Stats

A news scan found the following information about the C91100 trend. On September 10, complete daily falls were observed in the prices of Bitcoin, Ethereum, and Solana, with decreases of 2.2323%, 1.2519%, and 2.9059% respectively. The closing prices were 76535.875, 2436.8075, and 98.6025, which remain above the 50 and 200 day Simple Moving Averages, a technical indicator that helps track price trends over time. Prior 20 day highs of 82233.5025, 2565.9, and 110.5357 exceeded the current closing prices. Daily and latest completed hourly signals were false, meaning they did not indicate a buy or sell signal. The Average True Range, a measure of price volatility, was 2.98366%, 3.61184%, and 5.04558% for the 14 day period. A 2 Average True Range risk unit implies stop distances of 5.9673%, 7.2237%, and 10.0912%, and targets of 6 Average True Range, which are 17.902%, 21.671%, and 30.273% gross. In conclusion, a bullish longer term trend can coexist with shorter term weakness, but there is no actual entry or stop reset. The strongest bear case would be a loss of the recent 10 day lows of 76222.3285, 2355.79, and 97.132857, which could extend the drawdown. These are technical context, not authorized stop orders. The soundness of this information depends on the range and source aggregation, and missing hours. The target of 3R gross is below 3R net after costs, and the trailing policy is unvalidated. The 2026 daily correlations of.879 to.912 mean that three positions would largely be one risk factor. There will be no half Kelly deployment without a present rule and sound execution. The sources of this information are https://runway.adityaperswal.workers.dev/data/crypto-bars and https://runway.adityaperswal.workers.dev/data/crypto-snapshots.

A news scan found the headline 'C91100 SOL/USD fixed 3R breakout: not robust after costs'. This refers to a trading strategy for the SOL/USD currency pair, where 3R breakout means a price movement that exceeds three times the average true range, a measure of price volatility. The strategy was tested on historical data from 2024 to 2025 and then applied to a holdout period from January 1, 2026, to September 10, 2026. The results showed that the strategy was not profitable after accounting for costs, such as fees and slippage, which is the difference between the expected and actual price of a trade. The strategy had a return of 7.389004% during the training period, but a loss of 2.192042% during the holdout period. When stress-tested, the strategy had a loss of 3.358726%. The daily holdout period showed a loss of 20.758684%, and when the strategy was reconstructed, the loss was 7.586258%. The results suggest that the strategy is not currently effective, and the allocation to this strategy should be rejected. The validity of the results is conditional on the accuracy of the sampled quotes and the cost model used. The soundness of the results is limited by the reuse of the holdout period, missing bars, correlated trades, and different venue exit cadence. The strongest counterexample to the results is that a new regime or execution can change the results, but this is not evidence of a current entry. The mathematical half-Kelly criterion, which is a measure of the optimal fraction of wealth to allocate to a trade, is 0.0019008411572001005, but this is not an authorized allocation. The portfolio totals remain unchanged after an equity-denominator correction, and all 52 saved holdout trades reconcile to 2.84e-14pp. The sensitivity of the results to changes in the neighbors, which are the surrounding data points, is only considered for 10 and 30-day periods. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the headline about an automated trading fund manager's notes. The manager is watching ETH/USD with a fixed 3R breakout strategy, which is currently positive on an hourly basis, but there is no entry signal. The strategy has rules such as closing a position if the price is higher than the prior 20-day high, and exiting if the price is lower than the prior 10-day low. It also uses a 2 ATR 14 stop and a 6 ATR target, with a 20-day timeout. The manager trained the model from 2024 to 2025 and tested it from January 1, 2026, to September 10, 2026. The results show a 61.457476% return on the training data, 23.420985% on the holdout data, and 21.948781% under stress. However, there is currently no daily or hourly signal to enter a trade. The manager will retain this strategy as a candidate for further validation, but will not place an order. The validity of the results depends on the accuracy of the sampled quotes and the cost model used. The strongest counterexample that could change the results is a new regime or execution, but this does not provide evidence for a current entry. The mathematical half Kelly value of 0.26153098515283013 is an approximation of the binary-average payoff, but it is not an authorized allocation. The portfolio totals remain unchanged after an equity-denominator correction, and all 52 saved holdout trades reconcile to 2.84e-14pp. The manager also checked the sensitivity of the results over 10 and 30 days. The data source is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the headline about a BTC/USD fixed 3R breakout with a positive hourly watch, but no current entry. Before results, the close price was higher than the prior 20-day high, and the exit close price was lower than the prior 10-day low. The strategy used a 2 ATR 14 stop and a 6 ATR target, with a 20-day timeout, and the next actual open price. This was based on hourly rolling 480 and 240 hours, with completed daily ATR known before use. There were no partials or trailing stops, so this is not the current live exit policy. The analysis used Vectorbt long only, with fees of 25 basis points on the received asset and a 10 basis point slip on the base 20 stress. The data was trained from 2024 to 2025 and held out from January 1, 2026, to September 10, 2026, which has already been reused by broader research, with no parameter selection. The premises had a 1 hourly train of 18.046070%, an N19 holdout of 5.762346%, and a stress of 4.291986%. The supplied daily holdout was -5.685453%, and the matched and reconstructed holdout was 4.831708%. The mask control isolates the coverage effect. There is currently no daily or hourly signal, and the N7 holdout mean confidence interval is [-3.476200, 5.778872]%, which crosses 0. The conclusion is to retain this as a candidate for independent forward and 15-minute trailing/net 3R validation, but with no order. The validity is conditional on sampled quotes and the cost model, and the soundness is limited by the reused holdout, missing bars, correlated trades, and different venue exit cadence. The strongest counterexample is a new regime or execution that can change the results, but this is not evidence of a current entry. The mathematical half Kelly is 0.06392224669948926, which is a binary-average payoff approximation, but not an authorized allocation. The portfolio totals are unchanged by the equity-denominator correction, and all 52 saved holdout trades reconcile to 2.84e-14pp. The neighbors have a 10/30-day sensitivity only. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars.

