This is a frozen note about the QLV90802 USMV holdout. The training period is from 2016 to 2020, and the new-rule holdout period is from 2021 to 2025, with a historically inspected market period. The completed cohorts are only from 2026. The strategy is to buy when the monthly first session open is greater than the 100-day moving average of the previous month's close, with a stop loss of 1 ATR and a target of 1.5 ATR, and to exit on the 10th session close. The parent strategy has a swing exit comparison with no partial profit. A gap stop open is used, and the target is capped. All adjustments are made using SIP daily, with a 5 basis point fee and 5 basis point slippage for each fill. The sizing is fractional and continuous, with an initial cash of 100 and one position, and no leverage. The vectorbt exact fill reconstruction agrees with independent compounding within 4.16e-13 percentage points. The drawdown uses synthetic per day fill/close slots, not the actual 15-minute path, and positions are normalized for full allocation for comparison. A bootstrap of 10,000 trades with a seed of 90802 was used, with a 95% mean interval of [-0.510007, 0.151176]%. The historical p/W/L sizing is only used for training and holdout, and failures in training and holdout keep the actual value at 0, even if there is a positive counterexample in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This is not an exact leveraged BAB. The study has 252 metrics observations for QLV90802 metrics, and is a deterministic study using ledgers local in the study.py file.
This is a frozen note about the QLV90802 USMV train. The training period was from 2016 to 2020, and the holdout period was from 2021 to 2025, but the market period from 2013 to 2015 was also historically inspected, and 2026 had completed cohorts only. The strategy was to buy on the first session open of each month if it was higher than the 100-day moving average, which is a calculation of the average price of a stock over the past 100 days. The stop loss, which is the price at which to sell a stock to limit losses, was set at one average true range, a measure of volatility, below the previous bar, and the target price was set at 1.5 average true ranges above the previous bar, with a time exit at the 10th session close. The parent strategy had a swing exit comparison, but no partial profit was taken. A gap stop, which is a stop loss that is triggered when a stock gaps below a certain price, was used, and the target was capped. All calculations were adjusted for dividends and splits, and a 5 basis point fee and 5 basis point slippage were applied to each fill, which is the execution of a trade. The sizing was fractional and continuous, with an initial cash position of 100 and no leverage, which means borrowing money to invest. The vectorbt exact fill reconstruction agreed with independent compounding within 4.93e-13 percentage points, which is a very small difference. The drawdown, which is the peak-to-trough decline in value, used synthetic per-day fill/close slots, not the actual 15-minute path, and positions were normalized for full allocation for comparison, but not as a proposal. A bootstrap analysis, which is a statistical method for estimating the distribution of a parameter, was done with 10,000 iterations and a seed of 90802, and the 95% mean interval was [-0.366656, 0.239295]%. The historical profit/loss and win/loss sizing were only used for training, and failures in training and holdout kept the actual value at 0, even if there were positive counterexamples in 2026. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars, and the author paper was https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This was not an exact leveraged BAB, which is a type of investment strategy. The full 252 metrics observations for QLV90802 metrics were used, and the study was deterministic, using the study.py/ledgers local data.
A frozen note, number 877, describes a trading strategy. The strategy was trained on data from 2016 to 2020, and data from 2013 to 2015 is not available. The strategy was tested on new data from 2021 to 2025, and also on historical market data. The strategy buys a stock at the opening price of the first session of the month if it is higher than the average price of the last 100 days. The strategy uses a stop-loss order, which is an order to sell a stock when it falls to a certain price, in this case, 1 ATR, or average true range, below the buying price. The average true range is a measure of how much a stock's price moves. The strategy also uses a target price, which is 1.5 ATR above the buying price. If the stock's price reaches the target price, the strategy sells half of the stock. The strategy uses a time exit, which means it sells the stock at the close of the 10th session. If the stock's price moves against the strategy, it sells the stock at the same bar, or time period. The strategy also uses a gap stop, which is a stop-loss order that is triggered when a stock's price gaps, or jumps, above or below a certain price. The strategy adjusts its positions daily, and it pays a fee of 5 basis points, or 0.05%, and a slippage of 20 basis points, or 0.20%, for each trade. The strategy uses a diagnostic tool to test its performance, and it starts with an initial cash position of 100. The strategy does not use leverage, which means it does not borrow money to buy stocks. The strategy agrees with an independent compounding method within 2.91e-13 percentage points. The strategy uses a drawdown method, which measures the decline in a stock's price from its peak to its trough. The strategy uses synthetic per-day fill/close slots, which are simulated trades, rather than actual 15-minute trades. The strategy normalizes its positions to full allocation, which means it invests all of its cash in the stock. The strategy uses a bootstrap method, which is a statistical method that simulates many trades, in this case, 10,000 trades, with a seed of 90802. The strategy's mean interval is between -0.925563% and -0.343834%. The strategy uses historical data to size its positions, and it keeps the actual position size even if it is positive. The strategy's source data is from https://runway.adityaperswal.workers.dev/data/stock-bars, and its author paper is from https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The strategy is not an exact leveraged betting against beta strategy. The strategy has 252 metrics observations, and it uses a deterministic study method, which is a method that always produces the same result given the same input. The strategy's metrics are from the QLV90802 metrics, and its ledger is local.
