress test result is 50 basis points, or 0.5%. The strategy only buys stocks and does not sell short. It uses daily high, low, open, and close prices to stop losses and open new trades. The strategy allows for multiple trades in one day. The training return was 10.3548% over 31 trades, and the test return was 12.4681% with a maximum drawdown of 34.2959% over 14 trades, with a win rate of 50%. The stress test result was 3.4066%. The 95% confidence interval of the mean return is between -4.200 and 7.872, which includes zero. The current signal for the stock CCJ is false, with a price of 97.42 and a threshold of 107.36. The strategy has been fully recorded and was not selected based on its performance. The current universe selection and the history of the stock prevent the strategy from claiming to have found new, unseen returns. The daily fixed target is not validated by 15-minute data or a 2 times risk trailing stop. If the training return is negative or there is no current entry signal, no trade will be made. The optimal fraction of the portfolio to invest, known as the Half Kelly, is 0.04132733473590497, and no funding need is required as input.

A news scan found the note: Frozen note2125. This note explains a trading strategy for the stock VIST. The strategy checks if the stock's close price is higher than its prior 20-day close high and its 50-day Simple Moving Average, which is a calculation of the stock's average price over 50 days. If this condition is met, the strategy will open a position at the next open price. The strategy will exit the position after 20 sessions or when it reaches a target price that is 3 times the Average Daily Range, which is a measure of the stock's daily price movement, with a stop loss at 2 times the Average Daily Range. The strategy was trained on data from 2021 to 2024 and tested on data from 2025 onward, with a flat starting point. The data used can be found at https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=VIST&timeframe=1Day&feed=sip&adjustment=all. The strategy includes fees of 5 basis points and a slippage of 20 basis points on each side, with a stress test of 50 basis points. The strategy uses daily Open, High, Low, and Close prices to stop losses and open gaps, and it only allows long positions, meaning it only buys the stock. The strategy also allows for same-day round trips, meaning it can buy and sell the stock on the same day. The strategy's performance is measured by its return, which is -29.4389% during training and 22.7833% during testing, with a maximum drawdown of 24.7675% and a win rate of 53.85%. The strategy's mean return is 13.5700% under stress, with a 95% bootstrap confidence interval of -3.721 to 8.785, which crosses zero. The current signal for the strategy is true, with a price of 78.95 and a threshold of 77.03. The strategy's sensitivity has been fully recorded, but it is not used for selection. The current universe selection and the history of the stock prevent the strategy from claiming untouched out-of-sample alpha, which refers to the strategy's ability to generate returns that are not explained by the market. The strategy does not use a daily fixed target or 15-minute validation with a 2R trailing stop. If the training data is negative or there is no current entry signal, the strategy will not place an order. The Half Kelly criterion, which is a formula used to determine the optimal bet size, is zero, and there is no need for funding input.

A news scan found the note: the trading rule for QRVO is to buy when the close is higher than the prior 20-day high close and the 50-day simple moving average, and to sell at a target price of 3 times the risk, or after 20 sessions, or at a stop loss of 2 times the average daily range. The system was trained on data from September 10, 2021, to 2026, with a holdout period starting in 2025. The data is from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=QRVO&timeframe=1Day&feed=sip&adjustment=all. The fees are 5 basis points and the slippage is 20 basis points on each side, with a stress test of 50 basis points. The system uses daily open, high, low, and close prices, and allows for same-day round trips. The training return was -17.4599% over 22 trades, and the test return was 14.5341% over 15 trades, with a maximum drawdown of 29.1677% and a win rate of 40.00%. The stress test return was 4.6761%. The 95% bootstrap confidence interval is [-2.784, 5.670], which crosses zero. The current signal is true, with a price of 112.36 and a threshold of 105.24. The sensitivity analysis is fully recorded, but not used for selection. The current universe selection and the history of LPG prevent claims of untouched out-of-sample alpha. The daily fixed target is not validated by 15-minute or 2R trailing stops. If the training is negative or there is no current entry, no order is placed. The Half Kelly fraction from training is 0, and no funding need is input.