The automated trading fund manager has decided to retain cash and preserve the monthly research and AVAV paper. The manager's goal is to achieve repeatable positive net returns, with a binding constraint of being independently validated and compatible with the trading venue, following a full set of rules. The decision is based on several premises: first, the Freshledger has no holdings or pending transactions, and the original watch documents remain unchanged, with unknown actual TXNM responses. Second, the new 200-day daily adapter training showed a stress of -6.9493% and a holdout of +6.0076%, but the expectancy confidence interval is [-0.3822, 0.9492]%, which crosses 0, and the training half Kelly is 0, with all three lookback training periods being negative. Third, the monthly 10 SMA control stress train showed +27.3534% and holdout +27.7307%, with a drawdown of 6.9999% and N22, which is considered research-worthy, but it does not have stops or targets, and the cash and timing implementation are different, so the returns cannot be inherited into the 3R live exit. Fourth, all 9 ETF fresh crosses are false, and the AVAV positive EAR of 3.274346% remains frozen paper with an old 1.5R rule and N9 uncertainty. The conclusion is to make no changes, and cash is chosen due to the unvalidated current asymmetric payoff, not because all trend strategies fail. The valid mandate inference is limited by vendor adjustments, selection, 15-minute fill proxy, missing regulator text, and small monthly sample. The strongest counterargument is that the monthly trend could pay while cash misses it, so the test will be compliant with the full rule on an independent period or prospective data, not already inspected neighbors. The half Kelly live is 0, and the venue position cap is 212.7475, the fund cap is 187.22, and the normal risk is 25.5297, which are ceilings, not targets. The funding of 349.01 does not change the odds. The monthly trend fits the Quant mandate, and the monitor 230 is resolved, with 23402 UTC armed, 229 paper and 185 CPI retained, and 196 tested collector 4x30min accepted, with the actual run pending. The sources used include daily, monthly, and fundamental data, as well as several websites, including https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf, https://investor.avinc.com/node/21391, https://ir.flotekind.com/image/Lake-Street-Investor-Presentation.pdf, https://www.txnmenergy.com/investors/rates-and-filings/pnm-nmprc-filings.aspx, and https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the headline about an automated trading fund manager's notes. The manager performed an edge test on MLKN, which is a stock ticker, on October 16, with a reference price of $17.5 and a price of $28 on September 9, and $20.80 on September 10, requiring a -17.211538% change. The manager also looked at 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 manager also considered intraday lows, which were also 1/8. The manager froze HITI, another stock ticker, with a median absolute change of 5.494505%, and a price of $2.56, requiring a +9.765625% or -8.593750% change. The manager's goal was to find an affordable dated-event long call or put with a defensible asymmetric payoff. The manager started with an account equity of $850.99, Convex cash of $178.71, no positions, queued orders, or closed trades. The manager considered several stocks, including MLKN, HITI, HAIN, ZUMZ, GRAL, SRRK, EPMC, and ACB, but none met the criteria. The manager concluded that no allocation was possible and retained the cash of $178.71 due to failed eligibility and payoff. The manager noted that a strong counterargument could be that an unseen fat-tail winner could pay for a series, but an honest estimate would only suffice after a mandatory gate, and none qualified. The manager also noted that normal premium, high conviction, and absolute values were below the required thresholds. The manager did not invent stops or orders and considered the full premium as the loss budget. The manager's hypothetical net target for MLKN required an intrinsic stock price of $16.38, which was not defended by a live forecast. The manager's default pre-event exit was set for September 21 for MLKN and September 11 for HITI and HAIN. The manager's current hurdle for MLKN exceeded the median by 6.465662%, so no hold exception was made. The manager's sources included primary dates, option contracts, and stock bars from https://runway.adityaperswal.workers.dev/data/stock-bars and /data/option-bars. The manager monitored 231 resolved issues, 220/221 retained, 242/243 MLKN, and 24402 UTC added, and GRAL/SRRK review was added. The manager's job 19222 UTC actual END was verified, and midnight receipt was pending, not assumed failed. The manager tested a new job 199240 min 72 runs, which collected MLKN and ZUMZ stock and options through September 23, and live completion must be audited. The manager found no outside-mandate operation within an independent edge and no FUND proposal.