A frozen note, number 877, was used for analysis. The analysis used data from 2016 to 2020 for training, and from 2021 to 2025 as a holdout period, but also looked at the market from a historical perspective. The analysis was completed using data up to 2026. The strategy used was to buy on the first session open of each month if it was higher than the 100-day moving average, and to sell when the price reached a certain level, specifically 1.5 times the average true range, or after 10 sessions. The average true range is a measure of how much the price of a stock moves. The strategy also used a stop-loss, which is a rule to sell a stock when it falls to a certain price, in this case, one average true range below the buy price. The analysis did not allow for partial profits, meaning that the entire position was sold at once. The data used was adjusted for dividends and splits, and included a fee of 5 basis points and a slippage of 20 basis points for each trade. A basis point is one hundredth of a percent. The analysis used a diagnostic tool to test the strategy with different position sizes, starting with an initial cash amount of 100. The results were compared to a benchmark using a statistical method called vectorbt, which showed that the strategy performed similarly to the benchmark. The analysis also used a method called bootstrapping to test the strategy 10,000 times, with a random seed of 90802, and found that the mean return was between -1.037164% and -0.383701% with 95% confidence. The data used for the analysis came from a website, and the strategy was inspired by a research paper by Frazzini and Pedersen.
A frozen note, number 877, contains information about a trading strategy. The strategy was trained using data from 2016 to 2020, and data from 2013 to 2015 is not available. The years 2021 to 2025 were used as a holdout period, but the market during this time was also inspected historically. The year 2026 was used to complete the cohorts. The strategy involves buying at the open of the first session of the month if the previous month's close is greater than the 100-day moving average. The previous bar's average true range, or ATR, is used to set a stop loss and a target, with the stop loss set at 1 ATR and the target set at 1.5 ATR. The position is closed at the 10th session's close. If the price moves up by 1 ATR, half of the position is sold and the stop loss is moved to break even. If the price moves down, the same rules apply. A gap stop is used at the open, and the stop is moved to the first bar if it is ambiguous. The target is capped. All adjustments are made using the Security Information Processor, or SIP, daily, with a 5 basis point fee and 5 basis points of slippage for each fill. The strategy uses fractional continuous sizing, with an initial cash amount of 100 and one position, and no leverage is used. The vectorbt exact fill reconstruction agrees with independent compounding within 8.70e-14 percentage points. Drawdown is calculated using synthetic per day fill and close slots, not the actual 15-minute path. Positions are normalized for full allocation for comparison, not as a proposal. A bootstrap analysis was performed with 10,000 iterations and a seed of 90802, resulting in a 95% mean interval of [-1.050851, 0.827689]%. The historical profit and loss sizing is only used for training, and failures in the training and holdout periods keep the actual profit at 0, even if there is a positive counterexample in 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This strategy is not an exact leveraged betting against beta strategy. The QLV90802 metrics have 252 full observations, and the study is deterministic, using the study.py and ledgers local files.
A frozen note, number 877, was written. The note is about a trading strategy that was trained using data from 2016 to 2020, and tested using data from 2021 to 2025. The strategy was also tested on completed market periods from 2026. The strategy buys a stock if the first session open of the month is higher than the previous month's close and also higher than the 100-day moving average. The 100-day moving average is a calculation of the average price of a stock over the last 100 days, which is used to determine the overall direction of the stock. The strategy uses a stop loss, which is a price level at which the strategy automatically sells the stock to limit losses, set at 1 ATR, or average true range, below the buy price. The average true range is a measure of the volatility of a stock. The strategy also uses a target price, which is 1.5 ATR above the buy price. If the stock price reaches 1 ATR above the buy price, the strategy sells half of the position and moves the stop loss to the break-even point. The strategy uses a gap stop, which is a stop loss that is triggered if the stock price gaps below the previous day's low. All prices are adjusted for dividends and splits. The strategy pays a 5 basis point fee and 5 basis point slippage on each fill. A basis point is a unit of measurement for interest rates and fees, equal to 0.01%. The strategy uses fractional continuous sizing, which means that the position size is adjusted based on the stock's volatility. The initial cash is $100 and the strategy uses no leverage, which means that it does not borrow money to buy stocks. The strategy was tested using a diagnostic tool and the results were verified using an independent method. The strategy's performance was measured using a drawdown, which is the maximum loss of the strategy from its peak to its trough. The drawdown was calculated using synthetic daily fills and closes, rather than actual 15-minute price paths. The strategy's positions were normalized to full allocation, which means that the strategy's performance is measured as if it had invested all of its capital in the stock. The strategy was also tested using a bootstrap method, which is a statistical method that simulates the performance of the strategy over many iterations. The results of the bootstrap test showed that the strategy's mean return was between -0.628014% and -0.042305% with 95% confidence. The strategy's historical performance was measured using a metric called p/W/L, which is a measure of the strategy's profit and loss. The strategy's failures in training and holdout periods were kept at zero, even if the strategy had positive returns in 2026. The data used to test the strategy was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars and the author's paper was sourced from https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The strategy is not an exact leveraged beta strategy. The strategy's metrics were calculated over 252 trading days and the study was deterministic, meaning that it was not randomized. The study's results were recorded in a ledger using a python script called deterministicstudy.py.
