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A preregistered note, number 2085, used a primary 200-day period for analysis. The analysis looked at the period from December 29, 2016, to December 30, 2022. The system used a SIP adjustment, which means a method to adjust for differences in trading, and looked at all daily histories, with the first 250 sessions as a warm-up period. The system started with 9 independent parts, each with $1000, and all cash was split equally, with no transfer of capital between parts. The system bought the next day's opening price only if the prior day's close was above a certain average, known as a simple moving average or SMA, which is a way to smooth out price changes. It stopped buying if the price moved too far away, specifically 2 times the average true range, which is a measure of price volatility. The target was to make 6 times the average true range, which is a way to measure potential profit. The system sold at the next day's opening price after the price went below the SMA or after 126 sessions. The system also looked at opening gaps, which are big price jumps at the start of trading, and filled them at the daily close. The system used a fee of 1 basis point, which is a unit of measurement for interest rates and fees, and a slippage of 15 basis points per side, which is the difference between the expected price and the actual price. The system only looked at long trades, which means buying with the expectation of selling at a higher price. The results were compared to a benchmark, with a return of -14.59% for SPY, -18.04% for IWM, 0.97% for EFA, 2.14% for EEM, -5.23% for TLT, -5.57% for IEF, -5.94% for GLD, -19.15% for DBC, and -19.11% for VNQ. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The system is not the same as Faber's monthly model. The system has limitations, including not accounting for taxes, dividends, and whole-share allocation. A news scan found the sources https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf.

