A news scan found the headline about Skyworks Inc. financing. The company made two announcements: first, on September 1, it offered to buy back its notes at 90.55 to 93.36 percent, which expires on September 11 at 17 ET, subject to extension. This offer is not conditioned on the merger results. Second, on July 28, the company reported 935 million dollars in revenue, with 49 million dollars in GAAP operating income and 182 million dollars in adjusted income. The company also planned to take on 2 billion dollars in debt for an acquisition, stopped paying dividends, and authorized but did not make share buybacks. There are three steps in this financing chain: first, tender offers help the company get creditor consent and manage its debt; second, the company still faces risks from independent regulatory decisions and closing the deal; third, shareholders may still face financial and integration risks. This means that participating in the tender offer does not mean shareholders approve of the deal. The people who benefit from this deal are the company and its advisors, who get paid for their work, not because they proved they can make good investment choices. There are also three steps in the semiconductor chain: first, higher prices and more production capacity increase reported margins; second, customers buying in advance and new production capacity can absorb cash; third, normal pricing or buyer spending on new equipment could reverse the current momentum. Continuing to invest in artificial intelligence can lead to long-term gains, so this is not a signal to sell short. There are also three steps in the bank chain: first, high net interest income and fees increase return on tangible equity; second, credit losses and capital needs consume earnings; third, returns depend on interest rates and valuation. All these chains are based on conditional inference, not measured forecasts, and do not require additional books. The events and momentum fit the Quant model. The sources of this information are the Skyworks Inc. investor news releases and observations 23630-36 and fundamental 2425.
The current sector ranks do not allow for a mid-month entry. On September 1, the top sector scores were XLE with 43.466%, XLK with 40.999%, and XLI with 25.185%. The respective prices were $64.93, $185.22, and $170.55, with ATR values of 201.140475, 3.0465, and 2.204745. Although XLI is below its 200-day Simple Moving Average, its long momentum is positive, which is not a contradiction due to different time horizons. The reference prices for a 2 ATR stop and 6 ATR target are $62.64905 and $71.77285 for XLE, $179.127 and $203.499 for XLK, and $166.14051 and $183.77847 for XLI, but these are not executable entry prices. The monthly frozen rule only applies to the first session, so there will be no new mid-month entry. If the fund were to be divided into three equal parts, each part would be $62.4067, but even one share of XLE at $64.93 would not fit into an individual sleeve, while the total fund of $187.22 could hold two shares of XLE or one share of XLK, but those are different portfolios. The 12-1 scores for BTC, ETH, and SOL are negative at 44.253%, 56.753%, and 65.970%, but the 90-day scores are positive at 20.465%, 46.371%, and 47.621%. There were no breakouts above $8223.35, $2565.9, and $110.5357 on September 10. Therefore, there will be no forced catch-up or crypto entry. The strongest counterargument is that long momentum can persist, but the current timing and whole-share implementation still need separate validation. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars and https://runway.adityaperswal.workers.dev/data/crypto-bars, based on observations from 23916 to 23927 and frozen on 2124.
The automated trading fund manager made the following premises: they removed 13176 test issues from the current exchange roster and took a snapshot of 132 batches of historical data from September 8 to 10, which included 12704 symbols and 12128 current symbols, but excluded 384 preferred and warrant symbols and 88 other missing symbols, and also excluded 576 symbols with insufficient or stale data. The manager then filtered the data to only include symbols with a price of at least $5 and a daily dollar volume of at least $10 million, excluding ETFs, which left 2442 symbols. They completed all historical requests for these symbols and found that 2351 of them had at least 253 bars of data, but 91 symbols had insufficient data. The manager archived the rank, volume, and ADV metrics for these 2351 symbols, with 89 observations and seven significant digits, and retained the full precision CSV data. The top symbols by 12-1 momentum were AXTI with 2326.97%, SNDK with 1702.65%, and WOLF with 1556.04%, while the daily leaders were TNON with 117.21%, DBGI with 81.07%, and PCLA with 31.00%, which were not the same factor. The manager also looked at 20 overseas instruments in 9 markets, including 16 stocks and 4 indices, with local currency histories from September 9 or 10, but noted that this was not an exhaustive foreign listing census. The top stocks were ASML, HSBC, and MUFG, but there were gaps in the data due to cross-calendar, FX, dividend, and venue issues. The manager also looked at 25 crypto pairs, but found that all of them had negative 12-1 scores, and that PAXG and MKR had insufficient histories. Stablecoins were not considered to be risk momentum. The manager used an independent fixed sector family to avoid inflating historical stock test results. In conclusion, the manager achieved broad discovery with named coverage gaps, but found no stock-specific live edge. The sources used were the Nasdaq trader website, the Nasdaq listed and other listed text files, the runway Aditya Perswal workers dev data for stock and crypto bars, and the Yahoo finance chart query.