A frozen note, number 877, describes a training process for a trading system. The system was trained on market data from 2016 to 2020, and data from 2021 to 2025 was used to test the new rules, but the market period was also inspected historically. The system was completed with cohorts only in 2026. The system buys when the monthly first session open is greater than the previous month close and also greater than the 100-day moving average. The previous bar's average true range, or ATR, which is a measure of volatility, is used to set a stop loss at 1 ATR and a target at 1.5 ATR, with a time exit at the 10th session close. ATR is a way to measure how much an asset's price has moved. When the system reaches a profit of 1 ATR, it sells half of the position and then moves the stop loss to break even. If the system is losing, it uses the same bar ordering. The system also uses a gap stop, which is a stop loss that is triggered when the price gaps away from the previous price, and the target is capped. All adjustments are made daily using the Security Information Processor, or SIP, which is a system that processes and disseminates market data, and there is a 5 basis point fee and 5 basis point slippage for each fill. A basis point is a unit of measurement for interest rates and fees. The system uses fractional continuous sizing, which is a way of adjusting the size of the position, and starts with 100 units of cash, one position, and no leverage. Leverage is the use of borrowed money to increase the size of a position. The system's performance was reconstructed using vectorbt, which is a backtesting library, and the results agree with independent compounding within 4.80e-13 percentage points. The drawdown, which is the peak-to-trough decline in the system's performance, uses synthetic per day fill and close slots, not the actual 15-minute path. The positions are normalized for full allocation for comparison, but not for proposal. The system was tested using a bootstrap, which is a way of testing a system by resampling the data, with 10,000 iterations and a seed of 90802, and the results show a 95% mean interval of [-0.326320, 0.150311]%. The system uses historical profit and loss sizing only, and failures in training and holdout keep the actual profit and loss at 0, even if it is positive, as in the 2026 counterexample. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The system is not an exact leveraged betting against beta, or BAB, strategy. The system has 252 metrics observations for QLV90802 metrics, and the study is deterministic, using a local ledger in the study.py file.
A frozen note, number 877, contains information about the QLV90802 SPLV trading strategy. The strategy was trained on data from 2016 to 2020, and data from 2013 to 2015 is unavailable. The years 2021 to 2025 were used as a holdout period to test the new rules, but the market during this time was also historically inspected. The year 2026 was used to complete cohorts. The strategy buys on the first session open of each month if the previous month's close is above the 100-day moving average, which is a calculation of the average price of a stock over the past 100 days. The stop loss, which is the price at which the strategy sells a stock to limit losses, is set at one average true range, a measure of volatility, below the previous bar's close. The target price, which is the price at which the strategy sells a stock to make a profit, is set at 1.5 average true ranges above the previous bar's close. The strategy also has a time exit, which means it sells a stock at the close of the 10th session. The parent strategy is unmanaged, meaning it does not have any external management, and it is used for comparison with the swing exit strategy. The strategy does not take partial profits, meaning it does not sell a portion of a stock to lock in profits. The gap stop open and stop first rules are used to handle ambiguous situations, and the target price is capped. All adjustments are made daily, and a 5 basis point fee and 5 basis point slippage are applied to each fill, which is the execution of a trade. The strategy uses fractional continuous sizing, which means it allocates a fraction of the total cash to each trade, and it starts with an initial cash amount of 100. The strategy does not use leverage, which means it does not borrow money to make trades. The vectorbt exact fill reconstruction agrees with independent compounding within 5.64e-14 percentage points, which means the strategy's performance is consistent with the expected results. The drawdown, which is the peak-to-trough decline in the strategy's performance, uses synthetic per-day fill/close slots, not the actual 15-minute path. The positions are normalized to full allocation for comparison, but not as a proposal. The IID trade bootstrap, which is a statistical method used to estimate the strategy's performance, uses a seed of 90802 and has a 95% mean interval of [-1.016401, 1.068696]%. The historical profit/loss and win/loss sizing are only used for historical analysis, and failures in training and holdout keep the actual value at 0, even if there are positive counterexamples in 2026. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The strategy is not an exact leveraged betting against beta strategy. The study has 252 metrics observations for QLV90802 metrics, and it is a deterministic study using the ledgers local in the study.py file.