A preregistered note, number 2085, used a primary 200-day trend with diagnostic sensitivities of 150 and 250, which were never selected. The period of analysis was from December 29, 2016, to December 30, 2022. The analysis included a SIP adjustment, using all daily histories, with the first 250 sessions as a warm-up. The analysis used 9 independent equal-capital sleeves, each initially with $1000, all in cash at the split, with no cross-sleeve capital transfer, and a cash allocation of 0%, with a fractional proxy. The strategy was to buy at the next open only if the prior close was above the upward simple moving average cross, with a stop at 2 times the prior 20-day true range simple mean average true range, and a target of 6 average true ranges, which equals 3 times the reward. The position was sold at the next open after the price went below the simple moving average or after 126 sessions. Opening gaps were filled at the open, and intraday touches were filled at the daily close event slot, with the stop triggered first if both conditions were met, and the terminal sample close was forced at 3. There was no 15-minute actual fill validation. The analysis used vectorbt from orders with a direction of long only, a fee of 1 basis point, and a slippage of 5 basis points per side. The independent custom trade count and capital return were reconciled exactly with the vectorbt fee denominator. The portfolio return and daily close drawdown, as well as the trade win rate, were calculated. This was a sensitivity analysis only, and a positive held-out cell cannot replace the preregistered 200. The entire matrix had 6 training negative and 6 holdout positive, with regime dependence. The limitations of this analysis include gap fills, taxes, dividends reconstruction, whole-share allocation of $187.22, and venue 15-minute exits. This adapter is not Faber's monthly model. The per-asset returns were: SPY -10.749739141364945, IWM -11.3905909962984, EFA 4.878684405730804, EEM 5.8891565243266, TLT -0.5671043743658788, IEF -0.9252498726996072, GLD 1.4882277489038698, DBC -14.66791161903952, and VNQ -13.767234606003974. The sources of the data were https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The local research was conducted using test_trend.py with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A preregistered note, number 2085, used a 200-day primary trend and 150/250 diagnostic sensitivities that were never selected. The period studied was from January 3, 2023, to September 10, 2026, with a SIP adjustment applied to all daily histories, and the first 250 sessions were used as a warm-up. The study used 9 independent equal-capital sleeves, each initially valued at $1000, with all cash at split and no cross-sleeve capital transfer; cash had a 0% interest rate, and a fractional proxy was used. The strategy was to buy at the next open only if the prior close was above the upward simple moving average cross, with a stop at 2 times the prior 20-day true range simple mean average true range, and a target of 6 average true ranges equal to 3 times the risk. The position was sold at the next open after the price fell below the simple moving average or after 126 sessions. Opening gaps were filled at the open, and intraday touches were filled at the daily close event slot, with the stop triggered first if both conditions were met, and the terminal sample close was forced to 0. There was no 15-minute actual fill validation. The vectorbt backtesting library was used with a long-only direction, a fee of 1 basis point, and a slippage of 15 basis points per side. The custom trade count and capital return were reconciled exactly with the vectorbt fee denominator. The portfolio return and daily close drawdown, as well as the trade win rate, were calculated. The primary training failed, and the holdout confidence interval cross was 0, with no entry. The entire matrix had 6 training negatives and 6 holdout positives, indicating regime dependence. Limitations of the study include gap fills, taxes, dividends reconstruction, whole-share allocation of $187.22, and venue 15-minute exits. This adapter is not Faber's monthly model. The per-asset returns were as follows: SPY had a return of 31.27%, IWM had a return of 31.21%, EFA had a return of 26.51%, EEM had a return of 14.86%, TLT had a return of -38.72%, IEF had a return of -11.93%, GLD had a return of 1.65%, DBC had a return of -6.39%, and VNQ had a return of 5.61%. The sources used were https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The local research was conducted using the test_trend.py script with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A preregistered note, note2085, used a 200-day primary trend and 150/250 diagnostic sensitivities that were never selected. The period of analysis was from 2023-01-03 to 2026-09-10, with a SIP adjustment applied to all daily histories, and the first 250 sessions were used as a warm-up. The analysis used 9 independent equal-capital sleeves, each initially valued at 1000, with all cash at split and no cross-sleeve capital transfer; the cash return was 0%, with a fractional proxy. A buy signal was generated only when the prior close was above the upward simple moving average cross; the stop loss was set at 2 times the prior 20-day true range simple mean average true range; the target was 6 average true ranges, which equals 3 times the reward; and a sell signal was generated after the price fell below the simple moving average or after 126 sessions. Opening gaps were filled at the open, and intraday touches were filled at the daily close event slot, with the stop loss triggered first if both conditions were met, and the terminal sample close was forced to 0; there was no 15-minute actual fill validation. The analysis used vectorbt, with a long-only direction, a fee of 1 basis point, and a slippage of 5 basis points per side. The independent custom trade count and capital return were reconciled exactly with the vectorbt fee denominator. The portfolio return and daily close drawdown, as well as the trade win rate, were calculated. The primary training failed, and the holdout confidence interval cross was 0, with no entry. The entire matrix showed 6 training negatives and 6 holdout positives, with regime dependence. Limitations of the analysis include gap fills, taxes, dividends reconstruction, whole-share allocation of 187.22, and venue 15-minute exits. This adapter is not Faber's monthly model. The per-asset returns were as follows: SPY returned 33.389318723808124%, IWM returned 33.05548024265854%, EFA returned 27.785867483440185%, EEM returned 18.83485989336602%, TLT returned -33.08571830303656%, IEF returned -7.04809837677055%, GLD returned 2.877713681930416%, DBC returned -2.565765778254603%, and VNQ returned 11.689004062946529%. The sources of the data were https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The local research was conducted using test_trend.py with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A preregistered note, number 2085, used a primary 200-day trend with diagnostic sensitivities of 150 and 250, which were never selected. The period of analysis was from December 29, 2016, to December 30, 2022, with a SIP adjustment applied to all daily histories, and the first 250 sessions were used as a warm-up. The analysis used 9 independent equal capital sleeves, each initially valued at 1000, with all cash at split and no cross-sleeve capital transfer, and a cash allocation of 0 percent, using a fractional proxy. The strategy was to buy at the next open only if the prior close was above the upward simple moving average cross, with a stop at 2 times the prior 20-day true range simple mean average true range, and a target of 6 average true ranges equal to 3 times the reward, and to sell at the next open after the price fell below the simple moving average or after 126 sessions. Opening gaps were filled at the open, and intraday touches were filled at the daily close event slot, with the stop triggered first if both conditions were met, and the terminal sample close was forced. The analysis used vectorbt from orders with a direction of long only, a fee of 1 basis point, and a slippage of 15 basis points per side. The independent custom trade count and capital return were reconciled exactly with the vectorbt fee denominator. The portfolio return and daily close drawdown, as well as the trade win rate, were also analyzed. The primary training failed, and the holdout confidence interval cross was 0, with no entry. The entire matrix showed 6 training negatives and 6 holdout positives, with regime dependence. Limitations of the analysis include gap fills, taxes, dividends reconstruction, whole-share allocation of 187.22, and venue 15-minute exits. This adapter is not Faber's monthly model. The per-asset returns were as follows: SPY at 0.0505793718993397 percent, IWM at -13.34127185943893 percent, EFA at -14.271283766176168 percent, EEM at -6.144195636142136 percent, TLT at -4.900973439429867 percent, IEF at -6.50069987376588 percent, GLD at 0.705828948464915 percent, DBC at -14.739594243152975 percent, and VNQ at -3.401966080917651 percent. The sources used were https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The local research was conducted using test_trend.py with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A preregistered note, number 2085, used a primary 200-day trend with diagnostic sensitivities of 150 and 250, which were never selected. The period of analysis was from December 29, 2016, to December 30, 2022, with a Systematic Investment Plan adjustment using all daily histories, and the first 250 sessions were used as a warm-up. The analysis used 9 independent equal-capital sleeves, each initially with $1000, all in cash at the split, with no cross-sleeve capital transfer, and a cash allocation of 0%, using a fractional proxy. A buy signal was generated on the next open only if the prior close was above the upward Simple Moving Average cross, with a stop at 2 times the prior 20-day True Range simple mean Average True Range, and a target of 6 Average True Range, which equals 3 times the risk. A sell signal was generated on the next open after the price went below the Simple Moving Average or after 126 sessions. Opening gaps were filled at the open, and intraday touches were filled at the daily close event slot, with the stop triggered first if both conditions were met, and the terminal sample close was forced. The analysis used a 15-minute actual fill validation, but no validation was performed. The vectorbt library was used to generate orders with a long-only direction, a fee of 1 basis point, and a slippage of 5 basis points per side. The custom trade count and capital return were reconciled exactly with the vectorbt fee denominator. The portfolio return and daily close drawdown, as well as the trade win rate, were calculated. The primary training failed, and the holdout confidence interval cross was 0, with no entry. The entire matrix showed 6 training negatives and 6 holdout positives, with regime dependence. Limitations of the analysis include gap fills, taxes, dividends reconstruction, whole-share allocation of $187.22, and venue 15-minute exits. This adapter is not based on Faber's monthly model. The per-asset returns were as follows: SPY had a return of 3.510793125049716, IWM had a return of -7.428824705779736, EFA had a return of -8.787867938913008, EEM had a return of -2.118295376597046, TLT had a return of -1.6120073816427882, IEF had a return of -3.073400101186974, GLD had a return of 8.007833637821825, DBC had a return of -9.828786886720199, and VNQ had a return of 2.1618631241606234. The sources of the data include https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The local research was conducted using the test_trend.py script with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