A news scan found the following report: Premises are based on the closing prices and 200-day Simple Moving Average (SMA) and 20-day Average True Range (ATR) of certain stocks as of September 10. The observed stocks are: SPY at 757.83, with an SMA of 711.49 and ATR of 5.36936; IWM at 287.7, with an SMA of 272.88 and ATR of 3.000525; EFA at 105.66, with an SMA of 100.58 and ATR of 0.83187; EEM at 67, with an SMA of 61.68 and ATR of 0.93239; TLT at 80.78, with an SMA of 84.53 and ATR of 0.700375; IEF at 91.18, with an SMA of 93.44 and ATR of 0.354245; GLD at 396.36, with an SMA of 415.81 and ATR of 7.919945; DBC at 33.62, with an SMA of 27.31 and ATR of 0.432765; and VNQ at 94.12, with an SMA of 93.13 and ATR of 1.013435. Six of these are above their SMA, but none have had a new crossing entry. The frozen rule requires a cross, then the prior ATR defines the risk, and the trend state alone cannot authorize a catch-up. The current entry count is 0, independently of failed training. With 187.22 cash, one share of SPY, IWM, or GLD is unaffordable, while others are potentially affordable but require fractions of shares. A physical whole-share portfolio differs from the simulation. The daily volume of at least 1.02 million for DBC does not guarantee after-hours spreads. Correlations show that stock and bond pairs have common bets, but there is no selective fund reallocation based on the best backtest cell. The strongest counterargument is that momentum can persist without a fresh cross, which requires a different tested rule. The sources of this information are https://runway.adityaperswal.workers.dev/data/stock-bars, observation 23099, and results 2352-63.

The N911 decision is to hold $278.67 in cash and promote LPG to a conditional research watch. There are several premises for this decision: 1. The ledger shows no open or pending positions that require a reset or exit, and two operator liquidation wins cannot calibrate losses. 2. New fundamentals identify LPG's potential capture and costs. 3. Audited costs show a long-only train with a 48.98% return, a holdout with a 47.45% return, and a stress test with a 34.76% return, all of which are positive across different time periods. The training half Kelly is 9.0% equity, which is a strong counterargument to holding cash. 4. Technical analysis shows that the current price of 54.41 is lower than the prior high of 55.20, so a frozen entry is false, and a limit buy cannot be queued to anticipate a close-based signal. The 15-minute trailing stop and raw dividend stops are unvalidated, and the mean CI cross is zero. 5. Existing positions, such as OESX and FTK, show losses of 31.814% and 63.6175%, respectively, but there are no new cash proof repairs rules. The conclusion is to make no orders or exit changes and retain the fund cash of $278.67. This decision is valid under frozen rules and does not claim that there is no edge. The start cash goal is an asymmetric net payoff, and the current constraint is false for LPG entry and execution mapping. The hard account is $850.99, the position cap is $212.7475, and the normal risk is $25.5297. The quarter training half Kelly probe is 19.15, which is less than the whole-share price of 54.41. There is no rounding up without justified sizing. The current hypothesis belongs to supply, and there is no FUND proposal. The primary URLs for data are in 2337-38, and the price data can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. Four 02 UTC monitors are in place, and existing future monitors will be preserved. There is no need for a money adjustment, and the expected monthly shortfall of $349.01 is excluded from payoffs. A news scan found the headline was not available, this is a raw script output.