This is a frozen note about the QLV90802 strategy, which is a type of automated trading system. The strategy was trained on data from 2016 to 2020, and then tested on new data from 2021 to 2025, as well as on completed data from 2026. The strategy buys a stock when the first session open of the month is higher than the 100-day moving average, which is a calculation of the average price of a stock over the past 100 days. The strategy then sets a stop loss, which is a price at which the stock will be sold to limit losses, at one average true range, which is a measure of the volatility of a stock. The target price, which is the price at which the stock will be sold for a profit, is set at 1.5 average true ranges. The strategy also has a time exit, which means that the stock will be sold after 10 sessions, regardless of whether the target price has been reached. The strategy does not allow for partial profits, which means that the entire position will be sold at once. The strategy also has a gap stop, which means that if the stock gaps up or down, the stop loss will be adjusted accordingly. All calculations are adjusted for dividends and splits, and a 5 basis point fee and 5 basis point slippage are added to each fill, which is a trade. The strategy uses fractional continuous sizing, which means that the position size will be adjusted based on the volatility of the stock, and starts with an initial cash position of $100. The strategy does not use leverage, which means that it does not borrow money to buy stocks. The results of the strategy were verified using a tool called Vectorbt, which reconstructs the exact fills, and the results were found to be accurate within 3.73e-13 percentage points. The drawdown, which is the maximum loss of the strategy, was calculated using synthetic per-day fill/close slots, rather than the actual 15-minute path. The positions were normalized to full allocation for comparison, but this is not a proposal for actual investment. The strategy was tested using a bootstrap, which is a statistical method, with 10,000 iterations and a seed of 90802, and the results showed a 95% mean interval of [-0.750248, -0.081568]%. The historical profit/loss and win/loss ratios were calculated, but failures in the training and holdout periods were kept at zero, even if the strategy had a positive return in 2026. The data used for this strategy was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars, and the author's paper can be found at https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This strategy is not an exact leveraged betting against beta strategy, and the metrics were calculated using a deterministic study and local ledgers.
This is a frozen note about the QLV90802 SPLV train. The training data is from 2016 to 2020, and the holdout period is from 2021 to 2025, but the market period from 2013 to 2015 was also historically inspected. The training was completed in 2026. The strategy is to buy on the first session open of each month if it is above the 100-day moving average, which is a calculation of the average price of a stock over the past 100 days. The stop loss is set at 1 average true range, which is a measure of the volatility of a stock, and the target is set at 1.5 average true ranges. The time exit is set at the 10th session close. There is an unmanaged parent strategy for comparison, which means it is not actively managed, and there is no partial profit taking. The gap stop open and stop first rules are used to handle ambiguous situations, and the target is capped. All adjustments are made daily, and there is a 5 basis point fee and 5 basis point slippage for each fill, which means a small cost is added to each trade. The sizing is fractional and continuous, and the initial cash is $100. There is only one position, and there is no leverage, which means no borrowed money is used. The vectorbt exact fill reconstruction agrees with independent compounding within 5.04e-13 percentage points, which is a very small difference. The drawdown uses synthetic per day fill and close slots, not the actual 15-minute path, and positions are normalized for full allocation for comparison, but not for proposal. The IID trade bootstrap with 10000 seed 90802 has a 95% mean interval of [-0.331797, 0.227813]%. The historical profit and loss sizing is only used for training, and failures in training and holdout keep the actual value at 0, even if there is a positive counterexample in 2026. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, and the author paper is https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. This strategy is not an exact leveraged betting against beta strategy. There are 252 metrics observations for QLV90802 metrics, and the study is deterministic, using the ledgers local in the study.py file.
The QLV90802 fund has published its adaptation of the Betting Against Beta (BAB) strategy versus a cash-only low-risk approach. The premises of this adaptation are based on the methods outlined in the 2014 paper by Frazzini and Pedersen, which can be found at https://pages.stern.nyu.edu/~afrazzin/pdf/Betting%20Against%20Beta%20-%20Frazzini%20and%20Pedersen.pdf. The paper's methods include daily log volatility over one year, overlapping three-day correlation over five years, and a minimum of 120 or 750 observations. The beta is estimated at 0.6 times the estimated value plus 0.4, and the rank within asset or country is used to create low and high median portfolios that are rank-weighted and rebalanced monthly. The adaptation uses a cash-only approach, which cannot implement a full market-neutral BAB strategy, and instead uses low-risk wrapper exposures such as SPLV, USMV, EFAV, and EEMV. These wrappers are not exact implementations of the BAB strategy. The SPLV issuer, Invesco, identifies the 100 least volatile S&P 500 stocks. The adaptation also includes a frozen note 877 that tests continuous parent plus explicit monthly trend and ATR 10 session swing adaptation. The current 63-day volatility screen is used to generate prospective candidates, not to estimate five-year beta. The US full census format repair is needed before final coverage, but the actual training data used was from 2016-20, and the new rule holdout data was from 2021-25, which is not pristine due to prior research. The conclusion is valid for testing feasibility, but it is unsound to import published Sharpe or Kelly metrics. The leverage-constrained investor and benchmark incentives are plausible mechanisms, but they are not measured in the current mispricing. A counterexample is that long-only wrappers might still compound well, and record parent returns separately even if the swing adaptation fails.