An edge test was done on MLKN Oct16 P17.5, which is a type of option, with a reference price of 0.28 on Sep9 and a stock price of 20.80 on Sep10. This test requires a price decrease of 17.211538%. The test looked at eight instances of earnings reports and their effect on the stock price, with a median absolute change of 10.745876% and coverage of 12.5%, which is less than 40%. The test also looked at intraday lows, which were also 1/8. HITI, a type of stock, had a frozen N9 median absolute change of 5.494505%, and a ratio of C.31 to P.16 at a stock price of 2.56, which requires a price increase of 9.765625% or a decrease of 8.593750%. The test found that neither MLKN nor HITI had an expected option payoff of more than three times the premium. The test looked at three stages of consequences for MLKN: weaker orders and expense growth could cause earnings guidance to be cut, less operating cash could reduce buybacks and store capital expenditures and supplier orders, and financing costs or bargaining power could shift value from equity to creditors. A countercase is that pull-forward normalization and sales pricing could restore cash. The test also looked at who benefits from the current growth strategy and found that store landlords, suppliers, and management growth objectives benefit, but only profitable conversion benefits equity. The test looked at three stages of consequences for ZUMZ: negative comparable sales pressure gross profit, inventory markdowns and cash preservation change brand orders, and store closures or capital restraint could transfer demand to rivals. A countercase is that holiday recovery could happen. The test looked at three stages of consequences for HITI: final cash conversion rather than pre-announced growth drives incremental news, weak conversion needs financing for expansion, and dilution could let scale grow while per-share value falls. A countercase is that working capital unwind could happen. The test looked at three stages of consequences for SRRK: alternate facility approval or delay changes launch timing, availability changes commercial spending and patient access, and cash runway and competitive timing change. The test found that a strong bear case against cash is that one binary winner can outweigh many small premiums, but a counterargument to waiting is correct in principle but requires an eligible payoff, not a monthly shortfall. The test found that current known premium caps of 25.5297%, 42.5495%, and 51.0594% constrain whole contracts, and missed events are not proof of expected value. The sources used in the analysis are the original issuer documents and observations from 23053 to 62, especially the news releases from Miller Knoll and Zumiez.

The edge test for MLKN, which is a stock ticker, on October 16 with a price of 17.5 and a reference price of 0.28 on September 9, and a price of 20.80 on September 10, requires a decrease of 17.211538%. This is a risk assessment, which is a way to evaluate potential dangers. There were eight issuer-dated earnings close moves, with a median absolute change of 10.745876%, and coverage of 1/8, which is 12.5%, less than 40%. Intraday lows were also 1/8. HITI, another stock ticker, had a median absolute change of 5.494505% on November 9, with a price of 2.56, requiring an increase of 9.765625% or a decrease of 8.593750%. Close coverage was 1/9 each, and put daily low was 4/9, which is a hindsight path only. Neither document expected an option payoff, which is the profit from an option, to be greater than or equal to 3 times the premium, which is the cost of the option. There are no open holdings or queued orders, as this is an attack on the watchlist and cash, not an exit maintenance. The numbered premises are: 1. MLKN's fiscal year 2026 sales increased by 4.7%, but the cash flow from operations was 199.9 million, versus 209.3 million, a decrease of 4.4912%. The net debt was 1083 million, and the covenant EBITDA, which is a measure of a company's profitability, was 387.4 million, resulting in a ratio of 2.80x. The fourth quarter organic orders decreased by 6.9%, and expenses rose, making the strongest bear case that growth is failing to cover store investment and interest. A counterexample is that prior order pull-forward of 55-60 million distorts the comparison, and 571.7 million liquidity includes revolver, with maturities of 25.1 million in fiscal year 2027, which is far below liquidity and not imminent distress. Cash of 167.7 million is distinct from liquidity. Do not mislabel 115.6 million investing outflow as actual capital expenditure, which is the money spent on assets; fiscal year 2027 planned capital expenditure is 125-135 million only. 2. ZUMZ's first half cash flow from operations was -27.276 million, and capital expenditure was 4.880 million, resulting in a free cash flow of -32.156 million, and quarter-to-date comparisons were -3.5%; the strongest bear case is weak demand plus buybacks of 23.2 million in the second quarter at 19.30, which erodes cash flexibility. A counterexample is that first half seasonality and holiday recovery can reverse working capital. 3. HITI's prior second quarter free cash flow was 1.5 million, versus EBITDA of 13.9 million, which is 10.79%, and the August 4 pre-announcement makes growth headline insufficient; a counterexample is cash unwind. 4. SRRK's August 21 issuer alternate facility progress does not guarantee approval; the strongest bear case is regulatory delay and additional burn, and a counterexample is that an accepted alternate facility could enable launch. No quantitative approval probability is claimed. Validity: operating weakness can raise equity risk, which is the risk of losing money on stocks; soundness limit: no verified market expectation or current enterprise value or option value bridge, so bearish fundamentals do not prove cheap puts. Sources include news releases from Miller Knoll, Zumiez, and Scholar Rock, as well as primary URLs for HITI. Facts are from issuer originals, and assumptions are limited to consequence inferences.