The automated trading fund manager notes the following premises: 1. US leaders AXTI and SNDK have reported revenue and margin acceleration, as seen in observations 23630-31, but their extreme measured volatility of 166.42% and 136.27% and prior announcements mean there is no fresh surprise in their estimates. SNDK's whole share price of 1692.59 exceeds the fund's price of 187.22, and fractions of the share entail an exit from RTH only. 2. WOLF's 1556% rank from September 29, 2025, to the present is due to its emergence from Chapter 11, but the old and new share economics have not been reconciled, so this rank is rejected pending a corporate action audit. 3. Global ASML's 12-1 rank with a 120.67% increase in local currency accompanies a Q2 free cash flow of 1317m EUR, but its H1 free cash flow was -1290.9m EUR, and its accounts receivable were 4229.6m EUR; both periods can be true, and receipts may lag growth, which is not proof of bad debt. 4. HSBC's local currency increase of 61.40% and H1 return on tangible equity of 18.2% are accompanied by a common equity tier 1 ratio of 14.1% versus 14.9% and an expected credit loss of 47 basis points versus 42 basis points; profitability does not eliminate credit or valuation risk. In conclusion, the manager will watch operating leaders and not override unvalidated quantitative entry with fundamentals. The strongest counterargument is that persistent demand can justify rich prices, but this requires cash conversion and an independent costed rule, not assumed mean reversion. The sources of this information are the investors' pages of AXT, Sandisk, Wolfspeed, and HSBC, as well as the US Securities and Exchange Commission's archives, with original dates of July 15, July 30, August 4, and August 5, 2026, and WOLF's 2025 data. The manager acknowledges issuer bias and notes that the SEC copy is not independent corroboration.
This report is based on premises that include evidence for momentum from a 2014 study by AQR, which is a type of investment management firm. The study, found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf, is about fact and fiction in momentum investing. The report also references a frozen note from 2024 about nine original sector ETFs, which are a type of investment fund. The note explains that the prior-month-end close price divided by the close price 189 days earlier, minus 1, is used to rank the top 3 sectors. The next session's open price is then used, with three fixed independent cash sleeves and no leverage, which means borrowing money to invest. The report also mentions a drop-rank monthly or 63-session exit, which is a strategy for selling investments, and a 2ATR stop and 6ATR target, where ATR stands for Average True Range, a measure of price volatility. Additionally, a 2R daily high makes the break-even point effective the following day. A gap open is used as an actual proxy, with the same bar stop first. The report also references data from https://runway.adityaperswal.workers.dev/data/stock-bars, which was requested from 2009 but only received from 2016 onward. The warm-up period means the first 252 rule trades from 2017, with no pre-2016 data. The frozen 2019 split was retained and not moved to improve results. The vectorbt long-only cash-sharing strategy is used, with every daily equity and trade count reconciled. A fee of 1 basis point each side, plus a 15 basis point slip, is applied. Corporate action total return is not independently rebuilt, and no taxes, cash yield, or operator costs are included. The results show 190 trades, with a mean return of 0.625286%, and an entry month cluster of 3000 bootstrap CI, which is a measure of confidence interval, of [-0.437693, 1.761003]%. The p-value is 35.2632%, the W-value is 8.1038%, and the L-value is 3.4484%. The plug-in half Kelly risk is 0.038580. The primary 252 training was negative, but the positive 189 neighbor cannot replace the pre-registered rule. The historical holdout may overlap prior research, and the daily break-even proxy is not 15-minute live proof. The report rejects live adoption and retains the positive holdout for prospective research. The artifacts are found in research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on two premises. The first premise is that a 2014 study by AQR, which ranked stocks by momentum over a 12-month period, skipping the most recent month, provides evidence for momentum investing, but this specific strategy does not follow that approach. The study can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The second premise, noted in Frozen note 2124, involves nine original sector ETFs, where the momentum is calculated as the difference between the closing price 21 days ago and 189 days ago, and the top three positive sectors are selected. The next session's open is used, with three fixed independent cash sleeves and no leverage. The strategy exits positions monthly or after 63 sessions, using a stop-loss of 6 ATR and a target of 2 ATR. If the daily high is 2R above the entry price, the stop-loss