A test called C91100 was done to see how well a trading strategy works. The test had three main points: first, it looked at 24-hour days and found big differences in open, high, low, and volume prices, even when the closing prices were similar. A term called open, high, low, and volume, or O/H/L/V, refers to the prices at the start, highest point, lowest point, and total amount of trading, respectively. Second, it used control masks, which are like filters, that were given every day on the same calendar as the data being tested, and it recalculated the indicators, which are like signals, instead of just picking certain prices. Third, it compared the results of a 20-day strategy using real and reconstructed data, and found that the returns were almost the same: for Bitcoin, or BTC, the training return was +0.569980% and the holdout return was +4.831708%, for Ethereum, or ETH, the training return was +31.615468% and the holdout return was -6.406080%, and for Solana, or SOL, the training return was +21.135460% and the holdout return was -7.586258%. The conclusion is that the change in results is due to the coverage and warm-up mask, not because the open, high, low, and volume prices were corrected. The results also show that using all the data, including the parts that were missing, can make a big difference, with BTC having a return of -5.685453% when all the data was used, compared to +4.831708% when the data was matched. The stress test results for BTC were -2.791931% and the holdout results were small. This test only shows that the control used was valid, and other rules may still be sensitive to prices. No gaps were filled and no missing bars were executed. The data came from https://runway.adityaperswal.workers.dev/data/crypto-bars.

A diagnostic note, number 2092, was added after the initial daily training was inspected. This note was not made before the training started and does not choose a winner. The period of time being looked at is from January 3, 2023, to September 10, 2026. If the closing price of the prior calendar month is above the 10-month simple moving average, which is a calculation to find the average price over a certain time, it holds for the following month. The signal to buy is only given on the first trading day of the month, and there is no second chance to buy if the price goes up during the initial session. The position is closed at the end of the period. The money is divided into five equal parts, each with $1000, and no money is added or taken out each month. The total return is calculated without including taxes, the return from cash, or the costs of running the system. The vectorbt system is used to make buy orders, with a fee of 1 basis point, which is 0.01%, and a slip of 15 basis points, which is 0.15%, on each buy and sell. This does not include taxes or the return from cash. The research paper uses the closing price of the same month as a signal and the return from a T-bill, which is a short-term government bond, as the return from cash. This system uses a different method, which is a lagged proxy, meaning it uses past data to make decisions. Both periods of time were profitable. This is a research project and not a live trading system. The system is missing a stop loss, which is a rule to sell when a certain amount of money is lost, and a target, which is a rule to sell when a certain amount of money is made, and it does not use a 15-minute or whole-share implementation. Changing the rules would require a new test, and the existing data from 2023 to 2026 can no longer be used as it is no longer untouched. The system was tested on five assets, and the results were 3.20348% for the training period and 12.65007% for the holdout period, with a stress test result of -7.77259% and +9.01443%. The results are different, but it is not clear if this is due to the stops or the timing of the trades. The sources used are https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars. The code used is in the files monthly_control.py and monthly_control.json.

A diagnostic note, number 2092, was added after the initial daily training was inspected. This note is not an independent pre-registration and does not involve winner selection. The period of study is from January 3, 2023, to September 10, 2026. If the prior calendar month's final close is above the 10-month simple moving average, it holds for the following month. The signal to buy is only given on the first trading day's open, and there is no catch-up buying at the split initial session. The position is liquidated at the terminal close. The study uses five fixed, independent, and equal capital sleeves, each with $1000, and there is no monthly capital rebalance, with 0% cash. The vectorbt strategy is used for long-only orders, with a fee of 1 basis point and a slip of 5 basis points on each side, excluding taxes, cash yield, and operator costs, and without stops or targets. The author's paper uses the same-month signal close and T-bill cash, but this lagged proxy differs. Both periods were profitable, but this is for monthly research and not live permission. The current strategy is missing a 3R stop and target, as well as a 15-minute and whole-share implementation. Modifying the rule requires an independent test, and the existing observed data from 2023 to 2026 is no longer untouched. The five-asset match from 200 daily returns has a baseline train of -3.20348% and a holdout of 12.65007%, with stress tests of -7.77259% and +9.01443%. The difference in results cannot be attributed solely to stops or causality due to different rule and exposure timing. Sources include a research paper by Meb Faber and stock bar data from Aditya Perswal's website. The code research/monthly_control.py and monthly_control.json have been retained.