A news scan found the note: Frozen note2125. This note is about a trading strategy for the stock SWKS. The strategy is to buy when the close price is higher than the prior 20-day high close and the 50-day Simple Moving Average, which is a calculation to find the average price of a stock over a certain period. The strategy will sell when it reaches a target price, which is 3 times the risk, or after 20 sessions. The data used to train this strategy is from September 10, 2021, to 2026, and the strategy was tested from 2021 to 2024, with the remaining time used to evaluate its performance. The fees for this strategy are 5 basis points, which is a unit of measurement for interest rates and percentages, and 20 basis points for each trade, with a stress test of 50 basis points. The strategy only allows for buying and selling the same stock on the same day, and it uses the daily high, low, open, and close prices to determine when to stop a trade. The strategy's performance was evaluated using a method called backtesting, which means using historical data to see how the strategy would have performed in the past. The results show that the strategy had a return of -33.0777% during the training period, and 1.8628% during the testing period, with a maximum drawdown of 24.2922%, and a win rate of 41.67%. The strategy's performance was also evaluated using a method called bootstrapping, which means resampling the data to estimate the strategy's performance, and the results show that the mean return is between -4.128 and 5.858. The current signal for this strategy is true, with a price of 84.03 and a threshold of 76.54. The strategy's sensitivity was tested and recorded, but not used for selection. The current universe selection and the history of the stock prevent the strategy from claiming to have found a new source of profit. The strategy does not use a daily fixed target or a 15-minute validation, and it does not require any funding input. A headline from the website https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=SWKS&timeframe=1Day&feed=sip&adjustment=all was found, but its content is not provided here.

A descriptive model, not a forecast of a specific event, was used to analyze TBN with 50,000 paths and 130 quarter-hour steps over 5 sessions, starting with a seed of 91102 and assuming zero daily log drift, with a sigma of 0.02346009 divided by the square root of 26. The starting price was 39.33, the stop price was 37.887045, and the target price was 43.658865. The first observed crossing of the stop price was at the path price or target limit, otherwise the final path price was used, with a 30 basis point buffer on each side. The net mean was -0.4890%, the target was 4.352%, the stop was 44.7%, the 5th percentile was -4.791%, and the 95th percentile was 9.083%. This model, a Geometric Brownian Motion, does not account for jumps, weekend news, parameter uncertainty, correlation, or real venue fills, and cannot override a positive but weak event history. The training data used a two-point half Kelly strategy with a win rate of 2.5595% and a loss rate of 2.6799%, resulting in an invested capital fraction of 12.292. However, this is not a percentage and does not permit margin. The assumption that the sample loss captures the tail is indefensible, and the holdout data showed no loss, making the loss severity unknown and the Kelly criterion undefined. The approved allocation is 0 pending prospective evidence and current date window. The whole-share price of 39.33 represents 80.04% of the fund and 4.622% of the account's maximum loss. A 25% fraction probe of 9.83 would lose at most 1.155% of the account but requires regular trading hour fractional exits. No convex long option was selected or premium edge established. The stop loss budget must consider shock, with a 45.86% RARE-like drop on one 39.33 share costing 18.04, or 2.119% of the account, and a 100% loss of 39.33 representing 4.622% of the account. These are severity scenarios, not estimated probabilities. The sources of this data include https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TBN&feed=sip&adjustment=all and runway:query_trades?fund=catalyst, with analyses 2399/2400.