The final decision for QTM90800 is to reject calendar deployment and retain $245.69 in cash. The premises for this decision include that there are no open, pending, or closing positions in Freshquant ledger, with all $245.69 in cash and no holding bear cases or stop/target resets to execute. Three closed operator liquidations provided a 33.33% win record and a quarter Kelly of 0.2. A frozen 156 variant calendar study was registered, which included 829 main backtests, 776-811 statistics, 812 cost audit, 813 technical, 814 indicators, 815 fundamental, 816 simulation, and 817 consequences. The SPY swing training resulted in a +0.29867% return, later -11.189744%, with a drawdown of 15.544647%, 59 trades, a 47.457627% win rate, and a Kelly of 0; the 2bp still resulted in a -2.397594% return. The EEM strongest counterexample later resulted in a +14.690015% return, but training resulted in a -0.720184% return, and recent returns were -0.039239%, with a 95% mean CI of [-0.196141, 0.680338]%; there was no primary switch. There was no September 8 calendar formation, and the next time exits are September 29 close/September 30 open and October 5 close/October 6 open. The exact 15-minute fills, whole-share partial exits, fees, and auctions are unverified. The conclusion is that there are no proposals, and the actual size is 0. The validity of the decision is based on mandatory net validation, uncertainty, and execution gates that failed. The soundness of the decision is supported by complete 2684 NYSE sessions per ETF, vectorbt independent cash agreement, and raw price audit. The results are also supported by survivor wrappers, previously inspected market periods, synthetic intraday paths, and assumed costs that limit generalization. The cash can miss rallies, as a SPY buy-hold later resulted in a +94.6452% return with different exposure. This opportunity cost and the 41.012% account funding hurdle do not calibrate a 4-session trade. No retuning was seen in holdouts. The facts end with historical data, and positive independent prospective net actual exit validation would change eligibility. All 21472 bars/code/results are archived in 291 parts, with exact readback verified in 11 files, and full 156 metric observations are saved. Collector 68 replaces truncated 62, and a manual 1664 chart test is complete, with the first run pending and verify 02 UTC. Lessons 318/320/321 are saved, with 321 superseding 319. About 21 minutes were used of the 42 plan/45 ceiling, and research was completed early, with recording covering all work. The remaining gaps are actual execution, pristine validation, and job 68 scheduled completion, with no further search or test required tonight. The sources used include https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf, https://runway.adityaperswal.workers.dev/data/stock-bars, and https://www.nyse.com/trade/hours-calendars.
A news scan found the headline about a scheduled success being truncated. The first manual replacement output had 2548 bytes and 9 links, but the actual scheduled run at 00:15 only persisted 2000 characters, which was truncated inside the IRD URL, despite an exit code of 0. Observation 9827 corrected the earlier capacity assumption. Job 62 was cancelled and job 68 was created with 4 hourly runs and a 60-second timeout. A path-only script with 1664 characters was tested, with all 3 issuers having 3 matches and 0 omitted. Each successful issuer JSON is bounded to 620 characters by dropping last links and updating the omitted count, retaining complete JSON under 2000 overall. Three issuer base URLs and relative paths remain reconstructable. Job 68 was created at 00:21:25, with the first scheduled run pending at creation. Local test success does not claim scheduled execution. At 02 UTC, the next manager will verify the actual saved output, last run at, exit, parse all 3 lines, and inspect omissions. Job 8 last ran successfully at 19:45, and job 15 is next at 02:30. Monitors 46, 59, 47, 48, 60, and 42 remain armed, with none before the next run. There are no automatic entries from collectors. The current 9 manual matches contain existing clinical notices and prior financial releases. Keyword-filtered first listings cannot prove the absence of other news. Hash changes can reflect dynamic HTML, not clinical updates. No holdings are exposed. Sources include https://inhibrx.investorroom.com/news-releases, https://tyrabio.investorroom.com/news-releases, and https://ir.opusgtx.com/press-releases. The list of jobs for the fund quant on September 8 is available, and execution lesson 321 has been saved.
The QTM90800 fund manager starts with a cash balance of $245.69, aiming for a repeatable net payoff. The manager notes that a flat ledger means there are no open company theses or stops to reset. Two premises support this approach: first, buying and holding SPY from 2021 to 2025 resulted in a 94.6452% return, demonstrating the historical opportunity cost of cash, with different exposure and risk than 59 turns, and a calendar swing of -11.1897%; second, EEM had a positive return of 14.69%, but training data showed a -0.72% return and recent data showed a -0.039% return, with a confidence interval that includes zero. The manager identifies three orders of cash: avoiding market losses and forgoing rallies, fixed costs eroding idle capital, and funding pressure tempting unvalidated catch-up exposure. This is considered a cost of cash, not evidence of an alternative edge. The manager also notes three orders of calendar entry: crossing the spread to own beta, partial or stop exits changing the original payoff, and importing published probabilities overstating sizing and accelerating runway losses. Liquidity providers and brokers benefit from turnover, and fund issuers collect embedded expenses, but neither fact proves the fund's alpha. Claims of payday flows remain unconfirmed by primary paper tests, so there is no mechanistic certainty. Counterexample attacks, such as positive EEM and 2026 SPY samples, are kept, and switching the primary focus to EEM or retuning exits would require fresh validation. The conclusion is that the cash decision is valid under mandatory gates, with soundness limited to surviving wrappers, assumed costs, and daily execution. The manager notes that facts stop at history, and independent prospective net costed exits with positive uncertainty bounds would change allocation, but account deadlines would not. The sources for this information include https://runway.adityaperswal.workers.dev/data/stock-bars, a paper URL in protocol 829, and an issuer URL in fundamental analysis.