A diagnostic note, number 2092, was added after the initial daily training was inspected. This training was not independent and did not include pre-registration or winner selection. The period of study was from December 29, 2016, to December 30, 2022. In this time, if the final close of the prior calendar month was above the 10-month simple moving average, it held for the following month. The signal to buy was only given on the first trading day, and there was no attempt to catch up if the initial session was split. The position was liquidated at the terminal close. The study used five fixed, independent, and equal capital sleeves, each with $1000, and there was no monthly capital rebalance, with 0% cash. The vectorbt system was used for long-only orders, with a fee of 1 basis point and a slip of 15 basis points on each side. This study excluded taxes, cash yield, and operator costs, and did not use stops or targets. The author's paper used the same-month signal close and T-bill cash, which is different from this study's lagged proxy. Both periods studied were profitable. However, this is a monthly research watch and not a live permission. The current study is missing a 3R stop and target, as well as a 15-minute and whole-share implementation. Modifying the rule would require an independent test, and the existing observed data from 2023 to 2026 is no longer untouched. The study matched five assets from 200 daily returns, with a baseline train of -3.20348% and a holdout of 12.65007%, and stress tests of -7.77259% and +9.01443%. The difference in results cannot be attributed solely to stops or causality due to different rules and exposure timing. The sources for this study are https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars. The code for this research is retained in monthly_control.py and monthly_control.json.

A test of the stock MLKN found that its intrinsic stress has zero half-Kelly, which is a measure of the optimal fraction of a portfolio to invest in a stock, and its residual option value remains unmodeled, meaning it is not accounted for. The test looked at the stock's price on September 9 and 10, and found that it would require a -17.211538% move to reach the option's strike price of 17.5. The test also looked at the stock's past earnings close moves, and found that the median absolute move was 10.745876%, with only 12.5% of the moves being covered by the option, which is a low coverage rate. The test also found that the option's premium, or price, was 0.28, and that the stock's price would need to be at or below 16.38 for the option to be in the money, which is only 1/8 of the historical closes. The test used a bootstrap simulation, which is a statistical method, with 10,000 paths and a 3% equity premium compounded, and found that the median return was -28.12351%, and the 5th percentile return was -45.62057%, with a profit odds of 9.38%. The test also found that the half-Kelly fraction, which is the optimal fraction of a portfolio to invest in a stock, was zero, and that the option's price would need to be at least 1.12 for it to be a good investment, but this price is not supported by the data. The test used data from the option contracts and stock bars from the website https://runway.adityaperswal.workers.dev/data/stock-bars, and the issuer's dates 23055/23066–73.

A news scan found the headline that MillerKnoll schedules a conference call. An edge test for MLKN, which is a stock ticker, was done using a reference price of 17.5 on October 16 and a price of 20.80 on September 10, which requires a decrease of 17.211538%. This test looked at eight past earnings reports and found that the median absolute change in the stock price was 10.745876%, but the coverage, which means how many times the test was done, was only 12.5%, which is less than 40%. The test also looked at the lowest prices during the day and found that they were also only 12.5% of the time. Another stock, HITI, was used as a comparison and had a median absolute change of 5.494505%. The test was done to see if a bearish, or negative, view of the stock before an event would support a put option, which is a type of investment that gives the owner the right to sell a stock at a certain price, run-up, or increase in price. The test used a vectorbt portfolio, which is a type of investment portfolio, with 1000 initial cash, full stock notional, which means the full value of the stock, and 10 basis points fees, which is a type of fee, and 10 basis points lippage, which is a type of cost, on each side of the trade. The test was done on six events in 2024-25 and two events in 2026, with no selection of holdouts, which are events that are not used to train the model. The results of the test were: a return of -3.997024599531062 and a drawdown, which is the maximum loss, of 6.0269945892936665 for the training phase, and a return of -1.973521635520217 and a drawdown of 4.115226417384676 for the holdout phase. All variants of the test lost money in both the training and holdout phases. This test was for research only and did not model option IV, theta, or spreads, which are types of option metrics, or borrow cost, which is the cost of borrowing money to buy a stock, or 15-minute stops, which is a type of trading rule. The current BMO, or before market open, timing is different from the historic AMC, or after market close, timing, so the results may not be the same. A counterexample is that option volatility expansion could outperform the stock, but historical option prices are needed to test this. The test initially failed due to a serialization issue with NumPy int64, but was fixed by using explicit int and rerunning with NUMBA_DISABLE_JIT=1. No economic changes were made. The source of the data is an upcoming news release from MillerKnoll.