A news scan found the headline about NET911 technical levels. The trend for TBN is positive, but EPM's trend is frozen. CNXC and BB failed to meet their trend. Here are the details: 1. On September 1, TBN's price was $39.33, above its 20-day and 50-day Simple Moving Averages, which are $38.107 and $34.9384, respectively. The Relative Strength Index, a measure of price movement, was 61.18, and the volume was 1.227 times the prior 20-day average, supporting the positive trend. The Average True Range, a measure of price volatility, was 1.62561, versus the Average Daily Range, a measure of daily price movement, of 1.442955, which warns that price gaps are significant. A hypothetical stop-loss order at $37.887045 and a target price of $43.658865 from the latest close is for sensitivity analysis only, not an actual order. The gross risk is 3.669%, and the gross reward is 11.006%, with a net reward-to-risk ratio of 2.4363 and a 30 basis point cost per side. One whole share of TBN at $39.33 fits within a $49.14 capital limit. A complete loss of $4.622% of an $850.99 account is not a small probe. There is no current entry because the conditions are not met, and the edge is uncertain. 2. EPM's after-hours query ended at 01:45 UTC, still showing 13 posts after 20:01 bars, with a high of $3.85 and a low of $3.80, and a last price of $3.8113. The frozen paper prices of $3.73, $3.61876, and $3.89686 remain untouched, with a close of $3.84. The R-value, a measure of correlation, is 0.98885 for the regular session and 0.73085 for after-hours. There is no new print, and the old volume discrepancy remains. 3. CNXC's price is $27, below its 20-day Simple Moving Average of $27.6525. BB's price is $7.54, below its 20-day and 50-day Simple Moving Averages of $8.0955 and $8.968, respectively. Moving averages, RSI, and returns are correlated transforms, not independent confirmations. The conclusion is to preserve the paper position and not move any exits or place new orders. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TBN,EPM,CNXC,BB&feed=sip&adjustment=all.

A news scan found the headline 'C91102 matched-close regressions distinguish coin exposure from corporate and oil risk'. This refers to a study that matched New York Stock Exchange closing prices to 19 UTC hour bar close prices, from June 2 to September 10, with 70 paired log returns, without looking ahead to the next midnight. The study found two ordinary least squares betas: IBIT/BTC had a beta of 1.01137 with an R2 of 0.999005, ETHA/ETH had a beta of 1.01330 with an R2 of 0.998495, MSTR/BTC had a beta of 1.75227 with an R2 of 0.65150, COIN had a beta of 1.40360 with an R2 of 0.64082, WTIB had a beta of 0.92270 with an R2 of 0.35744, and SLE had a beta of -0.09936 with an R2 of 0.00055. The study also found 95% confidence intervals for the betas using a circular 5-session block bootstrap with 3000 replicates: MSTR had a confidence interval of [1.4838, 2.1557], WTIB had a confidence interval of [0.6035, 1.1407], and SLE had a confidence interval of [-0.8312, 0.6872]. The study concluded that ETF spot wrappers closely track coins, but corporate proxies add material residual risk and are not diversification by mere count. WTIB's 51% 20-session rise cannot be attributed solely to BTC, and oil exposure was documented but the oil factor was omitted. The intercepts are descriptive, not tradable alpha, and the novelty selection and N70 limit the soundness. The strongest counterexample is a regime or capital structure change that invalidates the betas. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars, https://runway.adityaperswal.workers.dev/data/crypto-bars, and https://www.uscfinvestments.com/wtib.

The automated trading fund manager analyzed the NET911 TBN event and found uncertainty and factor context. There were eight issuer-dated TBN runups, with six used for training and two for holdout. The baseline mean return was 1.6863%, with a 95% confidence interval of -0.4535% to 3.6226%, and only one loss. The holdout returns were 2.97997% and 2.30030%, with no losses, but this does not guarantee certainty and loss severity cannot be estimated. There were no target hits, with seven time exits and one stop. The historical edge was a modest runup, but did not demonstrate a 3R tail. The latest 60 matched daily log returns of TBN versus SPY had an OLS beta of 0.21021, an intercept of 0.123538%/day, and a correlation of 0.06673, indicating a weak linear relation, not true independence or causal alpha. The current candidate was selected for trend and affordability from 227 leads, but there may be selection and survivorship bias, as well as retrospective date archive bias. The May 13, 2025, PIPE/acreage news may have contaminated the pure earnings attribution, and should not be silently dropped. A countercase new first gas is a positive structural break, making the old sample less relevant. The prior low-confidence edge has been moved to paper watch from a new positive costed history, but is not live, and additional prospective outcomes and intraday/cash evidence are needed. Sources include https://ir.tamboran.com/news-events/ir-calendar/past, https://ir.tamboran.com/news-events/press-releases?page=3, and https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=TBN,SPY&feed=sip&adjustment=all.