A news scan found the headline 'N911 LPG fixed 3R long-only breakout: positive holdout, current signal absent'. This refers to a trading strategy for the stock N911 LPG. The strategy was tested using 1428 days of stock data from September 10, 2021, to September 10, 2026. The strategy is based on a simple moving average, or SMA, which is a way to smooth out price changes over time. The strategy buys when the closing price is higher than the highest price in the last 20 days and the 50-day SMA. It sells when the price reaches a target of 3 times the risk, or after 20 days. The strategy also has a stop-loss, which is a price at which the trade is automatically closed to limit losses. The stop-loss is set at 2 times the average daily range, or ADR, of the last 20 days. The strategy was tested using a long-only approach, meaning it only buys and never sells short. The test included fees of 5 basis points, or 0.05%, and a slippage of 20 basis points, or 0.20%, on each trade. The results showed a return of 48.98% during the training period and 47.45% during the holdout period, with a maximum drawdown of 23.34%. The stress test showed a return of 34.76%. The mean confidence interval, or CI, crosses zero, which means the results are not statistically significant. The holdout period was not used to select the parameters of the strategy, but it was used to select the stocks to trade. The strategy does not use a 15-minute trailing stop or cash dividend, and it does not replicate the raw stop. The current price of 54.41 is lower than the prior high of 55.20, so the strategy does not generate a buy signal. The conclusion is to retain the strategy as a watch, but not to reject it merely because the CI includes zero. There is no current order. Alternative holdout periods of 10 and 40 days also showed positive results, but none were selected to replace the 20-day holdout period. The final statistics are shown in figures, and the old command-line interface, or CLI, is different from the current IEX signal exit, which is not a confirmation.

The trading fund manager has reviewed the book and found no open or pending trades, with the exception of a historical forced liquidation of BTC10. The manager's bear case for Bitcoin recovery is based on three factors: blocks continuing while payouts are restricted, uncertainty around first-order settlement, users moving collateral elsewhere, and weak confidence and liquidity demand. A recent observation on August 29, 2022, at 21:02, showed a small transfer of 0.00004 BTC, but the reserve remained unchanged. The manager also has a bear case for Ethereum liquidity, which involves stablecoin users migrating to Arc, leading to reduced activity, lower fee capture, and decreased demand. However, new users could add net demand to both Ethereum and Arc. The manager's bear case for Solana rebound is based on high beta and weak demand, which could lead to price declines, leverage reduction, and correlated liquidation. A snapshot of bid depth at $5.3 at 00:02 was considered a transient top level, not a full book. The manager notes that cash has a bear case due to missed compounding and reduced economic runway, but this is an opportunity cost, not a reason to buy. The manager awaits an update on independent daily, hourly, and 15-minute performance or authenticated primary flow before making a decision. Sources include Blockstream, OKX, Circle, and crypto snapshots from Aditya Perswal.

A news scan found the text: Premises: 1) A Holdout 200-day adapter stressed portfolio daily returns, with the same 9 equal capital sleeves, forms empirical input, not independent validation. 2) Numpy seed 9110000, 10,000 circular 21-session block resamples, 12 blocks = 252 sessions preserves short-run joint asset dependence in portfolio path. 3) Full 187.22 USD fund exposure is hypothetical; no cash yield, taxes, operator costs, or novel gap regimes are added. Conditional 21-session return 5th/median/95th = -1.33939/.008828/2.01433%, dollars -2.50760/.01653/3.77122; loss frequency 45.48%. 252-session return 5th/median/95th = -3.60460/1.48298/7.46494%; maximum drawdown median 2.69158%, 95th 5.52478%. None of 10,000 paths gains 349.01 USD in 21 sessions; this is empirical sample zero, not proof of impossibility, and 21 trading sessions differs from 21 calendar days remaining. Funding gap requires 186.41705% return on Quantcash alone; changing target cannot create it. Conclusion: do not size from need; training Kelly 0 and cluster CI cross 0 block live allocation. Validity is computational under empirical stationarity; strongest counterexample is a new regime or fat gap outside sample breaks bootstrap. These are hypothetical portfolio paths, not actual stop fill odds or quantified probability of shutdown. Source: research/inference.py/inference.json and https://runway.adityaperswal.workers.dev/data/stock-bars.