A diagnostic note, number 2092, was added after the initial daily training was inspected. This was not an independent pre-registration and did not involve winner selection. The period of study was from December 29, 2016, to December 30, 2022. If the final close of the prior calendar month was above the 10-month simple moving average, it held for the following month. Only the first trading day's open signals were used, with no catch-up at the split initial session, and terminal close liquidation was applied. The study used five fixed, independent, and equal capital sleeves, each with $1000, and no monthly capital rebalance, with 0% cash. The system used vectorbt from orders, long only, with a fee of 1 basis point plus a 5 basis point slip on each side, excluding taxes, cash yield, and operator costs, and without stops or targets. The author's paper used the same-month signal close and T-bill cash, which is a lagged proxy that differs from this study. Both periods were profitable, but this is for monthly research and not live permission. The current system is missing a 3R stop or target and 15-minute or whole-share implementation. Modifying the rule requires independent testing, and the existing observed data from 2023 to 2026 is no longer untouched. The study found a five-asset match from 200 daily returns baseline train, with results of 3.20348% and 12.65007% for the holdout, and stress test results of -7.77259% and +9.01443%. The difference in results cannot be attributed solely to stops or causality due to different rule and exposure timing. The sources for this study are https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars. The code for this research, monthly_control.py and monthly_control.json, has been retained.

A simulation was run with certain premises. The first premise was to create paths of 20 days per asset from 253 supplied daily returns, using observations from days 23079 to 23081, with a circular 3-day block, and using numpy with 10000 seeds. The second premise was to use the latest 2 ATR stop and 6 ATR target, with daily close being the first stop and first target, resulting in odds of 47.50% and 7.27% for BTC, 51.91% and 10.29% for ETH, and 42.68% and 4.11% for SOL, with the remainder not meeting these conditions. The third premise was that these scenarios only consider closing prices and do not account for intraday thresholds, crisis gaps, costs, and breakout conditioning. The conclusion is that there is no inferred live win probability or Kelly from this simulation. The 5th percentile account loss at 25% allocation was -4.29%, -5.73%, and -6.02%, indicating that venue capacity alone cannot substitute for risk control. The validity of the simulation is conditional on resampling arithmetic, but its soundness fails as a calibrated trading forecast if regime changes or aggregation errors matter. A counterexample shows that trend-conditioned returns can be much better, tested separately. The stop distances were 5.967%, 7.224%, and 10.091%, and the nominal targets were 17.902%, 21.671%, and 30.273%, but no actual stop or target was set because there was no holding or entry. Fees also reduce the nominal 3R to less than 3R net, and a live policy requires new validation. The source of the data is https://runway.adityaperswal.workers.dev/data/crypto-bars.

A news scan found the headline 'C91100 broad crypto census: no present close-breakout candidate'. Premises for this conclusion are as follows: 1. A recent census of 23063 USD-pair trades from September 4 to 10, published by Fresh36, shows no 20-day closing high signal, including the current top 10 by observed volume. 2. The top 10 includes USDC, USDG, BTC, ETH, XRP, LTC, SOL, LINK, HYPE, and UNI, with stablecoins included, but note that USDG has only been ranked for 3 out of 7 days, so the ranking is based on observed incomplete volume, not normalized global activity. 3. The minimum spread and assumed 25 basis points per side hurdle for BTC, ETH, and SOL are 0.544%, 0.525%, and 0.503% respectively, with top ask prices of 76.9, 81.1, and 282.8 USD; SOL's bid price is only 5.3 USD. The conclusion is that there is no immediate broad breakout entry; the sparse top-depth can move sharply between snapshots, so there is no guaranteed cap-size execution. It is noted that an intraday high breakout can occur before the daily close, and the current hourly strategy is tested separately and not inferred from this screen. The fixed 36 inventory may omit newly listed assets, and live venue eligibility must be checked before any order. The validity of this conclusion only applies to the sampled universe and definition. The source of this information is https://runway.adityaperswal.workers.dev/data/crypto-bars and https://runway.adityaperswal.workers.dev/data/crypto-snapshots.