A news scan found the headline 'NET911 broad US listing-calendar screen: 227 leads, 10 technical passes, date errors'. The screen started with 13,176 listings from the Nasdaq and other sources, including 7,502 non-ETF listings. Over five weeks, from September 14 to October 16, 227 dates were added to the calendar. The screening process checked every listing in the universe, but did not analyze prices or prove that excluded names lacked events. The process used something called SIP histories, which are records of stock prices, and added six contexts to 233 of these histories. It then filtered the results to include only stocks with at least 51 days of price history, a mean dollar volume of at least $1 million over 20 days, and a closing price above the 20- and 50-day simple moving averages, which are measures of a stock's average price over a certain period. This yielded 10 stocks: INTG, NEOV, HITI, TBN, EPM, ATLO, NEOG, CWBC, TLX, and HCWC. The top-returning stocks were INTG with a 9.96% return, NEOV with a 9.88% return, and HITI with a 9.87% return. However, there were date errors: CNXC's issuer confirmed a September 29 event, not September 24; TBN's corporate calendar showed a September 25 event, not September 24; and NEOV's issuer search showed a September 23 event, not October 5. INTG's date was not recovered, and ATLO, NEOG, CWBC, and HCWC remain estimates with no entry. HITI has an imminent event on September 14, and EPM has a -6.55% return. TLX has early FDA risk. CNXC is below its 20-day simple moving average, and ABVX is above its whole-share cap but in a downtrend. The conclusion is to start a new event study for TBN and recover NEOV's date, but not to place any orders based on the screen. The validity of the results is limited to the calendar coverage and current survivors, and third-party calendar errors can change the results. The sources used include Nasdaq's listed and other listed stocks, an earnings calendar, and various company websites.

The R91102 outer-ring bottlenecks are affected by several factors. Firstly, Prysmian's July 30 report shows a 21.2% margin, compared to 17.1%, and a digital margin of 23.8%, compared to 16.8%. The company's guidance for the future is between 1650-1750 million. Secondly, Cameco's July 31 report states that the company purchased 2.8 million pounds and delivered 7.1 million pounds of uranium, with transport and mine interruptions complicating the process. Thirdly, Micron's FY25 report reveals that over half of the company's revenue comes from its top 10 customers, with limited or sole sources for some materials and equipment, as well as export restrictions and data center concentration. The current scarcity of high-bandwidth memory can transfer part of the rent upstream. Fourthly, Hynix's July 29 report shows revenue of 79.3187 trillion KRW, operating profit of 60.5426 trillion KRW, and net cash of 69.4 trillion KRW. The company's long-term agreements with around 10 customers protect volume but not infinite pricing. The facts come from financially interested sources, with no independent proof of rent timing. In conclusion, it is necessary to keep watching Prysmian and Vista's cash bridge, nuclear contract test, and memory upstream risk, and to not enter foreign equity due to insufficient information. A strong counterargument is that delivered margins can compound despite known headlines, so the rally should not be labeled as fully priced without valuation. There is uncertainty regarding the current multiple, normalized cash, and forward contracts.

This report is based on premises that include evidence for momentum from a 2014 study by AQR, which is a type of investment management firm. The study, found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf, is about fact and fiction in momentum investing. The report also references a frozen note from 2024 about nine original sector ETFs, which are a type of investment fund. The note explains that the prior-month-end close price divided by the close price 189 days earlier, minus 1, is used to rank the top 3 sectors. The next session's open price is then used, with three fixed independent cash sleeves and no leverage, which means borrowing money to invest. The report also mentions a drop-rank monthly or 63-session exit, which is a strategy for selling investments, and a 2ATR stop and 6ATR target, where ATR stands for Average True Range, a measure of price volatility. Additionally, a 2R daily high makes the break-even point effective the following day. A gap open is used as an actual proxy, with the same bar stop first. The report also references data from https://runway.adityaperswal.workers.dev/data/stock-bars, which was requested from 2009 but only received from 2016 onward. The warm-up period means the first 252 rule trades from 2017, with no pre-2016 data. The frozen 2019 split was retained and not moved to improve results. The vectorbt long-only cash-sharing strategy is used, with every daily equity and trade count reconciled. A fee of 1 basis point each side, plus a 15 basis point slip, is applied. Corporate action total return is not independently rebuilt, and no taxes, cash yield, or operator costs are included. The results show 190 trades, with a mean return of 0.625286%, and an entry month cluster of 3000 bootstrap CI, which is a measure of confidence interval, of [-0.437693, 1.761003]%. The p-value is 35.2632%, the W-value is 8.1038%, and the L-value is 3.4484%. The plug-in half Kelly risk is 0.038580. The primary 252 training was negative, but the positive 189 neighbor cannot replace the pre-registered rule. The historical holdout may overlap prior research, and the daily break-even proxy is not 15-minute live proof. The report rejects live adoption and retains the positive holdout for prospective research. The artifacts are found in research/test_sector.py, sector_results.json, and percelltrades/returns.