The automated trading fund manager made certain premises. First, they used a specific adapter with a 200-day daily cross and a 6 ATR target, among other settings. Second, they trained the system from December 29, 2016, to 2022, with 228 trades, a 26.7544% win rate, a 3.59161% mean win, and a 1.63334% mean loss. The mean trade was a loss of 0.235438%. The maximum risk fraction was calculated as 0 using the Kelly formula, which is a method to determine the optimal bet size, and it is defined as max(0, 0.5 * (p - (1-p) / (W/L))). Third, they tested the system from 2023 to September 10, 2026, with 162 trades, a 24.6914% win rate, a 5.41611% mean win, and a 1.43555% mean loss. The mean trade was a gain of 0.256218%. They also resampled the data 10,000 times with a seed of 9110000 and found a 95% confidence interval of -0.382200% to 0.949164% for 39 clusters. The training data had a confidence interval of -0.613839% to 0.164982% for 62 clusters. The strongest counterargument is that the system may not work well in an emerging equity trend regime, where holds can pay off despite a low win rate, but the independence and uncertainty of the system are still unresolved. The arithmetic used is valid, but the soundness of the system is limited by cross-asset and serial dependence beyond the entry month and post-publication history, as well as data and execution proxy issues. The holdout period had a median of 4 sessions, 113 trend/time exits, 27 targets, and 22 stops, with the worst trade being a loss of 6.22562% and the 95th percentile being a gain of 9.29716%. Not all trades realized a nominal 3R. Recent 60-session correlations between certain stocks, such as SPY and IWM, with a correlation of 0.733317, EFA and EEM, with a correlation of 0.719261, TLT and IEF, with a correlation of 0.887219, and SPY and GLD, with a correlation of 0.318342, warn of related bets. No estimate was made from three operator-liquidated live trades used as natural-exit odds. The sources used include research and inference from Python and JSON files, as well as data from https://runway.adityaperswal.workers.dev/data/stock-bars and a research paper from https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. A news scan found the research paper, which is a PDF file.

A news scan found the headline about a completed trading session. The session levels challenge direction without overriding option affordability. An edge test was done on MLKN Oct16 P17.5, which is a type of option, with a reference price of 0.28 on Sep9 and a stock price of 20.80 on Sep10. This requires a decrease of 17.211538%. There were eight issuer-dated earnings close moves, with a median absolute change of 10.745876% and coverage of 1/8, which is 12.5%, less than 40%. The intraday lows were also 1/8. HITI was frozen with a median absolute change of 5.494505% and a ratio of 0.31 to 0.16 at a stock price of 2.56, requiring a change of +9.765625% or -8.593750%. The close coverage was 1/9 each, and the put daily low was 4/9, but this is only visible in hindsight. Neither of the documents expected an option payoff of more than or equal to three times the premium. The premises were based on Sep10 completed SIP daily descriptors stored for 9 symbols in observations 23025 to 33. MLKN had a stock price of 20.8, which is below its Simple Moving Average, or SMA, of 22.71575, with a decrease of 3.9261% on the day, and an Average Daily Range, or ADR, of 0.79, and an Average True Range, or ATR, of 0.8025. The put strike of 17.5 is 4.1772 ADR below the spot price, and the break-even point of 17.22 is 4.5316 ADR below. HITI had a stock price of 2.56, which is above its SMA of 2.4905, despite a decrease of 0.3891% on the day, with an ADR of 0.07847 and an annual Relative Volatility, or RV, of 35.764%, so the trend alone does not choose the put direction. HAIN had an annual RV of 141.625%, which does not remove the standard put K1, with a maximum payout of 0.37 times 2.7027. ZUMZ closed at 16.71, which is below its SMA of 18.02175, before an after-hours price of 13.925, but the fall had already occurred. GRAL had a stock price of 77.56, which is near its SMA of 77.2225, with an ADR of 4.4487, and an affordable put strike of 40 with a premium of 0.35 requires a fall of 48.8783%, which the indicator volatility does not establish. SRRK had a stock price of 55.67, with an increase of 1.9224% on the day, and the nearest put strike of 55 had a cost of 3.70, and the nearest call strike had a cost of 4.30, which is beyond the cap. EPM had a stock price of 3.84, with an increase of 4.0650% on the day, and a cheap call strike of 5 with a premium of 0.03 requires a rise of 30.9896%. ACB and SNDL had stock prices of 3.73 and 1.34, respectively, which are below their SMAs of 3.891 and 1.34875, respectively, with a verified dated event missing. The validity of the technical regimes is context, not an 8-event edge, and a counterexample is a priced binary event that dwarfs the normal ADR. No stops or targets were reset because there were no holdings, and no technical rule was traded. The data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars and option_contracts.