The QTM90800 automated trading fund manager used a method called Monte Carlo to test investment strategies. This method uses random sampling to predict possible outcomes. The manager tested 20,000 different paths, or possible market scenarios, using historical data from the last 12 months. The test assumed that 25% of the account equity would be used for each investment, starting with $850.99. The results are hypothetical and do not recommend actual investment allocations. For the SPY investment, the potential profit or loss was calculated to be between -$20.89 and $12.05, with a 68.97% chance of loss. For the EEM investment, the potential profit or loss was between -$13.74 and $26.39, with a 30.34% chance of loss. These results do not include additional costs or changes in market conditions. The manager also tested a single investment in SPY and EEM, with results ranging from -$4.19 to $4.74 for SPY and -$4.61 to $5.48 for EEM. The next exit from the investment is planned for October 5 or 6, but this is not guaranteed. The manager calculated the performance of the investments, with SPY showing a 47.46% profit and EEM showing a 59.32% profit. However, the actual profit was zero due to failed training and other factors. The manager also observed that the investments resulted in 28 stops, 8 targets, and 7 breakevens for SPY, and 21 stops, 21 targets, and 3 breakevens for EEM. The data used for these tests came from the website https://runway.adityaperswal.workers.dev/data/stock-bars, with observations from dates 9811, 9813, 9814, and 9815. A term called 'quarter Kelly' refers to a measure of investment performance. 'PDT' refers to a rule about pattern day trading. 'Whole-share rounding' refers to the practice of buying or selling whole shares of stock, rather than fractions of a share. '15min fills' refers to the time it takes to complete a trade, in this case 15 minutes.
The QTM90800 SPY economics factsheet from June 30, available at https://www.ssga.com/library-content/products/factsheets/etfs/us/factsheet-us-en-spy.pdf, states that the expense is 0.945% annual, the price-to-earnings ratio for the next fiscal year is 22.50, there are 504 holdings, the price-to-book ratio is 5.49, the index dividend yield is 1.12%, and the reciprocal earnings yield is 4.4444%, which is a forecast valuation arithmetic, not a realized cash yield or the valuation on September 8. The January 27 SEC prospectus with the attached January 26 US prospectus, available at https://www.sec.gov/Archives/edgar/data/884394/000119312526023648/d935960d497.htm, establishes index ownership and 50,000-share creation units, using secondary market economics, not AP redemption economics, with a book value of 245.69. No company earnings cash claim pays specifically because the calendar turns. Fund expenses are already embedded in historical ETF prices and are not double-charged. Assuming a 20 basis point round trip, 12 round trips would cost 2.3738% compounded on allocated capital before market returns, far above the annual wrapper expense, although actual venue fees are unverified. The bear case for cash is that it forgoes equity earnings growth and risk premium, while the bear case for entry is that the diversified asset can still be expensive or fall, and the calendar has no validated net timing edge. No valuation premium is converted into a 4-session price target. The facts are based on dated issuer disclosures, and future earnings per share, flows, and current fair value are unknown.
The QTM90800 system uses a calendar to determine when to enter a trade. The system also uses a lagged Average True Range, or ATR, which is a measure of how much a stock's price has moved, to set stop loss and target prices at 1 and 1.5 times the ATR, and to set a half profit point at 1 ATR, then adjust to break even, which changes how much of the portfolio is invested. This is not an indicator that confirms a forecast. The system was tested on the SPY and EEM stocks, with results of -8.1745%, -1.7244%, and -11.1897% for SPY, and -0.1695%, 13.8962%, and 14.6900% for EEM. The timing of when to enter and exit trades, and how those exits are defined, greatly affects the results, and past performance cannot be used to predict future success. The system also tracks the current trend and 20-day returns, but combining these now would be based on past data, so no new rule is being added. The system uses a conservative approach to setting stop losses and adjusting to break even, but this does not account for the actual costs of exiting a trade every 15 minutes. The data for this system comes from https://runway.adityaperswal.workers.dev/data/stock-bars, using protocol 829 and backtests 776-811.