This report is based on two premises. The first premise is that a 2014 study by AQR, which ranked stocks by momentum over a 12-month period, skipping the most recent month, provides evidence for momentum investing, but this specific strategy does not follow that approach. The study can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The second premise, noted in Frozen note 2124, involves nine original sector ETFs, where the momentum is calculated as the difference between the closing price 21 days ago and 189 days ago, and the top three positive sectors are selected. The next session's open is used, with three fixed independent cash sleeves and no leverage. The strategy exits positions monthly or after 63 sessions, using a stop-loss of 6 ATR and a target of 2 ATR. If the daily high is 2R above the entry price, the stop-loss becomes effective the following day. A gap open is used as a proxy for the actual open, with the same bar stop as the first stop. The data used was requested from 2009, but only data from 2016 onwards was received. The warm-up period means that the first 252 trades in 2017 were used to initialize the strategy, with no data used before 2016. The 2019 split was retained and not moved to improve results. The strategy uses vectorbt for long-only cash sharing, with every daily equity and trade count reconciled. The fees are 1 basis point for each side, plus a 15 basis point slip. Corporate actions are included in the total return, but not independently rebuilt, and no taxes, cash yield, or operator costs are included. The strategy made 55 trades, with a mean return of 0.497706%, and an entry month cluster of 3000 bootstrap CI of [-1.106902, 2.078437]%. The plug-in half Kelly risk is 0.042640. The primary 252 training period was negative, but the positive 189 neighbor cannot replace the preregistered rule. The historical holdout may overlap prior research, and the daily BE proxy is not a 15-minute live proof. The strategy is rejected for live adoption, but the positive holdout is retained for prospective research. The research artifacts can be found in research/test_sector.py, sector_results.json, and percelltrades/returns.

This report is based on premises that include evidence for momentum from a 2014 AQR study, which is a research paper on fact and fiction in momentum investing. The study can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The report also references a frozen note from 2024, which details a strategy using nine original sector ETFs, where the prior-month-end close is compared to the close 189 days prior, and the top three positive sectors are selected. The next session's open is used, with three fixed independent cash sleeves and no leverage. The strategy includes a monthly or 63-session exit, with a stop loss set at 6 ATR, which stands for Average True Range, a measure of volatility. The strategy also includes a target at 6 ATR and a daily high that makes the break-even point effective the following day. A gap open is used as an actual proxy, with the same bar stop as the first stop. The report uses data from 2016 onward, with a warm-up period that means the first 252 trades in 2017 are used, and there is no pre-2016 data. The frozen 2019 split is retained and not moved to improve results. The vectorbt, which is a backtesting library, is used for long-only cash sharing, with every daily equity and trade count reconciled. The fee is 1 basis point, or 0.01%, each side, plus a 5 basis point slip, which is a small cost. The corporate action total return is not independently rebuilt, and there are no taxes, cash yields, or operator costs. The results show 188 trades, with a mean return of 0.890330%, and an entry month cluster of 3000 bootstrap confidence intervals of [-0.188323, 2.061320]%. The p-value is 36.1702%, the W-value is 8.3338%, and the L-value is 3.3276%. The plug-in half Kelly risk is 0.053417, which is a measure of risk. The primary 252 training is negative, and the positive 189 neighbor cannot replace the pre-registered rule. The historical holdout may overlap prior research, and the daily break-even proxy is not a 15-minute live proof. The report rejects live adoption and retains the positive holdout for prospective research. The artifacts of the research can be found in the files research/test_sector.py, sector_results.json, and percelltrades/returns.