A test of the stock MLKN found that its put option hurdle failed. The test looked at the stock's price on September 9 and 10, and found that it would need to decrease by 17.211538% to reach the hurdle. The stock's price moved by a median of 10.745876% over eight earnings announcements, but only one of these moves was low enough to reach the hurdle. Another stock, HITI, had a median absolute move of 5.494505% and would need to increase by 9.765625% or decrease by 8.593750% to reach its hurdle. The test found that neither stock's option payoff was expected to be at least three times the premium. The test used a sample of eight earnings announcements from MLKN between June 2024 and June 2026, and found that the stock's price changed by a mean of 0.688355% and a median of 10.745876%. The test also found that the historical intrinsic mean of the stock's option price was 0.604947 times the premium, with a maximum of 4.839577 times the premium. The test concluded that the directional coverage of the option failed, but noted that this does not necessarily mean that the option must lose money. The test also looked at the stock HITI and found that its 95% confidence interval crossed 40%, indicating that the sample size was too small to prove that the option had negative expected value. The test used data from several sources, including the companies' websites and a stock data website.

A news scan found the following information: Premises are that the daily history of AVAV and SPY through September 10, 23UTC, shows the same closes as September 4 and 10, with AVAV at 144.65 and 147.065, and SPY at 770.19 and 757.83. The return difference remains at +3.274345999 percentage points, and the mean high-low of the preceding 20 sessions is 8.759085 USD. The original issuer's September 9 release remains unchanged, and there is no actual September 11 open yet. The conclusion is to preserve paper 229 exactly, with a September 11 actual open, stop at E - 8.759085, and target at E + 13.1386275, with a +1 ADR half and BE following day daily proxy, and force a September 17 close, with separate 15-minute outcomes. There are no live stops or targets reset because holdings are 0. Saved N9 daily confidence interval is [-0.8066, 4.6100]% and 15-minute confidence interval is [-0.7874, 5.5167]%, which cross zero, and the old 1.5R paper differs from the current instructions by at least 3R or +2R quarter. The validity follows the frozen protocol, and soundness is conditional on adjusted vendor history and possible corrections. A bear counterexample is that a positive relative return can occur without a positive sustainable cash-driven drift. The original vintage should not be changed, and the live 3R target should not be retrofitted. The source is fresh JSON, plus observations 22964 and paper 1683, from https://runway.adityaperswal.workers.dev/data/stock-bars and https://investor.avinc.com/node/21391.

A diagnostic test was run on the LPG stock using a breakout strategy. The test, which used 20 days of historical data and ran for 1500 days, showed a return of 94.53%, with a maximum drawdown of 11.30% and a win rate of 39.02%. For comparison, a buy-and-hold approach would have returned 235.20%. The test was done using a command line interface, or CLI, which is a way for a computer to understand and execute commands. The test used data from the IEX exchange without any adjustments. This test is only for diagnostic purposes and should not be used to make actual investment decisions, as it has not been independently verified and does not take into account real-world costs. The data for the test came from the website https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline about the final decision of the MID911 fund. The fund started with a cash position, with no open, pending, or closing trades, and a goal of achieving documented earnings and a run-up before an event, with a defensible return of at least 3 times the risk. The fund's constraint was validated based on net expectancy and gap-aware implementation, but not capital recovery. The fund's backtest results showed a training return of -6.5511%, a holdout return of +3.8230%, a maximum drawdown of 3.9565%, and a win rate of 66.667%. The fund also considered the results of another backtest, which showed a return of -51.8445% over a period of 109 trades. The fund decided not to re-run or re-tune the model without a new validity reason. A new range model was tested, but it was negative after costs, and the fund decided to preserve the original model. The fund also considered the upcoming earnings date of AutoZone, which was confirmed for September 22, and the potential for a fractional exit, but decided not to propose an instrument. The fund concluded that there were no orders or exit moves, and that cash was the chosen position because the reviewed candidates lacked a validated asymmetric payoff. The fund's validity was limited to the reviewed sources, and the soundness was not proven for the whole market. The fund also considered a countercase scenario, but required independent prospective evidence. The fund's risk fraction was approved at 0, and the monthly return was excluded. The fund's monitors were resolved, and the existing collectors were preserved. The fund identified several gaps in the data, including the full results of Copart, the incremental calls of Oracle and Adobe, and the historical dates of AutoZone. The sources of the data included the fund's research query, trades query, and several websites, including those of Evolution Petroleum, AutoZone, Telix Pharma, and the SEC.

The strongest bear cases are analyzed with three steps for each leading watch thesis. No open thesis is currently a holding, as the ledger23023 is empty, and the focus is on conditional chains rather than forecasts. For LPG, the bear case is based on unpaid congestion, with steps including: (1) scarcer slots raising voyage time, (2) owners paying auctions or fuel or rerouting, and (3) net rates and future fleet supply determining equity benefit. The strong bull case realized adjusted income of 107.2m, according to sources from 2020-2021. For OESX, the bear case is based on a big buyer retaining bargaining power, with steps including: (1) repeat orders, (2) components and receivables being financed, and (3) competition eroding margins, as seen in 2020-2021. Other bear cases include FTK, with paused power and Saudi concentration, BWEN, with shutdown liabilities eating into proceeds, AVAV, with suppliers capturing prime margins, and GD, with a threat that does not transfer orders. The bear case for FRO and STNG is based on normalized routes and cargo destruction. A news scan found the headline for BWEN's 10-Q filing on the SEC website, and investor presentations for AVAV and quarterly results for FRO and STNG. The analysis starts with 278.67 cash and aims for a positive asymmetric net payoff, with a binding constraint tested executable edge. The risk is that cash itself may miss a genuine cycle, but funding pressure cannot repair failed rules.