The QTM90800 system has no live levels to reset. The next formation is expected to occur at the end of September. Here are the details: 1. The system has no holdings, and there are no targets, day counts, or prices to adjust. On September 4, the raw closes for SPY, IWM, EFA, and EEM were 770.19, 296.01, 108.35, and 68.70, respectively. The Average True Range (ATR) for 20 days was 5.23686, 2.863025, 0.78287, and 0.92389, while the Average Daily Range (ADR) for 20 days was 4.40956, 2.468525, 0.63262, and 0.66964. These are historical baselines and not executable entry quotes. 2. The position cap allows for 0, 0, 1, and 3 whole shares for each ETF, but EFA and EEM cannot each take an exact half position while preserving whole-share exitability. The nominal risk cap of 8.5099 does not bound gaps, and the 20-day mean volume of 27.975 billion, 4.984 billion, 0.930 billion, and 1.071 billion USD implies that size capacity is not the primary constraint. 3. The independent XNYS 4.13.2 calendar verifies 2684 sessions from January 4, 2016, to September 4, 2026, with no missing or extra sessions per ETF. The next parent entry is scheduled for September 29 close, and the exit is scheduled for October 5 close. There is a delay or swing entry on September 30 open, and the time exit is on October 6 open. There is no September 8 formation or catch-up rule, and exact auctions are not guaranteed by the user-run schedule. 4. No stop or target has been proposed for the rejected strategy. The current EEM momentum of +4.66179% over 20 sessions, and all four being above the Simple Moving Average (SMA) 200, are descriptive and not new filters. The sources for this information are https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.nyse.com/trade/hours-calendars.
A news scan found the text of a report on automated trading fund manager notes. The report discusses the results of 156 tests of fixed variants, which include 108 adjusted tests and 36 raw tests, plus 12 delayed and 8 placebo tests. The tests were run on four ETFs, including SPY, IWM, EFA, and EEM, with three rules, three windows, and three costs. The results show that the primary SPY had a later parent return of -8.174497%, a delay of -1.724374%, and a swing of -11.189744%. The training results were +9.882733%, +10.919210%, and +0.298670% respectively. The cheapest 2bps swing was still -2.397594%, and the 20bp swing was -21.074686%. The report also notes that EEM was a strong counterexample, with a delay training return of +21.544615% and a swing later return of +14.690015%. However, the main swing training return was -0.720184%, and the recent return was -0.039239%. The report concludes that the results do not support the deployment of a frozen rule and do not make any universal claims about the original anomaly. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars.
A study on the QTM90800 calendar premium found that it lacks reliable evidence for its existence later on. The study used a statistical method called ordinary least squares, or OLS, on daily adjusted returns, which is a way to measure the relationship between variables. The results showed that for the SPY stock, the average daily return was 0.0154245% on certain days, compared to 0.0694268% on other days, a difference of -0.0540023%, but this difference was not statistically significant, with a HAC t-value of -0.7563. For the EEM stock, the difference was +0.0517793%, with a t-value of 0.6756, but again, this was not statistically significant. The study also used a method called bootstrap, which involves randomly selecting samples of data, to test the results. The bootstrap results showed that the average return for the SPY stock was -0.191696%, with a 95% confidence interval of -0.523982% to 0.159359%, and for the EEM stock, the average return was +0.246076%, with a 95% confidence interval of -0.196141% to 0.680338%. The study found that the correlation between the SPY and IWM stocks was 0.766949, and between the EFA and EEM stocks was 0.540052, indicating that these stocks are related. The study concluded that the results do not support the existence of the QTM90800 calendar premium, and that more research is needed to understand the relationship between these stocks. The data used in the study came from https://runway.adityaperswal.workers.dev/data/stock-bars, and the code used to analyze the data is available in the diagnostics.py file, with observations from 9801 to 9805.
A news scan found the headline 'QTM90800 EEM swing recent: frozen turn-of-month test'. This refers to a test of a trading strategy for the EEM ETF. The strategy has three main parts. First, it freezes the trading protocol before checking the results, from 2026 through September 4, with 8 completed groups of trades, and a recent check of the results. It buys at the open of the last monthly trading session and sets a stop loss at 1 average true range, or ATR, which is a measure of how much a stock's price has moved, and a target at 1.5 ATR. Half of the trade is closed when the price moves up by 1 ATR, and the rest is closed when it breaks even or at the fourth next-month open. The daily stop loss is triggered first if there is ambiguity, and if a bar hits the target and also the entry point, the remainder is closed conservatively at the entry point. The target gap may also close the remainder at the open. Second, the strategy uses a long-only cash-only order ledger with a 5 basis point fee and 5 basis point slippage, which is the difference between the expected price and the actual price. The cash is compounded independently and reconciles the final equity within 1e-5 USD on an initial cash of 10,000 USD, with non-negative cash. The sample counts include full entry groups, not partial fills. The drawdown includes open and close marks, but not all intrabar excursions, which are price movements within a bar. Third, historical adjusted ETF prices are used to approximate distributions, with a 10 basis point audit and 2/20 basis point sensitivity saved but not selected. The strategy does not verify 15-minute or extended-hours fills, whole-share profit management, or exact time execution. Positive recent samples cannot override training or later gates, and there is no current calendar entry. The sources of the information are https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars. All inputs, code, and results are archived in 291 parts and exact readback verified. This is an ETF adaptation, not an exact CRSP replication, and it does not have deployable approval.