becomes effective the following day. A gap open is used as a proxy for the actual open, with the same bar stop as the first stop. The data used was requested from 2009, but only data from 2016 onwards was received. The warm-up period means that the first 252 trades in 2017 were used to initialize the strategy, with no data used before 2016. The 2019 split was retained and not moved to improve results. The strategy uses vectorbt for long-only cash sharing, with every daily equity and trade count reconciled. The fees are 1 basis point for each side, plus a 15 basis point slip. Corporate actions are included in the total return, but not independently rebuilt, and no taxes, cash yield, or operator costs are included. The strategy made 55 trades, with a mean return of 0.497706%, and an entry month cluster of 3000 bootstrap CI of [-1.106902, 2.078437]%. The plug-in half Kelly risk is 0.042640. The primary 252 training period was negative, but the positive 189 neighbor cannot replace the preregistered rule. The historical holdout may overlap prior research, and the daily BE proxy is not a 15-minute live proof. The strategy is rejected for live adoption, but the positive holdout is retained for prospective research. The research artifacts can be found in research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on premises that include evidence for momentum from a 2014 AQR study, which is a research paper on fact and fiction in momentum investing. The study can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The report also references a frozen note from 2024, which details a strategy using nine original sector ETFs, where the prior-month-end close is compared to the close 189 days prior, and the top three positive sectors are selected. The next session's open is used, with three fixed independent cash sleeves and no leverage. The strategy includes a monthly or 63-session exit, with a stop loss set at 6 ATR, which stands for Average True Range, a measure of volatility. The strategy also includes a target at 6 ATR and a daily high that makes the break-even point effective the following day. A gap open is used as an actual proxy, with the same bar stop as the first stop. The report uses data from 2016 onward, with a warm-up period that means the first 252 trades in 2017 are used, and there is no pre-2016 data. The frozen 2019 split is retained and not moved to improve results. The vectorbt, which is a backtesting library, is used for long-only cash sharing, with every daily equity and trade count reconciled. The fee is 1 basis point, or 0.01%, each side, plus a 5 basis point slip, which is a small cost. The corporate action total return is not independently rebuilt, and there are no taxes, cash yields, or operator costs. The results show 188 trades, with a mean return of 0.890330%, and an entry month cluster of 3000 bootstrap confidence intervals of [-0.188323, 2.061320]%. The p-value is 36.1702%, the W-value is 8.3338%, and the L-value is 3.3276%. The plug-in half Kelly risk is 0.053417, which is a measure of risk. The primary 252 training is negative, and the positive 189 neighbor cannot replace the pre-registered rule. The historical holdout may overlap prior research, and the daily break-even proxy is not a 15-minute live proof. The report rejects live adoption and retains the positive holdout for prospective research. The artifacts of the research can be found in the files research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on two premises. The first premise is that a 2014 study by AQR, which ranked momentum based on a 12-month period with a 1-month skip, is evidence for momentum, but this adapter does not use that method. The study can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The second premise, from a frozen note numbered 2124, involves nine original sector ETFs and a specific trading strategy. The strategy involves calculating the momentum of each sector based on the prior month's end close and the close 189 days prior, then selecting the top three positive sectors. The next session's open is used to determine the trade, with three fixed independent cash sleeves and no leverage. A stop-loss is set at 6 ATR, which stands for Average True Range, a measure of volatility. The strategy also includes a target at 6 ATR and a daily high that makes the break-even point effective the following day. A gap open is used as an actual proxy, with the same bar stop first. The data used for this strategy was requested from 2009, but only data from 2016 onward was received. The warm-up period means that the first 252 trades in 2017 were used to establish the baseline, with no data used from before 2016. The frozen 2019 split was retained and not moved to improve results. The vectorbt long-only cash-sharing strategy was used, with every daily equity and trade count reconciled. A fee of 1 basis point, or 0.01%, was charged on each side, plus a 5 basis point slip. Corporate actions and total return were not independently rebuilt, and no taxes, cash yield, or