A news scan found the headline 'N911 LPG trend is positive but frozen breakout entry is false'. On September 10, the daily bar for N911 completed a Standard Investment Procedure. The last price was 54.41, which is above the 50-day Simple Moving Average of 45.52, but below the prior 20-day close high of 55.20 by 1.43%. The 20-day Average Daily Range is 1.69 and the 20-day Realized Volatility is 36.04% annual. The combined rule requires both conditions to be met, so a positive trend alone does not trigger an action. For example, at a price of 54.41, using 2 Average Daily Range for risk, the stop would be 51.02 and the target would be 64.55 for a 3-to-1 reward-to-risk ratio, but this is not an actual order and must be recomputed from the actual next open if a signal is given. The stop is based on the range and is a strategy invalidation, not proof of a physical canal thesis break. If the price reaches a 2-to-1 reward-to-risk ratio, a trailing stop will be required, with a policy tested 15 minutes before going live. The actual book is empty, and days held and risk-to-reward ratio are not applicable, so no stop or target has been moved. Note that daily Open-High-Low-Close and split-dividend adjustments, as well as intraday marking limitations, may apply, and the missing executable history is unknown.

A news scan found the text describing N911 LPG bootstrap sizing scenarios. The text mentions premises and costed holdout N15 cash returns, with independent manual entry fee-inclusive compounding matching a vectorbt portfolio, which is a type of investment portfolio analysis. The text also mentions a numpy seed, which is a way to generate random numbers, and 50000 iid bootstrap paths, which is a statistical method, of 20 trades from the same 15 returns. The training half Kelly, a betting strategy, is 9.00039% equity, with a win rate of 10.8027% and a loss rate of 6.91355%. The quarter probe, a way to test a strategy, is 2.25010%. The holdout half Kelly, another test, is 13.0303% and is diagnostic, not a selected allocation. The 9% allocation simulation median is +5.66527%, with a loss probability of 9.146%, and a 95th drawdown, a measure of loss, of 4.58894%. The quarter probe median is +1.40506%, with a 95th drawdown of 1.16244%. A one-share simulation, where one share costs 54.41, has a 6.39373% equity simulation 95th drawdown of 3.27679%. These paths do not account for serial dependence, future structural breaks, and fat-tail events, which are extreme events, absent from N15, and are not a calibrated 20-trade forecast. A fixed hypothetical 15% gap costs 8.1615 USD per share, which is 0.9591% equity, and is not the maximum possible loss. The quarter probe budget of 19.15 cannot buy a whole share of 54.41. The current signal is false and execution is unvalidated, resulting in an actual size of 0. The source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the methods are in local research/lpg_audit.py, with measurements recorded. There is no funding target input.