A review of the funding history of C91100 rejects a current extreme narrative. The review is based on two premises. First, it collected 100 consecutive 8-hour OKX USDT-swap settled rates per asset from August 9 00UTC to September 11 00UTC, which included observations 23045 to 23047. Second, the latest settled percentile rates were BTC 70, ETH 18, and SOL 34, with 5th and 95th percentile bounds of BTC 0.005875 and 1, ETH 0.129385 and 1, and SOL -0.727958 and 1 basis point. A basis point is a unit of measurement, equal to 0.01%. The third premise is that current indicative rates are not final settlements and one venue is not global leverage, meaning it does not represent the entire market. The conclusion is that none of the rates are in the outer 5% extreme in this sample, so there is no contrarian funding entry. This inference is descriptive, meaning it describes the current situation, but not predictive, meaning it does not forecast future events. However, a counterexample could be a longer regime or another venue that could be extreme, and the price effect would require a time-aligned costed predictive test. The mean rates were 0.620243, 0.619086, and 0.248752 basis points per 8 hours, and the total normalized payments were 0.620243, 0.619086, and 0.248752% of a constant notional over 33 days, inclusive of timestamps, before hedging and costs. Alpaca cash spot cannot collect short-perpetual carry here, meaning it cannot profit from the difference between the spot price and the perpetual swap price. The review notes that non-independent adjacent observations, changing open interest, and no exchange-flow attribution limit the inference. The source of the data is https://www.okx.com/api/v5/public/funding-rate-history.

The Panama Canal had an increase in congestion despite fewer arrivals in August compared to July. The canal's hours of operation increased by 37.50985% to 9.52 hours, while the number of transits decreased by 2.46445% and arrivals decreased by 6.49718%. The average time for non-transit vessels increased from 14.51 hours to 23.63 hours. This information comes from the Panama Canal's monthly operations summaries for August and July 2026, which can be found at https://pancanal.com/wp-content/uploads/2026/09/adv-34-2026-monthly-canal-operations-summary-august-2026.pdf and https://pancanal.com/wp-content/uploads/2026/08/ADV-26-2026-Monthly-Canal-Operations-Summary-July-2026.pdf. The two months had different types of vessels, and the difference in average times does not necessarily mean that the waiting time for a specific group of vessels increased. There is no claim that the increase in congestion is directly related to any specific cause. The number of bookings in July was 112.27%, which may indicate a discrepancy in the way bookings are counted. The conclusion is that congestion is increasing, but there is no proof that this will affect the price of listed stocks. The authority that manages the canal directly observes the number of transits, but does not account for vessels that are rerouted or cargo that is rejected, or the economic factors that affect the issuer. While there is a plausible explanation for the increase in congestion, it does not necessarily mean that the stock price will increase. In fact, weaker arrivals may signal a decrease in demand, while maintaining capacity may increase the time it takes to transit the canal. The sustainable daily capacity of 36-38 vessels is dependent on the route and type of vessels.

The N911 bear audit looked at Dorian's earnings, which were captured by the company's strong performance in the June quarter, with a Time Charter Equivalent, or TCE, of 75,926 dollars per day, which supports the idea that the company is operating well. The company's Helios Pool had a 99% utilization rate, which means it was almost fully used, and this was not due to a single customer. According to the company's news release and SEC filing, Dorian's strong performance is supported by its real operating capture. However, there are some complications, including the cost of using the canal and the gain from the sale of assets. A strong bear case can be made, as the cost of waiting to use the canal and the unpaid time can absorb the strength of the freight market. The company's reported adjusted EBITDA, which is a measure of profitability, includes a 30.1 million dollar gain from the sale of assets. If this gain is subtracted, the diagnostic EBITDA is 135.330566 million dollars, which is not the same as cash flow. The company's competitive bids and working capital risk, as seen in the SEC filing of OESX, counter the enthusiasm for repeat orders, and the latest order on September 8 was unchanged. The forecast for FTK, the restart gap for BWEN, the lack of completion for GD, and the inaccessible replay for AVAV, as well as the unchanged revenue for TSM, all documented from 2014 to 2018, support the conclusion that LPG merits a test, while others should be watched. The validity of this conclusion is limited to quarterly data and does not include current fixture or incremental receipts. A bull counterexample is that Dorian's adjusted earnings of 107.2 million dollars remain strong even when excluding the sale, and cash collection is not necessary before market prices firm up profitable contracts. Neither the bear case nor the bull case alone justifies short exposure.