operator costs were considered. The results show 54 trades, with a mean return of 0.689527%. The entry month cluster had a 95% confidence interval of -0.960180% to 2.353536%, with a p-value of 37.0370% and a W-value of 5.9756%. The plug-in half Kelly risk was 0.057695. The primary 252 training period was negative, and the positive 189 neighbor cannot replace the pre-registered rule. The historical holdout may overlap prior research, and the daily break-even proxy was not live-proofed at 15 minutes. As a result, live adoption was rejected, but the positive holdout was retained for prospective research. The artifacts from this research can be found in the files research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on premises that include evidence for momentum from a 2014 study by AQR, which can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The report also references a frozen note, number 2124, which describes a strategy using nine original sector ETFs. The strategy involves ranking these ETFs based on their performance over the past 252 days, excluding the most recent month, and then selecting the top three. The next session's open is used to determine the entry point, with three fixed independent cash sleeves and no leverage. The strategy also includes a stop-loss and target, with a daily high of 2R making the stop-loss effective the following day. A gap open is used as a proxy, with the same bar stop being the first stop. The data used for this report was requested from 2009, but only data from 2016 onward was received. The warm-up period means that the first 252 rule trades were in 2017, with no pre-2016 data used. The frozen 2019 split was retained and not moved to improve results. The vectorbt long-only cash-sharing strategy was used, with every daily equity and trade count reconciled. The fees were 1 basis point on each side, plus a 15 basis point slip. The corporate action total return was not independently rebuilt, and no taxes, cash yield, or operator costs were included. The results show 190 trades, with a mean return of 1.011710%, and an entry month cluster of 3000 bootstrap CI of [-0.048485, 2.130268]%. The p-value was 38.4211%, the W-value was 8.2380%, and the L-value was 3.4970%. The plug-in half Kelly risk was 0.061405. The primary 252-day training period showed negative results, but a positive 189-neighbor cannot replace the pre-registered rule. The historical holdout may overlap prior research, and the daily BE proxy is not a 15-minute live proof. As a result, live adoption is rejected, but the positive holdout is retained for prospective research. The artifacts from this research can be found in the files research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on two premises. First, a 2014 study by AQR found evidence for momentum, which is a trading strategy that involves buying assets that have performed well in the past. The study is available at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. Second, a frozen note from 2021, note 2124, describes a trading strategy that uses nine original sector ETFs and a specific set of rules to buy and sell assets. The rules include using the prior-month-end close price and the close price 252 days ago to determine which assets to buy, and using a stop-loss order to limit losses. The strategy also uses a target price, which is six times the average true range, a measure of volatility. If the daily high price is two times the average true range above the entry price, the stop-loss order is moved to the break-even point the next day. The strategy uses a gap open as a proxy for the actual open price, and the same bar stop is used as the first stop. The data used for this strategy was obtained from https://runway.adityaperswal.workers.dev/data/stock-bars, but only data from 2016 onwards was available, although data from 2009 was requested. The first 252 trades in 2017 were used as a warm-up period, and no data from before 2016 was used. The strategy was split in 2019, but the split was not moved to improve the results. The vectorbt library was used to backtest the strategy, which is a long-only strategy that shares cash every day and reconciles the equity and trade count daily. The fee for each trade is 1 basis point, plus a 15 basis point slip. The total return does not include corporate actions, taxes, cash yield, or operator costs. The strategy made 48 trades, with a mean return of -0.511591%. The entry month cluster had a confidence interval of [-2.046171, 0.966631]%, and the p-value was 33.3333%. The W-score was 4.4882%, and the L-score was 3.0115%. The plug-in half Kelly risk was 0.000000. The primary 252-day training period had a negative return, and the positive 189-neighbor cannot replace the preregistered rule. The historical holdout period may overlap with prior research, and the daily break-even proxy is not a 15-minute live proof. Therefore, the strategy is not recommended for live adoption, but the positive holdout is retained for prospective research. The artifacts of