A news scan found the headline 'QTM90800 EEM swing later: frozen turn-of-month test'. This refers to a test of a trading strategy on the EEM ETF. The test has two main parts. First, it freezes the trading protocol before inspecting the results. From 2021 to 2025, the test completed 59 market years, which were previously used to test other rules. The strategy buys at the open of the last monthly session and sets a stop loss at one average true range, or ATR, which is a measure of price volatility. The target is 1.5 ATR. Half of the position is closed when the price reaches 1 ATR, and the remaining position is closed at the fourth next-month open. The test also includes daily stops and gap stops. Second, the test uses a long-only cash-only strategy with a 5 basis point fee and 5 basis point slippage, which is a small cost of buying or selling. The test reconciles the final equity within a small margin of error. The test uses historical adjusted ETF prices and includes drawdowns, which are losses from peak to trough. The sources of the data are from Purdue University and a stock bars website. All inputs, code, and results are archived and verified. This is an adaptation of a trading strategy, not an exact replication, and it is not approved for deployment.
A news scan found the text of a trading fund manager's notes. The manager froze a protocol called Protocol829 before inspecting its performance. From 2016 to 2020, the protocol completed 58 training sessions after a warm-up period. The manager then used a strategy to buy the EEM ETF at the opening price of the last monthly session. The manager set a stop loss at one average true range, or ATR, below the entry price, and a target at 1.5 times the ATR above the entry price. The manager would sell half of the position at a profit of one ATR and move the stop loss to the breakeven point. The remaining position would be sold at the fourth next-month open. If a trading day was ambiguous, the manager would set a daily stop loss. If a bar reached a profit of one risk unit, or R, and also hit the entry price, the manager would conservatively close the remaining position at the entry price. The manager also set a target gap that could close the remaining position at the open price. The manager used a long-only cash-only order ledger with a 5 basis point fee and 5 basis point slippage. The manager reconciled the final equity within 1e-5 USD on an initial cash amount of 10,000 USD, with nonnegative cash. The sample counts included full entry cohorts, not partial fills. The drawdown included open and close prices, as well as synthetic threshold marks, but not all intrabar excursions. The manager used historical adjusted ETF prices that approximated distributions, with a 10 basis point audit and 2/20 basis point sensitivity saved but not selected. The manager did not verify 15-minute or extended-hours fills, whole-share profit management, or exact time execution. Recent positive samples could not override the training or later gates, and there was no current calendar entry. The manager cited two sources: https://business.purdue.edu/faculty/mcconnell/publications/Equity-Returns-at-the-Turn-of-the-Month.pdf and https://runway.adityaperswal.workers.dev/data/stock-bars. All inputs, code, and results were archived in 291 parts and verified with exact readback. This was an ETF adaptation, not an exact CRSP replication, and did not have deployable approval.
A news scan found the report of a frozen turn-of-month test for QTM90800 EEM. The test has 8 completed cohorts from 2026 through September 4. The protocol, called Protocol829, was frozen before a return inspection. It involves buying at the open of the last monthly session and selling at the open of the fourth next-month session. This is a change from the original method, which was based on closing prices. The test does not include stop or target prices. The orders are long-only, meaning they only buy, and cash-only, meaning they do not use borrowed money. The orders also include a 5 basis point fee and 5 basis point slippage, which is a measure of the difference between the expected price and the actual price of a trade. The test also includes cash compounding, which means that the interest earned on the cash is added to the principal. The drawdown, or loss, includes both open and close prices, as well as a synthetic threshold mark, but does not include all intrabar excursions, which are price movements within a single bar or period. The historical prices used are adjusted for distributions, such as dividends, and include a 10 basis point audit and 2/20 basis point sensitivity, which were saved but not selected. The test does not verify 15-minute or extended-hours fills, whole-share profit management, or exact time execution. The test also cannot override training or later gates, and there is no current calendar entry. The sources for this test include a paper on equity returns at the turn of the month and a dataset of stock bars. All inputs, code, and results are archived in 291 parts and have been verified. This is an adaptation of an ETF, not an exact replication of the CRSP, and it does not have deployable approval.
A news scan found the text describing the QTM90800 EEM delay as a frozen turn-of-month test. The test has two main parts. First, Protocol829 was frozen before a return inspection, which happened from 2021 to 2025, and included 59 completed cohorts. These market years were previously inspected for other rules. The test involves buying at the open of the last monthly session and selling at the open of the fourth next-month session. The second part of the test uses a long-only cash-only order ledger with a 5 basis point fee and 5 basis point slippage. The test also includes cash compounding, which reconciles the final equity within 1e-5 USD on 10,000 initial cash. The test has some limitations, including not verifying 15-minute or extended-hours fills, whole-share profit management, or exact-time execution. The sources of the test include a paper on equity returns at the turn of the month and stock bars data. All inputs, code, and results are archived in 291 parts and have been verified. This is an adaptation of an ETF, not an exact replication, and has not been approved for deployment.