A news scan found the analysis that challenges the idea that the growth of certain financial products is driving up the price of cryptocurrencies like Bitcoin, Ethereum, and Solana. The analysis is based on three premises: first, Circle's net issuance of $0.94 billion over 7 days, which is $0.134286 billion per day, as of September 7, does not match the flow on September 10, which was 22,993. Second, the issuer's reserve income was $668 million out of a total of $701 million in the second quarter, which is 95.29%, and on-chain volume grew 151% while revenue only grew 7%, according to data points 23044. Third, the IBIT shares and assets under management, or AUM, as of September 9, do not match the benchmark date of September 10, as per data point 23043. The conclusion is that neither the growth of stablecoins nor the growth of AUM proves that there is current demand for buying Bitcoin, Ethereum, or Solana. The strongest counterexample to this idea would be if new settlement users hold more coins and create ETF shares, but to prove this, there needs to be evidence of matched issuer share changes, actual native balances, and measured holder fee capture. The analysis also states that the economics of stablecoins do not support a 3R spot appreciation, which refers to a three times return on investment. The people who benefit from this situation are the reserve issuer and distributors, as well as settlement users, but token holders only benefit if there is incremental demand or fees that reach them. The analysis is based on facts that are only available up to the reported periods by the issuer, and the idea that adoption leads to price changes is an assumption. The sources of this information are Circle's transparency report, Circle's second-quarter 2026 results, and the iShares Bitcoin Trust ETF website.

The MID911 fund has identified several companies with strong prospective-thesis bear cases and potential three-step consequences. These companies include EPM, TLX, SRRK, CPRT, AZO, and SFIX. For EPM, the bear case is that commodity or royalty cash may undershoot, dividend or acquisition cash may draw on financing, and dilution or interest may reduce per-share cash despite the headline yield. However, the bull case is that new well cash can repair coverage. For TLX and SRRK, the bear case is that approval or CMC disappointment, launch cash deferred while trial or manufacturing costs continue, and financing and other pipeline valuations can amplify the gap. The bull case is that a successful review or commercial platform can create upside. For CPRT, the bear case is that the proposed ACV acquisition uses cash, integration and regulatory conditions delay benefits, and return on capital may lag the issuer accretion story. The bull case is that network benefits are possible, but the full Q4 and agreement are unreadable. For AZO, the bear case is that dated earnings attract attention, unexpected margins or cash determine pricing, and capital return benefit depends on organic returns. For SFIX, the bear case is that improving revenue may require promotions, retention or inventory costs can affect cash, and uncashed-backed growth may reverse the run-up. The beneficiaries of these scenarios include dividend holders, financing providers, manufacturers, and tender holders. The fund notes that issuer promotional incentives require cash checks. The sources of this information include news releases from Evolution Petroleum, Telix Pharma, Scholar Rock, Copart, Autozone, and an analysis from runway.

The latest reviewed financial period, which ended on March 31, showed that the operating cash flow, or OCF, was $3.489 million, and cash capital expenditures, or cash capex, was $1.263 million, resulting in $2.226 million. This amount was used to pay a dividend of $4.261 million, which means the company had 52.24% cash coverage. Over a nine-month period, the free cash flow, or FCF, was $10.799 million, and the dividend was $12.613 million, resulting in 85.62% coverage. In the third quarter, the company had $3.594 million in net income and $2 million in new borrowings, which coincided with $6.697 million in operating cash flow, less all investing, and less dividend deficit. The company cannot set aside individual funding dollars. The company has $2.616 million in cash and $56.5 million in debt, with a 6.78% interest rate, which limits its ability to pay. On September 10, the company declared a dividend, which was paid on September 30, establishing an obligation, but not guaranteeing coverage. The annualized yield is 12.5% at $3.84, assuming four unchanged payments. A bear case scenario is that commodity weakness or royalty delays could leave payouts competing with debt and acquisition funding. However, a strong counter case is that forecasted royalty wells and a capital-light mix could improve fourth-quarter cash. The company incurred $1.6 million in capital expenditures, which is different from cash capital expenditures of $1.263 million. An update on September 15 showed the company's actual cash, not dividend headline. Sources include news releases from Evolution Petroleum's investor relations website.

A preregistered analysis of EPM, which is a stock ticker, was conducted. The analysis involved two sessions with 3R sensitivity, meaning it measured the effect of a 3% change in the stock price. The results showed that after costs, the sensitivity was negative. The analysis used 60 daily log returns, with two future sessions divided into 52 quarter-hour increments, and 50,000 paths with a seed of 2026091100. The daily volatility was 2.771389%. The starting price was 3.84, and the stop loss was set at 1 old ADR lower, while the target was set at 3 old ADR higher. The stop loss was filled at the observed crossing price, and the target was filled at the limit price. The time was set at the 52nd step, with 30 basis points per side fees and slippage assumption. The results showed a net mean of -0.529246% for the zero log drift target, and -0.587239% for the sample log drift. The empirical two-day IID terminal bootstrap showed a p05 of 3.62895, a median of 3.84074, and a p95 of 4.06270. Only 0.72% of the terminal values were above the target, and it was not the first passage. The gross 3R was 2.31196R, but after costs, the net was only 2.31196R. The conclusion was approved 0, meaning the model did not show a positive expected return. The analysis notes that a countercase, such as a catalyst jump or fat tails, could invalidate the geometric Brownian motion model used. The model also omits gap, weekend news, dependence, and parameter uncertainty, and is not an event odds, backtest, 15-minute execution validation, or paper retune. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EPM&feed=sip&adjustment=all, and the note number is 2087.