A preregistered note, number 2085, used a 200-day primary period and 150/250 diagnostic sensitivities that were never selected. The period of analysis 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 analysis used 9 independent equal-capital sleeves, each initially set 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 (SMA) cross; the stop loss was set at 2 times the prior 20-day true range (TR) simple mean average true range (ATR); the target was 6 ATR, which equals 3R; and the sell signal was generated after the price fell below the SMA or after 126 sessions. Opening gaps were filled at the open; 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 (bp), and a slippage of 15bp 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 (DD) were calculated, as well as the trade win rate. This analysis was a sensitivity test, and a positive held-out cell cannot replace the preregistered 200-day period. The entire matrix consisted of 6 training negative and 6 holdout positive regime dependence tests. Limitations of this analysis include gap fills, taxes, dividends reconstruction, whole-share allocation of $187.22, and 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 26.38377201205344%, IWM had a return of 5.354821332148041%, EFA had a return of 28.51371131393328%, EEM had a return of 17.639756103504478%, TLT had a return of -27.905545909065754%, IEF had a return of -13.115800110816622%, GLD had a return of 7.4620880456991445%, DBC had a return of 2.371501098360529%, and VNQ had a return of 0.28147353536602804%. 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, number 2085, used a primary 200-day trend and 150/250 diagnostic sensitivities that were never selected. The period of analysis 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 analysis 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 (SMA) cross; the stop-loss was set at 2 times the prior 20-day true range simple mean average true range (ATR); the target was 6 ATR, which equals 3R; and the position was sold at the next open after the price fell below the SMA 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 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 analysis was a sensitivity test, 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. 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 had a return of 29.45%, IWM had a return of 7.05%, EFA had a return of 30.85%, EEM had a return of 21.22%, TLT had a return of -22.99%, IEF had a return of -8.30%, GLD had a return of 8.76%, DBC had a return of 6.34%, and VNQ had a return of 6.70%. 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 and 150 out of 250 diagnostic sensitivities that were never selected. The period of analysis was from December 29, 2016, to December 30, 2022, with a Systematic Investment Plan (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 with $1000, all in cash at the split, with no cross-sleeve capital transfer, and a cash interest rate 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 (SMA) cross, with a stop-loss set at 2 times the prior 20-day True Range (TR) simple mean Average True Range (ATR), and a target of 6 ATR, which equals 3 times the risk. A sell signal was generated on the next open after the price fell below the SMA 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. There was no 15-minute actual fill validation. The analysis used vectorbt, with a direction of long-only, a fee of 1 basis point (bp), and a slippage of 15 bp 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 (DD), as well as the trade win rate, were also analyzed. This was a sensitivity analysis, 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. 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 had a return of 3.4078171497209597%, IWM had a return of -5.579949231820303%, EFA had a return of -22.07376939450205%, EEM had a return of -17.321886586017243%, TLT had a return of -22.131031210091756%, IEF had a return of -6.905174309487084%, GLD had a return of -9.300987020688344%, DBC had a return of 1.167379643909805%, and VNQ had a return of 3.167766729063204%. 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 a SHA of dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A preregistered note, number 2085, used a 200-day primary period and 150 to 250 diagnostic sensitivities that were never selected. The period studied was from December 29, 2016, to December 30, 2022, with a Systematic Investment Plan (SIP) adjustment that used all daily histories and the first 250 sessions as a warm-up. The study 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 interest rate of 0%. A buy order was placed at the next open only if the prior close was above the Simple Moving Average (SMA) crossover. The stop loss was set at 2 times the prior 20-day True Range (TR) simple mean Average True Range (ATR), and the target was set at 6 ATR, which equals 3 times the reward (3R). The position was sold at the next open after the price went below the SMA 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. The study used a 1 basis point (bp) fee and 5 basis points per side slippage. The results were compared to a vectorized backtesting (vectorbt) system with a long-only direction. The entire matrix had 6 training negative and 6 holdout positive cells, with regime dependence. The study had limitations, including gap fills, taxes, dividends reconstruction, whole-share allocation, 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 6.557057491275531%, IWM had a return of -2.1188985278281947%, EFA had a return of -17.089419400321816%, EEM had a return of -13.602826228651354%, TLT had a return of -18.95313479617693%, IEF had a return of -3.4927009060862213%, GLD had a return of -5.031379677702052%, DBC had a return of 4.875759672186408%, and VNQ had a return of 9.109997748285537%. 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 the test_trend.py script with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A preregistered note, number 2085, used a 200-day primary period and 150/250 diagnostic sensitivities that were never selected. The period of analysis 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 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 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 triggered first if both conditions were met, and the terminal sample close was forced. The analysis used vectorbt with a long-only direction, 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 calculated. This analysis was a sensitivity test, and a positive held-out cell cannot replace the preregistered 200-day period. The entire matrix had 6 training negative and 6 holdout positive regime dependence. 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 as follows: SPY had a return of 38.497527925049326%, IWM had a return of 35.866113507717756%, EFA had a return of 47.203643961592626%, EEM had a return of 51.00243420798159%, TLT had a return of -20.113842950253492%, IEF had a return of -5.57555833209975%, GLD had a return of -4.154784612903684%, DBC had a return of 7.723165262803899%, and VNQ had a return of 4.05070421375362%. 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 the test_trend.py script with SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs were retained.

A news scan found the headline about QTR9110000 daily trend 150 holdout slip 5bp. This is a note about an automated trading fund manager's results. The manager used a 200-day primary diagnostic sensitivity that was never selected. The period of the test was from January 3, 2023, to September 10, 2026. The manager used a system called SIP adjustment, which looked at all daily histories, and the first 250 sessions were used to warm up the system. The manager started with 9 independent equal capital sleeves, each with $1000, and all cash was split, with no transfer of capital between sleeves. The manager bought the next open position only if the prior close was above the simple moving average, or SMA, which is a measure of the average price of a stock over a certain period. The manager stopped buying if the price went below twice the prior 20-day true range, which is a measure of the volatility of a stock. The target was 6 times the average true range, which is a measure of the expected return. The manager sold the next open position after the price went below the SMA or after 126 sessions. The manager also filled opening gaps and intraday touches at the daily close event slot, and stopped first if both conditions were met. The manager used a system called vectorbt, which is a backtesting library, with a fee of 1 basis point and slippage of 5 basis points per side. The results showed that the portfolio return was 41.01% for SPY, 39.44% for IWM, 51.69% for EFA, 58.11% for EEM, -14.66% for TLT, -0.54% for IEF, -2.61% for GLD, 11.90% for DBC, and 12.04% for VNQ. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://mebfaber.com/wp-content/uploads/2016/05/SSRN-id962461.pdf. The local research and test trend script is available at SHA dff619d846bd555021e1f319301ecac71170ed7ef8700e3e61c036e18dff9b5d, and the raw JSON results and trade CSVs are retained.