this research are available in the files research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on premises that include evidence for momentum from a 2014 AQR study, which is a research paper by AQR, a company that studies investment strategies. The study can be found at https://www.aqr.com/-/media/AQR/Documents/Journal-Articles/JPM-Fact-Fiction-and-Momentum-Investing.pdf. The report also references a frozen note, number 2124, which describes a trading strategy using nine original sector ETFs, or exchange-traded funds, which are a type of investment fund. The strategy involves ranking the ETFs based on their performance over the prior 252 days, excluding the last 21 days, and then selecting the top three. The next session's open price is used, and three fixed independent cash sleeves, or portions of the investment, are used, with no leverage, or borrowing. The strategy also includes a stop-loss, which is a rule to sell a security when it falls to a certain price, and a target price, with a daily high that makes the stop-loss effective the following day. A gap open, which is when a security's price opens higher than its previous close, is used as a proxy, or substitute, for the actual price. The report also references a data request to a website, https://runway.adityaperswal.workers.dev/data/stock-bars, which was supposed to provide data from 2009, but only provided data from 2016 onward. The report states that the first 252 days of trades in 2017 were used as a warm-up period, and that there was no data before 2016. The report also states that the data from 2019 was retained, and not moved to improve the results. The report uses a vectorbt, or a type of backtesting software, to test the strategy, and reconciles the daily equity and trade count. The report also states that there is a fee of 1 basis point, or 0.01%, each side, plus a 5 basis point slip, which is a small reduction in the price of a security. The report provides statistics on the strategy's performance, including 189 trades, with a mean return of 1.268803%, and a confidence interval of [0.218449, 2.374473]%. The report also states that the primary 252-day training period was negative, but the positive 189-neighbor, or nearby, period cannot replace the pre-registered rule. The report concludes that the historical holdout, or test period, may overlap with prior research, and that the daily break-even, or BE, proxy is not a live proof. The report rejects live adoption, but retains the positive holdout for prospective research. The report references artifacts, or files, including research/test_sector.py, sector_results.json, and percelltrades/returns.
This report is based on premises that include evidence for momentum from a 2014 AQR study, which is a type of investment research, and a frozen note from 2124. The study found that momentum, or the tendency of stocks to continue rising if they have been rising, is a real effect. The frozen note described a trading strategy that used nine original sector ETFs, which are a type of investment fund, and ranked them based on their performance over the past 252 days, with a 12-month skip of one month. The top three performing sectors were selected, and the next session's open was used to determine the trade. The strategy used three fixed independent cash sleeves, which means that the investment was divided into three parts, and no leverage, which means that no borrowed money was used. The strategy also included a stop-loss, which is a rule to sell a stock if it falls by a certain amount, and a target, which is a price at which to sell a stock. If the daily high was two times the risk, the stop-loss was moved to the break-even point, which is the price at which the trade becomes profitable. The data used for this strategy was obtained from a website, but it only went back to 2016, although it was requested to go back to 2009. The strategy was tested using a method called vectorbt, which is a type of backtesting, and the results were reconciled, which means that they were checked for accuracy. The strategy had a fee of one basis point, which is a unit of measurement for interest rates, on each side of the trade, plus a five basis point slip, which is a fee for buying or selling a stock. The strategy made 47 trades, with a mean return of -0.085267%, and the results were analyzed using a method called bootstrapping, which is a way of estimating the accuracy of a statistic. The results showed that the strategy was not effective, and it was decided not to adopt it for live trading. The artifacts of this research, including the code and results, are available in files called research/test_sector.py, sector_results.json, and percelltrades/returns.
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 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.
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 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 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.
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.